pax_global_header00006660000000000000000000000064115257626420014524gustar00rootroot0000000000000052 comment=2cbfd6239911eae2656ae4327f51d3c1147cced1 dipy-0.5.0/000077500000000000000000000000001152576264200124735ustar00rootroot00000000000000dipy-0.5.0/.gitattributes000066400000000000000000000000741152576264200153670ustar00rootroot00000000000000dipy/COMMIT_INFO.txt export-subst ./debian export-ignore dipy-0.5.0/.gitignore000066400000000000000000000001641152576264200144640ustar00rootroot00000000000000*.pyc *.pyd *.so *.c build *~ doc/_build doc/*-stamp MANIFEST dist/ .project .pydevproject .pc *.swp dipy.egg-info/ dipy-0.5.0/AUTHOR000066400000000000000000000004621152576264200134220ustar00rootroot00000000000000Eleftherios Garyfallidis Ian Nimmo-Smith Matthew Brett Bago Amirbekian Christopher Nguyen Yaroslav Halchenko Emanuele Olivetti dipy-0.5.0/Changelog000066400000000000000000000023361152576264200143110ustar00rootroot00000000000000.. -*- mode: rst -*- .. vim:syntax=rest .. _changelog: Dipy Development Changelog ----------------------------- Dipy is a diffusion MR imaging library written in Python 'Close gh-' statements refer to GitHub issues that are available at:: http://github.com/Garyfallidis/dipy/issues The full VCS changelog is available here: http://github.com/Garyfallidis/dipy/commits/master Releases ~~~~~~~~ Dipy ++++ Most work on Dipy so far has been by Eleftherios Garyfallidis, Ian Nimmo-Smith, Matthew Brett, Bago Amirbekian, Christopher Nguyen, Yaroslav Halchenko and Emanuele Olivetti. * 0.5.0 (Friday, 11 Feb 2011) * Initial release. * Reconstruction algorithms e.g. GQI, DTI * Tractography generation algorithms e.g. EuDX * Intelligent downsampling of tracks * Ultra fast tractography clustering * Resampling datasets with anisotropic voxels to isotropic * Visualizing multiple brains simultaneously * Finding track correspondence between different brains * Reading many different file formats e.g. Trackvis or Nifti * Dealing with huge tractographies without memory restrictions * Playing with datasets interactively without storing * And much more and even more to come in next releases dipy-0.5.0/LICENSE000066400000000000000000000030041152576264200134750ustar00rootroot00000000000000Copyright (c) 2009-2010, dipy developers All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the dipy developers nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. dipy-0.5.0/MANIFEST.in000066400000000000000000000005141152576264200142310ustar00rootroot00000000000000include AUTHOR LICENSE Makefile* MANIFEST.in setup* README.* include Changelog TODO recursive-include doc * recursive-include bin * recursive-include tools * # put this stuff back into setup.py (package_data) once I'm enlightened # enough to accomplish this herculean task recursive-include dipy/data * include dipy/COMMIT_INFO.txt dipy-0.5.0/Makefile000066400000000000000000000042401152576264200141330ustar00rootroot00000000000000# Simple makefile to quickly access handy build commands for Cython extension # code generation. Note that the actual code to produce the extension lives in # the setup.py file, this Makefile is just meant as a command # convenience/reminder while doing development. PYTHON ?= python PKGDIR=dipy DOCSRC_DIR=doc DOCDIR=${PKGDIR}/${DOCSRC_DIR} TESTDIR=${PKGDIR}/tests help: @echo "Numpy/Cython tasks. Available tasks:" @echo "ext -> build the Cython extension module." @echo "cython-html -> create annotated HTML from the .pyx sources" @echo "test -> run a simple test demo." @echo "all -> Call ext, html and finally test." all: ext cython-html test ext: recspeed.so propspeed.so vox2track.so \ distances.so test: ext nosetests . cython-html: ${PKGDIR}/reconst/recspeed.html ${PKGDIR}/tracking/propspeed.html ${PKGDIR}/tracking/vox2track.html ${PKGDIR}/tracking/distances.html recspeed.so: ${PKGDIR}/reconst/recspeed.pyx propspeed.so: ${PKGDIR}/tracking/propspeed.pyx vox2track.so: ${PKGDIR}/tracking/vox2track.pyx distances.so: ${PKGDIR}/tracking/distances.pyx python setup.py build_ext --inplace # Phony targets for cleanup and similar uses .PHONY: clean clean: - find ${PKGDIR} -name "*.so" -print0 | xargs -0 rm - find ${PKGDIR} -name "*.pyd" -print0 | xargs -0 rm - find ${PKGDIR} -name "*.c" -print0 | xargs -0 rm - find ${PKGDIR} -name "*.html" -print0 | xargs -0 rm rm -rf build rm -rf docs/_build rm -rf docs/dist distclean: clean rm -rf dist # Suffix rules %.c : %.pyx cython $< %.html : %.pyx cython -a $< # Print out info for possible install methods check-version-info: $(PYTHON) -c 'from nisext.testers import info_from_here; info_from_here("dipy")' # Run tests from installed code installed-tests: $(PYTHON) -c 'from nisext.testers import tests_installed; tests_installed("dipy")' # Run tests from installed code sdist-tests: $(PYTHON) -c 'from nisext.testers import sdist_tests; sdist_tests("dipy")' bdist-egg-tests: $(PYTHON) -c 'from nisext.testers import bdist_egg_tests; bdist_egg_tests("dipy")' source-release: clean python -m compileall . python setup.py sdist --formats=gztar,zip binary-release: clean python setup_egg.py bdist_egg dipy-0.5.0/README.txt000066400000000000000000000021471152576264200141750ustar00rootroot00000000000000====== DiPy ====== Dipy is a python toolbox for analysis of MR diffusion imaging. Dipy is for research only; please do not use results from dipy for clinical decisions. Website ======= Current information can always be found at the NIPY dipy website - http://nipy.org/dipy - or directly from the DIPY website - http://dipy.org Mailing Lists ============= Please see the developer's list at http://mail.scipy.org/mailman/listinfo/nipy-devel Code ==== You can find our sources and single-click downloads: * `Main repository`_ on Github. * Documentation_ for all releases and current development tree. * Download as a tar/zip file the `current trunk`_. * Downloads of all `available releases`_. .. _main repository: http://github.com/Garyfallidis/dipy .. _Documentation: http://dipy.org .. _current trunk: http://github.com/Garyfallidis/dipy/archives/master .. _available releases: http://github.com/Garyfallidis/dipy/downloads License ======= dipy is licensed under the terms of the BSD license. Some code included with dipy is also licensed under the BSD license. Please the LICENSE file in the dipy distribution. dipy-0.5.0/debian/000077500000000000000000000000001152576264200137155ustar00rootroot00000000000000dipy-0.5.0/debian/blends000066400000000000000000000010631152576264200151070ustar00rootroot00000000000000Format: extended Tasks: debian-med/imaging-dev Depends: python-dipy Language: Python Author: Dipy Developers License: BSD Version: 0.5.0.dev Published-Title: Identification of corresponding tracks in diffusion MRI tractographies Published-Authors: Garyfallidis E, Brett M, Tsiaras V, Vogiatzis G, Nimmo-Smith I Published-In: Proc. Intl. Soc. Mag. Reson. Med. 18 Published-Year: 2010 Tasks: debian-med/imaging Recommends: python-dipy Why: Although listed in -dev task, it also has a strong focus on interactive data analysis. dipy-0.5.0/debian/changelog000066400000000000000000000004771152576264200155770ustar00rootroot00000000000000dipy (0.5.0-1) unstable; urgency=low * Initial release (Closes: #610347) -- Yaroslav Halchenko Wed, 09 Feb 2011 17:38:53 -0500 dipy (0.5.0~dev20110117-1~pre1) UNRELEASED; urgency=low * Initial packaging. -- Yaroslav Halchenko Mon, 17 Jan 2011 15:13:17 -0500 dipy-0.5.0/debian/compat000066400000000000000000000000021152576264200151130ustar00rootroot000000000000007 dipy-0.5.0/debian/control000066400000000000000000000045771152576264200153350ustar00rootroot00000000000000Source: dipy Section: python Priority: extra Maintainer: NeuroDebian Team Uploaders: Yaroslav Halchenko , Michael Hanke Build-Depends: debhelper (>= 7.0.50~), python-all-dev, python-support, python-numpy, python-scipy, cython (>= 0.13), python-matplotlib, python-sphinx (>= 1.0), python-nose, python-nibabel Standards-Version: 3.9.1 Homepage: http://nipy.org/dipy Vcs-Git: git://github.com/neurodebian/dipy.git Vcs-Browser: http://github.com/neurodebian/dipy Package: python-dipy Architecture: all Depends: ${Python:depends}, ${shlibs:Depends}, ${misc:Depends}, python-numpy, python-scipy, python-dipy-lib (>= ${source:Version}) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: ${python:Provides} XB-Python-Version: ${python:Versions} Description: toolbox for analysis of MR diffusion imaging data Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. It features: - Reconstruction algorithms, e.g. GQI, DTI - Tractography generation algorithms, e.g. EuDX - Intelligent downsampling of tracks - Ultra fast tractography clustering - Resampling datasets with anisotropic voxels to isotropic - Visualizing multiple brains simultaneously - Finding track correspondence between different brains - Warping tractographies into another space, e.g. MNI space - Reading many different file formats, e.g. Trackvis or NIfTI - Dealing with huge tractographies without memory restrictions - Playing with datasets interactively without storing Package: python-dipy-lib Architecture: any Depends: ${Python:depends}, ${shlibs:Depends}, ${misc:Depends} Provides: ${python:Provides} XB-Python-Version: ${python:Versions} Description: toolbox for analysis of MR diffusion imaging data -- extensions Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. . This package provides architecture-dependent builds of the extensions. Package: python-dipy-doc Architecture: all Section: doc Depends: ${misc:Depends}, libjs-jquery Suggests: python-dipy Description: toolbox for analysis of MR diffusion imaging data -- documentation Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. . This package provides the documentation in HTML format. dipy-0.5.0/debian/copyright000066400000000000000000000040051152576264200156470ustar00rootroot00000000000000Format-Specification: http://svn.debian.org/wsvn/dep/web/deps/dep5.mdwn?op=file&rev=135 Name: dipy Maintainer: Eleftherios Garyfallidis Source: http://github.com/Garyfallidis/dipy Files: * Copyright: 2009-2010, Dipy Developers License: BSD-3 Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: . * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the dipy developers nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission. . THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Files: doc/sphinxext/* Copyright: 2007-2010, Stefan van der Walt and Sphinx team License: BSD-3 Files: dipy/testing/lightunit.py Copyright: 2009, The IPython Development Team License: BSD-3 Files: debian/* Copyright: 2010, Yaroslav Halchenko License: BSD-3 dipy-0.5.0/debian/docs000066400000000000000000000000131152576264200145620ustar00rootroot00000000000000README.txt dipy-0.5.0/debian/gbp.conf000066400000000000000000000006641152576264200153420ustar00rootroot00000000000000[DEFAULT] upstream-branch = master debian-branch = debian upstream-tag = %(version)s debian-tag = debian/%(version)s # Options only affecting git-buildpackage [git-buildpackage] # ignore some any non-gitted files ignore-new = True #upstream-branch = dfsgclean # uncomment this to automatically GPG sign tags sign-tags = True # use this for more svn-buildpackage like bahaviour: export-dir = ../build-area/ tarball-dir = ../tarballs/ dipy-0.5.0/debian/patches/000077500000000000000000000000001152576264200153445ustar00rootroot00000000000000dipy-0.5.0/debian/patches/deb_no_sources_for_docs000066400000000000000000000005641152576264200221430ustar00rootroot00000000000000--- a/doc/conf.py +++ b/doc/conf.py @@ -93,6 +93,9 @@ rst_epilog = open('links_names.inc', 'rt # for source files. exclude_trees = ['_build', 'examples'] +# If true, the reST sources are included in the HTML build as _sources/. +html_copy_source = False + # The reST default role (used for this markup: `text`) to use for all documents. #default_role = None dipy-0.5.0/debian/patches/series000066400000000000000000000000301152576264200165520ustar00rootroot00000000000000deb_no_sources_for_docs dipy-0.5.0/debian/python-dipy-doc.doc-base000066400000000000000000000004131152576264200203410ustar00rootroot00000000000000Document: dipy Title: Dipy Manual Author: Dipy Developers Abstract: This manual provides Dipy user guide and examples Section: Science/Data Analysis Format: HTML Index: /usr/share/doc/python-dipy-doc/html/index.html Files: /usr/share/doc/python-dipy-doc/html/*.html dipy-0.5.0/debian/python-dipy-doc.docs000066400000000000000000000000201152576264200176060ustar00rootroot00000000000000doc/_build/html dipy-0.5.0/debian/python-dipy-doc.links000066400000000000000000000001331152576264200200030ustar00rootroot00000000000000usr/share/javascript/jquery/jquery.js usr/share/doc/python-dipy-doc/html/_static/jquery.js dipy-0.5.0/debian/python-dipy.install000066400000000000000000000000171152576264200175670ustar00rootroot00000000000000debian/tmp/usr dipy-0.5.0/debian/rules000077500000000000000000000065071152576264200150050ustar00rootroot00000000000000#!/usr/bin/make -f # -*- makefile -*- PACKAGE_NAME = python-dipy PACKAGE_ROOT_DIR = debian/${PACKAGE_NAME} INSTALL_PATH = $(CURDIR)/debian/tmp # default Python PYTHON=$(shell pyversions -d) PYVER=$(shell pyversions -d -v) srcpkg = $(shell LC_ALL=C dpkg-parsechangelog | grep '^Source:' | cut -d ' ' -f 2,2) debver = $(shell LC_ALL=C dpkg-parsechangelog | grep '^Version:' | cut -d ' ' -f 2,2 ) upver = $(shell echo $(debver) | cut -d '-' -f 1,1 ) %: dh --buildsystem=python_distutils $@ override_dh_auto_test: : # Do not test just after build, lets install and then test override_dh_auto_build: dh_auto_build echo "backend : Agg" >| build/matplotlibrc override_dh_auto_install: dh_auto_install : # Prune duplicate LICENSE file find debian/ -name LICENSE -delete : # Only now lets build docs ifeq (,$(filter nodoc,$(DEB_BUILD_OPTIONS))) : # Copy pregenerated examples cp -rp doc-examples/doc/examples_built/* doc/examples_built/ export PYTHONPATH=$$(/bin/ls -d $(INSTALL_PATH)/usr/lib/$(PYTHON)/*-packages) \ MPLCONFIGDIR=$(CURDIR)/build HOME=$(CURDIR)/build; \ cd doc; $(MAKE) html-after-examples -rm doc/_build/html/_static/jquery.js -rm -r doc/_build/html/_sources : # objects inventory is of no use for the package -rm doc/_build/html/objects.inv endif : # Run tests later on : # cd build to prevent use of local/not-built source tree ifeq (,$(filter nocheck,$(DEB_BUILD_OPTIONS))) cd build; \ for PYTHON in $(shell pyversions -r); do \ echo "I: Running Dipy unittests using $$PYTHON"; \ PYTHONPATH=$$(/bin/ls -d $(INSTALL_PATH)/usr/lib/$$PYTHON/*-packages) \ MPLCONFIGDIR=/tmp/ \ $$PYTHON /usr/bin/nosetests dipy; \ done endif override_dh_pysupport: : # I: Move libraries into the -lib and -lib-dbg packages find $(PACKAGE_ROOT_DIR)/ -iname *.so | \ while read so; do \ d=$$(dirname $$so); \ if [ $${so%_d.so} = $${so} ]; then suf=""; else suf="-dbg"; fi; \ d=$$(echo $$d | sed -e "s,$(PACKAGE_ROOT_DIR)/,$(PACKAGE_ROOT_DIR)-lib$$suf/,g"); \ mkdir -p $$d; echo "Moving $$so under $$d"; mv $$so $$d; \ done dh_pysupport ## immediately useable documentation ## and exemplar data (they are small excerpts anyway) override_dh_compress: dh_compress -X.py -X.html -X.css -X.jpg -X.txt -X.js -X.json -X.rtc -X.par -X.bin -Xobjects.inv override_dh_clean: : # I: Custom cleaning rm -rf build doc-stamp $(MAKE) -C doc clean dh_clean get-orig-source: -quilt pop -a : # I Testing for uncommited changes @git diff --quiet HEAD : # I Composing main tarball mkdir -p tarballs git archive --format=tar --prefix=$(srcpkg)-$(upver)/ HEAD \ | gzip -9 > ../tarballs/$(srcpkg)_$(upver).orig.tar.gz # : # I Building examples figures and tarball # python setup.py build # export PYTHONPATH=$$(/bin/ls -d $(CURDIR)/build/lib.*$(PYVER)); \ # cd doc; $(MAKE) examples-tgz # mv dist/dipy-*-doc-examples.tar.gz ../tarballs/$(srcpkg)_$(upver).orig-doc-examples.tar.gz wget -O ../tarballs/$(srcpkg)_$(upver).orig-doc-examples.tar.gz \ http://nipy.sourceforge.net/dipy/dipy-$(upver)-doc-examples.tar.gz : # Symlinking back into build-area for git-buildpackage not supporting multi-tarball atm [ -d ../build-area ] && ln -sf ../tarballs/$(srcpkg)_$(upver).orig*.tar.gz ../build-area inject-doc-examples: mkdir -p doc-examples tar -C doc-examples --strip-components 1 \ -xzvf ../tarballs/$(srcpkg)_$(upver).orig-doc-examples.tar.gz dipy-0.5.0/debian/source/000077500000000000000000000000001152576264200152155ustar00rootroot00000000000000dipy-0.5.0/debian/source/format000066400000000000000000000000141152576264200164230ustar00rootroot000000000000003.0 (quilt) dipy-0.5.0/debian/watch000066400000000000000000000002071152576264200147450ustar00rootroot00000000000000version=3 opts="filenamemangle=s/.*\/(.*)/dipy-$1\.tar\.gz/" \ http://github.com/Garyfallidis/dipy/downloads .*tarball/([\d\.a-z]+) dipy-0.5.0/dipy/000077500000000000000000000000001152576264200134405ustar00rootroot00000000000000dipy-0.5.0/dipy/COMMIT_INFO.txt000066400000000000000000000004141152576264200160030ustar00rootroot00000000000000# This is an ini file that may contain information about the code state [commit hash] # The line below may contain a valid hash if it has been substituted during 'git archive' archive_subst_hash=2cbfd62 # This line may be modified by the install process install_hash= dipy-0.5.0/dipy/__init__.py000066400000000000000000000011401152576264200155450ustar00rootroot00000000000000''' Load some modules ''' import os from .info import __version__, long_description as __doc__ import align import reconst import io import tracking import viz import external import core ''' try: from nibabel.nicom.dicomreaders import read_mosaic_dir as load_dcm_dir except ImportError: pass ''' # raise ImportError('nibabel.nicom.dicomreaders cannot be found') # Test callable from numpy.testing import Tester test = Tester().test del Tester # Plumb in version etc info stuff from .pkg_info import get_pkg_info as _get_pkg_info get_info = lambda : _get_pkg_info(os.path.dirname(__file__)) dipy-0.5.0/dipy/align/000077500000000000000000000000001152576264200145325ustar00rootroot00000000000000dipy-0.5.0/dipy/align/__init__.py000066400000000000000000000000031152576264200166340ustar00rootroot00000000000000 dipy-0.5.0/dipy/align/aniso2iso.py000066400000000000000000000052441152576264200170170ustar00rootroot00000000000000''' Anisotropic to isotropic voxel conversion ''' import numpy as np from scipy.ndimage import affine_transform def resample(data,affine,zooms,new_zooms,order=1): ''' Resample data from anisotropic to isotropic voxel size Parameters ----------- data : array, shape (I,J,K) or (I,J,K,N) 3d volume or 4d volume with datasets affine : array, shape (4,4) mapping from voxel coordinates to world coordinates zooms : tuple, shape (3,) voxel size for (i,j,k) dimensions new_zooms : tuple, shape (3,) new voxel size for (i,j,k) after resampling order : int, from 0 to 5 order of interpolation for resampling/reslicing, 0 nearest interpolation, 1 trilinear etc.. if you don't want any smoothing 0 is the option you need. Returns -------- data2 : array, shape (I,J,K) or (I,J,K,N) datasets resampled into isotropic voxel size Notes ------ It is also possible with this function to resample/reslice from isotropic voxel size to anisotropic or from isotropic to isotropic or even from anisotropic to anisotropic, as long as you provide the correct zooms (voxel sizes) and new_zooms (new voxel sizes). It is fairly easy to get the correct zooms using nibabel as show in the example below. Examples --------- >>> import nibabel as nib >>> from dipy.align.aniso2iso import resample >>> from dipy.data import get_data >>> fimg=get_data('aniso_vox') >>> img=nib.load(fimg) >>> data=img.get_data() >>> data.shape (58, 58, 24) >>> affine=img.get_affine() >>> zooms=img.get_header().get_zooms()[:3] >>> zooms (4.0, 4.0, 5.0) >>> new_zooms=(3.,3.,3.) >>> new_zooms (3.0, 3.0, 3.0) >>> data2,affine2=resample(data,affine,zooms,new_zooms) >>> data2.shape (77, 77, 40) ''' R=np.diag(np.array(new_zooms)/np.array(zooms)) new_shape=np.array(zooms)/np.array(new_zooms) * np.array(data.shape[:3]) new_shape=np.round(new_shape).astype('i8') if data.ndim==3: data2=affine_transform(input=data,matrix=R,offset=np.zeros(3,),output_shape=tuple(new_shape),order=order) if data.ndim==4: data2l=[] for i in range(data.shape[-1]): tmp=affine_transform(input=data[...,i],matrix=R,offset=np.zeros(3,),output_shape=tuple(new_shape),order=order) data2l.append(tmp) data2=np.zeros(tmp.shape+(data.shape[-1],),data.dtype) for i in range(data.shape[-1]): data2[...,i]=data2l[i] Rx=np.eye(4) Rx[:3,:3]=R affine2=np.dot(affine,Rx) return data2,affine2 dipy-0.5.0/dipy/boots/000077500000000000000000000000001152576264200145665ustar00rootroot00000000000000dipy-0.5.0/dipy/boots/__init__.py000066400000000000000000000000431152576264200166740ustar00rootroot00000000000000# Init for core.stat dipy objects dipy-0.5.0/dipy/boots/resampling.py000066400000000000000000000235771152576264200173170ustar00rootroot00000000000000#!/usr/bin/python #import modules import time import sys, os, traceback, optparse import numpy as np import scipy as sp from copy import copy, deepcopy import warnings warnings.warn("This module is most likely to change both as a name and in structure in the future",FutureWarning) def bs_se(bs_pdf): """ Calculates the bootstrap standard error estimate of a statistic """ N = len(bs_pdf) return np.std(bs_pdf) * np.sqrt(N / (N - 1)) def bootstrap(x, statistic = bs_se, B = 1000, alpha = 0.95): """ Bootstrap resampling _[1] to accurately estimate the standard error and confidence interval of a desired statistic of a probability distribution function (pdf). Parameters ------------ x : ndarray (N, 1) Observable sample to resample. N should be reasonably large. statistic : method (optional) Method to calculate the desired statistic. (Default: calculate bootstrap standard error) B : integer (optional) Total number of bootstrap resamples in bootstrap pdf. (Default: 1000) alpha : float (optional) Percentile for confidence interval of the statistic. (Default: 0.05) Returns --------- bs_pdf : ndarray (M, 1) Jackknife probabilisty distribution function of the statistic. se : float Standard error of the statistic. ci : ndarray (2, 1) Confidence interval of the statistic. See Also ----------- numpy.std, numpy.random.random Notes -------- Bootstrap resampling is non parametric. It is quite powerful in determining the standard error and the confidence interval of a sample distribution. The key characteristics of bootstrap is: 1) uniform weighting among all samples (1/n) 2) resampling with replacement In general, the sample size should be large to ensure accuracy of the estimates. The number of bootstrap resamples should be large as well as that will also influence the accuracy of the estimate. References ---------- .. [1] Efron, B., 1979. 1977 Rietz lecture--Bootstrap methods--Another look at the jackknife. Ann. Stat. 7, 1-26. """ N = len(x) pdf_mask = np.ones((N,),dtype='int16') bs_pdf = np.empty((B,)) for ii in range(0, B): #resample with replacement rand_index = np.int16(np.round(np.random.random(N) * (N - 1))) bs_pdf[ii] = statistic(x[rand_index]) return bs_pdf, bs_se(bs_pdf), abc(x, statistic, alpha = alpha) def abc(x, statistic = bs_se , alpha = 0.05, eps = 1e-5): """ Calculates the bootstrap confidence interval by approximating the BCa. Parameters ---------- x : np.ndarray Observed data (e.g. chosen gold standard estimate used for bootstrap) statistic : method Method to calculate the desired statistic given x and probability proportions (flat probability densities vector) alpha : float (0, 1) Desired confidence interval initial endpoint (Default: 0.05) eps : float (optional) Specifies step size in calculating numerical derivative T' and T''. Default: 1e-5 See Also -------- __tt, __tt_dot, __tt_dot_dot, __calc_z0 Notes ----- Unlike the BCa method of calculating the bootstrap confidence interval, the ABC method is computationally less demanding (about 3% computational power needed) and is fairly accurate (sometimes out performing BCa!). It does not require any bootstrap resampling and instead uses numerical derivatives via Taylor series to approximate the BCa calculation. However, the ABC method requires the statistic to be smooth and follow a multinomial distribution. References ---------- .. [1] DiCiccio, T.J., Efron, B., 1996. Bootstrap Confidence Intervals. Statistical Science. 11, 3, 189-228. """ #define base variables -- n, p_0, sigma_hat, delta_hat n = len(x) p_0 = np.ones(x.shape) / n sigma_hat = np.zeros(x.shape) delta_hat = np.zeros(x.shape) for i in range(0, n): sigma_hat[i] = __tt_dot(i, x, p_0, statistic, eps)**2 delta_hat[i] = __tt_dot(i, x, p_0, statistic, eps) sigma_hat = (sigma_hat / n**2)**0.5 #estimate the bias (z_0) and the acceleration (a_hat) a_hat = np.zeros(x.shape) a_num = np.zeros(x.shape) a_dem = np.zeros(x.shape) for i in range(0, n): a_num[i] = __tt_dot(i, x, p_0, statistic, eps)**3 a_dem[i] = __tt_dot(i, x, p_0, statistic, eps)**2 a_hat = 1 / 6 * a_num / a_dem**1.5 z_0 = __calc_z0(x, p_0, statistic, eps, a_hat, sigma_hat) #define helper variables -- w and l w = z_0 + __calc_z_alpha(1 - alpha) l = w / (1 - a_hat * w)**2 return __tt(x, p_0 + l * delta_hat / sigma_hat, statistic) def __calc_z_alpha(alpha): """ Classic "quantile function" that calculates inverse of cdf of standard normal. """ return 2**0.5 * sp.special.erfinv(2 * alpha - 1) def __calc_z0(x, p_0, statistic, eps, a_hat, sigma_hat): """ Function that calculates the bias z_0 for abc method. See Also ---------- abc, __tt, __tt_dot, __tt_dot_dot """ n = len(x) b_hat = np.ones(x.shape) c_q_hat = np.ones(x.shape) tt_dot = np.ones(x.shape) for i in range(0, n): b_hat[i] = __tt_dot_dot(i, x, p_0, statistic, eps) tt_dot[i] = __tt_dot(i, x, p_0, statistic, eps) b_hat = b_hat / (2 * n**2) c_q_hat = (__tt(x, (1 - eps) * p_0 + eps * tt_dot / (n**2 * sigma_hat) , statistic) + __tt(x, (1 - eps) * p_0 - eps * tt_dot / (n**2 * sigma_hat) , statistic) - 2 * __tt(x, p_0, statistic) ) / eps**2 return a_hat - (b_hat / sigma_hat - c_q_hat) def __tt(x, p_0, statistic = bs_se): """ Function that calculates desired statistic from observable data and a given proportional weighting. Parameters ------------ x : np.ndarray Observable data (e.g. from gold standard). p_0 : np.ndarray Proportional weighting vector (Default: uniform weighting 1/n) Returns ------- theta_hat : float Desired statistic of the observable data. See Also ----------- abc, __tt_dot, __tt_dot_dot """ return statistic(x / p_0) def __tt_dot(i, x, p_0, statistic, eps): """ First numerical derivative of __tt """ e = np.zeros(x.shape) e[i] = 1 return ( (__tt(x, (1 - eps) * p_0 + eps * e[i], statistic) - __tt(x, p_0, statistic)) / eps ) def __tt_dot_dot(i, x, p_0, statistic, eps): """ Second numerical derivative of __tt """ e = np.zeros(x.shape) e[i] = 1 return (__tt_dot(i, x, p_0, statistic, eps) / eps + (__tt(x, (1 - eps) * p_0 - eps * e[i], statistic) - __tt(x, p_0, statistic)) / eps**2) def jackknife(pdf, statistic = np.std, M = None): """ Jackknife resampling _[1] to quickly estimate the bias and standard error of a desired statistic in a probability distribution function (pdf). Parameters ------------ pdf : ndarray (N, 1) Probability distribution function to resample. N should be reasonably large. statistic : method (optional) Method to calculate the desired statistic. (Default: calculate standard deviation) M : integer (M < N) Total number of samples in jackknife pdf. (Default: M == N) Returns --------- jk_pdf : ndarray (M, 1) Jackknife probabilisty distribution function of the statistic. bias : float Bias of the jackknife pdf of the statistic. se : float Standard error of the statistic. See Also ----------- numpy.std, numpy.mean, numpy.random.random Notes -------- Jackknife resampling like bootstrap resampling is non parametric. However, it requires a large distribution to be accurate and in some ways can be considered deterministic (if one removes the same set of samples, then one will get the same estimates of the bias and variance). In the context of this implementation, the sample size should be at least larger than the asymptotic convergence of the statistic (ACstat); preferably, larger than ACstat + np.greater(ACbias, ACvar) The clear benefit of using jackknife is its ability to estimate the bias of the statistic. The most powerful application of this is estimating the bias of a bootstrap-estimated standard error. In fact, one could "bootstrap the bootstrap" (nested bootstrap) of the estimated standard error, but the inaccuracy of the bootstrap to characterize the true mean would incur a poor estimate of the bias (recall: bias = mean[sample_est] - mean[true population]) References ------------- .. [1] Efron, B., 1979. 1977 Rietz lecture--Bootstrap methods--Another look at the jackknife. Ann. Stat. 7, 1-26. """ N = len(pdf) pdf_mask = np.ones((N,),dtype='int16') #keeps track of all n - 1 indexes mask_index = np.copy(pdf_mask) if M == None: M = N M = np.minimum(M, N - 1) jk_pdf = np.empty((M,)) for ii in range(0, M): rand_index = np.round(np.random.random() * (N - 1)) #choose a unique random sample to remove while pdf_mask[rand_index] == 0 : rand_index = np.round(np.random.random() * (N - 1)) #set mask to zero for chosen random index so not to choose again pdf_mask[rand_index] = 0 mask_index[rand_index] = 0 jk_pdf[ii] = statistic(pdf[mask_index > 0]) #compute n-1 statistic mask_index[rand_index] = 1 return jk_pdf, (N - 1) * (np.mean(jk_pdf) - statistic(pdf)), np.sqrt(N - 1) * np.std(jk_pdf) def residual_bootstrap(data): pass def repetition_bootstrap(data): pass dipy-0.5.0/dipy/core/000077500000000000000000000000001152576264200143705ustar00rootroot00000000000000dipy-0.5.0/dipy/core/__init__.py000066400000000000000000000002061152576264200164770ustar00rootroot00000000000000# Init for core dipy objects """ Core objects """ # Test callable from numpy.testing import Tester test = Tester().test del Tester dipy-0.5.0/dipy/core/geometry.py000066400000000000000000000536651152576264200166140ustar00rootroot00000000000000''' Utility functions for algebra etc ''' import math import numpy as np import numpy.linalg as npl # epsilon for testing whether a number is close to zero _EPS = np.finfo(float).eps * 4.0 # axis sequences for Euler angles _NEXT_AXIS = [1, 2, 0, 1] # map axes strings to/from tuples of inner axis, parity, repetition, frame _AXES2TUPLE = { 'sxyz': (0, 0, 0, 0), 'sxyx': (0, 0, 1, 0), 'sxzy': (0, 1, 0, 0), 'sxzx': (0, 1, 1, 0), 'syzx': (1, 0, 0, 0), 'syzy': (1, 0, 1, 0), 'syxz': (1, 1, 0, 0), 'syxy': (1, 1, 1, 0), 'szxy': (2, 0, 0, 0), 'szxz': (2, 0, 1, 0), 'szyx': (2, 1, 0, 0), 'szyz': (2, 1, 1, 0), 'rzyx': (0, 0, 0, 1), 'rxyx': (0, 0, 1, 1), 'ryzx': (0, 1, 0, 1), 'rxzx': (0, 1, 1, 1), 'rxzy': (1, 0, 0, 1), 'ryzy': (1, 0, 1, 1), 'rzxy': (1, 1, 0, 1), 'ryxy': (1, 1, 1, 1), 'ryxz': (2, 0, 0, 1), 'rzxz': (2, 0, 1, 1), 'rxyz': (2, 1, 0, 1), 'rzyz': (2, 1, 1, 1)} _TUPLE2AXES = dict((v, k) for k, v in _AXES2TUPLE.items()) def sphere2cart(r, theta, phi): ''' Spherical to Cartesian coordinates This is the standard physics convention where `theta` is the inclination (polar) angle, and `phi` is the azimuth angle. Imagine a sphere with center (0,0,0). Orient it with the z axis running south->north, the y axis running west-east and the x axis from posterior to anterior. `theta` (the inclination angle) is the angle to rotate from the z-axis (the zenith) around the y-axis, towards the x axis. Thus the rotation is counter-clockwise from the point of view of positive y. `phi` (azimuth) gives the angle of rotation around the z-axis towards the y axis. The rotation is counter-clockwise from the point of view of positive z. Equivalently, given a point P on the sphere, with coordinates x, y, z, `theta` is the angle between P and the z-axis, and `phi` is the angle between the projection of P onto the XY plane, and the X axis. Geographical nomenclature designates theta as 'co-latitude', and phi as 'longitude' Parameters ------------ r : array-like radius theta : array-like inclination or polar angle phi : array-like azimuth angle Returns --------- x : array x coordinate(s) in Cartesion space y : array y coordinate(s) in Cartesian space z : array z coordinate Notes -------- See these pages: * http://en.wikipedia.org/wiki/Spherical_coordinate_system * http://mathworld.wolfram.com/SphericalCoordinates.html for excellent discussion of the many different conventions possible. Here we use the physics conventions, used in the wikipedia page. Derivations of the formulae are simple. Consider a vector x, y, z of length r (norm of x, y, z). The inclination angle (theta) can be found from: cos(theta) == z / r -> z == r * cos(theta). This gives the hypotenuse of the projection onto the XY plane, which we will call Q. Q == r*sin(theta). Now x / Q == cos(phi) -> x == r * sin(theta) * cos(phi) and so on. We have deliberately named this function ``sphere2cart`` rather than ``sph2cart`` to distinguish it from the Matlab function of that name, because the Matlab function uses an unusual convention for the angles that we did not want to replicate. The Matlab function is trivial to implement with the formulae given in the Matlab help. ''' sin_theta = np.sin(theta) x = r * np.cos(phi) * sin_theta y = r * np.sin(phi) * sin_theta z = r * np.cos(theta) return x, y, z def cart2sphere(x, y, z): r''' Return angles for Cartesian 3D coordinates `x`, `y`, and `z` See doc for ``sphere2cart`` for angle conventions and derivation of the formulae. $0 \le \theta \mathrm{(theta)} \le \pi$ and $0 \le \phi \mathrm{(phi)} \le 2 \pi$ Parameters ------------ x : array-like x coordinate in Cartesion space y : array-like y coordinate in Cartesian space z : array-like z coordinate Returns --------- r : array radius theta : array inclination (polar) angle phi : array azimuth angle ''' r = np.sqrt(x*x + y*y + z*z) theta = np.arccos(z/r) phi = np.arctan2(y, x) return r, theta, phi def normalized_vector(vec): ''' Return vector divided by its Euclidean (L2) norm See :term:`unit vector` and :term:`Euclidean norm` Parameters ------------ vec : array-like shape (3,) Returns ---------- nvec : array shape (3,) vector divided by L2 norm Examples ----------- >>> vec = [1, 2, 3] >>> l2n = np.sqrt(np.dot(vec, vec)) >>> nvec = normalized_vector(vec) >>> np.allclose(np.array(vec) / l2n, nvec) True >>> vec = np.array([[1, 2, 3]]) >>> vec.shape (1, 3) >>> normalized_vector(vec).shape (3,) ''' vec = np.asarray(vec).squeeze() return vec / math.sqrt((vec**2).sum()) def vector_norm(vec): ''' Return vector Euclidaan (L2) norm See :term:`unit vector` and :term:`Euclidean norm` Parameters ------------- vec : array-like shape (3,) Returns --------- norm : scalar Examples -------- >>> import numpy as np >>> vec = [1, 2, 3] >>> l2n = np.sqrt(np.dot(vec, vec)) >>> nvec = vector_norm(vec) >>> np.allclose(nvec, np.sqrt(np.dot(vec, vec))) True ''' vec = np.asarray(vec) return math.sqrt((vec**2).sum()) def nearest_pos_semi_def(B): ''' Least squares positive semi-definite tensor estimation Reference: Niethammer M, San Jose Estepar R, Bouix S, Shenton M, Westin CF. On diffusion tensor estimation. Conf Proc IEEE Eng Med Biol Soc. 2006;1:2622-5. PubMed PMID: 17946125; PubMed Central PMCID: PMC2791793. Parameters ------------ B : (3,3) array-like B matrix - symmetric. We do not check the symmetry. Returns --------- npds : (3,3) array Estimated nearest positive semi-definite array to matrix `B`. Examples ---------- >>> B = np.diag([1, 1, -1]) >>> nearest_pos_semi_def(B) array([[ 0.75, 0. , 0. ], [ 0. , 0.75, 0. ], [ 0. , 0. , 0. ]]) ''' B = np.asarray(B) vals, vecs = npl.eigh(B) # indices of eigenvalues in descending order inds = np.argsort(vals)[::-1] vals = vals[inds] cardneg = np.sum(vals < 0) if cardneg == 0: return B if cardneg == 3: return np.zeros((3,3)) lam1a, lam2a, lam3a = vals scalers = np.zeros((3,)) if cardneg == 2: b112 = np.max([0,lam1a+(lam2a+lam3a)/3.]) scalers[0] = b112 elif cardneg == 1: lam1b=lam1a+0.25*lam3a lam2b=lam2a+0.25*lam3a if lam1b >= 0 and lam2b >= 0: scalers[:2] = lam1b, lam2b else: # one of the lam1b, lam2b is < 0 if lam2b < 0: b111=np.max([0,lam1a+(lam2a+lam3a)/3.]) scalers[0] = b111 if lam1b < 0: b221=np.max([0,lam2a+(lam1a+lam3a)/3.]) scalers[1] = b221 # resort the scalers to match the original vecs scalers = scalers[np.argsort(inds)] return np.dot(vecs, np.dot(np.diag(scalers), vecs.T)) def sphere_distance(pts1, pts2, radius=None, check_radius=True): """ Distance across sphere surface between `pts1` and `pts2` Parameters ------------ pts1 : (N,R) or (R,) array-like where N is the number of points and R is the number of coordinates defining a point (``R==3`` for 3D) pts2 : (N,R) or (R,) array-like where N is the number of points and R is the number of coordinates defining a point (``R==3`` for 3D). It should be possible to broadcast `pts1` against `pts2` radius : None or float, optional Radius of sphere. Default is to work out radius from mean of the length of each point vector check_radius : bool, optional If True, check if the points are on the sphere surface - i.e check if the vector lengths in `pts1` and `pts2` are close to `radius`. Default is True. Returns --------- d : (N,) or (0,) array Distances between corresponding points in `pts1` and `pts2` across the spherical surface, i.e. the great circle distance See also ---------- cart_distance : cartesian distance between points vector_cosine : cosine of angle between vectors Examples ---------- >>> print '%.4f' % sphere_distance([0,1],[1,0]) 1.5708 >>> print '%.4f' % sphere_distance([0,3],[3,0]) 4.7124 """ pts1 = np.asarray(pts1) pts2 = np.asarray(pts2) lens1 = np.sqrt(np.sum(pts1**2, axis=-1)) lens2 = np.sqrt(np.sum(pts2**2, axis=-1)) if radius is None: radius = (np.mean(lens1) + np.mean(lens2)) / 2.0 if check_radius: if not (np.allclose(radius, lens1) and np.allclose(radius, lens2)): raise ValueError('Radii do not match sphere surface') # Get angle with vector cosine dots = np.inner(pts1, pts2) lens = lens1 * lens2 angle_cos = np.arccos(dots / lens) return angle_cos * radius def cart_distance(pts1, pts2): ''' Cartesian distance between `pts1` and `pts2` If either of `pts1` or `pts2` is 2D, then we take the first dimension to index points, and the second indexes coordinate. More generally, we take the last dimension to be the coordinate dimension. Parameters ------------ pts1 : (N,R) or (R,) array-like where N is the number of points and R is the number of coordinates defining a point (``R==3`` for 3D) pts2 : (N,R) or (R,) array-like where N is the number of points and R is the number of coordinates defining a point (``R==3`` for 3D). It should be possible to broadcast `pts1` against `pts2` Returns --------- d : (N,) or (0,) array Cartesian distances between corresponding points in `pts1` and `pts2` See also ---------- sphere_distance : distance between points on sphere surface Examples ---------- >>> cart_distance([0,0,0], [0,0,3]) 3.0 ''' sqs = np.subtract(pts1, pts2)**2 return np.sqrt(np.sum(sqs, axis=-1)) def vector_cosine(vecs1, vecs2): """ Cosine of angle between two (sets of) vectors The cosine of the angle between two vectors ``v1`` and ``v2`` is given by the inner product of ``v1`` and ``v2`` divided by the product of the vector lengths:: v_cos = np.inner(v1, v2) / (np.sqrt(np.sum(v1**2)) * np.sqrt(np.sum(v2**2))) Parameters ------------- vecs1 : (N, R) or (R,) array-like N vectors (as rows) or single vector. Vectors have R elements. vecs1 : (N, R) or (R,) array-like N vectors (as rows) or single vector. Vectors have R elements. It should be possible to broadcast `vecs1` against `vecs2` Returns ---------- vcos : (N,) or (0,) array Vector cosines. To get the angles you will need ``np.arccos`` Notes -------- The vector cosine will be the same as the correlation only if all the input vectors have zero mean. """ vecs1 = np.asarray(vecs1) vecs2 = np.asarray(vecs2) lens1 = np.sqrt(np.sum(vecs1**2, axis=-1)) lens2 = np.sqrt(np.sum(vecs2**2, axis=-1)) dots = np.inner(vecs1, vecs2) lens = lens1 * lens2 return dots / lens def lambert_equal_area_projection_polar(theta, phi): r''' Lambert Equal Area Projection from polar sphere to plane Return positions in (y1,y2) plane corresponding to the points with polar coordinates (theta, phi) on the unit sphere, under the Lambert Equal Area Projection mapping (see Mardia and Jupp (2000), Directional Statistics, p. 161). See doc for ``sphere2cart`` for angle conventions - $0 \le \theta \le \pi$ and $0 \le \phi \le 2 \pi$ - $|(y_1,y_2)| \le 2$ The Lambert EAP maps the upper hemisphere to the planar disc of radius 1 and the lower hemisphere to the planar annulus between radii 1 and 2, and *vice versa*. Parameters ---------- theta : array-like theta spherical coordinates phi : array-like phi spherical coordinates Returns --------- y : (N,2) array planar coordinates of points following mapping by Lambert's EAP. ''' return 2 * np.repeat(np.sin(theta/2),2).reshape((theta.shape[0],2)) * np.column_stack((np.cos(phi), np.sin(phi))) def lambert_equal_area_projection_cart(x,y,z): r''' Lambert Equal Area Projection from cartesian vector to plane Return positions in $(y_1,y_2)$ plane corresponding to the directions of the vectors with cartesian coordinates xyz under the Lambert Equal Area Projection mapping (see Mardia and Jupp (2000), Directional Statistics, p. 161). The Lambert EAP maps the upper hemisphere to the planar disc of radius 1 and the lower hemisphere to the planar annulus between radii 1 and 2, The Lambert EAP maps the upper hemisphere to the planar disc of radius 1 and the lower hemisphere to the planar annulus between radii 1 and 2. and *vice versa*. See doc for ``sphere2cart`` for angle conventions Parameters ------------ x : array-like x coordinate in Cartesion space y : array-like y coordinate in Cartesian space z : array-like z coordinate Returns ---------- y : (N,2) array planar coordinates of points following mapping by Lambert's EAP. ''' (r, theta, phi) = cart2sphere(x,y,z) return lambert_equal_area_projection_polar(theta, phi) def euler_matrix(ai, aj, ak, axes='sxyz'): """Return homogeneous rotation matrix from Euler angles and axis sequence. Code modified from the work of Christoph Gohlke link provided here http://www.lfd.uci.edu/~gohlke/code/transformations.py.html Parameters ------------ ai, aj, ak : Euler's roll, pitch and yaw angles axes : One of 24 axis sequences as string or encoded tuple Returns --------- matrix: 4x4 numpy array Code modified from the work of Christoph Gohlke link provided here http://www.lfd.uci.edu/~gohlke/code/transformations.py.html Examples -------- >>> import numpy >>> R = euler_matrix(1, 2, 3, 'syxz') >>> numpy.allclose(numpy.sum(R[0]), -1.34786452) True >>> R = euler_matrix(1, 2, 3, (0, 1, 0, 1)) >>> numpy.allclose(numpy.sum(R[0]), -0.383436184) True >>> ai, aj, ak = (4.0*math.pi) * (numpy.random.random(3) - 0.5) >>> for axes in _AXES2TUPLE.keys(): ... R = euler_matrix(ai, aj, ak, axes) >>> for axes in _TUPLE2AXES.keys(): ... R = euler_matrix(ai, aj, ak, axes) """ try: firstaxis, parity, repetition, frame = _AXES2TUPLE[axes] except (AttributeError, KeyError): _ = _TUPLE2AXES[axes] firstaxis, parity, repetition, frame = axes i = firstaxis j = _NEXT_AXIS[i+parity] k = _NEXT_AXIS[i-parity+1] if frame: ai, ak = ak, ai if parity: ai, aj, ak = -ai, -aj, -ak si, sj, sk = math.sin(ai), math.sin(aj), math.sin(ak) ci, cj, ck = math.cos(ai), math.cos(aj), math.cos(ak) cc, cs = ci*ck, ci*sk sc, ss = si*ck, si*sk M = np.identity(4) if repetition: M[i, i] = cj M[i, j] = sj*si M[i, k] = sj*ci M[j, i] = sj*sk M[j, j] = -cj*ss+cc M[j, k] = -cj*cs-sc M[k, i] = -sj*ck M[k, j] = cj*sc+cs M[k, k] = cj*cc-ss else: M[i, i] = cj*ck M[i, j] = sj*sc-cs M[i, k] = sj*cc+ss M[j, i] = cj*sk M[j, j] = sj*ss+cc M[j, k] = sj*cs-sc M[k, i] = -sj M[k, j] = cj*si M[k, k] = cj*ci return M def compose_matrix(scale=None, shear=None, angles=None, translate=None,perspective=None): """Return 4x4 transformation matrix from sequence of transformations. Code modified from the work of Christoph Gohlke link provided here http://www.lfd.uci.edu/~gohlke/code/transformations.py.html This is the inverse of the decompose_matrix function. Parameters ------------- scale : vector of 3 scaling factors shear : list of shear factors for x-y, x-z, y-z axes angles : list of Euler angles about static x, y, z axes translate : translation vector along x, y, z axes perspective : perspective partition of matrix Returns --------- matrix : 4x4 array Examples ---------- >>> import math >>> import numpy as np >>> import dipy.core.geometry as gm >>> scale = np.random.random(3) - 0.5 >>> shear = np.random.random(3) - 0.5 >>> angles = (np.random.random(3) - 0.5) * (2*math.pi) >>> trans = np.random.random(3) - 0.5 >>> persp = np.random.random(4) - 0.5 >>> M0 = gm.compose_matrix(scale, shear, angles, trans, persp) """ M = np.identity(4) if perspective is not None: P = np.identity(4) P[3, :] = perspective[:4] M = np.dot(M, P) if translate is not None: T = np.identity(4) T[:3, 3] = translate[:3] M = np.dot(M, T) if angles is not None: R = euler_matrix(angles[0], angles[1], angles[2], 'sxyz') M = np.dot(M, R) if shear is not None: Z = np.identity(4) Z[1, 2] = shear[2] Z[0, 2] = shear[1] Z[0, 1] = shear[0] M = np.dot(M, Z) if scale is not None: S = np.identity(4) S[0, 0] = scale[0] S[1, 1] = scale[1] S[2, 2] = scale[2] M = np.dot(M, S) M /= M[3, 3] return M def decompose_matrix(matrix): """Return sequence of transformations from transformation matrix. Code modified from the excellent work of Christoph Gohlke link provided here http://www.lfd.uci.edu/~gohlke/code/transformations.py.html Parameters ------------ matrix : array_like Non-degenerative homogeneous transformation matrix Returns --------- tuple of: scale : vector of 3 scaling factors shear : list of shear factors for x-y, x-z, y-z axes angles : list of Euler angles about static x, y, z axes translate : translation vector along x, y, z axes perspective : perspective partition of matrix Raise ValueError if matrix is of wrong type or degenerative. Examples ----------- >>> import numpy as np >>> T0=np.diag([2,1,1,1]) >>> scale, shear, angles, trans, persp = decompose_matrix(T0) """ M = np.array(matrix, dtype=np.float64, copy=True).T if abs(M[3, 3]) < _EPS: raise ValueError("M[3, 3] is zero") M /= M[3, 3] P = M.copy() P[:, 3] = 0, 0, 0, 1 if not np.linalg.det(P): raise ValueError("matrix is singular") scale = np.zeros((3, ), dtype=np.float64) shear = [0, 0, 0] angles = [0, 0, 0] if any(abs(M[:3, 3]) > _EPS): perspective = np.dot(M[:, 3], np.linalg.inv(P.T)) M[:, 3] = 0, 0, 0, 1 else: perspective = np.array((0, 0, 0, 1), dtype=np.float64) translate = M[3, :3].copy() M[3, :3] = 0 row = M[:3, :3].copy() scale[0] = vector_norm(row[0]) row[0] /= scale[0] shear[0] = np.dot(row[0], row[1]) row[1] -= row[0] * shear[0] scale[1] = vector_norm(row[1]) row[1] /= scale[1] shear[0] /= scale[1] shear[1] = np.dot(row[0], row[2]) row[2] -= row[0] * shear[1] shear[2] = np.dot(row[1], row[2]) row[2] -= row[1] * shear[2] scale[2] = vector_norm(row[2]) row[2] /= scale[2] shear[1:] /= scale[2] if np.dot(row[0], np.cross(row[1], row[2])) < 0: scale *= -1 row *= -1 angles[1] = math.asin(-row[0, 2]) if math.cos(angles[1]): angles[0] = math.atan2(row[1, 2], row[2, 2]) angles[2] = math.atan2(row[0, 1], row[0, 0]) else: #angles[0] = math.atan2(row[1, 0], row[1, 1]) angles[0] = math.atan2(-row[2, 1], row[1, 1]) angles[2] = 0.0 return scale, shear, angles, translate, perspective def vector_norm(data, axis=None, out=None): """Return length, i.e. euclidean norm, of ndarray along axis. Examples ---------- >>> import numpy >>> v = numpy.random.random(3) >>> n = vector_norm(v) >>> numpy.allclose(n, numpy.linalg.norm(v)) True >>> v = numpy.random.rand(6, 5, 3) >>> n = vector_norm(v, axis=-1) >>> numpy.allclose(n, numpy.sqrt(numpy.sum(v*v, axis=2))) True >>> n = vector_norm(v, axis=1) >>> numpy.allclose(n, numpy.sqrt(numpy.sum(v*v, axis=1))) True >>> v = numpy.random.rand(5, 4, 3) >>> n = numpy.empty((5, 3), dtype=numpy.float64) >>> vector_norm(v, axis=1, out=n) >>> numpy.allclose(n, numpy.sqrt(numpy.sum(v*v, axis=1))) True >>> vector_norm([]) 0.0 >>> vector_norm([1.0]) 1.0 """ data = np.array(data, dtype=np.float64, copy=True) if out is None: if data.ndim == 1: return math.sqrt(np.dot(data, data)) data *= data out = np.atleast_1d(np.sum(data, axis=axis)) np.sqrt(out, out) return out else: data *= data np.sum(data, axis=axis, out=out) np.sqrt(out, out) def circumradius(a, b, c): ''' a, b and c are 3-dimensional vectors which are the vertices of a triangle. The function returns the circumradius of the triangle, i.e the radius of the smallest circle that can contain the triangle. In the degenerate case when the 3 points are collinear it returns half the distance between the furthest apart points. Parameters ---------- a, b, c : (3,) arraylike the three vertices of the triangle Returns ------- circumradius : float the desired circumradius ''' x = a-c xx = np.linalg.norm(x)**2 y = b-c yy = np.linalg.norm(y)**2 z = np.cross(x,y) # test for collinearity if np.linalg.norm(z) == 0: return np.sqrt(np.max(np.dot(x,x), np.dot(y,y), np.dot(a-b,a-b)))/2. else: m = np.vstack((x,y,z)) w = np.dot(np.linalg.inv(m.T),np.array([xx/2.,yy/2.,0])) return np.linalg.norm(w)/2. dipy-0.5.0/dipy/core/graph.py000066400000000000000000000070431152576264200160470ustar00rootroot00000000000000""" A simple graph class """ class Graph(object): ''' A simple graph class ''' def __init__(self): ''' A graph class with nodes and edges :-) This class allows us to: 1. find the shortest path 2. find all paths 3. add/delete nodes and edges 4. get parent & children nodes Examples -------- >>> from dipy.core.graph import Graph >>> g=Graph() >>> g.add_node('a',5) >>> g.add_node('b',6) >>> g.add_node('c',10) >>> g.add_node('d',11) >>> g.add_edge('a','b') >>> g.add_edge('b','c') >>> g.add_edge('c','d') >>> g.add_edge('b','d') >>> g.up_short('d') ['d', 'b', 'a'] ''' self.node={} self.pred={} self.succ={} def add_node(self,n,attr=None): self.succ[n]={} self.pred[n]={} self.node[n]=attr def add_edge(self,n,m,ws=True,wp=True): self.succ[n][m]=ws self.pred[m][n]=wp def parents(self,n): return self.pred[n].keys() def children(self,n): return self.succ[n].keys() def up(self, n): return self.all_paths(self.pred,n) def down(self, n): return self.all_paths(self.succ,n) def up_short(self,n): return self.shortest_path(self.pred,n) def down_short(self,n): return self.shortest_path(self.succ,n) def all_paths(self,graph, start, end=None, path=[]): path = path + [start] if start==end or graph[start]=={}: return [path] if not graph.has_key(start): return [] paths = [] for node in graph[start]: if node not in path: newpaths = self.all_paths(graph, node, end, path) for newpath in newpaths: paths.append(newpath) return paths def shortest_path(self,graph, start, end=None, path=[]): path = path + [start] if graph[start]=={} or start == end: return path if not graph.has_key(start): return [] shortest = None for node in graph[start]: if node not in path: newpath = self.shortest_path(graph, node, end, path) if newpath: if not shortest or len(newpath) < len(shortest): shortest = newpath return shortest def del_node_and_edges(self,n): try: del self.node[n] except KeyError: raise KeyError('node not in the graph') for s in self.succ[n]: del self.pred[s][n] del self.succ[n] for p in self.pred[n]: del self.succ[p][n] del self.pred[n] def del_node(self,n): try: del self.node[n] except KeyError: raise KeyError('node not in the graph') for s in self.succ[n]: for p in self.pred[n]: self.succ[p][s]=self.succ[n][s] self.pred[s][p]=self.pred[s][n] for s in self.succ.keys(): try: del self.succ[s][n] except KeyError: pass for p in self.pred.keys(): try: del self.pred[p][n] except KeyError: pass del self.succ[n] del self.pred[n] dipy-0.5.0/dipy/core/meshes.py000066400000000000000000000265721152576264200162420ustar00rootroot00000000000000''' Mesh analysis ''' import numpy as np from scipy import sparse FLOAT64_EPS = np.finfo(np.float64).eps FLOAT_TYPES = np.sctypes['float'] def sym_hemisphere(vertices, hemisphere='z', equator_thresh=None, dist_thresh=None): """ Indices for hemisphere from an array of `vertices` on a sphere Selects the vertices from a sphere that lie in one hemisphere. If there are pairs of symmetric points on the equator, we return only the first occurring of each pair. Parameters ---------- vertices : (N,3) array-like (x, y, z) Point coordinates of N vertices hemisphere : str, optional Which hemisphere to select. Values of '-x', '-y', '-z' select, respectively negative x, y, and z hemispheres; 'x', 'y', 'z' select the positive x, y, and z hemispheres. Default is 'z' equator_thresh : None or float, optional Threshold (+-0) to identify points as being on the equator of the sphere. If None, generate a default based on the data type dist_thresh : None or float, optional For a vertex ``v`` on the equator, if there is a vertex ``v_dash`` in `vertices`, such that the Euclidean distance between ``v * -1`` and ``v_dash`` is <= `dist_thresh`, then ``v`` is taken to be in the opposite hemisphere to ``v_dash``, and only ``v``, not ``v_dash``, will appear in the output vertex indices `inds`. None results in a threshold based on the input data type of ``vertices`` Returns ------- inds : (P,) array Indices into `vertices` giving points in hemisphere Notes ----- We expect the sphere to be symmetric, and so there may well be points on the sphere equator that are both on the same diameter line. The routine returns the first of the two points in the original order of `vertices`. """ vertices = np.asarray(vertices) assert vertices.shape[1] == 3 if len(hemisphere) == 2: sign, hemisphere = hemisphere if sign not in '+-': raise ValueError('Hemisphere sign must be + or -') else: sign = '+' try: coord = 'xyz'.index(hemisphere) except ValueError: raise ValueError('Hemisphere must be (+-) x, y or z') if equator_thresh is None or dist_thresh is None: if not vertices.dtype.type in FLOAT_TYPES: EPS = FLOAT64_EPS else: EPS = np.finfo(vertices.dtype.type).eps if equator_thresh is None: equator_thresh = EPS * 10 if dist_thresh is None: dist_thresh = EPS * 20 # column with coordinates for selecting the hemisphere sel_col = vertices[:,coord] if sign == '+': inds = sel_col > -equator_thresh else: inds = sel_col < equator_thresh # find equator points eq_inds, = np.where( (sel_col < equator_thresh) & (sel_col > -equator_thresh)) # eliminate later points that are symmetric on equator untested_inds = list(eq_inds) out_inds = [] for ind in eq_inds: untested_inds.remove(ind) test_vert = vertices[ind,:] * -1 test_dists = np.sum( (vertices[untested_inds,:] - test_vert)**2, axis=1) sym_inds, = np.where(test_dists < dist_thresh) for si in sym_inds: out_ind = untested_inds[si] untested_inds.remove(out_ind) out_inds.append(out_ind) if len(untested_inds) == 0: break inds[out_inds] = False return np.nonzero(inds)[0] def vertinds_to_neighbors(vertex_inds, faces): """ Return indices of neighbors of vertices given `faces` Parameters ---------- vertex_inds : sequence length N. Indices of vertices faces : (F, 3) array-like Faces given by indices of vertices for each of ``F`` faces Returns ------- adj : list For each ``N`` vertex indicated by `vertex_inds`, the vertex indices that are neighbors according to the graph given by `faces`. """ full_adj = neighbors(faces) adj = [] for i, n in enumerate(full_adj): if i in vertex_inds: adj.append(n) return adj def neighbors(faces): """ Return indices of neighbors for each vertex within `faces` Parameters ---------- faces : (F, 3) array-like Faces given by indices of vertices for each of ``F`` faces Returns ------- adj : list For each vertex found within `faces`, the vertex indices that are neighbors according to the graph given by `faces`. We expand the list with empty lists in between non-empty neighbors. """ faces = np.asarray(faces) adj = {} for face in faces: a, b, c = face if a in adj: adj[a] += [b, c] else: adj[a] = [b, c] if b in adj: adj[b] += [a, c] else: adj[b] = [a, c] if c in adj: adj[c] += [a, b] else: adj[c] = [a, b] N = max(adj.keys())+1 out = [[] for i in range(N)] for i in range(N): if i in adj: out[i] = np.sort(np.unique(adj[i])) return out def vertinds_faces(vertex_inds, faces): """ Return faces containing any of `vertex_inds` Parameters ---------- vertex_inds : sequence length N. Indices of vertices faces : (F, 3) array-like Faces given by indices of vertices for each of ``F`` faces Returns --------- less_faces : (P, 3) array Only retaining rows in `faces` which contain any of `vertex_inds` """ in_inds = [] vertex_inds = set(vertex_inds) for ind, face in enumerate(faces): if vertex_inds.intersection(face): in_inds.append(ind) return faces[in_inds] def edges(vertex_inds, faces): r""" Return array of starts and ends of edges from list of faces taking regard of direction. Parameters ---------- vertex_inds : sequence length N. Indices of vertices faces : (F, 3) array-like Faces given by indices of vertices for each of F faces Returns ------- edgearray : (E2, 2) array where E2 = 2*E, twice the number of edges. If e= (a,b) is an edge then [a,b] and [b,a] are included in edgearray. """ edgedic = {} for face in faces: edgedic[(face[0],face[1])]=1 edgedic[(face[0],face[2])]=1 edgedic[(face[1],face[0])]=1 edgedic[(face[1],face[2])]=1 edgedic[(face[2],face[0])]=1 edgedic[(face[2],face[1])]=1 start, end = zip(*edgedic) edgearray = np.column_stack(zip(*edgedic)) return edgearray def vertex_adjacencies(vertex_inds, faces): """ Return matrix which shows the adjacent vertices of each vertex Parameters ---------- vertex_inds : sequence length N. Indices of vertices faces : (F, 3) array-like Faces given by indices of vertices for each of F faces Returns ------- """ edgearray = edges(vertex_inds, faces) V = len(vertex_inds) a = sparse.coo_matrix((np.ones(edgearray.shape[0]), (edgearray[:,0],edgearray[:,1])), shape=(V,V)) return a def argmax_from_adj(vals, vertex_inds, adj_inds): """ Indices of local maxima from `vals` given adjacent points See ``reconstruction_performance`` for optimized versions of this routine. Parameters ---------- vals : (N,) array-like values at all vertices referred to in either of `vertex_inds` or `adj_inds`' vertex_inds : None or (V,) array-like indices into `vals` giving vertices that may be local maxima. If None, then equivalent to ``np.arange(N)`` adj_inds : sequence For every vertex in ``vertex_inds``, the indices (into `vals`) of the neighboring points Returns ------- inds : (M,) array Indices into `vals` giving local maxima of vals, given topology from `adj_inds`, and restrictions from `vertex_inds`. Inds are returned sorted by value at that index - i.e. smallest value (at index) first. """ vals = np.asarray(vals) if vertex_inds is None: vertex_inds = np.arange(vals.shape[0]) else: vertex_inds = np.asarray(vertex_inds) maxes = [] for i, adj in enumerate(adj_inds): vert_ind = vertex_inds[i] val = vals[vert_ind] if np.all(val > vals[adj]): maxes.append((val, vert_ind)) if len(maxes) == 0: return np.array([]) maxes.sort(cmp=lambda x, y: cmp(x[0], y[0])) vals, inds = zip(*maxes) return np.array(inds) def peak_finding_compatible(vertices, hemisphere='z', equator_thresh=None, dist_thresh=None): """ Check that a sphere mesh is compatible with ``peak_finding`` Parameters ---------- vertices : (N,3) array-like (x, y, z) Point coordinates of N vertices hemisphere : str, optional Which hemisphere to select. Values of '-x', '-y', '-z' select, respectively negative x, y, and z hemispheres; 'x', 'y', 'z' select the positive x, y, and z hemispheres. Default is 'z' equator_thresh : None or float, optional Threshold (+-0) to identify points as being on the equator of the sphere. If None, generate a default based on the data type dist_thresh : None or float, optional For a vertex ``v`` on the equator, if there is a vertex ``v_dash`` in `vertices`, such that the Euclidean distance between ``v * -1`` and ``v_dash`` is <= `dist_thresh`, then ``v`` is taken to be in the opposite hemisphere to ``v_dash``, and only ``v``, not ``v_dash``, will appear in the output vertex indices `inds`. None results in a threshold based on the input data type of ``vertices`` Returns ------- compatible : bool True if the sphere mesh is compatible with ``peak_finding`` """ inds = sym_hemisphere(vertices, hemisphere, equator_thresh, dist_thresh) N = vertices.shape[0] // 2 return np.all(inds == np.arange(N)) def euler_characteristic_check(vertices, faces, chi=2): r''' If $f$ = number of faces, $e$ = number_of_edges and $v$ = number of vertices, the Euler formula says $f-e+v = 2$ for a mesh on a sphere. Here, assuming we have a healthy triangulation every face is a triangle, all 3 of whose edges should belong to exactly two faces. So $2*e = 3*f$. To avoid integer division and consequential integer rounding we test whether $2*f - 3*f + 2*v == 4$ or, more generally, whether $2*v - f == 2*\chi$ where $\chi$ is the Euler characteristic of the mesh. - Open chain (track) has $\chi=1$ - Closed chain (loop) has $\chi=0$ - Disk has $\chi=1$ - Sphere has $\chi=2$ Parameters ---------- vertices : (N,3) array-like (x, y, z) Point coordinates of N vertices faces : (M,3) array-like of type int (i1, i2, i3) Integer indices of the vertices of the (triangular) faces chi : int, or None The Euler characteristic of the mesh to be checked Returns ------- check : bool True if the mesh has Euler characteristic chi ''' v = vertices.shape[0] f = faces.shape[0] if 2*v-f==2*chi: return True else: return False dipy-0.5.0/dipy/core/onetime.py000066400000000000000000000046661152576264200164160ustar00rootroot00000000000000"""Descriptor support for NIPY. Utilities to support special Python descriptors ([1]_, [2]_), in particular the use of a useful pattern for properties we call 'one time properties'. These are object attributes which are declared as properties, but become regular attributes once they've been read the first time. They can thus be evaluated later in the object's life cycle, but once evaluated they become normal, static attributes with no function call overhead on access or any other constraints. References ------------ .. [1] How-To Guide for Descriptors, Raymond Hettinger. http://users.rcn.com/python/download/Descriptor.htm .. [2] Python data model, http://docs.python.org/reference/datamodel.html """ class OneTimeProperty(object): """A descriptor to make special properties that become normal attributes. """ def __init__(self,func): """Create a OneTimeProperty instance. Parameters ------------- func : method The method that will be called the first time to compute a value. Afterwards, the method's name will be a standard attribute holding the value of this computation. """ self.getter = func self.name = func.func_name def __get__(self,obj,type=None): """This will be called on attribute access on the class or instance. """ if obj is None: # Being called on the class, return the original function. This way, # introspection works on the class. return self.getter val = self.getter(obj) #print "** setattr_on_read - loading '%s'" % self.name # dbg setattr(obj, self.name, val) return val def setattr_on_read(func): # XXX - beetter names for this? # - cor_property (copy on read property) # - sor_property (set on read property) # - prop2attr_on_read #... ? """Decorator to create OneTimeProperty attributes. Parameters ------------ func : method The method that will be called the first time to compute a value. Afterwards, the method's name will be a standard attribute holding the value of this computation. Examples ----------- >>> class MagicProp(object): ... @setattr_on_read ... def a(self): ... return 99 ... >>> x = MagicProp() >>> 'a' in x.__dict__ False >>> x.a 99 >>> 'a' in x.__dict__ True """ return OneTimeProperty(func) dipy-0.5.0/dipy/core/profile.py000066400000000000000000000057351152576264200164140ustar00rootroot00000000000000""" Class for profiling cython code """ import os import subprocess from ..utils.optpkg import optional_package cProfile, _, _ = optional_package('cProfile') pstats, _, _ = optional_package('pstats', 'pstats is not installed. It is part of the' 'python-profiler package in Debian/Ubuntu') class Profiler(): ''' Profile python/cython files or functions If you are profiling cython code you need to add # cython: profile=True on the top of your .pyx file and for the functions that you do not want to profile you can use this decorator in your cython files @cython.profile(False) Parameters ------------- caller: file or function call args: function arguments Attributes ------------ stats: function, stats.print_stats(10) will prin the 10 slower functions Examples ----------- import dipy.core.profile as p import numpy as np p.Profiler(np.sum,np.random.rand(1000000,3)) fname='test.py' p.Profiler(fname) p.print_stats(10) p.print_stats('det') References ------------- http://docs.cython.org/src/tutorial/profiling_tutorial.html http://docs.python.org/library/profile.html http://packages.python.org/line_profiler/ ''' def __init__(self,call=None,*args): # Delay import until use of class instance. We were getting some very # odd build-as-we-go errors running tests and documentation otherwise import pyximport pyximport.install() try: ext=os.path.splitext(call)[1].lower() print('ext',ext) if ext == '.py' or ext == '.pyx': #python/cython file print('profiling python/cython file ...') subprocess.call(['python','-m','cProfile', \ '-o','profile.prof',call]) s = pstats.Stats('profile.prof') stats=s.strip_dirs().sort_stats('time') self.stats=stats except: print('profiling function call ...') self.args=args self.call=call cProfile.runctx('self._profile_function()',globals(),locals(),\ 'profile.prof') s = pstats.Stats('profile.prof') stats=s.strip_dirs().sort_stats('time') self.stats=stats def _profile_function(self): self.call(*self.args) def print_stats(self,N=10): ''' Print stats for profiling You can use it in all different ways developed in pstats for example print_stats(10) will give you the 10 slowest calls or print_stats('function_name') will give you the stats for all the calls with name 'function_name' Parameters ------------ N : stats.print_stats argument ''' self.stats.print_stats(N) dipy-0.5.0/dipy/core/pyalloc.pxd000066400000000000000000000005551152576264200165550ustar00rootroot00000000000000# -*- python -*- or rather like from python_string cimport PyString_FromStringAndSize, \ PyString_AS_STRING, PyString_Size # Function to allocate, wrap memory via Python string creation cdef inline object pyalloc_v(Py_ssize_t n, void **pp): cdef object ob = PyString_FromStringAndSize(NULL, n) pp[0] = PyString_AS_STRING(ob) return ob dipy-0.5.0/dipy/core/rng.py000066400000000000000000000065651152576264200155440ustar00rootroot00000000000000""" Random number generation utilities """ from math import floor from platform import architecture def WichmannHill2006(): ''' B.A. Wichmann, I.D. Hill, Generating good pseudo-random numbers, Computational Statistics & Data Analysis, Volume 51, Issue 3, 1 December 2006, Pages 1614-1622, ISSN 0167-9473, DOI: 10.1016/j.csda.2006.05.019. (http://www.sciencedirect.com/science/article/B6V8V-4K7F86W-2/2/a3a33291b8264e4c882a8f21b6e43351) for advice on generating many sequences for use together, and on alternative algorithms and codes Examples ---------- >>> from dipy.core import rng >>> rng.ix, rng.iy, rng.iz, rng.it = 100001, 200002, 300003, 400004 >>> N = 1000 >>> a = [rng.WichmannHill2006() for i in range(N)] ''' global ix, iy, iz, it if architecture()[0] == '64': #If 64 bits are available then the following lines of code will be faster. ix = (11600 * ix) % 2147483579 iy = (47003 * iy) % 2147483543 iz = (23000 * iz) % 2147483423 it = (33000 * it) % 2147483123 else: #If only 32 bits are available ix = 11600 * (ix % 185127) - 10379 * (ix / 185127) iy = 47003 * (ix % 45688) - 10479 * (iy / 45688) iz = 23000 * (iz % 93368) - 19423 * (iz / 93368) it = 33000 * (it % 65075) - 8123 * (it / 65075) if ix < 0: ix = ix + 2147483579 if iy < 0: iy = iy + 2147483543 if iz < 0: iz = iz + 2147483423 if it < 0: it = it + 2147483123 W = ix/2147483579.0 + iy/2147483543.0 + iz/2147483423.0 + it/2147483123.0 return W - floor(W) def WichmannHill1982(): ''' Algorithm AS 183 Appl. Statist. (1982) vol.31, no.2 Returns a pseudo-random number rectangularly distributed between 0 and 1. The cycle length is 6.95E+12 (See page 123 of Applied Statistics (1984) vol.33), not as claimed in the original article. ix, iy and iz should be set to integer values between 1 and 30000 before the first entry. Integer arithmetic up to 5212632 is required. ''' import numpy as np global ix, iy, iz ix = (171 * ix) % 30269 iy = (172 * iy) % 30307 iz = (170 * iz) % 30323 ''' If integer arithmetic only up to 30323 (!) is available, the preceding 3 statements may be replaced by: ix = 171 * (ix % 177) - 2 * (ix / 177) iy = 172 * (iy % 176) - 35 * (iy / 176) iz = 170 * (iz % 178) - 63 * (iz / 178) if ix < 0: ix = ix + 30269 if iy < 0: iy = iy + 30307 if iz < 0: iz = iz + 30323 ''' return np.remainder(np.float(ix) / 30269. + np.float(iy) / 30307. + np.float(iz) / 30323., 1.0) def LEcuyer(): ''' Generate uniformly distributed random numbers using the 32-bit generator from figure 3 of: L'Ecuyer, P. Efficient and portable combined random number generators, C.A.C.M., vol. 31, 742-749 & 774-?, June 1988. The cycle length is claimed to be 2.30584E+18 ''' global s1, s2 k = s1 / 53668 s1 = 40014 * (s1 - k * 53668) - k * 12211 if s1 < 0: s1 = s1 + 2147483563 k = s2 / 52774 s2 = 40692 * (s2 - k * 52774) - k * 3791 if s2 < 0: s2 = s2 + 2147483399 z = s1 - s2 if z < 0: z = z + 2147483562 return z / 2147483563. dipy-0.5.0/dipy/core/sphere_stats.py000066400000000000000000000057501152576264200174550ustar00rootroot00000000000000""" Statistics on spheres """ import numpy as np import dipy.core.geometry as geometry def eigenstats(points, alpha=0.05): r'''Principal direction and confidence ellipse Implements equations in section 6.3.1(ii) of Fisher, Lewis and Embleton, supplemented by equations in section 3.2.5. Parameters ---------- points : arraey_like (N,3) array of points on the sphere of radius 1 in $\mathbb{R}^3$ alpha : real or None 1 minus the coverage for the confidence ellipsoid, e.g. 0.05 for 95% coverage. Returns ------- centre : vector (3,) centre of ellipsoid b1 : vector (2,) lengths of semi-axes of ellipsoid ''' n = points.shape[0] # the number of points rad2deg = 180/np.pi # scale angles from radians to degrees # there is a problem with averaging and axis data. ''' centroid = np.sum(points, axis=0)/n normed_centroid = geometry.normalized_vector(centroid) x,y,z = normed_centroid #coordinates of normed centroid polar_centroid = np.array(geometry.cart2sphere(x,y,z))*rad2deg ''' cross = np.dot(points.T,points)/n # cross-covariance of points evals, evecs = np.linalg.eigh(cross) # eigen decomposition assuming that cross is symmetric order = np.argsort(evals) # eigenvalues don't necessarily come in an particular order? tau = evals[order] # the ordered eigenvalues h = evecs[:,order] # the eigenvectors in corresponding order h[:,2] = h[:,2]*np.sign(h[2,2]) # map the first principal direction into upper hemisphere centre = np.array(geometry.cart2sphere(*h[:,2]))[1:]*rad2deg # the spherical coordinates of the first principal direction e = np.zeros((2,2)) p0 = np.dot(points,h[:,0]) p1 = np.dot(points,h[:,1]) p2 = np.dot(points,h[:,2]) # the principal coordinates of the points e[0,0] = np.sum((p0**2)*(p2**2))/(n*(tau[0]-tau[2])**2) e[1,1] = np.sum((p1**2)*(p2**2))/(n*(tau[1]-tau[2])**2) e[0,1] = np.sum((p0*p1*(p2**2))/(n*(tau[0]-tau[2])*(tau[1]-tau[2]))) e[1,0] = e[0,1] # e is a 2x2 helper matrix b1 = np.array([np.NaN,np.NaN]) d = -2*np.log(alpha)/n s,w = np.linalg.eig(e) g = np.sqrt(d*s) b1= np.arcsin(g)*rad2deg # b1 are the estimated 100*(1-alpha)% confidence ellipsoid semi-axes # in degrees return centre, b1 ''' # b2 is equivalent to b1 above # try to invert e and calculate vector b the standard errors of # centre - these are forced to a mixture of NaN and/or 0 in singular cases b2 = np.array([np.NaN,np.NaN]) if np.abs(np.linalg.det(e)) < 10**-20: b2 = np.array([0,np.NaN]) else: try: f = np.linalg.inv(e) except np.linalg.LigAlgError: b2 = np.array([np.NaN, np.NaN]) else: t, y = np.linalg.eig(f) d = -2*np.log(alpha)/n g = np.sqrt(d/t) b2= np.arcsin(g)*rad2deg ''' dipy-0.5.0/dipy/core/tests/000077500000000000000000000000001152576264200155325ustar00rootroot00000000000000dipy-0.5.0/dipy/core/tests/__init__.py000066400000000000000000000001651152576264200176450ustar00rootroot00000000000000# init to make tests into a package # Test callable from numpy.testing import Tester test = Tester().test del Testerdipy-0.5.0/dipy/core/tests/test_geometry.py000066400000000000000000000146021152576264200210010ustar00rootroot00000000000000""" Testing utility functions """ import numpy as np from dipy.core.geometry import (sphere2cart, cart2sphere, nearest_pos_semi_def, sphere_distance, cart_distance, vector_cosine, lambert_equal_area_projection_polar, circumradius ) from nose.tools import assert_true, assert_false, \ assert_equal, assert_raises from numpy.testing import assert_array_equal, assert_array_almost_equal from dipy.testing import parametric, sphere_points @parametric def test_sphere_cart(): # test arrays of points rs, thetas, phis = cart2sphere(*(sphere_points.T)) xyz = sphere2cart(rs, thetas, phis) yield assert_array_almost_equal(xyz, sphere_points.T) # test radius estimation big_sph_pts = sphere_points * 10.4 rs, thetas, phis = cart2sphere(*big_sph_pts.T) yield assert_array_almost_equal(rs, 10.4) xyz = sphere2cart(rs, thetas, phis) yield assert_array_almost_equal(xyz, big_sph_pts.T, 6) # test a scalar point pt = sphere_points[3] r, theta, phi = cart2sphere(*pt) xyz = sphere2cart(r, theta, phi) yield assert_array_almost_equal(xyz, pt) @parametric def test_nearest_pos_semi_def(): B = np.diag(np.array([1,2,3])) yield assert_array_almost_equal(B, nearest_pos_semi_def(B)) B = np.diag(np.array([0,2,3])) yield assert_array_almost_equal(B, nearest_pos_semi_def(B)) B = np.diag(np.array([0,0,3])) yield assert_array_almost_equal(B, nearest_pos_semi_def(B)) B = np.diag(np.array([-1,2,3])) Bpsd = np.array([[0.,0.,0.],[0.,1.75,0.],[0.,0.,2.75]]) yield assert_array_almost_equal(Bpsd, nearest_pos_semi_def(B)) B = np.diag(np.array([-1,-2,3])) Bpsd = np.array([[0.,0.,0.],[0.,0.,0.],[0.,0.,2.]]) yield assert_array_almost_equal(Bpsd, nearest_pos_semi_def(B)) B = np.diag(np.array([-1.e-11,0,1000])) Bpsd = np.array([[0.,0.,0.],[0.,0.,0.],[0.,0.,1000.]]) yield assert_array_almost_equal(Bpsd, nearest_pos_semi_def(B)) B = np.diag(np.array([-1,-2,-3])) Bpsd = np.array([[0.,0.,0.],[0.,0.,0.],[0.,0.,0.]]) yield assert_array_almost_equal(Bpsd, nearest_pos_semi_def(B)) @parametric def test_cart_distance(): a = [0, 1] b = [1, 0] yield assert_array_almost_equal(cart_distance(a, b), np.sqrt(2)) yield assert_array_almost_equal(cart_distance([1,0], [-1,0]), 2) pts1 = [2, 1, 0] pts2 = [0, 1, -2] yield assert_array_almost_equal( cart_distance(pts1, pts2), np.sqrt(8)) pts2 = [[0, 1, -2], [-2, 1, 0]] yield assert_array_almost_equal( cart_distance(pts1, pts2), [np.sqrt(8), 4]) @parametric def test_sphere_distance(): # make a circle, go around... radius = 3.2 n = 5000 n2 = n / 2 # pi at point n2 in array angles = np.linspace(0, np.pi*2, n, endpoint=False) x = np.sin(angles) * radius y = np.cos(angles) * radius # dists around half circle, including pi half_x = x[:n2+1] half_y = y[:n2+1] half_dists = np.sqrt(np.diff(half_x)**2 + np.diff(half_y)**2) # approximate distances from 0 to pi (not including 0) csums = np.cumsum(half_dists) # concatenated with distances from pi to 0 again cdists = np.r_[0, csums, csums[-2::-1]] # check approximation close to calculated sph_d = sphere_distance([0,radius], np.c_[x, y]) yield assert_array_almost_equal(cdists, sph_d, decimal=5) # Now check with passed radius sph_d = sphere_distance([0,radius], np.c_[x, y], radius=radius) yield assert_array_almost_equal(cdists, sph_d, decimal=5) # Check points not on surface raises error when asked for yield assert_raises(ValueError, sphere_distance, [1, 0], [0, 2]) # Not when check is disabled sph_d = sphere_distance([1, 0], [0,2], None, False) # Error when radii don't match passed radius yield assert_raises(ValueError, sphere_distance, [1, 0], [0, 1], 2.0) @parametric def test_vector_cosine(): a = [0, 1] b = [1, 0] yield assert_array_almost_equal(vector_cosine(a, b), 0) yield assert_array_almost_equal(vector_cosine([1,0], [-1,0]), -1) yield assert_array_almost_equal(vector_cosine([1,0], [1,1]), 1 / np.sqrt(2)) yield assert_array_almost_equal(vector_cosine([2,0], [-4,0]), -1) pts1 = [2, 1, 0] pts2 = [-2, -1, 0] yield assert_array_almost_equal( vector_cosine(pts1, pts2), -1) pts2 = [[-2, -1, 0], [2, 1, 0]] yield assert_array_almost_equal( vector_cosine(pts1, pts2), [-1, 1]) # test relationship with correlation # not the same if non-zero vector mean a = np.random.uniform(size=(100,)) b = np.random.uniform(size=(100,)) cc = np.corrcoef(a, b)[0,1] vcos = vector_cosine(a, b) yield assert_false(np.allclose(cc, vcos)) # is the same if zero vector mean a_dm = a - np.mean(a) b_dm = b - np.mean(b) vcos = vector_cosine(a_dm, b_dm) yield assert_array_almost_equal(cc, vcos) @parametric def test_lambert_equal_area_projection_polar(): theta = np.repeat(np.pi/3,10) phi = np.linspace(0,2*np.pi,10) # points sit on circle with co-latitude pi/3 (60 degrees) leap = lambert_equal_area_projection_polar(theta,phi) yield assert_array_almost_equal(np.sqrt(np.sum(leap**2,axis=1)), np.array([ 1.,1.,1.,1.,1.,1.,1.,1.,1.,1.])) # points map onto the circle of radius 1 @parametric def test_lambert_equal_area_projection_cart(): xyz = np.array([[1,0,0],[0,1,0],[0,0,1],[-1,0,0],[0,-1,0],[0,0,-1]]) # points sit on +/-1 on all 3 axes r,theta,phi = cart2sphere(*xyz.T) leap = lambert_equal_area_projection_polar(theta,phi) r2 = np.sqrt(2) yield assert_array_almost_equal(np.sqrt(np.sum(leap**2,axis=1)), np.array([ r2,r2,0,r2,r2,2])) # x and y =+/-1 map onto circle of radius sqrt(2) # z=1 maps to origin, and z=-1 maps to (an arbitrary point on) the # outer circle of radius 2 @parametric def test_circumradius(): yield assert_array_almost_equal(np.sqrt(0.5), circumradius(np.array([0,2,0]),np.array([2,0,0]),np.array([0,0,0]))) dipy-0.5.0/dipy/core/tests/test_graph.py000066400000000000000000000017711152576264200202520ustar00rootroot00000000000000from dipy.core.graph import Graph from nose.tools import assert_equal def test_graph(): g=Graph() g.add_node('a',5) g.add_node('b',6) g.add_node('c',10) g.add_node('d',11) g.add_edge('a','b') g.add_edge('b','c') g.add_edge('c','d') g.add_edge('b','d') print('Nodes') print(g.node) print('Successors') print(g.succ) print('Predecessors') print(g.pred) print('Paths above d') print(g.up('d')) print('Paths below a') print(g.down('a')) print('Shortest path above d') print(g.up_short('d')) print('Shortest path below a') print(g.down_short('a')) print( 'Deleting node b') #g.del_node_and_edges('b') g.del_node('b') print( 'Nodes') print( g.node) print( 'Successors') print( g.succ) print( 'Predecessors') print( g.pred) assert_equal(len(g.node),3) assert_equal(len(g.succ),3) assert_equal(len(g.pred),3) dipy-0.5.0/dipy/core/triangle_subdivide.py000066400000000000000000000170151152576264200206110ustar00rootroot00000000000000'''Create a unit sphere by subdividing all triangles of an octahedron recursively. The unit sphere has a radius of 1, which also means that all points in this sphere (assumed to have centre at [0, 0, 0]) have an absolute value (modulus) of 1. Another feature of the unit sphere is that the unit normals of this sphere are exactly the same as the vertices. This recursive method will avoid the common problem of the polar singularity, produced by 2d (lon-lat) parameterization methods. If you require a sphere with another radius than that of 1, simply multiply every single value in the vertex array by this new radius (although this will break the "vertex array equal to unit normal array" property) ''' import numpy np = numpy octahedron_vertices = numpy.array( [ [ 1.0, 0.0, 0.0], # 0 [-1.0, 0.0, 0.0], # 1 [ 0.0, 1.0, 0.0], # 2 [ 0.0,-1.0, 0.0], # 3 [ 0.0, 0.0, 1.0], # 4 [ 0.0, 0.0,-1.0] # 5 ] ) octahedron_edges = numpy.array( [ [0, 4], #0 #0 [1, 5], #10 #1 [4, 2], #1 #2 [5, 3], #11 #3 [2, 0], #2 #4 [3, 1], #6 #5 [2, 1], #3 #6 [3, 0], #7 #7 [1, 4], #4 #8 [0, 5], #8 #9 [4, 3], #5 #10 [5, 2], #9 #11 ], dtype='uint16' ) octahedron_triangles = numpy.array( [ [ 0, 2, 4], [ 1, 3, 5], [ 2, 6, 8], [ 3, 7, 9], [ 8, 10, 5], [ 9, 11, 4], [ 0, 10, 7], [ 1, 11, 6], ], dtype='uint16') def normalize_v3(arr): ''' Normalize a numpy array of 3 component vectors shape=(n,3) ''' lens = numpy.sqrt( arr[:,0]**2 + arr[:,1]**2 + arr[:,2]**2 ) arr /= lens[:,None] def divide_all( vertices, edges, triangles ): r""" Subdivides a triangle Parameters ------------ vertices : ndarray A Vx3 array with the x, y, and z coordinates of of each vertex edges : ndarray An Ex2 array were each pair of values is an index Returns --------- vertices : array A 2d array with the x, y, and z coordinates of vertices edges : array A 2d array of vertex pairs for every set of neighboring vertexes triangles : array A 2d array of edge triplets representing triangles Notes ------- Subdivide each triangle in the old approximation and normalize the new points thus generated to lie on the surface of the unit sphere. Each input triangle with vertices labelled [0,1,2], as shown below, is represented by a set of edges. The edges are written in such a way so that the second vertex in each edge is the first vertex in the next edge. For example:: [0, 1] [1, 2] [2, 0] Make new points:: b = (0+1)/2 c = (1+2)/2 a = (2+0)/2 Construct new triangles:: t1 [0b,ba,a0] t2 [1c,cb,b1] t3 [2a,ac,c2] t4 [ba,ac,cb] Like this:: 1 /\ / \ b/____\ c /\ /\ / \ / \ /____\/____\ 0 a 2 Normalize a, b, c. When constructed this way edges[triangles,0] or edges[triangles,1] will both return the three vertices that make up each triangle (in a different order): Code was adjusted from dlampetest website http://sites.google.com/site/dlampetest/python/triangulating-a-sphere-recursively """ num_vertices = len(vertices) num_edges = len(edges) num_triangles = len(triangles) new_vertices = vertices[edges].sum(1) normalize_v3(new_vertices) vertices = np.vstack((vertices, new_vertices)) new_v_ind = np.arange(num_vertices, num_vertices+num_edges, dtype='uint16') v_b = new_v_ind[triangles[:,0]] v_c = new_v_ind[triangles[:,1]] v_a = new_v_ind[triangles[:,2]] edges = np.vstack((np.c_[edges[:,0], new_v_ind], np.c_[new_v_ind, edges[:,1]], np.c_[v_b, v_a], np.c_[v_a, v_c], np.c_[v_c, v_b], )) E_0b = triangles[:,0] E_b1 = triangles[:,0] + num_edges E_1c = triangles[:,1] E_c2 = triangles[:,1] + num_edges E_2a = triangles[:,2] E_a0 = triangles[:,2] + num_edges E_ba = np.arange(3*num_triangles, 4*num_triangles, dtype='uint16') E_ac = np.arange(4*num_triangles, 5*num_triangles, dtype='uint16') E_cb = np.arange(5*num_triangles, 6*num_triangles, dtype='uint16') triangles = np.vstack((np.c_[E_0b, E_ba, E_a0], np.c_[E_1c, E_cb, E_b1], np.c_[E_2a, E_ac, E_c2], np.c_[E_ba, E_ac, E_cb], )) return vertices, edges, triangles def create_unit_sphere( recursion_level=2 ): """ Creates a unit sphere by subdividing a unit octahedron. Starts with a unit octahedron and subdivides the faces, projecting the resulting points onto the surface of a unit sphere. Parameters ------------ recursion_level : int Level of subdivision, recursion_level=1 will return an octahedron, anything bigger will return a more subdivided sphere. Returns --------- vertices : array A 2d array with the x, y, and z coordinates of vertices on a unit sphere. edges : array A 2d array of vertex pairs for every set of neighboring vertexes on a unit sphere. triangles : array A 2d array of edge triplets representing triangles on the surface of a unit sphere. See Also ---------- create_half_sphere, divide_all """ vertex_array, edge_array, triangle_array = octahedron_vertices, \ octahedron_edges, \ octahedron_triangles for i in range( recursion_level - 1 ): vertex_array, edge_array, triangle_array = divide_all(vertex_array, edge_array, triangle_array) return vertex_array, edge_array, triangle_array def create_half_unit_sphere( recursion_level=2 ): """ Creates a unit sphere and returns the top half Starting with a symmetric sphere of points, removes half the points so that for any pair of points a, -a only one is kept. Removes half the edges so for any pair of edges [a, b]; [-a, -b] only one is kept. The new edges are constructed in a way so that references to any removed point, r, is replaced by a reference to -r. Removes half the triangles in the same way. Parameters ------------- recursion_level : int Level of subdivision, recursion_level=1 will return an octahedron, anything bigger will return a more subdivided sphere. Returns --------- vertices : array A 2d array with the x, y, and z coordinates of vertices on a unit sphere. edges : array A 2d array of vertex pairs for every set of neighboring vertexes on a unit sphere. triangles : array A 2d array of edge triplets representing triangles on the surface of a unit sphere. See Also ---------- create_half_sphere, divide_all """ v, e, t = create_unit_sphere( recursion_level ) return remove_half_sphere(v, e, t) def remove_half_sphere(v, e, t): """ Returns a triangulated half sphere Removes half the vertices, edges, and triangles from a unit sphere created by create_unit_sphere """ return v[::2], e[::2]/2, t[::2]/2 dipy-0.5.0/dipy/data/000077500000000000000000000000001152576264200143515ustar00rootroot00000000000000dipy-0.5.0/dipy/data/55dir_grad.bval000066400000000000000000000004261152576264200171460ustar00rootroot000000000000000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 dipy-0.5.0/dipy/data/55dir_grad.bvec000066400000000000000000000047631152576264200171510ustar00rootroot000000000000000 0.387747134121 0.946163995722 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'fib0', 'fib1' or 'fib2' Returns --------- dix : dictionary, where dix['data'] returns a 2d array where every row is a simulated voxel with different orientation Examples ---------- >>> from dipy.data import get_sim_voxels >>> sv=get_sim_voxels('fib1') >>> sv['data'].shape (100, 102) >>> sv['fibres'] '1' >>> sv['gradients'].shape (102, 3) >>> sv['bvals'].shape (102,) >>> sv['snr'] '60' >>> sv2=get_sim_voxels('fib2') >>> sv2['fibres'] '2' >>> sv2['snr'] '80' Notes ------- These sim voxels were provided by M.M. Correia using Rician noise. """ if name=='fib0': fname=pjoin(THIS_DIR,'fib0.pkl.gz') if name=='fib1': fname=pjoin(THIS_DIR,'fib1.pkl.gz') if name=='fib2': fname=pjoin(THIS_DIR,'fib2.pkl.gz') return cPickle.loads(gzip.open(fname,'rb').read()) def get_skeleton(name='C1'): """ provide skeletons generated from Local Skeleton Clustering (LSC) Parameters ----------- name : str, 'C1' or 'C3' Returns ------- dix : dictionary Examples --------- >>> from dipy.data import get_skeleton >>> C=get_skeleton('C1') >>> len(C.keys()) 117 >>> for c in C: break >>> C[c].keys() ['indices', 'most', 'hidden', 'N'] """ if name=='C1': fname=pjoin(THIS_DIR,'C1.pkl.gz') if name=='C3': fname=pjoin(THIS_DIR,'C3.pkl.gz') return cPickle.loads(gzip.open(fname,'rb').read()) def get_sphere(name='symmetric363'): ''' provide triangulated spheres Parameters ------------ name : str which sphere - one of: * 'symmetric362' * 'symmetric642' Returns ------- vertices : ndarray vertices for sphere faces : ndarray faces Examples -------- >>> import numpy as np >>> from dipy.data import get_sphere >>> verts, faces = get_sphere('symmetric362') >>> verts.shape (362, 3) >>> faces.shape (720, 3) >>> verts, faces = get_sphere('not a sphere name') Traceback (most recent call last): ... DataError: No sphere called "not a sphere name" ''' fname = SPHERE_FILES.get(name) if fname is None: raise DataError('No sphere called "%s"' % name) res = np.load(fname) # Set to native byte order to avoid errors in compiled routines for # big-endian platforms, when using these spheres. return (as_native_array(res['vertices']), as_native_array(res['faces'])) def get_data(name='small_64D'): ''' provides filenames of some test datasets Parameters ---------- name: str the filename/s of which dataset to return, one of: 'small_64D' small region of interest nifti,bvecs,bvals 64 directions 'small_101D' small region of interest nifti,bvecs,bvals 101 directions 'aniso_vox' volume with anisotropic voxel size as Nifti 'fornix' 300 tracks in Trackvis format (from Pittsburgh Brain Competition) Returns ------- fnames : tuple filenames for dataset Examples ---------- >>> import numpy as np >>> from dipy.data import get_data >>> fimg,fbvals,fbvecs=get_data('small_101D') >>> bvals=np.loadtxt(fbvals) >>> bvecs=np.loadtxt(fbvecs).T >>> import nibabel as nib >>> img=nib.load(fimg) >>> data=img.get_data() >>> data.shape (6, 10, 10, 102) >>> bvals.shape (102,) >>> bvecs.shape (102, 3) ''' if name=='small_64D': fbvals=pjoin(THIS_DIR,'small_64D.bvals.npy') fbvecs=pjoin(THIS_DIR,'small_64D.gradients.npy') fimg =pjoin(THIS_DIR,'small_64D.nii') return fimg,fbvals, fbvecs if name=='55dir_grad.bvec': return pjoin(THIS_DIR,'55dir_grad.bvec') if name=='small_101D': fbvals=pjoin(THIS_DIR,'small_101D.bval') fbvecs=pjoin(THIS_DIR,'small_101D.bvec') fimg=pjoin(THIS_DIR,'small_101D.nii.gz') return fimg,fbvals, fbvecs if name=='aniso_vox': return pjoin(THIS_DIR,'aniso_vox.nii.gz') if name=='fornix': return pjoin(THIS_DIR,'tracks300.trk') dipy-0.5.0/dipy/data/aniso_vox.nii.gz000066400000000000000000002612631152576264200175100ustar00rootroot00000000000000‹äå)Mÿ/tmp/new.niiĽe˜ÕÙ°½Ö½'n3Éd&3™ŒÆ=7b$Á5$¸C)îîV(VZ¼¸¦P\‚ÞbÅZ´H)V ”Ò÷<÷Õyú¼ï¯ïß—}ìÉ–û^׺ÜÖZ{«œþ?ý+¥<¤üŸÇÿ}SEª,pôœ”ŽžÏËüß÷ÿë¹r~¼ÿ¿ïþÿë¸|Óvù½G 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S'\x9a[\x0e\xba\x01B\xd5?\x83\xe8\x00eR\xef\xc1\xbf\xb4\xe5\xe1\xb3+\xd9\xed?' tbag4 (g5 (I0 tS'b' tRp43 (I1 (I3 tg8 I00 S'\xe1\n\r\xa4\x98\xc8\xee?v\x12m\xb5|S\xd1\xbf1iG\x81\xa3f\xa2?' tbag4 (g5 (I0 tS'b' tRp44 (I1 (I3 tg8 I00 S'\xe6=PTW\xb3\xee?\xf0\xb5@H\xbe\xe6\xca?\xa8\xd8\x8c(\xf5\x14\xc8?' tbag4 (g5 (I0 tS'b' tRp45 (I1 (I3 tg8 I00 S'X\x1e\xc6\xb9\xf8\xd6\xdc?\xa7,\xf2\x8da\x82\xec?v\xc8s\xfdP\x02\xad\xbf' tbag4 (g5 (I0 tS'b' tRp46 (I1 (I3 tg8 I00 S'\x11\xf6\x92\x19L\xa9\xe8?\xaa\x97%9\xbc5\xe4?wR\x90\xa6V\xbb\xb5?' tbag4 (g5 (I0 tS'b' tRp47 (I1 (I3 tg8 I00 S'\x178\x1dD\xee\xb3\xe6?xq\xc07"I\xda\xbfX@\x82\xb7{S\xe2\xbf' tbag4 (g5 (I0 tS'b' tRp48 (I1 (I3 tg8 I00 S'\xc7V(v?9\xe6?\xc0\xcd\x16P\xcf\x96\x99?\x1a%\xcd\xf5\x9e\x02\xe7?' tbag4 (g5 (I0 tS'b' tRp49 (I1 (I3 tg8 I00 S'\xed\x93\xa4\xc1\xbf\xcb\xe5?;\\\xfd\xd4\xe3-\xe1\xbf\x19\xc2\x1c\x0b\xeb\xdc\xdf?' tbag4 (g5 (I0 tS'b' tRp50 (I1 (I3 tg8 I00 S'\x0e\xf4m\x94V\x14\xc2?\x83\x1b\xa2Z\xbev\xe7\xbf\xdb\xa3+\x19\xc4H\xe5?' tbag4 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ÃB ²Br¶Bµ€ÁB·E²Bÿ,·B{ÀB ѱB.ï·B•¾BýP±Bû·¸B^%½B†×°B:¹BÆ»Be^°B‰¢ºB!‚ºBÕÚ¯B,Á»BY¹BÍM¯B€½B„H¸Brð®B’c¾BÆD·Bö®BÚÁ¿B&B¶B*¯®B¡ÁBï7µBñ®BvÂBÏ7´B½\®B÷ÕÃBÄ<³B"®Bç9ÅB°F²Bgî­B)£ÆBíX±B;¹­B ÈB°h°B(j­BäkÉB¼{¯BØ­B_ÞÊB‹®B±æ¬B?YÌBÊ­B‘À¬B;ØÍBþ­BDެB®WÏB³Q¬B„!¬B ÇÐB±«Bw«BÈ1ÒB}«BvЪB½™ÓB—\ªBîªBdipy-0.5.0/dipy/external/000077500000000000000000000000001152576264200152625ustar00rootroot00000000000000dipy-0.5.0/dipy/external/__init__.py000066400000000000000000000002221152576264200173670ustar00rootroot00000000000000# init for externals package """ Calls to external packages """ # Test callable from numpy.testing import Tester test = Tester().test del Tester dipy-0.5.0/dipy/external/fsl.py000066400000000000000000000332441152576264200164260ustar00rootroot00000000000000''' FSL IO ''' from __future__ import with_statement import os from os.path import join as pjoin from subprocess import Popen,PIPE import numpy as np import numpy.linalg as npl from numpy import newaxis from scipy.ndimage import map_coordinates as mc from scipy.ndimage import affine_transform from dipy.io.dpy import Dpy import nibabel as nib from nibabel.tmpdirs import InTemporaryDirectory _VAL_FMT = ' %e' class FSLError(Exception): """ Class signals error in FSL processing """ def have_flirt(): """ Return True if we can call flirt without error Relies on the fact that flirt produces text on stdout when called with no arguments """ p = Popen('flirt', stdout=PIPE, stderr=PIPE, shell=True) stdout, stderr = p.communicate() return stdout != '' def write_bvals_bvecs(bvals, bvecs, outpath=None, prefix=''): ''' Write FSL FDT bvals and bvecs files Parameters ------------- bvals : (N,) sequence Vector with diffusion gradient strength (one per diffusion acquisition, N=no of acquisitions) bvecs : (N, 3) array-like diffusion gradient directions outpath : None or str path to write FDT bvals, bvecs text files None results in current working directory. prefix : str prefix for bvals, bvecs files in directory. Defaults to '' ''' if outpath is None: outpath = os.getcwd() bvals = tuple(bvals) bvecs = np.asarray(bvecs) bvecs[np.isnan(bvecs)] = 0 N = len(bvals) fname = pjoin(outpath, prefix + 'bvals') fmt = _VAL_FMT * N + '\n' open(fname, 'wt').write(fmt % bvals) fname = pjoin(outpath, prefix + 'bvecs') bvf = open(fname, 'wt') for dim_vals in bvecs.T: bvf.write(fmt % tuple(dim_vals)) def flirt2aff(mat, in_img, ref_img): """ Transform from `in_img` voxels to `ref_img` voxels given `mat` Parameters ---------- mat : (4,4) array contents (as array) of output ``-omat`` transformation file from flirt in_img : img image passed (as filename) to flirt as ``-in`` image ref_img : img image passed (as filename) to flirt as ``-ref`` image Returns ------- aff : (4,4) array Transform from voxel coordinates in ``in_img`` to voxel coordinates in ``ref_img`` Notes ----- Thanks to Mark Jenkinson and Jesper Andersson for the correct statements here, apologies for any errors we've added. ``flirt`` registers an ``in`` image to a ``ref`` image. It can produce (with the ``-omat`` option) - a 4 x 4 affine matrix giving the mapping from *inspace* to *refspace*. The rest of this note is to specify what *inspace* and *refspace* are. In what follows, a *voxtrans* for an image is the 4 by 4 affine ``np.diag([vox_i, vox_j, vox_k, 1])`` where ``vox_i`` etc are the voxel sizes for the first second and third voxel dimension. ``vox_i`` etc are always positive. If the input image has an affine with a negative determinant, then the mapping from voxel coordinates in the input image to *inspace* is simply *voxtrans* for the input image. If the reference image has a negative determinant, the mapping from voxel space in the reference image to *refspace* is simply *voxtrans* for the reference image. A negative determinant for the image affine is the common case, of an image with a x voxel flip. Analyze images don't store affines and flirt assumes a negative determinant in these cases. For positive determinant affines, flirt starts *inspace* and / or *refspace* with an x voxel flip. The mapping implied for an x voxel flip for image with shape (N_i, N_j, N_k) is: [[-1, 0, 0, N_i - 1], [ 0, 1, 0, 0], [ 0, 0, 1, 0], [ 0, 0, 0, 1]] If the input image has an affine with a positive determinant, then mapping from input image voxel coordinates to *inspace* is ``np.dot(input_voxtrans, input_x_flip)`` - where ``input_x_flip`` is the matrix above with ``N_i`` given by the input image first axis length. Similarly the mapping from reference voxel coordinates to *refspace*, if the reference image has a positive determinant, is ``np.dot(ref_voxtrans, ref_x_flip)`` - where ``ref_x_flip`` is the matrix above with ``N_i`` given by the reference image first axis length. """ in_hdr = in_img.get_header() ref_hdr = ref_img.get_header() # get_zooms gets the positive voxel sizes as returned in the header inspace = np.diag(in_hdr.get_zooms() + (1,)) refspace = np.diag(ref_hdr.get_zooms() + (1,)) if npl.det(in_img.get_affine())>=0: inspace = np.dot(inspace, _x_flipper(in_hdr.get_data_shape()[0])) if npl.det(ref_img.get_affine())>=0: refspace = np.dot(refspace, _x_flipper(ref_hdr.get_data_shape()[0])) # Return voxel to voxel mapping return np.dot(npl.inv(refspace), np.dot(mat, inspace)) def _x_flipper(N_i): flipr = np.diag([-1, 1, 1, 1]) flipr[0,3] = N_i - 1 return flipr def flirt2aff_files(matfile, in_fname, ref_fname): """ Map from `in_fname` image voxels to `ref_fname` voxels given `matfile` See :func:`flirt2aff` docstring for details. Parameters ------------ matfile : str filename of output ``-omat`` transformation file from flirt in_fname : str filename for image passed to flirt as ``-in`` image ref_fname : str filename for image passed to flirt as ``-ref`` image Returns ------- aff : (4,4) array Transform from voxel coordinates in image for ``in_fname`` to voxel coordinates in image for ``ref_fname`` """ mat = np.loadtxt(matfile) in_img = nib.load(in_fname) ref_img = nib.load(ref_fname) return flirt2aff(mat, in_img, ref_img) def warp_displacements(ffa,flaff,fdis,fref,ffaw,order=1): ''' Warp an image using fsl displacements Parameters ------------ ffa : filename of nifti to be warped flaff : filename of .mat (flirt) fdis : filename of displacements (fnirtfileutils) fref : filename of reference volume e.g. (FMRIB58_FA_1mm.nii.gz) ffaw : filename for the output warped image ''' refaff=nib.load(fref).get_affine() disdata=nib.load(fdis).get_data() imgfa=nib.load(ffa) fadata=imgfa.get_data() fazooms=imgfa.get_header().get_zooms() #from fa index to ref index res=flirt2aff_files(flaff,ffa,fref) #from ref index to fa index ires=np.linalg.inv(res) #create the 4d volume which has the indices for the reference image reftmp=np.zeros(disdata.shape) ''' #create the grid indices for the reference #refinds = np.ndindex(disdata.shape[:3]) for ijk_t in refinds: i,j,k = ijk_t reftmp[i,j,k,0]=i reftmp[i,j,k,1]=j reftmp[i,j,k,2]=k ''' #same as commented above but much faster reftmp[...,0] = np.arange(disdata.shape[0])[:,newaxis,newaxis] reftmp[...,1] = np.arange(disdata.shape[1])[newaxis,:,newaxis] reftmp[...,2] = np.arange(disdata.shape[2])[newaxis,newaxis,:] #affine transform from reference index to the fa index A = np.dot(reftmp,ires[:3,:3].T)+ires[:3,3] #add the displacements but first devide them by the voxel sizes A2=A+disdata/fazooms #hold the displacements' shape reshaping di,dj,dk,dl=disdata.shape #do the interpolation using map coordinates #the list of points where the interpolation is done given by the reshaped in 2D A2 (list of 3d points in fa index) W=mc(fadata,A2.reshape(di*dj*dk,dl).T,order=order).reshape(di,dj,dk) #save the warped image Wimg=nib.Nifti1Image(W,refaff) nib.save(Wimg,ffaw) def warp_displacements_tracks(fdpy,ffa,fmat,finv,fdis,fdisa,fref,fdpyw): """ Warp tracks from native space to the FMRIB58/MNI space We use here the fsl displacements. Have a look at create_displacements to see an example of how to use these displacements. Parameters ------------ fdpy : filename of the .dpy file with the tractography ffa : filename of nifti to be warped fmat : filename of .mat (flirt) fdis : filename of displacements (fnirtfileutils) fdisa : filename of displacements (fnirtfileutils + affine) finv : filename of invwarp displacements (invwarp) fref : filename of reference volume e.g. (FMRIB58_FA_1mm.nii.gz) fdpyw : filename of the warped tractography See also ----------- dipy.external.fsl.create_displacements """ #read the tracks from the image space dpr=Dpy(fdpy,'r') T=dpr.read_tracks() dpr.close() #copy them in a new file dpw=Dpy(fdpyw,'w',compression=1) dpw.write_tracks(T) dpw.close() #from fa index to ref index res=flirt2aff_files(fmat,ffa,fref) #load the reference img imgref=nib.load(fref) refaff=imgref.get_affine() #load the invwarp displacements imginvw=nib.load(finv) invwdata=imginvw.get_data() invwaff = imginvw.get_affine() #load the forward displacements imgdis=nib.load(fdis) disdata=imgdis.get_data() #load the forward displacements + affine imgdis2=nib.load(fdisa) disdata2=imgdis2.get_data() #from their difference create the affine disaff=disdata2-disdata del disdata del disdata2 shape=nib.load(ffa).get_data().shape #transform the displacements affine back to image space disaff0=affine_transform(disaff[...,0],res[:3,:3],res[:3,3],shape,order=1) disaff1=affine_transform(disaff[...,1],res[:3,:3],res[:3,3],shape,order=1) disaff2=affine_transform(disaff[...,2],res[:3,:3],res[:3,3],shape,order=1) #remove the transformed affine from the invwarp displacements di=invwdata[:,:,:,0] + disaff0 dj=invwdata[:,:,:,1] + disaff1 dk=invwdata[:,:,:,2] + disaff2 dprw=Dpy(fdpyw,'r+') rows=len(dprw.f.root.streamlines.tracks) blocks=np.round(np.linspace(0,rows,10)).astype(int)#lets work in blocks #print rows for i in range(len(blocks)-1): #print blocks[i],blocks[i+1] #copy a lot of tracks together caboodle=dprw.f.root.streamlines.tracks[blocks[i]:blocks[i+1]] mci=mc(di,caboodle.T,order=1) #interpolations for i displacement mcj=mc(dj,caboodle.T,order=1) #interpolations for j displacement mck=mc(dk,caboodle.T,order=1) #interpolations for k displacement D=np.vstack((mci,mcj,mck)).T #go back to mni image space WI2=np.dot(caboodle,res[:3,:3].T)+res[:3,3]+D #and then to mni world space caboodlew=np.dot(WI2,refaff[:3,:3].T)+refaff[:3,3] #write back dprw.f.root.streamlines.tracks[blocks[i]:blocks[i+1]]=caboodlew.astype('f4') dprw.close() def pipe(cmd): """ A tine pipeline system to run external tools. For more advanced pipelining use nipype http://www.nipy.org/nipype """ p = Popen(cmd, shell=True,stdout=PIPE,stderr=PIPE) sto=p.stdout.readlines() ste=p.stderr.readlines() print(sto) print(ste) def dcm2nii(dname,outdir,filt='*.dcm',options='-d n -g n -i n -o'): cmd='dcm2nii '+options +' ' + outdir +' ' + dname + '/' + filt print(cmd) pipe(cmd) def eddy_correct(in_nii,out_nii,ref=0): cmd='eddy_correct '+in_nii+' '+ out_nii + ' '+str(ref) print(cmd) pipe(cmd) def bet(in_nii,out_nii,options=' -F -f .2 -g 0'): cmd='bet '+in_nii+' '+ out_nii + options print(cmd) pipe(cmd) def run_flirt_imgs(in_img, ref_img, dof=6, flags=''): """ Run flirt on nibabel images, returning affine Parameters ---------- in_img : `SpatialImage' image to register ref_img : `SpatialImage` image to register to dof : int, optional degrees of freedom for registration (default 6) flags : str, optional other flags to pass to flirt command string Returns ------- in_vox2out_vox : (4,4) ndarray affine such that, if [i, j, k] is a coordinate in voxels in the `in_img`, and [p, q, r] are the equivalent voxel coordinates in the reference image, then [p, q, r] = np.dot(in_vox2out_vox[:3,:3]), [i, j, k] + in_vox2out_vox[:3,3]) """ omat = 'reg.mat' with InTemporaryDirectory(): nib.save(in_img, 'in.nii') nib.save(ref_img, 'ref.nii') cmd = 'flirt %s -dof %d -in in.nii -ref ref.nii -omat %s' % ( flags, dof, omat) proc = Popen(cmd, shell=True,stdout=PIPE,stderr=PIPE) stdout, stderr = proc.communicate() if not os.path.isfile(omat): raise FSLError('Command "%s" failed somehow - stdout: %s\n' 'and stderr: %s\n' % (cmd, stdout, stderr)) res = np.loadtxt(omat) return flirt2aff(res, in_img, ref_img) def apply_warp(in_nii,affine_mat,nonlin_nii,out_nii): cmd='applywarp --ref=${FSLDIR}/data/standard/FMRIB58_FA_1mm --in='+in_nii+' --warp='+nonlin_nii+' --out='+out_nii print(cmd) pipe(cmd) def create_displacements(in_nii,affine_mat,nonlin_nii,invw_nii,disp_nii,dispa_nii): commands=[] commands.append('flirt -ref ${FSLDIR}/data/standard/FMRIB58_FA_1mm -in '+in_nii+' -omat ' + affine_mat) commands.append('fnirt --in='+in_nii+' --aff='+affine_mat+' --cout='+nonlin_nii+' --config=FA_2_FMRIB58_1mm') commands.append('invwarp --ref='+in_nii+' --warp='+nonlin_nii+' --out='+invw_nii) commands.append('fnirtfileutils --in='+nonlin_nii+' --ref=${FSLDIR}/data/standard/FMRIB58_FA_1mm --out='+disp_nii) commands.append('fnirtfileutils --in='+nonlin_nii+' --ref=${FSLDIR}/data/standard/FMRIB58_FA_1mm --out='+dispa_nii + ' --withaff') for c in commands: print(c) pipe(c) dipy-0.5.0/dipy/external/tests/000077500000000000000000000000001152576264200164245ustar00rootroot00000000000000dipy-0.5.0/dipy/external/tests/__init__.py000066400000000000000000000000751152576264200205370ustar00rootroot00000000000000# Externals test directory made into package with this file dipy-0.5.0/dipy/info.py000066400000000000000000000070321152576264200147470ustar00rootroot00000000000000""" This file contains defines parameters for dipy that we use to fill settings in setup.py, the dipy top-level docstring, and for building the docs. In setup.py in particular, we exec this file, so it cannot import dipy """ # dipy version information. An empty _version_extra corresponds to a # full release. '.dev' as a _version_extra string means this is a development # version _version_major = 0 _version_minor = 5 _version_micro = 0 #_version_extra = '.dev' _version_extra = '' # Format expected by setup.py and doc/source/conf.py: string of form "X.Y.Z" __version__ = "%s.%s.%s%s" % (_version_major, _version_minor, _version_micro, _version_extra) CLASSIFIERS = ["Development Status :: 3 - Alpha", "Environment :: Console", "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", "Operating System :: OS Independent", "Programming Language :: Python", "Topic :: Scientific/Engineering"] description = 'Diffusion MRI utilities in python' # Note: this long_description is actually a copy/paste from the top-level # README.txt, so that it shows up nicely on PyPI. So please remember to edit # it only in one place and sync it correctly. long_description = """ ====== DiPy ====== Dipy is a python toolbox for analysis of MR diffusion imaging. Dipy is for research only; please do not use results from dipy for clinical decisions. Website ======= Current information can always be found at the NIPY dipy website - http://nipy.org/dipy - or directly from the DIPY website - http://dipy.org Mailing Lists ============= Please see the developer's list at http://mail.scipy.org/mailman/listinfo/nipy-devel Code ==== You can find our sources and single-click downloads: * `Main repository`_ on Github. * Documentation_ for all releases and current development tree. * Download as a tar/zip file the `current trunk`_. * Downloads of all `available releases`_. .. _main repository: http://github.com/Garyfallidis/dipy .. _Documentation: http://dipy.org .. _current trunk: http://github.com/Garyfallidis/dipy/archives/master .. _available releases: http://github.com/Garyfallidis/dipy/downloads License ======= dipy is licensed under the terms of the BSD license. Some code included with dipy is also licensed under the BSD license. Please the LICENSE file in the dipy distribution. """ # versions for dependencies NUMPY_MIN_VERSION='1.2' SCIPY_MIN_VERSION='0.6' # Guessed CYTHON_MIN_VERSION='0.13' NIBABEL_MIN_VERSION='1.0.0' # Main setup parameters NAME = 'dipy' MAINTAINER = "Eleftherios Garyfallidis" MAINTAINER_EMAIL = "nipy-devel@neuroimaging.scipy.org" DESCRIPTION = description LONG_DESCRIPTION = long_description URL = "http://dipy.org" DOWNLOAD_URL = "http://github.com/Garyfallidis/dipy/archives/master" LICENSE = "BSD license" CLASSIFIERS = CLASSIFIERS AUTHOR = "dipy developers" AUTHOR_EMAIL = "nipy-devel@neuroimaging.scipy.org" PLATFORMS = "OS Independent" MAJOR = _version_major MINOR = _version_minor MICRO = _version_micro ISRELEASE = _version_extra == '' VERSION = __version__ PROVIDES = ["dipy"] REQUIRES = ["numpy (>=%s)" % NUMPY_MIN_VERSION, "scipy (>=%s)" % SCIPY_MIN_VERSION, "nibabel (>=%s)" % NIBABEL_MIN_VERSION] dipy-0.5.0/dipy/io/000077500000000000000000000000001152576264200140475ustar00rootroot00000000000000dipy-0.5.0/dipy/io/__init__.py000066400000000000000000000000301152576264200161510ustar00rootroot00000000000000# init for io routines dipy-0.5.0/dipy/io/bvectxt.py000066400000000000000000000033701152576264200161030ustar00rootroot00000000000000import numpy as np from os.path import splitext def read_bvec_file(filename, atol=.001): """ Read gradient table information from a pair of files with extentions .bvec and .bval. The bval file should have one row of values representing the bvalues of each volume in the dwi data set. The bvec file should have three rows, where the rows are the x, y, and z components of the normalized gradient direction for each of the volumes. Parameters ------------ filename : The path to the either the bvec or bval file atol : float, optional The tolorance used to check all the gradient directions are normalized. Defult is .001 """ base, ext = splitext(filename) if ext == '': bvec = base+'.bvec' bval = base+'.bval' elif ext == '.bvec': bvec = filename bval = base+'.bval' elif ext == '.bval': bvec = base+'.bvec' bval = filename else: raise ValueError('filename must have .bvec or .bval extension') b_values = np.loadtxt(bval) grad_table = np.loadtxt(bvec) if grad_table.shape[0] != 3: raise IOError('bvec file should have three rows') if b_values.ndim != 1: raise IOError('bval file should have one row') if b_values.shape[0] != grad_table.shape[1]: raise IOError('the gradient file and b value file should have the same number of columns') grad_norms = np.sqrt((grad_table**2).sum(0)) if not np.allclose(grad_norms[b_values > 0], 1, atol=atol): raise IOError('the magnitudes of the gradient directions are not within '+str(atol)+' of 1') grad_table[:,b_values > 0] = grad_table[:,b_values > 0]/grad_norms[b_values > 0] return (grad_table, b_values) dipy-0.5.0/dipy/io/dpy.py000066400000000000000000000107161152576264200152220ustar00rootroot00000000000000''' A class for handling large tractography datasets. It is built using the pytables tools which in turn implement key features of the HDF5 (hierachical data format) API [1]_. References ---------- .. [1] http://www.hdfgroup.org/HDF5/doc/H5.intro.html ''' import numpy as np # Conditional import machinery for pytables from ..utils.optpkg import optional_package # Allow import, but disable doctests, if we don't have pytables tables, have_tables, setup_module = optional_package('tables') class Dpy(object): def __init__(self,fname,mode='r',compression=0): ''' Advanced storage system for tractography based on HDF5 Parameters ------------ fname : str, full filename mode : 'r' read 'w' write 'r+' read and write only if file already exists 'a' read and write even if file doesn't exist (not used yet) compression : 0 no compression to 9 maximum compression Examples ---------- >>> import os >>> from tempfile import mkstemp #temp file >>> from dipy.io.dpy import Dpy >>> fd,fname = mkstemp() >>> fname = fname + '.dpy' #add correct extension >>> dpw = Dpy(fname,'w') >>> A=np.ones((5,3)) >>> B=2*A.copy() >>> C=3*A.copy() >>> dpw.write_track(A) >>> dpw.write_track(B) >>> dpw.write_track(C) >>> dpw.close() >>> dpr = Dpy(fname,'r') >>> A=dpr.read_track() >>> B=dpr.read_track() >>> T=dpr.read_tracksi([0,1,2,0,0,2]) >>> dpr.close() >>> os.remove(fname) #delete file from disk ''' self.mode=mode self.f = tables.openFile(fname, mode = self.mode) self.N = 5*10**9 self.compression = compression if self.mode=='w': self.streamlines=self.f.createGroup(self.f.root,'streamlines') #create a version number self.version=self.f.createArray(self.f.root,'version',['0.0.1'],'Dpy Version Number') self.tracks = self.f.createEArray(self.f.root.streamlines, 'tracks',tables.Float32Atom(), (0, 3), "scalar Float32 earray", tables.Filters(self.compression),expectedrows=self.N) self.offsets = self.f.createEArray(self.f.root.streamlines, 'offsets',tables.Int64Atom(), (0,), "scalar Int64 earray", tables.Filters(self.compression), expectedrows=self.N+1) self.curr_pos=0 self.offsets.append(np.array([self.curr_pos]).astype(np.int64)) if self.mode=='r': self.tracks=self.f.root.streamlines.tracks self.offsets=self.f.root.streamlines.offsets self.track_no=len(self.offsets)-1 self.offs_pos=0 def version(self): ver=self.f.root.version[:] return ver[0] def write_track(self,track): ''' write on track each time ''' self.tracks.append(track.astype(np.float32)) self.curr_pos+=track.shape[0] self.offsets.append(np.array([self.curr_pos]).astype(np.int64)) def write_tracks(self,T): ''' write many tracks together ''' for track in T: self.tracks.append(track.astype(np.float32)) self.curr_pos+=track.shape[0] self.offsets.append(np.array([self.curr_pos]).astype(np.int64)) def read_track(self): ''' read one track each time ''' off0,off1=self.offsets[self.offs_pos:self.offs_pos+2] self.offs_pos+=1 return self.tracks[off0:off1] def read_tracksi(self,indices): ''' read tracks with specific indices ''' T=[] for i in indices: #print(self.offsets[i:i+2]) off0,off1=self.offsets[i:i+2] T.append(self.tracks[off0:off1]) return T def read_tracks(self): ''' read the entire tractography ''' I=self.offsets[:] TR=self.tracks[:] T=[] for i in range(len(I)-1): off0,off1=I[i:i+2] T.append(TR[off0:off1]) return T def close(self): self.f.close() if __name__ == '__main__': pass dipy-0.5.0/dipy/io/pickles.py000066400000000000000000000022371152576264200160570ustar00rootroot00000000000000import cPickle def save_pickle(fname,dix): ''' Save `dix` to `fname` as pickle Parameters ------------ fname : str filename to save object e.g. a dictionary dix : str dictionary or other object Examples ---------- >>> import os >>> from tempfile import mkstemp >>> fd, fname = mkstemp() # make temporary file (opened, attached to fh) >>> d={0:{'d':1}} >>> save_pickle(fname, d) >>> d2=load_pickle(fname) We remove the temporary file we created for neatness >>> os.close(fd) # the file is still open, we need to close the fh >>> os.remove(fname) See also ---------- dipy.io.pickles.load_pickle ''' out=open(fname,'wb') cPickle.dump(dix,out) out.close() def load_pickle(fname): ''' Load object from pickle file `fname` Parameters ------------ fname : str filename to load dict or other python object Returns --------- dix : object dictionary or other object Examples ---------- dipy.io.pickles.save_pickle ''' inp=open(fname,'rb') dix=cPickle.load(inp) inp.close() return dix dipy-0.5.0/dipy/io/tests/000077500000000000000000000000001152576264200152115ustar00rootroot00000000000000dipy-0.5.0/dipy/io/tests/__init__.py000066400000000000000000000001721152576264200173220ustar00rootroot00000000000000# init to allow relative imports in tests # Test callable from numpy.testing import Tester test = Tester().test del Testerdipy-0.5.0/dipy/io/tests/test_dpy.py000066400000000000000000000021001152576264200174070ustar00rootroot00000000000000import os import numpy as np from tempfile import mkstemp from ..dpy import Dpy, have_tables from nose.tools import assert_true, assert_false, \ assert_equal, assert_raises from numpy.testing import assert_array_equal, assert_array_almost_equal import numpy.testing as npt # Decorator to protect tests from being run without pytables present iftables = npt.dec.skipif(not have_tables, 'Pytables does not appear to be installed') @iftables def test_dpy(): fd,fname = mkstemp() dpw = Dpy(fname,'w') A=np.ones((5,3)) B=2*A.copy() C=3*A.copy() dpw.write_track(A) dpw.write_track(B) dpw.write_track(C) dpw.write_tracks([C,B,A]) dpw.close() dpr = Dpy(fname,'r') assert_equal(dpr.version()=='0.0.1',True) T=dpr.read_tracksi([0,1,2,0,0,2]) print(T) T2=dpr.read_tracks() assert_equal(len(T2),6) dpr.close() assert_array_equal(A,T[0]) assert_array_equal(C,T[5]) # This might cause problems on windows os.remove(fname) dipy-0.5.0/dipy/io/utils.py000066400000000000000000000147071152576264200155720ustar00rootroot00000000000000''' Utility functions for file formats ''' import sys import numpy as np sys_is_le = sys.byteorder == 'little' native_code = sys_is_le and '<' or '>' swapped_code = sys_is_le and '>' or '<' default_compresslevel = 1 endian_codes = (# numpy code, aliases ('<', 'little', 'l', 'le', 'L', 'LE'), ('>', 'big', 'BIG', 'b', 'be', 'B', 'BE'), (native_code, 'native', 'n', 'N', '=', '|', 'i', 'I'), (swapped_code, 'swapped', 's', 'S', '!')) # We'll put these into the Recoder class after we define it class Recoder(object): ''' class to return canonical code(s) from code or aliases The concept is a lot easier to read in the implementation and tests than it is to explain, so... >>> # If you have some codes, and several aliases, like this: >>> code1 = 1; aliases1=['one', 'first'] >>> code2 = 2; aliases2=['two', 'second'] >>> # You might want to do this: >>> codes = [[code1]+aliases1,[code2]+aliases2] >>> recodes = Recoder(codes) >>> recodes.code['one'] 1 >>> recodes.code['second'] 2 >>> recodes.code[2] 2 >>> # Or maybe you have a code, a label and some aliases >>> codes=((1,'label1','one', 'first'),(2,'label2','two')) >>> # you might want to get back the code or the label >>> recodes = Recoder(codes, fields=('code','label')) >>> recodes.code['first'] 1 >>> recodes.code['label1'] 1 >>> recodes.label[2] 'label2' >>> # For convenience, you can get the first entered name by >>> # indexing the object directly >>> recodes[2] 2 ''' def __init__(self, codes, fields=('code',)): ''' Create recoder object ``codes`` give a sequence of code, alias sequences ``fields`` are names by which the entries in these sequences can be accessed. By default ``fields`` gives the first column the name "code". The first column is the vector of first entries in each of the sequences found in ``codes``. Thence you can get the equivalent first column value with ob.code[value], where value can be a first column value, or a value in any of the other columns in that sequence. You can give other columns names too, and access them in the same way - see the examples in the class docstring. Parameters ------------ codes : seqence of sequences Each sequence defines values (codes) that are equivalent fields : {('code',) string sequence}, optional names by which elements in sequences can be accesssed ''' self.fields = fields self.field1 = {} # a placeholder for the check below for name in fields: if name in self.__dict__: raise KeyError('Input name %s already in object dict' % name) self.__dict__[name] = {} self.field1 = self.__dict__[fields[0]] self.add_codes(codes) def add_codes(self, codes): ''' Add codes to object >>> codes = ((1, 'one'), (2, 'two')) >>> rc = Recoder(codes) >>> rc.value_set() == set((1,2)) True >>> rc.add_codes(((3, 'three'), (1, 'first'))) >>> rc.value_set() == set((1,2,3)) True ''' for vals in codes: for val in vals: for ind, name in enumerate(self.fields): self.__dict__[name][val] = vals[ind] def __getitem__(self, key): ''' Return value from field1 dictionary (first column of values) Returns same value as ``obj.field1[key]`` and, with the default initializing ``fields`` argument of fields=('code',), this will return the same as ``obj.code[key]`` >>> codes = ((1, 'one'), (2, 'two')) >>> Recoder(codes)['two'] 2 ''' return self.field1[key] def keys(self): ''' Return all available code and alias values Returns same value as ``obj.field1.keys()`` and, with the default initializing ``fields`` argument of fields=('code',), this will return the same as ``obj.code.keys()`` >>> codes = ((1, 'one'), (2, 'two'), (1, 'repeat value')) >>> k = Recoder(codes).keys() >>> k.sort() # Just to guarantee order for doctest output >>> k [1, 2, 'one', 'repeat value', 'two'] ''' return self.field1.keys() def value_set(self, name=None): ''' Return set of possible returned values for column By default, the column is the first column. Returns same values as ``set(obj.field1.values())`` and, with the default initializing ``fields`` argument of fields=('code',), this will return the same as ``set(obj.code.values())`` Parameters ------------ name : {None, string} Where default of None gives result for first column Returns --------- val_set : set set of all values for `name` Examples ----------- >>> codes = ((1, 'one'), (2, 'two'), (1, 'repeat value')) >>> vs = Recoder(codes).value_set() >>> vs == set([1, 2]) # Sets are not ordered, hence this test True >>> rc = Recoder(codes, fields=('code', 'label')) >>> rc.value_set('label') == set(('one', 'two', 'repeat value')) True ''' if name is None: d = self.field1 else: d = self.__dict__[name] return set(d.values()) # Endian code aliases endian_codes = Recoder(endian_codes) def allopen(fname, *args, **kwargs): ''' Generic file-like object open If input ``fname`` already looks like a file, pass through. If ``fname`` ends with recognizable compressed types, use python libraries to open as file-like objects (read or write) Otherwise, use standard ``open``. ''' if hasattr(fname, 'write'): return fname if args: mode = args[0] elif 'mode' in kwargs: mode = kwargs['mode'] else: mode = 'rb' if fname.endswith('.gz'): if ('w' in mode and len(args) < 2 and not 'compresslevel' in kwargs): kwargs['compresslevel'] = default_compresslevel import gzip opener = gzip.open elif fname.endswith('.bz2'): if ('w' in mode and len(args) < 3 and not 'compresslevel' in kwargs): kwargs['compresslevel'] = default_compresslevel import bz2 opener = bz2.BZ2File else: opener = open return opener(fname, *args, **kwargs) dipy-0.5.0/dipy/pkg_info.py000066400000000000000000000053271152576264200156150ustar00rootroot00000000000000import os import sys import subprocess from ConfigParser import ConfigParser COMMIT_INFO_FNAME = 'COMMIT_INFO.txt' def pkg_commit_hash(pkg_path): ''' Get short form of commit hash given directory `pkg_path` There should be a file called 'COMMIT_INFO.txt' in `pkg_path`. This is a file in INI file format, with at least one section: ``commit hash``, and two variables ``archive_subst_hash`` and ``install_hash``. The first has a substitution pattern in it which may have been filled by the execution of ``git archive`` if this is an archive generated that way. The second is filled in by the installation, if the installation is from a git archive. We get the commit hash from (in order of preference): * A substituted value in ``archive_subst_hash`` * A written commit hash value in ``install_hash` * git's output, if we are in a git repository If all these fail, we return a not-found placeholder tuple Parameters ------------- pkg_path : str directory containing package Returns --------- hash_from : str Where we got the hash from - description hash_str : str short form of hash ''' # Try and get commit from written commit text file pth = os.path.join(pkg_path, COMMIT_INFO_FNAME) if not os.path.isfile(pth): raise IOError('Missing commit info file %s' % pth) cfg_parser = ConfigParser() cfg_parser.read(pth) archive_subst = cfg_parser.get('commit hash', 'archive_subst_hash') if not archive_subst.startswith('$Format'): # it has been substituted return 'archive substitution', archive_subst install_subst = cfg_parser.get('commit hash', 'install_hash') if install_subst != '': return 'installation', install_subst # maybe we are in a repository proc = subprocess.Popen('git rev-parse --short HEAD', stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=pkg_path, shell=True) repo_commit, _ = proc.communicate() if repo_commit: return 'repository', repo_commit.strip() return '(none found)', '' def get_pkg_info(pkg_path): ''' Return dict describing the context of this package Parameters ------------ pkg_path : str path containing __init__.py for package Returns ---------- context : dict with named parameters of interest ''' src, hsh = pkg_commit_hash(pkg_path) import numpy return dict( pkg_path=pkg_path, commit_source=src, commit_hash=hsh, sys_version=sys.version, sys_executable=sys.executable, sys_platform=sys.platform, np_version=numpy.__version__) dipy-0.5.0/dipy/reconst/000077500000000000000000000000001152576264200151155ustar00rootroot00000000000000dipy-0.5.0/dipy/reconst/__init__.py000066400000000000000000000000641152576264200172260ustar00rootroot00000000000000#init for reconst aka the reconstruction module dipy-0.5.0/dipy/reconst/dandelion.py000066400000000000000000000102341152576264200174240ustar00rootroot00000000000000import warnings import numpy as np from dipy.reconst.recspeed import peak_finding from dipy.utils.spheremakers import sphere_vf_from warnings.warn("This module is most likely to change both as a name and in structure in the future",FutureWarning) class SphericalDandelion(object): ''' HIGHLY EXPERIMENTAL - PLEASE DO NOT USE. ''' def __init__(self, data, bvals, gradients, smoothing=1., odf_sphere='symmetric362', mask=None): ''' Parameters ----------- data : array, shape(X,Y,Z,D) bvals : array, shape (N,) gradients : array, shape (N,3) also known as bvecs smoothing : float, smoothing parameter odf_sphere : str or tuple, optional If str, then load sphere of given name using ``get_sphere``. If tuple, gives (vertices, faces) for sphere. See also ---------- dipy.reconst.dti.Tensor, dipy.reconst.gqi.GeneralizedQSampling ''' odf_vertices, odf_faces = sphere_vf_from(odf_sphere) self.odf_vertices=odf_vertices self.bvals=bvals gradients[np.isnan(gradients)] = 0. self.gradients=gradients self.weighting=np.abs(np.dot(gradients,self.odf_vertices.T)) #self.weighting=self.weighting/np.sum(self.weighting,axis=0) S=data datashape=S.shape #initial shape msk=None #tmp mask if len(datashape)==4: x,y,z,g=S.shape S=S.reshape(x*y*z,g) XA = np.zeros((x*y*z,5)) IN = np.zeros((x*y*z,5)) if mask != None: if mask.shape[:3]==datashape[:3]: msk=mask.ravel().copy() if len(datashape)==2: x,g= S.shape XA = np.zeros((x,5)) IN = np.zeros((x,5)) if mask !=None: for (i,s) in enumerate(S): if msk[i]>0: odf=self.spherical_diffusivity(s) peaks,inds=peak_finding(odf,odf_faces) l=min(len(peaks),5) XA[i][:l] = peaks[:l] IN[i][:l] = inds[:l] if mask==None: for (i,s) in enumerate(S): odf=self.spherical_diffusivity(s) peaks,inds=peak_finding(odf,odf_faces) l=min(len(peaks),5) XA[i][:l] = peaks[:l] IN[i][:l] = inds[:l] if len(datashape) == 4: self.XA=XA.reshape(x,y,z,5) self.IN=IN.reshape(x,y,z,5) if len(datashape) == 2: self.XA=XA self.IN=IN def spherical_diffusivity(self,s): ob=-1/self.bvals[1:] lg=np.log(s[1:])-np.log(s[0]) d=ob*(np.log(s[1:])-np.log(s[0])) #d=d.reshape(1,len(d)) #return np.squeeze(np.dot(d,self.weighting[1:,:])) ''' final_sphere=np.zeros(self.odf_vertices.shape[0]) for i in range(len(d)): final_sphere+=d[i]*np.abs(np.dot(self.gradients[i+1],self.odf_vertices.T)**(2)) #return (final_sphere-final_sphere.min())/float(len(d)) return final_sphere/float(len(d)) ''' d=np.zeros(d.shape) d[0]=12 #d=12*np.ones(d.shape) o=np.ones(d.shape) finald=self.koukou(d) finalo=self.koukou(o) #print finald.shape,finalo.shape return finald/finalo def koukou(self,d): width=1 final_sphere=np.zeros((len(d),self.odf_vertices.shape[0])) for i in range(len(d)): #f=np.abs(np.dot(self.gradients[i+1],self.odf_vertices.T)**(2)) cos2=np.dot(self.gradients[i+1],self.odf_vertices.T)**(2) sin2=1-cos2 Sinc=np.sinc(width*sin2)**2 final_sphere[i]=d[i]*Sinc #return final_sphere return np.sum(final_sphere,axis=0)#/float(len(d)) def xa(self): return self.XA def ind(self): return self.IN dipy-0.5.0/dipy/reconst/dti.py000066400000000000000000000447421152576264200162620ustar00rootroot00000000000000#!/usr/bin/python """ Classes and functions for fitting tensors """ # 5/17/2010 import numpy as np from dipy.reconst.maskedview import MaskedView, _makearray, _filled from dipy.reconst.modelarray import ModelArray from dipy.data import get_sphere class Tensor(ModelArray): """ Fits a diffusion tensor given diffusion-weighted signals and gradient info Tensor object that when initialized calculates single self diffusion tensor [1]_ in each voxel using selected fitting algorithm (DEFAULT: weighted least squares [2]_) Requires a given gradient table, b value for each diffusion-weighted gradient vector, and image data given all as arrays. Parameters ---------- data : array ([X, Y, Z, ...], g) Diffusion-weighted signals. The dimension corresponding to the diffusion weighting must be the last dimenssion bval : array (g,) Diffusion weighting factor b for each vector in gtab. gtab : array (g, 3) Diffusion gradient table found in DICOM header as a array. mask : array, optional The tensor will only be fit where mask is True. Mask must must broadcast to the shape of data and must have fewer dimensions than data thresh : float, default = None The tensor will not be fit where data[bval == 0] < thresh. If multiple b0 volumes are given, the minimum b0 signal is used. fit_method : funciton or string, default = 'WLS' The method to be used to fit the given data to a tensor. Any function that takes the B matrix and the data and returns eigen values and eigen vectors can be passed as the fit method. Any of the common fit methods can be passed as a string. *args, **kargs : Any other arguments or keywards will be passed to fit_method. common fit methods: 'WLS' : weighted least squares dti.wls_fit_tensor 'LS' : ordinary least squares dti.ols_fit_tensor Attributes ---------- D : array (..., 3, 3) Self diffusion tensor calculated from cached eigenvalues and eigenvectors. mask : array True in voxels where a tensor was fit, false if the voxel was skipped B : array (g, 7) Design matrix or B matrix constructed from given gradient table and b-value vector. evals : array (..., 3) Cached eigenvalues of self diffusion tensor for given index. (eval1, eval2, eval3) evecs : array (..., 3, 3) Cached associated eigenvectors of self diffusion tensor for given index. Note: evals[..., j] is associated with evecs[..., :, j] Methods ------- fa : array Calculates fractional anisotropy [2]_. md : array Calculates the mean diffusivity [2]_. Note: [units ADC] ~ [units b value]*10**-1 See Also -------- dipy.io.bvectxt.read_bvec_file, dipy.core.qball.ODF Notes ----- Due to the fact that diffusion MRI entails large volumes (e.g. [256,256, 50,64]), memory can be an issue. Therefore, only the following parameters of the self diffusion tensor are cached for each voxel: - All three eigenvalues - Primary and secondary eigenvectors From these cached parameters, one can presumably construct any desired parameter. References ---------- .. [1] Basser, P.J., Mattiello, J., LeBihan, D., 1994. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 103, 247-254. .. [2] Basser, P., Pierpaoli, C., 1996. Microstructural and physiological features of tissues elucidated by quantitative diffusion-tensor MRI. Journal of Magnetic Resonance 111, 209-219. Examples ---------- For a complete example have a look at the main dipy/examples folder """ ### Eigenvalues Property ### @property def evals(self): """ Returns the eigenvalues of the tensor as an array """ return _filled(self.model_params[..., :3]) ### Eigenvectors Property ### @property def evecs(self): """ Returns the eigenvectors of teh tensor as an array """ evecs = _filled(self.model_params[..., 3:]) return evecs.reshape(self.shape + (3, 3)) def __init__(self, data, b_values, grad_table, mask=True, thresh=None, fit_method='WLS', verbose=False, *args, **kargs): """ Fits a tensors to diffusion weighted data. """ if not callable(fit_method): try: fit_method = common_fit_methods[fit_method] except KeyError: raise ValueError('"'+str(fit_method)+'" is not a known fit '+ 'method, the fit method should either be a '+ 'function or one of the common fit methods') #64 bit design matrix makes for faster pinv B = design_matrix(grad_table.T, b_values) self.B = B mask = np.atleast_1d(mask) if thresh is not None: #Define total mask from thresh and mask #mask = mask & (np.min(data[..., b_values == 0], -1) > #thresh) #the assumption that the lowest b_value is always 0 is #incorrect the lowest b_value could also be higher than 0 #this is common with grid q-spaces min_b0_sig = np.min(data[..., b_values == b_values.min()], -1) mask = mask & (min_b0_sig > thresh) #if mask is all False if not mask.any(): raise ValueError('between mask and thresh, there is no data to '+ 'fit') #and the mask is not all True if not mask.all(): #leave only data[mask is True] data = data[mask] data = MaskedView(mask, data) #Perform WLS fit on masked data dti_params = fit_method(B, data, *args, **kargs) self.model_params = dti_params ### Self Diffusion Tensor Property ### def _getD(self): evals = self.evals evecs = self.evecs evals_flat = evals.reshape((-1, 3)) evecs_flat = evecs.reshape((-1, 3, 3)) D_flat = np.empty(evecs_flat.shape) for L, Q, D in zip(evals_flat, evecs_flat, D_flat): D[:] = np.dot(Q*L, Q.T) return D_flat.reshape(evecs.shape) D = property(_getD, doc = "Self diffusion tensor") def fa(self): r""" Fractional anisotropy (FA) calculated from cached eigenvalues. Returns --------- fa : array (V, 1) Calculated FA. Note: range is 0 <= FA <= 1. Notes -------- FA is calculated with the following equation: .. math:: FA = \sqrt{\frac{1}{2}\frac{(\lambda_1-\lambda_2)^2+(\lambda_1- \lambda_3)^2+(\lambda_2-lambda_3)^2}{\lambda_1^2+ \lambda_2^2+\lambda_3^2} } """ evals, wrap = _makearray(self.model_params[..., :3]) ev1 = evals[..., 0] ev2 = evals[..., 1] ev3 = evals[..., 2] fa = np.sqrt(0.5 * ((ev1 - ev2)**2 + (ev2 - ev3)**2 + (ev3 - ev1)**2) / (ev1*ev1 + ev2*ev2 + ev3*ev3)) fa = wrap(np.asarray(fa)) return _filled(fa) def md(self): r""" Mean diffusitivity (MD) calculated from cached eigenvalues. Returns --------- md : array (V, 1) Calculated MD. Notes -------- MD is calculated with the following equation: .. math:: ADC = \frac{\lambda_1+\lambda_2+\lambda_3}{3} """ #adc/md = (ev1+ev2+ev3)/3 return self.evals.mean(-1) def ind(self): ''' Quantizes eigenvectors with maximum eigenvalues on an evenly distributed sphere so that the can be used for tractography. Returns --------- IN : array, shape(x,y,z) integer indices for the points of the evenly distributed sphere representing tensor eigenvectors of maximum eigenvalue ''' return quantize_evecs(self.evecs,odf_vertices=None) def wls_fit_tensor(design_matrix, data, min_signal=1): r""" Computes weighted least squares (WLS) fit to calculate self-diffusion tensor using a linear regression model [1]_. Parameters ---------- design_matrix : array (g, 7) Design matrix holding the covariants used to solve for the regression coefficients. data : array ([X, Y, Z, ...], g) Data or response variables holding the data. Note that the last dimension should contain the data. It makes no copies of data. min_signal : default = 1 All values below min_signal are repalced with min_signal. This is done in order to avaid taking log(0) durring the tensor fitting. Returns ------- eigvals : array (..., 3) Eigenvalues from eigen decomposition of the tensor. eigvecs : array (..., 3, 3) Associated eigenvectors from eigen decomposition of the tensor. Eigenvectors are columnar (e.g. eigvecs[:,j] is associated with eigvals[j]) See Also -------- decompose_tensor Notes ----- In Chung, et al. 2006, the regression of the WLS fit needed an unbiased preliminary estimate of the weights and therefore the ordinary least squares (OLS) estimates were used. A "two pass" method was implemented: 1. calculate OLS estimates of the data 2. apply the OLS estimates as weights to the WLS fit of the data This ensured heteroscadasticity could be properly modeled for various types of bootstrap resampling (namely residual bootstrap). .. math:: y = \mathrm{data} \\ X = \mathrm{design matrix} \\ \hat{\beta}_\mathrm{WLS} = \mathrm{desired regression coefficients (e.g. tensor)}\\ \\ \hat{\beta}_\mathrm{WLS} = (X^T W X)^{-1} X^T W y \\ \\ W = \mathrm{diag}((X \hat{\beta}_\mathrm{OLS})^2), \mathrm{where} \hat{\beta}_\mathrm{OLS} = (X^T X)^{-1} X^T y References ---------- .. _[1] Chung, SW., Lu, Y., Henry, R.G., 2006. Comparison of bootstrap approaches for estimation of uncertainties of DTI parameters. NeuroImage 33, 531-541. """ if min_signal <= 0: raise ValueError('min_signal must be > 0') data, wrap = _makearray(data) data_flat = data.reshape((-1, data.shape[-1])) dti_params = np.empty((len(data_flat), 4, 3)) #obtain OLS fitting matrix #U,S,V = np.linalg.svd(design_matrix, False) #math: beta_ols = inv(X.T*X)*X.T*y #math: ols_fit = X*beta_ols*inv(y) #ols_fit = np.dot(U, U.T) ols_fit = _ols_fit_matrix(design_matrix) for param, sig in zip(dti_params, data_flat): param[0], param[1:] = _wls_iter(ols_fit, design_matrix, sig, min_signal=min_signal) dti_params.shape = data.shape[:-1]+(12,) dti_params = wrap(dti_params) return dti_params def _wls_iter(ols_fit, design_matrix, sig, min_signal=1): ''' Function used by wls_fit_tensor for later optimization. ''' sig = np.maximum(sig, min_signal) #throw out zero signals log_s = np.log(sig) w = np.exp(np.dot(ols_fit, log_s)) D = np.dot(np.linalg.pinv(design_matrix*w[:,None]), w*log_s) tensor = _full_tensor(D) return decompose_tensor(tensor) def _ols_iter(inv_design, sig, min_signal=1): ''' Function used by ols_fit_tensor for later optimization. ''' sig = np.maximum(sig, min_signal) #throw out zero signals log_s = np.log(sig) D = np.dot(inv_design, log_s) tensor = _full_tensor(D) return decompose_tensor(tensor) def ols_fit_tensor(design_matrix, data, min_signal=1): r""" Computes ordinary least squares (OLS) fit to calculate self-diffusion tensor using a linear regression model [1]_. Parameters ---------- design_matrix : array (g, 7) Design matrix holding the covariants used to solve for the regression coefficients. Use design_matrix to build a valid design matrix from bvalues and a gradient table. data : array ([X, Y, Z, ...], g) Data or response variables holding the data. Note that the last dimension should contain the data. It makes no copies of data. min_signal : default = 1 All values below min_signal are repalced with min_signal. This is done in order to avaid taking log(0) durring the tensor fitting. Returns ------- eigvals : array (..., 3) Eigenvalues from eigen decomposition of the tensor. eigvecs : array (..., 3, 3) Associated eigenvectors from eigen decomposition of the tensor. Eigenvectors are columnar (e.g. eigvecs[:,j] is associated with eigvals[j]) See Also -------- WLS_fit_tensor, decompose_tensor, design_matrix Notes ----- This function is offered mainly as a quick comparison to WLS. .. math:: y = \mathrm{data} \\ X = \mathrm{design matrix} \\ \hat{\beta}_\mathrm{OLS} = (X^T X)^{-1} X^T y References ---------- .. [1] Chung, SW., Lu, Y., Henry, R.G., 2006. Comparison of bootstrap approaches for estimation of uncertainties of DTI parameters. NeuroImage 33, 531-541. """ data, wrap = _makearray(data) data_flat = data.reshape((-1, data.shape[-1])) evals = np.empty((len(data_flat), 3)) evecs = np.empty((len(data_flat), 3, 3)) dti_params = np.empty((len(data_flat), 4, 3)) #obtain OLS fitting matrix #U,S,V = np.linalg.svd(design_matrix, False) #math: beta_ols = inv(X.T*X)*X.T*y #math: ols_fit = X*beta_ols*inv(y) #ols_fit = np.dot(U, U.T) inv_design = np.linalg.pinv(design_matrix) for param, sig in zip(dti_params, data_flat): param[0], param[1:] = _ols_iter(inv_design, sig, min_signal) dti_params.shape = data.shape[:-1]+(12,) dti_params = wrap(dti_params) return dti_params def _ols_fit_matrix(design_matrix): """ Helper function to calculate the ordinary least squares (OLS) fit as a matrix multiplication. Mainly used to calculate WLS weights. Can be used to calculate regression coefficients in OLS but not recommended. See Also: --------- wls_fit_tensor, ols_fit_tensor Example: -------- ols_fit = _ols_fit_matrix(design_mat) ols_data = np.dot(ols_fit, data) """ U,S,V = np.linalg.svd(design_matrix, False) return np.dot(U, U.T) def _full_tensor(D): """ Returns a tensor given the six unique tensor elements Given the six unique tensor elments (in the order: Dxx, Dyy, Dzz, Dxy, Dxz, Dyz) returns a 3 by 3 tensor. All elements after the sixth are ignored. """ tensor = np.empty((3,3),dtype=D.dtype) tensor[0, 0] = D[0] #Dxx tensor[1, 1] = D[1] #Dyy tensor[2, 2] = D[2] #Dzz tensor[1, 0] = tensor[0, 1] = D[3] #Dxy tensor[2, 0] = tensor[0, 2] = D[4] #Dxz tensor[2, 1] = tensor[1, 2] = D[5] #Dyz return tensor def _compact_tensor(tensor, b0=1): """ Returns the six unique values of the tensor and a dummy value in the order expected by the design matrix """ D = np.empty(tensor.shape[:-2] + (7,)) row = [0, 1, 2, 1, 2, 2] colm = [0, 1, 2, 0, 0, 1] D[..., :6] = tensor[..., row, colm] D[..., 6] = np.log(b0) return D def decompose_tensor(tensor): """ Returns eigenvalues and eigenvectors given a diffusion tensor Computes tensor eigen decomposition to calculate eigenvalues and eigenvectors of self-diffusion tensor. (Basser et al., 1994a) Parameters ---------- D : array (3,3) array holding a tensor. Assumes D has units on order of ~ 10^-4 mm^2/s Returns ------- eigvals : array (3,) Eigenvalues from eigen decomposition of the tensor. Negative eigenvalues are replaced by zero. Sorted from largest to smallest. eigvecs : array (3,3) Associated eigenvectors from eigen decomposition of the tensor. Eigenvectors are columnar (e.g. eigvecs[:,j] is associated with eigvals[j]) See Also -------- numpy.linalg.eig """ #outputs multiplicity as well so need to unique eigenvals, eigenvecs = np.linalg.eig(tensor) #need to sort the eigenvalues and associated eigenvectors order = eigenvals.argsort()[::-1] eigenvecs = eigenvecs[:, order] eigenvals = eigenvals[order] #Forcing negative eigenvalues to 0 eigenvals = np.maximum(eigenvals, 0) # b ~ 10^3 s/mm^2 and D ~ 10^-4 mm^2/s # eigenvecs: each vector is columnar return eigenvals, eigenvecs def design_matrix(gtab, bval, dtype=None): """ Constructs design matrix for DTI weighted least squares or least squares fitting. (Basser et al., 1994a) Parameters ---------- gtab : array with shape (3,g) Diffusion gradient table found in DICOM header as a numpy array. bval : array with shape (g,) Diffusion weighting factor b for each vector in gtab. dtype : string Parameter to control the dtype of returned designed matrix Returns ------- design_matrix : array (g,7) Design matrix or B matrix assuming Gaussian distributed tensor model. Note: design_matrix[j,:] = (Bxx,Byy,Bzz,Bxy,Bxz,Byz,dummy) """ G = gtab B = np.zeros((bval.size, 7), dtype = G.dtype) if gtab.shape[1] != bval.shape[0]: raise ValueError('The number of b values and gradient directions must' +' be the same') B[:, 0] = G[0, :] * G[0, :] * 1. * bval #Bxx B[:, 1] = G[1, :] * G[1, :] * 1. * bval #Byy B[:, 2] = G[2, :] * G[2, :] * 1. * bval #Bzz B[:, 3] = G[0, :] * G[1, :] * 2. * bval #Bxy B[:, 4] = G[0, :] * G[2, :] * 2. * bval #Bxz B[:, 5] = G[1, :] * G[2, :] * 2. * bval #Byz B[:, 6] = np.ones(bval.size) return -B def quantize_evecs(evecs, odf_vertices=None): ''' Find the closest orientation of an evenly distributed sphere Parameters ---------- evecs : ndarray odf_vertices : None or ndarray If None, then set vertices from symmetric362 sphere. Otherwise use passed ndarray as vertices Returns ------- IN : ndarray ''' max_evecs=evecs[...,:,0] if odf_vertices==None: odf_vertices, _ = get_sphere('symmetric362') tup=max_evecs.shape[:-1] mec=max_evecs.reshape(np.prod(np.array(tup)),3) IN=np.array([np.argmin(np.dot(odf_vertices,m)) for m in mec]) IN=IN.reshape(tup) return IN common_fit_methods = {'WLS': wls_fit_tensor, 'LS': ols_fit_tensor} dipy-0.5.0/dipy/reconst/gqi.py000066400000000000000000000234761152576264200162630ustar00rootroot00000000000000""" Classes and functions for generalized q-sampling """ import numpy as np import dipy.reconst.recspeed as rp from dipy.utils.spheremakers import sphere_vf_from class GeneralizedQSampling(object): """ Implements Generalized Q-Sampling Generates a model-free description for every voxel that can be used from simple to very complicated configurations like quintuple crossings if your datasets support them. You can use this class for every kind of DWI image but it will perform much better when you have a balanced sampling scheme. Implements equation [9] from Generalized Q-Sampling as described in Fang-Cheng Yeh, Van J. Wedeen, Wen-Yih Isaac Tseng. Generalized Q-Sampling Imaging. IEEE TMI, 2010. Parameters ----------- data : array, shape(X,Y,Z,D) bvals : array, shape (N,) gradients : array, shape (N,3) also known as bvecs Lambda : float, smoothing parameter - diffusion sampling length Properties ---------- QA : array, shape(X,Y,Z,5), quantitative anisotropy IN : array, shape(X,Y,Z,5), indices of QA, qa unit directions fwd : float, normalization parameter Notes ----- In order to reconstruct the spin distribution function a nice symmetric evenly distributed sphere is provided using 362 or 642 points. This is usually sufficient for most of the datasets. See also -------- dipy.tracking.propagation.EuDX, dipy.reconst.dti.Tensor, dipy.data.get_sphere """ def __init__(self, data, bvals, gradients, Lambda=1.2, odf_sphere='symmetric362', mask=None): """ Generates a model-free description for every voxel that can be used from simple to very complicated configurations like quintuple crossings if your datasets support them. You can use this class for every kind of DWI image but it will perform much better when you have a balanced sampling scheme. Implements equation [9] from Generalized Q-Sampling as described in Fang-Cheng Yeh, Van J. Wedeen, Wen-Yih Isaac Tseng. Generalized Q-Sampling Imaging. IEEE TMI, 2010. Parameters ----------- data: array, shape(X,Y,Z,D) bvals: array, shape (N,) gradients: array, shape (N,3) also known as bvecs Lambda: float, optional smoothing parameter - diffusion sampling length odf_sphere : None or str or tuple, optional input that will result in vertex, face arrays for a sphere. mask : None or ndarray, optional Key Properties --------------- QA : array, shape(X,Y,Z,5), quantitative anisotropy IN : array, shape(X,Y,Z,5), indices of QA, qa unit directions fwd : float, normalization parameter Notes ------- In order to reconstruct the spin distribution function a nice symmetric evenly distributed sphere is provided using 362 points. This is usually sufficient for most of the datasets. See also -------- dipy.tracking.propagation.EuDX, dipy.reconst.dti.Tensor, dipy.data.__init__.get_sphere """ odf_vertices, odf_faces = sphere_vf_from(odf_sphere) self.odf_vertices=odf_vertices # 0.01506 = 6*D where D is the free water diffusion coefficient # l_values sqrt(6 D tau) D free water diffusion coefficient and # tau included in the b-value scaling = np.sqrt(bvals*0.01506) tmp=np.tile(scaling, (3,1)) #the b vectors might have nan values where they correspond to b #value equals with 0 gradients[np.isnan(gradients)]= 0. gradsT = gradients.T b_vector=gradsT*tmp # element-wise also known as the Hadamard product #q2odf_params=np.sinc(np.dot(b_vector.T, odf_vertices.T) * Lambda/np.pi) q2odf_params=np.real(np.sinc(np.dot(b_vector.T, odf_vertices.T) * Lambda/np.pi)) #q2odf_params[np.isnan(q2odf_params)]= 1. #define total mask #tot_mask = (mask > 0) & (data[...,0] > thresh) S=data datashape=S.shape #initial shape msk=None #tmp mask if len(datashape)==4: x,y,z,g=S.shape S=S.reshape(x*y*z,g) QA = np.zeros((x*y*z,5)) IN = np.zeros((x*y*z,5)) if mask != None: if mask.shape[:3]==datashape[:3]: msk=mask.ravel().copy() #print 'msk.shape',msk.shape if len(datashape)==2: x,g= S.shape QA = np.zeros((x,5)) IN = np.zeros((x,5)) glob_norm_param = 0 self.q2odf_params=q2odf_params #Calculate Quantitative Anisotropy and #find the peaks and the indices #for every voxel if mask !=None: for (i,s) in enumerate(S): if msk[i]>0: #Q to ODF odf=np.dot(s,q2odf_params) peaks,inds=rp.peak_finding(odf,odf_faces) glob_norm_param=max(np.max(odf),glob_norm_param) #remove the isotropic part peaks = peaks - np.min(odf) l=min(len(peaks),5) QA[i][:l] = peaks[:l] IN[i][:l] = inds[:l] if mask==None: for (i,s) in enumerate(S): #Q to ODF odf=np.dot(s,q2odf_params) peaks,inds=rp.peak_finding(odf,odf_faces) glob_norm_param=max(np.max(odf),glob_norm_param) #remove the isotropic part peaks = peaks - np.min(odf) l=min(len(peaks),5) QA[i][:l] = peaks[:l] IN[i][:l] = inds[:l] #normalize QA/=glob_norm_param if len(datashape) == 4: self.QA=QA.reshape(x,y,z,5) self.IN=IN.reshape(x,y,z,5) if len(datashape) == 2: self.QA=QA self.IN=IN self.glob_norm_param = glob_norm_param def qa(self): """ quantitative anisotropy """ return self.QA def ind(self): """ indices on the sampling sphere """ return self.IN def odf(self,s): """ spin density orientation distribution function Parameters ----------- s : array, shape(D), diffusion signal for one point in the dataset Returns --------- odf : array, shape(len(odf_vertices)), spin density orientation distribution function """ return np.dot(s,self.q2odf_params) def npa(self,s,width=5): """ non-parametric anisotropy Nimmo-Smith et. al ISMRM 2011 """ odf=self.odf(s) t0,t1,t2=triple_odf_maxima(self.odf_vertices, odf, width) psi0 = t0[1]**2 psi1 = t1[1]**2 psi2 = t2[1]**2 npa = np.sqrt((psi0-psi1)**2+(psi1-psi2)**2+(psi2-psi0)**2)/np.sqrt(2*(psi0**2+psi1**2+psi2**2)) #print 'tom >>>> ',t0,t1,t2,npa return t0,t1,t2,npa def equatorial_zone_vertices(vertices, pole, width=5): """ finds the 'vertices' in the equatorial zone conjugate to 'pole' with width half 'width' degrees """ return [i for i,v in enumerate(vertices) if np.abs(np.dot(v,pole)) < np.abs(np.sin(np.pi*width/180))] def polar_zone_vertices(vertices, pole, width=5): """ finds the 'vertices' in the equatorial band around the 'pole' of radius 'width' degrees """ return [i for i,v in enumerate(vertices) if np.abs(np.dot(v,pole)) > np.abs(np.cos(np.pi*width/180))] def upper_hemi_map(v): """ maps a 3-vector into the z-upper hemisphere """ return np.sign(v[2])*v def equatorial_maximum(vertices, odf, pole, width): eqvert = equatorial_zone_vertices(vertices, pole, width) #need to test for whether eqvert is empty or not if len(eqvert) == 0: print('empty equatorial band at %s pole with width %f' % (np.array_str(pole), width)) return Null, Null eqvals = [odf[i] for i in eqvert] eqargmax = np.argmax(eqvals) eqvertmax = eqvert[eqargmax] eqvalmax = eqvals[eqargmax] return eqvertmax, eqvalmax #""" def patch_vertices(vertices,pole, width): """ find 'vertices' within the cone of 'width' degrees around 'pole' """ return [i for i,v in enumerate(vertices) if np.abs(np.dot(v,pole)) > np.abs(np.cos(np.pi*width/180))] #""" def patch_maximum(vertices, odf, pole, width): eqvert = patch_vertices(vertices, pole, width) #need to test for whether eqvert is empty or not if len(eqvert) == 0: print('empty cone around pole %s with with width %f' % (np.array_str(pole), width)) return np.Null, np.Null eqvals = [odf[i] for i in eqvert] eqargmax = np.argmax(eqvals) eqvertmax = eqvert[eqargmax] eqvalmax = eqvals[eqargmax] return eqvertmax, eqvalmax def triple_odf_maxima(vertices, odf, width): indmax1 = np.argmax([odf[i] for i,v in enumerate(vertices)]) odfmax1 = odf[indmax1] pole = vertices[indmax1] eqvert = equatorial_zone_vertices(vertices, pole, width) indmax2, odfmax2 = equatorial_maximum(vertices,\ odf, pole, width) indmax3 = eqvert[np.argmin([np.abs(np.dot(vertices[indmax2],vertices[p])) for p in eqvert])] odfmax3 = odf[indmax3] """ cross12 = np.cross(vertices[indmax1],vertices[indmax2]) cross12 = cross12/np.sqrt(np.sum(cross12**2)) indmax3, odfmax3 = patch_maximum(vertices, odf, cross12, 2*width) """ return [(indmax1, odfmax1),(indmax2, odfmax2),(indmax3, odfmax3)] dipy-0.5.0/dipy/reconst/maskedview.py000066400000000000000000000202361152576264200176310ustar00rootroot00000000000000""" Class to allow masked view of data array """ import numpy as np from operator import mul from copy import copy def _makearray(a): new = np.asarray(a) wrap = getattr(a, "__array_wrap__", new.__array_wrap__) return new, wrap def _filled(a, *args, **kargs): if hasattr(a, 'filled'): return a.filled(*args, **kargs) else: return a class MaskedView(object): """ An interface to allow the user to interact with a data array as if it is a container with the same shape as mask. The contents of data are mapped to the nonzero elements of mask, where mask is zero fill_value is used. Examples ----------- >>> mask = np.array([[True, False, True],[False, True, False]]) >>> data = np.arange(2*3*4) >>> data.shape = (2, 3, 4) >>> mv = MaskedView(mask, data[mask], fill_value=10) >>> mv.shape (2, 3, 4) >>> data[0, 0, :] array([0, 1, 2, 3]) >>> mv[0, 1] array([10, 10, 10, 10]) >>> mv[:,:,0] array([[ 0, 10, 8], [10, 16, 10]]) """ def __init__(self, mask, data, fill_value=None): """ Creates a MaskedView of data. Parameters ------------ mask : ndarray of bools mask indicating where the data belongs data : ndarray, ndim >= mask.ndim the first dimension of data should have size equal to the number of nonzero elements in mask fill_value : optional fill_value is returned when MaskedView is indexed and mask is zero, also fill_value is used to fill out an array when the filled method is called. By defult is NaN or 0 depending on the dtype of data. """ mask = mask.astype('bool') if len(data) != mask.sum(): raise ValueError('the number of data elements does not match mask') self._data = data try: self.fill_value = np.array(fill_value, dtype=data.dtype) except TypeError: if fill_value is None: self.fill_value = np.array(0, dtype=data.dtype) else: raise self.base = None self._imask = np.empty(mask.shape, 'int') self._imask.fill(-1) self._imask[mask] = np.arange(len(data)) @property def mask(self): return self._imask >= 0 @property def dtype(self): #the data type of a masked view is the same as the _data array return self._data.dtype @property def ndim(self): return self._data.ndim + self._imask.ndim - 1 def filled(self, fill_value=None): """ Returns an ndarray copy of itself. Where mask is zero, fill_value is used (self.fill_value defult). Parameters ------------ fill_value : Value to be used in place of data where mask is 0. """ if fill_value is None: fill_value = self.fill_value out_arr = np.empty(self.shape, self.dtype) out_arr[:] = fill_value out_arr[self.mask] = self.__array__() return out_arr def _get_shape_contents(self): return self._data.shape[1:] def _set_shape_contents(self, shape): self._data.shape = self._data.shape[0:1] + shape def _get_shape_mask(self): return self._imask.shape def _set_shape_mask(self, shape): self._imask.shape = shape def _get_shape(self): return self.shape_mask + self.shape_contents def _set_shape(self, shape): try: shape[1] except (TypeError, IndexError): raise ValueError("a 2d shape is required such that the size of " + "mask is unchanged") where_missing = [ii < 0 for ii in shape] count_missing = sum(where_missing) if count_missing == 1: ind = where_missing.index(True) tot_sz = reduce(mul, self.shape) other_sz = reduce(mul, shape[:ind] + shape[ind+1:]) if tot_sz % other_sz != 0: raise ValueError("total size of new array must be unchanged") missing = tot_sz / other_sz shape = shape[:ind] + (missing,) + shape[ind+1:] elif count_missing > 1: raise ValueError("can only specify one unknown dimension") elif reduce(mul, shape) != reduce(mul, self.shape): raise ValueError("total size of new array must be unchanged") first_n = 1 for ind, dim_i in enumerate(shape): first_n = dim_i*first_n if first_n == self._imask.size: self.shape_mask = shape[:ind+1] self.shape_contents = shape[ind+1:] break elif first_n > self._imask.size: raise ValueError("total size of mask must be unchanged") shape_contents = property(_get_shape_contents, _set_shape_contents, "Tuple of contents dimensions") shape_mask = property(_get_shape_mask, _set_shape_mask, "Tuple of mask dimensions") shape = property(_get_shape, _set_shape, "Tuple of array dimensions") def get_size(self): """ Returns the number of non-empty values in MaskedView, ie where mask > 0. """ return self.mask.sum() def copy(self): """ Returns a copy of the MaskedView. Copies the underlying data array. """ data = self._data[self._imask[self.mask]] return MaskedView(self.mask, data, self.fill_value) def __getitem__(self, index): """ Indexes the MaskedView without copying the underlying data. """ if type(index) is not tuple: index = (index,) #replace first Ellipsis with slices for ii, slc in enumerate(index): if slc is Ellipsis: n_ellipsis = len(self.shape) - len(index) + 1 index = index[:ii] + n_ellipsis*(slice(None),) + index[ii+1:] break ndim_mask = self._imask.ndim if len(index) > ndim_mask: index_mask = index[:ndim_mask] index_cont = index[ndim_mask:] else: index_mask = index index_cont = (slice(None),) imask = self._imask[index_mask] if isinstance(imask, int): if imask >= 0: return self._data[(imask,)+index_cont] else: result = np.empty(self.shape_contents, self.dtype) result[:] = self.fill_value return result[index_cont] else: new_mp = copy(self) new_mp._imask = imask if self.base is None: new_mp.base = self data = self._data[(slice(None),) + index_cont] new_mp._data = data if data.ndim < 2: return new_mp.filled() else: return new_mp def __setitem__(self, index, values): """ Sets part of the maskedview is this useful? """ imask = self._imask[index] if isinstance(imask, int): if imask >= 0: self._data[imask] = values else: self._imask[index] = len(self._data) self._data = np.r_[self._data, values[np.newaxis]] else: self._data[imask[imask >= 0]] = values def __array__(self, dtype=None): """ Returns the underlying data """ #to save time only index _data when base is not None if self.base is None: data = self._data else: data = self._data[self._imask[self.mask]] #only makes a copy of data when dtype does not match self.dtype if dtype is None or np.dtype(dtype) == self.dtype: return data else: return data.astype(dtype) def __array_wrap__(self, array, context=None): #fill_value is not updated #ie if new = old + 1 new.fill_value == old.fil_value. Fixing this might #be a useful feature to implement at some point for numeric fill_values new_container = MaskedView(self.mask, array, self.fill_value) return new_container dipy-0.5.0/dipy/reconst/modelarray.py000066400000000000000000000051441152576264200176320ustar00rootroot00000000000000#!/usr/bin/python """ Class to present model parameters as voxel-shaped array """ # 5/17/2010 #import modules from numpy import asarray, ones from copy import copy class ModelArray(object): """A class that has a shape and can be indexed like an ndarray When using a model to describe many voxels, ModelArray allows the parameters of a model to be stored as an ndarray where the last dimension of the array represents the parameters, and the first n-1 dimensions represent the shape or arrangement of the voxels. Model array is meant to be sub-classed to make more specific model classes. """ ### Shape Property ### def _getshape(self): """ Gives the shape of the ModelArray """ return self.model_params.shape[:-1] def _setshape(self, shape): """ Sets the shape of the ModelArray """ if type(shape) is not tuple: shape = (shape,) self.model_params.shape = shape + self.model_params.shape[-1:] shape = property(_getshape, _setshape, doc = "Shape of model array") ### Ndim Property ### @property def ndim(self): """Gives the number of dimensions of the ModelArray """ return self.model_params.ndim - 1 @property def mask(self): """If the model_params array has a mask, returns the mask """ if hasattr(self.model_params, 'mask'): return self.model_params.mask else: return ones(self.shape, 'bool') ### Getitem Property ### def __getitem__(self, index): """ Returns part of the model array """ if type(index) is not tuple: index = (index,) if len(index) > self.ndim: raise IndexError('invalid index') for ii, slc in enumerate(index): if slc is Ellipsis: n_ellipsis = len(self.shape) - len(index) + 1 index = index[:ii] + n_ellipsis*(slice(None),) + index[ii+1:] break new_model = copy(self) new_model.model_params = self.model_params[index] return new_model def _get_model_params(self): """Parameters of the model All the parameters needed for a model should be flattened into the last dimension of model_params. The shape of the ModelArray is determined by the model_params.shape[:-1]. """ return self._model_params def _set_model_params(self, params): """Sets model_params """ self._model_params = params model_params = property(_get_model_params, _set_model_params) dipy-0.5.0/dipy/reconst/qball.py000066400000000000000000000141301152576264200165610ustar00rootroot00000000000000#from enthought.mayavi import mlab import numpy as np from scipy.special import sph_harm, lpn from copy import copy, deepcopy import warnings warnings.warn("This module is most likely to change both as a name and in structure in the future",FutureWarning) def real_sph_harm(m, n, theta, phi): """ Compute real spherical harmonics, where the real harmonic $Y^m_n$ is defined to be: Real($Y^m_n$) * sqrt(2) if m > 0 $Y^m_n$ if m == 0 Imag($Y^m_n$) * sqrt(2) if m < 0 This may take scalar or array arguments. The inputs will be broadcasted against each other. Parameters ----------- - `m` : int |m| <= n The order of the harmonic. - `n` : int >= 0 The degree of the harmonic. - `theta` : float [0, 2*pi] The azimuthal (longitudinal) coordinate. - `phi` : float [0, pi] The polar (colatitudinal) coordinate. Returns -------- - `y_mn` : real float The real harmonic $Y^m_n$ sampled at `theta` and `phi`. :See also: scipy.special.sph_harm """ m = np.atleast_1d(m) # find where m is =,< or > 0 and broadcasts to the size of the output m_eq0,junk,junk,junk = np.broadcast_arrays(m == 0, n, theta, phi) m_gt0,junk,junk,junk = np.broadcast_arrays(m > 0, n, theta, phi) m_lt0,junk,junk,junk = np.broadcast_arrays(m < 0, n, theta, phi) sh = sph_harm(m, n, theta, phi) real_sh = np.empty(sh.shape, 'double') real_sh[m_eq0] = sh[m_eq0].real real_sh[m_gt0] = sh[m_gt0].real * np.sqrt(2) real_sh[m_lt0] = sh[m_lt0].imag * np.sqrt(2) return real_sh def sph_harm_ind_list(sh_order): """ Returns the degree (n) and order (m) of all the symmetric spherical harmonics of degree less then or equal it sh_order. The results, m_list and n_list are kx1 arrays, where k depends on sh_order. They can be passed to real_sph_harm. Parameters ---------- sh_order : int even int > 0, max degree to return Returns ------- m_list : array orders of even spherical harmonics n_list : array degrees of even spherical hormonics See also -------- real_sph_harm """ if sh_order % 2 != 0: raise ValueError('sh_order must be an even integer >= 0') n_range = np.arange(0, np.int(sh_order+1), 2) n_list = np.repeat(n_range, n_range*2+1) ncoef = (sh_order + 2)*(sh_order + 1)/2 offset = 0 m_list = np.empty(ncoef, 'int') for ii in n_range: m_list[offset:offset+2*ii+1] = np.arange(-ii, ii+1) offset = offset + 2*ii + 1 # makes the arrays ncoef by 1, allows for easy broadcasting later in code n_list = n_list[..., np.newaxis] m_list = m_list[..., np.newaxis] return (m_list, n_list) def cartesian2polar(x=0, y=0, z=0): """Converts cartesian coordinates to polar coordinates converts a list of cartesian coordinates (x, y, z) to polar coordinates (R, theta, phi). """ R = np.sqrt(x**2+y**2+z**2) theta = np.arctan2(y, x) phi = np.arccos(z) R, theta, phi = np.broadcast_arrays(R, theta, phi) return R, theta, phi class ODF(object): def _getshape(self): return self._coef.shape[:-1] shape = property(_getshape, doc="Shape of ODF array") def _getndim(self): return self._coef.ndim-1 ndim = property(_getndim, doc="Number of dimensions in ODF array") def __getitem__(self, index): if type(index) is not tuple: index = (index,) if len(index) > self.ndim: raise IndexError('invalid index') for ii in index: if ii is Ellipsis: index = index + (slice(None),) break new_odf = copy(self) new_odf._coef = self._coef[index] if new_odf._resid is not None: new_odf._resid = self._resid[index] return new_odf def __init__(self, data, sh_order, grad_table, b_values, smoothness=0, keep_resid=False): if (sh_order % 2 != 0 or sh_order < 0 ): raise ValueError('sh_order must be an even integer >= 0') self.sh_order = sh_order dwi = b_values > 0 self.ngrad = dwi.sum() R, theta, phi = cartesian2polar(grad_table[dwi, 0], grad_table[dwi, 0], grad_table[dwi, 2]) m_list, n_list = sph_harm_ind_list(self.sh_order) if m_list.size > self.ngrad: raise ValueError('sh_order seems too high, there are only '+ str(self.ngrad)+' diffusion weighted images in data') design_mat = real_sph_harm(m_list, n_list, theta, phi) if smoothness == 0: self.fit_matrix = np.linalg.pinv(design_mat) else: L = np.diag(n_list*(n_list+1))*sqrt(smoothness) self.fit_matrix = np.linalg.pinv(np.c_[design_mat, L])[:,:self.ngrad] legendre0, junk = lpn(self.sh_order, 0) funk_radon = legendre0[n_list] self.fit_matrix *= funk_radon.T self.b0 = data[..., np.logical_not(dwi)] self._coef = np.dot(data[..., dwi], self.fit_matrix) if keep_resid: unfit = design_mat / funk_radon self._resid = data[..., dwi] - np.dot(self._coef, unfit) else: self._resid = None def evaluate_at(self, theta_e, phi_e): m_list, n_list = sph_harm_ind_list(self.sh_order) design_mat = real_sph_harm(m_list, n_list, theta_e.ravel(), phi_e.ravel()) values = np.dot(self._coef, design_mat) values.shape = self.shape + np.broadcast(theta_e,phi_e).shape return values def evaluate_boot(self, theta_e, phi_e, permute=None): m_list, n_list = sph_harm_ind_list(self.sh_order) design_mat = real_sph_harm(m_list, n_list, theta_e.ravel(), phi_e.ravel()) if permute is None: permute = np.random.permutation(self.ngrad) values = np.dot(self._coef + np.dot(self._resid[..., permute], self.fit_matrix), design_mat) return values dipy-0.5.0/dipy/reconst/recspeed.pyx000066400000000000000000000233721152576264200174600ustar00rootroot00000000000000# Emacs should think this is a -*- python -*- file """ Optimized routines for creating voxel diffusion models """ # cython: profile=True # cython: embedsignature=True cimport cython import numpy as np cimport numpy as cnp cdef extern from "math.h" nogil: double floor(double x) float fabs(float x) double log2(double x) double cos(double x) double sin(double x) float acos(float x ) bint isnan(double x) double sqrt(double x) DEF PI=3.1415926535897931 # initialize numpy runtime cnp.import_array() #numpy pointers cdef inline float* asfp(cnp.ndarray pt): return pt.data cdef inline double* asdp(cnp.ndarray pt): return pt.data #@cython.boundscheck(False) @cython.wraparound(False) def peak_finding_edges(odf, edges_on_sphere): cdef: cnp.ndarray[cnp.uint16_t, ndim=2] cedges = np.ascontiguousarray(edges_on_sphere) cnp.ndarray[cnp.float64_t, ndim=1] codf = np.ascontiguousarray(odf) cnp.ndarray[cnp.uint8_t, ndim=1] cpeak = np.ones(odf.shape, np.uint8) int i=0 int lenedges = len(cedges) int find0,find1 double odf0,odf1 for i from 0 <= i < lenedges: find0 = cedges[i,0] find1 = cedges[i,1] odf0 = codf[find0] odf1 = codf[find1] if odf0 > odf1: cpeak[find1] = 0 elif odf0 < odf1: cpeak[find1] = 0 cpeak = np.array(cpeak) #find local maxima and give fiber orientation (inds) and magnitude #peaks in a descending order inds = cpeak.nonzero()[0] pinds = odf[inds].argsort() inds = inds[pinds][::-1] peaks = odf[inds] return peaks, inds @cython.boundscheck(False) @cython.wraparound(False) def peak_finding(odf, odf_faces): ''' Hemisphere local maxima from sphere values and faces Return local maximum values and indices. Local maxima (peaks) are given in descending order. The sphere mesh, as defined by the vertex coordinates ``vertices`` and the face indices ``odf_faces``, has to conform to the check in ``dipy.core.meshes.peak_finding_compatible``. If it does not, then the results from peak finding routine will be unpredictable. Parameters ------------ odf : (N,) array function values on the sphere, where N is the number of vertices on the sphere odf_faces : (M,3) array faces of the triangulation on the sphere, where M is the number of faces on the sphere Returns --------- peaks : (L,) array, dtype np.float64 peak values, shape (L,) where L can vary and is the number of local moximae (peaks). Values are sorted, largest first inds : (L,) array, dtype np.uint16 indices of the peak values on the `odf` array corresponding to the maxima in `peaks` Notes ----- In summary this function does the following: Where the smallest odf values in the vertices of a face put zeros on them. By doing that for the vertices of all faces at the end you only have the peak points with nonzero values. For precalculated odf_faces look under dipy/data/evenly*.npz to use them try numpy.load()['faces'] Examples ---------- This is called from GeneralizedQSampling or QBall and other models with orientation distribution functions. See also ----------- dipy.core.meshes ''' cdef: cnp.ndarray[cnp.uint16_t, ndim=2] cfaces = np.ascontiguousarray(odf_faces) cnp.ndarray[cnp.float64_t, ndim=1] codf = np.ascontiguousarray(odf) cnp.ndarray[cnp.float64_t, ndim=1] cpeak = odf.copy() int i=0 int test=0 int lenfaces = len(cfaces) double odf0,odf1,odf2 int find0,find1,find2 for i in range(lenfaces): find0 = cfaces[i,0] find1 = cfaces[i,1] find2 = cfaces[i,2] odf0=codf[find0] odf1=codf[find1] odf2=codf[find2] if odf0 >= odf1 and odf0 >= odf2: cpeak[find1] = 0 cpeak[find2] = 0 continue if odf1 >= odf0 and odf1 >= odf2: cpeak[find0] = 0 cpeak[find2] = 0 continue if odf2 >= odf0 and odf2 >= odf1: cpeak[find0] = 0 cpeak[find1] = 0 continue peak=np.array(cpeak) peak=peak[0:len(peak)/2] #find local maxima and give fiber orientation (inds) and magnitude #peaks in a descending order inds=np.where(peak>0)[0] pinds=np.argsort(peak[inds]) peaks=peak[inds[pinds]][::-1] return peaks, inds[pinds][::-1] def argmax_from_adj(vals, vertex_inds, adj_inds): """ Indices of local maxima from `vals` given adjacent points Parameters ------------ vals : (N,) array, dtype np.float64 values at all vertices referred to in either of `vertex_inds` or `adj_inds`' vertex_inds : (V,) array indices into `vals` giving vertices that may be local maxima. adj_inds : sequence For every vertex in ``vertex_inds``, the indices (into `vals`) of the neighboring points Returns --------- inds : (M,) array Indices into `vals` giving local maxima of vals, given topology from `adj_inds`, and restrictions from `vertex_inds`. Inds are returned sorted by value at that index - i.e. smallest value (at index) first. """ cvals, cvertinds = proc_reco_args(vals, vertex_inds) cadj_counts, cadj_inds = adj_to_countarrs(adj_inds) return argmax_from_countarrs(cvals, cvertinds, cadj_counts, cadj_inds) def proc_reco_args(vals, vertinds): vals = np.ascontiguousarray(vals.astype(np.float)) vertinds = np.ascontiguousarray(vertinds.astype(np.uint32)) return vals, vertinds def adj_to_countarrs(adj_inds): """ Convert adjacency sequence to counts and flattened indices We use this to provide expected input to ``argmax_from_countarrs`` Parameters ------------ adj_indices : sequence length V sequence of sequences, where sequence ``i`` contains the neighbors of a particular vertex. Returns --------- counts : (V,) array Number of neighbors for each vertex adj_inds : (n,) array flat array containing `adj_indices` unrolled as a vector """ counts = [] all_inds = [] for verts in adj_inds: v = list(verts) all_inds += v counts.append(len(v)) adj_inds = np.array(all_inds, dtype=np.uint32) counts = np.array(counts, dtype=np.uint32) return counts, adj_inds # prefetch argsort for small speedup cdef object argsort = np.argsort def argmax_from_countarrs(cnp.ndarray vals, cnp.ndarray vertinds, cnp.ndarray adj_counts, cnp.ndarray adj_inds): """ Indices of local maxima from `vals` from count, array neighbors Parameters ------------ vals : (N,) array, dtype float values at all vertices referred to in either of `vertex_inds` or `adj_inds`' vertinds : (V,) array, dtype uint32 indices into `vals` giving vertices that may be local maxima. adj_counts : (V,) array, dtype uint32 For every vertex ``i`` in ``vertex_inds``, the number of neighbors for vertex ``i`` adj_inds : (P,) array, dtype uint32 Indices for neighbors for each point. ``P=sum(adj_counts)`` Returns --------- inds : (M,) array Indices into `vals` giving local maxima of vals, given topology from `adj_counts` and `adj_inds`, and restrictions from `vertex_inds`. Inds are returned sorted by value at that index - i.e. smallest value (at index) first. """ cdef: cnp.ndarray[cnp.float64_t, ndim=1] cvals = vals cnp.ndarray[cnp.uint32_t, ndim=1] cvertinds = vertinds cnp.ndarray[cnp.uint32_t, ndim=1] cadj_counts = adj_counts cnp.ndarray[cnp.uint32_t, ndim=1] cadj_inds = adj_inds # temporary arrays for storing maxes cnp.ndarray[cnp.float64_t, ndim=1] maxes = vals.copy() cnp.ndarray[cnp.uint32_t, ndim=1] maxinds = vertinds.copy() cnp.npy_intp i, j, V, C, n_maxes=0, adj_size, adj_pos=0 int is_max cnp.float64_t *vals_ptr double val cnp.uint32_t vert_ind, *vertinds_ptr, *counts_ptr, *adj_ptr, ind cnp.uint32_t vals_size, vert_size if not (cnp.PyArray_ISCONTIGUOUS(cvals) and cnp.PyArray_ISCONTIGUOUS(cvertinds) and cnp.PyArray_ISCONTIGUOUS(cadj_counts) and cnp.PyArray_ISCONTIGUOUS(cadj_inds)): raise ValueError('Need contiguous arrays as input') vals_size = cvals.shape[0] vals_ptr = cvals.data vertinds_ptr = cvertinds.data adj_ptr = cadj_inds.data counts_ptr = cadj_counts.data V = cadj_counts.shape[0] adj_size = cadj_inds.shape[0] if cvertinds.shape[0] < V: raise ValueError('Too few indices for adj arrays') for i in range(V): vert_ind = vertinds_ptr[i] if vert_ind >= vals_size: raise IndexError('Overshoot on vals') val = vals_ptr[vert_ind] C = counts_ptr[i] # check for overshoot adj_pos += C if adj_pos > adj_size: raise IndexError('Overshoot on adj_inds array') is_max = 1 for j in range(C): ind = adj_ptr[j] if ind >= vals_size: raise IndexError('Overshoot on vals') if val <= vals_ptr[ind]: is_max = 0 break if is_max: maxinds[n_maxes] = vert_ind maxes[n_maxes] = val n_maxes +=1 adj_ptr += C if n_maxes == 0: return np.array([]) # fancy indexing always produces a copy return maxinds[argsort(maxes[:n_maxes])] dipy-0.5.0/dipy/reconst/tests/000077500000000000000000000000001152576264200162575ustar00rootroot00000000000000dipy-0.5.0/dipy/reconst/tests/__init__.py000066400000000000000000000000401152576264200203620ustar00rootroot00000000000000# tests for reconstruction code dipy-0.5.0/dipy/reconst/tests/test_dandelion.py000066400000000000000000000044411152576264200216300ustar00rootroot00000000000000import numpy as np from nose.tools import assert_true, assert_false, assert_equal, assert_raises from numpy.testing import assert_array_equal, assert_array_almost_equal import nibabel as nib from dipy.data import get_data, get_sphere from dipy.reconst.dandelion import SphericalDandelion from dipy.reconst.recspeed import peak_finding from dipy.reconst.gqi import GeneralizedQSampling def test_dandelion(): fimg,fbvals,fbvecs=get_data('small_101D') bvals=np.loadtxt(fbvals) gradients=np.loadtxt(fbvecs).T data=nib.load(fimg).get_data() print(bvals.shape, gradients.shape, data.shape) sd=SphericalDandelion(data,bvals,gradients) sdf=sd.spherical_diffusivity(data[5,5,5]) XA=sd.xa() np.set_printoptions(2) print XA.min(),XA.max(),XA.mean() print sdf*10**4 """ print(sdf.shape) gq=GeneralizedQSampling(data,bvals,gradients) sodf=gq.odf(data[5,5,5]) vertices, faces = get_sphere('symmetric362') print(faces.shape) peaks,inds=peak_finding(np.squeeze(sdf),faces) print(peaks, inds) peaks2,inds2=peak_finding(np.squeeze(sodf),faces) print(peaks2, inds2) """ ''' from fos.data import get_sphere from fos.comp_geom.gen_normals import auto_normals vertices=100*vertices.astype(np.float32)#make a bigger sphere faces=faces.astype(np.uint32) colors=np.ones((len(vertices),4)) colors[:,0]=np.interp(sdf,[sdf.min(),sdf.max()],[0,1]) colors[inds[0]]=np.array([1,0,0,1]) colors[inds2[0]]=np.array([0,1,0,1]) colors=colors.astype('f4') len(vertices) print 'vertices.shape', vertices.shape, vertices.dtype print 'faces.shape', faces.shape,faces.dtype normals=auto_normals(vertices,faces) print vertices.min(),vertices.max(),vertices.mean() print normals.min(),normals.max(), normals.mean() print vertices.dtype,faces.dtype, colors.dtype, normals.dtype from fos.actor.surf import Surface from fos import Window, World, DefaultCamera aff = np.eye(4, dtype = np.float32) #aff[0,3] = 30 s=Surface(vertices,faces,colors,normals=normals, affine = aff,) w=World() w.add(s) #w.add(s2) cam=DefaultCamera() w.add(cam) wi=Window() wi.attach(w) ''' dipy-0.5.0/dipy/reconst/tests/test_dti.py000066400000000000000000000162651152576264200204620ustar00rootroot00000000000000""" Testing DTI """ import numpy as np import dipy.reconst.dti as dti from dipy.reconst.maskedview import MaskedView import nibabel as nib from dipy.io.bvectxt import read_bvec_file from dipy.data import get_data from nose.tools import assert_true, assert_false, \ assert_equal, assert_almost_equal, assert_raises from numpy.testing import assert_array_equal, assert_array_almost_equal from dipy.testing import parametric import os @parametric def test_tensor_scalar_attributes(): """ Tests that the tensor class scalar attributes (FA, ADC, etc...) are calculating properly. """ ### DEFINING ANALYTICAL VALUES ### evals = np.array([2., 1., 0.]) a = 1. / np.sqrt(2) #evec[:,j] is pair with eval[j] evecs = np.array([[a, 0, -a], [a, 0, a], [0, 1., 0]]) D = np.array([[1., 1., 0], [1., 1., 0], [0, 0, 1.]]) FA = np.sqrt(1./2*(1+4+1)/(1+4+0)) # 0.7745966692414834 MD = 1. ### CALCULATE ESTIMATE VALUES ### dummy_data = np.ones((1,10)) #single voxel dummy_gtab = np.zeros((10,3)) dummy_bval = np.zeros((10,)) tensor = dti.Tensor(dummy_data,dummy_bval,dummy_gtab) tensor.model_params = np.r_['-1,2', evals, evecs.ravel()] ### TESTS ### yield assert_almost_equal(np.abs(np.dot(evecs[:, 2], tensor[0].evecs[:, 2].T)), 1., msg = "Calculation of third eigenvector is not right") yield assert_array_almost_equal(D, tensor[0].D, err_msg = "Recovery of self diffusion tensor from eig not adaquate") yield assert_almost_equal(FA, tensor.fa(), msg = "Calculation of FA of self diffusion tensor is not adequate") yield assert_almost_equal(MD, tensor.md(), msg = "Calculation of MD of self diffusion tensor is not adequate") yield assert_equal(True, tensor.mask.all()) #yield assert_equal(m_list.shape, n_list.shape) #yield assert_equal(m_list.ndim, 2) #yield assert_equal(m_list.shape, (45,1)) #yield assert_true(np.all(np.abs(m_list) <= n_list)) #yield assert_array_equal(n_list % 2, 0) #yield assert_raises(ValueError, qball.sph_harm_ind_list, 1) @parametric def test_WLS_and_LS_fit(): """ Tests the WLS and LS fitting functions to see if they returns the correct eigenvalues and eigenvectors. Uses data/55dir_grad.bvec as the gradient table and 3by3by56.nii as the data. """ ### Defining Test Voxel (avoid nibabel dependency) ### #Recall: D = [Dxx,Dyy,Dzz,Dxy,Dxz,Dyz,log(S_0)] and D ~ 10^-4 mm^2 /s D = np.array([1., 1., 1., 1., 0., 0., np.log(1000) * 10.**4]) * 10.**-4 evals = np.array([2., 1., 0.]) * 10.**-4 md = evals.mean() tensor = np.empty((3,3)) tensor[0, 0] = D[0] tensor[1, 1] = D[1] tensor[2, 2] = D[2] tensor[0, 1] = tensor[1, 0] = D[3] tensor[0, 2] = tensor[2, 0] = D[4] tensor[1, 2] = tensor[2, 1] = D[5] #Design Matrix gtab, bval = read_bvec_file(get_data('55dir_grad.bvec')) X = dti.design_matrix(gtab, bval) #Signals Y = np.exp(np.dot(X,D)) Y.shape = (-1,) + Y.shape ### Testing WLS Fit on Single Voxel ### #Estimate tensor from test signals tensor_est = dti.Tensor(Y,bval,gtab.T,min_signal=1e-8) yield assert_equal(tensor_est.shape, Y.shape[:-1]) yield assert_array_almost_equal(tensor_est.evals[0], evals) yield assert_array_almost_equal(tensor_est.D[0], tensor,err_msg= "Calculation of tensor from Y does not compare to analytical solution") yield assert_almost_equal(tensor_est.md()[0], md) #test 0d tensor y = Y[0] tensor_est = dti.Tensor(y, bval, gtab.T, min_signal=1e-8) yield assert_equal(tensor_est.shape, tuple()) yield assert_array_almost_equal(tensor_est.evals, evals) yield assert_array_almost_equal(tensor_est.D, tensor) yield assert_almost_equal(tensor_est.md(), md) tensor_est = dti.Tensor(y, bval, gtab.T, min_signal=1e-8, fit_method='LS') yield assert_equal(tensor_est.shape, tuple()) yield assert_array_almost_equal(tensor_est.evals, evals) yield assert_array_almost_equal(tensor_est.D, tensor) yield assert_almost_equal(tensor_est.md(), md) @parametric def test_masked_array_with_Tensor(): data = np.ones((2,4,56)) mask = np.array([[True, False, False, True], [True, False, True, False]]) gtab, bval = read_bvec_file(get_data('55dir_grad.bvec')) tensor = dti.Tensor(data, bval, gtab.T, mask=mask, min_signal=1e-9) yield assert_equal(tensor.shape, (2,4)) yield assert_equal(tensor.fa().shape, (2,4)) yield assert_equal(tensor.evals.shape, (2,4,3)) yield assert_equal(tensor.evecs.shape, (2,4,3,3)) yield assert_equal(type(tensor.model_params), MaskedView) yield assert_array_equal(tensor.mask, mask) tensor = tensor[0] yield assert_equal(tensor.shape, (4,)) yield assert_equal(tensor.fa().shape, (4,)) yield assert_equal(tensor.evals.shape, (4,3)) yield assert_equal(tensor.evecs.shape, (4,3,3)) yield assert_equal(type(tensor.model_params), MaskedView) yield assert_array_equal(tensor.mask, mask[0]) tensor = tensor[0] yield assert_equal(tensor.shape, tuple()) yield assert_equal(tensor.fa().shape, tuple()) yield assert_equal(tensor.evals.shape, (3,)) yield assert_equal(tensor.evecs.shape, (3,3)) yield assert_equal(type(tensor.model_params), np.ndarray) @parametric def test_passing_maskedview(): data = np.ones((2,4,56)) mask = np.array([[True, False, False, True], [True, False, True, False]]) gtab, bval = read_bvec_file(get_data('55dir_grad.bvec')) data = data[mask] mv = MaskedView(mask, data) tensor = dti.Tensor(mv, bval, gtab.T, min_signal=1e-9) yield assert_equal(tensor.shape, (2,4)) yield assert_equal(tensor.fa().shape, (2,4)) yield assert_equal(tensor.evals.shape, (2,4,3)) yield assert_equal(tensor.evecs.shape, (2,4,3,3)) yield assert_equal(type(tensor.model_params), MaskedView) yield assert_array_equal(tensor.mask, mask) tensor = tensor[0] yield assert_equal(tensor.shape, (4,)) yield assert_equal(tensor.fa().shape, (4,)) yield assert_equal(tensor.evals.shape, (4,3)) yield assert_equal(tensor.evecs.shape, (4,3,3)) yield assert_equal(type(tensor.model_params), MaskedView) yield assert_array_equal(tensor.mask, mask[0]) tensor = tensor[0] yield assert_equal(tensor.shape, tuple()) yield assert_equal(tensor.fa().shape, tuple()) yield assert_equal(tensor.evals.shape, (3,)) yield assert_equal(tensor.evecs.shape, (3,3)) yield assert_equal(type(tensor.model_params), np.ndarray) @parametric def test_init(): data = np.ones((2,4,56)) mask = np.ones((2,4),'bool') gtab, bval = read_bvec_file(get_data('55dir_grad.bvec')) tensor = dti.Tensor(data, bval, gtab.T, mask, thresh=0) mask[:] = False yield assert_raises(ValueError, dti.Tensor, data, bval, gtab.T, mask) yield assert_raises(ValueError, dti.Tensor, data, bval, gtab.T, min_signal=-1) yield assert_raises(ValueError, dti.Tensor, data, bval, gtab.T, thresh=1) yield assert_raises(ValueError, dti.Tensor, data, bval, gtab.T, fit_method='s') yield assert_raises(ValueError, dti.Tensor, data, bval, gtab.T, fit_method=0) dipy-0.5.0/dipy/reconst/tests/test_gqsampling.py000066400000000000000000000216761152576264200220460ustar00rootroot00000000000000import time import numpy as np import nibabel as nib from .. import recspeed as rp from .. import gqi as gq from .. import dti as dt from ...core import meshes from ...data import get_data, get_sphere from nose.tools import assert_true, assert_false, assert_equal, assert_raises from numpy.testing import assert_array_equal, assert_array_almost_equal def test_gqiodfmask(): fimg,fbvals,fbvecs=get_data('small_64D') bvals=np.load(fbvals) gradients=np.load(fbvecs) data=nib.load(fimg).get_data() mask =np.random.random(data.shape[:3]) mask[mask >.5]=1 mask[mask<=.5]=0 gqs = gq.GeneralizedQSampling(data,bvals,gradients,mask=mask) mask=np.zeros(data.shape[:3]) gqs = gq.GeneralizedQSampling(data,bvals,gradients,mask=mask) assert_equal(np.sum(np.isnan(gqs.QA)),5000) #all voxels should be NULL def test_gqiodf(): #read bvals,gradients and data fimg,fbvals,fbvecs=get_data('small_64D') bvals=np.load(fbvals) gradients=np.load(fbvecs) data=nib.load(fimg).get_data() #print(bvals.shape) #print(gradients.shape) #print(data.shape) t1=time.clock() gqs = gq.GeneralizedQSampling(data,bvals,gradients) ten = dt.Tensor(data,bvals,gradients,thresh=50) fa=ten.fa() x,y,z,a,b=ten.evecs.shape evecs=ten.evecs xyz=x*y*z evecs = evecs.reshape(xyz,3,3) #vs = np.sign(evecs[:,2,:]) #print vs.shape #print np.hstack((vs,vs,vs)).reshape(1000,3,3).shape #evecs = np.hstack((vs,vs,vs)).reshape(1000,3,3) #print evecs.shape evals=ten.evals evals = evals.reshape(xyz,3) #print evals.shape t2=time.clock() #print('GQS in %d' %(t2-t1)) odf_vertices, odf_faces = get_sphere('symmetric362') #Yeh et.al, IEEE TMI, 2010 #calculate the odf using GQI scaling=np.sqrt(bvals*0.01506) # 0.01506 = 6*D where D is the free #water diffusion coefficient #l_values sqrt(6 D tau) D free water #diffusion coefficiet and tau included in the b-value tmp=np.tile(scaling,(3,1)) b_vector=gradients.T*tmp Lambda = 1.2 # smoothing parameter - diffusion sampling length q2odf_params=np.sinc(np.dot(b_vector.T, odf_vertices.T) * Lambda/np.pi) #implements equation no. 9 from Yeh et.al. S=data.copy() x,y,z,g=S.shape S=S.reshape(x*y*z,g) QA = np.zeros((x*y*z,5)) IN = np.zeros((x*y*z,5)) fwd = 0 #Calculate Quantitative Anisotropy and find the peaks and the indices #for every voxel summary = {} summary['vertices'] = odf_vertices v = odf_vertices.shape[0] summary['faces'] = odf_faces f = odf_faces.shape[0] ''' If e = number_of_edges the Euler formula says f-e+v = 2 for a mesh on a sphere Here, assuming we have a healthy triangulation every face is a triangle, all 3 of whose edges should belong to exactly two faces = so 2*e = 3*f to avoid division we test whether 2*f - 3*f + 2*v == 4 or equivalently 2*v - f == 4 ''' assert_equal(2*v-f, 4,'Euler test fails') for (i,s) in enumerate(S): #print 'Volume %d' % i istr = str(i) summary[istr] = {} odf = Q2odf(s,q2odf_params) peaks,inds=rp.peak_finding(odf,odf_faces) fwd=max(np.max(odf),fwd) peaks = peaks - np.min(odf) l=min(len(peaks),5) QA[i][:l] = peaks[:l] IN[i][:l] = inds[:l] summary[istr]['odf'] = odf summary[istr]['peaks'] = peaks summary[istr]['inds'] = inds summary[istr]['evecs'] = evecs[i,:,:] summary[istr]['evals'] = evals[i,:] QA/=fwd QA=QA.reshape(x,y,z,5) IN=IN.reshape(x,y,z,5) peaks_1 = [i for i in range(1000) if len(summary[str(i)]['inds'])==1] peaks_2 = [i for i in range(1000) if len(summary[str(i)]['inds'])==2] peaks_3 = [i for i in range(1000) if len(summary[str(i)]['inds'])==3] # correct numbers of voxels with respectively 1,2,3 ODF/QA peaks assert_array_equal((len(peaks_1),len(peaks_2),len(peaks_3)), (790,196,14), 'error in numbers of QA/ODF peaks') # correct indices of odf directions for voxels 0,10,44 # with respectively 1,2,3 ODF/QA peaks assert_array_equal(summary['0']['inds'],[116], 'wrong peak indices for voxel 0') assert_array_equal(summary['10']['inds'],[105, 78], 'wrong peak indices for voxel 10') assert_array_equal(summary['44']['inds'],[95, 84, 108], 'wrong peak indices for voxel 44') assert_equal(np.argmax(summary['0']['odf']), 116) assert_equal(np.argmax(summary['10']['odf']), 105) def upper_hemi_map(v): ''' maps a 3-vector into the z-upper hemisphere ''' return np.sign(v[2])*v def equatorial_maximum(vertices, odf, pole, width): eqvert = meshes.equatorial_vertices(vertices, pole, width) ''' need to test for whether eqvert is empty or not ''' if len(eqvert) == 0: print 'empty equatorial band at pole', pole, 'with width', width return Null, Null eqvals = [odf[i] for i in eqvert] eqargmax = np.argmax(eqvals) eqvertmax = eqvert[eqargmax] eqvalmax = eqvals[eqargmax] return eqvertmax, eqvalmax def patch_vertices(vertices,pole, width): ''' find 'vertices' within the cone of 'width' around 'pole' ''' return [i for i,v in enumerate(vertices) if np.dot(v,pole) > 1- width] def patch_maximum(vertices, odf, pole, width): eqvert = patch_vertices(vertices, pole, width) ''' need to test for whether eqvert is empty or not ''' if len(eqvert) == 0: print 'empty cone around pole', pole, 'with width', width return Null, Null eqvals = [odf[i] for i in eqvert] eqargmax = np.argmax(eqvals) eqvertmax = eqvert[eqargmax] eqvalmax = eqvals[eqargmax] return eqvertmax, eqvalmax def triple_odf_maxima(vertices, odf, width): indmax1 = np.argmax([odf[i] for i,v in enumerate(vertices)]) odfmax1 = odf[indmax1] indmax2, odfmax2 = equatorial_maximum(vertices, odf, vertices[indmax1], width) cross12 = np.cross(vertices[indmax1],vertices[indmax2]) indmax3, odfmax3 = patch_maximum(vertices, odf, cross12, width) return [(indmax1, odfmax1),(indmax2, odfmax2),(indmax3, odfmax3)] def test_gqi_small(): #read bvals,gradients and data fimg,fbvals,fbvecs=get_data('small_64D') bvals=np.load(fbvals) gradients=np.load(fbvecs) data=nib.load(fimg).get_data() print(bvals.shape) print(gradients.shape) print(data.shape) t1=time.clock() gqs = gq.GeneralizedQSampling(data,bvals,gradients) t2=time.clock() print('GQS in %d' %(t2-t1)) odf_vertices, odf_faces = get_sphere('symmetric362') #Yeh et.al, IEEE TMI, 2010 #calculate the odf using GQI scaling=np.sqrt(bvals*0.01506) # 0.01506 = 6*D where D is the free #water diffusion coefficient #l_values sqrt(6 D tau) D free water #diffusion coefficiet and tau included in the b-value tmp=np.tile(scaling,(3,1)) b_vector=gradients.T*tmp Lambda = 1.2 # smoothing parameter - diffusion sampling length q2odf_params=np.sinc(np.dot(b_vector.T, odf_vertices.T) * Lambda/np.pi) #implements equation no. 9 from Yeh et.al. S=data.copy() x,y,z,g=S.shape S=S.reshape(x*y*z,g) QA = np.zeros((x*y*z,5)) IN = np.zeros((x*y*z,5)) fwd = 0 #Calculate Quantitative Anisotropy and find the peaks and the indices #for every voxel for (i,s) in enumerate(S): odf = Q2odf(s,q2odf_params) peaks,inds=rp.peak_finding(odf,odf_faces) fwd=max(np.max(odf),fwd) peaks = peaks - np.min(odf) l=min(len(peaks),5) QA[i][:l] = peaks[:l] IN[i][:l] = inds[:l] QA/=fwd QA=QA.reshape(x,y,z,5) IN=IN.reshape(x,y,z,5) print('Old %d secs' %(time.clock() - t2)) assert_equal((gqs.QA-QA).max(),0.,'Frank QA different than dipy QA') assert_equal((gqs.QA.shape),QA.shape, 'Frank QA shape is different') def Q2odf(s,q2odf_params): odf=np.dot(s,q2odf_params) return odf def peak_finding(odf,odf_faces): #proton density already include from the scaling b_table[0][0] and s[0] #find local maxima peak=odf.copy() # where the smallest odf values in the vertices of a face remove the # two smallest vertices for face in odf_faces: i, j, k = face check=np.array([odf[i],odf[j],odf[k]]) zeroing=check.argsort() peak[face[zeroing[0]]]=0 peak[face[zeroing[1]]]=0 #for later testing expecting peak.max 794595.94774980657 and #np.where(peak>0) (array([166, 347]),) #we just need the first half of peak peak=peak[0:len(peak)/2] #find local maxima and give fiber orientation (inds) and magnitute #peaks in a descending order inds=np.where(peak>0)[0] pinds=np.argsort(peak[inds]) peaks=peak[inds[pinds]][::-1] return peaks, inds[pinds][::-1] if __name__ == "__main__": T=test_gqiodf() dipy-0.5.0/dipy/reconst/tests/test_maskedview.py000066400000000000000000000052271152576264200220350ustar00rootroot00000000000000""" Testing maskedview """ import numpy as np import dipy.reconst.maskedview as maskedview from nose.tools import assert_true, assert_false, \ assert_equal, assert_raises from numpy.testing import assert_array_equal, assert_array_almost_equal def test_MaskedView(): mask = np.random.random((21,22,23)) > .5 mask[0,0,0] = True mask[0,0,1] = False shape_contents = (2,2,4) data = np.random.random((mask.sum(),)+shape_contents) fill_value = 99 dataS = maskedview.MaskedView(mask, data, fill_value) dataS_part = dataS[10:20,10:20,10:20] dataS_zero = dataS[0,0,0] dataS_one = dataS[0,0,1] dataS_ind = dataS[...,0,0,0] #test shape sz = 21*22*23*2*2*4 copy_dataS = dataS[:] assert_raises(ValueError, copy_dataS._set_shape, (sz,)) assert_raises(ValueError, copy_dataS._set_shape, sz) assert_raises(ValueError, copy_dataS._set_shape, (13,-1)) assert_raises(ValueError, copy_dataS._set_shape, (21,-1,-1)) assert_raises(ValueError, copy_dataS._set_shape, (2,3,4,5,6,7,8)) assert_raises(ValueError, copy_dataS._set_shape, (22,23,2,2,4,21)) sz = 21*22*23 copy_dataS.shape = (sz, -1) assert_equal(copy_dataS.shape_mask, (sz,)) assert_equal(copy_dataS.shape_contents, (16,)) copy_dataS.shape = (21, 22, 23, -1) copy_dataS.shape = (21, 22, 23, -1) assert_equal(copy_dataS.shape_mask, (21, 22, 23)) assert_equal(copy_dataS.shape_contents, (16,)) copy_dataS.shape = (2, 3, 7, 11, 23, 2, 2, 2, 2) assert_equal(copy_dataS.shape_mask, (2, 3, 7, 11, 23)) assert_equal(copy_dataS.shape_contents, (2, 2, 2, 2)) #test __init__ assert_equal(dataS.shape, mask.shape+shape_contents) assert_array_equal(dataS.mask, mask) assert_equal(dataS.get_size(), np.asarray(dataS).shape[0]) assert_true(dataS.base is None) #test filld slice_one = dataS[:,:,10,:,:,:].filled() assert_equal(type(slice_one), np.ndarray) assert_equal(slice_one.shape, mask.shape[:2]+shape_contents) #test __getitem__ assert_equal(dataS_part.shape, (10, 10, 10)+shape_contents) assert_equal(dataS_part.get_size(), np.asarray(dataS_part).shape[0]) assert_array_equal(dataS_part.mask, mask[10:20,10:20,10:20]) #this is the correct behaviour assert_array_equal(dataS_zero, data[0]) #this is the correct behaviour assert_array_equal(dataS_one, fill_value) assert_equal(type(dataS_ind), np.ndarray) assert_equal(dataS_ind.shape, mask.shape) assert_array_equal(dataS_ind[mask], data[:,0,0,0]) #test __array_wrap__ new_view = dataS + np.array(0) assert_equal(type(new_view), type(dataS)) dipy-0.5.0/dipy/reconst/tests/test_modelarray.py000066400000000000000000000026151152576264200220330ustar00rootroot00000000000000""" Testing ModelArray """ import numpy as np from numpy.ma import MaskedArray from dipy.reconst.modelarray import ModelArray #for reading in nifti test data from nose.tools import assert_true, assert_false, \ assert_equal, assert_almost_equal, assert_raises from numpy.testing import assert_array_equal, assert_array_almost_equal import os def test_model_shape(): shape = (10,11,12) ma = ModelArray() ma.model_params = np.zeros(shape+(4,)) assert_equal(ma[..., 0].shape, shape[:-1]) assert_equal(ma[0].shape, shape[1:]) ma.shape = (-1) assert(ma.ndim == 1) ma.shape = shape[::-1] assert(ma.ndim == len(shape)) def test_model_mask(): shape = (10,11,12) data = np.zeros(shape+(4,)) ma = ModelArray() ma.model_params = data assert(ma.mask.all()) mask = np.random.random(shape+(4,)) > .3 maskedarray = MaskedArray(data, mask) ma.model_params = maskedarray assert_array_equal(ma.mask, mask) def test_indexing_and_setting(): shape = (10,11,12) data = np.random.random(shape+(4,)) ma = ModelArray() ma.model_params = data assert_array_equal(ma[0].model_params, data[0]) assert_array_equal(ma[:, 1].model_params, data[:, 1]) assert_array_equal(ma[:, :, 2].model_params, data[:, :, 2]) assert_array_equal(ma[3:5].model_params, data[3:5]) assert_array_equal(ma[..., 4:].model_params, data[..., 4:, :]) dipy-0.5.0/dipy/reconst/tests/test_qball.py000066400000000000000000000041231152576264200207630ustar00rootroot00000000000000""" Testing qball """ import numpy as np import dipy.reconst.qball as qball from nose.tools import assert_true, assert_false, \ assert_equal, assert_raises from numpy.testing import assert_array_equal, assert_array_almost_equal def test_sph_harm_ind_list(): m_list, n_list = qball.sph_harm_ind_list(8) assert_equal(m_list.shape, n_list.shape) assert_equal(m_list.ndim, 2) assert_equal(m_list.shape, (45,1)) assert_true(np.all(np.abs(m_list) <= n_list)) assert_array_equal(n_list % 2, 0) assert_raises(ValueError, qball.sph_harm_ind_list, 1) def test_real_sph_harm(): # Tests derived from tables in # http://en.wikipedia.org/wiki/Table_of_spherical_harmonics # where real spherical harmonic $Y^m_n$ is defined to be: # Real($Y^m_n$) * sqrt(2) if m > 0 # $Y^m_n$ if m == 0 # Imag($Y^m_n$) * sqrt(2) if m < 0 rsh = qball.real_sph_harm pi = np.pi exp = np.exp sqrt = np.sqrt sin = np.sin cos = np.cos assert_array_almost_equal(rsh(0,0,0,0), 0.5/sqrt(pi)) assert_array_almost_equal(rsh(2,2,pi/3,pi/5), 0.25*sqrt(15./(2.*pi))* (sin(pi/5.))**2.*cos(0+2.*pi/3)*sqrt(2)) assert_array_almost_equal(rsh(-2,2,pi/3,pi/5), 0.25*sqrt(15./(2.*pi))* (sin(pi/5.))**2.*sin(0-2.*pi/3)*sqrt(2)) assert_array_almost_equal(rsh(2,2,pi,pi/2), 0.25*sqrt(15/(2.*pi))* cos(2.*pi)*sin(pi/2.)**2.*sqrt(2)) assert_array_almost_equal(rsh(-2,4,pi/4.,pi/3.), (3./8.)*sqrt(5./(2.*pi))* sin(0-2.*pi/4.)* sin(pi/3.)**2.* (7.*cos(pi/3.)**2.-1)*sqrt(2)) assert_array_almost_equal(rsh(4,4,pi/8.,pi/6.), (3./16.)*sqrt(35./(2.*pi))* cos(0+4.*pi/8.)*sin(pi/6.)**4.*sqrt(2)) assert_array_almost_equal(rsh(-4,4,pi/8.,pi/6.), (3./16.)*sqrt(35./(2.*pi))* sin(0-4.*pi/8.)*sin(pi/6.)**4.*sqrt(2)) aa = np.ones((3,1,1,1)) bb = np.ones((1,4,1,1)) cc = np.ones((1,1,5,1)) dd = np.ones((1,1,1,6)) assert_equal(rsh(aa, bb, cc, dd).shape, (3, 4, 5, 6)) dipy-0.5.0/dipy/reconst/tests/test_reco_utils.py000066400000000000000000000045041152576264200220430ustar00rootroot00000000000000""" Testing reconstruction utilities """ import numpy as np from dipy.reconst.recspeed import (adj_to_countarrs, argmax_from_countarrs) from nose.tools import assert_true, assert_false, \ assert_equal, assert_raises from numpy.testing import assert_array_equal, assert_array_almost_equal def test_adj_countarrs(): adj = [[0, 1, 2], [2, 3], [4, 5, 6, 7]] counts, inds = adj_to_countarrs(adj) assert_array_equal(counts, [3, 2, 4]) assert_equal(counts.dtype.type, np.uint32) assert_array_equal(inds, [0, 1, 2, 2, 3, 4, 5, 6, 7]) assert_equal(inds.dtype.type, np.uint32) def test_argmax_from_countarrs(): # basic case vals = np.arange(10, dtype=np.float) vertinds = np.arange(10, dtype=np.uint32) adj_counts = np.ones((10,), dtype=np.uint32) adj_inds_raw = np.arange(10, dtype=np.uint32)[::-1] # when contigous - OK adj_inds = adj_inds_raw.copy() inds = argmax_from_countarrs(vals, vertinds, adj_counts, adj_inds) #yield assert_array_equal(inds, [5, 6, 7, 8, 9]) # test for errors - first - not contiguous # # The tests below cause odd errors and segfaults with numpy SVN # vintage June 2010 (sometime after 1.4.0 release) - see # http://groups.google.com/group/cython-users/browse_thread/thread/624c696293b7fe44?pli=1 """ yield assert_raises(ValueError, argmax_from_countarrs, vals, vertinds, adj_counts, adj_inds_raw) # too few vertices yield assert_raises(ValueError, argmax_from_countarrs, vals, vertinds[:-1], adj_counts, adj_inds) # adj_inds too short yield assert_raises(IndexError, argmax_from_countarrs, vals, vertinds, adj_counts, adj_inds[:-1]) # vals too short yield assert_raises(IndexError, argmax_from_countarrs, vals[:-1], vertinds, adj_counts, adj_inds) """ dipy-0.5.0/dipy/reconst/tests/test_sphere_max.py000066400000000000000000000153611152576264200220310ustar00rootroot00000000000000""" Testing sphere maxima finding and associated routines """ from os.path import join as pjoin, dirname import numpy as np from dipy.data import get_sphere from dipy.core.meshes import ( sym_hemisphere, neighbors, vertinds_to_neighbors, vertinds_faces, argmax_from_adj, peak_finding_compatible, edges, vertex_adjacencies) import dipy.reconst.recspeed as dcr from nose.tools import assert_true, assert_false, \ assert_equal, assert_raises from numpy.testing import assert_array_equal, assert_array_almost_equal # 8 faces (two square pyramids) VERTICES = np.array([ [0, 0, 1], [1, 0, 0], [0, 1, 0], [-1, 0, 0], [0, -1, 0], [0, 0, -1]], dtype=np.float) FACES = np.array([ [0, 1, 2], [0, 2, 3], [0, 3, 4], [0, 4, 1], [5, 1, 2], [5, 2, 3], [5, 3, 4], [5, 4, 1]]) N_VERTICES = VERTICES.shape[0] VERTEX_INDS = np.array([0,1,2,3,4,5]) DATA_PATH = pjoin(dirname(__file__), '..', 'matrices') # vertex, face tuple SPHERE_DATA = get_sphere('symmetric362') def test_sym_hemisphere(): assert_raises(ValueError, sym_hemisphere, VERTICES, 'k') assert_raises(ValueError, sym_hemisphere, VERTICES, '%z') for hem in ('x', 'y', 'z', '-x', '-y', '-z'): vert_inds = sym_hemisphere(VERTICES, hem) assert_equal(vert_inds.shape, (3,)) verts = VERTICES[vert_inds] # there are no symmetrical points remanining in vertices for vert in verts: assert_false(np.any(np.all( vert * -1 == verts))) # Test the sphere mesh data vertices, _ = SPHERE_DATA n_vertices = vertices.shape[0] vert_inds = sym_hemisphere(vertices) assert_array_equal(vert_inds, np.arange(n_vertices / 2)) def test_vertinds_neighbors(): adj = neighbors(FACES) assert_array_equal(adj, [[1, 2, 3, 4], [0, 2, 4, 5], [0, 1, 3, 5], [0, 2, 4, 5], [0, 1, 3, 5], [1, 2, 3, 4]]) adj = vertinds_to_neighbors(np.arange(6), FACES) assert_array_equal(adj, [[1, 2, 3, 4], [0, 2, 4, 5], [0, 1, 3, 5], [0, 2, 4, 5], [0, 1, 3, 5], [1, 2, 3, 4]]) # subset of inds gives subset of faces adj = vertinds_to_neighbors(np.arange(3), FACES) assert_array_equal(adj, [[1, 2, 3, 4], [0, 2, 4, 5], [0, 1, 3, 5]]) # can be any subset adj = vertinds_to_neighbors(np.arange(3,6), FACES) assert_array_equal(adj, [[0, 2, 4, 5], [0, 1, 3, 5], [1, 2, 3, 4]]) # just test right size for the real mesh vertices, faces = SPHERE_DATA n_vertices = vertices.shape[0] adj = vertinds_to_neighbors(np.arange(n_vertices), faces) assert_equal(len(adj), n_vertices) assert_equal(len(adj[1]), 6) def test_vertinds_faces(): # routines to strip out faces f2 = vertinds_faces(range(6), FACES) assert_array_equal(f2, FACES) f2 = vertinds_faces([0, 5], FACES) assert_array_equal(f2, FACES) f2 = vertinds_faces([0], FACES) assert_array_equal(f2, FACES[:4]) def test_neighbor_max(): # test ability to find maxima on sphere using neighbors vert_inds = sym_hemisphere(VERTICES) adj_inds = vertinds_to_neighbors(vert_inds, FACES) # test slow and fast routine for func in (argmax_from_adj, dcr.argmax_from_adj): # all equal, no maxima vert_vals = np.zeros((N_VERTICES,)) inds = func(vert_vals, vert_inds, adj_inds) assert_equal(inds.size, 0) # just ome max for max_pos in range(3): vert_vals = np.zeros((N_VERTICES,)) vert_vals[max_pos] = 1 inds = func(vert_vals, vert_inds, adj_inds) assert_array_equal(inds, [max_pos]) # maxima outside hemisphere don't appear for max_pos in range(3,6): vert_vals = np.zeros((N_VERTICES,)) vert_vals[max_pos] = 1 inds = func(vert_vals, vert_inds, adj_inds) assert_equal(inds.size, 0) # use whole mesh, with two maxima w_vert_inds = np.arange(6) w_adj_inds = vertinds_to_neighbors(w_vert_inds, FACES) vert_vals = np.array([1.0, 0, 0, 0, 0, 2]) inds = func(vert_vals, w_vert_inds, w_adj_inds) assert_array_equal(inds, [0, 5]) # check too few vals raises sensible error. For the Cython # version of the routine, the test below causes odd errors and # segfaults with numpy SVN vintage June 2010 (sometime after # 1.4.0 release) - see # http://groups.google.com/group/cython-users/browse_thread/thread/624c696293b7fe44?pli=1 # assert_raises(IndexError, func, vert_vals[:3], # w_vert_inds, w_adj_inds) def test_performance(): # test this implementation against Frank Yeh implementation vertices, faces = SPHERE_DATA n_vertices = vertices.shape[0] vert_inds = sym_hemisphere(vertices) adj = vertinds_to_neighbors(vert_inds, faces) np.random.seed(42) vert_vals = np.random.uniform(size=(n_vertices,)) maxinds = argmax_from_adj(vert_vals, vert_inds, adj) maxes, pfmaxinds = dcr.peak_finding(vert_vals, faces) assert_array_equal(maxinds, pfmaxinds[::-1]) def test_sym_check(): assert_true(peak_finding_compatible(VERTICES)) vertices, faces = SPHERE_DATA assert_true(peak_finding_compatible(vertices)) assert_false(peak_finding_compatible(vertices[::-1])) def test_adjacencies(): faces = FACES vertex_inds = VERTEX_INDS edgearray = edges(vertex_inds, faces) assert_array_equal(edgearray.shape,(24,2)) assert_array_equal(edgearray, [[3, 0], [5, 4], [2, 1], [5, 1], [2, 5], [0, 3], [4, 0], [1, 2], [1, 5], [0, 4], [5, 3], [4, 1], [3, 2], [4, 5], [1, 4], [2, 3], [1, 0], [3, 5], [0, 1], [5, 2], [2, 0] ,[4, 3], [3, 4], [0, 2]]) assert_array_equal(vertex_adjacencies(vertex_inds, faces).shape,(6,6)) dipy-0.5.0/dipy/testing/000077500000000000000000000000001152576264200151155ustar00rootroot00000000000000dipy-0.5.0/dipy/testing/__init__.py000066400000000000000000000010771152576264200172330ustar00rootroot00000000000000''' Utilities for testing ''' from os.path import dirname, abspath, join as pjoin # set path to example data IO_DATA_PATH = abspath(pjoin(dirname(__file__), '..', 'io', 'tests', 'data')) from .spherepoints import sphere_points # Allow failed import of nose if not now running tests try: import nose.tools as nt except ImportError: pass else: from lightunit import ParametricTestCase, parametric from nose.tools import (assert_equal, assert_not_equal, assert_true, assert_false, assert_raises) dipy-0.5.0/dipy/testing/_paramtestpy2.py000066400000000000000000000055031152576264200202640ustar00rootroot00000000000000"""Implementation of the parametric test support for Python 2.x """ #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- # Stdlib import unittest from compiler.consts import CO_GENERATOR #----------------------------------------------------------------------------- # Classes and functions #----------------------------------------------------------------------------- def isgenerator(func): try: return func.func_code.co_flags & CO_GENERATOR != 0 except AttributeError: return False class ParametricTestCase(unittest.TestCase): """Write parametric tests in normal unittest testcase form. Limitations: the last iteration misses printing out a newline when running in verbose mode. """ def run_parametric(self, result, testMethod): # But if we have a test generator, we iterate it ourselves testgen = testMethod() while True: try: # Initialize test result.startTest(self) # SetUp try: self.setUp() except KeyboardInterrupt: raise except: result.addError(self, self._exc_info()) return # Test execution ok = False try: testgen.next() ok = True except StopIteration: # We stop the loop break except self.failureException: result.addFailure(self, self._exc_info()) except KeyboardInterrupt: raise except: result.addError(self, self._exc_info()) # TearDown try: self.tearDown() except KeyboardInterrupt: raise except: result.addError(self, self._exc_info()) ok = False if ok: result.addSuccess(self) finally: result.stopTest(self) def run(self, result=None): if result is None: result = self.defaultTestResult() testMethod = getattr(self, self._testMethodName) # For normal tests, we just call the base class and return that if isgenerator(testMethod): return self.run_parametric(result, testMethod) else: return super(ParametricTestCase, self).run(result) def parametric(func): """Decorator to make a simple function into a normal test via unittest.""" class Tester(ParametricTestCase): test = staticmethod(func) Tester.__name__ = func.__name__ return Tester dipy-0.5.0/dipy/testing/_paramtestpy3.py000066400000000000000000000036531152576264200202710ustar00rootroot00000000000000"""Implementation of the parametric test support for Python 3.x. Thanks for the py3 version to Robert Collins, from the Testing in Python mailing list. """ #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- # Stdlib import unittest from unittest import TestSuite #----------------------------------------------------------------------------- # Classes and functions #----------------------------------------------------------------------------- def isgenerator(func): return hasattr(func,'_generator') class IterCallableSuite(TestSuite): def __init__(self, iterator, adapter): self._iter = iterator self._adapter = adapter def __iter__(self): yield self._adapter(self._iter.__next__) class ParametricTestCase(unittest.TestCase): """Write parametric tests in normal unittest testcase form. Limitations: the last iteration misses printing out a newline when running in verbose mode. """ def run(self, result=None): testMethod = getattr(self, self._testMethodName) # For normal tests, we just call the base class and return that if isgenerator(testMethod): def adapter(next_test): return unittest.FunctionTestCase(next_test, self.setUp, self.tearDown) return IterCallableSuite(testMethod(), adapter).run(result) else: return super(ParametricTestCase, self).run(result) def parametric(func): """Decorator to make a simple function into a normal test via unittest.""" # Hack, until I figure out how to write isgenerator() for python3!! func._generator = True class Tester(ParametricTestCase): test = staticmethod(func) Tester.__name__ = func.__name__ return Tester dipy-0.5.0/dipy/testing/lightunit.py000066400000000000000000000035051152576264200175010ustar00rootroot00000000000000"""Lightweight testing that remains unittest-compatible. This module exposes decorators and classes to make lightweight testing possible in a manner similar to what nose allows, where standalone functions can be tests. It also provides parametric test support that is vastly easier to use than nose's for debugging, because if a test fails, the stack under inspection is that of the test and not that of the test framework. - An @as_unittest decorator can be used to tag any normal parameter-less function as a unittest TestCase. Then, both nose and normal unittest will recognize it as such. Authors ------- - Fernando Perez """ #----------------------------------------------------------------------------- # Copyright (C) 2009 The IPython Development Team # # Distributed under the terms of the BSD License. The full license is in # the file COPYING, distributed as part of this software. #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- # Stdlib import sys import unittest # Our own import nosepatch if sys.version[0]=='2': from _paramtestpy2 import ParametricTestCase, parametric else: from _paramtestpy3 import ParametricTestCase, parametric #----------------------------------------------------------------------------- # Classes and functions #----------------------------------------------------------------------------- # Simple example of the basic idea def as_unittest(func): """Decorator to make a simple function into a normal test via unittest.""" class Tester(unittest.TestCase): def test(self): func() Tester.__name__ = func.__name__ return Tester dipy-0.5.0/dipy/testing/nosepatch.py000066400000000000000000000036031152576264200174550ustar00rootroot00000000000000"""Monkeypatch nose to accept any callable as a method. By default, nose's ismethod() fails for static methods. Once this is fixed in upstream nose we can disable it. Note: merely importing this module causes the monkeypatch to be applied.""" import unittest import nose.loader from inspect import ismethod, isfunction def getTestCaseNames(self, testCaseClass): """Override to select with selector, unless config.getTestCaseNamesCompat is True """ if self.config.getTestCaseNamesCompat: return unittest.TestLoader.getTestCaseNames(self, testCaseClass) def wanted(attr, cls=testCaseClass, sel=self.selector): item = getattr(cls, attr, None) # MONKEYPATCH: replace this: #if not ismethod(item): # return False # return sel.wantMethod(item) # With: if ismethod(item): return sel.wantMethod(item) # static method or something. If this is a static method, we # can't get the class information, and we have to treat it # as a function. Thus, we will miss things like class # attributes for test selection if isfunction(item): return sel.wantFunction(item) return False # END MONKEYPATCH cases = filter(wanted, dir(testCaseClass)) for base in testCaseClass.__bases__: for case in self.getTestCaseNames(base): if case not in cases: cases.append(case) # add runTest if nothing else picked if not cases and hasattr(testCaseClass, 'runTest'): cases = ['runTest'] if self.sortTestMethodsUsing: cases.sort(self.sortTestMethodsUsing) return cases ########################################################################## # Apply monkeypatch here nose.loader.TestLoader.getTestCaseNames = getTestCaseNames ########################################################################## dipy-0.5.0/dipy/testing/spherepoints.py000066400000000000000000000013671152576264200202210ustar00rootroot00000000000000''' Create example sphere points ''' import numpy as np def _make_pts(): ''' Make points around sphere quadrants ''' thetas = np.arange(1,4) * np.pi/4 phis = np.arange(8) * np.pi/4 north_pole = (0,0,1) south_pole = (0,0,-1) points = [north_pole, south_pole] for theta in thetas: for phi in phis: x = np.sin(theta) * np.cos(phi) y = np.sin(theta) * np.sin(phi) z = np.cos(theta) points.append((x,y,z)) return np.array(points) sphere_points = _make_pts() def _show_pts(): ''' Show 3D scatter plot of sphere points; requires Mayavi ''' from enthought.mayavi import mlab pts = sphere_points mlab.points3d(pts[:,0], pts[:,1], pts[:,2], scale_factor=0.2) dipy-0.5.0/dipy/tracking/000077500000000000000000000000001152576264200152425ustar00rootroot00000000000000dipy-0.5.0/dipy/tracking/__init__.py000066400000000000000000000000331152576264200173470ustar00rootroot00000000000000#init for tracking module dipy-0.5.0/dipy/tracking/distances.pyx000066400000000000000000001760161152576264200177740ustar00rootroot00000000000000 # A type of -*- python -*- file """ Optimized track distances, similarities and clustering algorithms using track distances """ # cython: profile=True # cython: embedsignature=True cimport cython import time import numpy as np cimport numpy as cnp cdef extern from "math.h" nogil: double floor(double x) float sqrt(float x) float fabs(float x) double log2(double x) float acos(float x ) bint isnan(double x) #cdef extern from "stdio.h": # void printf ( const char * format, ... ) cdef extern from "stdlib.h" nogil: ctypedef unsigned long size_t void free(void *ptr) void *malloc(size_t size) void *calloc(size_t nelem, size_t elsize) void *realloc (void *ptr, size_t size) void *memcpy(void *str1, void *str2, size_t n) #@cython.boundscheck(False) #@cython.wraparound(False) DEF biggest_double = 1.79769e+308 #np.finfo('f8').max DEF biggest_float = 3.4028235e+38 #np.finfo('f4').max cdef inline cnp.ndarray[cnp.float32_t, ndim=1] as_float_3vec(object vec): ''' Utility function to convert object to 3D float vector ''' return np.squeeze(np.asarray(vec, dtype=np.float32)) cdef inline float* asfp(cnp.ndarray pt): return pt.data def normalized_3vec(vec): ''' Return normalized 3D vector Vector divided by Euclidean (L2) norm Parameters ------------ vec : array-like shape (3,) Returns --------- vec_out : array shape (3,) ''' cdef cnp.ndarray[cnp.float32_t, ndim=1] vec_in = as_float_3vec(vec) cdef cnp.ndarray[cnp.float32_t, ndim=1] vec_out = np.zeros((3,), np.float32) cnormalized_3vec(vec_in.data, vec_out.data) return vec_out def norm_3vec(vec): ''' Euclidean (L2) norm of length 3 vector Parameters ------------ vec : array-like shape (3,) Returns --------- norm : float Euclidean norm ''' cdef cnp.ndarray[cnp.float32_t, ndim=1] vec_in = as_float_3vec(vec) return cnorm_3vec(vec_in.data) cdef inline float cnorm_3vec(float *vec): ''' Calculate Euclidean norm of input vector Parameters ------------ vec : float * length 3 float vector Returns --------- norm : float Euclidean norm ''' cdef float v0, v1, v2 v0 = vec[0] v1 = vec[1] v2 = vec[2] return sqrt(v0 * v0 + v1*v1 + v2*v2) cdef inline void cnormalized_3vec(float *vec_in, float *vec_out): ''' Calculate and fill normalized 3D vector Parameters ------------ vec_in : float * Length 3 vector to normalize vec_out : float * Memory into which to write normalized length 3 vector Returns --------- void ''' cdef float norm = cnorm_3vec(vec_in) cdef int i for i in range(3): vec_out[i] = vec_in[i] / norm def inner_3vecs(vec1, vec2): cdef cnp.ndarray[cnp.float32_t, ndim=1] fvec1 = as_float_3vec(vec1) cdef cnp.ndarray[cnp.float32_t, ndim=1] fvec2 = as_float_3vec(vec2) return cinner_3vecs(fvec1.data, fvec2.data) cdef inline float cinner_3vecs(float *vec1, float *vec2) nogil: cdef int i cdef float ip = 0 for i from 0<=i<3: ip += vec1[i]*vec2[i] return ip def sub_3vecs(vec1, vec2): cdef cnp.ndarray[cnp.float32_t, ndim=1] fvec1 = as_float_3vec(vec1) cdef cnp.ndarray[cnp.float32_t, ndim=1] fvec2 = as_float_3vec(vec2) cdef cnp.ndarray[cnp.float32_t, ndim=1] vec_out = np.zeros((3,), np.float32) csub_3vecs(fvec1.data, fvec2.data, vec_out.data) return vec_out cdef inline void csub_3vecs(float *vec1, float *vec2, float *vec_out) nogil: cdef int i for i from 0<=i<3: vec_out[i] = vec1[i]-vec2[i] def add_3vecs(vec1, vec2): cdef cnp.ndarray[cnp.float32_t, ndim=1] fvec1 = as_float_3vec(vec1) cdef cnp.ndarray[cnp.float32_t, ndim=1] fvec2 = as_float_3vec(vec2) cdef cnp.ndarray[cnp.float32_t, ndim=1] vec_out = np.zeros((3,), np.float32) cadd_3vecs(fvec1.data, fvec2.data, vec_out.data) return vec_out cdef inline void cadd_3vecs(float *vec1, float *vec2, float *vec_out) nogil: cdef int i for i from 0<=i<3: vec_out[i] = vec1[i]+vec2[i] def mul_3vecs(vec1, vec2): cdef cnp.ndarray[cnp.float32_t, ndim=1] fvec1 = as_float_3vec(vec1) cdef cnp.ndarray[cnp.float32_t, ndim=1] fvec2 = as_float_3vec(vec2) cdef cnp.ndarray[cnp.float32_t, ndim=1] vec_out = np.zeros((3,), np.float32) cmul_3vecs(fvec1.data, fvec2.data, vec_out.data) return vec_out cdef inline void cmul_3vecs(float *vec1, float *vec2, float *vec_out) nogil: cdef int i for i from 0<=i<3: vec_out[i] = vec1[i]*vec2[i] def mul_3vec(a, vec): cdef cnp.ndarray[cnp.float32_t, ndim=1] fvec = as_float_3vec(vec) cdef cnp.ndarray[cnp.float32_t, ndim=1] vec_out = np.zeros((3,), np.float32) cmul_3vec(a,fvec.data, vec_out.data) return vec_out cdef inline void cmul_3vec(float a, float *vec, float *vec_out) nogil: cdef int i for i from 0<=i<3: vec_out[i] = a*vec[i] # float 32 dtype for casting cdef cnp.dtype f32_dt = np.dtype(np.float32) def cut_plane(tracks,ref): ''' Extract divergence vectors and points of intersection between planes normal to the reference fiber and other tracks Parameters ------------ tracks : sequence of tracks as arrays, shape (N1,3) .. (Nm,3) ref : array, shape (N,3) reference track Returns --------- hits : sequence list of points and rcds (radial coefficient of divergence)`` Examples ---------- >>> from dipy.tracking.distances import cut_plane >>> refx = np.array([[0,0,0],[1,0,0],[2,0,0],[3,0,0]],dtype='float32') >>> bundlex = [np.array([[0.5,1,0],[1.5,2,0],[2.5,3,0]],dtype='float32')] >>> cut_plane(bundlex,refx) [array([[ 1. , 1.5 , 0. , 0.70710683, 0,]], dtype=float32), array([[ 2. , 2.5 , 0. , 0.70710677, 0.]], dtype=float32)] The orthogonality relationship np.inner(hits[p][q][0:3]-ref[p+1],ref[p+2]-ref[r][p+1]) will hold throughout for every point q in the hits plane at point (p+1) on the reference track. ''' cdef: size_t n_hits, hit_no, max_hit_len float alpha,beta,lrq,rcd,lhp,ld cnp.ndarray[cnp.float32_t, ndim=2] ref32 cnp.ndarray[cnp.float32_t, ndim=2] track object hits cnp.ndarray[cnp.float32_t, ndim=1] one_hit float *hit_ptr cnp.ndarray[cnp.float32_t, ndim=2] hit_arr object Hit=[] # make reference fiber usable type ref32 = np.ascontiguousarray(ref, f32_dt) # convert all the tracks to something we can work with. Get track # lengths cdef: size_t N_tracks=len(tracks) cnp.ndarray[cnp.uint64_t, ndim=1] track_lengths size_t t_no, N_track cdef object tracks32 = [] track_lengths = np.empty((N_tracks,), dtype=np.uint64) for t_no in range(N_tracks): track = np.ascontiguousarray(tracks[t_no], f32_dt) track_lengths[t_no] = track.shape[0] tracks32.append(track) # set up loop across reference fiber points cdef: size_t N_ref = ref32.shape[0] size_t p_no, q_no float *this_ref_p, *next_ref_p, *this_trk_p, *next_trk_p float along[3], normal[3] float qMp[3], rMp[3], rMq[3], pMq[3] float hit[3], hitMp[3], *delta # List used for storage of hits. We will fill this with lots of # small numpy arrays, and reuse them over the reference track point # loops. max_hit_len = 0 hits = [] # for every point along the reference track next_ref_p = asfp(ref32[0]) for p_no in range(N_ref-1): # extract point to point vector into `along` this_ref_p = next_ref_p next_ref_p = asfp(ref32[p_no+1]) csub_3vecs(next_ref_p, this_ref_p, along) # normalize cnormalized_3vec(along, normal) # initialize index for hits hit_no = 0 # for every track for t_no in range(N_tracks): track=tracks32[t_no] N_track = track_lengths[t_no] # for every point on the track next_trk_p = asfp(track[0]) for q_no in range(N_track-1): # p = ref32[p_no] # q = track[q_no] # r = track[q_no+1] # float* versions of above: p == this_ref_p this_trk_p = next_trk_p # q next_trk_p = asfp(track[q_no+1]) # r #if np.inner(normal,q-p)*np.inner(normal,r-p) <= 0: csub_3vecs(this_trk_p, this_ref_p, qMp) # q-p csub_3vecs(next_trk_p, this_ref_p, rMp) # r-p if (cinner_3vecs(normal, qMp) * cinner_3vecs(normal, rMp)) <=0: #if np.inner((r-q),normal) != 0: csub_3vecs(next_trk_p, this_trk_p, rMq) beta = cinner_3vecs(rMq, normal) if beta !=0: #alpha = np.inner((p-q),normal)/np.inner((r-q),normal) csub_3vecs(this_ref_p, this_trk_p, pMq) alpha = (cinner_3vecs(pMq, normal) / cinner_3vecs(rMq, normal)) if alpha < 1: # hit = q+alpha*(r-q) hit[0] = this_trk_p[0]+alpha*rMq[0] hit[1] = this_trk_p[1]+alpha*rMq[1] hit[2] = this_trk_p[2]+alpha*rMq[2] # h-p csub_3vecs(hit, this_ref_p, hitMp) # |h-p| lhp = cnorm_3vec(hitMp) delta = rMq # just renaming # |r-q| == |delta| ld = cnorm_3vec(delta) ''' # Summary of stuff in comments # divergence =((r-q)-inner(r-q,normal)*normal)/|r-q| div[0] = (rMq[0]-beta*normal[0]) / ld div[1] = (rMq[1]-beta*normal[1]) / ld div[2] = (rMq[2]-beta*normal[2]) / ld # radial coefficient of divergence d.(h-p)/|h-p| ''' # radial divergence # np.inner(delta, (hit-p)) / (ld * lhp) if lhp > 0: rcd = fabs(cinner_3vecs(delta, hitMp) / (ld*lhp)) else: rcd=0 # hit data into array if hit_no >= max_hit_len: one_hit = np.empty((5,), dtype=f32_dt) hits.append(one_hit) else: one_hit = hits[hit_no] hit_ptr = one_hit.data hit_ptr[0] = hit[0] hit_ptr[1] = hit[1] hit_ptr[2] = hit[2] hit_ptr[3] = rcd hit_ptr[4] = t_no hit_no += 1 # convert hits list to hits array n_hits = hit_no if n_hits > max_hit_len: max_hit_len = n_hits hit_arr = np.empty((n_hits,5), dtype=f32_dt) for hit_no in range(n_hits): hit_arr[hit_no] = hits[hit_no] Hit.append(hit_arr) #Div.append(divs[1:]) return Hit[1:] def most_similar_track_mam(tracks,metric='avg'): ''' Find the most similar track in a bundle using distances calculated from Zhang et. al 2008. Parameters ------------ tracks : sequence of tracks as arrays, shape (N1,3) .. (Nm,3) metric : str 'avg', 'min', 'max' Returns ---------- si : int index of the most similar track in tracks. This can be used as a reference track for a bundle. s : array, shape (len(tracks),) similarities between tracks[si] and the rest of the tracks in the bundle Notes ----- A vague description of this function is given below: for (i,j) in tracks_combinations_of_2: calculate the mean_closest_distance from i to j (mcd_i) calculate the mean_closest_distance from j to i (mcd_j) if 'avg': s holds the average similarities if 'min': s holds the minimum similarities if 'max': s holds the maximum similarities si holds the index of the track with min {avg,min,max} average metric ''' cdef: size_t i, j, lent int metric_type if metric=='avg': metric_type = 0 elif metric == 'min': metric_type = 1 elif metric == 'max': metric_type = 2 else: raise ValueError('Metric should be one of avg, min, max') # preprocess tracks cdef: size_t longest_track_len = 0, track_len cnp.ndarray[object, ndim=1] tracks32 lent = len(tracks) tracks32 = np.zeros((lent,), dtype=object) # process tracks to predictable memory layout, find longest track for i in range(lent): tracks32[i] = np.ascontiguousarray(tracks[i], dtype=f32_dt) track_len = tracks32[i].shape[0] if track_len > longest_track_len: longest_track_len = track_len # buffer for distances of found track to other tracks cdef: cnp.ndarray[cnp.double_t, ndim=1] track2others track2others = np.zeros((lent,), dtype=np.double) # use this buffer also for working space containing summed distances # of candidate track to all other tracks cdef cnp.double_t *sum_track2others = track2others.data # preallocate buffer array for track distance calculations cdef: cnp.ndarray [cnp.float32_t, ndim=1] distances_buffer cnp.float32_t *t1_ptr, *t2_ptr, *min_buffer, distance distances_buffer = np.zeros((longest_track_len*2,), dtype=np.float32) min_buffer = distances_buffer.data # cycle over tracks cdef: cnp.ndarray [cnp.float32_t, ndim=2] t1, t2 size_t t1_len, t2_len for i from 0 <= i < lent-1: t1 = tracks32[i] t1_len = t1.shape[0] t1_ptr = t1.data for j from i+1 <= j < lent: t2 = tracks32[j] t2_len = t2.shape[0] t2_ptr = t2.data distance = czhang(t1_len, t1_ptr, t2_len, t2_ptr, min_buffer, metric_type) # get metric sum_track2others[i]+=distance sum_track2others[j]+=distance # find track with smallest summed metric with other tracks cdef double mn = sum_track2others[0] cdef size_t si = 0 for i in range(lent): if sum_track2others[i] < mn: si = i mn = sum_track2others[i] # recalculate distance of this track from the others t1 = tracks32[si] t1_len = t1.shape[0] t1_ptr = t1.data for j from 0 <= j < lent: t2 = tracks32[j] t2_len = t2.shape[0] t2_ptr = t2.data track2others[j] = czhang(t1_len, t1_ptr, t2_len, t2_ptr, min_buffer, metric_type) return si, track2others def bundles_distances_mam(tracksA, tracksB, metric='avg'): ''' Calculate distances between list of tracks A and list of tracks B Parameters ------------ tracksA : sequence of tracks as arrays, shape (N1,3) .. (Nm,3) tracksB : sequence of tracks as arrays, shape (N1,3) .. (Nm,3) metric : str 'avg', 'min', 'max' Returns --------- DM : array, shape (len(tracksA), len(tracksB)) distances between tracksA and tracksB according to metric ''' cdef: size_t i, j, lentA, lentB int metric_type if metric=='avg': metric_type = 0 elif metric == 'min': metric_type = 1 elif metric == 'max': metric_type = 2 else: raise ValueError('Metric should be one of avg, min, max') # preprocess tracks cdef: size_t longest_track_len = 0, track_len longest_track_lenA, longest_track_lenB cnp.ndarray[object, ndim=1] tracksA32 cnp.ndarray[object, ndim=1] tracksB32 cnp.ndarray[cnp.double_t, ndim=2] DM lentA = len(tracksA) lentB = len(tracksB) tracksA32 = np.zeros((lentA,), dtype=object) tracksB32 = np.zeros((lentB,), dtype=object) DM = np.zeros((lentA,lentB), dtype=np.double) # process tracks to predictable memory layout, find longest track for i in range(lentA): tracksA32[i] = np.ascontiguousarray(tracksA[i], dtype=f32_dt) track_len = tracksA32[i].shape[0] if track_len > longest_track_lenA: longest_track_lenA = track_len for i in range(lentB): tracksB32[i] = np.ascontiguousarray(tracksB[i], dtype=f32_dt) track_len = tracksB32[i].shape[0] if track_len > longest_track_lenB: longest_track_lenB = track_len if longest_track_lenB > longest_track_lenA: longest_track_lenA = longest_track_lenB # preallocate buffer array for track distance calculations cdef: cnp.ndarray [cnp.float32_t, ndim=1] distances_buffer cnp.float32_t *t1_ptr, *t2_ptr, *min_buffer distances_buffer = np.zeros((longest_track_lenA*2,), dtype=np.float32) min_buffer = distances_buffer.data # cycle over tracks cdef: cnp.ndarray [cnp.float32_t, ndim=2] t1, t2 size_t t1_len, t2_len for i from 0 <= i < lentA: t1 = tracksA32[i] t1_len = t1.shape[0] t1_ptr = t1.data for j from 0 <= j < lentB: t2 = tracksB32[j] t2_len = t2.shape[0] t2_ptr = t2.data DM[i,j] = czhang(t1_len, t1_ptr, t2_len, t2_ptr, min_buffer, metric_type) return DM cdef cnp.float32_t inf = np.inf @cython.cdivision(True) cdef inline cnp.float32_t czhang(size_t t1_len, cnp.float32_t *track1_ptr, size_t t2_len, cnp.float32_t *track2_ptr, cnp.float32_t *min_buffer, int metric_type) nogil: ''' Note ``nogil`` - no python calls allowed in this function ''' cdef: cnp.float32_t *min_t2t1, *min_t1t2 min_t2t1 = min_buffer min_t1t2 = min_buffer + t2_len min_distances(t1_len, track1_ptr, t2_len, track2_ptr, min_t2t1, min_t1t2) cdef: size_t t1_pi, t2_pi cnp.float32_t mean_t2t1 = 0, mean_t1t2 = 0, dist_val for t1_pi from 0<= t1_pi < t1_len: mean_t1t2+=min_t1t2[t1_pi] mean_t1t2=mean_t1t2/t1_len for t2_pi from 0<= t2_pi < t2_len: mean_t2t1+=min_t2t1[t2_pi] mean_t2t1=mean_t2t1/t2_len if metric_type == 0: dist_val=(mean_t2t1+mean_t1t2)/2.0 elif metric_type == 1: if mean_t2t1 < mean_t1t2: dist_val=mean_t2t1 else: dist_val=mean_t1t2 elif metric_type == 2: if mean_t2t1 > mean_t1t2: dist_val=mean_t2t1 else: dist_val=mean_t1t2 return dist_val @cython.cdivision(True) cdef inline void min_distances(size_t t1_len, cnp.float32_t *track1_ptr, size_t t2_len, cnp.float32_t *track2_ptr, cnp.float32_t *min_t2t1, cnp.float32_t *min_t1t2) nogil: cdef: cnp.float32_t *t1_pt, *t2_pt, d0, d1, d2 cnp.float32_t delta2 int t1_pi, t2_pi for t2_pi from 0<= t2_pi < t2_len: min_t2t1[t2_pi] = inf for t1_pi from 0<= t1_pi < t1_len: min_t1t2[t1_pi] = inf # pointer to current point in track 1 t1_pt = track1_ptr # calculate min squared distance between each point in the two # lines. Squared distance to delay doing the sqrt until after this # speed-critical loop for t1_pi from 0<= t1_pi < t1_len: # pointer to current point in track 2 t2_pt = track2_ptr for t2_pi from 0<= t2_pi < t2_len: d0 = t1_pt[0] - t2_pt[0] d1 = t1_pt[1] - t2_pt[1] d2 = t1_pt[2] - t2_pt[2] delta2 = d0*d0 + d1*d1 + d2*d2 if delta2 < min_t2t1[t2_pi]: min_t2t1[t2_pi]=delta2 if delta2 < min_t1t2[t1_pi]: min_t1t2[t1_pi]=delta2 t2_pt += 3 # to next point in track 2 t1_pt += 3 # to next point in track 1 # sqrt to get Euclidean distance from squared distance for t1_pi from 0<= t1_pi < t1_len: min_t1t2[t1_pi]=sqrt(min_t1t2[t1_pi]) for t2_pi from 0<= t2_pi < t2_len: min_t2t1[t2_pi]=sqrt(min_t2t1[t2_pi]) def mam_distances(xyz1,xyz2,metric='all'): ''' Min/Max/Mean Average Minimum Distance between tracks xyz1 and xyz2 Based on the metrics in Zhang, Correia, Laidlaw 2008 http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4479455 which in turn are based on those of Corouge et al. 2004 Parameters ------------ xyz1 : array, shape (N1,3), dtype float32 xyz2 : array, shape (N2,3), dtype float32 arrays representing x,y,z of the N1 and N2 points of two tracks metrics : {'avg','min','max','all'} Metric to calculate. {'avg','min','max'} return a scalar. 'all' returns a tuple Returns --------- avg_mcd : float average_mean_closest_distance min_mcd : float minimum_mean_closest_distance max_mcd : float maximum_mean_closest_distance Notes ----- Algorithmic description Lets say we have curves A and B. For every point in A calculate the minimum distance from every point in B stored in minAB For every point in B calculate the minimum distance from every point in A stored in minBA find average of minAB stored as avg_minAB find average of minBA stored as avg_minBA if metric is 'avg' then return (avg_minAB + avg_minBA)/2.0 if metric is 'min' then return min(avg_minAB,avg_minBA) if metric is 'max' then return max(avg_minAB,avg_minBA) ''' cdef: cnp.ndarray[cnp.float32_t, ndim=2] track1 cnp.ndarray[cnp.float32_t, ndim=2] track2 size_t t1_len, t2_len track1 = np.ascontiguousarray(xyz1, dtype=f32_dt) t1_len = track1.shape[0] track2 = np.ascontiguousarray(xyz2, dtype=f32_dt) t2_len = track2.shape[0] # preallocate buffer array for track distance calculations cdef: cnp.float32_t *min_t2t1, *min_t1t2 cnp.ndarray [cnp.float32_t, ndim=1] distances_buffer distances_buffer = np.zeros((t1_len + t2_len,), dtype=np.float32) min_t2t1 = distances_buffer.data min_t1t2 = min_t2t1 + t2_len min_distances(t1_len, track1.data, t2_len, track2.data, min_t2t1, min_t1t2) cdef: size_t t1_pi, t2_pi cnp.float32_t mean_t2t1 = 0, mean_t1t2 = 0 for t1_pi from 0<= t1_pi < t1_len: mean_t1t2+=min_t1t2[t1_pi] mean_t1t2=mean_t1t2/t1_len for t2_pi from 0<= t2_pi < t2_len: mean_t2t1+=min_t2t1[t2_pi] mean_t2t1=mean_t2t1/t2_len if metric=='all': return ((mean_t2t1+mean_t1t2)/2.0, np.min((mean_t2t1,mean_t1t2)), np.max((mean_t2t1,mean_t1t2))) elif metric=='avg': return (mean_t2t1+mean_t1t2)/2.0 elif metric=='min': return np.min((mean_t2t1,mean_t1t2)) elif metric =='max': return np.max((mean_t2t1,mean_t1t2)) else : ValueError('Wrong argument for metric') def minimum_closest_distance(xyz1,xyz2): ''' Find the minimum distance between two curves xyz1, xyz2 Parameters ------------- xyz1 : array, shape (N1,3), dtype float32 xyz2 : array, shape (N2,3), dtype float32 arrays representing x,y,z of the N1 and N2 points of two tracks Returns --------- md : minimum distance Notes -------- Algorithmic description Lets say we have curves A and B for every point in A calculate the minimum distance from every point in B stored in minAB for every point in B calculate the minimum distance from every point in A stored in minBA find min of minAB stored in min_minAB find min of minBA stored in min_minBA Then return (min_minAB + min_minBA)/2.0 ''' cdef: cnp.ndarray[cnp.float32_t, ndim=2] track1 cnp.ndarray[cnp.float32_t, ndim=2] track2 size_t t1_len, t2_len track1 = np.ascontiguousarray(xyz1, dtype=f32_dt) t1_len = track1.shape[0] track2 = np.ascontiguousarray(xyz2, dtype=f32_dt) t2_len = track2.shape[0] # preallocate buffer array for track distance calculations cdef: cnp.float32_t *min_t2t1, *min_t1t2 cnp.ndarray [cnp.float32_t, ndim=1] distances_buffer distances_buffer = np.zeros((t1_len + t2_len,), dtype=np.float32) min_t2t1 = distances_buffer.data min_t1t2 = min_t2t1 + t2_len min_distances(t1_len, track1.data, t2_len, track2.data, min_t2t1, min_t1t2) cdef: size_t t1_pi, t2_pi double min_min_t2t1 = inf double min_min_t1t2 = inf for t1_pi in range(t1_len): if min_min_t1t2 > min_t1t2[t1_pi]: min_min_t1t2 = min_t1t2[t1_pi] for t2_pi in range(t2_len): if min_min_t2t1 > min_t2t1[t2_pi]: min_min_t2t1 = min_t2t1[t2_pi] return (min_min_t1t2+min_min_t2t1)/2.0 def lee_perpendicular_distance(start0, end0, start1, end1): ''' Based on Lee , Han & Whang SIGMOD07. Calculates perpendicular distance metric for the distance between two line segments This function assumes that norm(end0-start0)>norm(end1-start1) i.e. that the first segment will be bigger than the second one. Parameters ------------ start0 : float array(3,) end0 : float array(3,) start1 : float array(3,) end1 : float array(3,) Returns -------- perpendicular_distance: float Examples --------- >>> import dipy.core.performance as pf >>> pf.lee_perpendicular_distance([0,0,0],[1,0,0],[3,4,5],[5,4,3]) 5.9380966767403436 Notes ------- l0 = np.inner(end0-start0,end0-start0) l1 = np.inner(end1-start1,end1-start1) k0=end0-start0 u1 = np.inner(start1-start0,k0)/l0 u2 = np.inner(end1-start0,k0)/l0 ps = start0+u1*k0 pe = start0+u2*k0 lperp1 = np.sqrt(np.inner(ps-start1,ps-start1)) lperp2 = np.sqrt(np.inner(pe-end1,pe-end1)) if lperp1+lperp2 > 0.: return (lperp1**2+lperp2**2)/(lperp1+lperp2) else: return 0. ''' cdef cnp.ndarray[cnp.float32_t, ndim=1] fvec1,fvec2,fvec3,fvec4 fvec1 = as_float_3vec(start0) fvec2 = as_float_3vec(end0) fvec3 = as_float_3vec(start1) fvec4 = as_float_3vec(end1) return clee_perpendicular_distance(fvec1.data,fvec2.data,fvec3.data,fvec4.data) cdef float clee_perpendicular_distance(float *start0, float *end0,float *start1, float *end1): ''' This function assumes that norm(end0-start0)>norm(end1-start1) ''' cdef: float l0,l1,ltmp,u1,u2,lperp1,lperp2 float *s_tmp,*e_tmp,k0[3],ps[3],pe[3],ps1[3],pe1[3],tmp[3] csub_3vecs(end0,start0,k0) l0 = cinner_3vecs(k0,k0) csub_3vecs(end1,start1,tmp) l1 = cinner_3vecs(tmp, tmp) #csub_3vecs(end0,start0,k0) #u1 = np.inner(start1-start0,k0)/l0 #u2 = np.inner(end1-start0,k0)/l0 csub_3vecs(start1,start0,tmp) u1 = cinner_3vecs(tmp,k0)/l0 csub_3vecs(end1,start0,tmp) u2 = cinner_3vecs(tmp,k0)/l0 cmul_3vec(u1,k0,tmp) cadd_3vecs(start0,tmp,ps) cmul_3vec(u2,k0,tmp) cadd_3vecs(start0,tmp,pe) #lperp1 = np.sqrt(np.inner(ps-start1,ps-start1)) #lperp2 = np.sqrt(np.inner(pe-end1,pe-end1)) csub_3vecs(ps,start1,ps1) csub_3vecs(pe,end1,pe1) lperp1 = sqrt(cinner_3vecs(ps1,ps1)) lperp2 = sqrt(cinner_3vecs(pe1,pe1)) if lperp1+lperp2 > 0.: return (lperp1*lperp1+lperp2*lperp2)/(lperp1+lperp2) else: return 0. def lee_angle_distance(start0, end0, start1, end1): ''' Based on Lee , Han & Whang SIGMOD07. Calculates angle distance metric for the distance between two line segments This function assumes that norm(end0-start0)>norm(end1-start1) i.e. that the first segment will be bigger than the second one. Parameters ------------ start0 : float array(3,) end0 : float array(3,) start1 : float array(3,) end1 : float array(3,) Returns --------- angle_distance : float Examples --------- >>> import dipy.tracking.distances as pf >>> pf.lee_angle_distance([0,0,0],[1,0,0],[3,4,5],[5,4,3]) 2.0 Notes ------- l_0 = np.inner(end0-start0,end0-start0) l_1 = np.inner(end1-start1,end1-start1) cos_theta_squared = np.inner(end0-start0,end1-start1)**2/ (l_0*l_1) return np.sqrt((1-cos_theta_squared)*l_1) ''' cdef cnp.ndarray[cnp.float32_t, ndim=1] fvec1,fvec2,fvec3,fvec4 fvec1 = as_float_3vec(start0) fvec2 = as_float_3vec(end0) fvec3 = as_float_3vec(start1) fvec4 = as_float_3vec(end1) return clee_angle_distance(fvec1.data,fvec2.data,fvec3.data,fvec4.data) cdef float clee_angle_distance(float *start0, float *end0,float *start1, float *end1): ''' This function assumes that norm(end0-start0)>norm(end1-start1) ''' cdef: float l0,l1,ltmp,cos_theta_squared float *s_tmp,*e_tmp,k0[3],k1[3],tmp[3] csub_3vecs(end0,start0,k0) l0 = cinner_3vecs(k0,k0) #print l0 csub_3vecs(end1,start1,k1) l1 = cinner_3vecs(k1, k1) #print l1 ltmp=cinner_3vecs(k0,k1) cos_theta_squared = (ltmp*ltmp)/ (l0*l1) #print cos_theta_squared return sqrt((1-cos_theta_squared)*l1) def approx_polygon_track(xyz,alpha=0.392): ''' Fast and simple trajectory approximation algorithm by Eleftherios and Ian It will reduce the number of points of the track by keeping intact the start and endpoints of the track and trying to remove as many points as possible without distorting much the shape of the track Parameters ------------ xyz : array(N,3) initial trajectory alpha : float smoothing parameter (<0.392 smoother, >0.392 rougher) if the trajectory was a smooth circle then with alpha =0.393 ~=pi/8. the circle would be approximated with an decahexagon if alpha = 0.7853 ~=pi/4. with an octagon. Returns --------- characteristic_points: list of M array(3,) points Examples ---------- >>> from fos.tracking.distances import approx_polygon_track >>> #approximating a helix >>> t=np.linspace(0,1.75*2*np.pi,100) >>> x = np.sin(t) >>> y = np.cos(t) >>> z = t >>> xyz=np.vstack((x,y,z)).T >>> xyza = approx_polygon_track(xyz) >>> len(xyza) < len(xyz) True Notes ------- Assuming that a good approximation for a circle is an octagon then that means that the points of the octagon will have angle alpha = 2*pi/8 = pi/4 . We calculate the angle between every two neighbour segments of a trajectory and if the angle is higher than pi/4 we choose that point as a characteristic point otherwise we move at the next point. ''' cdef : int mid_index cnp.ndarray[cnp.float32_t, ndim=2] track float *fvec0,*fvec1,*fvec2 object characteristic_points size_t t_len double angle,tmp float vec0[3],vec1[3] angle=alpha track = np.ascontiguousarray(xyz, dtype=f32_dt) t_len=len(track) characteristic_points=[track[0]] mid_index = 1 angle=0 while mid_index < t_len-1: #fvec0 = as_float_3vec(track[mid_index-1]) #track[0].data fvec0 = asfp(track[mid_index-1]) fvec1 = asfp(track[mid_index]) fvec2 = asfp(track[mid_index+1]) #csub_3vecs(fvec1.data,fvec0.data,vec0) csub_3vecs(fvec1,fvec0,vec0) csub_3vecs(fvec2,fvec1,vec1) tmp=fabs(acos(cinner_3vecs(vec0,vec1)/(cnorm_3vec(vec0)*cnorm_3vec(vec1)))) if isnan(tmp) : angle+=0. else: angle+=tmp if angle > alpha: characteristic_points.append(track[mid_index]) angle=0 mid_index+=1 characteristic_points.append(track[-1]) return np.array(characteristic_points) def approximate_mdl_trajectory(xyz, alpha=1.): ''' Implementation of Lee et al Approximate Trajectory Partitioning Algorithm This is base on the minimum description length principle Parameters ------------ xyz : array(N,3) initial trajectory alpha : float smoothing parameter (>1 smoother, <1 rougher) Returns ------------ characteristic_points : list of M array(3,) points ''' cdef : int start_index,length,current_index, i double cost_par,cost_nopar,alphac object characteristic_points size_t t_len cnp.ndarray[cnp.float32_t, ndim=2] track float tmp[3] cnp.ndarray[cnp.float32_t, ndim=1] fvec1,fvec2,fvec3,fvec4 track = np.ascontiguousarray(xyz, dtype=f32_dt) t_len=len(track) alphac=alpha characteristic_points=[xyz[0]] start_index = 0 length = 2 #print t_len while start_index+length < t_len-1: current_index = start_index+length fvec1 = as_float_3vec(track[start_index]) fvec2 = as_float_3vec(track[current_index]) # L(H) csub_3vecs(fvec2.data,fvec1.data,tmp) cost_par=log2(sqrt(cinner_3vecs(tmp,tmp))) cost_nopar=0 #print start_index,current_index # L(D|H) #for i in range(start_index+1,current_index):#+1): for i in range(start_index,current_index+1): #print i fvec3 = as_float_3vec(track[i]) fvec4 = as_float_3vec(track[i+1]) cost_par += log2(clee_perpendicular_distance(fvec3.data,fvec4.data,fvec1.data,fvec2.data)) cost_par += log2(clee_angle_distance(fvec3.data,fvec4.data,fvec1.data,fvec2.data)) csub_3vecs(fvec4.data,fvec3.data,tmp) cost_nopar += log2(cinner_3vecs(tmp,tmp)) cost_nopar /= 2 #print cost_par, cost_nopar, start_index,length if alphac*cost_par>cost_nopar: characteristic_points.append(track[current_index-1]) start_index = current_index-1 length = 2 else: length+=1 characteristic_points.append(track[-1]) return np.array(characteristic_points) def intersect_segment_cylinder(sa,sb,p,q,r): ''' Intersect Segment S(t) = sa +t(sb-sa), 0 <=t<= 1 against cylinder specified by p,q and r Look p.197 from Real Time Collision Detection C. Ericson Examples ---------- >>> from dipy.tracking.distances import intersect_segment_cylinder as isc >>> # Define cylinder using a segment defined by >>> p=np.array([0,0,0],dtype=float32) >>> q=np.array([1,0,0],dtype=float32) >>> r=0.5 >>> # Define segment >>> sa=np.array([0.5,1 ,0],dtype=float32) >>> sb=np.array([0.5,-1,0],dtype=float32) >>> isc(sa,sb,p,q,r) ''' cdef: float *csa,*csb,*cp,*cq float cr float ct[2] csa = asfp(sa) csb = asfp(sb) cp = asfp(p) cq = asfp(q) cr=r ct[0]=-100 ct[1]=-100 tmp= cintersect_segment_cylinder(csa,csb,cp, cq, cr, ct) return tmp,ct[0],ct[1] cdef float cintersect_segment_cylinder(float *sa,float *sb,float *p, float *q, float r, float *t): ''' Intersect Segment S(t) = sa +t(sb-sa), 0 <=t<= 1 against cylinder specified by p,q and r Look p.197 from Real Time Collision Detection C. Ericson 0 no intersection 1 intersection ''' cdef: float d[3],m[3],n[3] float md,nd,dd, nn, mn, a, k, c,b, discr float epsilon_float=5.96e-08 csub_3vecs(q,p,d) csub_3vecs(sa,p,m) csub_3vecs(sb,sa,n) md=cinner_3vecs(m,d) nd=cinner_3vecs(n,d) dd=cinner_3vecs(d,d) #test if segment fully outside either endcap of cylinder if md < 0. and md + nd < 0.: return 0 #segment outside p side if md > dd and md + nd > dd: return 0 #segment outside q side nn=cinner_3vecs(n,n) mn=cinner_3vecs(m,n) a=dd*nn-nd*nd k=cinner_3vecs(m,m) -r*r c=dd*k-md*md if fabs(a) < epsilon_float: #segment runs parallel to cylinder axis if c>0.: return 0. # segment lies outside cylinder if md < 0.: t[0]=-mn/nn # intersect against p endcap elif md > dd : t[0]=(nd-mn)/nn # intersect against q endcap else: t[0]=0. # lies inside cylinder return 1 b=dd*mn -nd*md discr=b*b-a*c if discr < 0.: return 0. # no real roots ; no intersection t[0]=(-b-sqrt(discr))/a t[1]=(-b+sqrt(discr))/a if t[0]<0. or t[0] > 1. : return 0. # intersection lies outside segment if md + t[0]* nd < 0.: #intersection outside cylinder on 'p' side if nd <= 0. : return 0. # segment pointing away from endcap t[0]=-md/nd #keep intersection if Dot(S(t)-p,S(t)-p) <= r^2 if k+2*t[0]*(mn+t[0]*nn) <=0.: return 1. elif md+t[0]*nd > dd : #intersection outside cylinder on 'q' side if nd >= 0.: return 0. # segment pointing away from endcap t[0]= (dd-md)/nd #keep intersection if Dot(S(t)-q,S(t)-q) <= r^2 if k+dd-2*md+t[0]*(2*(mn-nd)+t[0]*nn) <= 0.: return 1. # segment intersects cylinder between the endcaps; t is correct return 1. def point_segment_sq_distance(a,b,c): ''' Calculate the squared distance from a point c to a finite line segment ab. Examples ---------- >>> from dipy.tracking.distances import point_segment_sq_distance >>> a=np.array([0,0,0],dtype=float32) >>> b=np.array([1,0,0],dtype=float32) >>> c=np.array([0,1,0],dtype=float32) >>> point_segment_sq_distance(a,b,c) 1.0 >>> c=np.array([0,3,0],dtype=float32) >>> pf.point_segment_sq_distance(a,b,c) 9.0 >>> c=np.array([-1,1,0],dtype=float32) >>> pf.point_segment_sq_distance(a,b,c) 2.0 ''' cdef: float *ca,*cb,*cc float cr float ct[2] ca = asfp(a) cb = asfp(b) cc = asfp(c) return cpoint_segment_sq_dist(ca, cb, cc) @cython.cdivision(True) cdef inline float cpoint_segment_sq_dist(float * a, float * b, float * c) nogil: ''' Calculate the squared distance from a point c to a line segment ab. ''' cdef: float ab[3],ac[3],bc[3] float e,f csub_3vecs(b,a,ab) csub_3vecs(c,a,ac) csub_3vecs(c,b,bc) e = cinner_3vecs(ac, ab) #Handle cases where c projects outside ab if e <= 0.: return cinner_3vecs(ac, ac) f = cinner_3vecs(ab, ab) if e >= f : return cinner_3vecs(bc, bc) #Handle case where c projects onto ab return cinner_3vecs(ac, ac) - e * e / f def track_dist_3pts(tracka,trackb): ''' Calculate the euclidean distance between two 3pt tracks both direct and flip distances are calculated but only the smallest is returned Parameters ------------ a : array, shape (3,3) a three point track b : array, shape (3,3) a three point track Returns --------- dist :float Examples ---------- >>> import numpy as np >>> from dipy.tracking.distances import track_dist_3pts >>> a=np.array([[0,0,0],[1,0,0,],[2,0,0]]) >>> b=np.array([[3,0,0],[3.5,1,0],[4,2,0]]) >>> track_dist_3pts(a,b) ''' cdef cnp.ndarray[cnp.float32_t, ndim=2] a,b cdef float d[2] a=np.ascontiguousarray(tracka,dtype=f32_dt) b=np.ascontiguousarray(trackb,dtype=f32_dt) track_direct_flip_3dist(asfp(a[0]),asfp(a[1]),asfp(a[2]), asfp(b[0]),asfp(b[1]),asfp(b[2]),d) if d[0]rows out[1]=distf/rows @cython.cdivision(True) cdef inline void track_direct_flip_3dist(float *a1, float *b1,float *c1,float *a2, float *b2, float *c2, float *out) nogil: ''' Calculate the euclidean distance between two 3pt tracks both direct and flip are given as output Parameters ---------------- a1,b1,c1 : 3 float[3] arrays representing the first track a2,b2,c2 : 3 float[3] arrays representing the second track Returns ----------- out : a float[2] array having the euclidean distance and the fliped euclidean distance ''' cdef: int i float tmp1=0,tmp2=0,tmp3=0,tmp1f=0,tmp3f=0 #for i in range(3): for i from 0<=i<3: tmp1=tmp1+(a1[i]-a2[i])*(a1[i]-a2[i]) tmp2=tmp2+(b1[i]-b2[i])*(b1[i]-b2[i]) tmp3=tmp3+(c1[i]-c2[i])*(c1[i]-c2[i]) tmp1f=tmp1f+(a1[i]-c2[i])*(a1[i]-c2[i]) tmp3f=tmp3f+(c1[i]-a2[i])*(c1[i]-a2[i]) out[0]=(sqrt(tmp1)+sqrt(tmp2)+sqrt(tmp3))/3.0 out[1]=(sqrt(tmp1f)+sqrt(tmp2)+sqrt(tmp3f))/3.0 #out[0]=(tmp1+tmp2+tmp3)/3.0 #out[1]=(tmp1f+tmp2+tmp3f)/3.0 ctypedef struct LSC_Cluster: long *indices float *hidden long N @cython.boundscheck(False) @cython.wraparound(False) @cython.cdivision(True) def local_skeleton_clustering(tracks, d_thr=10): """ Efficient tractography clustering Every track can needs to have the same number of points. Use dipy.tracking.metrics.downsample to restrict the number of points Parameters ----------- tracks : sequence of tracks as arrays, shape (N,3) .. (N,3) where N=points d_thr : float, average euclidean distance threshold Returns -------- C : dict Examples ---------- >>> from dipy.tracking.distances import local_skeleton_clustering >>> tracks=[np.array([[0,0,0],[1,0,0,],[2,0,0]]), np.array([[3,0,0],[3.5,1,0],[4,2,0]]), np.array([[3.2,0,0],[3.7,1,0],[4.4,2,0]]), np.array([[3.4,0,0],[3.9,1,0],[4.6,2,0]]), np.array([[0,0.2,0],[1,0.2,0],[2,0.2,0]]), np.array([[2,0.2,0],[1,0.2,0],[0,0.2,0]]), np.array([[0,0,0],[0,1,0],[0,2,0]])] >>> C=local_skeleton_clustering(tracks,d_thr=0.5,3) Notes ------ The distance calculated between two tracks t_1 t_2 0* a *0 \ | \ | 1* | | b *1 | \ 2* \ c *2 is equal to (a+b+c)/3 where a the euclidean distance between t_1[0] and t_2[0], b between t_1[1] and t_2[1] and c between t_1[2] and t_2[2]. Also the fliped Visualization -------------- It is possible to visualize the clustering C from the example above using the fvtk module from dipy.viz import fvtk r=fvtk.ren() for c in C: color=np.random.rand(3) for i in C[c]['indices']: fvtk.add(r,fvtk.line(tracks[i],color)) fvtk.show(r) See also --------- dipy.tracking.metrics.downsample """ cdef : cnp.ndarray[cnp.float32_t, ndim=2] track LSC_Cluster *cluster long lent = 0,lenC = 0, dim = 0, points=0 long i=0, j=0, c=0, i_k=0, rows=0 ,cit=0 float *ptr, *hid, *alld float d[2],m_d,cd_thr long *flip points=len(tracks[0]) dim = points*3 rows = points cd_thr = d_thr #Allocate and copy memory for first cluster cluster=realloc(NULL,sizeof(LSC_Cluster)) cluster[0].indices=realloc(NULL,sizeof(long)) cluster[0].hidden=realloc(NULL,dim*sizeof(float)) cluster[0].indices[0]=0 track=np.ascontiguousarray(tracks[0],dtype=f32_dt) ptr=track.data for i from 0<=irealloc(NULL,dim*sizeof(float)) #Work with the rest of the tracks lent=len(tracks) for it in range(1,lent): track=np.ascontiguousarray(tracks[it],dtype=f32_dt) ptr=track.data cit=it with nogil: alld=calloc(lenC,sizeof(float)) flip=calloc(lenC,sizeof(long)) for k from 0<=kcluster[k].N #track_direct_flip_3dist(&ptr[0],&ptr[3],&ptr[6],&hid[0],&hid[3],&hid[6],d) #track_direct_flip_3dist(ptr,ptr+3,ptr+6,hid,hid+3,hid+6,d) track_direct_flip_dist(ptr, hid,rows,d) if d[1]realloc(cluster[i_k].indices,cluster[i_k].N*sizeof(long)) cluster[i_k].indices[cluster[i_k].N-1]=cit else:#New cluster added lenC+=1 cluster=realloc(cluster,lenC*sizeof(LSC_Cluster)) cluster[lenC-1].indices=realloc(NULL,sizeof(long)) cluster[lenC-1].hidden=realloc(NULL,dim*sizeof(float)) cluster[lenC-1].indices[0]=cit for i from 0<=i>> from dipy.viz import fvtk >>> tracks=[np.array([[0,0,0],[1,0,0,],[2,0,0]]), np.array([[3,0,0],[3.5,1,0],[4,2,0]]), np.array([[3.2,0,0],[3.7,1,0],[4.4,2,0]]), np.array([[3.4,0,0],[3.9,1,0],[4.6,2,0]]), np.array([[0,0.2,0],[1,0.2,0],[2,0.2,0]]), np.array([[2,0.2,0],[1,0.2,0],[0,0.2,0]]), np.array([[0,0,0],[0,1,0],[0,2,0]])] >>> C=local_skeleton_clustering(tracks,d_thr=0.5) Notes ------ It is possible to visualize the clustering C from the example above using the fvtk module r=fvtk.ren() for c in C: color=np.random.rand(3) for i in C[c]['indices']: fvtk.add(r,fos.line(tracks[i],color)) fvtk.show(r) ''' cdef : cnp.ndarray[cnp.float32_t, ndim=2] track cnp.ndarray[cnp.float32_t, ndim=2] h int lent,k,it float d[2] #float d_sq=d_thr**2 lent=len(tracks) #Network C C={0:{'indices':[0],'hidden':tracks[0].copy(),'N':1}} ts=np.zeros((3,3),dtype=np.float32) #for (it,t) in enumerate(tracks[1:]): for it in range(1,lent): track=np.ascontiguousarray(tracks[it],dtype=f32_dt) lenC=len(C.keys()) #if it%1000==0: # print it,lenC alld=np.zeros(lenC) flip=np.zeros(lenC) for k in range(lenC): h=np.ascontiguousarray(C[k]['hidden']/C[k]['N'],dtype=f32_dt) #print track #print h track_direct_flip_3dist( asfp(track[0]),asfp(track[1]),asfp(track[2]), asfp(h[0]), asfp(h[1]),asfp(h[2]),d) #d=np.sum(np.sqrt(np.sum((t-h)**2,axis=1)))/3.0 #ts[0]=t[-1];ts[1]=t[1];ts[-1]=t[0] #ds=np.sum(np.sqrt(np.sum((ts-h)**2,axis=1)))/3.0 #print d[0],d[1] if d[1]>> import numpy as np >>> from dipy.viz import fvtk >>> from dipy.tracking.distances as pf >>> tracks=[np.array([[0,0,0],[1,0,0,],[2,0,0]],dtype=np.float32), >>> np.array([[3,0,0],[3.5,1,0],[4,2,0]],dtype=np.float32), >>> np.array([[3.2,0,0],[3.7,1,0],[4.4,2,0]],dtype=np.float32), >>> np.array([[3.4,0,0],[3.9,1,0],[4.6,2,0]],dtype=np.float32), >>> np.array([[0,0.2,0],[1,0.2,0],[2,0.2,0]],dtype=np.float32), >>> np.array([[2,0.2,0],[1,0.2,0],[0,0.2,0]],dtype=np.float32), >>> np.array([[0,0,0],[0,1,0],[0,2,0]],dtype=np.float32), >>> np.array([[0.2,0,0],[0.2,1,0],[0.2,2,0]],dtype=np.float32), >>> np.array([[-0.2,0,0],[-0.2,1,0],[-0.2,2,0]],dtype=np.float32)] >>> C=pf.larch_fast_split(tracks,None,0.5) Here is an example of how to visualize the clustering above r=fvtk.ren() fvtk.add(r,fvtk.line(tracks,fvtk.red)) fvtk.show(r) for c in C: color=np.random.rand(3) for i in C[c]['indices']: fos.add(r,fvtk.line(tracks[i],color)) fvtk.show(r) for c in C: fvtk.add(r,fos.line(C[c]['rep3']/C[c]['N'],fos.white)) fvtk.show(r) Notes ----- If a 3 point track (3track) is far away from all clusters then add a new cluster and assign this 3track as the rep(representative) track for the new cluster. Otherwise the rep 3track of each cluster is the average track of the cluster. ''' cdef : cnp.ndarray[cnp.float32_t, ndim=2] track cnp.ndarray[cnp.float32_t, ndim=2] h int lent,k,it float d[2] lent=len(tracks) if indices==None: C={0:{'indices':[0],'rep3':tracks[0].copy(),'N':1}} itrange=range(1,lent) else: C={0:{'indices':[indices[0]],'rep3':tracks[indices[0]].copy(),'N':1}} itrange=indices[1:] ts=np.zeros((3,3),dtype=np.float32) for it in itrange: track=np.ascontiguousarray(tracks[it],dtype=f32_dt) lenC=len(C.keys()) alld=np.zeros(lenC) flip=np.zeros(lenC) for k in range(lenC): h=np.ascontiguousarray(C[k]['rep3']/C[k]['N'],dtype=f32_dt) track_direct_flip_3dist(asfp(track[0]),asfp(track[1]),asfp(track[2]), asfp(h[0]), asfp(h[1]), asfp(h[2]),d) if d[1]>> from dipy.tracking.distances import point_track_sq_distance_check >>> t=np.random.rand(10,3).astype(np.float32) >>> p=np.array([0.5,0.5,0.5],dtype=np.float32) >>> point_track_sq_distance_check(t,p,2**2) True >>> t=np.array([[0,0,0],[1,1,1],[2,2,2]],dtype='f4') >>> p=np.array([-1,-1.,-1],dtype='f4') >>> point_track_sq_distance_check(t,p,.2**2) False >>> point_track_sq_distance_check(t,p,2**2) True ''' cdef: float *t=track.data float *p=point.data float a[3],b[3] int tlen = len(track) int curr = 0 float dist = 0 int i int intersects = 0 with nogil: for i from 0<=ia,b,p) if dist<=sq_dist_thr: intersects=1 break if intersects==1: return True else: return False def track_roi_intersection_check(cnp.ndarray[float,ndim=2] track, cnp.ndarray[float,ndim=2] roi, double sq_dist_thr): ''' Check if a track is intersecting a region of interest Parameters ---------- track: array,float32, shape (N,3) roi: array,float32, shape (M,3) sq_dist_thr: double, threshold, check squared euclidean distance from every roi point Returns ------- bool: True, if sq_distance <= sq_dist_thr, otherwise False. Examples -------- >>> from dipy.tracking.distances import track_roi_intersection_check >>> roi=np.array([[0,0,0],[1,0,0],[2,0,0]],dtype='f4') >>> t=np.array([[0,0,0],[1,1,1],[2,2,2]],dtype='f4') >>> track_roi_intersection_check(t,roi,1) True >>> track_roi_intersection_check(t,np.array([[10,0,0]],dtype='f4'),1) False ''' cdef: float *t=track.data float *r=roi.data float a[3],b[3],p[3] int tlen = len(track) int rlen = len(roi) int curr = 0 int currp = 0 float dist = 0 int i,j int intersects=0 with nogil: for i from 0<=ia,b,p) if dist<=sq_dist_thr: intersects=1 break if intersects==1: break if intersects==1: return True else: return False dipy-0.5.0/dipy/tracking/learning.py000066400000000000000000000072161152576264200174210ustar00rootroot00000000000000''' Learning algorithms for tractography''' import numpy as np import dipy.tracking.distances as pf def detect_corresponding_tracks(indices,tracks1,tracks2): ''' Detect corresponding tracks from list tracks1 to list tracks2 where tracks1 & tracks2 are lists of tracks Parameters ------------ indices : sequence of indices of tracks1 that are to be detected in tracks2 tracks1 : sequence of tracks as arrays, shape (N1,3) .. (Nm,3) tracks2 : sequence of tracks as arrays, shape (M1,3) .. (Mm,3) Returns --------- track2track : array (N,2) where N is len(indices) of int it shows the correspondance in the following way: the first column is the current index in tracks1 the second column is the corresponding index in tracks2 Examples ---------- >>> import numpy as np >>> import dipy.tracking.learning as tl >>> A=np.array([[0,0,0],[1,1,1],[2,2,2]]) >>> B=np.array([[1,0,0],[2,0,0],[3,0,0]]) >>> C=np.array([[0,0,-1],[0,0,-2],[0,0,-3]]) >>> bundle1=[A,B,C] >>> bundle2=[B,A] >>> indices=[0,1] >>> arr=tl.detect_corresponding_tracks(indices,bundle1,bundle2) Notes ------- To find the corresponding tracks we use mam_distances with 'avg' option. Then we calculate the argmin of all the calculated distances and return it for every index. (See 3rd column of arr in the example given below. ''' li=len(indices) track2track=np.zeros((li,2)) cnt=0 for i in indices: rt=[pf.mam_distances(tracks1[i],t,'avg') for t in tracks2] rt=np.array(rt) track2track[cnt]=np.array([i,rt.argmin()]) cnt+=1 return track2track.astype(int) def detect_corresponding_tracks_plus(indices,tracks1,indices2,tracks2): ''' Detect corresponding tracks from 1 to 2 where tracks1 & tracks2 are sequences of tracks Parameters ------------ indices : sequence of indices of tracks1 that are to be detected in tracks2 tracks1 : sequence of tracks as arrays, shape (N1,3) .. (Nm,3) indices2 : sequence of indices of tracks2 in the initial brain tracks2 : sequence of tracks as arrays, shape (M1,3) .. (Mm,3) Returns --------- track2track : array (N,2) where N is len(indices) of int showing the correspondance in th following way the first colum is the current index of tracks1 the second column is the corresponding index in tracks2 Examples ---------- >>> import numpy as np >>> import dipy.tracking.learning as tl >>> A=np.array([[0,0,0],[1,1,1],[2,2,2]]) >>> B=np.array([[1,0,0],[2,0,0],[3,0,0]]) >>> C=np.array([[0,0,-1],[0,0,-2],[0,0,-3]]) >>> bundle1=[A,B,C] >>> bundle2=[B,A] >>> indices=[0,1] >>> indices2=indices >>> arr=tl.detect_corresponding_tracks_plus(indices,bundle1,indices2,bundle2) Notes ------- To find the corresponding tracks we use mam_distances with 'avg' option. Then we calculate the argmin of all the calculated distances and return it for every index. (See 3rd column of arr in the example given below. See also ---------- distances.mam_distances ''' li=len(indices) track2track=np.zeros((li,2)) cnt=0 for i in indices: rt=[pf.mam_distances(tracks1[i],t,'avg') for t in tracks2] rt=np.array(rt) track2track[cnt]=np.array([i,indices2[rt.argmin()]]) cnt+=1 return track2track.astype(int) dipy-0.5.0/dipy/tracking/metrics.py000066400000000000000000000574561152576264200173030ustar00rootroot00000000000000''' Metrics for tracks, where tracks are arrays of points ''' import numpy as np from scipy.interpolate import splprep, splev def length(xyz, along=False): ''' Euclidean length of track line This will give length in mm if tracks are expressed in world coordinates. Parameters ------------ xyz : array-like shape (N,3) array representing x,y,z of N points in a track along : bool, optional If True, return array giving cumulative length along track, otherwise (default) return scalar giving total length. Returns --------- L : scalar or array shape (N-1,) scalar in case of `along` == False, giving total length, array if `along` == True, giving cumulative lengths. Examples ---------- >>> from dipy.tracking.metrics import length >>> xyz = np.array([[1,1,1],[2,3,4],[0,0,0]]) >>> expected_lens = np.sqrt([1+2**2+3**2, 2**2+3**2+4**2]) >>> length(xyz) == expected_lens.sum() True >>> len_along = length(xyz, along=True) >>> np.allclose(len_along, expected_lens.cumsum()) True >>> length([]) 0 >>> length([[1, 2, 3]]) 0 >>> length([], along=True) array([0]) ''' xyz = np.asarray(xyz) if xyz.shape[0] < 2: if along: return np.array([0]) return 0 dists = np.sqrt((np.diff(xyz, axis=0)**2).sum(axis=1)) if along: return np.cumsum(dists) return np.sum(dists) def bytes(xyz): ''' Size of track in bytes Parameters ------------ xyz : array-like shape (N,3) array representing x,y,z of N points in a track Returns --------- int : number of bytes ''' return xyz.nbytes def midpoint(xyz): ''' Midpoint of track Parameters ------------ xyz : array-like shape (N,3) array representing x,y,z of N points in a track Returns --------- mp : array shape (3,) Middle point of line, such that, if L is the line length then `np` is the point such that the length xyz[0] to `mp` and from `mp` to xyz[-1] is L/2. If the middle point is not a point in `xyz`, then we take the interpolation between the two nearest `xyz` points. If `xyz` is empty, return a ValueError Examples ----------- >>> from dipy.tracking.metrics import midpoint >>> midpoint([]) Traceback (most recent call last): ... ValueError: xyz array cannot be empty >>> midpoint([[1, 2, 3]]) array([1, 2, 3]) >>> xyz = np.array([[1,1,1],[2,3,4]]) >>> midpoint(xyz) array([ 1.5, 2. , 2.5]) >>> xyz = np.array([[0,0,0],[1,1,1],[2,2,2]]) >>> midpoint(xyz) array([ 1., 1., 1.]) >>> xyz = np.array([[0,0,0],[1,0,0],[3,0,0]]) >>> midpoint(xyz) array([ 1.5, 0. , 0. ]) >>> xyz = np.array([[0,9,7],[1,9,7],[3,9,7]]) >>> midpoint(xyz) array([ 1.5, 9. , 7. ]) ''' xyz = np.asarray(xyz) n_pts = xyz.shape[0] if n_pts == 0: raise ValueError('xyz array cannot be empty') if n_pts == 1: return xyz.copy().squeeze() cumlen = np.zeros(n_pts) cumlen[1:] = length(xyz, along=True) midlen=cumlen[-1]/2.0 ind=np.where((cumlen-midlen)>0)[0][0] len0=cumlen[ind-1] len1=cumlen[ind] Ds=midlen-len0 Lambda = Ds/(len1-len0) return Lambda*xyz[ind]+(1-Lambda)*xyz[ind-1] def center_of_mass(xyz): ''' Center of mass of streamline Parameters ------------ xyz : array-like shape (N,3) array representing x,y,z of N points in a track Returns --------- com : array shape (3,) center of mass of streamline Examples ---------- >>> from dipy.tracking.metrics import center_of_mass >>> center_of_mass([]) Traceback (most recent call last): ... ValueError: xyz array cannot be empty >>> center_of_mass([[1,1,1]]) array([ 1., 1., 1.]) >>> xyz = np.array([[0,0,0],[1,1,1],[2,2,2]]) >>> center_of_mass(xyz) array([ 1., 1., 1.]) ''' xyz = np.asarray(xyz) if xyz.size == 0: raise ValueError('xyz array cannot be empty') return np.mean(xyz,axis=0) def magn(xyz,n=1): ''' magnitude of vector ''' mag=np.sum(xyz**2,axis=1)**0.5 imag=np.where(mag==0) mag[imag]=np.finfo(float).eps if n>1: return np.tile(mag,(n,1)).T return mag.reshape(len(mag),1) def frenet_serret(xyz): r''' Frenet-Serret Space Curve Invariants Calculates the 3 vector and 2 scalar invariants of a space curve defined by vectors r = (x,y,z). If z is omitted (i.e. the array xyz has shape (N,2), then the curve is only 2D (planar), but the equations are still valid. Similar to http://www.mathworks.com/matlabcentral/fileexchange/11169 In the following equations the prime ($'$) indicates differentiation with respect to the parameter $s$ of a parametrised curve $\mathbf{r}(s)$. - $\mathbf{T}=\mathbf{r'}/|\mathbf{r'}|\qquad$ (Tangent vector)} - $\mathbf{N}=\mathbf{T'}/|\mathbf{T'}|\qquad$ (Normal vector) - $\mathbf{B}=\mathbf{T}\times\mathbf{N}\qquad$ (Binormal vector) - $\kappa=|\mathbf{T'}|\qquad$ (Curvature) - $\mathrm{\tau}=-\mathbf{B'}\cdot\mathbf{N}$ (Torsion) Parameters ------------ xyz : array-like shape (N,3) array representing x,y,z of N points in a track Returns --------- T : array shape (N,3) array representing the tangent of the curve xyz N : array shape (N,3) array representing the normal of the curve xyz B : array shape (N,3) array representing the binormal of the curve xyz k : array shape (N,1) array representing the curvature of the curve xyz t : array shape (N,1) array representing the torsion of the curve xyz Examples ---------- Create a helix and calculate its tangent, normal, binormal, curvature and torsion >>> from dipy.tracking import metrics as tm >>> import numpy as np >>> theta = 2*np.pi*np.linspace(0,2,100) >>> x=np.cos(theta) >>> y=np.sin(theta) >>> z=theta/(2*np.pi) >>> xyz=np.vstack((x,y,z)).T >>> T,N,B,k,t=tm.frenet_serret(xyz) ''' xyz = np.asarray(xyz) n_pts = xyz.shape[0] if n_pts == 0: raise ValueError('xyz array cannot be empty') dxyz=np.gradient(xyz)[0] ddxyz=np.gradient(dxyz)[0] #Tangent T=np.divide(dxyz,magn(dxyz,3)) #Derivative of Tangent dT=np.gradient(T)[0] #Normal N = np.divide(dT,magn(dT,3)) #Binormal B = np.cross(T,N) #Curvature k = magn(np.cross(dxyz,ddxyz),1)/(magn(dxyz,1)**3) #Torsion #(In matlab was t=dot(-B,N,2)) t = np.sum(-B*N,axis=1) #return T,N,B,k,t,dxyz,ddxyz,dT return T,N,B,k,t def mean_curvature(xyz): ''' Calculates the mean curvature of a curve Parameters ------------ xyz : array-like shape (N,3) array representing x,y,z of N points in a curve Returns ----------- m : float float representing the mean curvature Examples -------- Create a straight line and a semi-circle and print their mean curvatures >>> from dipy.tracking import metrics as tm >>> import numpy as np >>> x=np.linspace(0,1,100) >>> y=0*x >>> z=0*x >>> xyz=np.vstack((x,y,z)).T >>> m=tm.mean_curvature(xyz) #mean curvature straight line >>> theta=np.pi*np.linspace(0,1,100) >>> x=np.cos(theta) >>> y=np.sin(theta) >>> z=0*x >>> xyz=np.vstack((x,y,z)).T >>> m=tm.mean_curvature(xyz) #mean curvature for semi-circle ''' xyz = np.asarray(xyz) n_pts = xyz.shape[0] if n_pts == 0: raise ValueError('xyz array cannot be empty') dxyz=np.gradient(xyz)[0] ddxyz=np.gradient(dxyz)[0] #Curvature k = magn(np.cross(dxyz,ddxyz),1)/(magn(dxyz,1)**3) return np.mean(k) def mean_orientation(xyz): ''' Calculates the mean orientation of a curve Parameters ------------ xyz : array-like shape (N,3) array representing x,y,z of N points in a curve Returns ----------- m : float float representing the mean orientation ''' xyz = np.asarray(xyz) n_pts = xyz.shape[0] if n_pts == 0: raise ValueError('xyz array cannot be empty') dxyz=np.gradient(xyz)[0] return np.mean(dxyz,axis=0) def generate_combinations(items, n): """ Combine sets of size n from items Parameters ------------ items : sequence n : int Returns -------- ic : iterator Examples -------- >>> from dipy.tracking.metrics import generate_combinations >>> ic=generate_combinations(range(3),2) >>> for i in ic: print i [0, 1] [0, 2] [1, 2] """ if n == 0: yield [] elif n == 2: #if n=2 non_recursive for i in xrange(len(items)-1): for j in xrange(i+1,len(items)): yield [i,j] else: #if n>2 uses recursion for i in xrange(len(items)): for cc in generate_combinations(items[i+1:], n-1): yield [items[i]] + cc def longest_track_bundle(bundle,sort=False): ''' Return longest track or length sorted track indices in `bundle` If `sort` == True, return the indices of the sorted tracks in the bundle, otherwise return the longest track. Parameters ------------ bundle : sequence of tracks as arrays, shape (N1,3) ... (Nm,3) sort : bool, optional If False (default) return longest track. If True, return length sorted indices for tracks in bundle Returns --------- longest_or_indices : array longest track - shape (N,3) - (if `sort` is False), or indices of length sorted tracks (if `sort` is True) Examples -------- >>> from dipy.tracking.metrics import longest_track_bundle >>> import numpy as np >>> bundle = [np.array([[0,0,0],[2,2,2]]),np.array([[0,0,0],[4,4,4]])] >>> longest_track_bundle(bundle) array([[0, 0, 0], [4, 4, 4]]) >>> longest_track_bundle(bundle,True) array([0, 1]) ''' alllengths=[length(t) for t in bundle] alllengths=np.array(alllengths) if sort: ilongest=alllengths.argsort() return ilongest else: ilongest=alllengths.argmax() return bundle[ilongest] def intersect_sphere(xyz,center,radius): ''' If any segment of the track is intersecting with a sphere of specific center and radius return True otherwise False Parameters ------------ xyz : array, shape (N,3) representing x,y,z of the N points of the track center : array, shape (3,) center of the sphere radius : float radius of the sphere Returns ---------- tf : {True,False} True if track `xyz` intersects sphere >>> from dipy.tracking.metrics import intersect_sphere >>> line=np.array(([0,0,0],[1,1,1],[2,2,2])) >>> sph_cent=np.array([1,1,1]) >>> sph_radius = 1 >>> intersect_sphere(line,sph_cent,sph_radius) True Notes ----- The ray to sphere intersection method used here is similar with http://local.wasp.uwa.edu.au/~pbourke/geometry/sphereline/ http://local.wasp.uwa.edu.au/~pbourke/geometry/sphereline/source.cpp we just applied it for every segment neglecting the intersections where the intersecting points are not inside the segment ''' center=np.array(center) #print center lt=xyz.shape[0] for i in xrange(lt-1): #first point x1=xyz[i] #second point x2=xyz[i+1] #do the calculations as given in the Notes x=x2-x1 a=np.inner(x,x) x1c=x1-center b=2*np.inner(x,x1c) c=np.inner(center,center)+np.inner(x1,x1)-2*np.inner(center,x1) - radius**2 bb4ac =b*b-4*a*c #print 'bb4ac',bb4ac if abs(a) 0: #two intersection points p1 and p2 mu=(-b+np.sqrt(bb4ac))/(2*a) p1=x1+mu*x mu=(-b-np.sqrt(bb4ac))/(2*a) p2=x1+mu*x #check if points are inside the line segment #print 'p1,p2',p1,p2 if np.inner(p1-x1,p1-x1) <= a or np.inner(p2-x1,p2-x1) <= a: return True return False def inside_sphere(xyz,center,radius): r''' If any point of the track is inside a sphere of a specified center and radius return True otherwise False. Mathematicaly this can be simply described by $|x-c|\le r$ where $x$ a point $c$ the center of the sphere and $r$ the radius of the sphere. Parameters ------------- xyz : array, shape (N,3) representing x,y,z of the N points of the track center : array, shape (3,) center of the sphere radius : float radius of the sphere Returns ---------- tf : {True,False} Examples -------- >>> from dipy.tracking.metrics import inside_sphere >>> line=np.array(([0,0,0],[1,1,1],[2,2,2])) >>> sph_cent=np.array([1,1,1]) >>> sph_radius = 1 >>> inside_sphere(line,sph_cent,sph_radius) True ''' return (np.sqrt(np.sum((xyz-center)**2,axis=1))<=radius).any()==True def inside_sphere_points(xyz,center,radius): ''' If a track intersects with a sphere of a specified center and radius return the points that are inside the sphere otherwise False. Mathematicaly this can be simply described by $|x-c| \le r$ where $x$ a point $c$ the center of the sphere and $r$ the radius of the sphere. Parameters ------------ xyz : array, shape (N,3) representing x,y,z of the N points of the track center : array, shape (3,) center of the sphere radius : float radius of the sphere Returns --------- xyzn : array, shape(M,3) array representing x,y,z of the M points inside the sphere Examples ---------- >>> from dipy.tracking.metrics import inside_sphere_points >>> line=np.array(([0,0,0],[1,1,1],[2,2,2])) >>> sph_cent=np.array([1,1,1]) >>> sph_radius = 1 >>> inside_sphere_points(line,sph_cent,sph_radius) array([[1, 1, 1]]) ''' return xyz[(np.sqrt(np.sum((xyz-center)**2,axis=1))<=radius)] def spline(xyz,s=3,k=2,nest=-1): ''' Generate B-splines as documented in http://www.scipy.org/Cookbook/Interpolation The scipy.interpolate packages wraps the netlib FITPACK routines (Dierckx) for calculating smoothing splines for various kinds of data and geometries. Although the data is evenly spaced in this example, it need not be so to use this routine. Parameters --------------- xyz : array, shape (N,3) array representing x,y,z of N points in 3d space s : float, optional A smoothing condition. The amount of smoothness is determined by satisfying the conditions: sum((w * (y - g))**2,axis=0) <= s where g(x) is the smoothed interpolation of (x,y). The user can use s to control the tradeoff between closeness and smoothness of fit. Larger satisfying the conditions: sum((w * (y - g))**2,axis=0) <= s where g(x) is the smoothed interpolation of (x,y). The user can use s to control the tradeoff between closeness and smoothness of fit. Larger s means more smoothing while smaller values of s indicate less smoothing. Recommended values of s depend on the weights, w. If the weights represent the inverse of the standard-deviation of y, then a: good s value should be found in the range (m-sqrt(2*m),m+sqrt(2*m)) where m is the number of datapoints in x, y, and w. k : int, optional Degree of the spline. Cubic splines are recommended. Even values of k should be avoided especially with a small s-value. for the same set of data. If task=-1 find the weighted least square spline for a given set of knots, t. nest : None or int, optional An over-estimate of the total number of knots of the spline to help in determining the storage space. None results in value m+2*k. -1 results in m+k+1. Always large enough is nest=m+k+1. Default is -1. Returns ---------- xyzn : array, shape (M,3) Examples ---------- >>> import numpy as np >>> t=np.linspace(0,1.75*2*np.pi,100)# make ascending spiral in 3-space >>> x = np.sin(t) >>> y = np.cos(t) >>> z = t >>> x+= np.random.normal(scale=0.1, size=x.shape) # add noise >>> y+= np.random.normal(scale=0.1, size=y.shape) >>> z+= np.random.normal(scale=0.1, size=z.shape) >>> xyz=np.vstack((x,y,z)).T >>> xyzn=spline(xyz,3,2,-1) >>> len(xyzn) > len(xyz) True See also ---------- From scipy documentation scipy.interpolate.splprep and scipy.interpolate.splev ''' # find the knot points tckp,u = splprep([xyz[:,0],xyz[:,1],xyz[:,2]],s=s,k=k,nest=nest) # evaluate spline, including interpolated points xnew,ynew,znew = splev(np.linspace(0,1,400),tckp) return np.vstack((xnew,ynew,znew)).T def startpoint(xyz): ''' First point of the track Parameters ------------- xyz: array, shape(N,3) representing the track Returns --------- sp: array, shape(3,) first track point Examples ---------- >>> from dipy.tracking.metrics import startpoint >>> import numpy as np >>> theta=np.pi*np.linspace(0,1,100) >>> x=np.cos(theta) >>> y=np.sin(theta) >>> z=0*x >>> xyz=np.vstack((x,y,z)).T >>> sp=startpoint(xyz) >>> sp.any()==xyz[0].any() True ''' return xyz[0] def endpoint(xyz): ''' Parameters ------------- xyz : array, shape(N,3) representing the track Returns --------- ep : array, shape(3,) first track point Examples ---------- >>> from dipy.tracking.metrics import endpoint >>> import numpy as np >>> theta=np.pi*np.linspace(0,1,100) >>> x=np.cos(theta) >>> y=np.sin(theta) >>> z=0*x >>> xyz=np.vstack((x,y,z)).T >>> ep=endpoint(xyz) >>> ep.any()==xyz[-1].any() True ''' return xyz[-1] def arbitrarypoint(xyz,distance): ''' Select an arbitrary point along distance on the track (curve) Parameters ------------ xyz : array-like shape (N,3) array representing x,y,z of N points in a track distance : float float representing distance travelled from the xyz[0] point of the curve along the curve. Returns --------- ap : array shape (3,) arbitrary point of line, such that, if the arbitrary point is not a point in `xyz`, then we take the interpolation between the two nearest `xyz` points. If `xyz` is empty, return a ValueError Examples ----------- >>> import numpy as np >>> from dipy.tracking.metrics import arbitrarypoint, length >>> theta=np.pi*np.linspace(0,1,100) >>> x=np.cos(theta) >>> y=np.sin(theta) >>> z=0*x >>> xyz=np.vstack((x,y,z)).T >>> ap=arbitrarypoint(xyz,length(xyz)/3) ''' xyz = np.asarray(xyz) n_pts = xyz.shape[0] if n_pts == 0: raise ValueError('xyz array cannot be empty') if n_pts == 1: return xyz.copy().squeeze() cumlen = np.zeros(n_pts) cumlen[1:] = length(xyz, along=True) if cumlen[-1]0)[0][0] len0=cumlen[ind-1] len1=cumlen[ind] Ds=distance-len0 Lambda = Ds/(len1-len0) return Lambda*xyz[ind]+(1-Lambda)*xyz[ind-1] def _extrap(xyz,cumlen,distance): ''' Helper function for extrapolate ''' ind=np.where((cumlen-distance)>0)[0][0] len0=cumlen[ind-1] len1=cumlen[ind] Ds=distance-len0 Lambda = Ds/(len1-len0) return Lambda*xyz[ind]+(1-Lambda)*xyz[ind-1] def downsample(xyz,n_pols=3): ''' downsample for a specific number of points along the curve/track Uses the length of the curve. It works in a similar fashion to midpoint and arbitrarypoint but it also reduces the number of segments of a track. Parameters ------------ xyz : array-like shape (N,3) array representing x,y,z of N points in a track n_pol : int integer representing number of points (poles) we need along the curve. Returns --------- xyz2 : array shape (M,3) array representing x,y,z of M points that where extrapolated. M should be equal to n_pols Examples ----------- >>> import numpy as np >>> # a semi-circle >>> theta=np.pi*np.linspace(0,1,100) >>> x=np.cos(theta) >>> y=np.sin(theta) >>> z=0*x >>> xyz=np.vstack((x,y,z)).T >>> xyz2=downsample(xyz,3) >>> # a cosine >>> x=np.pi*np.linspace(0,1,100) >>> y=np.cos(theta) >>> z=0*y >>> xyz=np.vstack((x,y,z)).T >>> xyz2=downsample(xyz,3) >>> len(xyz2) 3 >>> xyz3=downsample(xyz,10) >>> len(xyz3) 10 ''' xyz = np.asarray(xyz) n_pts = xyz.shape[0] if n_pts == 0: raise ValueError('xyz array cannot be empty') if n_pts == 1: return xyz.copy().squeeze() cumlen = np.zeros(n_pts) cumlen[1:] = length(xyz, along=True) step=cumlen[-1]/(n_pols-1) if cumlen[-1]>> import numpy as np >>> from dipy.tracking.metrics import principal_components >>> theta=np.pi*np.linspace(0,1,100) >>> x=np.cos(theta) >>> y=np.sin(theta) >>> z=0*x >>> xyz=np.vstack((x,y,z)).T >>> va, ve = principal_components(xyz) >>> np.allclose(va, [0.51010101, 0.09883545, 0]) True ''' C=np.cov(xyz.T) va,ve=np.linalg.eig(C) return va,ve def midpoint2point(xyz,p): ''' Calculate distance from midpoint of a curve to arbitrary point p Parameters ------------- xyz : array-like shape (N,3) array representing x,y,z of N points in a track p : array shape (3,) array representing an arbitrary point with x,y,z coordinates in space. Returns --------- d : float a float number representing Euclidean distance Examples ----------- >>> import numpy as np >>> from dipy.tracking.metrics import midpoint2point, midpoint >>> theta=np.pi*np.linspace(0,1,100) >>> x=np.cos(theta) >>> y=np.sin(theta) >>> z=0*x >>> xyz=np.vstack((x,y,z)).T >>> dist=midpoint2point(xyz,np.array([0,0,0])) ''' mid=midpoint(xyz) return np.sqrt(np.sum((xyz-mid)**2)) if __name__ == "__main__": pass dipy-0.5.0/dipy/tracking/propagation.py000066400000000000000000000134361152576264200201460ustar00rootroot00000000000000import numpy as np from dipy.tracking.propspeed import eudx_both_directions from dipy.tracking.metrics import length from dipy.data import get_sphere class EuDX(object): ''' Euler Delta Crossings Generates tracks with termination criteria defined by a delta function [1]_ and it has similarities with FACT algorithm [2]_ and Basser's method but uses trilinear interpolation. Can be used with any reconstruction method as DTI, DSI, QBI, GQI which can calculate an orientation distribution function and find the local peaks of that function. For example a single tensor model can give you only one peak a dual tensor model 2 peaks and quantitative anisotropy method as used in GQI can give you 3,4,5 or even more peaks. The parameters of the delta function are checking thresholds for the direction propagation magnitude and the angle of propagation. A specific number of seeds is defined randomly and then the tracks are generated for that seed if the delta function returns true. Trilinear interpolation is being used for defining the weights of the propagation. References ------------ .. [1] Yeh. et al. Generalized Q-Sampling Imaging, TMI 2010. .. [2] Mori et al. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 1999. ''' def __init__(self, a, ind, seeds=10000, odf_vertices=None, a_low=0.0239, step_sz=0.5, ang_thr=60., length_thr=0., total_weight=.5): ''' Euler integration with multiple stopping criteria and supporting multiple peaks Parameters ------------ a : array, shape(x,y,z,Np) magnitude of the peak of a scalar anisotropic function e.g. QA (quantitative anisotropy) or a different function of shape(x,y,z) e.g FA or GFA. ind : array, shape(x,y,z,Np) indices of orientations of the scalar anisotropic peaks found on the resampling sphere seeds : int or sequence, optional number of random seeds or list of seeds odf_vertices : None or ndarray, optional sphere points which define a discrete representation of orientations for the peaks, the same for all voxels. None results in a_low : float, optional low threshold for QA(typical 0.023) or FA(typical 0.2) or any other anisotropic function step_sz : float, optional euler propagation step size ang_thr : float, optional if turning angle is bigger than this threshold then tracking stops. length_thr: float, optional total_weight : float, optional total weighting threshold Examples -------- >>> import nibabel as nib >>> from dipy.reconst.dti import Tensor >>> from dipy.data import get_data >>> fimg,fbvals,fbvecs=get_data('small_101D') >>> img=nib.load(fimg) >>> affine=img.get_affine() >>> bvals=np.loadtxt(fbvals) >>> gradients=np.loadtxt(fbvecs).T >>> data=img.get_data() >>> ten=Tensor(data,bvals,gradients,thresh=50) >>> eu=EuDX(a=ten.fa(),ind=ten.ind(),seeds=100,a_low=.2) >>> tracks=[e for e in eu] Notes ------- This works as an iterator class because otherwise it could fill your entire RAM if you generate many tracks. Something very common as you can easily generate millions of tracks if you have many seeds. ''' self.a=a.copy() self.ind=ind.copy() self.a_low=a_low self.ang_thr=ang_thr self.step_sz=step_sz self.length_thr=length_thr self.total_weight=total_weight if len(self.a.shape)==3: self.a.shape=self.a.shape+(1,) self.ind.shape=self.ind.shape+(1,) #store number of maximum peacks x,y,z,g=self.a.shape self.Np=g tlist=[] if odf_vertices==None: vertices, faces = get_sphere('symmetric362') self.odf_vertices = vertices ''' print 'Shapes' print 'a',self.a.shape, self.a.dtype print 'ind',self.ind.shape, self.ind.dtype print 'odf_vertices',self.odf_vertices.shape, self.odf_vertices.dtype ''' try: if len(seeds)>0: self.seed_list=seeds self.seed_no=len(seeds) except TypeError: self.seed_no=seeds self.seed_list=None self.ind=self.ind.astype(np.double) def __iter__(self): ''' This is were all the fun starts ''' x,y,z,g=self.a.shape #for all seeds for i in range(self.seed_no): if self.seed_list==None: rx=(x-1)*np.random.rand() ry=(y-1)*np.random.rand() rz=(z-1)*np.random.rand() seed=np.ascontiguousarray(np.array([rx,ry,rz]),dtype=np.float64) else: seed=np.ascontiguousarray(self.seed_list[i],dtype=np.float64) #for all peaks for ref in range(self.a.shape[-1]): #propagate up and down track =eudx_both_directions(seed.copy(),ref,self.a,self.ind,self.odf_vertices,self.a_low,self.ang_thr,self.step_sz,self.total_weight) if track == None: pass else: #return a track from that seed if length(track)>self.length_thr: yield track dipy-0.5.0/dipy/tracking/propspeed.pyx000066400000000000000000000346161152576264200200170ustar00rootroot00000000000000# A type of -*- python -*- file """ Track propagation performance functions """ # cython: profile=True # cython: embedsignature=True cimport cython import numpy as np cimport numpy as cnp cdef extern from "math.h" nogil: double floor(double x) float sqrt(float x) float fabs(float x) double log2(double x) double cos(double x) double sin(double x) float acos(float x ) bint isnan(double x) double sqrt(double x) DEF PI=3.1415926535897931 DEF PEAK_NO=5 # initialize numpy runtime cnp.import_array() #numpy pointers cdef inline float* asfp(cnp.ndarray pt): return pt.data cdef inline double* asdp(cnp.ndarray pt): return pt.data @cython.cdivision(True) cdef long offset(long *indices,long *strides,int lenind, int typesize) nogil: ''' Very general way to access any element of any ndimensional numpy array using cython. Parameters ------------ indices : long * (int64 *), indices of the array which we want to find the offset strides : long * strides lenind : int, len(indices) typesize : int, number of bytes for data type e.g. if double is 8 if int32 is 4 Returns ---------- offset : integer, offset from 0 pointer in memory normalized by dtype ''' cdef int i cdef long summ=0 for i from 0<=itypesize return summ def ndarray_offset(cnp.ndarray[long, ndim=1] indices, \ cnp.ndarray[long, ndim=1] strides,int lenind, int typesize): ''' find offset in an ndarray using strides Parameters ---------- indices : array, shape(N,), indices of the array which we want to find the offset strides : array, shape(N,), strides lenind : int, len(indices) typesize : int, number of bytes for data type e.g. if double is 8 if int32 is 4 Returns ------- offset : integer, offset from 0 pointer in memory normalized by dtype Examples -------- >>> import numpy as np >>> from dipy.tracking.propspeed import ndarray_offset >>> I=np.array([1,1]) >>> A=np.array([[1,0,0],[0,2,0],[0,0,3]]) >>> S=np.array(A.strides) >>> ndarray_offset(I,S,2,8) 4 >>> A.ravel()[4]==A[1,1] True ''' return offset(indices.data,strides.data,lenind, typesize) cdef void _trilinear_interpolation(double *X, double *W, long *IN) nogil: ''' interpolate in 3d volumes given point X Returns ------- W : weights IN : indices of the volume ''' cdef double Xf[3],d[3],nd[3] cdef long i #define the rectangular box where every corner is a neighboring voxel (assuming center) #!!! this needs to change for the affine case for i from 0<=i<3: Xf[i]=floor(X[i]) d[i]=X[i]-Xf[i] nd[i]=1-d[i] #weights #the weights are actualy the volumes of the 8 smaller boxes that define the initial rectangular box #for more on trilinear have a look here #http://en.wikipedia.org/wiki/Trilinear_interpolation #http://local.wasp.uwa.edu.au/~pbourke/miscellaneous/interpolation/index.html W[0]=nd[0] * nd[1] * nd[2] W[1]= d[0] * nd[1] * nd[2] W[2]=nd[0] * d[1] * nd[2] W[3]=nd[0] * nd[1] * d[2] W[4]= d[0] * d[1] * nd[2] W[5]=nd[0] * d[1] * d[2] W[6]= d[0] * nd[1] * d[2] W[7]= d[0] * d[1] * d[2] #indices #the indices give you the indices of the neighboring voxels (the corners of the box) e.g. the qa coordinates IN[0] =Xf[0]; IN[1] =Xf[1]; IN[2] =Xf[2] IN[3] =Xf[0]+1; IN[4] =Xf[1]; IN[5] =Xf[2] IN[6] =Xf[0]; IN[7] =Xf[1]+1; IN[8] =Xf[2] IN[9] =Xf[0]; IN[10]=Xf[1]; IN[11]=Xf[2]+1 IN[12]=Xf[0]+1; IN[13]=Xf[1]+1; IN[14]=Xf[2] IN[15]=Xf[0]; IN[16]=Xf[1]+1; IN[17]=Xf[2]+1 IN[18]=Xf[0]+1; IN[19]=Xf[1]; IN[20]=Xf[2]+1 IN[21]=Xf[0]+1; IN[22]=Xf[1]+1; IN[23]=Xf[2]+1 return cdef long _nearest_direction(double* dx,double* qa,\ double *ind,long peaks,double *odf_vertices,\ double qa_thr, double ang_thr,\ double *direction) nogil: ''' Give the nearest direction to a point and also check for the threshold and the angle Parameters ------------ dx : array, shape(3,), as float, moving direction of the current tracking qa : array, shape(Np,), float, quantitative anisotropy matrix, where Np the number of peaks, found using self.Np ind : array, shape(Np,), float, index of the track orientation odf_vertices : array, shape(N,3), float, sampling directions on the sphere qa_thr : float, threshold for QA, we want everything higher than this threshold ang_thr : float, theshold, we only select fiber orientation with this range Returns -------- delta : bool, delta funtion, if 1 we give it weighting if it is 0 we don't give any weighting direction : array, shape(3,), the fiber orientation to be consider in the interpolation ''' cdef: double max_dot=0 double angl,curr_dot double odfv[3] long i,j,max_doti=0 #calculate the cos with radians angl=cos((PI*ang_thr)/180.) #if the maximum peak is lower than the threshold then there is no point continuing tracking if qa[0] <= qa_thr: return 0 #for all peaks find the minimum angle between odf_vertices and dx for i from 0<=iind[i]+j] #calculate the absolute dot product between dx and odf_vertices curr_dot = dx[0]*odfv[0]+dx[1]*odfv[1]+dx[2]*odfv[2] if curr_dot < 0: #abs check curr_dot = -curr_dot #maximum dot means minimum angle #store tha maximum dot and the corresponding index from the neighboring voxel in maxdoti if curr_dot > max_dot: max_dot=curr_dot max_doti = i #if maxdot smaller than our angular *dot* threshold stop tracking if max_dot < angl: return 0 #copy the odf_vertices for the voxel qa indices which have the smaller angle for j from 0<=j<3: odfv[j]=odf_vertices[3*ind[max_doti]+j] #if the dot product is negative then return the opposite direction otherwise return the same direction if dx[0]*odfv[0]+dx[1]*odfv[1]+dx[2]*odfv[2] < 0: for j from 0<=j<3: direction[j]=-odf_vertices[3*ind[max_doti]+j] return 1 else: for j from 0<=j<3: direction[j]= odf_vertices[3*ind[max_doti]+j] return 1 @cython.cdivision(True) cdef long _propagation_direction(double *point,double* dx,double* qa,\ double *ind, double *odf_vertices,\ double qa_thr, double ang_thr,\ long *qa_shape,long* strides,\ double *direction,double total_weight) nogil: cdef: double total_w=0 #total weighting useful for interpolation double delta=0 #store delta function (stopping function) result double new_direction[3] #new propagation direction double w[8],qa_tmp[PEAK_NO],ind_tmp[PEAK_NO] long index[24],i,j,m,xyz[4] double normd long peaks=qa_shape[3]#number of allowed peaks e.g. for fa is 1 for gqi.qa is 5 #calculate qa & ind of each of the 8 neighboring voxels #to do that we use trilinear interpolation and return the weights #and the indices for the weights i.e. xyz in qa[x,y,z] _trilinear_interpolation(point,w,index) #check if you are outside of the volume for i from 0<=i<3: new_direction[i]=0 if index[7*3+i] >= qa_shape[i] or index[i] < 0: return 0 #for every weight sum the total weighting for m from 0<=m<8: for i from 0<=i<3: xyz[i]=index[m*3+i] #fill qa_tmp and ind_tmp for j from 0<=jxyz,strides,4,8) qa_tmp[j]=qa[off] ind_tmp[j]=ind[off] #return the nearest direction by searching in all peaks delta=_nearest_direction(dx,qa_tmp,ind_tmp,peaks,odf_vertices,\ qa_thr, ang_thr,direction) #if delta is 0 then that means that there was no good direction (obeying the thresholds) #from that neighboring voxel, so this voxel is not adding to the total weight if delta==0: continue #add in total total_w+=w[m] for i from 0<=i<3: new_direction[i]+=w[m]*direction[i] #if less than half the volume is time to stop propagating if total_w < total_weight: #termination return 0 #all good return normalized weighted next direction normd=new_direction[0]**2+new_direction[1]**2+new_direction[2]**2 normd=1/sqrt(normd) for i from 0<=i<3: direction[i]=new_direction[i]*normd return 1 cdef long _initial_direction(double* seed,double *qa,\ double* ind, double* odf_vertices,\ double qa_thr, long* strides, long ref,\ double* direction) nogil: ''' First direction that we get from a seeding point ''' cdef: long point[4],off long i double qa_tmp,ind_tmp #very tricky/cool addition/flooring that helps create a valid #neighborhood (grid) for the trilinear interpolation to run smoothly #find the index for qa for i from 0<=i<3: point[i]=floor(seed[i]+.5) point[3]=ref #find the offcet in memory to access the qa value off=offset(point,strides,4,8) qa_tmp=qa[off] #check for scalar threshold if qa_tmp < qa_thr: return 0 else: #find the correct direction from the indices ind_tmp=ind[off] #similar to ind[point] in numpy syntax #return initial direction through odf_vertices by ind for i from 0<=i<3: direction[i]=odf_vertices[3*ind_tmp+i] return 1 def eudx_both_directions(cnp.ndarray[double,ndim=1] seed,\ long ref,\ cnp.ndarray[double,ndim=4] qa,\ cnp.ndarray[double,ndim=4] ind,\ cnp.ndarray[double,ndim=2] odf_vertices,\ double qa_thr,double ang_thr,double step_sz,double total_weight): ''' Parameters ------------ seed : array, shape(3,), point where the tracking starts ref : long int, which peak to follow first qa : array, shape(Np,), float, quantitative anisotropy matrix, where Np the number of peaks, found using self.Np ind : array, shape(Np,), float, index of the track orientation total_weight : double Returns ------- track : array, shape(N,3) ''' cdef: double *ps=seed.data double *pqa=qa.data double *pin=ind.data double *pverts=odf_vertices.data long *pstr=qa.strides long *qa_shape=qa.shape long *pvstr=odf_vertices.strides long d,i,j double direction[3],dx[3],idirection[3],ps2[3],tmp,ftmp """ #don't track seeds on the boundaries for i from 0<=i<3: if seed[i] ==qa_shape[i]-1 or seed[i] == 0: return None """ d=_initial_direction(ps,pqa,pin,pverts,qa_thr,pstr,ref,idirection) if d==0: return None for i from 0<=i<3: #store the initial direction dx[i]=idirection[i] #ps2 is for downwards and ps for upwards propagation ps2[i]=ps[i] point=seed.copy() track = [] track.append(point.copy()) #track towards one direction while d: d= _propagation_direction(ps,dx,pqa,pin,pverts,qa_thr,\ ang_thr,qa_shape,pstr,direction,total_weight) if d==0: break #update the track for i from 0<=i<3: dx[i]=direction[i] #check for boundaries tmp=ps[i]+step_sz*dx[i] #ftmp=floor(tmp+.5) if ftmp > qa_shape[i]-1 or tmp < 0.: d=0 break #propagate ps[i]=tmp point[i]=ps[i] #print('point up',point) if d==1: track.append(point.copy()) d=1 for i from 0<=i<3: dx[i]=-idirection[i] #track towards the opposite direction while d: d= _propagation_direction(ps2,dx,pqa,pin,pverts,qa_thr,\ ang_thr,qa_shape,pstr,direction,total_weight) if d==0: break #update the track for i from 0<=i<3: dx[i]=direction[i] #check for boundaries tmp=ps2[i]+step_sz*dx[i] #ftmp=floor(tmp+.5) if tmp > qa_shape[i]-1 or tmp < 0.: d=0 break #propagate ps2[i]=tmp point[i]=ps2[i] #to be changed #add track point if d==1: track.insert(0,point.copy()) #prepare to return final track for the current seed tmp_track=np.array(track,dtype=np.float32) #some times one of the ends takes small negative values #needs to be investigated further """ try: if tmp_track[0,0]<0 or tmp_track[0,1] or tmp_track[0,2]: tmp_track=np.delete(tmp_track,0,0) except: pass try: if tmp_track[-1,0]<0 or tmp_track[-1,1] or tmp_track[-1,2]: tmp_track=np.delete(tmp_track,len(tmp_track)-1,0) except: pass """ #return track for the current seed point and ref return tmp_track dipy-0.5.0/dipy/tracking/tests/000077500000000000000000000000001152576264200164045ustar00rootroot00000000000000dipy-0.5.0/dipy/tracking/tests/__init__.py000066400000000000000000000001211152576264200205070ustar00rootroot00000000000000# Test callable from numpy.testing import Tester test = Tester().test del Tester dipy-0.5.0/dipy/tracking/tests/test_distances.py000066400000000000000000000210421152576264200217710ustar00rootroot00000000000000import numpy as np import nose from nose.tools import assert_true, assert_false, assert_equal, assert_almost_equal from numpy.testing import assert_array_equal, assert_array_almost_equal from dipy.tracking import metrics as tm from dipy.tracking import distances as pf def test_LSCv2(): xyz1=np.array([[1,0,0],[2,0,0],[3,0,0]],dtype='float32') xyz2=np.array([[1,0,0],[1,2,0],[1,3,0]],dtype='float32') xyz3=np.array([[1.1,0,0],[1,2,0],[1,3,0]],dtype='float32') xyz4=np.array([[1,0,0],[2.1,0,0],[3,0,0]],dtype='float32') xyz5=np.array([[100,0,0],[200,0,0],[300,0,0]],dtype='float32') xyz6=np.array([[0,20,0],[0,40,0],[300,50,0]],dtype='float32') T=[xyz1,xyz2,xyz3,xyz4,xyz5,xyz6] C=pf.local_skeleton_clustering(T,0.2) #print C #print len(C) C2=pf.local_skeleton_clustering_3pts(T,0.2) #print C2 #print len(C2) #""" for i in range(40): xyz=np.random.rand(3,3).astype('f4') T.append(xyz) from time import time t1=time() C3=pf.local_skeleton_clustering(T,.5) t2=time() print t2-t1 print len(C3) t1=time() C4=pf.local_skeleton_clustering_3pts(T,.5) t2=time() print t2-t1 print len(C4) for c in C3: assert_equal(np.sum(C3[c]['hidden']-C4[c]['hidden']),0) T2=[] for i in range(10**4): xyz=np.random.rand(10,3).astype('f4') T2.append(xyz) t1=time() C5=pf.local_skeleton_clustering(T2,.5) t2=time() print t2-t1 print len(C5) from dipy.data import get_data from nibabel import trackvis as tv try: from dipy.viz import fvtk except ImportError, e: raise nose.plugins.skip.SkipTest( 'Fails to import dipy.viz due to %s' % str(e)) streams,hdr=tv.read(get_data('fornix')) T3=[tm.downsample(s[0],6) for s in streams] print 'lenT3',len(T3) C=pf.local_skeleton_clustering(T3,10.) print 'lenC',len(C) """ r=fvtk.ren() colors=np.zeros((len(C),3)) for c in C: color=np.random.rand(3) for i in C[c]['indices']: fvtk.add(r,fvtk.line(T3[i],color)) colors[c]=color fvtk.show(r) fvtk.clear(r) skeleton=[] def width(w): if w<1: return 1 else: return w for c in C: bundle=[T3[i] for i in C[c]['indices']] si,s=pf.most_similar_track_mam(bundle,'avg') skeleton.append(bundle[si]) fvtk.label(r,text=str(len(bundle)),pos=(bundle[si][-1]),scale=(2,2,2)) fvtk.add(r,fvtk.line(skeleton,colors,opacity=1,linewidth=width(len(bundle)/10.))) fvtk.show(r) """ def test_bundles_distances_mam(): xyz1A = np.array([[0,0,0],[1,0,0],[2,0,0],[3,0,0]],dtype='float32') xyz2A = np.array([[0,1,1],[1,0,1],[2,3,-2]],dtype='float32') xyz1B = np.array([[-1,0,0],[2,0,0],[2,3,0],[3,0,0]],dtype='float32') tracksA = [xyz1A, xyz2A] tracksB = [xyz1B, xyz1A, xyz2A] for metric in ('avg', 'min', 'max'): DM2 = pf.bundles_distances_mam(tracksA, tracksB, metric=metric) def test_mam_distances(): xyz1 = np.array([[0,0,0],[1,0,0],[2,0,0],[3,0,0]]) xyz2 = np.array([[0,1,1],[1,0,1],[2,3,-2]]) # dm=array([[ 2, 2, 17], [ 3, 1, 14], [6, 2, 13], [11, 5, 14]]) # this is the distance matrix between points of xyz1 # and points of xyz2 xyz1=xyz1.astype('float32') xyz2=xyz2.astype('float32') zd2 = pf.mam_distances(xyz1,xyz2) assert_almost_equal( zd2[0], 1.76135602742) def test_approx_ei_traj(): segs=100 t=np.linspace(0,1.75*2*np.pi,segs) x =t y=5*np.sin(5*t) z=np.zeros(x.shape) xyz=np.vstack((x,y,z)).T xyza=pf.approx_polygon_track(xyz) assert_equal(len(xyza), 27) def test_approx_mdl_traj(): t=np.linspace(0,1.75*2*np.pi,100) x = np.sin(t) y = np.cos(t) z = t xyz=np.vstack((x,y,z)).T xyza1 = pf.approximate_mdl_trajectory(xyz,alpha=1.) xyza2 = pf.approximate_mdl_trajectory(xyz,alpha=2.) assert_equal(len(xyza1), 10) assert_equal(len(xyza2), 8) assert_array_almost_equal( xyza1, np.array([[ 0.00000000e+00, 1.00000000e+00, 0.00000000e+00], [ 9.39692621e-01, 3.42020143e-01, 1.22173048e+00], [ 6.42787610e-01, -7.66044443e-01, 2.44346095e+00], [ -5.00000000e-01, -8.66025404e-01, 3.66519143e+00], [ -9.84807753e-01, 1.73648178e-01, 4.88692191e+00], [ -1.73648178e-01, 9.84807753e-01, 6.10865238e+00], [ 8.66025404e-01, 5.00000000e-01, 7.33038286e+00], [ 7.66044443e-01, -6.42787610e-01, 8.55211333e+00], [ -3.42020143e-01, -9.39692621e-01, 9.77384381e+00], [ -1.00000000e+00, -4.28626380e-16, 1.09955743e+01]])) assert_array_almost_equal(xyza2, np.array([[ 0.00000000e+00, 1.00000000e+00, 0.00000000e+00], [ 9.95471923e-01, -9.50560433e-02, 1.66599610e+00], [ -1.89251244e-01, -9.81928697e-01, 3.33199221e+00], [ -9.59492974e-01, 2.81732557e-01, 4.99798831e+00], [ 3.71662456e-01, 9.28367933e-01, 6.66398442e+00], [ 8.88835449e-01, -4.58226522e-01, 8.32998052e+00], [ -5.40640817e-01, -8.41253533e-01, 9.99597663e+00], [ -1.00000000e+00, -4.28626380e-16, 1.09955743e+01]])) def test_point_track_sq_distance(): t=np.array([[0,0,0],[1,1,1],[2,2,2]],dtype='f4') p=np.array([-1,-1.,-1],dtype='f4') assert_equal( pf.point_track_sq_distance_check(t,p,.2**2), False) pf.point_track_sq_distance_check(t,p,2**2), True t=np.array([[0,0,0],[1,0,0],[2,2,0]],dtype='f4') p=np.array([.5,0,0],dtype='f4') assert_equal( pf.point_track_sq_distance_check(t,p,.2**2), True) p=np.array([.5,1,0],dtype='f4') assert_equal( pf.point_track_sq_distance_check(t,p,.2**2), False) def test_track_roi_intersection_check(): roi=np.array([[0,0,0],[1,0,0],[2,0,0]],dtype='f4') t=np.array([[0,0,0],[1,1,1],[2,2,2]],dtype='f4') assert_equal( pf.track_roi_intersection_check(t,roi,1), True) t=np.array([[0,0,0],[1,0,0],[2,2,2]],dtype='f4') assert_equal(pf.track_roi_intersection_check(t,roi,1), True) t=np.array([[1,1,0],[1,0,0],[1,-1,0]],dtype='f4') assert_equal( pf.track_roi_intersection_check(t,roi,1), True) t=np.array([[4,0,0],[4,1,1],[4,2,0]],dtype='f4') assert_equal(pf.track_roi_intersection_check(t,roi,1), False) def test_minimum_distance(): xyz1=np.array([[1,0,0],[2,0,0]],dtype='float32') xyz2=np.array([[3,0,0],[4,0,0]],dtype='float32') assert_equal(pf.minimum_closest_distance(xyz1,xyz2), 1.0) def test_most_similar_mam(): xyz1 = np.array([[0,0,0],[1,0,0],[2,0,0],[3,0,0]],dtype='float32') xyz2 = np.array([[0,1,1],[1,0,1],[2,3,-2]],dtype='float32') xyz3 = np.array([[-1,0,0],[2,0,0],[2,3,0],[3,0,0]],dtype='float32') tracks=[xyz1,xyz2,xyz3] for metric in ('avg', 'min', 'max'): #pf should be much faster and the results equivalent si2,s2=pf.most_similar_track_mam(tracks,metric=metric) def test_cut_plane(): dt = np.dtype(np.float32) refx = np.array([[0,0,0],[1,0,0],[2,0,0],[3,0,0]],dtype=dt) bundlex = [np.array([[0.5,1,0],[1.5,2,0],[2.5,3,0]],dtype=dt), np.array([[0.5,2,0],[1.5,3,0],[2.5,4,0]],dtype=dt), np.array([[0.5,1,1],[1.5,2,2],[2.5,3,3]],dtype=dt), np.array([[-0.5,2,-1],[-1.5,3,-2],[-2.5,4,-3]],dtype=dt)] expected_hit0 = [ [ 1. , 1.5 , 0. , 0.70710683, 0. ], [ 1. , 2.5 , 0. , 0.70710677, 1. ], [ 1. , 1.5 , 1.5 , 0.81649661, 2. ]] expected_hit1 = [ [ 2. , 2.5 , 0. , 0.70710677, 0. ], [ 2. , 3.5 , 0. , 0.70710677, 1. ], [ 2. , 2.5 , 2.5 , 0.81649655, 2. ]] hitx=pf.cut_plane(bundlex,refx) assert_array_almost_equal(hitx[0], expected_hit0) assert_array_almost_equal(hitx[1], expected_hit1) # check that algorithm allows types other than float32 bundlex[0] = np.asarray(bundlex[0], dtype=np.float64) hitx=pf.cut_plane(bundlex,refx) assert_array_almost_equal(hitx[0], expected_hit0) assert_array_almost_equal(hitx[1], expected_hit1) refx = np.asarray(refx, dtype=np.float64) hitx=pf.cut_plane(bundlex,refx) assert_array_almost_equal( hitx[0], expected_hit0) assert_array_almost_equal( hitx[1], expected_hit1) dipy-0.5.0/dipy/tracking/tests/test_learning.py000066400000000000000000000016131152576264200216150ustar00rootroot00000000000000''' Testing track_metrics module ''' import numpy as np from nose.tools import assert_true, assert_false, assert_equal, assert_almost_equal from numpy.testing import assert_array_equal, assert_array_almost_equal from dipy.tracking import metrics as tm from dipy.tracking import distances as td from dipy.tracking import learning as tl def test_det_corr_tracks(): A=np.array([[0,0,0],[1,1,1],[2,2,2]]) B=np.array([[1,0,0],[2,0,0],[3,0,0]]) C=np.array([[0,0,-1],[0,0,-2],[0,0,-3]]) bundle1=[A,B,C] bundle2=[B,A] indices=[0,1] print(A) print(B) print(C) arr=tl.detect_corresponding_tracks(indices,bundle1,bundle2) print(arr) assert_array_equal(arr,np.array([[0, 1],[1, 0]])) indices2=[0,1] arr2=tl.detect_corresponding_tracks_plus(indices,bundle1,indices2,bundle2) print(arr2) assert_array_equal(arr,arr2) dipy-0.5.0/dipy/tracking/tests/test_metrics.py000066400000000000000000000043431152576264200214670ustar00rootroot00000000000000''' Testing track_metrics module ''' from StringIO import StringIO import numpy as np from nose.tools import assert_true, assert_false, assert_equal, assert_almost_equal from numpy.testing import assert_array_equal, assert_array_almost_equal from dipy.tracking import metrics as tm from dipy.tracking import distances as pf def test_splines(): #create a helix t=np.linspace(0,1.75*2*np.pi,100) x = np.sin(t) y = np.cos(t) z = t # add noise x+= np.random.normal(scale=0.1, size=x.shape) y+= np.random.normal(scale=0.1, size=y.shape) z+= np.random.normal(scale=0.1, size=z.shape) xyz=np.vstack((x,y,z)).T # get the B-splines smoothed result xyzn=tm.spline(xyz,3,2,-1) def test_segment_intersection(): xyz=np.array([[1,1,1],[2,2,2],[2,2,2]]) center=[10,4,10] radius=1 assert_equal(tm.intersect_sphere(xyz,center,radius), False) xyz=np.array([[1,1,1],[2,2,2],[3,3,3],[4,4,4]]) center=[10,10,10] radius=2 assert_equal( tm.intersect_sphere(xyz,center,radius), False) xyz=np.array([[1,1,1],[2,2,2],[3,3,3],[4,4,4]]) center=[2.1,2,2.2] radius=2 assert_equal( tm.intersect_sphere(xyz,center,radius), True) def test_normalized_3vec(): vec = [1, 2, 3] l2n = np.sqrt(np.dot(vec, vec)) assert_array_almost_equal(l2n, pf.norm_3vec(vec)) nvec = pf.normalized_3vec(vec) assert_array_almost_equal( np.array(vec) / l2n, nvec) vec = np.array([[1, 2, 3]]) assert_equal(vec.shape, (1, 3)) assert_equal(pf.normalized_3vec(vec).shape, (3,)) def test_inner_3vecs(): vec1 = [1, 2.3, 3] vec2 = [2, 3, 4.3] assert_array_almost_equal(np.inner(vec1, vec2), pf.inner_3vecs(vec1, vec2)) vec2 = [2, -3, 4.3] assert_array_almost_equal(np.inner(vec1, vec2), pf.inner_3vecs(vec1, vec2)) def test_add_sub_3vecs(): vec1 = np.array([1, 2.3, 3]) vec2 = np.array([2, 3, 4.3]) assert_array_almost_equal( vec1 - vec2, pf.sub_3vecs(vec1, vec2)) assert_array_almost_equal( vec1 + vec2, pf.add_3vecs(vec1, vec2)) vec2 = [2, -3, 4.3] assert_array_almost_equal( vec1 - vec2, pf.sub_3vecs(vec1, vec2)) assert_array_almost_equal( vec1 + vec2, pf.add_3vecs(vec1, vec2)) dipy-0.5.0/dipy/tracking/tests/test_propagation.py000066400000000000000000000072331152576264200223450ustar00rootroot00000000000000import os import numpy as np from dipy.data import get_data from dipy.reconst.gqi import GeneralizedQSampling from dipy.reconst.dti import Tensor from dipy.tracking.propagation import EuDX from dipy.tracking.propspeed import ndarray_offset from dipy.tracking.metrics import length import nibabel as ni from nose.tools import assert_true, assert_false, \ assert_equal, assert_raises, assert_almost_equal from numpy.testing import assert_array_equal, assert_array_almost_equal def test_eudx(): #read bvals,gradients and data fimg,fbvals, fbvecs = get_data('small_64D') bvals=np.load(fbvals) gradients=np.load(fbvecs) img =ni.load(fimg) data=img.get_data() print(data.shape) gqs = GeneralizedQSampling(data,bvals,gradients) ten = Tensor(data,bvals,gradients,thresh=50) seed_list=np.dot(np.diag(np.arange(10)),np.ones((10,3))) iT=iter(EuDX(gqs.qa(),gqs.ind(),seeds=seed_list)) T=[] for t in iT: T.append(t) iT2=iter(EuDX(ten.fa(),ten.ind(),seeds=seed_list)) T2=[] for t in iT2: T2.append(t) print('length T ',sum([length(t) for t in T])) print('length T2',sum([length(t) for t in T2])) print(gqs.QA[1,4,8,0]) print(gqs.QA.ravel()[ndarray_offset(np.array([1,4,8,0]),np.array(gqs.QA.strides),4,8)]) assert_almost_equal(gqs.QA[1,4,8,0], gqs.QA.ravel()[ndarray_offset(np.array([1,4,8,0]),np.array(gqs.QA.strides),4,8)]) assert_almost_equal(sum([length(t) for t in T ]) , 70.999996185302734,places=3) assert_almost_equal(sum([length(t) for t in T2]) , 56.999997615814209,places=3) def test_eudx_further(): """ Cause we love testin.. ;-) """ fimg,fbvals,fbvecs=get_data('small_101D') img=ni.load(fimg) affine=img.get_affine() bvals=np.loadtxt(fbvals) gradients=np.loadtxt(fbvecs).T data=img.get_data() ten=Tensor(data,bvals,gradients,thresh=50) x,y,z=data.shape[:3] seeds=np.zeros((10**4,3)) for i in range(10**4): rx=(x-1)*np.random.rand() ry=(y-1)*np.random.rand() rz=(z-1)*np.random.rand() seeds[i]=np.ascontiguousarray(np.array([rx,ry,rz]),dtype=np.float64) #print seeds #""" eu=EuDX(a=ten.fa(),ind=ten.ind(),seeds=seeds,a_low=.2) T=[e for e in eu] #check that there are no negative elements for t in T: assert_equal(np.sum(t.ravel()<0),0) """ for (i,t) in enumerate(T): for row in t: if row[0]<0 or row[1]<0 or row[2]<0: print 'l======' print i,row print t[0] print t[-1] if row[0]>=data.shape[0] or row[1]>=data.shape[1] or row[2]>=data.shape[2]: print 'h======' print i,row print t[0] print t[-1] from dipy.viz import fvtk r=fvtk.ren() fvtk.add(r,fvtk.line(T,fvtk.red)) fvtk.add(r,fvtk.point(seeds,fvtk.green)) fvtk.show(r) """ def uniform_seed_grid(): #read bvals,gradients and data fimg,fbvals, fbvecs = get_data('small_64D') bvals=np.load(fbvals) gradients=np.load(fbvecs) img =ni.load(fimg) data=img.get_data() x,y,z,g=data.shape M=np.mgrid[.5:x-.5:np.complex(0,x),.5:y-.5:np.complex(0,y),.5:z-.5:np.complex(0,z)] M=M.reshape(3,x*y*z).T print(M.shape) print(M.dtype) for m in M: print(m) gqs = GeneralizedQSampling(data,bvals,gradients) iT=iter(EuDX(gqs.QA,gqs.IN,seeds=M)) T=[] for t in iT: T.append(i) print('lenT',len(T)) assert_equal(len(T), 1221) dipy-0.5.0/dipy/tracking/tests/test_track_volumes.py000066400000000000000000000046531152576264200227030ustar00rootroot00000000000000 import numpy as np from nose.tools import assert_true, assert_false, \ assert_equal, assert_raises from numpy.testing import assert_array_equal, assert_array_almost_equal import dipy.tracking.vox2track as tvo def tracks_to_expected(tracks, vol_dims): # simulate expected behavior of module vol_dims = np.array(vol_dims, dtype=np.int32) counts = np.zeros(vol_dims, dtype=np.int32) elements = {} for t_no, t in enumerate(tracks): u_ps = set() ti = np.round(t).astype(np.int32) for p_no, p in enumerate(ti): if np.any(p < 0): p[p<0] = 0 too_high = p >= vol_dims if np.any(too_high): p[too_high] = vol_dims[too_high]-1 p = tuple(p) if p in u_ps: continue u_ps.add(p) val = t_no if counts[p]: elements[p].append(val) else: elements[p] = [val] counts[p] +=1 return counts, elements def test_track_volumes(): # simplest case vol_dims = (1, 2, 3) tracks = ([[0, 0, 0], [0, 1, 1]],) tracks = [np.array(t) for t in tracks] ex_counts, ex_els = tracks_to_expected(tracks, vol_dims) tcs, tes = tvo.track_counts(tracks, vol_dims, [1,1,1]) assert_array_equal(tcs, ex_counts) assert_array_equal( tes, ex_els) # check only counts returned for return_elements=False tcs = tvo.track_counts(tracks, vol_dims, [1,1,1], False) assert_array_equal(tcs, ex_counts) # non-unique points, non-integer points, points outside vol_dims = (5, 10, 15) tracks = ([[-1, 0, 1], [0, 0.1, 0], [1, 1, 1], [1, 1, 1], [2, 2, 2]], [[0.7, 0, 0], [1, 1, 1], [1, 2, 2], [1, 11, 0]]) tracks = [np.array(t) for t in tracks] ex_counts, ex_els = tracks_to_expected(tracks, vol_dims) tcs, tes = tvo.track_counts(tracks, vol_dims, [1,1,1]) assert_array_equal( tcs, ex_counts) assert_array_equal( tes, ex_els) # points with non-unit voxel sizes vox_sizes = [1.4, 2.1, 3.7] float_tracks = [] for t in tracks: float_tracks.append(t * vox_sizes) tcs, tes = tvo.track_counts(float_tracks, vol_dims, vox_sizes) assert_array_equal(tcs, ex_counts) assert_array_equal(tes, ex_els) dipy-0.5.0/dipy/tracking/vox2track.pyx000066400000000000000000000100241152576264200177240ustar00rootroot00000000000000# A type of -*- python -*- file """ Counting incidence of tracks in voxels of volume """ import numpy as np cimport numpy as cnp cdef extern from "math.h": double floor(double x) def track_counts(tracks, vol_dims, vox_sizes, return_elements=True): ''' Counts of points in `tracks` that pass through voxels in volume We find whether a point passed through a track by rounding the mm point values to voxels. For a track that passes through a voxel more than once, we only record counts and elements for the first point in the line that enters the voxel. Parameters ------------ tracks : sequence sequence of tracks. Tracks are ndarrays of shape (N, 3), where N is the number of points in that track, and ``tracks[t][n]`` is the n-th point in the t-th track. Points are of form x, y, z in *mm* coordinates. vol_dim : sequence length 3 volume dimensions in voxels, x, y, z. vox_sizes : sequence length 3 voxel sizes in mm return_elements : {True, False}, optional If True, also return object array with one list per voxel giving track indices and point indices passing through the voxel (see below) Returns --------- tcs : ndarray shape `vol_dim` An array where entry ``tcs[x, y, z]`` is the number of tracks that passed through voxel at voxel coordinate x, y, z tes : ndarray dtype np.object, shape `vol_dim` If `return_elements` is True, we also return an object array with one object per voxel. The objects at each voxel are a list of integers, where the integers are the indices of the track that passed through the voxel. ''' vol_dims = np.asarray(vol_dims).astype(np.int) vox_sizes = np.asarray(vox_sizes).astype(np.double) n_voxels = np.prod(vol_dims) # output track counts array, flattened cdef cnp.ndarray[cnp.int_t, ndim=1] tcs = \ np.zeros((n_voxels,), dtype=np.int) # pointer to output track indices cdef cnp.npy_intp i if return_elements: el_inds = {} # cython numpy pointer to individual track array cdef cnp.ndarray[cnp.float_t, ndim=2] t # cython numpy pointer to point in track array cdef cnp.ndarray[cnp.float_t, ndim=1] in_pt # processed point cdef int out_pt[3] # various temporary loop and working variables cdef int tno, pno, cno cdef cnp.npy_intp el_no, v # fill native C arrays from inputs cdef int vd[3] cdef double vxs[3] for cno in range(3): vd[cno] = vol_dims[cno] vxs[cno] = vox_sizes[cno] # return_elements to C native cdef int ret_elf = return_elements # x slice size (C array ordering) cdef cnp.npy_intp yz = vd[1] * vd[2] for tno in range(len(tracks)): t = tracks[tno].astype(np.float) # set to find unique voxel points in track in_inds = set() # the loop below is time-critical for pno in range(t.shape[0]): in_pt = t[pno] # Round to voxel coordinates, and set coordinates outside # volume to volume edges for cno in range(3): v = floor(in_pt[cno] / vxs[cno] + 0.5) if v < 0: v = 0 elif v >= vd[cno]: v = vd[cno]-1 # last index for this dimension out_pt[cno] = v # calculate element number in flattened tcs array el_no = out_pt[0] * yz + out_pt[1] * vd[2] + out_pt[2] # discard duplicates if el_no in in_inds: continue in_inds.add(el_no) # set elements into object array if ret_elf: key = (out_pt[0], out_pt[1], out_pt[2]) val = tno if tcs[el_no]: el_inds[key].append(val) else: el_inds[key] = [val] # set value into counts tcs[el_no] += 1 if ret_elf: return tcs.reshape(vol_dims), el_inds return tcs.reshape(vol_dims) dipy-0.5.0/dipy/utils/000077500000000000000000000000001152576264200146005ustar00rootroot00000000000000dipy-0.5.0/dipy/utils/__init__.py000066400000000000000000000000421152576264200167050ustar00rootroot00000000000000# code support utilities for dipy dipy-0.5.0/dipy/utils/arrfuncs.py000066400000000000000000000013741152576264200170020ustar00rootroot00000000000000""" Utilities to manipulate numpy arrays """ import sys import numpy as np from nibabel.volumeutils import endian_codes, native_code, swapped_code def as_native_array(arr): """ Return `arr` as native byteordered array If arr is already native byte ordered, return unchanged. If it is opposite endian, then make a native byte ordered copy and return that Parameters ---------- arr : ndarray Returns ------- native_arr : ndarray If `arr` was native order, this is just `arr`. Otherwise it's a new array such that ``np.all(native_arr == arr)``, with native byte ordering. """ if endian_codes[arr.dtype.byteorder] == native_code: return arr return arr.byteswap().newbyteorder() dipy-0.5.0/dipy/utils/optpkg.py000066400000000000000000000041071152576264200164600ustar00rootroot00000000000000""" Routines to support optional packages """ try: import nose except ImportError: have_nose = False else: have_nose = True from .tripwire import TripWire, is_tripwire def optional_package(name, trip_msg=None): """ Return package-like thing and module setup for package `name` Parameters ---------- name : str package name trip_msg : None or str message to give when someone tries to use the return package, but we could not import it, and have returned a TripWire object instead. Default message if None. Returns ------- pkg_like : module or ``TripWire`` instance If we can import the package, return it. Otherwise return an object raising an error when accessed have_pkg : bool True if import for package was succesful, false otherwise module_setup : function callable usually set as ``setup_module`` in calling namespace, to allow skipping tests. Example ------- Typical use would be something like this at the top of a module using an optional package: >>> from dipy.utils.optpkg import optional_package >>> pkg, have_pkg, setup_module = optional_package('not_a_package') Of course in this case the package doesn't exist, and so, in the module: >>> have_pkg False and >>> pkg.some_function() Traceback (most recent call last): ... TripWireError: We need package not_a_package for these functions, but ``import not_a_package`` raised an ImportError """ try: pkg = __import__(name) except ImportError: pass else: # import worked return pkg, True, lambda : None if trip_msg is None: trip_msg = ('We need package %s for these functions, but ' '``import %s`` raised an ImportError' % (name, name)) pkg = TripWire(trip_msg) def setup_module(): if have_nose: raise nose.plugins.skip.SkipTest('No %s for these tests' % name) return pkg, False, setup_module dipy-0.5.0/dipy/utils/spheremakers.py000066400000000000000000000013161152576264200176440ustar00rootroot00000000000000""" Factory function(s) for spheres """ from dipy.data import get_sphere def sphere_vf_from(input): """ Return sphere vertices and faces from a variety of inputs Parameters ---------- input : str or tuple or dict * str - a named sphere from dipy.data.get_sphere * tuple - the vertex, face tuple all ready to go * dict - with keys 'vertices', 'faces' Returns ------- vertices : ndarray N,3 ndarray of sphere vertex coordinates faces : ndarray Indices into `vertices` """ if hasattr(input, 'keys'): return input['vertices'], input['faces'] if isinstance(input, basestring): return get_sphere(input) return input dipy-0.5.0/dipy/utils/tests/000077500000000000000000000000001152576264200157425ustar00rootroot00000000000000dipy-0.5.0/dipy/utils/tests/__init__.py000066400000000000000000000000431152576264200200500ustar00rootroot00000000000000# Tests for utilities - as package dipy-0.5.0/dipy/utils/tests/test_arrfuncs.py000066400000000000000000000015001152576264200211720ustar00rootroot00000000000000""" Testing array utilities """ import sys import numpy as np from ..arrfuncs import as_native_array from numpy.testing import (assert_array_almost_equal, assert_array_equal) from nose.tools import assert_true, assert_false, assert_equal, assert_raises NATIVE_ORDER = '<' if sys.byteorder == 'little' else '>' SWAPPED_ORDER = '>' if sys.byteorder == 'little' else '<' def test_as_native(): arr = np.arange(5) # native assert_equal(arr.dtype.byteorder, '=') narr = as_native_array(arr) assert_true(arr is narr) sdt = arr.dtype.newbyteorder('s') barr = arr.astype(sdt) assert_equal(barr.dtype.byteorder, SWAPPED_ORDER) narr = as_native_array(barr) assert_false(barr is narr) assert_array_equal(barr, narr) assert_equal(narr.dtype.byteorder, NATIVE_ORDER) dipy-0.5.0/dipy/utils/tests/test_spheremakers.py000066400000000000000000000013751152576264200220520ustar00rootroot00000000000000""" Testing sphere makers """ import numpy as np from ..spheremakers import sphere_vf_from from numpy.testing import (assert_array_almost_equal, assert_array_equal) from nose.tools import assert_true, assert_equal, assert_raises def test_spheremakers(): # Test inputs to spheremakers # Example data given string v, f = sphere_vf_from('symmetric362') assert_equal(f.shape[1], 3) assert_equal(v.shape[1], 3) # Given tuple vdash, fdash = sphere_vf_from((v, f)) assert_array_equal(vdash, v) assert_array_equal(fdash, f) # Given dict vdash, fdash = sphere_vf_from({'vertices': v, 'faces': f}) assert_array_equal(vdash, v) assert_array_equal(fdash, f) dipy-0.5.0/dipy/utils/tripwire.py000066400000000000000000000022201152576264200170130ustar00rootroot00000000000000""" Class to raise error for missing modules or other misfortunes """ class TripWireError(Exception): """ Exception if trying to use TripWire object """ def is_tripwire(obj): """ Returns True if `obj` appears to be a TripWire object Examples -------- >>> is_tripwire(object()) False >>> is_tripwire(TripWire('some message')) True """ try: obj.any_attribute except TripWireError: return True except: pass return False class TripWire(object): """ Class raising error if used Standard use is to proxy modules that we could not import Examples -------- >>> try: ... import silly_module_name ... except ImportError: ... silly_module_name = TripWire('We do not have silly_module_name') >>> silly_module_name.do_silly_thing('with silly string') Traceback (most recent call last): ... TripWireError: We do not have silly_module_name """ def __init__(self, msg): self._msg = msg def __getattr__(self, attr_name): ''' Raise informative error accessing attributes ''' raise TripWireError(self._msg) dipy-0.5.0/dipy/viz/000077500000000000000000000000001152576264200142505ustar00rootroot00000000000000dipy-0.5.0/dipy/viz/__init__.py000066400000000000000000000000501152576264200163540ustar00rootroot00000000000000# Init file for visualization package dipy-0.5.0/dipy/viz/colormap.py000066400000000000000000000105771152576264200164500ustar00rootroot00000000000000import numpy as np def cc(na,nd): return ( na * np.cos( nd * np.pi/180.0 ) ); def ss(na,nd): return na * np.sin( nd * np.pi/180.0 ) ; def boys2rgb(v): """ boys 2 rgb cool colormap Maps a given field of undirected lines (line field) to rgb colors using Boy's Surface immersion of the real projective plane. Boy's Surface is one of the three possible surfaces obtained by gluing a Mobius strip to the edge of a disk. The other two are the crosscap and Roman surface, Steiner surfaces that are homeomorphic to the real projective plane (Pinkall 1986). The Boy's surface is the only 3D immersion of the projective plane without singularities. Visit http://www.cs.brown.edu/~cad/rp2coloring for further details. Cagatay Demiralp, 9/7/2008. Code was initially in matlab and was rewritten in Python for dipy by the Dipy Team. Thank you Cagatay for putting this online. Parameters ------------ v : array, shape (N, 3) of unit vectors (e.g., principal eigenvectors of tensor data) representing one of the two directions of the undirected lines in a line field. Returns --------- c : array, shape (N, 3) matrix of rgb colors corresponding to the vectors given in V. Examples ---------- >>> from dipy.viz import colormap >>> v=np.array([[1,0,0],[0,1,0],[0,0,1]]) >>> c=colormap.boys2rgb(v) """ if v.ndim==1: x=v[0] y=v[1] z=v[2] if v.ndim==2: x=v[:,0] y=v[:,1] z=v[:,2] #return x,y,z x2 = x**2 y2 = y**2 z2 = z**2 x3 = x*x2 y3 = y*y2 z3 = z*z2 z4 = z*z2 xy = x*y xz = x*z yz = y*z hh1 = .5 * (3 * z2 - 1)/1.58 hh2 = 3 * xz/2.745 hh3 = 3 * yz/2.745 hh4 = 1.5 * (x2 - y2)/2.745 hh5 = 6 * xy/5.5 hh6 = (1/1.176) * .125 * (35 * z4 - 30 * z2 + 3) hh7 = 2.5 * x * (7 * z3 - 3*z)/3.737 hh8 = 2.5 * y * (7 * z3 - 3*z)/3.737 hh9 = ((x2 - y2) * 7.5 * (7 * z2 - 1))/15.85 hh10 = ((2 * xy) * (7.5 * (7 * z2 - 1)))/15.85 hh11 = 105 * ( 4 * x3 * z - 3 * xz * (1 - z2))/59.32 hh12 = 105 * (-4 * y3 * z + 3 * yz * (1 - z2))/59.32 s0 = -23.0 s1 = 227.9 s2 = 251.0 s3 = 125.0 ss23 = ss(2.71,s0); cc23 = cc(2.71, s0) ss45 = ss(2.12, s1); cc45 = cc(2.12, s1); ss67 = ss(.972, s2); cc67 = cc(.972, s2); ss89 = ss(.868, s3); cc89 = cc(.868, s3); X = 0.0 X =X+ hh2 * cc23 X =X+ hh3 * ss23 X =X+ hh5 * cc45 X =X+ hh4 * ss45 X =X+ hh7 * cc67 X =X+ hh8 * ss67 X =X+ hh10 * cc89 X =X+ hh9 * ss89 Y = 0.0 Y =Y+ hh2 * -ss23 Y =Y+ hh3 * cc23 Y =Y+ hh5 * -ss45 Y =Y+ hh4 * cc45 Y =Y+ hh7 * -ss67 Y =Y+ hh8 * cc67 Y =Y+ hh10 * -ss89 Y =Y+ hh9 * cc89 Z = 0.0 Z =Z+ hh1 * -2.8 Z =Z+ hh6 * -0.5 Z =Z+ hh11 * 0.3 Z =Z+ hh12 * -2.5 # scale and normalize to fit # in the rgb space w_x = 4.1925 trl_x = -2.0425 w_y = 4.0217 trl_y = -1.8541 w_z = 4.0694 trl_z = -2.1899 if v.ndim==2: N = len(x) C = np.zeros((N, 3)) C[:,0] = 0.9 * np.abs(((X-trl_x)/w_x)) + 0.05 C[:,1] = 0.9 * np.abs(((Y-trl_y)/w_y)) + 0.05 C[:,2] = 0.9 * np.abs(((Z-trl_z)/w_z)) + 0.05 if v.ndim==1: C = np.zeros((3,)) C[0] = 0.9 * np.abs(((X-trl_x)/w_x)) + 0.05 C[1] = 0.9 * np.abs(((Y-trl_y)/w_y)) + 0.05 C[2] = 0.9 * np.abs(((Z-trl_z)/w_z)) + 0.05 return C def orient2rgb(v): """ standard orientation 2 rgb colormap v : array, shape (N, 3) of vectors not necessarily normalized Returns --------- c : array, shape (N, 3) matrix of rgb colors corresponding to the vectors given in V. Examples ---------- >>> from dipy.viz import colormap >>> v=np.array([[1,0,0],[0,1,0],[0,0,1]]) >>> c=colormap.orient2rgb(v) """ if v.ndim==1: orient=v orient=np.abs(orient/np.linalg.norm(orient)) if v.ndim==2: orientn=np.sqrt(v[:,0]**2+v[:,1]**2+v[:,2]**2) orientn.shape=orientn.shape+(1,) orient=np.abs(v/orientn) return orient dipy-0.5.0/dipy/viz/fvtk.py000066400000000000000000001452131152576264200156020ustar00rootroot00000000000000''' Fvtk module implements simple visualization functions using VTK. Fos means light in Greek. The main idea is the following: A window can have one or more renderers. A renderer can have none, one or more actors. Examples of actors are a sphere, line, point etc. You basically add actors in a renderer and in that way you can visualize the forementioned objects e.g. sphere, line ... Examples ---------------- >>> from dipy.viz import fvtk >>> r=fvtk.ren() >>> a=fvtk.axes() >>> fvtk.add(r,a) >>> #fvtk.show(r) ''' import types import numpy as np import scipy as sp # Conditional import machinery for vtk from ..utils.optpkg import optional_package # Allow import, but disable doctests if we don't have vtk vtk, have_vtk, setup_module = optional_package('vtk') ''' For more color names see http://www.colourlovers.com/blog/2007/07/24/32-common-color-names-for-easy-reference/ ''' #Some common colors red=np.array([1,0,0]) green=np.array([0,1,0]) blue=np.array([0,0,1]) yellow=np.array([1,1,0]) cyan=np.array([0,1,1]) azure=np.array([0,0.49,1]) golden=np.array([1,0.84,0]) white=np.array([1,1,1]) black=np.array([0,0,0]) aquamarine=np.array([0.498,1.,0.83]) indigo=np.array([ 0.29411765, 0., 0.50980392]) lime=np.array([ 0.74901961, 1., 0.]) hot_pink=np.array([ 0.98823529, 0.05882353, 0.75294118]) gray=np.array([0.5,0.5,0.5]) dark_red=np.array([0.5,0,0]) dark_green=np.array([0,0.5,0]) dark_blue=np.array([0,0,0.5]) tan=np.array([ 0.82352941, 0.70588235, 0.54901961]) chartreuse=np.array([ 0.49803922, 1. , 0. ]) coral=np.array([ 1. , 0.49803922, 0.31372549]) #a track buffer used only with picking tracks track_buffer=[] #indices buffer for the tracks ind_buffer=[] #tempory renderer used only with picking tracks tmp_ren=None if have_vtk: # Create a text mapper and actor to display the results of picking. textMapper = vtk.vtkTextMapper() tprop = textMapper.GetTextProperty() tprop.SetFontFamilyToArial() tprop.SetFontSize(10) #tprop.BoldOn() #tprop.ShadowOn() tprop.SetColor(1, 0, 0) textActor = vtk.vtkActor2D() textActor.VisibilityOff() textActor.SetMapper(textMapper) # Create a cell picker. picker = vtk.vtkCellPicker() def ren(): ''' Create a renderer Returns -------- a vtkRenderer() object Examples --------- >>> from dipy.viz import fvtk >>> import numpy as np >>> r=fvtk.ren() >>> lines=[np.random.rand(10,3)] >>> c=fvtk.line(lines,fvtk.red) >>> fvtk.add(r,c) >>> #fvtk.show(r) ''' return vtk.vtkRenderer() def add(ren,a): ''' Add a specific actor ''' if isinstance(a,vtk.vtkVolume): ren.AddVolume(a) else: ren.AddActor(a) def rm(ren,a): ''' Remove a specific actor ''' ren.RemoveActor(a) def clear(ren): ''' Remove all actors from the renderer ''' ren.RemoveAllViewProps() def rm_all(ren): ''' Remove all actors from the renderer ''' clear(ren) def _arrow(pos=(0,0,0),color=(1,0,0),scale=(1,1,1),opacity=1): ''' Internal function for generating arrow actors. ''' arrow = vtk.vtkArrowSource() #arrow.SetTipLength(length) arrowm = vtk.vtkPolyDataMapper() arrowm.SetInput(arrow.GetOutput()) arrowa= vtk.vtkActor() arrowa.SetMapper(arrowm) arrowa.GetProperty().SetColor(color) arrowa.GetProperty().SetOpacity(opacity) arrowa.SetScale(scale) return arrowa def axes(scale=(1,1,1),colorx=(1,0,0),colory=(0,1,0),colorz=(0,0,1),opacity=1): ''' Create an actor with the coordinate system axes where red = x, green = y, blue =z. ''' arrowx=_arrow(color=colorx,scale=scale,opacity=opacity) arrowy=_arrow(color=colory,scale=scale,opacity=opacity) arrowz=_arrow(color=colorz,scale=scale,opacity=opacity) arrowy.RotateZ(90) arrowz.RotateY(-90) ass=vtk.vtkAssembly() ass.AddPart(arrowx) ass.AddPart(arrowy) ass.AddPart(arrowz) return ass def _lookup(colors): ''' Internal function Creates a lookup table with given colors. Parameters ------------ colors : array, shape (N,3) Colormap where every triplet is encoding red, green and blue e.g. r1,g1,b1 r2,g2,b2 ... rN,gN,bN where 0=2: raise ValueError('Incorrect shape of array in colors') if colors.ndim==1: N=1 if colors.ndim==2: N=colors.shape[0] lut=vtk.vtkLookupTable() lut.SetNumberOfColors(N) lut.Build() if colors.ndim==2: scalar=0 for (r,g,b) in colors: lut.SetTableValue(scalar,r,g,b,1.0) scalar+=1 if colors.ndim==1: lut.SetTableValue(0,colors[0],colors[1],colors[2],1.0) return lut def line(lines,colors,opacity=1,linewidth=1): ''' Create an actor for one or more lines. Parameters ------------ lines : list of arrays representing lines as 3d points for example lines=[np.random.rand(10,3),np.random.rand(20,3)] represents 2 lines the first with 10 points and the second with 20 points in x,y,z coordinates. colors : array, shape (N,3) Colormap where every triplet is encoding red, green and blue e.g. r1,g1,b1 r2,g2,b2 ... rN,gN,bN where 0=>> from dipy.viz import fvtk >>> r=fvtk.ren() >>> lines=[np.random.rand(10,3),np.random.rand(20,3)] >>> colors=np.random.rand(2,3) >>> c=fvtk.line(lines,colors) >>> fvtk.add(r,c) >>> #fvtk.show(r) ''' if not isinstance(lines,types.ListType): lines=[lines] points= vtk.vtkPoints() lines_=vtk.vtkCellArray() linescalars=vtk.vtkFloatArray() #lookuptable=vtk.vtkLookupTable() lookuptable=_lookup(colors) scalarmin=0 if colors.ndim==2: scalarmax=colors.shape[0]-1 if colors.ndim==1: scalarmax=0 curPointID=0 m=(0.0,0.0,0.0) n=(1.0,0.0,0.0) scalar=0 #many colors if colors.ndim==2: for Line in lines: inw=True mit=iter(Line) nit=iter(Line) nit.next() while(inw): try: m=mit.next() n=nit.next() #scalar=sp.rand(1) linescalars.SetNumberOfComponents(1) points.InsertNextPoint(m) linescalars.InsertNextTuple1(scalar) points.InsertNextPoint(n) linescalars.InsertNextTuple1(scalar) lines_.InsertNextCell(2) lines_.InsertCellPoint(curPointID) lines_.InsertCellPoint(curPointID+1) curPointID+=2 except StopIteration: break scalar+=1 #one color only if colors.ndim==1: for Line in lines: inw=True mit=iter(Line) nit=iter(Line) nit.next() while(inw): try: m=mit.next() n=nit.next() #scalar=sp.rand(1) linescalars.SetNumberOfComponents(1) points.InsertNextPoint(m) linescalars.InsertNextTuple1(scalar) points.InsertNextPoint(n) linescalars.InsertNextTuple1(scalar) lines_.InsertNextCell(2) lines_.InsertCellPoint(curPointID) lines_.InsertCellPoint(curPointID+1) curPointID+=2 except StopIteration: break polydata = vtk.vtkPolyData() polydata.SetPoints(points) polydata.SetLines(lines_) polydata.GetPointData().SetScalars(linescalars) mapper = vtk.vtkPolyDataMapper() mapper.SetInput(polydata) mapper.SetLookupTable(lookuptable) mapper.SetColorModeToMapScalars() mapper.SetScalarRange(scalarmin,scalarmax) mapper.SetScalarModeToUsePointData() actor=vtk.vtkActor() actor.SetMapper(mapper) actor.GetProperty().SetLineWidth(linewidth) actor.GetProperty().SetOpacity(opacity) return actor def dots(points,color=(1,0,0),opacity=1): ''' Create one or more 3d dots(points) returns one actor handling all the points ''' if points.ndim==2: points_no=points.shape[0] else: points_no=1 polyVertexPoints = vtk.vtkPoints() polyVertexPoints.SetNumberOfPoints(points_no) aPolyVertex = vtk.vtkPolyVertex() aPolyVertex.GetPointIds().SetNumberOfIds(points_no) cnt=0 if points.ndim>1: for point in points: polyVertexPoints.InsertPoint(cnt, point[0], point[1], point[2]) aPolyVertex.GetPointIds().SetId(cnt, cnt) cnt+=1 else: polyVertexPoints.InsertPoint(cnt, points[0], points[1], points[2]) aPolyVertex.GetPointIds().SetId(cnt, cnt) cnt+=1 aPolyVertexGrid = vtk.vtkUnstructuredGrid() aPolyVertexGrid.Allocate(1, 1) aPolyVertexGrid.InsertNextCell(aPolyVertex.GetCellType(), aPolyVertex.GetPointIds()) aPolyVertexGrid.SetPoints(polyVertexPoints) aPolyVertexMapper = vtk.vtkDataSetMapper() aPolyVertexMapper.SetInput(aPolyVertexGrid) aPolyVertexActor = vtk.vtkActor() aPolyVertexActor.SetMapper(aPolyVertexMapper) aPolyVertexActor.GetProperty().SetColor(color) aPolyVertexActor.GetProperty().SetOpacity(opacity) return aPolyVertexActor def point(points,colors,opacity=1,point_radius=0.001,theta=3,phi=3): if np.array(colors).ndim==1: #return dots(points,colors,opacity) colors=np.tile(colors,(len(points),1)) scalars=vtk.vtkUnsignedCharArray() scalars.SetNumberOfComponents(3) pts=vtk.vtkPoints() cnt_colors=0 for p in points: pts.InsertNextPoint(p[0],p[1],p[2]) scalars.InsertNextTuple3(round(255*colors[cnt_colors][0]),round(255*colors[cnt_colors][1]),round(255*colors[cnt_colors][2])) #scalars.InsertNextTuple3(255,255,255) cnt_colors+=1 ''' src = vtk.vtkDiskSource() src.SetRadialResolution(1) src.SetCircumferentialResolution(10) src.SetInnerRadius(0.0) src.SetOuterRadius(0.001) ''' #src = vtk.vtkPointSource() src = vtk.vtkSphereSource() src.SetRadius(point_radius) src.SetThetaResolution(theta) src.SetPhiResolution(phi) polyData = vtk.vtkPolyData() polyData.SetPoints(pts) polyData.GetPointData().SetScalars(scalars) glyph = vtk.vtkGlyph3D() glyph.SetSourceConnection(src.GetOutputPort()) glyph.SetInput(polyData) glyph.SetColorModeToColorByScalar() glyph.SetScaleModeToDataScalingOff() mapper=vtk.vtkPolyDataMapper() mapper.SetInput(glyph.GetOutput()) actor=vtk.vtkActor() actor.SetMapper(mapper) return actor def sphere(position=(0,0,0),radius=0.5,thetares=8,phires=8,color=(0,0,1),opacity=1,tessel=0): ''' Create a sphere actor ''' sphere = vtk.vtkSphereSource() sphere.SetRadius(radius) sphere.SetLatLongTessellation(tessel) sphere.SetThetaResolution(thetares) sphere.SetPhiResolution(phires) spherem = vtk.vtkPolyDataMapper() spherem.SetInput(sphere.GetOutput()) spherea = vtk.vtkActor() spherea.SetMapper(spherem) spherea.SetPosition(position) spherea.GetProperty().SetColor(color) spherea.GetProperty().SetOpacity(opacity) return spherea def ellipsoid(R=np.array([[2, 0, 0],[0, 1, 0],[0, 0, 1] ]),position=(0,0,0),thetares=20,phires=20,color=(0,0,1),opacity=1,tessel=0): ''' Create a ellipsoid actor. Stretch a unit sphere to make it an ellipsoid under a 3x3 translation matrix R R=sp.array([[2, 0, 0], [0, 1, 0], [0, 0, 1] ]) ''' Mat=sp.identity(4) Mat[0:3,0:3]=R ''' Mat=sp.array([[2, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1] ]) ''' mat=vtk.vtkMatrix4x4() for i in sp.ndindex(4,4): mat.SetElement(i[0],i[1],Mat[i]) radius=1 sphere = vtk.vtkSphereSource() sphere.SetRadius(radius) sphere.SetLatLongTessellation(tessel) sphere.SetThetaResolution(thetares) sphere.SetPhiResolution(phires) trans=vtk.vtkTransform() trans.Identity() #trans.Scale(0.3,0.9,0.2) trans.SetMatrix(mat) trans.Update() transf=vtk.vtkTransformPolyDataFilter() transf.SetTransform(trans) transf.SetInput(sphere.GetOutput()) transf.Update() spherem = vtk.vtkPolyDataMapper() spherem.SetInput(transf.GetOutput()) spherea = vtk.vtkActor() spherea.SetMapper(spherem) spherea.SetPosition(position) spherea.GetProperty().SetColor(color) spherea.GetProperty().SetOpacity(opacity) #spherea.GetProperty().SetRepresentationToWireframe() return spherea def label(ren,text='Origin',pos=(0,0,0),scale=(0.2,0.2,0.2),color=(1,1,1)): ''' Create a label actor This actor will always face the camera Parameters ------------ ren : vtkRenderer() object as returned from ren() text : a text for the label pos : left down position of the label scale : change the size of the label color : (r,g,b) and RGB tuple Returns ---------- vtkActor object Examples ---------- >>> from dipy.viz import fvtk >>> r=fvtk.ren() >>> l=fvtk.label(r) >>> fvtk.add(r,l) >>> #fvtk.show(r) ''' atext=vtk.vtkVectorText() atext.SetText(text) textm=vtk.vtkPolyDataMapper() textm.SetInput(atext.GetOutput()) texta=vtk.vtkFollower() texta.SetMapper(textm) texta.SetScale(scale) texta.GetProperty().SetColor(color) texta.SetPosition(pos) ren.AddActor(texta) texta.SetCamera(ren.GetActiveCamera()) return texta def volume(vol,voxsz=(1.0,1.0,1.0),affine=None,center_origin=1,info=0,maptype=0,trilinear=1,iso=0,iso_thr=100,opacitymap=None,colormap=None): ''' Create a volume and return a volumetric actor using volumetric rendering. This function has many different interesting capabilities. The maptype, opacitymap and colormap are the most crucial parameters here. Parameters ---------------- vol : array, shape (N, M, K), dtype uint8 an array representing the volumetric dataset that we want to visualize using volumetric rendering voxsz : sequence of 3 floats default (1., 1., 1.) affine : array, shape (4,4), default None as given by volumeimages center_origin : int {0,1}, default 1 it considers that the center of the volume is the point (-vol.shape[0]/2.0+0.5,-vol.shape[1]/2.0+0.5,-vol.shape[2]/2.0+0.5) info : int {0,1}, default 1 if 1 it prints out some info about the volume, the method and the dataset. trilinear: int {0,1}, default 1 Use trilinear interpolation, default 1, gives smoother rendering. If you want faster interpolation use 0 (Nearest). maptype : int {0,1}, default 0, The maptype is a very important parameter which affects the raycasting algorithm in use for the rendering. The options are: If 0 then vtkVolumeTextureMapper2D is used. If 1 then vtkVolumeRayCastFunction is used. iso : int {0,1} default 0, If iso is 1 and maptype is 1 then we use vtkVolumeRayCastIsosurfaceFunction which generates an isosurface at the predefined iso_thr value. If iso is 0 and maptype is 1 vtkVolumeRayCastCompositeFunction is used. iso_thr : int, default 100, if iso is 1 then then this threshold in the volume defines the value which will be used to create the isosurface. opacitymap : array, shape (N,2), default None. The opacity map assigns a transparency coefficient to every point in the volume. The default value uses the histogram of the volume to calculate the opacitymap. colormap : array, shape (N,4), default None. The color map assigns a color value to every point in the volume. When None from the histogram it uses a red-blue colormap. Returns ---------- vtkVolume Notes -------- What is the difference between TextureMapper2D and RayCastFunction? Coming soon... See VTK user's guide [book] & The Visualization Toolkit [book] and VTK's online documentation & online docs. What is the difference between RayCastIsosurfaceFunction and RayCastCompositeFunction? Coming soon... See VTK user's guide [book] & The Visualization Toolkit [book] and VTK's online documentation & online docs. What about trilinear interpolation? Coming soon... well when time permits really ... :-) Examples ------------ First example random points >>> from dipy.viz import fvtk >>> import numpy as np >>> vol=100*np.random.rand(100,100,100) >>> vol=vol.astype('uint8') >>> print vol.min(), vol.max() 0 99 >>> r = fvtk.ren() >>> v = fvtk.volume(vol) >>> fvtk.add(r,v) >>> #fvtk.show(r) Second example with a more complicated function >>> from dipy.viz import fvtk >>> import numpy as np >>> x, y, z = np.ogrid[-10:10:20j, -10:10:20j, -10:10:20j] >>> s = np.sin(x*y*z)/(x*y*z) >>> r = fvtk.ren() >>> v = fvtk.volume(s) >>> fvtk.add(r,v) >>> #fvtk.show(r) If you find this function too complicated you can always use mayavi. Please do not forget to use the -wthread switch in ipython if you are running mayavi. from enthought.mayavi import mlab import numpy as np x, y, z = np.ogrid[-10:10:20j, -10:10:20j, -10:10:20j] s = np.sin(x*y*z)/(x*y*z) mlab.pipeline.volume(mlab.pipeline.scalar_field(s)) mlab.show() More mayavi demos are available here: http://code.enthought.com/projects/mayavi/docs/development/html/mayavi/mlab.html ''' if vol.ndim!=3: raise ValueError('3d numpy arrays only please') if info : print('Datatype',vol.dtype,'converted to uint8' ) vol=np.interp(vol,[vol.min(),vol.max()],[0,255]) vol=vol.astype('uint8') if opacitymap==None: bin,res=np.histogram(vol.ravel()) res2=np.interp(res,[vol.min(),vol.max()],[0,1]) opacitymap=np.vstack((res,res2)).T opacitymap=opacitymap.astype('float32') ''' opacitymap=np.array([[ 0.0, 0.0], [50.0, 0.9]]) ''' if info: print 'opacitymap', opacitymap if colormap==None: bin,res=np.histogram(vol.ravel()) res2=np.interp(res,[vol.min(),vol.max()],[0,1]) zer=np.zeros(res2.shape) colormap=np.vstack((res,res2,zer,res2[::-1])).T colormap=colormap.astype('float32') ''' colormap=np.array([[0.0, 0.5, 0.0, 0.0], [64.0, 1.0, 0.5, 0.5], [128.0, 0.9, 0.2, 0.3], [196.0, 0.81, 0.27, 0.1], [255.0, 0.5, 0.5, 0.5]]) ''' if info: print 'colormap', colormap im = vtk.vtkImageData() im.SetScalarTypeToUnsignedChar() im.SetDimensions(vol.shape[0],vol.shape[1],vol.shape[2]) #im.SetOrigin(0,0,0) #im.SetSpacing(voxsz[2],voxsz[0],voxsz[1]) im.AllocateScalars() for i in range(vol.shape[0]): for j in range(vol.shape[1]): for k in range(vol.shape[2]): im.SetScalarComponentFromFloat(i,j,k,0,vol[i,j,k]) if affine != None: aff = vtk.vtkMatrix4x4() aff.DeepCopy((affine[0,0],affine[0,1],affine[0,2],affine[0,3],affine[1,0],affine[1,1],affine[1,2],affine[1,3],affine[2,0],affine[2,1],affine[2,2],affine[2,3],affine[3,0],affine[3,1],affine[3,2],affine[3,3])) #aff.DeepCopy((affine[0,0],affine[0,1],affine[0,2],0,affine[1,0],affine[1,1],affine[1,2],0,affine[2,0],affine[2,1],affine[2,2],0,affine[3,0],affine[3,1],affine[3,2],1)) #aff.DeepCopy((affine[0,0],affine[0,1],affine[0,2],127.5,affine[1,0],affine[1,1],affine[1,2],-127.5,affine[2,0],affine[2,1],affine[2,2],-127.5,affine[3,0],affine[3,1],affine[3,2],1)) reslice = vtk.vtkImageReslice() reslice.SetInput(im) #reslice.SetOutputDimensionality(2) #reslice.SetOutputOrigin(127,-145,147) reslice.SetResliceAxes(aff) #reslice.SetOutputOrigin(-127,-127,-127) #reslice.SetOutputExtent(-127,128,-127,128,-127,128) #reslice.SetResliceAxesOrigin(0,0,0) #print 'Get Reslice Axes Origin ', reslice.GetResliceAxesOrigin() #reslice.SetOutputSpacing(1.0,1.0,1.0) reslice.SetInterpolationModeToLinear() #reslice.UpdateWholeExtent() #print 'reslice GetOutputOrigin', reslice.GetOutputOrigin() #print 'reslice GetOutputExtent',reslice.GetOutputExtent() #print 'reslice GetOutputSpacing',reslice.GetOutputSpacing() changeFilter=vtk.vtkImageChangeInformation() changeFilter.SetInput(reslice.GetOutput()) #changeFilter.SetInput(im) if center_origin: changeFilter.SetOutputOrigin(-vol.shape[0]/2.0+0.5,-vol.shape[1]/2.0+0.5,-vol.shape[2]/2.0+0.5) print 'ChangeFilter ', changeFilter.GetOutputOrigin() opacity = vtk.vtkPiecewiseFunction() for i in range(opacitymap.shape[0]): opacity.AddPoint(opacitymap[i,0],opacitymap[i,1]) color = vtk.vtkColorTransferFunction() for i in range(colormap.shape[0]): color.AddRGBPoint(colormap[i,0],colormap[i,1],colormap[i,2],colormap[i,3]) if(maptype==0): property = vtk.vtkVolumeProperty() property.SetColor(color) property.SetScalarOpacity(opacity) if trilinear: property.SetInterpolationTypeToLinear() else: property.SetInterpolationTypeToNearest() if info: print('mapper VolumeTextureMapper2D') mapper = vtk.vtkVolumeTextureMapper2D() if affine == None: mapper.SetInput(im) else: #mapper.SetInput(reslice.GetOutput()) mapper.SetInput(changeFilter.GetOutput()) if (maptype==1): property = vtk.vtkVolumeProperty() property.SetColor(color) property.SetScalarOpacity(opacity) property.ShadeOn() if trilinear: property.SetInterpolationTypeToLinear() else: property.SetInterpolationTypeToNearest() if iso: isofunc=vtk.vtkVolumeRayCastIsosurfaceFunction() isofunc.SetIsoValue(iso_thr) else: compositeFunction = vtk.vtkVolumeRayCastCompositeFunction() if info: print('mapper VolumeRayCastMapper') mapper = vtk.vtkVolumeRayCastMapper() if iso: mapper.SetVolumeRayCastFunction(isofunc) if info: print('Isosurface') else: mapper.SetVolumeRayCastFunction(compositeFunction) #mapper.SetMinimumImageSampleDistance(0.2) if info: print('Composite') if affine == None: mapper.SetInput(im) else: #mapper.SetInput(reslice.GetOutput()) mapper.SetInput(changeFilter.GetOutput()) #Return mid position in world space #im2=reslice.GetOutput() #index=im2.FindPoint(vol.shape[0]/2.0,vol.shape[1]/2.0,vol.shape[2]/2.0) #print 'Image Getpoint ' , im2.GetPoint(index) volum = vtk.vtkVolume() volum.SetMapper(mapper) volum.SetProperty(property) if info : print 'Origin', volum.GetOrigin() print 'Orientation', volum.GetOrientation() print 'OrientationW', volum.GetOrientationWXYZ() print 'Position', volum.GetPosition() print 'Center', volum.GetCenter() print 'Get XRange', volum.GetXRange() print 'Get YRange', volum.GetYRange() print 'Get ZRange', volum.GetZRange() print 'Volume data type', vol.dtype return volum def contour(vol,voxsz=(1.0,1.0,1.0),affine=None,levels=[50],colors=[np.array([1.0,0.0,0.0])],opacities=[0.5]): ''' Take a volume and draw surface contours for any any number of thresholds (levels) where every contour has its own color and opacity Parameters ---------------- vol : array, shape (N, M, K) an array representing the volumetric dataset for which we will draw some beautiful contours . voxsz : sequence of 3 floats default (1., 1., 1.) affine : not used here levels : sequence of thresholds for the contours taken from image values needs to be same datatype as vol colors : array, shape (N,3) with the rgb values in where r,g,b belong to [0,1] opacities : sequence of floats [0,1] Returns ----------- ass: assembly of actors representing the contour surfaces Examples ------------- >>> import numpy as np >>> from dipy.viz import fvtk >>> A=np.zeros((10,10,10)) >>> A[3:-3,3:-3,3:-3]=1 >>> r=fvtk.ren() >>> fvtk.add(r,fvtk.contour(A,levels=[1])) >>> #fvtk.show(r) ''' im = vtk.vtkImageData() im.SetScalarTypeToUnsignedChar() im.SetDimensions(vol.shape[0],vol.shape[1],vol.shape[2]) #im.SetOrigin(0,0,0) #im.SetSpacing(voxsz[2],voxsz[0],voxsz[1]) im.AllocateScalars() for i in range(vol.shape[0]): for j in range(vol.shape[1]): for k in range(vol.shape[2]): im.SetScalarComponentFromFloat(i,j,k,0,vol[i,j,k]) ass=vtk.vtkAssembly() #ass=[] for (i,l) in enumerate(levels): #print levels skinExtractor = vtk.vtkContourFilter() skinExtractor.SetInput(im) skinExtractor.SetValue(0, l) skinNormals = vtk.vtkPolyDataNormals() skinNormals.SetInputConnection(skinExtractor.GetOutputPort()) skinNormals.SetFeatureAngle(60.0) skinMapper = vtk.vtkPolyDataMapper() skinMapper.SetInputConnection(skinNormals.GetOutputPort()) skinMapper.ScalarVisibilityOff() skin = vtk.vtkActor() skin.SetMapper(skinMapper) skin.GetProperty().SetOpacity(opacities[i]) #print colors[i] skin.GetProperty().SetColor(colors[i][0],colors[i][1],colors[i][2]) #skin.Update() ass.AddPart(skin) del skin del skinMapper del skinExtractor #ass=ass+[skin] return ass def _cm2colors(colormap='Blues'): ''' Colormaps from matplotlib ['Spectral', 'summer', 'RdBu', 'gist_earth', 'Set1', 'Set2', 'Set3', 'Dark2', 'hot', 'PuOr_r', 'PuBuGn_r', 'RdPu', 'gist_ncar_r', 'gist_yarg_r', 'Dark2_r', 'YlGnBu', 'RdYlBu', 'hot_r', 'gist_rainbow_r', 'gist_stern', 'cool_r', 'cool', 'gray', 'copper_r', 'Greens_r', 'GnBu', 'gist_ncar', 'spring_r', 'gist_rainbow', 'RdYlBu_r', 'gist_heat_r', 'OrRd_r', 'bone', 'gist_stern_r', 'RdYlGn', 'Pastel2_r', 'spring', 'Accent', 'YlOrRd_r', 'Set2_r', 'PuBu', 'RdGy_r', 'spectral', 'flag_r', 'jet_r', 'RdPu_r', 'gist_yarg', 'BuGn', 'Paired_r', 'hsv_r', 'YlOrRd', 'Greens', 'PRGn', 'gist_heat', 'spectral_r', 'Paired', 'hsv', 'Oranges_r', 'prism_r', 'Pastel2', 'Pastel1_r', 'Pastel1', 'gray_r', 'PuRd_r', 'Spectral_r', 'BuGn_r', 'YlGnBu_r', 'copper', 'gist_earth_r', 'Set3_r', 'OrRd', 'PuBu_r', 'winter_r', 'jet', 'bone_r', 'BuPu', 'Oranges', 'RdYlGn_r', 'PiYG', 'YlGn', 'binary_r', 'gist_gray_r', 'BuPu_r', 'gist_gray', 'flag', 'RdBu_r', 'BrBG', 'Reds', 'summer_r', 'GnBu_r', 'BrBG_r', 'Reds_r', 'RdGy', 'PuRd', 'Accent_r', 'Blues', 'Greys', 'autumn', 'PRGn_r', 'Greys_r', 'pink', 'binary', 'winter', 'pink_r', 'prism', 'YlOrBr', 'Purples_r', 'PiYG_r', 'YlGn_r', 'Blues_r', 'YlOrBr_r', 'Purples', 'autumn_r', 'Set1_r', 'PuOr', 'PuBuGn'] ''' try: from pylab import cm except ImportError: ImportError('pylab is not installed') blue=cm.datad[colormap]['blue'] blue1=[b[0] for b in blue] blue2=[b[1] for b in blue] red=cm.datad[colormap]['red'] red1=[b[0] for b in red] red2=[b[1] for b in red] green=cm.datad[colormap]['green'] green1=[b[0] for b in green] green2=[b[1] for b in green] return red1,red2,green1,green2,blue1,blue2 def colors(v,colormap,auto=True): ''' Create colors from a specific colormap and return it as an array of shape (N,3) where every row gives the corresponding r,g,b value. The colormaps we use are similar with that of pylab. Current options for colormaps are 'jet','blues','blue_red', 'accent' Notes ------- If you want to add more colormaps here is what you could do. Go to this website http://www.scipy.org/Cookbook/Matplotlib/Show_colormaps see which colormap you need and then get in pylab using the cm.datad dictionary. e.g. cm.datad['jet'] {'blue': ((0.0, 0.5, 0.5), (0.11, 1, 1), (0.34000000000000002, 1, 1), (0.65000000000000002, 0, 0), (1, 0, 0)), 'green': ((0.0, 0, 0), (0.125, 0, 0), (0.375, 1, 1), (0.64000000000000001, 1, 1), (0.91000000000000003, 0, 0), (1, 0, 0)), 'red': ((0.0, 0, 0), (0.34999999999999998, 0, 0), (0.66000000000000003, 1, 1), (0.89000000000000001, 1, 1), (1, 0.5, 0.5))} ''' if v.ndim>1: ValueError('This function works only with 1d arrays. Use ravel()') if auto: v=np.interp(v,[v.min(),v.max()],[0,1]) else: v=np.interp(v,[0,1],[0,1]) if colormap=='jet': #print 'jet' red=np.interp(v,[0,0.35,0.66,0.89,1],[0,0,1,1,0.5]) green=np.interp(v,[0,0.125,0.375,0.64,0.91,1],[0,0,1,1,0,0]) blue=np.interp(v,[0,0.11,0.34,0.65,1],[0.5,1,1,0,0]) if colormap=='blues': #cm.datad['Blues'] #print 'blues' red=np.interp(v,[0.0,0.125,0.25,0.375,0.5,0.625,0.75,0.875,1.0],[0.96862745285,0.870588243008,0.776470601559,0.61960786581,0.419607847929,0.258823543787,0.129411771894,0.0313725508749,0.0313725508749]) green=np.interp(v,[0.0,0.125,0.25,0.375,0.5,0.625,0.75,0.875,1.0],[0.984313726425,0.921568632126,0.858823537827,0.792156875134,0.68235296011,0.572549045086,0.443137258291,0.317647069693,0.188235297799]) blue=np.interp(v,[0.0,0.125,0.25,0.375,0.5,0.625,0.75,0.875,1.0] , [1.0,0.96862745285,0.937254905701,0.882352948189,0.839215695858,0.776470601559,0.709803938866,0.611764729023,0.419607847929]) if colormap=='blue_red': #print 'blue_red' #red=np.interp(v,[],[]) red=np.interp(v,[0.0,0.125,0.25,0.375,0.5,0.625,0.75,0.875,1.0],[0.0,0.125,0.25,0.375,0.5,0.625,0.75,0.875,1.0]) green=np.zeros(red.shape) blue=np.interp(v,[0.0,0.125,0.25,0.375,0.5,0.625,0.75,0.875,1.0],[1.0,0.875,0.75,0.625,0.5,0.375,0.25,0.125,0.0]) blue=green if colormap=='accent': #print 'accent' red=np.interp(v,[0.0, 0.14285714285714285, 0.2857142857142857, 0.42857142857142855, 0.5714285714285714, 0.7142857142857143, 0.8571428571428571,1.0], [0.49803921580314636, 0.7450980544090271, 0.99215686321258545, 1.0, 0.21960784494876862, 0.94117647409439087, 0.74901962280273438, 0.40000000596046448]) green=np.interp(v,[0.0, 0.14285714285714285, 0.2857142857142857, 0.42857142857142855, 0.5714285714285714, 0.7142857142857143, 0.8571428571428571, 1.0], [0.78823530673980713, 0.68235296010971069, 0.75294119119644165,1.0, 0.42352941632270813, 0.0078431377187371254, 0.35686275362968445, 0.40000000596046448]) blue=np.interp(v,[0.0, 0.14285714285714285, 0.2857142857142857, 0.42857142857142855, 0.5714285714285714, 0.7142857142857143, 0.8571428571428571, 1.0], [0.49803921580314636, 0.83137255907058716, 0.52549022436141968, 0.60000002384185791, 0.69019609689712524, 0.49803921580314636, 0.090196080505847931, 0.40000000596046448]) return np.vstack((red,green,blue)).T def tube(point1=(0,0,0),point2=(1,0,0),color=(1,0,0),opacity=1,radius=0.1,capson=1,specular=1,sides=8): ''' Deprecated Wrap a tube around a line connecting point1 with point2 with a specific radius ''' points = vtk.vtkPoints() points.InsertPoint(0,point1[0],point1[1],point1[2]) points.InsertPoint(1,point2[0],point2[1],point2[2]) lines=vtk.vtkCellArray() lines.InsertNextCell(2) lines.InsertCellPoint(0) lines.InsertCellPoint(1) profileData=vtk.vtkPolyData() profileData.SetPoints(points) profileData.SetLines(lines) # Add thickness to the resulting line. profileTubes = vtk.vtkTubeFilter() profileTubes.SetNumberOfSides(sides) profileTubes.SetInput(profileData) profileTubes.SetRadius(radius) if capson: profileTubes.SetCapping(1) else: profileTubes.SetCapping(0) profileMapper = vtk.vtkPolyDataMapper() profileMapper.SetInputConnection(profileTubes.GetOutputPort()) profile = vtk.vtkActor() profile.SetMapper(profileMapper) profile.GetProperty().SetDiffuseColor(color) profile.GetProperty().SetSpecular(specular) profile.GetProperty().SetSpecularPower(30) profile.GetProperty().SetOpacity(opacity) return profile def _closest_track(p,tracks): ''' Return the index of the closest track from tracks to point p ''' d=[] #enumt= enumerate(tracks) for (ind,t) in enumerate(tracks): for i in range(len(t[:-1])): d.append((ind, np.sqrt(np.sum(np.cross((p-t[i]),(p-t[i+1]))**2))/np.sqrt(np.sum((t[i+1]-t[i])**2)))) d=np.array(d) imin=d[:,1].argmin() return int(d[imin,0]) def crossing(a,ind,sph,scale,orient=False): """ visualize a volume of crossings Examples ---------- See 'dipy/doc/examples/visualize_crossings.py' at :ref:`examples` """ T=[] Tor=[] if a.ndim == 4 or a.ndim ==3: x,y,z=ind.shape[:3] for pos in np.ndindex(x,y,z): i,j,k=pos pos_=np.array(pos) ind_=ind[i,j,k] a_=a[i,j,k] try: len(ind_) except TypeError: ind_=[ind_] a_=[a_] for (i,_i) in enumerate(ind_): T.append(pos_ + scale*a_[i]*np.vstack((sph[_i],-sph[_i]))) if orient: Tor.append(sph[_i]) if a.ndim == 1: for (i,_i) in enumerate(ind): T.append(scale*a[i]*np.vstack((sph[_i],-sph[_i]))) if orient: Tor.append(sph[_i]) if orient: return T,Tor return T def slicer(ren,vol,voxsz=(1.0,1.0,1.0),affine=None,contours=1,planes=1,levels=[20,30,40],opacities=[0.8,0.7,0.3],colors=None,planesx=[20,30],planesy=[30,40],planesz=[20,30]): ''' Slicer and contour rendering of 3d volumes Parameters ---------------- vol : array, shape (N, M, K), dtype uint8 an array representing the volumetric dataset that we want to visualize using volumetric rendering voxsz : sequence of 3 floats default (1., 1., 1.) affine : array, shape (4,4), default None as given by volumeimages contours : bool 1 to show contours planes : boolean 1 show planes levels : contour levels opacities : opacity for every contour level colors : None or planesx : saggital planesy : coronal planesz : axial Examples -------------- >>> import numpy as np >>> from dipy.viz import fvtk >>> x, y, z = np.ogrid[-10:10:80j, -10:10:80j, -10:10:80j] >>> s = np.sin(x*y*z)/(x*y*z) >>> r=fvtk.ren() >>> #fvtk.slicer(r,s) #does showing too ''' vol=np.interp(vol,xp=[vol.min(),vol.max()],fp=[0,255]) vol=vol.astype('uint8') im = vtk.vtkImageData() im.SetScalarTypeToUnsignedChar() im.SetDimensions(vol.shape[0],vol.shape[1],vol.shape[2]) #im.SetOrigin(0,0,0) im.SetSpacing(voxsz[2],voxsz[0],voxsz[1]) im.AllocateScalars() for i in range(vol.shape[0]): for j in range(vol.shape[1]): for k in range(vol.shape[2]): im.SetScalarComponentFromFloat(i,j,k,0,vol[i,j,k]) Contours=[] for le in levels: # An isosurface, or contour value of 500 is known to correspond to the # skin of the patient. Once generated, a vtkPolyDataNormals filter is # is used to create normals for smooth surface shading during rendering. # The triangle stripper is used to create triangle strips from the # isosurface these render much faster on may systems. skinExtractor = vtk.vtkContourFilter() #skinExtractor.SetInputConnection(im.GetOutputPort()) skinExtractor.SetInput(im) skinExtractor.SetValue(0, le) skinNormals = vtk.vtkPolyDataNormals() skinNormals.SetInputConnection(skinExtractor.GetOutputPort()) skinNormals.SetFeatureAngle(60.0) skinStripper = vtk.vtkStripper() skinStripper.SetInputConnection(skinNormals.GetOutputPort()) skinMapper = vtk.vtkPolyDataMapper() skinMapper.SetInputConnection(skinStripper.GetOutputPort()) skinMapper.ScalarVisibilityOff() skin = vtk.vtkActor() skin.SetMapper(skinMapper) if colors==None: skin.GetProperty().SetDiffuseColor(1, .49, .25) else: colorskin=colors[le] skin.GetProperty().SetDiffuseColor(colorskin[0], colorskin[1], colorskin[2]) skin.GetProperty().SetSpecular(.3) skin.GetProperty().SetSpecularPower(20) Contours.append(skin) # An outline provides context around the data. outlineData = vtk.vtkOutlineFilter() #outlineData.SetInputConnection(im.GetOutputPort()) outlineData.SetInput(im) mapOutline = vtk.vtkPolyDataMapper() mapOutline.SetInputConnection(outlineData.GetOutputPort()) outline = vtk.vtkActor() outline.SetMapper(mapOutline) outline.GetProperty().SetColor(1, 0, 0) # Now we are creating three orthogonal planes passing through the # volume. Each plane uses a different texture map and therefore has # diferent coloration. # Start by creatin a black/white lookup table. lut = vtk.vtkLookupTable() lut.SetTableRange(vol.min(), vol.max()) lut.SetSaturationRange(0, 0) lut.SetHueRange(0, 0) lut.SetValueRange(0, 1) lut.SetRampToLinear() lut.Build() x1,x2,y1,y2,z1,z2=im.GetExtent() #print x1,x2,y1,y2,z1,z2 # Create the first of the three planes. The filter vtkImageMapToColors # maps the data through the corresponding lookup table created above. # The vtkImageActor is a type of vtkProp and conveniently displays an # image on a single quadrilateral plane. It does this using texture # mapping and as a result is quite fast. (Note: the input image has to # be unsigned char values, which the vtkImageMapToColors produces.) # Note also that by specifying the DisplayExtent, the pipeline # requests data of this extent and the vtkImageMapToColors only # processes a slice of data. planeColors = vtk.vtkImageMapToColors() #saggitalColors.SetInputConnection(im.GetOutputPort()) planeColors.SetInput(im) planeColors.SetLookupTable(lut) planeColors.Update() saggitals=[] for x in planesx: saggital = vtk.vtkImageActor() saggital.SetInput(planeColors.GetOutput()) saggital.SetDisplayExtent(x,x,y1,y2,z1,z2) saggitals.append(saggital) axials=[] for z in planesz: axial = vtk.vtkImageActor() axial.SetInput(planeColors.GetOutput()) axial.SetDisplayExtent(x1, x2, y1, y2, z, z) axials.append(axial) coronals=[] for y in planesy: coronal = vtk.vtkImageActor() coronal.SetInput(planeColors.GetOutput()) coronal.SetDisplayExtent(x1, x2, y, y, z1, z2) coronals.append(coronal) # It is convenient to create an initial view of the data. The FocalPoint # and Position form a vector direction. Later on (ResetCamera() method) # this vector is used to position the camera to look at the data in # this direction. aCamera = vtk.vtkCamera() aCamera.SetViewUp(0, 0, -1) aCamera.SetPosition(0, 1, 0) aCamera.SetFocalPoint(0, 0, 0) aCamera.ComputeViewPlaneNormal() #saggital.SetOpacity(0.1) # Actors are added to the renderer. ren.AddActor(outline) if planes: for sag in saggitals: ren.AddActor(sag) for ax in axials: ren.AddActor(ax) for cor in coronals: ren.AddActor(cor) if contours: cnt=0 for actor in Contours: actor.GetProperty().SetOpacity(opacities[cnt]) ren.AddActor(actor) cnt+=1 # Turn off bone for this example. #bone.VisibilityOff() # Set skin to semi-transparent. # An initial camera view is created. The Dolly() method moves # the camera towards the FocalPoint, thereby enlarging the image. ren.SetActiveCamera(aCamera) ren.ResetCamera() aCamera.Dolly(1.5) # Set a background color for the renderer and set the size of the # render window (expressed in pixels). ren.SetBackground(0, 0, 0) #renWin.SetSize(640, 480) # Note that when camera movement occurs (as it does in the Dolly() # method), the clipping planes often need adjusting. Clipping planes # consist of two planes: near and far along the view direction. The # near plane clips out objects in front of the plane the far plane # clips out objects behind the plane. This way only what is drawn # between the planes is actually rendered. #ren.ResetCameraClippingRange() #return ren renWin = vtk.vtkRenderWindow() renWin.AddRenderer(ren) iren = vtk.vtkRenderWindowInteractor() iren.SetRenderWindow(renWin) ren.ResetCameraClippingRange() # Interact with the data. iren.Initialize() renWin.Render() iren.Start() def annotatePick(object, event): ''' Create a Python function to create the text for the text mapper used to display the results of picking. ''' global picker, textActor, textMapper,track_buffer if picker.GetCellId() < 0: textActor.VisibilityOff() else: if len(track_buffer)!=0: selPt = picker.GetSelectionPoint() pickPos = picker.GetPickPosition() closest=_closest_track(np.array([pickPos[0],pickPos[1],pickPos[2]]),track_buffer) textMapper.SetInput("(%.6f, %.6f, %.6f)"%pickPos) textActor.SetPosition(selPt[:2]) textActor.VisibilityOn() label(tmp_ren,text=str(ind_buffer[closest]),pos=(track_buffer[closest][0][0],track_buffer[closest][0][1],track_buffer[closest][0][2])) tmp_ren.AddActor(line(track_buffer[closest],golden,opacity=1)) def show(ren,title='dipy.viz.fvtk',size=(300,300),png_magnify=1): ''' Show window Notes ------ To save a screenshot press 's' and check your current directory for ``fvtk.png`` Parameters ------------ ren : vtkRenderer() object as returned from function ren() title : string a string for the window title bar size : (int, int) (width,height) of the window png_magnify : int number of times to magnify the screenshot Notes ------- If you want to: * navigate in the the 3d world use the left - middle - right mouse buttons * reset the screen press 'r' * save a screenshot press 's' * quit press 'q' See also --------- dipy.viz.fvtk.record Examples ---------- >>> import numpy as np >>> from dipy.viz import fvtk >>> r=fvtk.ren() >>> lines=[np.random.rand(10,3),np.random.rand(20,3)] >>> colors=np.array([[0.2,0.2,0.2],[0.8,0.8,0.8]]) >>> c=fvtk.line(lines,colors) >>> fvtk.add(r,c) >>> l=fvtk.label(r) >>> fvtk.add(r,l) >>> #fvtk.show(r) See also ---------- dipy.viz.fvtk.record ''' ren.AddActor2D(textActor) ren.ResetCamera() window = vtk.vtkRenderWindow() window.AddRenderer(ren) window.SetWindowName(title) window.SetSize(size[0],size[1]) style=vtk.vtkInteractorStyleTrackballCamera() iren = vtk.vtkRenderWindowInteractor() iren.SetRenderWindow(window) iren.SetPicker(picker) def key_press(obj,event): key = obj.GetKeySym() if key=='s' or key=='S': print('Saving image...') renderLarge = vtk.vtkRenderLargeImage() renderLarge.SetInput(ren) renderLarge.SetMagnification(png_magnify) renderLarge.Update() writer = vtk.vtkPNGWriter() writer.SetInputConnection(renderLarge.GetOutputPort()) writer.SetFileName('fvtk.png') writer.Write() print('Look for fvtk.png in your current dir.') iren.AddObserver('KeyPressEvent',key_press) iren.SetInteractorStyle(style) iren.Initialize() picker.Pick(85, 126, 0, ren) window.Render() iren.Start() def record(ren=None,cam_pos=None,cam_focal=None,cam_view=None,out_path=None,n_frames=10, az_ang=10, magnification=1,size=(300,300),bgr_color=(0,0,0)): ''' This will record a video of your scene Records a video as a series of .png files of your scene by rotating the azimuth angle az_angle in every frame. Parameters ----------- ren : vtkRenderer() object as returned from function ren() cam_pos : None or sequence (3,), optional camera position cam_focal : None or sequence (3,), optional camera focal point cam_view : None or sequence (3,), optional camera view up out_path : str, optional output directory for the frames n_frames : int, optional number of frames to save, default 10 az_ang : float, optional azimuthal angle of camera rotation. magnification : int, optional how much to magnify the saved frame Examples --------- >>> from dipy.viz import fvtk >>> r=fvtk.ren() >>> a=fvtk.axes() >>> from dipy.viz import fvtk >>> r=fvtk.ren() >>> fvtk.add(r,fvtk.axes()) >>> #uncomment below to record >>> #fvtk.record(r,cam_pos=(0,0,-10)) ''' if ren==None: ren = vtk.vtkRenderer() ren.SetBackground(bgr_color) renWin = vtk.vtkRenderWindow() renWin.AddRenderer(ren) renWin.SetSize(size[0],size[1]) iren = vtk.vtkRenderWindowInteractor() iren.SetRenderWindow(renWin) #ren.GetActiveCamera().Azimuth(180) ''' # We'll set up the view we want. ren.GetActiveCamera().SetPosition(0, 1, 0) ren.GetActiveCamera().SetFocalPoint(0, 0, 0) ren.GetActiveCamera().SetViewUp(0, 0, 1) # Let the renderer compute a good position and focal point. ren.ResetCamera() ren.GetActiveCamera().Dolly(1.4) ren.ResetCameraClippingRange() ''' ren.ResetCamera() renderLarge = vtk.vtkRenderLargeImage() renderLarge.SetInput(ren) renderLarge.SetMagnification(magnification) renderLarge.Update() writer = vtk.vtkPNGWriter() ang=0 if cam_pos!=None: cx,cy,cz=cam_pos ren.GetActiveCamera().SetPosition(cx,cy,cz) if cam_focal!=None: fx,fy,fz=cam_focal ren.GetActiveCamera().SetFocalPoint(fx,fy,fz) if cam_view!=None: ux,uy,uz=cam_view ren.GetActiveCamera().SetViewUp(ux, uy, uz) cam=ren.GetActiveCamera() print('------------------------------------') print('Camera Position (%.2f,%.2f,%.2f)' % cam.GetPosition()) print('Camera Focal Point (%.2f,%.2f,%.2f)' % cam.GetFocalPoint()) print('Camera View Up (%.2f,%.2f,%.2f)' % cam.GetViewUp()) print('------------------------------------') for i in range(n_frames): ren.GetActiveCamera().Azimuth(ang) renderLarge = vtk.vtkRenderLargeImage() renderLarge.SetInput(ren) renderLarge.SetMagnification(magnification) renderLarge.Update() writer.SetInputConnection(renderLarge.GetOutputPort()) #filename='/tmp/'+str(3000000+i)+'.png' if out_path==None: filename=str(1000000+i)+'.png' else: filename=out_path+str(1000000+i)+'.png' writer.SetFileName(filename) writer.Write() ang=+az_ang if __name__ == "__main__": pass dipy-0.5.0/dipy/viz/tests/000077500000000000000000000000001152576264200154125ustar00rootroot00000000000000dipy-0.5.0/dipy/viz/tests/__init__.py000066400000000000000000000001641152576264200175240ustar00rootroot00000000000000# init to make tests into a package # Test callable from numpy.testing import Tester test = Tester().test del Testerdipy-0.5.0/dipy/viz/tests/test_fvtk.py000066400000000000000000000017131152576264200177770ustar00rootroot00000000000000""" Testing vizualization with fvtk """ import numpy as np from .. import fvtk from nose.tools import assert_true, assert_false, \ assert_equal, assert_raises from numpy.testing import assert_array_equal, assert_array_almost_equal import numpy.testing as npt @npt.dec.skipif(not fvtk.have_vtk) def test_fvtk_functions(): # Create a renderer r=fvtk.ren() # Create 2 lines with 2 different colors lines=[np.random.rand(10,3),np.random.rand(20,3)] colors=np.random.rand(2,3) c=fvtk.line(lines,colors) fvtk.add(r,c) # Create a volume and return a volumetric actor using volumetric rendering vol=100*np.random.rand(100,100,100) vol=vol.astype('uint8') r = fvtk.ren() v = fvtk.volume(vol) fvtk.add(r,v) # Remove all objects fvtk.rm_all(r) # Put some text l=fvtk.label(r,text='Yes Men') fvtk.add(r,l) # Show everything #fvtk.show(r) dipy-0.5.0/doc/000077500000000000000000000000001152576264200132405ustar00rootroot00000000000000dipy-0.5.0/doc/Makefile000066400000000000000000000103131152576264200146760ustar00rootroot00000000000000# Makefile for Sphinx documentation # # "https://sourceforge.net/apps/trac/sourceforge/wiki/Release files for # download". To use, set your sourceforge username with 'export # SF_USER=garyfallidis' or similar, then 'make upload-website' #SF_USER ?= matthewbrett SF_USER ?= garyfallidis WWW_SF=nipy@web.sourceforge.net:/home/groups/n/ni/nipy/htdocs/dipy RSYNC_SF=rsync -rzhvp --delete --chmod=Dg+s,g+rw # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build PAPER = # Internal variables. PAPEROPT_a4 = -D latex_paper_size=a4 PAPEROPT_letter = -D latex_paper_size=letter ALLSPHINXOPTS = -d _build/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . .PHONY: help clean html dirhtml pickle json htmlhelp qthelp latex changes linkcheck doctest help: @echo "Please use \`make ' where is one of" @echo " html to make standalone HTML files" @echo " dirhtml to make HTML files named index.html in directories" @echo " pickle to make pickle files" @echo " json to make JSON files" @echo " htmlhelp to make HTML files and a HTML help project" @echo " qthelp to make HTML files and a qthelp project" @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" @echo " changes to make an overview of all changed/added/deprecated items" @echo " linkcheck to check all external links for integrity" @echo " doctest to run all doctests embedded in the documentation (if enabled)" clean: examples-clean -rm -rf _build/* -rm *-stamp examples-clean: -cd examples_built && rm -rf *.py *.rst *.png fig examples-tgz: examples-clean rstexamples ../tools/pack_examples.py ../dist gitwash-update: python ../tools/gitwash_dumper.py devel dipy --repo-name=dipy --github-user=Garyfallidis html: rstexamples html-after-examples # build full docs including examples html-after-examples: # Standard html build after examples have been prepared $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) _build/html @echo @echo "Build finished. The HTML pages are in _build/html." dirhtml: $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) _build/dirhtml @echo @echo "Build finished. The HTML pages are in _build/dirhtml." pickle: $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) _build/pickle @echo @echo "Build finished; now you can process the pickle files." json: $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) _build/json @echo @echo "Build finished; now you can process the JSON files." htmlhelp: $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) _build/htmlhelp @echo @echo "Build finished; now you can run HTML Help Workshop with the" \ ".hhp project file in _build/htmlhelp." qthelp: $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) _build/qthelp @echo @echo "Build finished; now you can run "qcollectiongenerator" with the" \ ".qhcp project file in _build/qthelp, like this:" @echo "# qcollectiongenerator _build/qthelp/dipy.qhcp" @echo "To view the help file:" @echo "# assistant -collectionFile _build/qthelp/dipy.qhc" latex: rstexamples latex-after-examples latex-after-examples: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) _build/latex @echo @echo "Build finished; the LaTeX files are in _build/latex." @echo "Run \`make all-pdf' or \`make all-ps' in that directory to" \ "run these through (pdf)latex." changes: $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) _build/changes @echo @echo "The overview file is in _build/changes." linkcheck: $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) _build/linkcheck @echo @echo "Link check complete; look for any errors in the above output " \ "or in _build/linkcheck/output.txt." doctest: $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) _build/doctest @echo "Testing of doctests in the sources finished, look at the " \ "results in _build/doctest/output.txt." rstexamples: rstexamples-stamp rstexamples-stamp: ../tools/make_examples.py touch $@ pdf: pdf-stamp pdf-stamp: latex cd _build/latex && make all-pdf touch $@ # This one udates for the specific user named at the top of the makefile upload-website: upload-website-$(SF_USER) upload-website-%: html $(RSYNC_SF) _build/html/* _build/latex/*.pdf $*,$(WWW_SF) #upload-website-%: pdf-stamp html # $(RSYNC_SF) _build/html/* _build/latex/*.pdf $*,$(WWW_SF) dipy-0.5.0/doc/_static/000077500000000000000000000000001152576264200146665ustar00rootroot00000000000000dipy-0.5.0/doc/_static/dipy-banner.png000066400000000000000000000530341152576264200176110ustar00rootroot00000000000000‰PNG  IHDRå˜ÚûñÉsRGB®ÎébKGDÿÿÿ ½§“ pHYs-†-†ѪSCtIMEÚØ)óS IDATx^ìwœTÕÝÿßg¶Ð‘"¢‚‚ijÔX€e½ÆIdѸ‹%Uã“'½üžM3‰OªÉ“D¢,Q•$&jDl¡(VŒ¨(ˆ QúRvwîùýñ¹wçÞ3wvØ¥yÞ¯×}ÍÜsîÌÜ6÷{¾õ€Çãñxvgöú¹;#€R·Ñ³udÜÇãñìVôt·‚ ðà:·c+ù(¸Çãñ|˜8ø¸ÛX$€y@ØtOvM)ðY·Ñ³õxMÙãñxv}Ê€ŽncÈ& ÷µÄ!ÀãÀþ€~¬KlQ<€ÍncŒnnƒ'/”=g×ç#@¥Û² Å­¡#ðw  倯#÷£HHo Ñ~¤Ñ8èàvxòñBÙãñxv}%ÀP·i¨…4åBê÷þH˜¿ üWØÞ샂¶ ‘fâîDºPÎ'O[œ>O ^({<Ï®fG=“]lA¯Ëpà·…} iÅ€«HšžçC€¾±¶ˆ.À¹ákœBæë£5À+n‡'/”=g÷ x u•8} $µå®HÛ}4Öq°EJ?ü+ÙÍF`6úWøÖÏ':íišòþÈo¶žx¡ìñx<»§ ß®ûŒ~ x8ÞißHR( <„h|:|ß øc¬/Î2$|O%?÷x!ÒÖ÷µ¹>å@Òîbí{g¡Èoƒ{Á=dzóyH]@RøE}ûcm[€ÎáûÞè3/些ù2_wFBô¯Éî •HcŽËŠx}W„«)^Þµe€£€s€×7c} AßÇiûÐá…²ÇãñìzlFR•Àhr~㦰o$2SCRS>˜r]ÎBiO’orv™‡4ÝSHjÌo„¯Ã׸Oùp¤…Ï×Aþé P¡“;—Ÿ‚‹i{óÂ…t}í5îèÝ›cV­â…Aƒøôa‡±íÛÁH˜¿†´æÆè‹Zà<à)à·£ŽBšú<·Ããñx<žöÄÀ‹a Rþ䓬³k-öùçi|ôQq>1™ŒßFþÞm¡3J³:àî»ùÝÆúí-[°wÝÅT¤±žŽ4ë½s-Š¡hàáÒ™¾]J‘f^({·Ãû”=g×ÂMwŠcQ×À†õëéÙ¹sN uéBi6Kÿæ­“D¦ï,ðïxÇV°¥QÝ®÷ëÖ+/‡ÒRú"x)ðO)Þî±¾†®lÚì´Š´êõnǦ/ÊÉÞn¼cÜãñxv-Æ!?i# ²j@ÑÕ N[ÃñÇ“½÷^îíÚ•³JKáå—yæôÓy$ú"‡A(H¬/JUÚ,@Y¿{úiÎ0€NË—ÓÐØÈŸ‘ ,GBªªÍç3$µÑ P¾¶K”úu6¹(ðöæÈpÙn\»½ÇãñxvO^BfÔB•²@Üw;¶ƒ!ÀŠ‹¬Ž“´g (‹[s×’› £Š0_žëTÖó|U~/°!Ù½ëã…²Çãñì¬FBjˆÛ2EOßîvl#EK¿®GÅZ£ -ÏBð¬#cmëÈ™°@e@#:¡'#0~üâ$»^({<ÏžÃBTTÄ¥¥'½€òŒÛ‚~HH. ×OB¾îÖè‡Ò²Zã)¤ùG3]-Aº#Ò _ÛA¦ìPÄ÷ê°}GâF‘ƒ)ñR¤.Ð@"qμPöx<ž=‡—ùÚÍÛ=i¶miº> É(íéEòµô ù‚ºùfç4à>”•yðAÏœÉ?Ì‚;ïlι>E`ß ü'üŒK)*ÃÙžòn3ùźR¸–wä÷^Eþ„ÇãÙC(®#_Cû( ô*$$¶…k€ cëWÅ…Sò§yŒJƒÆ‚*†döl–FER.¤iî\Ž@^®°íÒ²F! 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ç”МH(ÑŽi`²ÞŒÁqÎÓÂÚeؕ܎j¢öFÓ!uÔâpyg2uy«ŠÓ|u˜5ëñó™)_Óµ½+*[»§wñ˜) è#Ø¶Ž’+q_ó{VæùŒ÷0_ƒÍë LÃ43ØŒ²6ÏZæ;îœÅÖY±öD›ÿß2=ÈãIEND®B`‚dipy-0.5.0/doc/_templates/000077500000000000000000000000001152576264200153755ustar00rootroot00000000000000dipy-0.5.0/doc/_templates/layout.html000066400000000000000000000054451152576264200176100ustar00rootroot00000000000000{% extends "!layout.html" %} {% set title = 'Dipy' %} {% block rootrellink %} {% endblock %} {% block extrahead %} {% endblock %} {% block header %} {% endblock %} {# This block gets put at the top of the sidebar #} {% block sidebarlogo %}

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{% endblock %} {# I had to copy the whole search block just to change the rendered text, so it doesn't mention modules or classes #} {%- block sidebarsearch %} {%- if pagename != "search" %} {%- endif %} {# The sidebarsearch block is the last one available in the default sidebar() macro, so the only way to add something to the bottom of the sidebar is to put it here, at the end of the sidebarsearch block (before it closes). #} {%- endblock %} dipy-0.5.0/doc/cite.rst000066400000000000000000000026651152576264200147270ustar00rootroot00000000000000 Publications ============== [1] Garyfallidis E, Brett M, Nimmo-Smith I (2010), “Fast Dimensionality Reduction for Brain Tractography Clusteringâ€, 16th Annual Meeting of the Organization for Human Brain Mapping. [2] Garyfallidis E, Brett M, Tsiaras V, Vogiatzis G, Nimmo-Smith I (2010), “Identification of corresponding tracks in diffusion MRI tractographies†Proc. Intl. Soc. Mag. Reson. Med. 18 [3] Correia M.M, Williams G.B, Yeh F-C, Nimmo-Smith I, Garyfallidis E (2011), “Robustness of diffusion scalar metrics when estimated with Generalized Q-Sampling Imaging acquisition schemesâ€, Proc. Intl. Soc. Mag. Reson. Med. 19 [4] Chamberlain SR, Hampshire A, Menzies LA, Garyfallidis E, Grant JE, Odlaug BL, Craig K, Fineberg N, Sahakian BJ (2010), “Reduced brain white matter integrity in trichotillomania: a diffusion tensor imaging study.†Arch Gen Psychiatry 67(9):965-71 [5] Garyfallidis E, Brett M, Amirbekian B, Nguyen C, Yeh F-C, Olivetti E, Halchenko Y, Nimmo-Smith I "Dipy - a novel software library for diffusion MR and tractography", 17th Annual Meeting of the Organization for Human Brain Mapping. [6] Yeh F-C, Wedeen VJ, Tseng WY, "Generalized Q-Sampling Imaging" A short note -------------- * If you are using the Local Skeleton Clustering (LSC) method please cite [1]. * If you are using track correspondence use [2]. * If you are using Generalized Q-sampling please cite [7]. * For everything else please cite [6]. dipy-0.5.0/doc/conf.py000066400000000000000000000170731152576264200145470ustar00rootroot00000000000000# -*- coding: utf-8 -*- # # dipy documentation build configuration file, created by # sphinx-quickstart on Thu Feb 4 15:23:20 2010. # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys, os # Doc generation depends on being able to import dipy try: import dipy except ImportError: raise RuntimeError('Cannot import dipy, please investigate') from distutils.version import LooseVersion import sphinx if LooseVersion(sphinx.__version__) < LooseVersion('1'): raise RuntimeError('Need sphinx >= 1 for numpydoc to work correctly') # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. sys.path.append(os.path.abspath('sphinxext')) # -- General configuration ----------------------------------------------------- # We load the nibabel release info into a dict by explicit execution rel = {} execfile(os.path.join('..', 'dipy', 'info.py'), rel) # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = ['sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.intersphinx', 'sphinx.ext.todo', 'sphinx.ext.coverage', 'sphinx.ext.pngmath', 'sphinx.ext.ifconfig', 'sphinx.ext.autosummary', 'math_dollar', # has to go before numpydoc 'numpydoc'] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8' # The master toctree document. master_doc = 'index' # General information about the project. project = u'dipy' copyright = u'2008-2011, %(AUTHOR)s <%(AUTHOR_EMAIL)s>' % rel # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = rel['__version__'] # The full version, including alpha/beta/rc tags. release = version # Include common links rst_epilog = open('links_names.inc', 'rt').read() # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of documents that shouldn't be included in the build. #unused_docs = [] # List of directories, relative to source directory, that shouldn't be searched # for source files. exclude_trees = ['_build', 'examples'] # The reST default role (used for this markup: `text`) to use for all documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. Major themes that come with # Sphinx are currently 'default' and 'sphinxdoc'. html_theme = 'sphinxdoc' # The style sheet to use for HTML and HTML Help pages. A file of that name # must exist either in Sphinx' static/ path, or in one of the custom paths # given in html_static_path. html_style = 'dipy.css' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {'index': 'indexsidebar.html'} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. # Setting to false fixes double module listing under header html_use_modindex = False # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # If nonempty, this is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = '' # Output file base name for HTML help builder. htmlhelp_basename = 'dipydoc' # -- Options for LaTeX output -------------------------------------------------- # The paper size ('letter' or 'a4'). #latex_paper_size = 'letter' # The font size ('10pt', '11pt' or '12pt'). #latex_font_size = '10pt' # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ('index', 'dipy.tex', u'dipy Documentation', u'Eleftherios Garyfallidis, Ian Nimmo-Smith, Matthew Brett', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # Additional stuff for the LaTeX preamble. #latex_preamble = '' # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_use_modindex = True # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = {'http://docs.python.org/': None} dipy-0.5.0/doc/devel/000077500000000000000000000000001152576264200143375ustar00rootroot00000000000000dipy-0.5.0/doc/devel/commit_codes.rst000066400000000000000000000010321152576264200175320ustar00rootroot00000000000000.. _commit-codes: Commit message codes --------------------- Please prefix all commit summaries with one (or more) of the following labels. 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!„¸ôÝ‹ÏÊâq±¦Ç#¼?¦ƒl±ÿ–œ<9û;¯Èõ‡Yž–Mü°I<9(A‚sѳ];evŒA!èOsá´[©ÆˆÉP¯c´Û‰ÅZÑdB÷+Ï<\V 6M A†Æ¯%ÑB!‰nÃ#7ù[3)íD·8³l±ÿŹYdnÜÉáR' =naPg¹†-$ÑB!‰®B]!„B]!„B!NEF/B!„’è !„B!‰®B!„’è !„BñÛ:ë“Ñöïß/Q—Ö­[ÿ!õÊ÷I!„øýŽá2ê‚B!„¸,I×!„B!‰®B!„’è !„B!‰®B!„’è !„B!‰®B!„DW!„BIt…B!„¸”ý h«E$IEND®B`‚dipy-0.5.0/doc/devel/gitwash/configure_git.rst000066400000000000000000000043231152576264200213650ustar00rootroot00000000000000.. _configure-git: =============== Configure git =============== .. _git-config-basic: Overview ======== :: git config --global user.email you@yourdomain.example.com git config --global user.name "Your Name Comes Here" In detail ========= This is to tell git_ who you are, for labeling any changes you make to the code. The simplest way to do this is from the command line:: git config --global user.email you@yourdomain.example.com git config --global user.name "Your Name Comes Here" This will write the settings into your git configuration file - a file called ``.gitconfig`` in your home directory. Advanced git configuration ========================== You might well benefit from some aliases to common commands. For example, you might well want to be able to shorten ``git checkout`` to ``git co``. The easiest way to do this, is to create a ``.gitconfig`` file in your home directory, with contents like this:: [core] editor = emacs [user] email = you@yourdomain.example.com name = Your Name Comes Here [alias] st = status stat = status co = checkout [color] diff = auto status = true (of course you'll need to set your email and name, and may want to set your editor). If you prefer, you can do the same thing from the command line:: git config --global core.editor emacs git config --global user.email you@yourdomain.example.com git config --global user.name "Your Name Comes Here" git config --global alias.st status git config --global alias.stat status git config --global alias.co checkout git config --global color.diff auto git config --global color.status true These commands will write to your user's git configuration file ``~/.gitconfig``. To set up on another computer, you can copy your ``~/.gitconfig`` file, or run the commands above. Other configuration recommended by Yarik ======================================== In your ``~/.gitconfig`` file alias section:: wdiff = diff --color-words so that ``git wdiff`` gives a nicely formatted output of the diff. To enforce summaries when doing merges(``~/.gitconfig`` file again):: [merge] summary = true .. include:: git_links.txt dipy-0.5.0/doc/devel/gitwash/development_workflow.rst000066400000000000000000000155321152576264200230210ustar00rootroot00000000000000.. _development-workflow: ==================== Development workflow ==================== You already have your own forked copy of the dipy_ repository, by following :ref:`forking`, :ref:`set-up-fork`, and you have configured git_ by following :ref:`configure-git`. Workflow summary ================ * Keep your ``master`` branch clean of edits that have not been merged to the main dipy_ development repo. Your ``master`` then will follow the main dipy_ repository. * Start a new *feature branch* for each set of edits that you do. * If you can avoid it, try not to merge other branches into your feature branch while you are working. * Ask for review! This way of working really helps to keep work well organized, and in keeping history as clear as possible. See - for example - `linux git workflow`_. Making a new feature branch =========================== :: git branch my-new-feature git checkout my-new-feature Generally, you will want to keep this also on your public github_ fork of dipy_. To do this, you `git push`_ this new branch up to your github_ repo. Generally (if you followed the instructions in these pages, and by default), git will have a link to your github_ repo, called ``origin``. You push up to your own repo on github_ with:: git push origin my-new-feature From now on git_ will know that ``my-new-feature`` is related to the ``my-new-feature`` branch in the github_ repo. The editing workflow ==================== Overview -------- :: # hack hack git add my_new_file git commit -am 'NF - some message' git push In more detail -------------- #. Make some changes #. See which files have changed with ``git status`` (see `git status`_). You'll see a listing like this one:: # On branch ny-new-feature # Changed but not updated: # (use "git add ..." to update what will be committed) # (use "git checkout -- ..." to discard changes in working directory) # # modified: README # # Untracked files: # (use "git add ..." to include in what will be committed) # # INSTALL no changes added to commit (use "git add" and/or "git commit -a") #. Check what the actual changes are with ``git diff`` (`git diff`_). #. Add any new files to version control ``git add new_file_name`` (see `git add`_). #. To commit all modified files into the local copy of your repo,, do ``git commit -am 'A commit message'``. Note the ``-am`` options to ``commit``. The ``m`` flag just signals that you're going to type a message on the command line. The ``a`` flag - you can just take on faith - or see `why the -a flag?`_. See also the `git commit`_ manual page. #. To push the changes up to your forked repo on github_, do a ``git push`` (see `git push`). Asking for code review ====================== #. Go to your repo URL - e.g. ``http://github.com/your-user-name/dipy``. #. Click on the *Branch list* button: .. image:: branch_list.png #. Click on the *Compare* button for your feature branch - here ``my-new-feature``: .. image:: branch_list_compare.png #. If asked, select the *base* and *comparison* branch names you want to compare. Usually these will be ``master`` and ``my-new-feature`` (where that is your feature branch name). #. At this point you should get a nice summary of the changes. Copy the URL for this, and post it to the `dipy mailing list`_, asking for review. The URL will look something like: ``http://github.com/your-user-name/dipy/compare/master...my-new-feature``. There's an example at http://github.com/matthew-brett/nipy/compare/master...find-install-data See: http://github.com/blog/612-introducing-github-compare-view for more detail. The generated comparison, is between your feature branch ``my-new-feature``, and the place in ``master`` from which you branched ``my-new-feature``. In other words, you can keep updating ``master`` without interfering with the output from the comparison. More detail? Note the three dots in the URL above (``master...my-new-feature``) and see :ref:`dot2-dot3`. Asking for your changes to be merged with the main repo ======================================================= When you are ready to ask for the merge of your code: #. Go to the URL of your forked repo, say ``http://github.com/your-user-name/dipy.git``. #. Click on the 'Pull request' button: .. image:: pull_button.png Enter a message; we suggest you select only ``dipy`` as the recipient. The message will go to the `dipy mailing list`_. Please feel free to add others from the list as you like. Merging from trunk ================== This updates your code from the upstream `dipy github`_ repo. Overview -------- :: # go to your master branch git checkout master # pull changes from github git fetch upstream # merge from upstream git merge upstream master In detail --------- We suggest that you do this only for your ``master`` branch, and leave your 'feature' branches unmerged, to keep their history as clean as possible. This makes code review easier:: git checkout master Make sure you have done :ref:`linking-to-upstream`. Merge the upstream code into your current development by first pulling the upstream repo to a copy on your local machine:: git fetch upstream then merging into your current branch:: git merge upstream/master Deleting a branch on github_ ============================ :: git checkout master # delete branch locally git branch -D my-unwanted-branch # delete branch on github git push origin :my-unwanted-branch (Note the colon ``:`` before ``test-branch``. See also: http://github.com/guides/remove-a-remote-branch Several people sharing a single repository ========================================== If you want to work on some stuff with other people, where you are all committing into the same repository, or even the same branch, then just share it via github_. First fork dipy into your account, as from :ref:`forking`. Then, go to your forked repository github page, say ``http://github.com/your-user-name/dipy`` Click on the 'Admin' button, and add anyone else to the repo as a collaborator: .. image:: pull_button.png Now all those people can do:: git clone git@githhub.com:your-user-name/dipy.git Remember that links starting with ``git@`` use the ssh protocol and are read-write; links starting with ``git://`` are read-only. Your collaborators can then commit directly into that repo with the usual:: git commit -am 'ENH - much better code' git push origin master # pushes directly into your repo Exploring your repository ========================= To see a graphical representation of the repository branches and commits:: gitk --all To see a linear list of commits for this branch:: git log You can also look at the `network graph visualizer`_ for your github_ repo. .. include:: git_links.txt dipy-0.5.0/doc/devel/gitwash/dot2_dot3.rst000066400000000000000000000012511152576264200203370ustar00rootroot00000000000000.. _dot2-dot3: ======================================== Two and three dots in difference specs ======================================== Thanks to Yarik Halchenko for this explanation. Imagine a series of commits A, B, C, D... Imagine that there are two branches, *topic* and *master*. You branched *topic* off *master* when *master* was at commit 'E'. The graph of the commits looks like this:: A---B---C topic / D---E---F---G master Then:: git diff master..topic will output the difference from G to C (i.e. with effects of F and G), while:: git diff master...topic would output just differences in the topic branch (i.e. only A, B, and C). dipy-0.5.0/doc/devel/gitwash/following_latest.rst000066400000000000000000000015001152576264200221070ustar00rootroot00000000000000.. _following-latest: ============================= Following the latest source ============================= These are the instructions if you just want to follow the latest *dipy* source, but you don't need to do any development for now. The steps are: * :ref:`install-git` * get local copy of the git repository from github_ * update local copy from time to time Get the local copy of the code ============================== From the command line:: git clone git://github.com/Garyfallidis/dipy.git You now have a copy of the code tree in the new ``dipy`` directory. Updating the code ================= From time to time you may want to pull down the latest code. Do this with:: cd dipy git pull The tree in ``dipy`` will now have the latest changes from the initial repository. .. include:: git_links.txt dipy-0.5.0/doc/devel/gitwash/forking_button.png000066400000000000000000000314441152576264200215530ustar00rootroot00000000000000‰PNG  IHDR]Vl8EÀ pHYs  šœ IDATxí]|TÅöþv7›Ý´M‡„N]&„O ¨Ø ‚Xÿ‚(ú¤("‚}€`@z!Ò!@¨$†’ I6»ÉöÝÿ™{wÓHÅ ™ñ—½w§ÏwÎ|sæÌ,JlÀG€#ÀàÔÒi…7Âàp8œt¹"p8D€“n ‚Í›âp8œt¹p8D€“n ‚Í›âp8œt¹p8DÀ©ÛªSMY­Vܺu jµºN»:ëååH¥åÛ÷;ƇêЦš­£22“ð{ºwG(7oÞ#77·»ÓÀ}Z+»6žŸŸ/®ŸŸ_¹£¼Ÿ1æ8”+ú{2±²2+ß”¸'‡öÏèTnn.\\\þ½‡z)‘HÜ~…ûcŽCEÒ¿÷Ò++3î^¸K²³X,`B`«UC€áÆð«(Üïs*Ò€{/½22ã–î½'7Þ#ŽGà>F€“î},\>´ÒÈËË믾ŠåË— »‘ÒsÝ¿±/^ûã¡vàî…»„;s+p׃[ìîcFºÌ5ÁnGÈd2˜Íæ{VVÕÙ3g°hÑ"A0“'OF»víîٱ߹öÔnÉŠdÆ-ÝÚ•Ðzzz:¶mÛ†9sæì=33³Âžé¯¬C¿~ý0~U¡Õreõ$!nõm…åï,ƒWŽìÀŽ#q(ÏëšgïÛÝëÇõžÝŒX¶l™P844´\K7nÝ'– ã¢USÖ½Ûý__­äÎú\]¥ ÆÎê\²d 222ÊÅ@h[Ÿô/ŽA¿aã±îdrù]3\Ä»TnüJ¦Ÿ„ÃëýÑoüª8äaÓ»%ê&¼‡¾þ5ÎÜ2—_¤VNß«ÒnéV­ªä­„¥«Ñh°aÃ9rþþþ0`€0vïÞM›6 ýé§Ÿ.óÚ™Ù¦z$“ºÂ])‡Fg,°Z¤n.P:Ù 3U¥Ó•È+1áô7s°Bòõo ›±ô‰a³÷MáåMý0V½•9€¬ÆEG¤Óéðí·ßâêÕ«3f úôéS`å–f ùBqŸ¶]ÑÞ_¼ú—w3þ4&k%ÇdƒAG»=ÝÊpS‚„„*­V999˜;w.ŒFc$Ìâÿæ›o0}út¸ººèNANj̈́›B§ãá!@ö<E~‚Ð=«ÑÖ¹Œå—úžGå ‚~ÊÄúõJ¨k6hubC¡}Ÿ@#ºð“S1»ðÞÔöØÿã@H­ŽŽÔ³’ú^¬gÈŒ“n1´jîË•+WŸ"› AAA˜5kؽS6ùû÷ïÏ>û |o¯½öZ¶lYö¤`Ý–Ü>•ö,À´u©hÖ Ø½ë¼õÀÄ™Ÿ¢}Æ&|²ð~4ÚÄ`æ{óa{h¦i‹½_}‚UêΘóå(dí]†Ù³ÃDÕ7ê4ã'¾ãî™X®fm­Àû Z`ÖÝ¿òÍóµzd Þš0Ô¤¶/Ÿðƒ{qÙ«!ÆLœ‰qý‚í)5û(J¸/¼ð‚°À1û+p‹öîÅ©Kð|K+¬ˆÉbÕ§žÆÓ>ƺ³9BÖc>ÃGãúÁS‡ïÞƒôfDNÇþD‹™?À](§€fì[<kÏÝÀƒc¦c\ÍÜÕwviÁ‚prr\*ì”6v†ÉTþêìJÙ%m‡ã¿³ŸƒÔbÅù%/á•åQ8—`„[ÜBLÝjÀ´o?@ eöýw26؆cÞ»EÆ'6Wê  ºáÓù_¢¥ÕB8'áÓŸÁ^m6@m¸²_Ïšƒð˜A‡'|òñ„%=s§}‰]±jô8HGýǦ`â£ÀwgÀð¯iøà©ÐÇíÀ»S¶bÈœy ¯8¹ê|µb7r$*ôù&¦Œ¾ætìøá+ÌÙ|NècçãðÎ;#pí—‹ëûøžP”:ŠÊGJ+Ÿ•ç¬.™2«ƒ. ƒ÷½÷ÞÔ)S••…'Ÿ|RHcï_}õŽ=Zñ6P(Qø¡K¿‚ؘØUïN€œäø|úN¸6®Ø¤l޼Eäqb’ñçÆsÐ!»öœE²S0nlÇ"\íCo૯Bvn>ýlB{áo6IBѽs#˜Î-ÆF¸!#ðæØ¾ˆ=° ï̇D*βăÉèÿþ84ÉIÆêÏ!ÁVóë<³è.#\¶¨iµZÁÊe?`©(l˜÷ ¦6 Ó>üÎÙŽ›š«˜;ä pOøoʼnUŸãå— %/ÇÄàø®Í8. 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The instructions here are very similar to the instructions at http://help.github.com/forking/ - please see that page for more detail. We're repeating some of it here just to give the specifics for the dipy_ project, and to suggest some default names. Set up and configure a github_ account ====================================== If you don't have a github_ account, go to the github_ page, and make one. You then need to configure your account to allow write access - see the ``Generating SSH keys`` help on `github help`_. Create your own forked copy of dipy_ ========================================= #. Log into your github_ account. #. Go to the dipy_ github home at `dipy github`_. #. Click on the *fork* button: .. image:: forking_button.png Now, after a short pause and some 'Hardcore forking action', you should find yourself at the home page for your own forked copy of dipy_. .. include:: git_links.txt dipy-0.5.0/doc/devel/gitwash/git_development.rst000066400000000000000000000003151152576264200217230ustar00rootroot00000000000000.. _git-development: ===================== Git for development ===================== Contents: .. toctree:: :maxdepth: 2 forking_hell set_up_fork configure_git development_workflow dipy-0.5.0/doc/devel/gitwash/git_install.rst000066400000000000000000000011151152576264200210460ustar00rootroot00000000000000.. _install-git: ============= Install git ============= Overview ======== ================ ============= Debian / Ubuntu ``sudo apt-get install git-core`` Fedora ``sudo yum install git-core`` Windows Download and install msysGit_ OS X Use the git-osx-installer_ ================ ============= In detail ========= See the git_ page for the most recent information. Have a look at the github_ install help pages available from `github help`_ There are good instructions here: http://book.git-scm.com/2_installing_git.html .. include:: git_links.txt dipy-0.5.0/doc/devel/gitwash/git_intro.rst000066400000000000000000000010361152576264200205350ustar00rootroot00000000000000============== Introduction ============== These pages describe a git_ and github_ workflow for the dipy_ project. There are several different workflows here, for different ways of working with *dipy*. This is not a comprehensive git_ reference, it's just a workflow for our own project. It's tailored to the github_ hosting service. You may well find better or quicker ways of getting stuff done with git_, but these should get you started. For general resources for learning git_ see :ref:`git-resources`. .. include:: git_links.txt dipy-0.5.0/doc/devel/gitwash/git_links.txt000066400000000000000000000070161152576264200205350ustar00rootroot00000000000000.. This (-*- rst -*-) format file contains commonly used link targets and name substitutions. It may be included in many files, therefore it should only contain link targets and name substitutions. Try grepping for "^\.\. _" to find plausible candidates for this list. .. NOTE: reST targets are __not_case_sensitive__, so only one target definition is needed for nipy, NIPY, Nipy, etc... .. PROJECTNAME placeholders .. _PROJECTNAME: http://neuroimaging.scipy.org .. _`PROJECTNAME github`: http://github.com/nipy .. _`PROJECTNAME mailing list`: http://projects.scipy.org/mailman/listinfo/nipy-devel .. nipy .. _nipy: http://nipy.org/nipy .. _`nipy github`: http://github.com/nipy/nipy .. _`nipy mailing list`: http://mail.scipy.org/mailman/listinfo/nipy-devel .. ipython .. _ipython: http://ipython.scipy.org .. _`ipython github`: http://github.com/ipython .. _`ipython mailing list`: http://mail.scipy.org/mailman/listinfo/IPython-dev .. nipy .. _dipy: http://nipy.org/dipy .. _`dipy github`: http://github.com/Garyfallidis/dipy .. _`dipy mailing list`: http://mail.scipy.org/mailman/listinfo/nipy-devel .. git stuff .. _git: http://git-scm.com/ .. _github: http://github.com .. _github help: http://help.github.com .. _msysgit: http://code.google.com/p/msysgit/downloads/list .. _git-osx-installer: http://code.google.com/p/git-osx-installer/downloads/list .. _subversion: http://subversion.tigris.org/ .. _git cheat sheet: http://github.com/guides/git-cheat-sheet .. _pro git book: http://progit.org/ .. _git svn crash course: http://git-scm.com/course/svn.html .. _learn.github: http://learn.github.com/ .. _network graph visualizer: http://github.com/blog/39-say-hello-to-the-network-graph-visualizer .. _git user manual: http://www.kernel.org/pub/software/scm/git/docs/user-manual.html .. _git tutorial: http://www.kernel.org/pub/software/scm/git/docs/gittutorial.html .. _git community book: http://book.git-scm.com/ .. _git ready: http://www.gitready.com/ .. _git casts: http://www.gitcasts.com/ .. _Fernando's git page: http://www.fperez.org/py4science/git.html .. _git magic: http://www-cs-students.stanford.edu/~blynn/gitmagic/index.html .. _git concepts: http://www.eecs.harvard.edu/~cduan/technical/git/ .. _git clone: http://www.kernel.org/pub/software/scm/git/docs/git-clone.html .. _git checkout: http://www.kernel.org/pub/software/scm/git/docs/git-checkout.html .. _git commit: http://www.kernel.org/pub/software/scm/git/docs/git-commit.html .. _git push: http://www.kernel.org/pub/software/scm/git/docs/git-push.html .. _git pull: http://www.kernel.org/pub/software/scm/git/docs/git-pull.html .. _git add: http://www.kernel.org/pub/software/scm/git/docs/git-add.html .. _git status: http://www.kernel.org/pub/software/scm/git/docs/git-status.html .. _git diff: http://www.kernel.org/pub/software/scm/git/docs/git-diff.html .. _git log: http://www.kernel.org/pub/software/scm/git/docs/git-log.html .. _git branch: http://www.kernel.org/pub/software/scm/git/docs/git-branch.html .. _git remote: http://www.kernel.org/pub/software/scm/git/docs/git-remote.html .. _git config: http://www.kernel.org/pub/software/scm/git/docs/git-config.html .. _why the -a flag?: http://www.gitready.com/beginner/2009/01/18/the-staging-area.html .. _git staging area: http://www.gitready.com/beginner/2009/01/18/the-staging-area.html .. _git management: http://kerneltrap.org/Linux/Git_Management .. _linux git workflow: http://www.mail-archive.com/dri-devel@lists.sourceforge.net/msg39091.html .. _git parable: http://tom.preston-werner.com/2009/05/19/the-git-parable.html dipy-0.5.0/doc/devel/gitwash/git_resources.rst000066400000000000000000000033071152576264200214170ustar00rootroot00000000000000.. _git-resources: ================ git_ resources ================ Tutorials and summaries ======================= * `github help`_ has an excellent series of how-to guides. * `learn.github`_ has an excellent series of tutorials * The `pro git book`_ is a good in-depth book on git. * A `git cheat sheet`_ is a page giving summaries of common commands. * The `git user manual`_ * The `git tutorial`_ * The `git community book`_ * `git ready`_ - a nice series of tutorials * `git casts`_ - video snippets giving git how-tos. * `git magic`_ - extended introduction with intermediate detail * Fernando Perez' git page - `Fernando's git page`_ - many links and tips * A good but technical page on `git concepts`_ * Th `git parable`_ is an easy read explaining the concepts behind git. * `git svn crash course`_: git_ for those of us used to subversion_ Advanced git workflow ===================== There are many ways of working with git_; here are some posts on the rules of thumb that other projects have come up with: * Linus Torvalds on `git management`_ * Linus Torvalds on `linux git workflow`_ . Summary; use the git tools to make the history of your edits as clean as possible; merge from upstream edits as little as possible in branches where you are doing active development. Manual pages online =================== You can get these on your own machine with (e.g) ``git help push`` or (same thing) ``git push --help``, but, for convenience, here are the online manual pages for some common commands: * `git add`_ * `git branch`_ * `git checkout`_ * `git clone`_ * `git commit`_ * `git config`_ * `git diff`_ * `git log`_ * `git pull`_ * `git push`_ * `git remote`_ * `git status`_ .. include:: git_links.txt dipy-0.5.0/doc/devel/gitwash/index.rst000066400000000000000000000003431152576264200176460ustar00rootroot00000000000000.. _using-git: Working with *dipy* source code ====================================== Contents: .. toctree:: :maxdepth: 2 git_intro git_install following_latest patching git_development git_resources dipy-0.5.0/doc/devel/gitwash/patching.rst000066400000000000000000000075631152576264200203470ustar00rootroot00000000000000================ Making a patch ================ You've discovered a bug or something else you want to change in dipy_ - excellent! You've worked out a way to fix it - even better! You want to tell us about it - best of all! The easiest way is to make a *patch* or set of patches. Here we explain how. Making a patch is the simplest and quickest, but if you're going to be doing anything more than simple quick things, please consider following the :ref:`git-development` model instead. .. _making-patches: Making patches ============== Overview -------- :: # tell git who you are git config --global user.email you@yourdomain.example.com git config --global user.name "Your Name Comes Here" # get the repository if you don't have it git clone git://github.com/Garyfallidis/dipy.git # make a branch for your patching cd dipy git branch the-fix-im-thinking-of git checkout the-fix-im-thinking-of # hack, hack, hack # Tell git about any new files you've made git add somewhere/tests/test_my_bug.py # commit work in progress as you go git commit -am 'BF - added tests for Funny bug' # hack hack, hack git commit -am 'BF - added fix for Funny bug' # make the patch files git format-patch -M -C master Then, send the generated patch files to the `dipy mailing list`_ - where we will thank you warmly. In detail --------- #. Tell git_ who you are so it can label the commits you've made:: git config --global user.email you@yourdomain.example.com git config --global user.name "Your Name Comes Here" #. If you don't already have one, clone a copy of the dipy_ repository:: git clone git://github.com/Garyfallidis/dipy.git cd dipy #. Make a 'feature branch'. This will be where you work on your bug fix. It's nice and safe and leaves you with access to an unmodified copy of the code in the main branch:: git branch the-fix-im-thinking-of git checkout the-fix-im-thinking-of #. Do some edits, and commit them as you go:: # hack, hack, hack # Tell git about any new files you've made git add somewhere/tests/test_my_bug.py # commit work in progress as you go git commit -am 'BF - added tests for Funny bug' # hack hack, hack git commit -am 'BF - added fix for Funny bug' Note the ``-am`` options to ``commit``. The ``m`` flag just signals that you're going to type a message on the command line. The ``a`` flag - you can just take on faith - or see `why the -a flag?`_. #. When you have finished, check you have committed all your changes:: git status #. Finally, make your commits into patches. You want all the commits since you branched from the ``master`` branch:: git format-patch -M -C master You will now have several files named for the commits:: 0001-BF-added-tests-for-Funny-bug.patch 0002-BF-added-fix-for-Funny-bug.patch Send these files to the `dipy mailing list`_. When you are done, to switch back to the main copy of the code, just return to the ``master`` branch:: git checkout master Moving from patching to development =================================== If you find you have done some patches, and you have one or more feature branches, you will probably want to switch to development mode. You can do this with the repository you have. Fork the dipy_ repository on github_ - :ref:`forking`. Then:: # checkout and refresh master branch from main repo git checkout master git pull origin master # rename pointer to main repository to 'upstream' git remote rename origin upstream # point your repo to default read / write to your fork on github git remote add origin git@github.com:your-user-name/dipy.git # push up any branches you've made and want to keep git push origin the-fix-im-thinking-of Then you can, if you want, follow the :ref:`development-workflow`. .. include:: git_links.txt dipy-0.5.0/doc/devel/gitwash/pull_button.png000066400000000000000000000311351152576264200210650ustar00rootroot00000000000000‰PNG  IHDR~\iÉÞu pHYs  šœ IDATxí]|TÅÖÿoß”MHH€ ¡„–P¤KTôå,€¢"ÊS, Oð‰øÀ§‚}€%"]zï„HR’@©›dûîwæÞ½ÉfIBB$a&¿Ý{ïÔ3ÿsæÌ™3s72ðÀàp8÷ òû¦§¼£ŽG€# À?ŽG€#pŸ!Àÿ}ÆpÞ]ŽG€#À?—ŽG€#pŸ!Àÿ}ÆpÞ]ŽG€#À?—ŽG€#pŸ! ¬ýµÛíHKKCVVVuèN¹ûP£F øûûC./y>çx•bpŒËÝßU’óìVäeÕáÿÍ›7Á”™——×­=¼ObØëyyy‚Ò(±×¯á)6‘c\,4÷lçYѬ)Ù4,ºÌ=› {Ž®Ê$H&“ 0,n8^·C¨ètŽqѸÜ˱œgEs§Z¸zl6ƒÙì~?†Ãâvãu;„ŠOçͽšÂyv+gª…Åk·x G€#Àà‡À}¯ø÷ìÙƒ©S§âÔ©Sª¡8 xyò$¾ûî;¶wß}­Zµ*—ø¸.6.O[ÑB$É´ÜÓ r)›ÉÄ»›BñúèÕ? 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First you follow the instructions for :ref:`forking`. Overview ======== :: git clone git@github.com/your-user-name/dipy.git cd dipy git remote add upstream git://github.com/Garyfallidis/dipy.git In detail ========= Clone your fork --------------- #. Clone your fork to the local computer with ``git clone git@github.com:your-user-name/dipy.git`` #. Investigate. Change directory to your new repo: ``cd dipy``. Then ``git branch -a`` to show you all branches. You'll get something like:: * master remotes/origin/master This tells you that you are currently on the ``master`` branch, and that you also have a ``remote`` connection to ``origin/master``. What remote repository is ``remote/origin``? Try ``git remote -v`` to see the URLs for the remote. They will point to your github_ fork. Now you want to connect to the upstream `dipy github`_ repository, so you can merge in changes from trunk. .. _linking-to-upstream: Linking your repository to the upstream repo -------------------------------------------- :: cd dipy git remote add upstream git://github.com/Garyfallidis/dipy.git ``upstream`` here is just the arbitrary name we're using to refer to the main dipy_ repository at `dipy github`_. Note that we've used ``git://`` for the URL rather than ``git@``. The ``git://`` URL is read only. This means we that we can't accidentally (or deliberately) write to the upstream repo, and we are only going to use it to merge into our own code. Just for your own satisfaction, show yourself that you now have a new 'remote', with ``git remote -v show``, giving you something like:: upstream git://github.com/Garyfallidis/dipy.git (fetch) upstream git://github.com/Garyfallidis/dipy.git (push) origin git@github.com:your-user-name/dipy.git (fetch) origin git@github.com:your-user-name/dipy.git (push) .. include:: git_links.txt dipy-0.5.0/doc/devel/index.rst000066400000000000000000000003661152576264200162050ustar00rootroot00000000000000 .. _development: DiPy development ================ Contents: .. toctree:: :maxdepth: 2 intro gitwash/index make_release commit_codes Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` dipy-0.5.0/doc/devel/intro.rst000066400000000000000000000016471152576264200162340ustar00rootroot00000000000000============== Introduction ============== dipy_ is in development mode at the moment. Please do join in the fun. The lead developer is Eleftherios Garyfallidis, with support from Ian Nimmo-Smith, Matthew Brett, Bago Amirbekian, Frank Yeh, Christopher Nguyen and (your name here). See the main documentation for the full list of dipy developers and contributors. The primary develpoment repository is `dipy github`_ Please do contribute. Have a look at :ref:`using-git` for some ideas on how to get going. Have a look at the `nipy development guidelines`_ for our coding habits. In summary, please follow the `numpy coding style`_ - and of course - PEP8_ . Test everything! We are using nose_ ; see the existing code for example tests. If you can please use our :ref:`commit-codes`. But - just pitch in - send us some code - we'll give you feedback if you want it - that way we learn from each other. And - welcome... dipy-0.5.0/doc/devel/make_release.rst000066400000000000000000000155351152576264200175170ustar00rootroot00000000000000.. _release-guide: ********************************* A guide to making a dipy release ********************************* A guide for developers who are doing a dipy release * Edit :file:`info.py` and bump the version number .. _release-tools: Release tools ============= There are some release utilities that come with nibabel_. nibabel should install these as the ``nisext`` package, and the testing stuff is understandably in the ``testers`` module of that package. Dipy has Makefile targets for their use. The relevant targets are:: make check-version-info make sdist-tests The first installs the code from a git archive, from the repository, and for in-place use, and runs the ``get_info()`` function to confirm that installation is working and information parameters are set correctly. The second (``sdist-tests``) makes an sdist source distribution archive, installs it to a temporary directory, and runs the tests of that install. If you have a version of nibabel trunk past February 11th 2011, there will also be a functional make target:: make bdist-egg-tests This builds an egg (which is a zip file), hatches it (unzips the egg) and runs the tests from the resulting directory. .. _release-checklist: Release checklist ================= * Review the open list of `issues `_ . Check whether there are outstanding issues that can be closed, and whether there are any issues that should delay the release. Label them ! * Review and update the release notes. Review and update the :file:`Changelog` file. Get a partial list of contributors with something like:: git log 0.4.0.. | grep '^Author' | cut -d' ' -f 2- | sort | uniq where ``0.4.0`` was the last release tag name. Then manually go over the *git log* to make sure the release notes are as complete as possible and that every contributor was recognized. * Check the ``long_description`` in ``dipy/info.py``. Check it matches the ``README`` in the root directory. * Clean and compile:: make distclean python setup.py build_ext --inplace * Make sure all tests pass (from the dipy root directory):: cd .. nosetests --with-doctest dipy cd dipy # back to the root directory * Check the documentation doctests:: cd doc make doctest cd .. At the moment this generates lots of errors from the autodoc documentation running the doctests in the code, where the doctests pass when run in nose - we should find out why this is at some point, but leave it for now. * Make sure all tests pass from sdist:: make sdist-tests and bdist_egg:: make bdist-egg-tests and the three ways of installing (from tarball, repo, local in repo):: make check-version-info The last may not raise any errors, but you should detect in the output lines of this form:: {'sys_version': '2.6.6 (r266:84374, Aug 31 2010, 11:00:51) \n[GCC 4.0.1 (Apple Inc. build 5493)]', 'commit_source': 'archive substitution', 'np_version': '1.5.0', 'commit_hash': '25b4125', 'pkg_path': '/var/folders/jg/jgfZ12ZXHwGSFKD85xLpLk+++TI/-Tmp-/tmpGPiD3E/pylib/dipy', 'sys_executable': '/Library/Frameworks/Python.framework/Versions/2.6/Resources/Python.app/Contents/MacOS/Python', 'sys_platform': 'darwin'} /var/folders/jg/jgfZ12ZXHwGSFKD85xLpLk+++TI/-Tmp-/tmpGPiD3E/pylib/dipy/__init__.pyc {'sys_version': '2.6.6 (r266:84374, Aug 31 2010, 11:00:51) \n[GCC 4.0.1 (Apple Inc. build 5493)]', 'commit_source': 'installation', 'np_version': '1.5.0', 'commit_hash': '25b4125', 'pkg_path': '/var/folders/jg/jgfZ12ZXHwGSFKD85xLpLk+++TI/-Tmp-/tmpGPiD3E/pylib/dipy', 'sys_executable': '/Library/Frameworks/Python.framework/Versions/2.6/Resources/Python.app/Contents/MacOS/Python', 'sys_platform': 'darwin'} Files not taken across by the installation: [] /Users/mb312/dev_trees/dipy/dipy/__init__.pyc {'sys_version': '2.6.6 (r266:84374, Aug 31 2010, 11:00:51) \n[GCC 4.0.1 (Apple Inc. build 5493)]', 'commit_source': 'repository', 'np_version': '1.5.0', 'commit_hash': '25b4125', 'pkg_path': '/Users/mb312/dev_trees/dipy/dipy', 'sys_executable': '/Library/Frameworks/Python.framework/Versions/2.6/Resources/Python.app/Contents/MacOS/Python', 'sys_platform': 'darwin'} * The release should now be ready. * Edit :file:`dipy/info.py` to set ``_version_extra`` to ``''``; commit * Build the release files:: make distclean make source-release * Once everything looks good, upload the source release to PyPi. See `setuptools intro`_:: python setup.py register python setup.py sdist --formats=gztar,zip upload * Then upload the binary release for the platform you are currently on:: python setup.py bdist_egg upload * Do binary builds for any virtualenvs you have:: workon python25 python setup.py bdist_egg upload deactivate etc. (``workon`` is a virtualenvwrapper command). * Repeat binary builds for Linux 32, 64 bit and OS X. * Get to a windows machine and do egg and wininst builds:: make distclean c:\Python26\python.exe setup.py bdist_egg upload c:\Python26\python.exe setup.py bdist_wininst --target-version=2.6 register upload Maybe virtualenvs for the different versions of python? I haven't explored that yet. * Tag the release with tag of form ``0.5.0``:: git tag -am 'First public release' 0.5.0 * Now the version number is OK, push the docs to sourceforge with:: make upload-htmldoc-mysfusername where ``mysfusername`` is obviously your own sourceforge username. * Set up maintenance / development branches If this is this is a full release you need to set up two branches, one for further substantial development (often called 'trunk') and another for maintenance releases. * Branch to maintainance:: git co -b maint/1.0.x Set ``_version_extra`` back to ``.dev`` and bump ``_version_micro`` by 1. Thus the maintenance series will have version numbers like - say - '0.5.1.dev' until the next maintenance release - say '0.5.1'. Commit. * Start next development series:: git co main-master then restore ``.dev`` to ``_version_extra``, and bump ``_version_minor`` by 1. Thus the development series ('trunk') will have a version number here of '0.6.0.dev' and the next full release will be '0.6.0'. If this is just a maintenance release from ``maint/0.5.x`` or similar, just tag and set the version number to - say - ``0.5.2.dev``. * Make a tarball for the examples, for packagers to get away without having vtk or a display on the build machines:: cd doc make examples-tgz The command requires pytables_ and python vtk on your machine. It writes an archive named for the dipy version and the docs, e.g:: /dist/dipy-0.5.0.dev-doc-examples.tar.gz We need to decide where to put this tarball. * Announce to the mailing lists. .. _setuptools intro: http://packages.python.org/an_example_pypi_project/setuptools.html dipy-0.5.0/doc/developers.rst000066400000000000000000000012371152576264200161450ustar00rootroot00000000000000.. _dipy_developers: Developers ================ The core development team consists of the following individuals: - **Eleftherios Garyfallidis**, University of Cambridge, UK - **Ian Nimmo-Smith**, MRC Cognition and Brain Sciences Unit, Cambridge, UK - **Matthew Brett**, University of California, Berkeley, CA - **Bago Amirbekian**, University of California, San Fransisco, CA - **Frank Yeh**, Carnegie Mellon University, Pittsburgh, PA - **Christopher Nguyen**, University of California, Los Angeles, CA - **Emanuele Olivetti**, NeuroInformatics Laboratory (NILab), Trento, Italy - **Yaroslav Halchenco**, PBS Department, Dartmouth, NH - **And your name here ...** dipy-0.5.0/doc/diffusion.bib000066400000000000000000020313131152576264200157070ustar00rootroot00000000000000@comment{This file has been generated by Pybliographer} @Article{Garyfallidis2009b, Author = {Garyfallidis, Eleftherios and Brett, Matthew and Nimmo-smith, Ian}, Title = {{Fast Dimensionality Reduction for Brain Tractography}}, Journal = {Computer}, Volume = {15}, Number = {6}, Pages = {2009--2009}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Garyfallidis, Brett, Nimmo-smith - 2009 - Fast Dimensionality Reduction for Brain Tractography.pdf:pdf}, year = 2009 } @Article{Ese2006, Author = {Ese, T H}, Title = {{Analysis and Classification of EEG Signals using Probabilistic Models for Brain Computer Interfaces Ecole Polytechnique F ´ ed ´ erale de Lausanne Silvia Chiappa}}, Journal = {Learning}, Volume = {3547}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Ese - 2006 - Analysis and Classification of EEG Signals using Probabilistic Models for Brain Computer Interfaces Ecole Polytechnique F ´ ed ´ erale de Lausanne Silvia Chiappa.pdf:pdf}, year = 2006 } @Article{Oliphant2010, Author = {Oliphant, Travis E}, Title = {{Guide to NumPy}}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Oliphant - 2010 - Guide to NumPy.pdf:pdf}, year = 2010 } @Article{yamamoto2007dtf, Author = {Yamamoto, A. and Miki, Y. and Urayama, S. and Fushimi, Y. and Okada, T. and Hanakawa, T. and Fukuyama, H. and Togashi, K.}, Title = {{Diffusion tensor fiber tractography of the optic radiation: analysis with 6-, 12-, 40-, and 81-directional motion-probing gradients, a preliminary study}}, Journal = {American Journal of Neuroradiology}, Volume = {28}, Number = {1}, Pages = {92}, publisher = {Am Soc Neuroradiology}, year = 2007 } @Article{FW05, Author = {Friman, O. and Westin, C. F.}, Title = {Uncertainty in white matter fiber tractography.}, Journal = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv}, Volume = {8}, Number = {Pt 1}, Pages = {107-14}, abstract = {In this work we address the uncertainty associated with fiber paths obtained in white matter fiber tractography. This uncertainty, which arises for example from noise and partial volume effects, is quantified using a Bayesian modeling framework. The theory for estimating the probability of a connection between two areas in the brain is presented, and a new model of the local water diffusion profile is introduced. We also provide a theorem that facilitates the estimation of the parameters in this diffusion model, making the presented method simple to implement.}, authoraddress = {Laboratory of Mathematics in Imaging, Department of Radiology Brigham and Women's Hospital, Harvard Medical School, USA.}, keywords = {*Algorithms ; Artificial Intelligence ; Brain/*anatomy \& histology ; Diffusion Magnetic Resonance Imaging/*methods ; Humans ; Image Enhancement/*methods ; Image Interpretation, Computer-Assisted/*methods ; Imaging, Three-Dimensional/*methods ; Nerve Fibers, Myelinated/*ultrastructure ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity}, language = {eng}, medline-crdt = {2006/05/12 09:00}, medline-da = {20060511}, medline-dcom = {20060609}, medline-edat = {2006/05/12 09:00}, medline-fau = {Friman, Ola ; Westin, Carl-Fredrik}, medline-gr = {P41-RR13218/RR/NCRR NIH HHS/United States}, medline-jid = {101249582}, medline-jt = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}, medline-lr = {20071114}, medline-mhda = {2006/06/10 09:00}, medline-own = {NLM}, medline-pl = {Germany}, medline-pmid = {16685835}, medline-pst = {ppublish}, medline-pt = {Journal Article ; Research Support, N.I.H., Extramural}, medline-sb = {IM}, medline-so = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. 2005;8(Pt 1):107-14.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=16685835}, year = 2005 } @Article{BaoPMB2009, Author = {Bao, LJ and Zhu, YM and Liu, WY and Croisille, P. and Pu, ZB and Robini, M. and Magnin, IE}, Title = {{Denoising human cardiac diffusion tensor magnetic resonance images using sparse representation combined with segmentation}}, Journal = {Physics in Medicine and Biology}, Volume = {54}, Number = {6}, Pages = {1435--1456}, abstract = {Cardiac diffusion tensor magnetic resonance imaging (DT-MRI) is noise sensitive, and the noise can induce numerous systematic errors in subsequent parameter calculations. This paper proposes a sparse representation-based method for denoising cardiac DT-MRI images. The method first generates a dictionary of multiple bases according to the features of the observed image. A segmentation algorithm based on nonstationary degree detector is then introduced to make the selection of atoms in the dictionary adapted to the image's features. The denoising is achieved by gradually approximating the underlying image using the atoms selected from the generated dictionary. The results on both simulated image and real cardiac DT-MRI images from ex vivo human hearts show that the proposed denoising method performs better than conventional denoising techniques by preserving image contrast and fine structures.}, year = 2009 } @Article{Baldi, Author = {Baldi, P and Kerkyacharian, G and Matematica, Dipartimento and Tor, Roma}, Title = {{arXiv : 0807 . 5059v1 [ math . ST ] 31 Jul 2008 Adaptive density estimation for directional data using needlets}}, arxivid = {arXiv:0807.5059v1}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Baldi et al. - Unknown - arXiv 0807 . 5059v1 math . ST 31 Jul 2008 Adaptive density estimation for directional data using needlets.pdf:pdf}, keywords = {and phrases,density estimation,needlets,spherical and directional data,thresholding} } @Article{Science2008, Author = {Science, Computer and Supervisor, Thesis and Wells, William M and Westin, Carl-fredrik and Orlando, Terry P}, Title = {{Quantitative Analysis of Cerebral White Matter Anatomy from Diffusion MRI by}}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Science et al. - 2008 - Quantitative Analysis of Cerebral White Matter Anatomy from Diffusion MRI by.pdf:pdf}, year = 2008 } @Article{Simon2005NeuroImage, Author = {Simon, Tony J. and Ding, Lijun and Bish, Joel P. and McDonald-McGinn, Donna M. and Zackai, Elaine H. and Geeb, James}, Title = {Volumetric, connective, and morphologic changes in the brains of children with chromosome 22q11.2 deletion syndrome: an integrative study}, Journal = {NeuroImage}, Volume = {25}, Pages = {169-180}, abstract = {Chromosome 22q11.2 deletion syndrome is a highly prevalent genetic disorder whose manifestations include developmental disability and sometimes mental retardation. The few studies that have examined brain morphology in different samples from this population have found similar general patterns, mostly using region of interest measures. We employed voxel-based techniques to concurrently examine specific morphologic changes in multiple brain tissue measures. Results were similar to previous findings of volumetric reductions in the posterior brain. They also extended them in two ways. First, our methods provided greater specificity in the localization of changes detected. Second, the combination of our measures of gray and white matter along with cerebrospinal fluid volume and fractional anisotropy, which indicates the structure of white matter, showed a posterior displacement of and morphologic changes to the corpus callosum in affected children.}, doi = {j.neuroimage.2004.11.018}, file = {attachment\:Simon2005NeuroImage.pdf:attachment\:Simon2005NeuroImage.pdf:PDF}, publisher = {Elsevier}, year = 2005 } @Article{Hagmann2008PLoSBiol, Author = {Hagmann, P and Cammoun, L and Gigandet, X and Meuli, R and Honey, C J and Wedeen, Van J. and Sporns, Olaf }, Title = {Mapping the structural core of human cerebral cortex}, Journal = {PLoS Biol}, Volume = {6}, Number = {7}, Pages = {e159}, abstract = {Structurally segregated and functionally specialized regions of the human cerebral cortex are interconnected by a dense network of cortico-cortical axonal pathways. By using diffusion spectrum imaging, we noninvasively mapped these pathways within and across cortical hemispheres in individual human participants. An analysis of the resulting large-scale structural brain networks reveals a structural core within posterior medial and parietal cerebral cortex, as well as several distinct temporal and frontal modules. Brain regions within the structural core share high degree, strength, and betweenness centrality, and they constitute connector hubs that link all major structural modules. The structural core contains brain regions that form the posterior components of the human default network. Looking both within and outside of core regions, we observed a substantial correspondence between structural connectivity and resting-state functional connectivity measured in the same participants. The spatial and topological centrality of the core within cortex suggests an important role in functional integration.}, doi = {doi:10.1371/journal.pbio.0060159}, file = {attachment\:Hagmann2008PLoSBiol.pdf:attachment\:Hagmann2008PLoSBiol.pdf:PDF}, year = 2008 } @Article{menzies2008wma, Author = {Menzies, L. and Williams, G.B. and Chamberlain, S.R. and Ooi, C. and Fineberg, N. and Suckling, J. and Sahakian, B.J. and Robbins, T.W. and Bullmore, E.T.}, Title = {{White matter abnormalities in patients with obsessive-compulsive disorder and their first-degree relatives}}, Journal = {American Journal of Psychiatry}, Volume = {165}, Number = {10}, Pages = {1308}, publisher = {Am Psychiatric Assoc}, year = 2008 } @Article{Gong2008CerebralCortex, Author = {Gong, Gaolang and He, Yong and Concha, Luis and Lebel, Catherine and Gross, Donald W. and Evans, Alan C. and Beaulieu, Christian}, Title = {{Mapping Anatomical Connectivity Patterns of Human Cerebral Cortex Using In Vivo Diffusion Tensor Imaging Tractography}}, Journal = {Cereb. Cortex}, Pages = {bhn102}, abstract = {The characterization of the topological architecture of complex networks underlying the structural and functional organization of the brain is a basic challenge in neuroscience. However, direct evidence for anatomical connectivity networks in the human brain remains scarce. Here, we utilized diffusion tensor imaging deterministic tractography to construct a macroscale anatomical network capturing the underlying common connectivity pattern of human cerebral cortex in a large sample of subjects (80 young adults) and further quantitatively analyzed its topological properties with graph theoretical approaches. The cerebral cortex was divided into 78 cortical regions, each representing a network node, and 2 cortical regions were considered connected if the probability of fiber connections exceeded a statistical criterion. The topological parameters of the established cortical network (binarized) resemble that of a "small-world" architecture characterized by an exponentially truncated power-law distribution. These characteristics imply high resilience to localized damage. Furthermore, this cortical network was characterized by major hub regions in association cortices that were connected by bridge connections following long-range white matter pathways. Our results are compatible with previous structural and functional brain networks studies and provide insight into the organizational principles of human brain anatomical networks that underlie functional states.}, doi = {10.1093/cercor/bhn102}, eprint = {http://cercor.oxfordjournals.org/cgi/reprint/bhn102v1.pdf}, file = { attachment\:Gong2008CerebralCortex.pdf: attachment\:Gong2008CerebralCortex.pdf:PDF}, url = {http://cercor.oxfordjournals.org/cgi/content/abstract/bhn102v1}, year = 2008 } @Article{Carlsson2009, Author = {Carlsson, Gunnar and Emoli, Facundo M}, Title = {{Characterization, stability and convergence of hierarchical clustering methods ´}}, Journal = {Methods}, Number = {April}, Pages = {1--23}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Carlsson, Emoli - 2009 - Characterization, stability and convergence of hierarchical clustering methods ´.pdf:pdf}, year = 2009 } @Article{Avram2008NMRBiomed, Author = {Avram, Liat and \¨{O} zarslan, Evren and Assaf, Yaniv and Bar-Shir, Amnon and Cohen, Yoram and Basser, Peter J.}, Title = {Three-dimensional water diffusion in impermeable cylindrical tubes: theory versus experiments}, Journal = {NMR IN BIOMEDICINE}, Volume = {21}, Pages = {888–898}, abstract = {Characterizing diffusion of gases and liquids within pores is important in understanding numerous transport processes and affects a wide range of practical applications. Previous measurements of the pulsed gradient stimulated echo (PGSTE) signal attenuation, E(q), of water within nerves and impermeable cylindrical microcapillary tubes showed it to be exquisitely sensitive to the orientation of the applied wave vector, q, with respect to the tube axis in the high-q regime. Here, we provide a simple three-dimensional model to explain this angular dependence by decomposing the average propagator, which describes the net displacement of water molecules, into components parallel and perpendicular to the tube wall, in which axial diffusion is free and radial diffusion is restricted. The model faithfully predicts the experimental data, not only the observed diffraction peaks in E(q) when the diffusion gradients are approximately normal to the tube wall, but their sudden disappearance when the gradient orientation possesses a small axial component. The model also successfully predicts the dependence of E(q) on gradient pulse duration and on gradient strength as well as tube inner diameter. To account for the deviation from the narrow pulse approximation in the PGSTE sequence, we use Callaghan’s matrix operator framework, which this study validates experimentally for the first time. We also show how to combine average propagators derived for classical one-dimensional and two-dimensional models of restricted diffusion (e.g. between plates, within cylinders) to construct composite three-dimensional models of diffusion in complex media containing pores (e.g. rectangular prisms and/ or capped cylinders) having a distribution of orientations, sizes, and aspect ratios. This three-dimensional modeling framework should aid in describing diffusion in numerous biological systems and in a myriad of materials sciences applications.}, owner = {ian}, timestamp = {2009.03.05}, year = 2008 } @Article{Barmpoutis2007IEEETransMedImag, Author = {Barmpoutis, A. and Vemuri, B. C. and Shepherd, T. M. and Forder, J. R.}, Title = {Tensor splines for interpolation and approximation of \{{D}{T}-{MRI}\} with applications to segmentation of isolated rat hippocampi}, Journal = {IEEE Transactions on Medical Imaging}, Volume = {26}, Number = {11}, Pages = {1537-1546}, abstract = {In this paper, we present novel algorithms for statistically robust interpolation and approximation of diffusion tensors-which are symmetric positive definite (SPD) matrices-and use them in developing a significant extension to an existing probabilistic algorithm for scalar field segmentation, in order to segment diffusion tensor magnetic resonance imaging (DT-MRI) datasets. Using the Riemannian metric on the space of SPD matrices, we present a novel and robust higher order (cubic) continuous tensor product of -splines algorithm to approximate the SPD diffusion tensor fields. The resulting approximations are appropriately dubbed tensor splines. Next, we segment the diffusion tensor field by jointly estimating the label (assigned to each voxel) field, which is modeled by a Gauss Markov measure field (GMMF) and the parameters of each smooth tensor spline model representing the labeled regions. Results of interpolation, approximation, and segmentation are presented for synthetic data and real diffusion tensor fields from an isolated rat hippocampus, along with validation. We also present comparisons of our algorithms with existing methods and show significantly improved results in the presence of noise as well as outliers. }, doi = {10.1109/TMI.2007.903195}, year = 2007 } @Article{Kanaan2006, Author = {Kanaan, Richard a and Shergill, Sukhwinder S and Barker, Gareth J and Catani, Marco and Ng, Virginia W and Howard, Robert and McGuire, Philip K and Jones, Derek K}, Title = {{Tract-specific anisotropy measurements in diffusion tensor imaging.}}, Journal = {Psychiatry research}, Volume = {146}, Number = {1}, Pages = {73--82}, abstract = {Diffusion tensor magnetic resonance imaging (DT-MRI) has been used to examine the microstructure of individual white matter tracts, often in neuropsychiatric conditions without identifiable focal pathology. However, the voxel-based group-mapping and region-of-interest (ROI) approaches used to analyse the data have inherent conceptual and practical difficulties. Taking the example of the genu of the corpus callosum in a sample of schizophrenic patients, we discuss the difficulties in attempting to replicate a voxel-based finding of reduced anisotropy using two ROI methods. Firstly we consider conventional ROIs; secondly, we present a novel tractography-based approach. The problems of both methods are explored, particularly of high variance and ROI definition. The potential benefits of the tractographic method for neuropsychiatric conditions with subtle and diffuse pathology are outlined.}, doi = {10.1016/j.pscychresns.2005.11.002}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Kanaan et al. - 2006 - Tract-specific anisotropy measurements in diffusion tensor imaging..pdf:pdf}, issn = {0165-1781}, keywords = {Adult,Anisotropy,Brain,Brain: pathology,Diffusion Magnetic Resonance Imaging,Female,Humans,Male,Middle Aged,Schizophrenia,Schizophrenia: pathology}, pmid = {16376059}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16376059}, year = 2006 } @Article{MCC+99, Author = {Mori, S. and Crain, B. J. and Chacko, V. P. and van Zijl, P. C.}, Title = {Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging.}, Journal = {Ann Neurol}, Volume = {45}, Number = {2}, Pages = {265-9}, abstract = {The relationship between brain structure and complex behavior is governed by large-scale neurocognitive networks. The availability of a noninvasive technique that can visualize the neuronal projections connecting the functional centers should therefore provide new keys to the understanding of brain function. By using high-resolution three-dimensional diffusion magnetic resonance imaging and a newly designed tracking approach, we show that neuronal pathways in the rat brain can be probed in situ. The results are validated through comparison with known anatomical locations of such fibers.}, authoraddress = {Department of Radiology, Johns Hopkins Medical School, Baltimore, MD, USA.}, keywords = {Animals ; Axons/*physiology ; Brain/*anatomy \& histology ; Magnetic Resonance Imaging/*methods ; Rats}, language = {eng}, medline-crdt = {1999/02/16 00:00}, medline-da = {19990329}, medline-dcom = {19990329}, medline-edat = {1999/02/16}, medline-fau = {Mori, S ; Crain, B J ; Chacko, V P ; van Zijl, P C}, medline-is = {0364-5134 (Print)}, medline-jid = {7707449}, medline-jt = {Annals of neurology}, medline-lr = {20061115}, medline-mhda = {1999/02/16 00:01}, medline-own = {NLM}, medline-pl = {UNITED STATES}, medline-pmid = {9989633}, medline-pst = {ppublish}, medline-pt = {Journal Article ; Research Support, Non-U.S. Gov't}, medline-sb = {IM}, medline-so = {Ann Neurol. 1999 Feb;45(2):265-9.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=9989633}, year = 1999 } @Article{Parker2003, Author = {Parker, Geoffrey J M and Haroon, Hamied a and Wheeler-Kingshott, Claudia a M}, Title = {{A framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements.}}, Journal = {Journal of magnetic resonance imaging : JMRI}, Volume = {18}, Number = {2}, Pages = {242--54}, abstract = {PURPOSE: To establish a general methodology for quantifying streamline-based diffusion fiber tracking methods in terms of probability of connection between points and/or regions. MATERIALS AND METHODS: The commonly used streamline approach is adapted to exploit the uncertainty in the orientation of the principal direction of diffusion defined for each image voxel. Running the streamline process repeatedly using Monte Carlo methods to exploit this inherent uncertainty generates maps of connection probability. Uncertainty is defined by interpreting the shape of the diffusion orientation profile provided by the diffusion tensor in terms of the underlying microstructure. RESULTS: Two candidates for describing the uncertainty in the diffusion tensor are proposed and maps of probability of connection to chosen start points or regions are generated in a number of major tracts. CONCLUSION: The methods presented provide a generic framework for utilizing streamline methods to generate probabilistic maps of connectivity.}, doi = {10.1002/jmri.10350}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Parker, Haroon, Wheeler-Kingshott - 2003 - A framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements..pdf:pdf}, issn = {1053-1807}, keywords = {Anisotropy,Brain,Brain: anatomy \& histology,Diffusion,Diffusion Magnetic Resonance Imaging,Diffusion Magnetic Resonance Imaging: methods,Echo-Planar Imaging,Humans,Models, Statistical,Monte Carlo Method,Probability,Uncertainty}, pmid = {12884338}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12884338}, year = 2003 } @Article{December2006, Author = {December, Draft}, Title = {{A n I n t r o d u c t i o n t o P r o g r a m m i n g f o r M e d i c a l I m a g e A n a l y s i s w i t h T h e V i s u a l i z a t i o n T o o l k i t X e n o p h o n P a p a d e m e t r i s}}, Journal = {Control}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/December - 2006 - A n I n t r o d u c t i o n t o P r o g r a m m i n g f o r M e d i c a l I m a g e A n a l y s i s w i t h T h e V i s u a l i z a t i o n T o o l k i t X e n o p h o n P a p a d e m e t r i s.pdf:pdf}, year = 2006 } @Article{Komodakis2006, Author = {Komodakis, Nikos}, Title = {{Optimization Algorithms for Discrete Markov Random Fields , with Applications to Computer Vision}}, Journal = {Optimization}, Number = {May}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Komodakis - 2006 - Optimization Algorithms for Discrete Markov Random Fields , with Applications to Computer Vision.pdf:pdf}, year = 2006 } @Article{Duru2010a, Author = {Duru, Dilek G\"{o}ksel and Ozkan, Mehmed}, Title = {{Determination of neural fiber connections based on data structure algorithm.}}, Journal = {Computational intelligence and neuroscience}, Volume = {2010}, Pages = {251928}, abstract = {The brain activity during perception or cognition is mostly examined by functional magnetic resonance imaging (fMRI). However, the cause of the detected activity relies on the anatomy. Diffusion tensor magnetic resonance imaging (DTMRI) as a noninvasive modality providing in vivo anatomical information allows determining neural fiber connections which leads to brain mapping. Still a complete map of fiber paths representing the human brain is missing in literature. One of the main drawbacks of reliable fiber mapping is the correct detection of the orientation of multiple fibers within a single imaging voxel. In this study a method based on linear data structures is proposed to define the fiber paths regarding their diffusivity. Another advantage of the proposed method is that the analysis is applied on entire brain diffusion tensor data. The implementation results are promising, so that the method will be developed as a rapid fiber tractography algorithm for the clinical use as future study.}, doi = {10.1155/2010/251928}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Duru, Ozkan - 2010 - Determination of neural fiber connections based on data structure algorithm..pdf:pdf}, issn = {1687-5273}, keywords = {Algorithms,Brain,Brain: anatomy \& histology,Diffusion Tensor Imaging,Diffusion Tensor Imaging: methods,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: methods,Linear Models,Neural Pathways,Neural Pathways: anatomy \& histology,Uncertainty}, month = jan, pmid = {20069047}, url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2801001\&tool=pmcentrez\&rendertype=abstract}, year = 2010 } @Article{Cook2006, Author = {Cook, P A and Bai, Y and Seunarine, K K and Hall, M G and Parker, G J and Alexander, D C}, Title = {{Camino : Open-Source Diffusion-MRI Reconstruction and Processing}}, Journal = {Statistics}, Volume = {14}, Pages = {22858--22858}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Cook et al. - 2006 - Camino Open-Source Diffusion-MRI Reconstruction and Processing.pdf:pdf}, year = 2006 } @Article{CLC+99, Author = {Conturo, T. E. and Lori, N. F. and Cull, T. S. and Akbudak, E. and Snyder, A. Z. and Shimony, J. S. and McKinstry, R. C. and Burton, H. and Raichle, M. E.}, Title = {Tracking neuronal fiber pathways in the living human brain.}, Journal = {Proc Natl Acad Sci U S A}, Volume = {96}, Number = {18}, Pages = {10422-7}, abstract = {Functional imaging with positron emission tomography and functional MRI has revolutionized studies of the human brain. Understanding the organization of brain systems, especially those used for cognition, remains limited, however, because no methods currently exist for noninvasive tracking of neuronal connections between functional regions [Crick, F. \& Jones, E. (1993) Nature (London) 361, 109-110]. Detailed connectivities have been studied in animals through invasive tracer techniques, but these invasive studies cannot be done in humans, and animal results cannot always be extrapolated to human systems. We have developed noninvasive neuronal fiber tracking for use in living humans, utilizing the unique ability of MRI to characterize water diffusion. We reconstructed fiber trajectories throughout the brain by tracking the direction of fastest diffusion (the fiber direction) from a grid of seed points, and then selected tracks that join anatomically or functionally (functional MRI) defined regions. We demonstrate diffusion tracking of fiber bundles in a variety of white matter classes with examples in the corpus callosum, geniculo-calcarine, and subcortical association pathways. Tracks covered long distances, navigated through divergences and tight curves, and manifested topological separations in the geniculo-calcarine tract consistent with tracer studies in animals and retinotopy studies in humans. Additionally, previously undescribed topologies were revealed in the other pathways. This approach enhances the power of modern imaging by enabling study of fiber connections among anatomically and functionally defined brain regions in individual human subjects.}, authoraddress = {Department of Radiology and Neuroimaging Laboratory, Mallinckrodt Institute of Radiology, Washington University School of Medicine, 4525 Scott Avenue, St. Louis, MO 63110, USA. tconturo@npg.wustl.edu}, keywords = {Brain/anatomy \& histology/*physiology ; *Brain Mapping ; Humans ; Magnetic Resonance Imaging ; Nerve Fibers/*physiology ; Neural Pathways/physiology ; Neurons/*physiology}, language = {eng}, medline-crdt = {1999/09/01 00:00}, medline-da = {19991007}, medline-dcom = {19991007}, medline-edat = {1999/09/01}, medline-fau = {Conturo, T E ; Lori, N F ; Cull, T S ; Akbudak, E ; Snyder, A Z ; Shimony, J S ; McKinstry, R C ; Burton, H ; Raichle, M E}, medline-gr = {P01 NS06833/NS/NINDS NIH HHS/United States}, medline-is = {0027-8424 (Print)}, medline-jid = {7505876}, medline-jt = {Proceedings of the National Academy of Sciences of the United States of America}, medline-lr = {20081120}, medline-mhda = {1999/09/01 00:01}, medline-oid = {NLM: PMC17904}, medline-own = {NLM}, medline-pl = {UNITED STATES}, medline-pmc = {PMC17904}, medline-pmid = {10468624}, medline-pst = {ppublish}, medline-pt = {Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, P.H.S.}, medline-sb = {IM}, medline-so = {Proc Natl Acad Sci U S A. 1999 Aug 31;96(18):10422-7.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=10468624}, year = 1999 } @Article{Bradski, Author = {Bradski, Gary and Kaehler, Adrian}, Title = {{No Title}}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Bradski, Kaehler - Unknown - No Title.pdf:pdf} } @Article{Qazi_Neuroimage08, Author = {Qazi, A. A. and Radmanesh, A. and O'Donnell, L. and Kindlmann, G. and Peled, S. and Whalen, S. and Westin, C. F. and Golby, A. J.}, Title = {Resolving crossings in the corticospinal tract by two-tensor streamline tractography: {M}ethod and clinical assessment using f{MRI}.}, Journal = {Neuroimage}, abstract = {An inherent drawback of the traditional diffusion tensor model is its limited ability to provide detailed information about multidirectional fiber architecture within a voxel. This leads to erroneous fiber tractography results in locations where fiber bundles cross each other. This may lead to the inability to visualize clinically important tracts such as the lateral projections of the corticospinal tract. In this report, we present a deterministic two-tensor eXtended Streamline Tractography (XST) technique, which successfully traces through regions of crossing fibers. We evaluated the method on simulated and in vivo human brain data, comparing the results with the traditional single-tensor and with a probabilistic tractography technique. By tracing the corticospinal tract and correlating with fMRI-determined motor cortex in both healthy subjects and patients with brain tumors, we demonstrate that two-tensor deterministic streamline tractography can accurately identify fiber bundles consistent with anatomy and previously not detected by conventional single-tensor tractography. When compared to the dense connectivity maps generated by probabilistic tractography, the method is computationally efficient and generates discrete geometric pathways that are simple to visualize and clinically useful. Detection of crossing white matter pathways can improve neurosurgical visualization of functionally relevant white matter areas.}, authoraddress = {Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, USA; University of Copenhagen, Denmark.}, language = {ENG}, medline-aid = {S1053-8119(08)00779-9 [pii] ; 10.1016/j.neuroimage.2008.06.034 [doi]}, medline-crdt = {2008/07/29 09:00}, medline-da = {20080811}, medline-dep = {20080708}, medline-edat = {2008/07/29 09:00}, medline-is = {1095-9572 (Electronic)}, medline-jid = {9215515}, medline-jt = {NeuroImage}, medline-mhda = {2008/07/29 09:00}, medline-own = {NLM}, medline-phst = {2008/04/30 [received] ; 2008/06/19 [revised] ; 2008/06/19 [accepted]}, medline-pmid = {18657622}, medline-pst = {aheadofprint}, medline-pt = {JOURNAL ARTICLE}, medline-so = {Neuroimage. 2008 Jul 8.}, medline-stat = {Publisher}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=18657622}, year = 2008 } @Article{Nannen2003, Author = {Nannen, Volker}, Title = {{The Paradox of Overfitting}}, Journal = {Computer}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Nannen - 2003 - The Paradox of Overfitting.pdf:pdf}, year = 2003 } @Article{Klein2007NeuroImage, Author = {Klein, J. C. and Behrens, T. E. and Robson, M. D. and Mackay, C. E. and Higham, D.J. and Johansen-Berg, H.}, Title = {Connectivity-based parcellation of human cortex using diffusion \{{M}{RI}\}: establishing reproducibility, validity and observer independence in \{{B}{A}\} 44/45 and \{{S}{MA}\}/pre-\{{S}{MA}\}}, Journal = {NeuroImage}, Volume = {34}, Number = {1}, Pages = {204-211}, abstract = {The identification of specialized, functional regions of the human cortex is a vital precondition for neuroscience and clinical neurosurgery. Functional imaging modalities are used for their delineation in living subjects, but these methods rely on subject cooperation, and many regions of the human brain cannot be activated specifically. Diffusion tractography is a novel tool to identify such areas in the human brain, utilizing underlying white matter pathways to separate regions of differing specialization. We explore the reproducibility, generalizability and validity of diffusion tractography-based localization in four functional areas across subjects, timepoints and scanners, and validate findings against fMRI and post-mortem cytoarchitectonic data. With reproducibility across modalities, clustering methods, scanners, timepoints, and subjects in the order of 80-90%, we conclude that diffusion tractography represents a useful and objective tool for parcellation of the human cortex into functional regions, enabling studies into individual functional anatomy even when there are no specific activation paradigms available.}, file = {attachment\:Klein2007NeuroImage.pdf:attachment\:Klein2007NeuroImage.pdf:PDF}, year = 2007 } @Article{McNab2008MRM, Author = {Jennifer A. McNab and Karla L. Miller}, Title = {Sensitivity of diffusion weighted steady state free precession to anisotropic diffusion}, Journal = {Magnetic Resonance in Medicine}, Volume = {60}, Number = {2}, Pages = {405-413}, abstract = {Diffusion-weighted steady-state free precession (DW-SSFP) accumulates signal from multiple echoes over several TRs yielding a strong sensitivity to diffusion with short gradient durations and imaging times. Although the DW-SSFP signal is well characterized for isotropic, Gaussian diffusion, it is unclear how the DW-SSFP signal propagates in inhomogeneous media such as brain tissue. This article presents a more general analytical expression for the DW-SSFP signal which accommodates Gaussian and non-Gaussian spin displacement probability density functions. This new framework for calculating the DW-SSFP signal is used to investigate signal behavior for a single fiber, crossing fibers, and reflective barriers. DW-SSFP measurements in the corpus callosum of a fixed brain are shown to be in good agreement with theoretical predictions. Further measurements in fixed brain tissue also demonstrate that 3D DW-SSFP out-performs 3D diffusion weighted spin echo in both SNR and CNR efficiency providing a compelling example of its potential to be used for high resolution diffusion tensor imaging.}, owner = {ian}, timestamp = {2009.03.27}, year = 2008 } @Article{Corney2007, Author = {Corney, David and Lotto, R Beau}, Title = {{What are lightness illusions and why do we see them?}}, Journal = {PLoS computational biology}, Volume = {3}, Number = {9}, Pages = {1790--800}, abstract = {Lightness illusions are fundamental to human perception, and yet why we see them is still the focus of much research. Here we address the question by modelling not human physiology or perception directly as is typically the case but our natural visual world and the need for robust behaviour. Artificial neural networks were trained to predict the reflectance of surfaces in a synthetic ecology consisting of 3-D "dead-leaves" scenes under non-uniform illumination. The networks learned to solve this task accurately and robustly given only ambiguous sense data. In addition--and as a direct consequence of their experience--the networks also made systematic "errors" in their behaviour commensurate with human illusions, which includes brightness contrast and assimilation--although assimilation (specifically White's illusion) only emerged when the virtual ecology included 3-D, as opposed to 2-D scenes. Subtle variations in these illusions, also found in human perception, were observed, such as the asymmetry of brightness contrast. These data suggest that "illusions" arise in humans because (i) natural stimuli are ambiguous, and (ii) this ambiguity is resolved empirically by encoding the statistical relationship between images and scenes in past visual experience. Since resolving stimulus ambiguity is a challenge faced by all visual systems, a corollary of these findings is that human illusions must be experienced by all visual animals regardless of their particular neural machinery. The data also provide a more formal definition of illusion: the condition in which the true source of a stimulus differs from what is its most likely (and thus perceived) source. As such, illusions are not fundamentally different from non-illusory percepts, all being direct manifestations of the statistical relationship between images and scenes.}, doi = {10.1371/journal.pcbi.0030180}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Corney, Lotto - 2007 - What are lightness illusions and why do we see them.pdf:pdf}, issn = {1553-7358}, keywords = {Artificial Intelligence,Biomimetics,Biomimetics: methods,Humans,Image Interpretation, Computer-Assisted,Image Interpretation, Computer-Assisted: methods,Nerve Net,Nerve Net: physiology,Optical Illusions,Optical Illusions: physiology,Photometry,Photometry: methods,Visual Perception,Visual Perception: physiology}, pmid = {17907795}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17907795}, year = 2007 } @Article{Commowick2008, Author = {Commowick, O and Arsigny, V and Isambert, a and Costa, J and Dhermain, F and Bidault, F and Bondiau, P-Y and Ayache, N and Malandain, G}, Title = {{An efficient locally affine framework for the smooth registration of anatomical structures.}}, Journal = {Medical image analysis}, Volume = {12}, Number = {4}, Pages = {427--41}, abstract = {Intra-subject and inter-subject nonlinear registration based on dense transformations requires the setting of many parameters, mainly for regularization. This task is a major issue, as the global quality of the registration will depend on it. Setting these parameters is, however, very hard, and they may have to be tuned for each patient when processing data acquired by different centers or using different protocols. Thus, we present in this article a method to introduce more coherence in the registration by using fewer degrees of freedom than with a dense registration. This is done by registering the images only on user-defined areas, using a set of affine transformations, which are optimized together in a very efficient manner. Our framework also ensures a smooth and coherent transformation thanks to a new regularization of the affine components. Finally, we ensure an invertible transformation thanks to the Log-Euclidean polyaffine framework. This allows us to get a more robust and very efficient registration method, while obtaining good results as explained below. We performed a qualitative and quantitative evaluation of the obtained results on two applications: first on atlas-based brain segmentation, comparing our results with a dense registration algorithm. Then the second application for which our framework is particularly well suited concerns bone registration in the lower-abdomen area. We obtain in this case a better positioning of the femoral heads than with a dense registration. For both applications, we show a significant improvement in computation time, which is crucial for clinical applications.}, doi = {10.1016/j.media.2008.01.002}, file = {::}, issn = {1361-8423}, keywords = {Algorithms,Brain,Brain: anatomy \& histology,Diagnostic Imaging,Diagnostic Imaging: methods,Humans,Image Processing, Computer-Assisted,Radiotherapy Planning, Computer-Assisted,Radiotherapy Planning, Computer-Assisted: methods,Sensitivity and Specificity}, month = aug, pmid = {18325825}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18325825}, year = 2008 } @Article{Kerkyacharian2007a, Author = {Kerkyacharian, G´ Erard and Petrushev, Pencho and Picard, Dominique and Willer, Thomas}, Title = {{Needlet algorithms for estimation in inverse problems}}, Journal = {Electron. J. Stat}, Volume = {1}, Pages = {30--76}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Kerkyacharian et al. - 2007 - Needlet algorithms for estimation in inverse problems.pdf:pdf}, year = 2007 } @InProceedings{Leow2008ISBI, Author = {Leow, Alex D. and Zhu, Siwei and McMahon, Katie L. and {de Zubicaray}, Greig I. and Meredith, G. Matthew and Wright, Margaret and Thompson, Paul M.}, Title = {The Tensor Distribution Function}, BookTitle = {5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro}, Pages = {FR-P2a (poster)}, abstract = {Diffusion weighted MR imaging is a powerful tool that can be employed to study white matter microstructure by examing the 3D displacement profile of water molecules in brain tissue. By applying diffusion-sensitizing gradients along a minimum of 6 directions, second-order tensors can be computed to model dominant diffusion processes. However, it has been shown that conventional DTI is not sufficient to resolve crossing fiber tracts. More recently, High Angular Resolution Diffusion Imaging (HARDI) seeks to address this issue by employing more than 6 gradient directions. In this paper, we introduce the Tensor Distribution Function (TDF), a probability function defined on the space of symmetric and positive definite matrices. Here, fiber crossing is modeled as an ensemble of Gaussian diffusion processes with weights specified by the TDF. Once this optimal TDF is determined, ODF can easily be computed by analytic integration of the resulting displacement probability function. Moreover, principal fiber directions can also be directly derived from the TDF.}, file = {attachment\:Leow2008ISBI.pdf:attachment\:Leow2008ISBI.pdf:PDF}, year = 2008 } @Article{Wainwright, Author = {Wainwright, Martin}, Title = {{Graphical models and variational methods : Message-passing , convex relaxations , and all that}}, Journal = {Electrical Engineering}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Wainwright - Unknown - Graphical models and variational methods Message-passing , convex relaxations , and all that.pdf:pdf} } @Article{Tuch2004, Author = {Tuch, DS}, Title = {{Q-ball imaging}}, Journal = {change}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Tuch - 2004 - Q-ball imaging.pdf:pdf}, url = {http://noodle.med.yale.edu/\~{}mjack/papers/tuch-2004.pdf}, year = 2004 } @Article{RHW+03, Author = {Reese, T. G. and Heid, O. and Weisskoff, R. M. and Wedeen, V. J.}, Title = {Reduction of eddy-current-induced distortion in diffusion {MRI} using a twice-refocused spin echo.}, Journal = {Magn Reson Med}, Volume = {49}, Number = {1}, Pages = {177-82}, abstract = {Image distortion due to field gradient eddy currents can create image artifacts in diffusion-weighted MR images. These images, acquired by measuring the attenuation of NMR signal due to directionally dependent diffusion, have recently been shown to be useful in the diagnosis and assessment of acute stroke and in mapping of tissue structure. This work presents an improvement on the spin-echo (SE) diffusion sequence that displays less distortion and consequently improves image quality. Adding a second refocusing pulse provides better image quality with less distortion at no cost in scanning efficiency or effectiveness, and allows more flexible diffusion gradient timing. By adjusting the timing of the diffusion gradients, eddy currents with a single exponential decay constant can be nulled, and eddy currents with similar decay constants can be greatly reduced. This new sequence is demonstrated in phantom measurements and in diffusion anisotropy images of normal human brain.}, authoraddress = {Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA. reese@nmr.MGH.harvard.edu}, keywords = {*Artifacts ; Brain/anatomy \& histology/pathology ; Echo-Planar Imaging/methods ; Humans ; Magnetic Resonance Imaging/*methods ; Phantoms, Imaging ; Stroke/diagnosis}, language = {eng}, medline-aid = {10.1002/mrm.10308 [doi]}, medline-ci = {Copyright 2003 Wiley-Liss, Inc.}, medline-crdt = {2003/01/02 04:00}, medline-da = {20030101}, medline-dcom = {20030422}, medline-edat = {2003/01/02 04:00}, medline-fau = {Reese, T G ; Heid, O ; Weisskoff, R M ; Wedeen, V J}, medline-gr = {R01 MH64044/MH/NIMH NIH HHS/United States}, medline-is = {0740-3194 (Print)}, medline-jid = {8505245}, medline-jt = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine}, medline-lr = {20071115}, medline-mhda = {2003/04/23 05:00}, medline-own = {NLM}, medline-pl = {United States}, medline-pmid = {12509835}, medline-pst = {ppublish}, medline-pt = {Journal Article ; Research Support, U.S. Gov't, P.H.S.}, medline-sb = {IM}, medline-so = {Magn Reson Med. 2003 Jan;49(1):177-82.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=12509835}, year = 2003 } @Article{hyvarinen2000ica, Author = {Hyv{\"a}rinen, A. and Oja, E.}, Title = {{Independent component analysis: algorithms and applications}}, Journal = {Neural networks}, Volume = {13}, Number = {4-5}, Pages = {411--430}, publisher = {Elsevier}, year = 2000 } @Article{Friedman2008, Author = {Friedman, Jerome and Hastie, Trevor}, Title = {{Regularization Paths for Generalized Linear Models via Coordinate Descent}}, Pages = {1--22}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Friedman, Hastie - 2008 - Regularization Paths for Generalized Linear Models via Coordinate Descent.pdf:pdf}, year = 2008 } @conference{lee2007trajectory, author = {Lee, J.G. and Han, J. and Whang, K.Y.}, booktitle = {Proceedings of the 2007 ACM SIGMOD international conference on Management of data}, organization = {ACM}, pages = {604}, title = {{Trajectory clustering: a partition-and-group framework}}, year = 2007 } @Article{Ipython2008, Author = {Ipython, The and Team, Development}, Title = {{IPython Documentation}}, Journal = {Development}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Ipython, Team - 2008 - IPython Documentation.pdf:pdf}, year = 2008 } @Book{DiffMRIBook, Author = {{Heidi Johansen-Berg}}, Editor = {Heidi Johansen-Berg, Oxford Centre for Functional MRI of the Brain (FMRIB), Department of Clinical Neurology and Timothy E.J. Behrens, Department of Experimental Psychology, University of Oxford; Centre for Functional MRI of the Brain (FMRIB)}, Title = {Diffusion {MRI}}, Publisher = {Academic Press}, year = 2009 } @Article{Guo2005, Author = {Guo, D. and Shamai, S. and Verdu, S.}, Title = {{Mutual Information and Minimum Mean-Square Error in Gaussian Channels}}, Journal = {IEEE Transactions on Information Theory}, Volume = {51}, Number = {4}, Pages = {1261--1282}, doi = {10.1109/TIT.2005.844072}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Guo, Shamai, Verdu - 2005 - Mutual Information and Minimum Mean-Square Error in Gaussian Channels.pdf:pdf}, issn = {0018-9448}, month = apr, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1412024}, year = 2005 } @Article{Mishra2007, Author = {Mishra, Arabinda and Lu, Yonggang and Choe, Ann S and Aldroubi, Akram and Gore, John C and Anderson, Adam W and Ding, Zhaohua}, Title = {{An image-processing toolset for diffusion tensor tractography.}}, Journal = {Magnetic resonance imaging}, Volume = {25}, Number = {3}, Pages = {365--76}, abstract = {Diffusion tensor imaging (DTI)-based fiber tractography holds great promise in delineating neuronal fiber tracts and, hence, providing connectivity maps of the neural networks in the human brain. An array of image-processing techniques has to be developed to turn DTI tractography into a practically useful tool. To this end, we have developed a suite of image-processing tools for fiber tractography with improved reliability. This article summarizes the main technical developments we have made to date, which include anisotropic smoothing, anisotropic interpolation, Bayesian fiber tracking and automatic fiber bundling. A primary focus of these techniques is the robustness to noise and partial volume averaging, the two major hurdles to reliable fiber tractography. Performance of these techniques has been comprehensively examined with simulated and in vivo DTI data, demonstrating improvements in the robustness and reliability of DTI tractography.}, doi = {10.1016/j.mri.2006.10.006}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Mishra et al. - 2007 - An image-processing toolset for diffusion tensor tractography..pdf:pdf}, issn = {0730-725X}, keywords = {Algorithms,Artificial Intelligence,Brain,Brain: anatomy \& histology,Diffusion Magnetic Resonance Imaging,Diffusion Magnetic Resonance Imaging: methods,Humans,Image Enhancement,Image Enhancement: methods,Image Interpretation, Computer-Assisted,Image Interpretation, Computer-Assisted: methods,Nerve Net,Nerve Net: anatomy \& histology,Neural Pathways,Neural Pathways: anatomy \& histology,Reproducibility of Results,Sensitivity and Specificity,Software}, pmid = {17371726}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17371726}, year = 2007 } @conference{deriche1990dcm, author = {Deriche, R. and Faugeras, O.}, booktitle = {Pattern Recognition, 1990. Proceedings., 10th International Conference on}, title = {{2-D curve matching using high curvature points: application tostereo vision}}, volume = {1}, year = 1990 } @Article{Vogiatzis, Author = {Vogiatzis, George}, Title = {{Visual Estimation of Shape , Reflectance and Illumination}}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Vogiatzis - Unknown - Visual Estimation of Shape , Reflectance and Illumination.pdf:pdf} } @Article{vernooij2007fda, Author = {Vernooij, M W and Smits, M. and Wielopolski, P A and Houston, G C and Krestin, G P and van der Lugt, A.}, Title = {{Fiber density asymmetry of the arcuate fasciculus in relation to functional hemispheric language lateralization in both right-and left-handed healthy subjects: A combined fMRI and DTI study}}, Journal = {Neuroimage}, Volume = {35}, Number = {3}, Pages = {1064--1076}, file = {attachment\:vernooij_arcuate_fasciculus_2007.pdf:attachment\:vernooij_arcuate_fasciculus_2007.pdf:PDF}, publisher = {Elsevier}, year = 2007 } @Article{KanaanPsych2006, Author = {Kanaan, R. A. and Shergill, S. S. and Barker, G. J. and Catani, M. and Ng, V. W. and Howard, R. and McGuire, P. K. and Jones, D. K.}, Title = {Tract-specific anisotropy measurements in diffusion tensor imaging.}, Journal = {Psychiatry Res}, Volume = {146}, Number = {1}, Pages = {73-82}, abstract = {Diffusion tensor magnetic resonance imaging (DT-MRI) has been used to examine the microstructure of individual white matter tracts, often in neuropsychiatric conditions without identifiable focal pathology. However, the voxel-based group-mapping and region-of-interest (ROI) approaches used to analyse the data have inherent conceptual and practical difficulties. Taking the example of the genu of the corpus callosum in a sample of schizophrenic patients, we discuss the difficulties in attempting to replicate a voxel-based finding of reduced anisotropy using two ROI methods. Firstly we consider conventional ROIs; secondly, we present a novel tractography-based approach. The problems of both methods are explored, particularly of high variance and ROI definition. The potential benefits of the tractographic method for neuropsychiatric conditions with subtle and diffuse pathology are outlined.}, authoraddress = {King's College London, Institute of Psychiatry, London, UK. r.kanaan@iop.kcl.ac.uk}, keywords = {Adult ; Anisotropy ; Brain/*pathology ; *Diffusion Magnetic Resonance Imaging ; Female ; Humans ; Male ; Middle Aged ; Schizophrenia/*pathology}, language = {eng}, medline-aid = {S0925-4927(05)00197-6 [pii] ; 10.1016/j.pscychresns.2005.11.002 [doi]}, medline-crdt = {2005/12/27 09:00}, medline-da = {20060227}, medline-dcom = {20060425}, medline-dep = {20051220}, medline-edat = {2005/12/27 09:00}, medline-fau = {Kanaan, Richard A ; Shergill, Sukhwinder S ; Barker, Gareth J ; Catani, Marco ; Ng, Virginia W ; Howard, Robert ; McGuire, Philip K ; Jones, Derek K}, medline-gr = {Wellcome Trust/United Kingdom}, medline-is = {0165-1781 (Print)}, medline-jid = {7911385}, medline-jt = {Psychiatry research}, medline-lr = {20080417}, medline-mhda = {2006/04/28 09:00}, medline-own = {NLM}, medline-phst = {2005/05/24 [received] ; 2005/09/13 [revised] ; 2005/11/03 [accepted] ; 2005/12/20 [aheadofprint]}, medline-pl = {Ireland}, medline-pmid = {16376059}, medline-pst = {ppublish}, medline-pt = {Journal Article ; Research Support, Non-U.S. Gov't}, medline-sb = {IM}, medline-so = {Psychiatry Res. 2006 Jan 30;146(1):73-82. Epub 2005 Dec 20.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=16376059}, year = 2006 } @Article{Mallo, Author = {Mallo, O and Peikert, R and Sigg, C and Sadlo, F}, Title = {{Illuminated lines revisited}}, Journal = {In Proceedings of IEEE Visualization}, Volume = {pages}, Pages = {19--26}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Mallo et al. - Unknown - Illuminated lines revisited.pdf:pdf} } @Article{HM96, Author = {Haselgrove, J. C. and Moore, J. R.}, Title = {Correction for distortion of echo-planar images used to calculate the apparent diffusion coefficient.}, Journal = {Magn Reson Med}, Volume = {36}, Number = {6}, Pages = {960-4}, abstract = {An algorithm for correcting the distortions that occur in diffusion-weighted echo-planar images due to the strong diffusion-sensitizing gradients is presented. The dominant distortions may be considered to be only changes of scale coupled with a shear and linear translation in the phase-encoding direction. It is then possible to correct for them by using an algorithm in which each line of the image in the phase-encoding direction is considered in turn, with only one parameter (the scale) to be found by searching.}, authoraddress = {Department of Radiology, Children's Hospital of Philadelphia, PA 19104, USA.}, keywords = {*Algorithms ; Brain/pathology ; Echo-Planar Imaging/*methods ; Humans ; Image Enhancement/*methods ; Sensitivity and Specificity}, language = {eng}, medline-crdt = {1996/12/01 00:00}, medline-da = {19970225}, medline-dcom = {19970225}, medline-edat = {1996/12/01}, medline-fau = {Haselgrove, J C ; Moore, J R}, medline-is = {0740-3194 (Print)}, medline-jid = {8505245}, medline-jt = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine}, medline-lr = {20041117}, medline-mhda = {1996/12/01 00:01}, medline-own = {NLM}, medline-pl = {UNITED STATES}, medline-pmid = {8946363}, medline-pst = {ppublish}, medline-pt = {Journal Article}, medline-sb = {IM}, medline-so = {Magn Reson Med. 1996 Dec;36(6):960-4.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=8946363}, year = 1996 } @Article{Kume2005, Author = {Kume, a.}, Title = {{Saddlepoint approximations for the Bingham and Fisher-Bingham normalising constants}}, Journal = {Biometrika}, Volume = {92}, Number = {2}, Pages = {465--476}, doi = {10.1093/biomet/92.2.465}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Kume - 2005 - Saddlepoint approximations for the Bingham and Fisher-Bingham normalising constants.pdf:pdf}, issn = {0006-3444}, month = jun, url = {http://biomet.oxfordjournals.org/cgi/doi/10.1093/biomet/92.2.465}, year = 2005 } @Article{Koev2006, Author = {Koev, Plamen and Edelman, Alan}, Title = {{OF THE HYPERGEOMETRIC FUNCTION OF A MATRIX ARGUMENT}}, Journal = {Mathematics of Computation}, Volume = {75}, Number = {254}, Pages = {833--846}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Koev, Edelman - 2006 - OF THE HYPERGEOMETRIC FUNCTION OF A MATRIX ARGUMENT.pdf:pdf}, keywords = {and phrases,c 2006 american mathematical,eigenvalues of random matrices,grant dms-0314286,hypergeometric function of a,in part by nsf,jack function,matrix argument,polynomial,society,this work was supported,zonal}, year = 2006 } @conference{corouge2004towards, author = {Corouge, I. and Gouttard, S. and Gerig, G.}, booktitle = {International Symposium on Biomedical Imaging}, organization = {Citeseer}, pages = {344--347}, title = {{Towards a shape model of white matter fiber bundles using diffusion tensor MRI}}, year = 2004 } @Article{Koles1991a, Author = {Koles, Z J}, Title = {{The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG.}}, Journal = {Electroencephalography and clinical neurophysiology}, Volume = {79}, Number = {6}, Pages = {440--7}, abstract = {A method is described which seems to be effective for extracting the abnormal components from the clinical EEG. The approach involves the use of a set a spatial patterns which are common to recorded and 'normal' EEGs and which can account for maximally different proportions of the combined variances in both EEGs. These spatial factors are used to decompose the EEG into orthogonal temporal wave forms which can be judged by the expert electroencephalographer to be abnormal, normal or of artifactual origin. The original EEG is then reconstructed using only the abnormal components and principal component analysis is used to present the spatial topography of the abnormal components. The effectiveness of the method is discussed along with its value for localization of abnormal sources. It is suggested, in conclusion, that the approach described may be optimal for interpretation of the clinical EEG since it allows what is best in terms of quantitative analysis of the EEG to be combined with the best that is available in terms of expert qualitative analysis.}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Koles - 1991 - The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG..pdf:pdf}, issn = {0013-4694}, keywords = {Brain,Brain Mapping,Brain: physiology,Electroencephalography,Electroencephalography: methods,Humans,Signal Processing, Computer-Assisted}, month = dec, pmid = {1721571}, url = {http://www.ncbi.nlm.nih.gov/pubmed/1721571}, year = 1991 } @Article{Kim, Author = {Kim, Min-soo}, Title = {{A Particle-and-Density Based Evolutionary Clustering Method for Dynamic Networks}}, Number = {1}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Kim - Unknown - A Particle-and-Density Based Evolutionary Clustering Method for Dynamic Networks.pdf:pdf} } @Article{Marinucci2008, Author = {Marinucci, D and Pietrobon, D and Balbi, A and Baldi, P and Cabella, P and Kerkyacharian, G and Natoli, P and Picard, D and Vittorio, N}, Title = {{Spherical Needlets for CMB Data Analysis}}, Volume = {000}, Number = {February}, arxivid = {arXiv:0707.0844v1}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Marinucci et al. - 2008 - Spherical Needlets for CMB Data Analysis.pdf:pdf}, year = 2008 } @Article{ODonnell_IEEETMI07, Author = {O'Donnell, L. J. and Westin, C. F.}, Title = {Automatic tractography segmentation using a high-dimensional white matter atlas.}, Journal = {IEEE Trans Med Imaging}, Volume = {26}, Number = {11}, Pages = {1562-75}, abstract = {We propose a new white matter atlas creation method that learns a model of the common white matter structures present in a group of subjects. We demonstrate that our atlas creation method, which is based on group spectral clustering of tractography, discovers structures corresponding to expected white matter anatomy such as the corpus callosum, uncinate fasciculus, cingulum bundles, arcuate fasciculus, and corona radiata. The white matter clusters are augmented with expert anatomical labels and stored in a new type of atlas that we call a high-dimensional white matter atlas. We then show how to perform automatic segmentation of tractography from novel subjects by extending the spectral clustering solution, stored in the atlas, using the Nystrom method. We present results regarding the stability of our method and parameter choices. Finally we give results from an atlas creation and automatic segmentation experiment. We demonstrate that our automatic tractography segmentation identifies corresponding white matter regions across hemispheres and across subjects, enabling group comparison of white matter anatomy.}, authoraddress = {Golby Laboratory, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA. lauren@csail.mit.edu}, keywords = {Algorithms ; Artificial Intelligence ; Computer Simulation ; Corpus Callosum/*anatomy \& histology ; Diffusion Magnetic Resonance Imaging/*methods ; Humans ; Image Enhancement/*methods ; Image Interpretation, Computer-Assisted/*methods ; Imaging, Three-Dimensional/*methods ; Models, Anatomic ; Models, Neurological ; Nerve Fibers, Myelinated/*ultrastructure ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Subtraction Technique}, language = {eng}, medline-aid = {10.1109/TMI.2007.906785 [doi]}, medline-crdt = {2007/11/29 09:00}, medline-da = {20071128}, medline-dcom = {20080122}, medline-edat = {2007/11/29 09:00}, medline-fau = {O'Donnell, Lauren J ; Westin, Carl-Fredrik}, medline-gr = {P41-RR13218/RR/NCRR NIH HHS/United States ; P41-RR15241/RR/NCRR NIH HHS/United States ; R01-AG20012/AG/NIA NIH HHS/United States ; R01-MH074794/MH/NIMH NIH HHS/United States ; U24-RR021382/RR/NCRR NIH HHS/United States ; U41-RR019703/RR/NCRR NIH HHS/United States}, medline-is = {0278-0062 (Print)}, medline-jid = {8310780}, medline-jt = {IEEE transactions on medical imaging}, medline-mhda = {2008/01/23 09:00}, medline-own = {NLM}, medline-pl = {United States}, medline-pmid = {18041271}, medline-pst = {ppublish}, medline-pt = {Journal Article ; Research Support, N.I.H., Extramural}, medline-sb = {IM}, medline-so = {IEEE Trans Med Imaging. 2007 Nov;26(11):1562-75.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=18041271}, year = 2007 } @Article{Bihan2001, Author = {Bihan, MD Denis Le and Mangin, JF and Poupon, C}, Title = {{Diffusion tensor imaging: concepts and applications}}, Journal = {Journal of Magnetic \ldots}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Bihan, Mangin, Poupon - 2001 - Diffusion tensor imaging concepts and applications.pdf:pdf}, url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.114.9156\&rep=rep1\&type=pdf}, year = 2001 } @Article{LWT+03, Author = {Lazar, M. and Weinstein, D. M. and Tsuruda, J. S. and Hasan, K. M. and Arfanakis, K. and Meyerand, M. E. and Badie, B. and Rowley, H. A. and Haughton, V. and Field, A. and Alexander, A. L.}, Title = {White matter tractography using diffusion tensor deflection.}, Journal = {Hum Brain Mapp}, Volume = {18}, Number = {4}, Pages = {306-21}, abstract = {Diffusion tensor MRI provides unique directional diffusion information that can be used to estimate the patterns of white matter connectivity in the human brain. In this study, the behavior of an algorithm for white matter tractography is examined. The algorithm, called TEND, uses the entire diffusion tensor to deflect the estimated fiber trajectory. Simulations and imaging experiments on in vivo human brains were performed to investigate the behavior of the tractography algorithm. The simulations show that the deflection term is less sensitive than the major eigenvector to image noise. In the human brain imaging experiments, estimated tracts were generated in corpus callosum, corticospinal tract, internal capsule, corona radiata, superior longitudinal fasciculus, inferior longitudinal fasciculus, fronto-occipital fasciculus, and uncinate fasciculus. This approach is promising for mapping the organizational patterns of white matter in the human brain as well as mapping the relationship between major fiber trajectories and the location and extent of brain lesions.}, authoraddress = {Department of Physics, University of Utah, Salt Lake City, Utah, USA.}, keywords = {Algorithms ; Brain Mapping/*methods ; Corpus Callosum/physiology ; Humans ; Nerve Fibers, Myelinated/*physiology ; Neural Pathways/physiology ; Pyramidal Tracts/physiology}, language = {eng}, medline-aid = {10.1002/hbm.10102 [doi]}, medline-ci = {Copyright 2003 Wiley-Liss, Inc.}, medline-crdt = {2003/03/13 04:00}, medline-da = {20030312}, medline-dcom = {20030530}, medline-edat = {2003/03/13 04:00}, medline-fau = {Lazar, Mariana ; Weinstein, David M ; Tsuruda, Jay S ; Hasan, Khader M ; Arfanakis, Konstantinos ; Meyerand, M Elizabeth ; Badie, Benham ; Rowley, Howard A ; Haughton, Victor ; Field, Aaron ; Alexander, Andrew L}, medline-gr = {MH62015/MH/NIMH NIH HHS/United States ; P30 CA42014/CA/NCI NIH HHS/United States}, medline-is = {1065-9471 (Print)}, medline-jid = {9419065}, medline-jt = {Human brain mapping}, medline-lr = {20071114}, medline-mhda = {2003/05/31 05:00}, medline-own = {NLM}, medline-pl = {United States}, medline-pmid = {12632468}, medline-pst = {ppublish}, medline-pt = {Comparative Study ; Journal Article ; Research Support, U.S. Gov't, P.H.S.}, medline-sb = {IM}, medline-so = {Hum Brain Mapp. 2003 Apr;18(4):306-21.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=12632468}, year = 2003 } @Article{Heil, Author = {Heil, Christopher}, Title = {{No Title}}, Journal = {Proofs}, Number = {1}, Pages = {2--5}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Heil - Unknown - No Title.pdf:pdf} } @Article{Rules2004, Author = {Rules, Association}, Title = {{Outline of the Course 1 . Introduction and Terminology 2 . Data Warehousing ( sketch ) Statement of the Problem}}, Pages = {155--187}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Rules - 2004 - Outline of the Course 1 . Introduction and Terminology 2 . Data Warehousing ( sketch ) Statement of the Problem.pdf:pdf}, year = 2004 } @Article{Papadakis1999, Author = {Papadakis, NG and Xing, D and Houston, GC and Smith, JM}, Title = {{A study of rotationally invariant and symmetric indices of diffusion anisotropy}}, Journal = {Magnetic resonance \ldots}, url = {http://linkinghub.elsevier.com/retrieve/pii/S0730725X99000296}, year = 1999 } @Article{Drepper2007, Author = {Drepper, Ulrich and Hat, Red}, Title = {{What Every Programmer Should Know About Memory}}, Journal = {Changes}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Drepper, Hat - 2007 - What Every Programmer Should Know About Memory.pdf:pdf}, year = 2007 } @Article{WWS+08, Author = {Wedeen, V. J. and Wang, R. P. and Schmahmann, J. D. and Benner, T. and Tseng, W. Y. and Dai, G. and Pandya, D. N. and Hagmann, P. and D'Arceuil, H. and de Crespigny, A. J.}, Title = {Diffusion spectrum magnetic resonance imaging ({DSI}) tractography of crossing fibers.}, Journal = {Neuroimage}, Volume = {41}, Number = {4}, Pages = {1267-77}, abstract = {MRI tractography is the mapping of neural fiber pathways based on diffusion MRI of tissue diffusion anisotropy. Tractography based on diffusion tensor imaging (DTI) cannot directly image multiple fiber orientations within a single voxel. To address this limitation, diffusion spectrum MRI (DSI) and related methods were developed to image complex distributions of intravoxel fiber orientation. Here we demonstrate that tractography based on DSI has the capacity to image crossing fibers in neural tissue. DSI was performed in formalin-fixed brains of adult macaque and in the brains of healthy human subjects. Fiber tract solutions were constructed by a streamline procedure, following directions of maximum diffusion at every point, and analyzed in an interactive visualization environment (TrackVis). We report that DSI tractography accurately shows the known anatomic fiber crossings in optic chiasm, centrum semiovale, and brainstem; fiber intersections in gray matter, including cerebellar folia and the caudate nucleus; and radial fiber architecture in cerebral cortex. In contrast, none of these examples of fiber crossing and complex structure was identified by DTI analysis of the same data sets. These findings indicate that DSI tractography is able to image crossing fibers in neural tissue, an essential step toward non-invasive imaging of connectional neuroanatomy.}, authoraddress = {Department of Radiology, MGH Martinos Center for Biomedical Imaging, Harvard Medical School, Charlestown, MA 02129, USA. van@nmr.mgh.harvard.edu}, keywords = {Adult ; Algorithms ; Animals ; Brain/anatomy \& histology ; Diffusion Magnetic Resonance Imaging/*methods ; Female ; Humans ; Image Processing, Computer-Assisted/methods ; Macaca fascicularis ; Male ; Middle Aged ; Nerve Fibers/*physiology ; Neural Pathways/*anatomy \& histology/*physiology}, language = {eng}, medline-aid = {S1053-8119(08)00253-X [pii] ; 10.1016/j.neuroimage.2008.03.036 [doi]}, medline-crdt = {2008/05/23 09:00}, medline-da = {20080616}, medline-dcom = {20080829}, medline-dep = {20080408}, medline-edat = {2008/05/23 09:00}, medline-fau = {Wedeen, V J ; Wang, R P ; Schmahmann, J D ; Benner, T ; Tseng, W Y I ; Dai, G ; Pandya, D N ; Hagmann, P ; D'Arceuil, H ; de Crespigny, A J}, medline-gr = {1R01 MH 64044/MH/NIMH NIH HHS/United States ; 1R01 MH67980/MH/NIMH NIH HHS/United States ; 1R01EB00790/EB/NIBIB NIH HHS/United States ; 1R01NS401285/NS/NINDS NIH HHS/United States ; 1S10RR016811-01/RR/NCRR NIH HHS/United States ; P41RR14075/RR/NCRR NIH HHS/United States}, medline-is = {1053-8119 (Print)}, medline-jid = {9215515}, medline-jt = {NeuroImage}, medline-mhda = {2008/08/30 09:00}, medline-own = {NLM}, medline-phst = {2007/11/30 [received] ; 2008/03/14 [revised] ; 2008/03/17 [accepted] ; 2008/04/08 [aheadofprint]}, medline-pl = {United States}, medline-pmid = {18495497}, medline-pst = {ppublish}, medline-pt = {Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't}, medline-sb = {IM}, medline-so = {Neuroimage. 2008 Jul 15;41(4):1267-77. Epub 2008 Apr 8.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=18495497}, year = 2008 } @Article{Indyk2003, Author = {Indyk, Piotr and Venkatasubramanian, Suresh}, Title = {{Approximate congruence in nearly linear time}}, Journal = {Computational Geometry}, Volume = {24}, Pages = {115--128}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Indyk, Venkatasubramanian - 2003 - Approximate congruence in nearly linear time.pdf:pdf}, keywords = {bottleneck distance,computational geometry,hall,metric entropy,pattern matching,point set matching,s}, year = 2003 } @Article{Arsigny2009, Author = {Arsigny, Vincent and Commowick, Olivier and Ayache, Nicholas and Pennec, Xavier}, Title = {{A Fast and Log-Euclidean Polyaffine Framework for Locally Linear Registration}}, Journal = {Journal of Mathematical Imaging and Vision}, Volume = {33}, Number = {2}, Pages = {222--238}, doi = {10.1007/s10851-008-0135-9}, file = {::}, issn = {0924-9907}, keywords = {arsigny,ayache,commowick,diffeomorphisms,ing,locally affine transformations,log-euclidean,medical imag-,n,non-rigid registration,o,ode,pennec,polyaffine transformations,v,x}, month = jan, url = {http://www.springerlink.com/index/10.1007/s10851-008-0135-9}, year = 2009 } @Article{Close2009, Author = {Close, Thomas G and Tournier, Jacques-Donald and Calamante, Fernando and Johnston, Leigh a and Mareels, Iven and Connelly, Alan}, Title = {{A software tool to generate simulated white matter structures for the assessment of fibre-tracking algorithms.}}, Journal = {NeuroImage}, Volume = {47}, Number = {4}, Pages = {1288--300}, abstract = {The assessment of Diffusion-Weighted MRI (DW-MRI) fibre-tracking algorithms has been limited by the lack of an appropriate 'gold standard'. Practical limitations of alternative methods and physical models have meant that numerical simulations have become the method of choice in practice. However, previous numerical phantoms have consisted of separate fibres embedded in homogeneous backgrounds, which do not capture the true nature of white matter. In this paper we describe a method that is able to randomly generate numerical structures consisting of densely packed bundles of fibres, which are much more representative of human white matter, and simulate the DW-MR images that would arise from them under many imaging conditions. User-defined parameters may be adjusted to produce structures with a range of complexities that spans the levels we would expect to find in vivo. These structures are shown to contain many different features that occur in human white matter and which could confound fibre-tracking algorithms, such as tract kissing and crossing. Furthermore, combinations of such features can be sampled by the random generation of many different structures with consistent levels of complexity. The proposed software provides means for quantitative assessment via direct comparison between tracking results and the exact location of the generated fibres. This should greatly improve our understanding of algorithm performance and therefore prove an important tool for fibre tracking development.}, doi = {10.1016/j.neuroimage.2009.03.077}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Close et al. - 2009 - A software tool to generate simulated white matter structures for the assessment of fibre-tracking algorithms..pdf:pdf}, issn = {1095-9572}, keywords = {Algorithms,Brain,Brain: anatomy \& histology,Computer Simulation,Humans,Image Enhancement,Image Enhancement: methods,Image Interpretation, Computer-Assisted,Image Interpretation, Computer-Assisted: methods,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Models, Anatomic,Models, Neurological,Nerve Fibers, Myelinated,Nerve Fibers, Myelinated: ultrastructure,Pattern Recognition, Automated,Pattern Recognition, Automated: methods,Reproducibility of Results,Sensitivity and Specificity,Software}, pmid = {19361565}, publisher = {Elsevier Inc.}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19361565}, year = 2009 } @Misc{TheMendeleySupportTeam2010, Author = {{The Mendeley Support Team}}, Title = {{Getting Started with Mendeley}}, abstract = {A quick introduction to Mendeley. Learn how Mendeley creates your personal digital library, how to organize and annotate documents, how to collaborate and share with colleagues, and how to generate citations and bibliographies.}, address = {London}, booktitle = {Mendeley Desktop}, file = {:usr/share/doc/mendeleydesktop/FAQ.pdf:pdf}, keywords = {Mendeley,how-to,user manual}, pages = {1--14}, publisher = {Mendeley Ltd.}, url = {http://www.mendeley.com}, year = 2010 } @conference{pickalov2006tra, author = {Pickalov, V. and Basser, P.J.}, booktitle = {3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006}, pages = {710--713}, title = {{3d tomographic reconstruction of the average propagator from mri data}}, year = 2006 } @Article{PCC+08, Author = {Perrin, M. and Cointepas, Y. and Cachia, A. and Poupon, C. and Thirion, B. and Riviere, D. and Cathier, P. and El Kouby, V. and Constantinesco, A. and Le Bihan, D. and Mangin, J. F.}, Title = {Connectivity-{B}ased {P}arcellation of the {C}ortical {M}antle {U}sing q-{B}all {D}iffusion {I}maging.}, Journal = {Int J Biomed Imaging}, Volume = {2008}, Pages = {368406}, abstract = {This paper exploits the idea that each individual brain region has a specific connection profile to create parcellations of the cortical mantle using MR diffusion imaging. The parcellation is performed in two steps. First, the cortical mantle is split at a macroscopic level into 36 large gyri using a sulcus recognition system. Then, for each voxel of the cortex, a connection profile is computed using a probabilistic tractography framework. The tractography is performed from q fields using regularized particle trajectories. Fiber ODF are inferred from the q-balls using a sharpening process focusing the weight around the q-ball local maxima. A sophisticated mask of propagation computed from a T1-weighted image perfectly aligned with the diffusion data prevents the particles from crossing the cortical folds. During propagation, the particles father child particles in order to improve the sampling of the long fascicles. For each voxel, intersection of the particle trajectories with the gyri lead to a connectivity profile made up of only 36 connection strengths. These profiles are clustered on a gyrus by gyrus basis using a K-means approach including spatial regularization. The reproducibility of the results is studied for three subjects using spatial normalization.}, authoraddress = {NeuroSpin Institut d'Imagerie BioMedicale, Commissariat l'Energie Atomique (CEA), Gif-sur-Yvette 91191, France.}, language = {eng}, medline-aid = {10.1155/2008/368406 [doi]}, medline-crdt = {2008/04/11 09:00}, medline-da = {20080410}, medline-edat = {2008/04/11 09:00}, medline-fau = {Perrin, Muriel ; Cointepas, Yann ; Cachia, Arnaud ; Poupon, Cyril ; Thirion, Bertrand ; Riviere, Denis ; Cathier, Pascal ; El Kouby, Vincent ; Constantinesco, Andre ; Le Bihan, Denis ; Mangin, Jean-Francois}, medline-is = {1687-4188 (Print)}, medline-jid = {101250756}, medline-jt = {International journal of biomedical imaging}, medline-mhda = {2008/04/11 09:00}, medline-oid = {NLM: PMC2288697}, medline-own = {NLM}, medline-phst = {2007/09/01 [received] ; 2007/11/30 [revised] ; 2007/12/16 [accepted]}, medline-pl = {United States}, medline-pmc = {PMC2288697}, medline-pmid = {18401457}, medline-pst = {ppublish}, medline-pt = {Journal Article}, medline-so = {Int J Biomed Imaging. 2008;2008:368406.}, medline-stat = {In-Data-Review}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=18401457}, year = 2008 } @Article{Tsiaras2009, Author = {Tsiaras, Vassilis L}, Title = {{Algorithms for the Analysis and Visualization of Biomedical Networks}}, Journal = {October}, Number = {October}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Tsiaras - 2009 - Algorithms for the Analysis and Visualization of Biomedical Networks.pdf:pdf}, year = 2009 } @Article{Maaten2008, Author = {Maaten, L and Hinton, G}, Title = {{Visualizing data using t-sne}}, Journal = {Journal of Machine Learning Research}, url = {http://scholar.google.co.uk/scholar?q=hinton t-sne\&oe=utf-8\&rls=com.ubuntu:en-GB:official\&client=firefox-a\&um=1\&ie=UTF-8\&sa=N\&hl=en\&tab=ws\#2}, year = 2008 } @Article{NedjatiGilani2008ISMRM, Author = {Nedjati-Gilani, S. and Parker, G. J. and Alexander, D. C.}, Title = {Regularized super-resolution for diffusion \{{M}{RI}\}}, Journal = {Proc. Intl. Soc. Mag. Reson. Med.}, Volume = {16}, Pages = {41}, abstract = {We present a new regularized super-resolution method, which finds fibre orientations and volume fractions on a sub-voxel scale and helps distinguish various fibre configurations such as fanning, bending and partial volume effects. We treat the task as a general inverse problem, which we solve by regularization and optimization, and run our method on human brain data.}, file = {attachment\:NedjatiGilani2008ISMRM.pdf:attachment\:NedjatiGilani2008ISMRM.pdf:PDF}, year = 2008 } @Article{Jones1999, Author = {Jones, D K and Horsfield, M a and Simmons, a}, Title = {{Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging.}}, Journal = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine}, Volume = {42}, Number = {3}, Pages = {515--25}, abstract = {The optimization of acquisition parameters for precise measurement of diffusion in anisotropic systems is described. First, an algorithm is presented that minimizes the bias inherent in making measurements with a fixed set of gradient vector directions by spreading out measurements in 3-dimensional gradient vector space. Next, it is shown how the set of b-matrices and echo time can be optimized for estimating the diffusion tensor and its scalar invariants. The standard deviation in the estimate of the tensor trace in a water phantom was reduced by more than 40\% and the artefactual anisotropy was reduced by more than 60\% when using the optimized scheme compared with a more conventional scheme for the same scan time, and marked improvements are demonstrated in the human brain with the optimized sequences. Use of these optimal schemes results in reduced scan times, increased precision, or improved resolution in diffusion tensor images. Magn Reson Med 42:515-525, 1999.}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Jones, Horsfield, Simmons - 1999 - Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging..pdf:pdf}, issn = {0740-3194}, keywords = {Adult,Algorithms,Anisotropy,Brain,Brain: anatomy \& histology,Diffusion,Humans,Linear Models,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Models, Structural,Phantoms, Imaging,Water}, month = sep, pmid = {10467296}, url = {http://www.ncbi.nlm.nih.gov/pubmed/10467296}, year = 1999 } @Article{Szeliski2006, Author = {Szeliski, Richard}, Title = {{Image Alignment and Stitching: A Tutorial}}, Journal = {Foundations and Trends® in Computer Graphics and Vision}, Volume = {2}, Number = {1}, Pages = {1--104}, doi = {10.1561/0600000009}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Szeliski - 2006 - Image Alignment and Stitching A Tutorial.pdf:pdf}, issn = {1572-2740}, url = {http://www.nowpublishers.com/product.aspx?product=CGV\&doi=0600000009}, year = 2006 } @Article{Maddah_IEEEBI2008, Author = {Maddah, M. and Zollei, L. and Grimson, W. E. and Westin, C. F. and Wells, W. M.}, Title = {A {M}athematical {F}ramework for {I}ncorporating {A}natomical {K}nowledge in {DT}-{MRI} {A}nalysis.}, Journal = {Proc IEEE Int Symp Biomed Imaging}, Volume = {4543943}, Pages = {105-108}, abstract = {We propose a Bayesian approach to incorporate anatomical information in the clustering of fiber trajectories. An expectation-maximization (EM) algorithm is used to cluster the trajectories, in which an atlas serves as the prior on the labels. The atlas guides the clustering algorithm and makes the resulting bundles anatomically meaningful. In addition, it provides the seed points for the tractography and initial settings of the EM algorithm. The proposed approach provides a robust and automated tool for tract-oriented analysis both in a single subject and over a population.}, authoraddress = {Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.}, language = {ENG}, medline-aid = {10.1109/ISBI.2008.4540943 [doi]}, medline-crdt = {2009/02/13 09:00}, medline-da = {20090305}, medline-edat = {2009/02/13 09:00}, medline-gr = {P41 RR013218-09/NCRR NIH HHS/United States ; R01 MH074794-02/NIMH NIH HHS/United States ; R01 NS051826-04/NINDS NIH HHS/United States ; U41 RR019703-03/NCRR NIH HHS/United States ; U54 EB005149-04/NIBIB NIH HHS/United States}, medline-is = {1945-7928 (Print)}, medline-jid = {101492570}, medline-jt = {Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging}, medline-mhda = {2009/02/13 09:00}, medline-mid = {NIHMS88086}, medline-own = {NLM}, medline-pmc = {PMC2638065}, medline-pmid = {19212449}, medline-pst = {ppublish}, medline-pt = {JOURNAL ARTICLE}, medline-so = {Proc IEEE Int Symp Biomed Imaging. 2008;4543943:105-108.}, medline-stat = {Publisher}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=19212449}, year = 2008 } @Article{Santana2010, Author = {Santana, Roberto and Bielza, Concha and Larra, Pedro}, Title = {{Classification of MEG data using a combined machine learning approach Problem definition}}, Journal = {Challenge}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Santana, Bielza, Larra - 2010 - Classification of MEG data using a combined machine learning approach Problem definition.pdf:pdf}, year = 2010 } @Article{Kohn2009, Author = {K\"{o}hn, Alexander and Klein, Jan and Weiler, Florian and Peitgen, Heinz-Otto}, Title = {{A GPU-based fiber tracking framework using geometry shaders}}, Journal = {Proceedings of SPIE}, Pages = {72611J--72611J--10}, doi = {10.1117/12.812219}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/K\"{o}hn et al. - 2009 - A GPU-based fiber tracking framework using geometry shaders.pdf:pdf}, keywords = {diffusion tensor imaging,fiber tracking,gpu,visualization}, publisher = {Spie}, url = {http://link.aip.org/link/PSISDG/v7261/i1/p72611J/s1\&Agg=doi}, year = 2009 } @Misc{okada2006dtf, Author = {Okada, T. and Miki, Y. and Fushimi, Y. and Hanakawa, T. and Kanagaki, M. and Yamamoto, A. and Urayama, S. and Fukuyama, H. and Hiraoka, M. and Togashi, K.}, Title = {{Diffusion-Tensor Fiber Tractography: Intraindividual Comparison of 3.0-T and 1.5-T MR Imaging 1}}, journal = {Radiology}, number = {2}, pages = {668--678}, publisher = {RSNA}, volume = {238}, year = 2006 } @Article{Mittmann2010, Author = {Mittmann, Adiel and Nobrega, Tiago H C and Comunello, Eros and Pinto, Juliano P O and Dellani, Paulo R and Stoeter, Peter and von Wangenheim, Aldo}, Title = {{Performing Real-Time Interactive Fiber Tracking.}}, Journal = {Journal of digital imaging : the official journal of the Society for Computer Applications in Radiology}, abstract = {Fiber tracking is a technique that, based on a diffusion tensor magnetic resonance imaging dataset, locates the fiber bundles in the human brain. Because it is a computationally expensive process, the interactivity of current fiber tracking tools is limited. We propose a new approach, which we termed real-time interactive fiber tracking, which aims at providing a rich and intuitive environment for the neuroradiologist. In this approach, fiber tracking is executed automatically every time the user acts upon the application. Particularly, when the volume of interest from which fiber trajectories are calculated is moved on the screen, fiber tracking is executed, even while it is being moved. We present our fiber tracking tool, which implements the real-time fiber tracking concept by using the video card's graphics processing units to execute the fiber tracking algorithm. Results show that real-time interactive fiber tracking is feasible on computers equipped with common, low-cost video cards.}, doi = {10.1007/s10278-009-9266-9}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Mittmann et al. - 2010 - Performing Real-Time Interactive Fiber Tracking..pdf:pdf}, issn = {1618-727X}, keywords = {11 which finds,diffusion tensor imaging,fiber tracking,fiber trajectories by following,graphics processing units,of them being the,real-time applications,streamline method,the main diffusion}, month = feb, pmid = {20155382}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20155382}, year = 2010 } @Article{Kindlmann2007, Author = {Kindlmann, Gordon and Tricoche, Xavier and Westin, Carl-Fredrik}, Title = {{Delineating white matter structure in diffusion tensor MRI with anisotropy creases.}}, Journal = {Medical image analysis}, Volume = {11}, Number = {5}, Pages = {492--502}, abstract = {Geometric models of white matter architecture play an increasing role in neuroscientific applications of diffusion tensor imaging, and the most popular method for building them is fiber tractography. For some analysis tasks, however, a compelling alternative may be found in the first and second derivatives of diffusion anisotropy. We extend to tensor fields the notion from classical computer vision of ridges and valleys, and define anisotropy creases as features of locally extremal tensor anisotropy. Mathematically, these are the loci where the gradient of anisotropy is orthogonal to one or more eigenvectors of its Hessian. We propose that anisotropy creases provide a basis for extracting a skeleton of the major white matter pathways, in that ridges of anisotropy coincide with interiors of fiber tracts, and valleys of anisotropy coincide with the interfaces between adjacent but distinctly oriented tracts. The crease extraction algorithm we present generates high-quality polygonal models of crease surfaces, which are further simplified by connected-component analysis. We demonstrate anisotropy creases on measured diffusion MRI data, and visualize them in combination with tractography to confirm their anatomic relevance.}, doi = {10.1016/j.media.2007.07.005}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Kindlmann, Tricoche, Westin - 2007 - Delineating white matter structure in diffusion tensor MRI with anisotropy creases..pdf:pdf}, issn = {1361-8415}, keywords = {Algorithms,Anisotropy,Artificial Intelligence,Brain,Brain: cytology,Cluster Analysis,Diffusion Magnetic Resonance Imaging,Diffusion Magnetic Resonance Imaging: methods,Humans,Image Enhancement,Image Enhancement: methods,Image Interpretation, Computer-Assisted,Image Interpretation, Computer-Assisted: methods,Imaging, Three-Dimensional,Imaging, Three-Dimensional: methods,Nerve Fibers, Myelinated,Nerve Fibers, Myelinated: ultrastructure,Neural Pathways,Neural Pathways: cytology,Pattern Recognition, Automated,Pattern Recognition, Automated: methods,Reproducibility of Results,Sensitivity and Specificity}, pmid = {17804278}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17804278}, year = 2007 } @Article{Koles1991, Author = {Koles, Z J}, Title = {{The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG.}}, Journal = {Electroencephalography and clinical neurophysiology}, Volume = {79}, Number = {6}, Pages = {440--7}, abstract = {A method is described which seems to be effective for extracting the abnormal components from the clinical EEG. The approach involves the use of a set a spatial patterns which are common to recorded and 'normal' EEGs and which can account for maximally different proportions of the combined variances in both EEGs. These spatial factors are used to decompose the EEG into orthogonal temporal wave forms which can be judged by the expert electroencephalographer to be abnormal, normal or of artifactual origin. The original EEG is then reconstructed using only the abnormal components and principal component analysis is used to present the spatial topography of the abnormal components. The effectiveness of the method is discussed along with its value for localization of abnormal sources. It is suggested, in conclusion, that the approach described may be optimal for interpretation of the clinical EEG since it allows what is best in terms of quantitative analysis of the EEG to be combined with the best that is available in terms of expert qualitative analysis.}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Koles - 1991 - The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG..pdf:pdf}, issn = {0013-4694}, keywords = {Brain,Brain Mapping,Brain: physiology,Electroencephalography,Electroencephalography: methods,Humans,Signal Processing, Computer-Assisted}, month = dec, pmid = {1721571}, url = {http://www.ncbi.nlm.nih.gov/pubmed/1721571}, year = 1991 } @Article{andersson2002mbm, Author = {Andersson, J.L.R. and Skare, S.}, Title = {{A model-based method for retrospective correction of geometric distortions in diffusion-weighted EPI}}, Journal = {Neuroimage}, Volume = {16}, Number = {1}, Pages = {177--199}, publisher = {Elsevier Inc.}, year = 2002 } @Article{behrens2005rca, Author = {Behrens, T E and Johansen-Berg, H.}, Title = {{Relating connectional architecture to grey matter function using diffusion imaging.}}, Journal = {Philos Trans R Soc Lond B Biol Sci}, Volume = {360}, Number = {1457}, Pages = {903--11}, file = {attachment\:behrens_dti_connectivity_function_2005.pdf:attachment\:behrens_dti_connectivity_function_2005.pdf:PDF}, year = 2005 } @Article{Hall2009, Author = {Hall, Matt G and Alexander, Daniel C}, Title = {{Convergence and parameter choice for Monte-Carlo simulations of diffusion MRI.}}, Journal = {IEEE transactions on medical imaging}, Volume = {28}, Number = {9}, Pages = {1354--64}, abstract = {This paper describes a general and flexible Monte- Carlo simulation framework for diffusing spins that generates realistic synthetic data for diffusion magnetic resonance imaging. Similar systems in the literature consider only simple substrates and their authors do not consider convergence and parameter optimization. We show how to run Monte-Carlo simulations within complex irregular substrates. We compare the results of the Monte-Carlo simulation to an analytical model of restricted diffusion to assess precision and accuracy of the generated results. We obtain an optimal combination of spins and updates for a given run time by trading off number of updates in favor of number of spins such that precision and accuracy of sythesized data are both optimized. Further experiments demonstrate the system using a tissue environment that current analytic models cannot capture. This tissue model incorporates swelling, abutting, and deformation. Swelling-induced restriction in the extracellular space due to the effects of abutting cylinders leads to large departures from the predictions of the analytical model, which does not capture these effects. This swelling-induced restriction may be an important mechanism in explaining the changes in apparent diffusion constant observed in the aftermath of acute ischemic stroke.}, doi = {10.1109/TMI.2009.2015756}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Hall, Alexander - 2009 - Convergence and parameter choice for Monte-Carlo simulations of diffusion MRI..pdf:pdf}, issn = {1558-0062}, keywords = {Algorithms,Brain Edema,Brain Edema: pathology,Brain Ischemia,Brain Ischemia: pathology,Computer Simulation,Diffusion Magnetic Resonance Imaging,Diffusion Magnetic Resonance Imaging: methods,Humans,Monte Carlo Method,Reproducibility of Results,Stroke,Stroke: pathology}, month = sep, pmid = {19273001}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19273001}, year = 2009 } @Article{Perbet, Author = {Perbet, Frank}, Title = {{Correlated Probabilistic Trajectories for Pedestrian Motion Detection}}, Journal = {Image (Rochester, N.Y.)}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Perbet - Unknown - Correlated Probabilistic Trajectories for Pedestrian Motion Detection.pdf:pdf} } @Article{Vazirani1994, Author = {Vazirani, Vijay V}, Title = {{MAXIMUM MATCHING ALGORITHM}}, Journal = {Combinatorica}, Volume = {14}, Number = {i}, Pages = {71--109}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Vazirani - 1994 - MAXIMUM MATCHING ALGORITHM.pdf:pdf}, year = 1994 } @Article{boykov2004ecm, Author = {Boykov, Y. and Kolmogorov, V.}, Title = {{An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision}}, Journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, Volume = {26}, Number = {9}, Pages = {1124--1137}, year = 2004 } @Article{Fillard2009a, Author = {Fillard, Pierre and Poupon, Cyril}, Title = {{A Novel Global Tractography Algorithm based on an Adaptive Spin Glass Model}}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Fillard, Poupon - 2009 - A Novel Global Tractography Algorithm based on an Adaptive Spin Glass Model.pdf:pdf}, year = 2009 } @Article{0266-5611-19-5-303, Author = {Jansons, Kalvis M and Alexander, Daniel C}, Title = {Persistent angular structure: new insights from diffusion magnetic resonance imaging data}, Journal = {Inverse Problems}, Volume = {19}, Number = {5}, Pages = {1031-1046}, abstract = {We determine a statistic called the (radially) persistent angular structure (PAS) from samples of the Fourier transform of a three-dimensional function. The method has applications in diffusion magnetic resonance imaging (MRI), which samples the Fourier transform of the probability density function of particle displacements. The PAS is then a representation of the relative mobility of particles in each direction. In PAS-MRI, we compute the PAS in each voxel of an image. This technique has biomedical applications, where it reveals the orientations of microstructural fibres, such as white-matter fibres in the brain. Scanner time is a significant factor in determining the amount of data available in clinical brain scans. Here, we use measurements acquired for diffusion-tensor MRI, which is a routine diffusion imaging technique, but extract richer information. In particular, PAS-MRI can resolve the orientations of crossing fibres.We test PAS-MRI on human brain data and on synthetic data. The human brain data set comes from a standard acquisition scheme for diffusion-tensor MRI in which the samples in each voxel lie on a sphere in Fourier space.}, url = {http://stacks.iop.org/0266-5611/19/1031}, year = 2003 } @Article{ODonnell_AJNR06, Author = {O'Donnell, L. J. and Kubicki, M. and Shenton, M. E. and Dreusicke, M. H. and Grimson, W. E. and Westin, C. F.}, Title = {A method for clustering white matter fiber tracts.}, Journal = {AJNR Am J Neuroradiol}, Volume = {27}, Number = {5}, Pages = {1032-6}, abstract = {BACKGROUND/PURPOSE: Despite its potential for visualizing white matter fiber tracts in vivo, diffusion tensor tractography has found only limited applications in clinical research in which specific anatomic connections between distant regions need to be evaluated. We introduce a robust method for fiber clustering that guides the separation of anatomically distinct fiber tracts and enables further estimation of anatomic connectivity between distant brain regions. METHODS: Line scanning diffusion tensor images (LSDTI) were acquired on a 1.5T magnet. Regions of interest for several anatomically distinct fiber tracts were manually drawn; then, white matter tractography was performed by using the Runge-Kutta method to interpolate paths (fiber traces) following the major directions of diffusion, in which traces were seeded only within the defined regions of interest. Next, a fully automatic procedure was applied to fiber traces, grouping them according to a pairwise similarity function that takes into account the shapes of the fibers and their spatial locations. RESULTS: We demonstrated the ability of the clustering algorithm to separate several fiber tracts which are otherwise difficult to define (left and right fornix, uncinate fasciculus and inferior occipitofrontal fasciculus, and corpus callosum fibers). CONCLUSION: This method successfully delineates fiber tracts that can be further analyzed for clinical research purposes. Hypotheses regarding specific fiber connections and their abnormalities in various neuropsychiatric disorders can now be tested.}, authoraddress = {MIT Computer Science and AI Lab, Cambridge, MA 02139, USA.}, keywords = {Adolescent ; Adult ; Brain/*anatomy \& histology ; *Diffusion Magnetic Resonance Imaging/methods ; Humans ; Middle Aged}, language = {eng}, medline-aid = {27/5/1032 [pii]}, medline-crdt = {2006/05/12 09:00}, medline-da = {20060511}, medline-dcom = {20061030}, medline-edat = {2006/05/12 09:00}, medline-fau = {O'Donnell, L J ; Kubicki, M ; Shenton, M E ; Dreusicke, M H ; Grimson, W E L ; Westin, C F}, medline-gr = {1-R01-NS051826-01/NS/NINDS NIH HHS/United States ; K02 MH 01110/MH/NIMH NIH HHS/United States ; P41 RR13218/RR/NCRR NIH HHS/United States ; R03 MH 068464-02/MH/NIMH NIH HHS/United States ; U54 EB005149/EB/NIBIB NIH HHS/United States}, medline-is = {0195-6108 (Print)}, medline-jid = {8003708}, medline-jt = {AJNR. American journal of neuroradiology}, medline-lr = {20080214}, medline-mhda = {2006/10/31 09:00}, medline-own = {NLM}, medline-pl = {United States}, medline-pmid = {16687538}, medline-pst = {ppublish}, medline-pt = {Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.}, medline-sb = {IM}, medline-so = {AJNR Am J Neuroradiol. 2006 May;27(5):1032-6.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=16687538}, year = 2006 } @Article{Perrin2008, Author = {Perrin, Muriel and Cointepas, Yann and Cachia, Arnaud and Poupon, Cyril and Thirion, Bertrand and Rivi\`{e}re, Denis and Cathier, Pascal and {El Kouby}, Vincent and Constantinesco, Andr\'{e} and {Le Bihan}, Denis and Mangin, Jean-Fran\c{c}ois}, Title = {{Connectivity-Based Parcellation of the Cortical Mantle Using q-Ball Diffusion Imaging.}}, Journal = {International journal of biomedical imaging}, Volume = {2008}, Pages = {368406}, abstract = {This paper exploits the idea that each individual brain region has a specific connection profile to create parcellations of the cortical mantle using MR diffusion imaging. The parcellation is performed in two steps. First, the cortical mantle is split at a macroscopic level into 36 large gyri using a sulcus recognition system. Then, for each voxel of the cortex, a connection profile is computed using a probabilistic tractography framework. The tractography is performed from q fields using regularized particle trajectories. Fiber ODF are inferred from the q-balls using a sharpening process focusing the weight around the q-ball local maxima. A sophisticated mask of propagation computed from a T1-weighted image perfectly aligned with the diffusion data prevents the particles from crossing the cortical folds. During propagation, the particles father child particles in order to improve the sampling of the long fascicles. For each voxel, intersection of the particle trajectories with the gyri lead to a connectivity profile made up of only 36 connection strengths. These profiles are clustered on a gyrus by gyrus basis using a K-means approach including spatial regularization. The reproducibility of the results is studied for three subjects using spatial normalization.}, doi = {10.1155/2008/368406}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Perrin et al. - 2008 - Connectivity-Based Parcellation of the Cortical Mantle Using q-Ball Diffusion Imaging..pdf:pdf}, issn = {1687-4188}, pmid = {18401457}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18401457}, year = 2008 } @PhdThesis{maddah2008quantitative, Author = {Maddah, M.}, Title = {{Quantitative Analysis of Cerebral White Matter Anatomy from Diffusion MRI}}, School = {Citeseer}, year = 2008 } @Article{Schmahmann2007, Author = {Schmahmann, Jeremy D and Pandya, Deepak N and Wang, Ruopeng and Dai, Guangping and Arceuil, Helen E D and Crespigny, Alex J De and Wedeen, Van J}, Title = {{Association fibre pathways of the brain : parallel observations from diffusion spectrum imaging and autoradiography}}, Journal = {Brain}, Pages = {630--653}, doi = {10.1093/brain/awl359}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Schmahmann et al. - 2007 - Association fibre pathways of the brain parallel observations from diffusion spectrum imaging and autoradiography.pdf:pdf}, keywords = {abbreviations,af ¼ arcuate fasciculus,ass ¼ superior limb,callosum,cb ¼ cingulum bundle,cc ¼ corpus,cs ¼ central sulcus,diffusion tensor imaging,disconnection,dsi ¼ diffusion spectrum,dti ¼ diffusion tensor,dwi ¼ diffusion weighted,emc ¼ extreme capsule,epi ¼ echoplanar imaging,fibre bundles,fof ¼ fronto-occipital fasciculus,ilf ¼ inferior longitudinal,image,imaging,isotope,of the arcuate sulcus,tract tracing,tractography}, year = 2007 } @Article{PPC+05, Author = {Perrin, M. and Poupon, C. and Cointepas, Y. and Rieul, B. and Golestani, N. and Pallier, C. and Riviere, D. and Constantinesco, A. and Le Bihan, D. and Mangin, J. F.}, Title = {Fiber tracking in q-ball fields using regularized particle trajectories.}, Journal = {Inf Process Med Imaging}, Volume = {19}, Pages = {52-63}, abstract = {Most of the approaches dedicated to fiber tracking from diffusion-weighted MR data rely on a tensor model. However, the tensor model can only resolve a single fiber orientation within each imaging voxel. New emerging approaches have been proposed to obtain a better representation of the diffusion process occurring in fiber crossing. In this paper, we adapt a tracking algorithm to the q-ball representation, which results from a spherical Radon transform of high angular resolution data. This algorithm is based on a Monte-Carlo strategy, using regularized particle trajectories to sample the white matter geometry. The method is validated using a phantom of bundle crossing made up of haemodialysis fibers. The method is also applied to the detection of the auditory tract in three human subjects.}, authoraddress = {Service Hospitalier Frederic Joliot, CEA, 91401 Orsay, France. perrin@shfj.cea.fr}, keywords = {Algorithms ; *Artificial Intelligence ; Brain/*cytology ; Diffusion Magnetic Resonance Imaging/*methods ; Humans ; Image Enhancement/methods ; Image Interpretation, Computer-Assisted/*methods ; Imaging, Three-Dimensional/*methods ; Nerve Fibers, Myelinated/*ultrastructure ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity}, language = {eng}, medline-crdt = {2007/03/16 09:00}, medline-da = {20070314}, medline-dcom = {20070406}, medline-edat = {2007/03/16 09:00}, medline-fau = {Perrin, M ; Poupon, C ; Cointepas, Y ; Rieul, B ; Golestani, N ; Pallier, C ; Riviere, D ; Constantinesco, A ; Le Bihan, D ; Mangin, J F}, medline-is = {1011-2499 (Print)}, medline-jid = {9216871}, medline-jt = {Information processing in medical imaging : proceedings of the ... conference}, medline-mhda = {2007/04/07 09:00}, medline-own = {NLM}, medline-pl = {Germany}, medline-pmid = {17354684}, medline-pst = {ppublish}, medline-pt = {Journal Article}, medline-sb = {IM}, medline-so = {Inf Process Med Imaging. 2005;19:52-63.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=17354684}, year = 2005 } @Article{Neji2008a, Author = {Neji, Radhou\`{e}ne and Gilles, Jean-fran\c{c}ois Deux and Mezri, Fleury and Georg, Maatouk}, Title = {{A Kernel-based Approach to Diffusion Tensor and Fiber Clustering in the Human Skeletal Muscle}}, Journal = {October}, Number = {October}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Neji et al. - 2008 - A Kernel-based Approach to Diffusion Tensor and Fiber Clustering in the Human Skeletal Muscle.pdf:pdf}, year = 2008 } @Article{Tournier2007, Author = {Tournier, J-Donald and Calamante, Fernando and Connelly, Alan}, Title = {{Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution.}}, Journal = {NeuroImage}, Volume = {35}, Number = {4}, Pages = {1459--72}, abstract = {Diffusion-weighted (DW) MR images contain information about the orientation of brain white matter fibres that potentially can be used to study human brain connectivity in vivo using tractography techniques. Currently, the diffusion tensor model is widely used to extract fibre directions from DW-MRI data, but fails in regions containing multiple fibre orientations. The spherical deconvolution technique has recently been proposed to address this limitation. It provides an estimate of the fibre orientation distribution (FOD) by assuming the DW signal measured from any fibre bundle is adequately described by a single response function. However, the deconvolution is ill-conditioned and susceptible to noise contamination. This tends to introduce artefactual negative regions in the FOD, which are clearly physically impossible. In this study, the introduction of a constraint on such negative regions is proposed to improve the conditioning of the spherical deconvolution. This approach is shown to provide FOD estimates that are robust to noise whilst preserving angular resolution. The approach also permits the use of super-resolution, whereby more FOD parameters are estimated than were actually measured, improving the angular resolution of the results. The method provides much better defined fibre orientation estimates, and allows orientations to be resolved that are separated by smaller angles than previously possible. This should allow tractography algorithms to be designed that are able to track reliably through crossing fibre regions.}, doi = {10.1016/j.neuroimage.2007.02.016}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Tournier, Calamante, Connelly - 2007 - Robust determination of the fibre orientation distribution in diffusion MRI non-negativity constrained super-resolved spherical deconvolution..pdf:pdf}, issn = {1053-8119}, keywords = {Algorithms,Brain,Brain: cytology,Computer Simulation,Data Interpretation, Statistical,Diffusion Magnetic Resonance Imaging,Humans,Image Processing, Computer-Assisted,Models, Statistical,Nerve Fibers,Nerve Fibers: physiology,Reproducibility of Results}, pmid = {17379540}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17379540}, year = 2007 } @Article{Descoteaux2009, Author = {Descoteaux, Maxime and Deriche, Rachid and Kn\"{o}sche, Thomas R and Anwander, Alfred}, Title = {{Deterministic and probabilistic tractography based on complex fibre orientation distributions.}}, Journal = {IEEE transactions on medical imaging}, Volume = {28}, Number = {2}, Pages = {269--86}, abstract = {We propose an integral concept for tractography to describe crossing and splitting fibre bundles based on the fibre orientation distribution function (ODF) estimated from high angular resolution diffusion imaging (HARDI). We show that in order to perform accurate probabilistic tractography, one needs to use a fibre ODF estimation and not the diffusion ODF. We use a new fibre ODF estimation obtained from a sharpening deconvolution transform (SDT) of the diffusion ODF reconstructed from q-ball imaging (QBI). This SDT provides new insight into the relationship between the HARDI signal, the diffusion ODF, and the fibre ODF. We demonstrate that the SDT agrees with classical spherical deconvolution and improves the angular resolution of QBI. Another important contribution of this paper is the development of new deterministic and new probabilistic tractography algorithms using the full multidirectional information obtained through use of the fibre ODF. An extensive comparison study is performed on human brain datasets comparing our new deterministic and probabilistic tracking algorithms in complex fibre crossing regions. Finally, as an application of our new probabilistic tracking, we quantify the reconstruction of transcallosal fibres intersecting with the corona radiata and the superior longitudinal fasciculus in a group of eight subjects. Most current diffusion tensor imaging (DTI)-based methods neglect these fibres, which might lead to incorrect interpretations of brain functions.}, doi = {10.1109/TMI.2008.2004424}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Descoteaux et al. - 2009 - Deterministic and probabilistic tractography based on complex fibre orientation distributions..pdf:pdf}, issn = {1558-0062}, keywords = {Algorithms,Brain,Brain: anatomy \& histology,Diffusion Magnetic Resonance Imaging,Diffusion Magnetic Resonance Imaging: methods,Echo-Planar Imaging,Echo-Planar Imaging: methods,Humans,Image Enhancement,Image Enhancement: methods,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: methods,Models, Neurological,Models, Statistical,Nerve Fibers,Nerve Fibers: ultrastructure,Normal Distribution,Reproducibility of Results,Sensitivity and Specificity}, month = feb, pmid = {19188114}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19188114}, year = 2009 } @Article{zhang1997birch, Author = {Zhang, T. and Ramakrishnan, R. and Livny, M.}, Title = {{BIRCH: A new data clustering algorithm and its applications}}, Journal = {Data Mining and Knowledge Discovery}, Volume = {1}, Number = {2}, Pages = {141--182}, publisher = {Springer}, year = 1997 } @Article{Leemans2005MagResMed, Author = {Leemans, A. and Sijbers, J. and Verhoye, M. and {Van der Linden}, A. and {Van Dyck}, D. }, Title = {Mathematical framework for simulating diffusion tensor \{{M}{R}\} neural fiber bundles}, Journal = {Magnetic Resonance in Medicine}, Volume = {53}, Number = {4}, Pages = {944-953}, doi = {10.1002/mrm.20418}, file = {attachment\:Leemans2005MagResMed.pdf:attachment\:Leemans2005MagResMed.pdf:PDF}, publisher = {Wiley-Liss, Inc.}, url = {http://dx.doi.org/10.1002/mrm.20418}, year = 2005 } @Article{Jones2002, Author = {Jones, Derek K. and Basser, Peter J.}, Title = {{Diffusion-tensor MRI: theory, experimental design and data analysis - a technical review}}, Journal = {NMR in Biomedicine}, Volume = {15}, Number = {7-8}, Pages = {456--467}, abstract = {This article treats the theoretical underpinnings of diffusion-tensor magnetic resonance imaging (DT-MRI), as well as experimental design and data analysis issues. We review the mathematical model underlying DT-MRI, discuss the quantitative parameters that are derived from the measured effective diffusion tensor, and describe artifacts thet arise in typical DT-MRI acquisitions. We also discuss difficulties in identifying appropriate models to describe water diffusion in heterogeneous tissues, as well as in interpreting experimental data obtained in such issues. Finally, we describe new statistical methods that have been developed to analyse DT-MRI data, and their potential uses in clinical and multi-site studies. Copyright � 2002 John Wiley \& Sons, Ltd.}, doi = {10.1002/nbm.783}, shorttitle = {Diffusion-tensor MRI}, url = {http://dx.doi.org/10.1002/nbm.783}, year = 2002 } @Article{Mining1997, Author = {Mining, Data and Discovery, Knowledge}, Title = {{BIRCH : A New Data Clustering Algorithm and Its Applications}}, Journal = {Knowledge Creation Diffusion Utilization}, Volume = {182}, Pages = {141--182}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Mining, Discovery - 1997 - BIRCH A New Data Clustering Algorithm and Its Applications.pdf:pdf}, keywords = {data classification and compression,data clustering,incremental algorithm,very large databases}, year = 1997 } @Article{Chan, Author = {Chan, Cy and Drensky, Vesselin and Edelman, Alan and Kan, Raymond and Koev, Plamen}, Title = {{On Computing Schur Functions and Series Thereof}}, Journal = {Journal of Algebraic Combinatorics}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Chan et al. - Unknown - On Computing Schur Functions and Series Thereof.pdf:pdf}, keywords = {computing,hypergeometric function of a,matrix argument,schur function} } @Article{MKW+08, Author = {Maddah, M. and Kubicki, M. and Wells, W. M. and Westin, C. F. and Shenton, M. E. and Grimson, W. E.}, Title = {Findings in schizophrenia by tract-oriented {DT}-{MRI} analysis.}, Journal = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv}, Volume = {11}, Number = {Pt 1}, Pages = {917-24}, abstract = {This paper presents a tract-oriented analysis of diffusion tensor (DT) images of the human brain. We demonstrate that unlike the commonly used ROI-based methods for population studies, our technique is sensitive to the local variation of diffusivity parameters along the fiber tracts. We show the strength of the proposed approach in identifying the differences in schizophrenic data compared to controls. Statistically significant drops in fractional anisotropy are observed along the genu and bilaterally in the splenium, as well as an increase in principal eigenvalue in uncinate fasciculus. This is the first tract-oriented clinical study in which an anatomical atlas is used to guide the algorithm.}, authoraddress = {Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. mmaddah@mit.edu}, keywords = {Algorithms ; *Artificial Intelligence ; Brain Diseases/*diagnosis ; Diffusion Magnetic Resonance Imaging/*methods ; Female ; Humans ; Image Enhancement/methods ; Image Interpretation, Computer-Assisted/*methods ; Male ; Nerve Fibers, Myelinated/*pathology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Schizophrenia/*diagnosis ; Sensitivity and Specificity}, language = {eng}, medline-crdt = {2008/11/05 09:00}, medline-da = {20081104}, medline-dcom = {20081209}, medline-edat = {2008/11/05 09:00}, medline-fau = {Maddah, Mahnaz ; Kubicki, Marek ; Wells, William M ; Westin, Carl-Fredrik ; Shenton, Martha E ; Grimson, W Eric L}, medline-jid = {101249582}, medline-jt = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}, medline-mhda = {2008/12/17 09:00}, medline-own = {NLM}, medline-pl = {Germany}, medline-pmid = {18979833}, medline-pst = {ppublish}, medline-pt = {Evaluation Studies ; Journal Article}, medline-sb = {IM}, medline-so = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):917-24.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=18979833}, year = 2008 } @Article{MaddahMIA2008, Author = {Maddah, M. and Grimson, W. E. and Warfield, S. K. and Wells, W. M.}, Title = {A unified framework for clustering and quantitative analysis of white matter fiber tracts.}, Journal = {Med Image Anal}, Volume = {12}, Number = {2}, Pages = {191-202}, abstract = {We present a novel approach for joint clustering and point-by-point mapping of white matter fiber pathways. Knowledge of the point correspondence along the fiber pathways is not only necessary for accurate clustering of the trajectories into fiber bundles, but also crucial for any tract-oriented quantitative analysis. We employ an expectation-maximization (EM) algorithm to cluster the trajectories in a gamma mixture model context. The result of clustering is the probabilistic assignment of the fiber trajectories to each cluster, an estimate of the cluster parameters, i.e. spatial mean and variance, and point correspondences. The fiber bundles are modeled by the mean trajectory and its spatial variation. Point-by-point correspondence of the trajectories within a bundle is obtained by constructing a distance map and a label map from each cluster center at every iteration of the EM algorithm. This offers a time-efficient alternative to pairwise curve matching of all trajectories with respect to each cluster center. The proposed method has the potential to benefit from an anatomical atlas of fiber tracts by incorporating it as prior information in the EM algorithm. The algorithm is also capable of handling outliers in a principled way. The presented results confirm the efficiency and effectiveness of the proposed framework for quantitative analysis of diffusion tensor MRI.}, authoraddress = {Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, USA. mmaddah@mit.edu}, keywords = {Algorithms ; *Artificial Intelligence ; Brain/*anatomy \& histology ; *Cluster Analysis ; Diffusion Magnetic Resonance Imaging/*methods ; Humans ; Image Enhancement/methods ; Image Interpretation, Computer-Assisted/*methods ; Imaging, Three-Dimensional/methods ; Likelihood Functions ; Models, Biological ; Models, Statistical ; Nerve Fibers, Myelinated/*ultrastructure ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity}, language = {eng}, medline-aid = {S1361-8415(07)00099-0 [pii] ; 10.1016/j.media.2007.10.003 [doi]}, medline-crdt = {2008/01/09 09:00}, medline-da = {20080416}, medline-dcom = {20080520}, medline-dep = {20071025}, medline-edat = {2008/01/09 09:00}, medline-fau = {Maddah, Mahnaz ; Grimson, W Eric L ; Warfield, Simon K ; Wells, William M}, medline-gr = {P30 HD018655/HD/NICHD NIH HHS/United States ; P30 HD018655-26/HD/NICHD NIH HHS/United States ; P41 RR013218/RR/NCRR NIH HHS/United States ; P41 RR013218-010001/RR/NCRR NIH HHS/United States ; P41 RR013218-010002/RR/NCRR NIH HHS/United States ; P41 RR013218-010010/RR/NCRR NIH HHS/United States ; R01 RR021885/RR/NCRR NIH HHS/United States ; R01 RR021885-01A1/RR/NCRR NIH HHS/United States ; R01 RR021885-02/RR/NCRR NIH HHS/United States ; R03 CA126466/CA/NCI NIH HHS/United States ; R03 CA126466-01A1/CA/NCI NIH HHS/United States ; R03 CA126466-02/CA/NCI NIH HHS/United States ; R21 MH067054/MH/NIMH NIH HHS/United States ; R21 MH067054-01A1/MH/NIMH NIH HHS/United States ; R21 MH067054-02/MH/NIMH NIH HHS/United States ; U41 RR019703/RR/NCRR NIH HHS/United States ; U54 EB005149/EB/NIBIB NIH HHS/United States}, medline-is = {1361-8423 (Electronic)}, medline-jid = {9713490}, medline-jt = {Medical image analysis}, medline-lr = {20090406}, medline-mhda = {2008/05/21 09:00}, medline-mid = {NIHMS49862}, medline-oid = {NLM: NIHMS49862 ; NLM: PMC2615202}, medline-own = {NLM}, medline-phst = {2006/11/18 [received] ; 2007/10/02 [revised] ; 2007/10/02 [accepted] ; 2007/10/25 [aheadofprint]}, medline-pl = {Netherlands}, medline-pmc = {PMC2615202}, medline-pmid = {18180197}, medline-pst = {ppublish}, medline-pt = {Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't}, medline-sb = {IM}, medline-so = {Med Image Anal. 2008 Apr;12(2):191-202. Epub 2007 Oct 25.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=18180197}, year = 2008 } @Article{Tuch2005, Author = {Tuch, David S and Wisco, Jonathan J and Khachaturian, Mark H and Vanduffel, Wim and Ekstrom, Leeland B and Ko, Rolf}, Title = {{Q-ball imaging of macaque white matter architecture}}, Number = {May}, Pages = {869--879}, doi = {10.1098/rstb.2005.1651}, keywords = {connectivity,diffusion magnetic resonance imaging,high angular resolution diffusion,imaging,macaque,tractography,white matter}, year = 2005 } @Article{George2009, Author = {George, Kyriazis and Erwan, Le Pennec and Pencho, Petrushev and Dominique, Picard}, Title = {{Inversion of noisy Radon transform by SVD based needlets arXiv : 0809 . 3332v2 [ math . ST ] 17 Aug 2009}}, Pages = {1--35}, arxivid = {arXiv:0809.3332v2}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/George et al. - 2009 - Inversion of noisy Radon transform by SVD based needlets arXiv 0809 . 3332v2 math . ST 17 Aug 2009.pdf:pdf}, year = 2009 } @Article{Hagmann2007PLoSONE, Author = {Hagmann, Patric and Kurant, Maciej and Gigandet, Xavier and Thiran, Patrick and Wedeen, Van J. and Meuli, Reto and Thiran, Jean-Philippe }, Title = {Mapping human whole-brain structural networks with diffusion {MRI}.}, Journal = {PLoS ONE}, Volume = {2}, Number = {7}, Pages = {e597}, abstract = {Understanding the large-scale structural network formed by neurons is a major challenge in system neuroscience. A detailed connectivity map covering the entire brain would therefore be of great value. Based on diffusion MRI, we propose an efficient methodology to generate large, comprehensive and individual white matter connectional datasets of the living or dead, human or animal brain. This non-invasive tool enables us to study the basic and potentially complex network properties of the entire brain. For two human subjects we find that their individual brain networks have an exponential node degree distribution and that their global organization is in the form of a small world.}, doi = {10.1371/journal.pone.0000597}, file = {attachment\:Hagmann2007PLoSONE.pdf:attachment\:Hagmann2007PLoSONE.pdf:PDF}, year = 2007 } @Article{torrey1956bed, Author = {Torrey, H.C.}, Title = {{Bloch equations with diffusion terms}}, Journal = {Physical Review}, Volume = {104}, Number = {3}, Pages = {563--565}, publisher = {APS}, year = 1956 } @Article{Perrin2005PhilTransRoySoc, Author = {Perrin, Muriel and Poupon, Cyril and Rieul, Bernard and Leroux, Patrick and Constantinesco, André and Mangin, Jean-François and LeBihan, Denis}, Title = {Validation of q-ball imaging with a diffusion fibre-crossing phantom on a clinical scanner}, Journal = {Philosophical Transactions of the Royal Society B: Biological Sciences}, Volume = {360}, Number = {1457}, Pages = {881-91}, abstract = {Magnetic resonance (MR) diffusion imaging provides a valuable tool used for inferring structural anisotropy of brain white matter connectivity from diffusion tensor imaging. Recently, several high angular resolution diffusion models were introduced in order to overcome the inadequacy of the tensor model for describing fibre crossing within a single voxel. Among them, q-ball imaging (QBI), inherited from the q-space method, relies on a spherical Radon transform providing a direct relationship between the diffusion-weighted MR signal and the orientation distribution function (ODF). Experimental validation of these methods in a model system is necessary to determine the accuracy of the methods and to optimize them. A diffusion phantom made up of two textile rayon fibre (comparable in diameter to axons) bundles, crossing at $90^o$, was designed and dedicated to ex vivo q-ball validation on a clinical scanner. Normalized ODFs were calculated inside regions of interest corresponding to monomodal and bimodal configurations of underlying structures. Threedimensional renderings of ODFs revealed monomodal shapes for voxels containing single-fibre population and bimodal patterns for voxels located within the crossing area. Principal orientations were estimated from ODFs and were compared with a priori structural fibre directions, validating efficiency of QBI for depicting fibre crossing. In the homogeneous regions, QBI detected the fibre angle with an accuracy of $19^o$ and in the fibre-crossing region with an accuracy of $30^o$.}, doi = {10.1098/rstb.2005.1650}, file = {attachment\:Perrin2005PhilTransRoySoc.pdf:attachment\:Perrin2005PhilTransRoySoc.pdf:PDF}, url = {http://journals.royalsociety.org/content/mldn6494e2xf23ta}, year = 2005 } @Article{Frey2008, Author = {Frey, S and Campbell, JSW and Pike, GB}, Title = {\ldots human language pathways with high angular resolution diffusion fiber tractography}, Journal = {Journal of Neuroscience}, url = {http://neuro.cjb.net/cgi/content/abstract/28/45/11435}, year = 2008 } @Article{Stejskal1965JChemPhys, Author = {E. O. Stejskal and J. E. Tanner}, Title = {Spin Diffusion Measurements: Spin Echoes in the Presence of a Time-Dependent Field Gradient}, Journal = {The Journal of Chemical Physics}, Volume = {42}, Number = {1}, Pages = {288-292}, doi = {10.1063/1.1695690}, publisher = {AIP}, url = {http://link.aip.org/link/?JCP/42/288/1}, year = 1965 } @Article{Reisert, Author = {Reisert, Marco and Mader, Irina and Kiselev, Valerij}, Title = {{Global Reconstruction of Neuronal Fibres}}, Journal = {Lecture Notes in Computer Science}, Pages = {1--12}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Reisert, Mader, Kiselev - Unknown - Global Reconstruction of Neuronal Fibres.pdf:pdf} } @Article{Koles1998, Author = {Koles, Z J and Soong, a C}, Title = {{EEG source localization: implementing the spatio-temporal decomposition approach.}}, Journal = {Electroencephalography and clinical neurophysiology}, Volume = {107}, Number = {5}, Pages = {343--52}, abstract = {OBJECTIVES: The spatio-temporal decomposition (STD) approach was used to localize the sources of simulated electroencephalograms (EEGs) to gain experience with the approach for analyzing real data. METHODS: The STD approach used is similar to the multiple signal classification method (MUSIC) in that it requires the signal subspace containing the sources of interest to be isolated in the EEG measurement space. It is different from MUSIC in that it allows more general methods of spatio-temporal decomposition to be used that may be better suited to the background EEG. RESULTS: If the EEG data matrix is not corrupted by noise, the STD approach can be used to locate multiple dipole sources of the EEG one at a time without a priori knowledge of the number of active sources in the signal space. In addition, the common-spatial-patterns method of spatio-temporal decomposition is superior to the eigenvector decomposition for localizing activity that is ictal in nature. CONCLUSIONS: The STD approach appears to be able to provide a means of localizing the equivalent dipole sources of realistic brain sources and that, even under difficult noise conditions and only 2 or 3 s of available EEG, the precision of the localization can be as low as a few mm.}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Koles, Soong - 1998 - EEG source localization implementing the spatio-temporal decomposition approach..pdf:pdf}, issn = {0013-4694}, keywords = {Artifacts,Brain,Brain Mapping,Brain Mapping: methods,Brain: physiology,Computer Simulation,Electrodes,Electroencephalography,Electroencephalography: instrumentation,Evaluation Studies as Topic,Humans,Models, Neurological,Time Factors}, month = nov, pmid = {9872437}, url = {http://www.ncbi.nlm.nih.gov/pubmed/9872437}, year = 1998 } @Article{roebroeck2008hrd, Author = {Roebroeck, A. and Galuske, R. and Formisano, E. and Chiry, O. and Bratzke, H. and Ronen, I. and Kim, D. and Goebel, R.}, Title = {{High-resolution diffusion tensor imaging and tractography of the human optic chiasm at 9.4 T}}, Journal = {Neuroimage}, Volume = {39}, Number = {1}, Pages = {157--168}, publisher = {Elsevier}, year = 2008 } @Article{To, Author = {To, Introduction}, Title = {{INTRODUCTION TO PROBABILITY}}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/To - Unknown - INTRODUCTION TO PROBABILITY.pdf:pdf} } @Article{Catani2008, Author = {Catani, M and Mesulam, M}, Title = {{The arcuate fasciculus and the disconnection theme in language and aphasia: \ldots}}, Journal = {Cortex}, url = {http://linkinghub.elsevier.com/retrieve/pii/S0010945208001111}, year = 2008 } @Article{Walter2010, Author = {Walter, Thomas and Shattuck, David W and Baldock, Richard and Bastin, Mark E and Carpenter, Anne E and Duce, Suzanne and Ellenberg, Jan and Fraser, Adam and Hamilton, Nicholas and Pieper, Steve and Ragan, Mark A and Schneider, Jurgen E and Tomancak, Pavel and H\'{e}rich\'{e}, Jean-karim}, Title = {{Visualization of image data from cells to organisms}}, Journal = {Nature Publishing Group}, Volume = {7}, Number = {3s}, Pages = {S26--S41}, doi = {10.1038/nmeth.1431}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Walter et al. - 2010 - Visualization of image data from cells to organisms.pdf:pdf}, issn = {1548-7091}, publisher = {Nature Publishing Group}, url = {http://dx.doi.org/10.1038/nmeth.1431}, year = 2010 } @Article{Jbabdi2007, Author = {Jbabdi, S and Woolrich, M W and Andersson, J L and Behrens, T E}, Title = {{A Bayesian framework for global tractography}}, Journal = {NeuroImage}, Volume = {37}, Pages = {116--129}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Jbabdi et al. - 2007 - A Bayesian framework for global tractography.pdf:pdf}, year = 2007 } @Article{Lewis2005, Author = {Lewis, David a and Hashimoto, Takanori and Volk, David W}, Title = {{Cortical inhibitory neurons and schizophrenia.}}, Journal = {Nature reviews. Neuroscience}, Volume = {6}, Number = {4}, Pages = {312--24}, abstract = {Impairments in certain cognitive functions, such as working memory, are core features of schizophrenia. Convergent findings indicate that a deficiency in signalling through the TrkB neurotrophin receptor leads to reduced GABA (gamma-aminobutyric acid) synthesis in the parvalbumin-containing subpopulation of inhibitory GABA neurons in the dorsolateral prefrontal cortex of individuals with schizophrenia. Despite both pre- and postsynaptic compensatory responses, the resulting alteration in perisomatic inhibition of pyramidal neurons contributes to a diminished capacity for the gamma-frequency synchronized neuronal activity that is required for working memory function. These findings reveal specific targets for therapeutic interventions to improve cognitive function in individuals with schizophrenia.}, doi = {10.1038/nrn1648}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Lewis, Hashimoto, Volk - 2005 - Cortical inhibitory neurons and schizophrenia..pdf:pdf}, issn = {1471-003X}, keywords = {Animals,Cerebral Cortex,Cerebral Cortex: cytology,Cerebral Cortex: physiology,Cerebral Cortex: physiopathology,Humans,Nerve Net,Nerve Net: pathology,Nerve Net: physiopathology,Neural Inhibition,Neural Inhibition: physiology,Neurons,Neurons: cytology,Neurons: physiology,Schizophrenia,Schizophrenia: pathology,Schizophrenia: physiopathology}, month = apr, pmid = {15803162}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15803162}, year = 2005 } @Article{Kenkre1997JMagRes, Author = {V. M. Kenkre and Eiichi Fukushima and D. Sheltraw}, Title = {Simple Solutions of the Torrey-Bloch Equations in the NMR Study of Molecular Diffusion}, Journal = {Journal of Magnetic Resonance}, Volume = {128}, Number = {1}, Pages = {62 - 69}, abstract = {A simple technique for solving the Torrey-Bloch equations appearing in the calculation of the NMR signal under gradient fields is presented. It is applicable to arbitrary time dependence of the gradient field to arbitrary initial distribution of spins, and to spin motion on discrete lattices as well as in the continuum under conditions of unrestricted diffusion. Known results are recovered as particular cases and new results are presented. The discrete lattice results are shown to be similar to known results for restricted diffusion in the continuum. Also presented is a surprising equivalence between results for a simple two-site hopping model and earlier expressions for the NMR signal for spins undergoing restricted diffusion in a continuum.}, doi = {DOI: 10.1006/jmre.1997.1216}, issn = {1090-7807}, url = {http://www.sciencedirect.com/science/article/B6WJX-45KN26H-6/2/817cb1d5d119831cc0ccf5284d324a37}, year = 1997 } @Article{Ashburner2000NeuroImage, Author = {Ashburner, John and Friston, Karl J.}, Title = {Voxel-Based Morphometry - The Methods}, Journal = {NeuroImage}, Volume = {11}, Pages = {805-821}, abstract = {At its simplest, voxel-based morphometry (VBM) involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing high-resolution images from all the subjects in the study into the same stereotactic space. This is followed by segmenting the gray matter from the spatially normalized images and smoothing the gray-matter segments. Voxel-wise parametric statistical tests which compare the smoothed gray-matter images from the two groups are performed. Corrections for multiple comparisons are made using the theory of Gaussian random fields. This paper describes the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with nonuniformity artifact. We provide evaluations of the assumptions that underpin the method, including the accuracy of the segmentation and the assumptions made about the statistical distribution of the data.-}, doi = {10.1006/nimg.2000.0582}, file = {attachment\:Ashburner2000NeuroImage.pdf:attachment\:Ashburner2000NeuroImage.pdf:PDF}, publisher = {Elsevier}, year = 2000 } @Article{Ding2003a, Author = {Ding, Zhaohua and Gore, John C and Anderson, Adam W}, Title = {{Classification and quantification of neuronal fiber pathways using diffusion tensor MRI.}}, Journal = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine}, Volume = {49}, Number = {4}, Pages = {716--21}, abstract = {Quantitative characterization of neuronal fiber pathways in vivo is of significant neurological and clinical interest. Using the capability of MR diffusion tensor imaging to determine the local orientations of neuronal fibers, novel algorithms were developed to bundle neuronal fiber pathways reconstructed in vivo with diffusion tensor images and to quantify various physical and geometric properties of fiber bundles. The reliability of the algorithms was examined with reproducibility tests. Illustrative results show that consistent physical and geometric measurements of novel properties of neuronal tissue can be obtained, which offer considerable potential for the quantitative study of fiber pathways in vivo.}, doi = {10.1002/mrm.10415}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Ding, Gore, Anderson - 2003 - Classification and quantification of neuronal fiber pathways using diffusion tensor MRI..pdf:pdf}, issn = {0740-3194}, keywords = {Algorithms,Brain Mapping,Brain Mapping: methods,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: methods,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Nerve Fibers,Nerve Fibers: classification,Neural Pathways,Neural Pathways: anatomy \& histology,Reproducibility of Results}, pmid = {12652543}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12652543}, year = 2003 } @Article{powell2005mtp, Author = {Powell, HWR and Parker, GJM and Alexander, DC and Symms, MR and Boulby, PA and Wheeler-Kingshott, CAM and Barker, GJ and Koepp, MJ and Duncan, JS}, Title = {{MR tractography predicts visual field defects following temporal lobe resection}}, Journal = {Neurology}, Volume = {65}, Number = {4}, Pages = {596--599}, publisher = {AAN Enterprises}, year = 2005 } @Article{lawes2008abs, Author = {Lawes, I. N. C. and Barrick, T.R. and Murugam, V. and Spierings, N. and Evans, D.R. and Song, M. and Clark, C. A.}, Title = {{Atlas-based segmentation of white matter tracts of the human brain using diffusion tensor tractography and comparison with classical dissection.}}, Journal = {Neuroimage}, Volume = {39}, Pages = {62--79}, file = {attachment\:lawes_dti_atlas-based_segmentation_2008.pdf:attachment\:lawes_dti_atlas-based_segmentation_2008.pdf:PDF}, year = 2008 } @Article{Tanaka1999, Author = {Tanaka, Hidefumi}, Title = {{Circular asymmetry of the paleomagnetic directions observed at low latitude volcanic sites}}, Journal = {Simulation}, Number = {4}, Pages = {1279--1286}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Tanaka - 1999 - Circular asymmetry of the paleomagnetic directions observed at low latitude volcanic sites.pdf:pdf}, year = 1999 } @Article{MaddahIPMI2007, Author = {Maddah, M. and Wells, 3rd, W. M. and Warfield, S. K. and Westin, C. F. and Grimson, W. E.}, Title = {Probabilistic clustering and quantitative analysis of white matter fiber tracts.}, Journal = {Inf Process Med Imaging}, Volume = {20}, Pages = {372-83}, abstract = {A novel framework for joint clustering and point-by-point mapping of white matter fiber pathways is presented. Accurate clustering of the trajectories into fiber bundles requires point correspondence determined along the fiber pathways. This knowledge is also crucial for any tract-oriented quantitative analysis. We employ an expectation-maximization (EM) algorithm to cluster the trajectories in a Gamma mixture model context. The result of clustering is the probabilistic assignment of the fiber trajectories to each cluster, an estimate of the cluster parameters, and point correspondences along the trajectories. Point-by-point correspondence of the trajectories within a bundle is obtained by constructing a distance map and a label map from each cluster center at every iteration of the EM algorithm. This offers a time-efficient alternative to pairwise curve matching of all trajectories with respect to each cluster center. Probabilistic assignment of the trajectories to clusters is controlled by imposing a minimum threshold on the membership probabilities, to remove outliers in a principled way. The presented results confirm the efficiency and effectiveness of the proposed framework for quantitative analysis of diffusion tensor MRI.}, authoraddress = {Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. mmaddah@mit.edu}, keywords = {Algorithms ; Artificial Intelligence ; Brain/*cytology ; Cluster Analysis ; Diffusion Magnetic Resonance Imaging/*methods ; Humans ; Image Enhancement/methods ; Image Interpretation, Computer-Assisted/*methods ; Imaging, Three-Dimensional/*methods ; Models, Neurological ; Models, Statistical ; Nerve Fibers, Myelinated/*ultrastructure ; Neural Pathways/*cytology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity}, language = {eng}, medline-crdt = {2007/07/19 09:00}, medline-da = {20070718}, medline-dcom = {20070831}, medline-edat = {2007/07/19 09:00}, medline-fau = {Maddah, Mahnaz ; Wells, William M 3rd ; Warfield, Simon K ; Westin, Carl-Fredrik ; Grimson, W Eric L}, medline-gr = {P30 HD018655/HD/NICHD NIH HHS/United States ; P41 RR013218/RR/NCRR NIH HHS/United States ; R01 RR021885/RR/NCRR NIH HHS/United States ; R03 CA126466/CA/NCI NIH HHS/United States ; R21 MH067054/MH/NIMH NIH HHS/United States ; U41 RR019703/RR/NCRR NIH HHS/United States ; U54 EB005149/EB/NIBIB NIH HHS/United States}, medline-is = {1011-2499 (Print)}, medline-jid = {9216871}, medline-jt = {Information processing in medical imaging : proceedings of the ... conference}, medline-lr = {20071203}, medline-mhda = {2007/09/01 09:00}, medline-own = {NLM}, medline-pl = {Germany}, medline-pmid = {17633714}, medline-pst = {ppublish}, medline-pt = {Evaluation Studies ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.}, medline-sb = {IM}, medline-so = {Inf Process Med Imaging. 2007;20:372-83.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=17633714}, year = 2007 } @Article{descoteaux2009deterministic, Author = {Descoteaux, M. and Deriche, R. and Knoesche, T. and Anwander, A.}, Title = {{Deterministic and probabilistic tractography based on complex fibre orientation distributions}}, Journal = {IEEE Trans Med Imaging}, Volume = {28}, Number = {2}, Pages = {269--86}, year = 2009 } @Article{Smith2006NeuroImage, Author = {Smith, Stephen M. and Jenkinson, Mark and Johansen-Berg, Heidi and Rueckert, Daniel and Nichols, Thomas E. and Mackay, Clare E. and Watkins, Kate E. and Ciccarelli, Olga and Cader, Zaheer and Matthews, Paul M. and Behrens, Timothy E.J.}, Title = {Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data}, Journal = {NeuroImage}, Volume = {31}, Pages = {1487-1505}, abstract = {There has been much recent interest in using magnetic resonance diffusion imaging to provide information about anatomical connectivity in the brain, by measuring the anisotropic diffusion of water in white matter tracts. One of the measures most commonly derived from diffusion data is fractional anisotropy (FA), which quantifies how strongly directional the local tract structure is. Many imaging studies are starting to use FA images in voxelwise statistical analyses, in order to localise brain changes related to development, degeneration and disease. However, optimal analysis is compromised by the use of standard registration algorithms; there has not to date been a satisfactory solution to the question of how to align FA images from multiple subjects in a way that allows for valid conclusions to be drawn from the subsequent voxelwise analysis. Furthermore, the arbitrariness of the choice of spatial smoothing extent has not yet been resolved. In this paper, we present a new method that aims to solve these issues via (a) carefully tuned non-linear registration, followed by (b) projection onto an alignment-invariant tract representation (the ‘‘mean FA skeleton’’). We refer to this new approach as Tract-Based Spatial Statistics (TBSS). TBSS aims to improve the sensitivity, objectivity and interpretability of analysis of multi-subject diffusion imaging studies. We describe TBSS in detail and present example TBSS results from several diffusion imaging studies.}, file = {attachment\:Smith2006NeuroImage.pdf:attachment\:Smith2006NeuroImage.pdf:PDF}, publisher = {Elsevier}, year = 2006 } @Article{SAM+05, Author = {Sherbondy, A. and Akers, D. and Mackenzie, R. and Dougherty, R. and Wandell, B.}, Title = {Exploring connectivity of the brain's white matter with dynamic queries.}, Journal = {IEEE Trans Vis Comput Graph}, Volume = {11}, Number = {4}, Pages = {419-30}, abstract = {Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging method that can be used to measure local information about the structure of white matter within the human brain. Combining DTI data with the computational methods of MR tractography, neuroscientists can estimate the locations and sizes of nerve bundles (white matter pathways) that course through the human brain. Neuroscientists have used visualization techniques to better understand tractography data, but they often struggle with the abundance and complexity of the pathways. In this paper, we describe a novel set of interaction techniques that make it easier to explore and interpret such pathways. Specifically, our application allows neuroscientists to place and interactively manipulate box or ellipsoid-shaped regions to selectively display pathways that pass through specific anatomical areas. These regions can be used in coordination with a simple and flexible query language which allows for arbitrary combinations of these queries using Boolean logic operators. A representation of the cortical surface is provided for specifying queries of pathways that may be relevant to gray matter structures and for displaying activation information obtained from functional magnetic resonance imaging. By precomputing the pathways and their statistical properties, we obtain the speed necessary for interactive question-and-answer sessions with brain researchers. We survey some questions that researchers have been asking about tractography data and show how our system can be used to answer these questions efficiently.}, authoraddress = {Department of Electrical Engineering, James H. Clark Center, 318 Campus Dr., Room S324, Stanford University, Stanford, CA 94305, USA. Sherbond@stanford.edu}, keywords = {Algorithms ; Animals ; Brain/*cytology ; *Computer Graphics ; Computer Simulation ; Diffusion Magnetic Resonance Imaging/*methods ; Humans ; Image Enhancement/*methods ; Image Interpretation, Computer-Assisted/*methods ; Imaging, Three-Dimensional/methods ; Models, Neurological ; Nerve Fibers, Myelinated/*ultrastructure ; Nerve Net/cytology ; Neural Pathways/*cytology ; Numerical Analysis, Computer-Assisted ; Online Systems ; *User-Computer Interface}, language = {eng}, medline-aid = {10.1109/TVCG.2005.59 [doi]}, medline-crdt = {2005/09/06 09:00}, medline-da = {20050905}, medline-dcom = {20050923}, medline-edat = {2005/09/06 09:00}, medline-fau = {Sherbondy, Anthony ; Akers, David ; Mackenzie, Rachel ; Dougherty, Robert ; Wandell, Brian}, medline-is = {1077-2626 (Print)}, medline-jid = {9891704}, medline-jt = {IEEE transactions on visualization and computer graphics}, medline-mhda = {2005/09/24 09:00}, medline-own = {NLM}, medline-pl = {United States}, medline-pmid = {16138552}, medline-pst = {ppublish}, medline-pt = {Evaluation Studies ; Journal Article}, medline-sb = {IM}, medline-so = {IEEE Trans Vis Comput Graph. 2005 Jul-Aug;11(4):419-30.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=16138552}, year = 2005 } @Article{ODonnell_MICCAI09, Author = {O'Donnell, L. J. and Westin, C. F. and Golby, A. J.}, Title = {Tract-based morphometry for white matter group analysis.}, Journal = {Neuroimage}, Volume = {45}, Number = {3}, Pages = {832-44}, abstract = {We introduce an automatic method that we call tract-based morphometry, or TBM, for measurement and analysis of diffusion MRI data along white matter fiber tracts. Using subject-specific tractography bundle segmentations, we generate an arc length parameterization of the bundle with point correspondences across all fibers and all subjects, allowing tract-based measurement and analysis. In this paper we present a quantitative comparison of fiber coordinate systems from the literature and we introduce an improved optimal match method that reduces spatial distortion and improves intra- and inter-subject variability of FA measurements. We propose a method for generating arc length correspondences across hemispheres, enabling a TBM study of interhemispheric diffusion asymmetries in the arcuate fasciculus (AF) and cingulum bundle (CB). The results of this study demonstrate that TBM can detect differences that may not be found by measuring means of scalar invariants in entire tracts, such as the mean diffusivity (MD) differences found in AF. We report TBM results of higher fractional anisotropy (FA) in the left hemisphere in AF (caused primarily by lower lambda(3), the smallest eigenvalue of the diffusion tensor, in the left AF), and higher left hemisphere FA in CB (related to higher lambda(1), the largest eigenvalue of the diffusion tensor, in the left CB). By mapping the significance levels onto the tractography trajectories for each structure, we demonstrate the anatomical locations of the interhemispheric differences. The TBM approach brings analysis of DTI data into the clinically and neuroanatomically relevant framework of the tract anatomy.}, authoraddress = {Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston MA, USA. odonnell@bwh.harvard.edu}, language = {eng}, medline-aid = {S1053-8119(08)01282-2 [pii] ; 10.1016/j.neuroimage.2008.12.023 [doi]}, medline-crdt = {2009/01/22 09:00}, medline-da = {20090309}, medline-dep = {20081225}, medline-edat = {2009/01/22 09:00}, medline-fau = {O'Donnell, Lauren J ; Westin, Carl-Fredrik ; Golby, Alexandra J}, medline-gr = {K08NS048063/NS/NINDS NIH HHS/United States ; P41RR13218/RR/NCRR NIH HHS/United States ; P41RR15241/RR/NCRR NIH HHS/United States ; R01AG20012/AG/NIA NIH HHS/United States ; R01MH074794/MH/NIMH NIH HHS/United States ; U41RR019703/RR/NCRR NIH HHS/United States ; U54EB005149/EB/NIBIB NIH HHS/United States}, medline-is = {1095-9572 (Electronic)}, medline-jid = {9215515}, medline-jt = {NeuroImage}, medline-mhda = {2009/01/22 09:00}, medline-own = {NLM}, medline-phst = {2008/08/18 [received] ; 2008/11/13 [revised] ; 2008/12/08 [accepted] ; 2008/12/25 [aheadofprint]}, medline-pl = {United States}, medline-pmid = {19154790}, medline-pst = {ppublish}, medline-pt = {Journal Article ; Research Support, N.I.H., Extramural}, medline-sb = {IM}, medline-so = {Neuroimage. 2009 Apr 15;45(3):832-44. Epub 2008 Dec 25.}, medline-stat = {In-Process}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=19154790}, year = 2009 } @Article{Kuo, Author = {Kuo, L W and Chen, J H and Wedeen, V J and Tseng, W Y}, Title = {{Optimization of diffusion spectrum imaging and q-ball imaging on clinical MRI system}}, Journal = {Neuroimage}, Volume = {vol}, Pages = {41pp7--18}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Kuo et al. - Unknown - Optimization of diffusion spectrum imaging and q-ball imaging on clinical MRI system.pdf:pdf} } @Article{candes2008ics, Author = {Cand{\`e}s, E.J. and Wakin, M.B.}, Title = {{An introduction to compressive sampling}}, Journal = {IEEE Signal Processing Magazine}, Volume = {25}, Number = {2}, Pages = {21--30}, publisher = {New York, NY: Institute of Electrical \& Electronic Engineers, c1991-}, year = 2008 } @Article{Poldrack2008, Author = {Poldrack, Russell a and Fletcher, Paul C and Henson, Richard N and Worsley, Keith J and Brett, Matthew and Nichols, Thomas E}, Title = {{Guidelines for reporting an fMRI study.}}, Journal = {NeuroImage}, Volume = {40}, Number = {2}, Pages = {409--14}, abstract = {In this editorial, we outline a set of guidelines for the reporting of methods and results in functional magnetic resonance imaging studies and provide a checklist to assist authors in preparing manuscripts that meet these guidelines.}, doi = {10.1016/j.neuroimage.2007.11.048}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Poldrack et al. - 2008 - Guidelines for reporting an fMRI study..pdf:pdf}, issn = {1053-8119}, keywords = {Guidelines as Topic,Magnetic Resonance Imaging,Publishing,Publishing: standards}, pmid = {18191585}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18191585}, year = 2008 } @Article{Miki2007, Author = {Miki, Y and Urayama, S and Fushimi, Y and Okada, T and Hanakawa, T and Fukuyama, H}, Title = {{Diffusion Tensor Fiber Tractography of the Optic Radiation : Analysis with 6- , 12- , 40- , and 81-}}, Journal = {Ajnr. American Journal Of Neuroradiology}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Miki et al. - 2007 - Diffusion Tensor Fiber Tractography of the Optic Radiation Analysis with 6- , 12- , 40- , and 81-.pdf:pdf}, year = 2007 } @Article{Glasser2008, Author = {Glasser, MF and Rilling, JK}, Title = {{DTI tractography of the human brain's language pathways}}, Journal = {Cerebral Cortex}, url = {http://cercor.oxfordjournals.org/cgi/content/abstract/bhn011}, year = 2008 } @Article{Wedeen, Author = {Wedeen, V and Wang, R and Schmahmann, J and Benner, T and Tseng, W and Dai, G and Pandya, D and Hagmann, P and D\^a arceuil, H and A}, Title = {{de Crespigny, "Diffusion spectrum magnetic resonance imaging (dsi) tractography of crossing fibers,"}}, Journal = {NeuroImage}, Volume = {vol}, Pages = {41no4pp1267--1277}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Wedeen et al. - Unknown - de Crespigny, Diffusion spectrum magnetic resonance imaging (dsi) tractography of crossing fibers,.pdf:pdf} } @Article{Nannen2003c, Author = {Nannen, Volker and Groningen, Rijksuniversiteit}, Title = {{The Paradox of Overfitting}}, Journal = {Artificial Intelligence}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Nannen, Groningen - 2003 - The Paradox of Overfitting.pdf:pdf}, year = 2003 } @Article{Chen2009, Author = {Chen, Wei and Ding, Zi'ang and Zhang, Song and MacKay-Brandt, Anna and Correia, Stephen and Qu, Huamin and Crow, John Allen and Tate, David F and Yan, Zhicheng and Peng, Qunsheng}, Title = {{A novel interface for interactive exploration of DTI fibers.}}, Journal = {IEEE transactions on visualization and computer graphics}, Volume = {15}, Number = {6}, Pages = {1433--40}, abstract = {Visual exploration is essential to the visualization and analysis of densely sampled 3D DTI fibers in biological specimens, due to the high geometric, spatial, and anatomical complexity of fiber tracts. Previous methods for DTI fiber visualization use zooming, color-mapping, selection, and abstraction to deliver the characteristics of the fibers. However, these schemes mainly focus on the optimization of visualization in the 3D space where cluttering and occlusion make grasping even a few thousand fibers difficult. This paper introduces a novel interaction method that augments the 3D visualization with a 2D representation containing a low-dimensional embedding of the DTI fibers. This embedding preserves the relationship between the fibers and removes the visual clutter that is inherent in 3D renderings of the fibers. This new interface allows the user to manipulate the DTI fibers as both 3D curves and 2D embedded points and easily compare or validate his or her results in both domains. The implementation of the framework is GPU based to achieve real-time interaction. The framework was applied to several tasks, and the results show that our method reduces the user's workload in recognizing 3D DTI fibers and permits quick and accurate DTI fiber selection.}, doi = {10.1109/TVCG.2009.112}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Chen et al. - 2009 - A novel interface for interactive exploration of DTI fibers..pdf:pdf}, issn = {1077-2626}, keywords = {Algorithms,Animals,Brain,Brain: anatomy \& histology,Cluster Analysis,Computer Graphics,Diffusion Magnetic Resonance Imaging,Diffusion Magnetic Resonance Imaging: methods,Heart,Heart: anatomy \& histology,Hindlimb,Models, Biological,Myofibrils,Nerve Fibers,Swine,User-Computer Interface}, pmid = {19834218}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19834218}, year = 2009 } @Article{ZLW+03, Author = {Zhai, G. and Lin, W. and Wilber, K. P. and Gerig, G. and Gilmore, J. H.}, Title = {Comparisons of regional white matter diffusion in healthy neonates and adults performed with a 3.0-{T} head-only {MR} imaging unit.}, Journal = {Radiology}, Volume = {229}, Number = {3}, Pages = {673-81}, abstract = {PURPOSE: To evaluate the normal brains of adults and neonates for regional and age-related differences in apparent diffusion coefficient (ADC) and fractional anisotropy (FA). MATERIALS AND METHODS: Eight healthy adults and 20 healthy neonates were examined with a 3.0-T head-only magnetic resonance (MR) imaging unit by using a single-shot diffusion-tensor sequence. Trace ADC maps, FA maps, directional maps of the putative directions of white matter (WM) tracts, and fiber-tracking maps were obtained. Regions of interest-eight in WM and one in gray matter (GM)-were predefined for the ADC and FA measurements. The Student t test was used to compare FA and ADC between adults and neonates, whereas the Tukey multiple-comparison test was used to compare FA and ADC in different brain regions in the adult and neonate groups. RESULTS: A global elevation in ADC (P <.001) in both GM and WM and a reduction in FA (P <.001) in WM were observed in neonates as compared with these values in adults. In addition, significant regional variations in FA and ADC were observed in both groups. Regional variations in FA and ADC were less remarkable in adults, whereas neonates had consistently higher FA values and lower ADC values in the central WM as compared with these values in the peripheral WM. Fiber tracking revealed only major WM tracts in the neonates but fibers extending to the peripheral WM in the adults. CONCLUSION: There were regional differences in FA and ADC values in the neonates; such variations were less remarkable in the adults.}, authoraddress = {Department of Biomedical Engineering, University of North Carolina at Chapel Hill, CB \#7515, Chapel Hill, NC 27599, USA.}, keywords = {Adult ; Age Factors ; Brain/*anatomy \& histology ; Diffusion Magnetic Resonance Imaging/*instrumentation ; Humans ; Infant, Newborn ; ROC Curve}, language = {eng}, medline-aid = {10.1148/radiol.2293021462 [doi] ; 229/3/673 [pii]}, medline-crdt = {2003/12/06 05:00}, medline-da = {20031205}, medline-dcom = {20040112}, medline-edat = {2003/12/06 05:00}, medline-fau = {Zhai, Guihua ; Lin, Weili ; Wilber, Kathy P ; Gerig, Guido ; Gilmore, John H}, medline-gr = {HD03110/HD/NICHD NIH HHS/United States ; MH 33127/MH/NIMH NIH HHS/United States ; R01 NS 37312/NS/NINDS NIH HHS/United States}, medline-is = {0033-8419 (Print)}, medline-jid = {0401260}, medline-jt = {Radiology}, medline-lr = {20071114}, medline-mhda = {2004/01/13 05:00}, medline-own = {NLM}, medline-pl = {United States}, medline-pmid = {14657305}, medline-pst = {ppublish}, medline-pt = {Comparative Study ; Journal Article ; Research Support, U.S. Gov't, P.H.S.}, medline-sb = {AIM ; IM}, medline-so = {Radiology. 2003 Dec;229(3):673-81.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=14657305}, year = 2003 } @Article{Voineskos_Neuroimage09, Author = {Voineskos, A. N. and O'Donnell, L. J. and Lobaugh, N. J. and Markant, D. and Ameis, S. H. and Niethammer, M. and Mulsant, B. H. and Pollock, B. G. and Kennedy, J. L. and Westin, C. F. and Shenton, M. E.}, Title = {Quantitative examination of a novel clustering method using magnetic resonance diffusion tensor tractography.}, Journal = {Neuroimage}, Volume = {45}, Number = {2}, Pages = {370-6}, abstract = {MR diffusion tensor imaging (DTI) can measure and visualize organization of white matter fibre tracts in vivo. DTI is a relatively new imaging technique, and new tools developed for quantifying fibre tracts require evaluation. The purpose of this study was to compare the reliability of a novel clustering approach with a multiple region of interest (MROI) approach in both healthy and disease (schizophrenia) populations. DTI images were acquired in 20 participants (n=10 patients with schizophrenia: 56+/-15 years; n=10 controls: 51+/-20 years) (1.5 T GE system) with diffusion gradients applied in 23 non-collinear directions, repeated three times. Whole brain seeding and creation of fibre tracts were then performed. Interrater reliability of the clustering approach, and the MROI approach, were each evaluated and the methods compared. There was high spatial (voxel-based) agreement within and between the clustering and MROI methods. Fractional anisotropy, trace, and radial and axial diffusivity values showed high intraclass correlation (p<0.001 for all tracts) for each approach. Differences in scalar indices of diffusion between the clustering and MROI approach were minimal. The excellent interrater reliability of the clustering method and high agreement with the MROI method, quantitatively and spatially, indicates that the clustering method can be used with confidence. The clustering method avoids biases of ROI drawing and placement, and, not limited by a priori predictions, may be a more robust and efficient way to identify and measure white matter tracts of interest.}, authoraddress = {Geriatric Mental Health Program, Centre for Addiction and Mental Health, Department of Psychiatry, University of Toronto, Canada.}, language = {eng}, medline-aid = {S1053-8119(08)01281-0 [pii] ; 10.1016/j.neuroimage.2008.12.028 [doi]}, medline-crdt = {2009/01/23 09:00}, medline-da = {20090223}, medline-dep = {20081229}, medline-edat = {2009/01/23 09:00}, medline-fau = {Voineskos, Aristotle N ; O'Donnell, Lauren J ; Lobaugh, Nancy J ; Markant, Doug ; Ameis, Stephanie H ; Niethammer, Marc ; Mulsant, Benoit H ; Pollock, Bruce G ; Kennedy, James L ; Westin, Carl Fredrik ; Shenton, Martha E}, medline-gr = {1P50 MH08272/MH/NIMH NIH HHS/United States ; P41 RR13218/RR/NCRR NIH HHS/United States ; R01 MH 50740/MH/NIMH NIH HHS/United States ; R01 MH074794/MH/NIMH NIH HHS/United States ; U41-RR019703/RR/NCRR NIH HHS/United States ; U54GM072977-01/GM/NIGMS NIH HHS/United States}, medline-is = {1095-9572 (Electronic)}, medline-jid = {9215515}, medline-jt = {NeuroImage}, medline-mhda = {2009/01/23 09:00}, medline-mid = {NIHMS85018}, medline-oid = {NLM: NIHMS85018 [Available on 04/01/10] ; NLM: PMC2646811 [Available on 04/01/10]}, medline-own = {NLM}, medline-phst = {2008/08/25 [received] ; 2008/11/05 [revised] ; 2008/12/08 [accepted] ; 2008/12/29 [aheadofprint]}, medline-pl = {United States}, medline-pmc = {PMC2646811}, medline-pmcr = {2010/04/01}, medline-pmid = {19159690}, medline-pst = {ppublish}, medline-pt = {Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.}, medline-sb = {IM}, medline-so = {Neuroimage. 2009 Apr 1;45(2):370-6. Epub 2008 Dec 29.}, medline-stat = {In-Process}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=19159690}, year = 2009 } @Article{Hagmann2006Radiographics, Author = {Hagmann, Patric and Jonasson, Lisa and Maeder, Philippe and Thiran, Jean-Philippe and Wedeen, Van J. and Meuli, Reto}, Title = {Understanding diffusion \{{M}{R}\} imaging techniques: \{{F}\}rom scalar diffusion-weighted imaging to diffusion tensor imaging and beyond}, Journal = {Radiographics}, Volume = {26}, Number = {suppl_1}, Pages = {S205-223}, abstract = {The complex structural organization of the white matter of the brain can be depicted in vivo in great detail with advanced diffusion magnetic resonance (MR) imaging schemes. Diffusion MR imaging techniques are increasingly varied, from the simplest and most commonly used technique-the mapping of apparent diffusion coefficient values-to the more complex, such as diffusion tensor imaging, q-ball imaging, diffusion spectrum imaging, and tractography. The type of structural information obtained differs according to the technique used. To fully understand how diffusion MR imaging works, it is helpful to be familiar with the physical principles of water diffusion in the brain and the conceptual basis of each imaging technique. Knowledge of the technique-specific requirements with regard to hardware and acquisition time, as well as the advantages, limitations, and potential interpretation pitfalls of each technique, is especially useful.}, doi = {10.1148/rg.26si065510}, eprint = {http://radiographics.rsnajnls.org/cgi/reprint/26/suppl_1/S205.pdf}, file = {attachment\:Hagmann2006Radiographics.pdf:attachment\:Hagmann2006Radiographics.pdf:PDF}, url = {http://radiographics.rsnajnls.org/cgi/content/abstract/26/suppl_1/S205}, year = 2006 } @Misc{Mendeley2009, Author = {Mendeley}, Title = {{Getting Started with Mendeley}}, address = {London}, annote = {Double click on the entry on the left to view the PDF.}, booktitle = {Mendeley Desktop}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Mendeley - 2009 - Getting Started with Mendeley.pdf:pdf}, keywords = {Mendeley}, publisher = {Mendeley Ltd.}, url = {http://www.mendeley.com}, year = 2009 } @Article{Schmid2010, Author = {Schmid, Benjamin and Schindelin, Johannes and Cardona, Albert and Longair, Mark and Heisenberg, Martin}, Title = {{A high-level 3D visualization API for Java and ImageJ.}}, Journal = {BMC bioinformatics}, Volume = {11}, Number = {1}, Pages = {274}, abstract = {ABSTRACT: BACKGROUND: Current imaging methods such as Magnetic Resonance Imaging (MRI), Confocal microscopy, Electron Microscopy (EM) or Selective Plane Illumination Microscopy (SPIM) yield three-dimensional (3D) data sets in need of appropriate computational methods for their analysis. The reconstruction, segmentation and registration are best approached from the 3D representation of the data set. RESULTS: Here we present a platform-independent framework based on Java and Java 3D for accelerated rendering of biological images. Our framework is seamlessly integrated into ImageJ, a free image processing package with a vast collection of community-developed biological image analysis tools. Our framework enriches the ImageJ software libraries with methods that greatly reduce the complexity of developing image analysis tools in an interactive 3D visualization environment. In particular, we provide high-level access to volume rendering, volume editing, surface extraction, and image annotation. The ability to rely on a library that removes the low-level details enables concentrating software development efforts on the algorithm implementation parts. CONCLUSIONS: Our framework enables biomedical image software development to be built with 3D visualization capabilities with very little effort. We offer the source code and convenient binary packages along with extensive documentation at http://3dviewer.neurofly.de.}, doi = {10.1186/1471-2105-11-274}, issn = {1471-2105}, month = may, pmid = {20492697}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20492697}, year = 2010 } @Article{Durrleman2009, Author = {Durrleman, Stanley and Fillard, Pierre and Pennec, Xavier and Trouv\'{e}, Alain and Ayache, Nicholas}, Title = {{A statistical model of white matter fiber bundles based on currents.}}, Journal = {Information processing in medical imaging : proceedings of the ... conference}, Volume = {21}, Pages = {114--25}, abstract = {The purpose of this paper is to measure the variability of a population of white matter fiber bundles without imposing unrealistic geometrical priors. In this respect, modeling fiber bundles as currents seems particularly relevant, as it gives a metric between bundles which relies neither on point nor on fiber correspondences and which is robust to fiber interruption. First, this metric is included in a diffeomorphic registration scheme which consistently aligns sets of fiber bundles. In particular, we show that aligning directly fiber bundles may solve the aperture problem which appears when fiber mappings are constrained by tensors only. Second, the measure of variability of a population of fiber bundles is based on a statistical model which considers every bundle as a random diffeomorphic deformation of a common template plus a random non-diffeomorphic perturbation. Thus, the variability is decomposed into a geometrical part and a "texture" part. Our results on real data show that both parts may contain interesting anatomical features.}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Durrleman et al. - 2009 - A statistical model of white matter fiber bundles based on currents..pdf:pdf}, issn = {1011-2499}, keywords = {Algorithms,Artificial Intelligence,Brain,Brain: anatomy \& histology,Cluster Analysis,Computer Simulation,Diffusion Magnetic Resonance Imaging,Diffusion Magnetic Resonance Imaging: methods,Humans,Image Enhancement,Image Enhancement: methods,Image Interpretation, Computer-Assisted,Image Interpretation, Computer-Assisted: methods,Imaging, Three-Dimensional,Imaging, Three-Dimensional: methods,Models, Neurological,Models, Statistical,Nerve Fibers, Myelinated,Nerve Fibers, Myelinated: ultrastructure,Pattern Recognition, Automated,Pattern Recognition, Automated: methods,Reproducibility of Results,Sensitivity and Specificity}, month = jan, pmid = {19694257}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19694257}, year = 2009 } @Article{Wedeen2008, Author = {Wedeen, VJ and Wang, RP and Schmahmann, JD and Benner, T}, Title = {{\ldots spectrum magnetic resonance imaging (DSI) tractography of crossing fibers}}, Journal = {Neuroimage}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Wedeen et al. - Unknown - de Crespigny, Diffusion spectrum magnetic resonance imaging (dsi) tractography of crossing fibers,.pdf:pdf}, url = {http://linkinghub.elsevier.com/retrieve/pii/S105381190800253X}, year = 2008 } @Article{Sotiras2009, Author = {Sotiras, Aristeidis and Neji, Radhou\`{e}ne and Nikos, Jean-fran\c{c}ois Deux and Mezri, Komodakis}, Title = {{Diffusion Tensor Registration Using Probability Kernels and Discrete Optimization}}, Journal = {Computer}, Number = {May}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Sotiras et al. - 2009 - Diffusion Tensor Registration Using Probability Kernels and Discrete Optimization.pdf:pdf}, year = 2009 } @Article{Basser1994, Author = {Basser, PJ and Mattiello, J and LeBihan, D}, Title = {{MR diffusion tensor spectroscopy and imaging}}, Journal = {Biophysical journal}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Basser, Mattiello, LeBihan - 1994 - MR diffusion tensor spectroscopy and imaging.pdf:pdf}, url = {http://linkinghub.elsevier.com/retrieve/pii/S0006349594807751}, year = 1994 } @Article{wakana2004ftba, Author = {Wakana, S. and Jiang, H. and Nagae-Poetscher, L. and van Zijl, P. and Mori, S.}, Title = {Fiber tract-based atlas of human white matter anatomy}, Journal = {Radiology}, Volume = {230}, Pages = {77-87}, file = {attachment\:wakana_fiber_tract-based_atlas_2004.pdf:attachment\:wakana_fiber_tract-based_atlas_2004.pdf:PDF}, publisher = {RSNA}, year = 2004 } @Article{Joy, Author = {Joy, Kenneth I}, Title = {{Numerical Methods for Particle Tracing in Vector Fields}}, Journal = {Science}, Pages = {1--7}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Joy - Unknown - Numerical Methods for Particle Tracing in Vector Fields.pdf:pdf} } @Article{Staempfli2006NeuroImage, Author = {Staempfli, P. and Jaermann, T. and Crelier, G.R. and Kollias, S. and Valavanis, A. and Boesiger, P.}, Title = {Resolving fiber crossing using advanced fast marching tractography based on diffusion tensor imaging}, Journal = {NeuroImage}, Volume = {30}, Number = {1}, Pages = {110-120}, abstract = {Magnetic resonance diffusion tensor tractography is a powerful tool for the non-invasive depiction of the white matter architecture in the human brain. However, due to limitations in the underlying tensor model, the technique is often unable to reconstruct correct trajectories in heterogeneous fiber arrangements, such as axonal crossings. A novel tractography method based on fast marching (FM) is proposed which is capable of resolving fiber crossings and also permits trajectories to branch. It detects heterogeneous fiber arrangements by incorporating information from the entire diffusion tensor. The FM speed function is adapted to the local tensor characteristics, allowing in particular to maintain the front evolution direction in crossing situations. In addition, the FM's discretization error is reduced by increasing the number of considered possible front evolution directions. The performance of the technique is demonstrated in artificial data and in the healthy human brain. Comparisons with standard FM tractography and conventional line propagation algorithms show that, in the presence of interfering structures, the proposed method is more accurate in reconstructing trajectories. The in vivo results illustrate that the elucidated major white matter pathways are consistent with known anatomy and that multiple crossings and tract branching are handled correctly.}, file = {attachment\:Staempfli2006NeuroImage.pdf:attachment\:Staempfli2006NeuroImage.pdf:PDF}, url = {http://www.sciencedirect.com/science/article/B6WNP-4HD8DK8-3/2/c67092fe40d5854eaa7e5e78808d9983}, year = 2006 } @Article{Aksoy2008MRM, Author = {Aksoy, Murat andi Liu, Chunle and Moseley, Michael E. and Bammer, Roland}, Title = {Single-Step Nonlinear Diffusion Tensor Estimation in the Presence of Microscopic and Macroscopic Motion}, Journal = {Magnetic Resonance in Medicine}, Volume = {59}, Pages = {1138–1150}, abstract = {Patient motion can cause serious artifacts in diffusion tensor imaging (DTI), diminishing the reliability of the estimated diffusion tensor information. Studies in this field have so far been limited mainly to the correction of miniscule physiological motion. In order to correct for gross patient motion it is not sufficient to correct for misregistration between successive shots; the change in the diffusion-encoding direction must also be accounted for. This becomes particularly important for multishot sequences, whereby—in the presence of motion—each shot is encoded with a different diffusion weighting. In this study a general mathematical framework to correct for gross patient motion present in a multishot and multicoil DTI scan is presented. A signal model is presented that includes the effect of rotational and translational motion in the patient frame of reference. This model was used to create a nonlinear leastsquares formulation, from which the diffusion tensors were obtained using a nonlinear conjugate gradient algorithm. Applications to both phantom simulations and in vivo studies showed that in the case of gross motion the proposed algorithm performs superiorly compared to conventional methods used for tensor estimation.}, owner = {ian}, timestamp = {2009.03.04}, year = 2008 } @Article{IturriaMedina2007NeuroImage, Author = {Iturria-Medina, Y. and Canales-Rodr{\'\i}guez, EJ and Melie-Garc{\'\i}a, L. and Vald{\'e}s-Hern{\'a}ndez, PA and Mart{\'\i}nez-Montes, E. and Alem{\'a}n-G{\'o}mez, Y. and S{\'a}nchez-Bornot, J M}, Title = {Characterizing brain anatomical connections using diffusion weighted \{{M}{RI}\} and graph theory}, Journal = {Neuroimage}, Volume = {36}, Number = {3}, Pages = {645-660}, abstract = {A new methodology based on Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) and Graph Theory is presented for characterizing the anatomical connections between brain gray matter areas. In a first step, brain voxels are modeled as nodes of a non-directed graph in which the weight of an arc linking two neighbor nodes is assumed to be proportional to the probability of being connected by nervous fibers. This probability is estimated by means of probabilistic tissue segmentation and intravoxel white matter orientational distribution function, obtained from anatomical MRI and DW-MRI, respectively. A new tractography algorithm for finding white matter routes is also introduced. This algorithm solves the most probable path problem between any two nodes, leading to the assessment of probabilistic brain anatomical connection maps. In a second step, for assessing anatomical connectivity between K gray matter structures, the previous graph is redefined as a K+1 partite graph by partitioning the initial nodes set in K non-overlapped gray matter subsets and one subset clustering the remaining nodes. Three different measures are proposed for quantifying anatomical connections between any pair of gray matter subsets: Anatomical Connection Strength (ACS), Anatomical Connection Density (ACD) and Anatomical Connection Probability (ACP). This methodology was applied to both artificial and actual human data. Results show that nervous fiber pathways between some regions of interest were reconstructed correctly. Additionally, mean connectivity maps of ACS, ACD and ACP between 71 gray matter structures for five healthy subjects are presented.}, file = {attachment\:IturriaMedina2007NeuroImage.pdf:attachment\:IturriaMedina2007NeuroImage.pdf:PDF}, publisher = {Elsevier}, year = 2007 } @Misc{hyvarinen1998fim, Author = {Hyvarinen, A. and Oja, E.}, Title = {{The Fast-ICA MATLAB package}}, year = 1998 } @Article{Rosen2008, Author = {Rosen, Bruce}, Title = {2 -' ' >7}, Journal = {Engineering}, Number = {2001}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Rosen - 2008 - 2 -' ' 7.pdf:pdf}, year = 2008 } @PhdThesis{Tuch2002ThesisMIT, Author = {Tuch, D.S.}, Title = {Diffusion \{{M}{RI}\} of complex tissue structure}, School = {Massachusetts Institute of Technology, Division of Health Sciences and Technology}, abstract = {Magnetic resonance diffusion imaging provides an exquisitely sensitive probe of tissue microstructure. Owing to the microscopic length scale of diffusion in biological tissues, diffusion imaging can reveal histological architecture irresolvable by conventional magnetic resonance imaging methods. However, diffusion imaging methods to date have chiefly been based on analytical models of the underlying diffusion process. For example, diffusion tensor imaging assumes homogeneous Gaussian diffusion within each voxel, an assumption which is clearly invalid for the vast majority of the brain at presently achievable voxel resolutions. In this thesis I developed a diffusion imaging method capable of measuring the microscopic diffusion function within each voxel. In contrast to previous approaches to diffusion imaging, the method presented here does not require any assumptions on the underlying diffusion function. The model-independent approach can resolve complex intravoxel tissue structure including fiber crossing and fiber divergence within a single voxel. The method is capable of resolving not only deep white matter intersections, but also composite tissue structure at the cortical margin, and fiber-specific degeneration in neurodegenerative pathology. In sum, the approach can reveal complex intravoxel tissue structure previously thought to be beyond the scope of diffusion imaging methodology.}, publisher = {Massachusetts Institute of Technology}, year = 2002 } @Article{Durrleman2009a, Author = {Durrleman, Stanley and Fillard, Pierre and Pennec, Xavier and Trouv\'{e}, Alain and Ayache, Nicholas}, Title = {{A statistical model of white matter fiber bundles based on currents.}}, Journal = {Information processing in medical imaging : proceedings of the ... conference}, Volume = {21}, Pages = {114--25}, abstract = {The purpose of this paper is to measure the variability of a population of white matter fiber bundles without imposing unrealistic geometrical priors. In this respect, modeling fiber bundles as currents seems particularly relevant, as it gives a metric between bundles which relies neither on point nor on fiber correspondences and which is robust to fiber interruption. First, this metric is included in a diffeomorphic registration scheme which consistently aligns sets of fiber bundles. In particular, we show that aligning directly fiber bundles may solve the aperture problem which appears when fiber mappings are constrained by tensors only. Second, the measure of variability of a population of fiber bundles is based on a statistical model which considers every bundle as a random diffeomorphic deformation of a common template plus a random non-diffeomorphic perturbation. Thus, the variability is decomposed into a geometrical part and a "texture" part. Our results on real data show that both parts may contain interesting anatomical features.}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Durrleman et al. - 2009 - A statistical model of white matter fiber bundles based on currents..pdf:pdf}, issn = {1011-2499}, keywords = {Algorithms,Artificial Intelligence,Brain,Brain: anatomy \& histology,Cluster Analysis,Computer Simulation,Diffusion Magnetic Resonance Imaging,Diffusion Magnetic Resonance Imaging: methods,Humans,Image Enhancement,Image Enhancement: methods,Image Interpretation, Computer-Assisted,Image Interpretation, Computer-Assisted: methods,Imaging, Three-Dimensional,Imaging, Three-Dimensional: methods,Models, Neurological,Models, Statistical,Nerve Fibers, Myelinated,Nerve Fibers, Myelinated: ultrastructure,Pattern Recognition, Automated,Pattern Recognition, Automated: methods,Reproducibility of Results,Sensitivity and Specificity}, month = jan, pmid = {19694257}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19694257}, year = 2009 } @Article{Hill2002, Author = {Hill, Murray}, Title = {{McLaren’s Improved Snub Cube and Other New Spherical Designs in Three Dimensions}}, Journal = {Sciences-New York}, Number = {1}, arxivid = {arXiv:math/0207211v1}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Hill - 2002 - McLaren’s Improved Snub Cube and Other New Spherical Designs in Three Dimensions.pdf:pdf}, year = 2002 } @Article{zhang2008identifying, Author = {Zhang, S. and Correia, S. and Laidlaw, D.H.}, Title = {{Identifying White-Matter Fiber Bundles in DTI Data Using an Automated Proximity-Based Fiber Clustering Method}}, Journal = {IEEE transactions on visualization and computer graphics}, Volume = {14}, Number = {5}, Pages = {1044}, publisher = {NIH Public Access}, year = 2008 } @Book{einstein1956itb, Author = {Einstein, A.}, Title = {Investigations on the {T}heory of the {B}rownian {M}ovement}, Publisher = {Dover Publications}, year = 1956 } @Article{Garyfallidis, Author = {Garyfallidis, Eleftherios}, Title = {{Diffusion MRI and Tractography Tracks vs Tracts}}, Journal = {Sciences-New York}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Garyfallidis - Unknown - Di usion MRI and Tractography Tracks vs Tracts.pdf:pdf} } @Article{Tegmark2008, Author = {Tegmark, Max}, Title = {{No Title}}, arxivid = {arXiv:astro-ph/9610094v1}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Tegmark - 2008 - No Title.pdf:pdf}, year = 2008 } @Article{wakana2007roq, Author = {Wakana, S. and Caprihan, A. and Panzenboeck, M. M. and Fallon, J.H. and Perry, M. and Gollub, R. L. and Hua, K. and Zhang, J. and Jiang, H. and Dubey, P. and Blitz, A. and van Zijl, P. and Mori, S.}, Title = {Reproducibility of quantitative tractography methods applied to cerebral white matter}, Journal = {Neuroimage}, Volume = {36}, Pages = {630-644}, file = {attachment\:wakana_reproducibility_2007.pdf:attachment\:wakana_reproducibility_2007.pdf:PDF}, publisher = {Elsevier}, year = 2007 } @Article{Dale2009, Author = {Dale, Darren and Droettboom, Michael and Firing, Eric and Hunter, John}, Title = {{Matplotlib}}, Journal = {Building}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Dale et al. - 2009 - Matplotlib.pdf:pdf}, year = 2009 } @Article{Hasan2007MRI, Author = {Hasan, Khader M.}, Title = {A framework for quality control and parameter optimization in diffusion tensor imaging: theoretical analysis and validation}, Journal = {Magnetic Resonance Imaging}, Volume = {25}, Pages = {1196–1202}, abstract = {In this communication, a theoretical framework for quality control and parameter optimization in diffusion tensor imaging (DTI) is presented and validated. The approach is based on the analytical error propagation of the mean diffusivity (Dav) obtained directly from the diffusion-weighted data acquired using rotationally invariant and uniformly distributed icosahedral encoding schemes. The error propagation of a recently described and validated cylindrical tensor model is further extrapolated to the spherical tensor case (diffusion anisotropy 0) to relate analytically the precision error in fractional tensor anisotropy (FA) with the mean diffusion-to-noise ratio (DNR). The approach provided simple analytical and empirical quality control measures for optimization of diffusion parameter space in an isotropic medium that can be tested using widely available water phantoms.}, file = {attachment\:Hasan2007MRI.pdf:attachment\:Hasan2007MRI.pdf:PDF}, year = 2007 } @Article{Jian2007bNeuroImage, Author = {Jian, Bing and Vemuri, Baba C. and Ozarslan, Evren and Carney, Paul R. and Mareci, Thomas H.}, Title = {Erratum to '\{{A}\} novel tensor distribution model for the diffusion-weighted \{{M}{R}\} signal'}, Journal = {NeuroImage}, Volume = {37}, Number = {2}, file = {attachment\:Jian2007bNeuroImage.pdf:attachment\:Jian2007bNeuroImage.pdf:PDF}, url = {http://www.sciencedirect.com/science/article/B6WNP-4S62RMR-5/2/160bb8aa9bf75adcf495557cec86868f}, year = 2007 } @InProceedings{Haro2008ISBI, Author = {Haro, Gloria and Lenglet, Christophe and Sapiro, Guillermo and Thompson, Paul M.}, Title = {On the Non-Uniform Complexity of Brain Connectivity}, BookTitle = {5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro}, Pages = {FR-P2a (poster)}, abstract = {A stratification and manifold learning approach for analyzing High Angular Resolution Diffusion Imaging (HARDI) data is introduced in this paper. HARDI data provides highdimensional signals measuring the complex microstructure of biological tissues, such as the cerebral white matter. We show that these high-dimensional spaces may be understood as unions of manifolds of varying dimensions/complexity and densities. With such analysis, we use clustering to characterize the structural complexity of the white matter. We briefly present the underlying framework and numerical experiments illustrating this original and promising approach.}, file = {attachment\:Haro2008ISBI.pdf:attachment\:Haro2008ISBI.pdf:PDF}, url = {http://www.ieeexplore.ieee.org/search/freesrchabstract.jsp?arnumber=4541139&isnumber=4540908&punumber=4534844&k2dockey=4541139@ieeecnfs&query=&pos=0}, year = 2008 } @Article{Buchel2004CerebralCortex, Author = {Büchel, C. and Raedler, T. and Sommer, M. and Sach, M. and Weiller, C. and Koch, M. A.}, Title = {White matter asymmetry in the human brain: a diffusion tensor \{{M}{RI}\} study}, Journal = {Cerebral Cortex}, Volume = {14}, Pages = {945-951}, abstract = {Language ability and handedness are likely to be associated with asymmetry of the cerebral cortex (grey matter) and connectivity (white matter). Grey matter asymmetry, most likely linked to language has been identified with voxel-based morphometry (VBM) using T1-weighted images. Differences in white matter obtained with this technique are less consistent, probably due to the relative insensitivity of the T1 contrast to the ultrastructure of white matter. Furthermore, previous VBM studies failed to find differences related to handedness in either grey or white matter. We revisited these issues and investigated two independent groups of subjects with diffusion-tensor imaging (DTI) for asymmetries in white matter composition. Using voxel-based statistical analyses an asymmetry of the arcuate fascicle was observed, with higher fractional anisotropy in the left hemisphere. In addition, we show differences related to handedness in the white matter underneath the precentral gyrus contralateral to the dominant hand. Remarkably, these findings were very robust, even when investigating small groups of subjects. This highlights the sensitivity of DTI for white matter tissue differences, making it an ideal tool to study small patient populations.}, doi = {10.1093/cercor/bhh055}, file = {attachment\:Buchel2004CerebralCortex.pdf:attachment\:Buchel2004CerebralCortex.pdf:PDF}, year = 2004 } @Article{Ding2003, Author = {Ding, Z and Gore, J and Anderson, A}, Title = {{Classification and quantification of neuronal fiber pathways using diffusion tensor MRI}}, Journal = {Magn. Reson. Med.}, Volume = {49}, Pages = {716--721}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Ding, Gore, Anderson - 2003 - Classification and quantification of neuronal fiber pathways using diffusion tensor MRI.pdf:pdf}, year = 2003 } @Article{Canales-Rodriguez2009, Author = {Canales-Rodr\'{\i}guez, Erick Jorge and Melie-Garc\'{\i}a, Lester and Iturria-Medina, Yasser}, Title = {{Mathematical description of q-space in spherical coordinates: exact q-ball imaging.}}, Journal = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine}, Volume = {61}, Number = {6}, Pages = {1350--67}, abstract = {Novel methodologies have been recently developed to characterize the microgeometry of neural tissues and porous structures via diffusion MRI data. In line with these previous works, this article provides a detailed mathematical description of q-space in spherical coordinates that helps to highlight the differences and similarities between various related q-space methodologies proposed to date such as q-ball imaging (QBI), diffusion spectrum imaging (DSI), and diffusion orientation transform imaging (DOT). This formulation provides a direct relationship between the orientation distribution function (ODF) and the diffusion data without using any approximation. Under this relationship, the exact ODF can be computed by means of the Radon transform of the radial projection (in q-space) of the diffusion MRI signal. This new methodology, termed exact q-ball imaging (EQBI), was put into practice using an analytical ODF estimation in terms of spherical harmonics that allows obtaining model-free and model-based reconstructions. This work provides a new framework for combining information coming from diffusion data recorded on multiple spherical shells in q-space (hybrid diffusion imaging encoding scheme), which is capable of mapping ODF to a high accuracy. This represents a step toward a more efficient development of diffusion MRI experiments for obtaining better ODF estimates.}, doi = {10.1002/mrm.21917}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Canales-Rodr\'{\i}guez, Melie-Garc\'{\i}a, Iturria-Medina - 2009 - Mathematical description of q-space in spherical coordinates exact q-ball imaging..pdf:pdf}, issn = {1522-2594}, keywords = {Algorithms,Computer Simulation,Diffusion Magnetic Resonance Imaging,Diffusion Magnetic Resonance Imaging: methods,Image Enhancement,Image Enhancement: methods,Image Interpretation, Computer-Assisted,Image Interpretation, Computer-Assisted: methods,Imaging, Three-Dimensional,Imaging, Three-Dimensional: methods,Models, Biological,Reproducibility of Results,Sensitivity and Specificity}, pmid = {19319889}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19319889}, year = 2009 } @Article{Wakana2007NeuroImage, Author = {Wakana, Setsu and Caprihan, Arvind and Panzenboeck, Martina M. and Fallon, James H. and Perry, Michele and Gollub, Randy L. and Hua, Kegang and Zhang, Jiangyang and Jiang, Hangyi and Dubey, Prachi and Blitz, Ari and {van Zijl}, Peter and Mori, Susumu}, Title = {Reproducibility of quantitative tractography methods applied to cerebral white matter}, Journal = {NeuroImage}, Volume = {36}, Number = {1}, Pages = {630-644}, abstract = {Tractography based on diffusion tensor imaging (DTI) allows visualization of white matter tracts. In this study, protocols to reconstruct eleven major white matter tracts are described. The protocols were refined by several iterations of intra- and inter-rater measurements and identification of sources of variability. Reproducibility of the established protocols was then tested by raters who did not have previous experience in tractography. The protocols were applied to a DTI database of adult normal subjects to study size, fractional anisotropy (FA), and T2 of individual white matter tracts. Distinctive features in FA and T2 were found for the corticospinal tract and callosal fibers. Hemispheric asymmetry was observed for the size of white matter tracts projecting to the temporal lobe. This protocol provides guidelines for reproducible DTI-based tract-specific quantification.}, file = {attachment\:Wakana2007NeuroImage.pdf:attachment\:Wakana2007NeuroImage.pdf:PDF}, publisher = {Elevier}, url = {http://www.sciencedirect.com/science/article/B6WNP-4N9DK04-1/2/6f4d33fa634a866aa907f16091a9bb67}, year = 2007 } @Article{Grady2006, Author = {Grady, Leo}, Title = {{Random walks for image segmentation.}}, Journal = {IEEE transactions on pattern analysis and machine intelligence}, Volume = {28}, Number = {11}, Pages = {1768--83}, abstract = {A novel method is proposed for performing multilabel, interactive image segmentation. Given a small number of pixels with user-defined (or predefined) labels, one can analytically and quickly determine the probability that a random walker starting at each unlabeled pixel will first reach one of the prelabeled pixels. By assigning each pixel to the label for which the greatest probability is calculated, a high-quality image segmentation may be obtained. Theoretical properties of this algorithm are developed along with the corresponding connections to discrete potential theory and electrical circuits. This algorithm is formulated in discrete space (i.e., on a graph) using combinatorial analogues of standard operators and principles from continuous potential theory, allowing it to be applied in arbitrary dimension on arbitrary graphs.}, doi = {10.1109/TPAMI.2006.233}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Grady - 2006 - Random walks for image segmentation..pdf:pdf}, issn = {0162-8828}, keywords = {Algorithms,Artificial Intelligence,Image Enhancement,Image Enhancement: methods,Image Interpretation, Computer-Assisted,Image Interpretation, Computer-Assisted: methods,Information Storage and Retrieval,Information Storage and Retrieval: methods,Models, Statistical,Pattern Recognition, Automated,Pattern Recognition, Automated: methods,Reproducibility of Results,Sensitivity and Specificity}, month = nov, pmid = {17063682}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17063682}, year = 2006 } @Article{Good2001NeuroImage, Author = {Good, Catriona D. and Johnsrude, Ingrid S. and Ashburner, John and Henson, Richard N. A. and Friston, Karl J. and Frackowiak, Richard S. J.}, Title = {A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains}, Journal = {NeuroImage}, Volume = {14}, Pages = {21-36}, doi = {10.1006/nimg.2001.0786}, file = {attachment\:Good2001NeuroImage.pdf:attachment\:Good2001NeuroImage.pdf:PDF}, publisher = {Elsevier}, year = 2001 } @Article{Savadjiev2008, Author = {Savadjiev, Peter and Campbell, Jennifer S W and Descoteaux, Maxime and Deriche, Rachid and Pike, G Bruce and Siddiqi, Kaleem}, Title = {{Labeling of ambiguous subvoxel fibre bundle configurations in high angular resolution diffusion MRI.}}, Journal = {NeuroImage}, Volume = {41}, Number = {1}, Pages = {58--68}, abstract = {Whereas high angular resolution reconstruction methods for diffusion MRI can estimate multiple dominant fibre orientations within a single imaging voxel, they are fundamentally limited in certain cases of complex subvoxel fibre structures, resulting in ambiguous local orientation distribution functions. In this article we address the important problem of disambiguating such complex subvoxel fibre tract configurations, with the purpose of improving the performance of fibre tractography. We do so by extending a curve inference method to distinguish between the cases of curving and fanning fibre bundles using differential geometric estimates in a local neighbourhood. The key benefit of this method is the inference of curves, instead of only fibre orientations, to model the underlying fibre bundles. This in turn allows distinct fibre geometries that contain nearly identical sets of fibre orientations at a voxel, to be distinguished from one another. Experimental results demonstrate the ability of the method to successfully label voxels into one of the above categories and improve the performance of a fibre-tracking algorithm.}, doi = {10.1016/j.neuroimage.2008.01.028}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Savadjiev et al. - 2008 - Labeling of ambiguous subvoxel fibre bundle configurations in high angular resolution diffusion MRI..pdf:pdf}, issn = {1053-8119}, keywords = {Adult,Algorithms,Brain,Brain: anatomy \& histology,Brain: cytology,Diffusion Magnetic Resonance Imaging,Diffusion Magnetic Resonance Imaging: methods,Diffusion Magnetic Resonance Imaging: statistics \&,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: methods,Image Processing, Computer-Assisted: statistics \& ,Motor Cortex,Motor Cortex: cytology,Motor Cortex: physiology,Nerve Fibers,Nerve Fibers: physiology,Neural Pathways,Neural Pathways: anatomy \& histology,Neural Pathways: cytology,Neural Pathways: physiology}, pmid = {18367409}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18367409}, year = 2008 } @Article{olver2010nist, Author = {Olver, F.W. and Lozier, D.W. and Boisvert, R.F. and Clark, C.W.}, Title = {{NIST handbook of mathematical functions}}, publisher = {Cambridge University Press New York, NY, USA}, year = 2010 } @Article{Cohen-adad, Author = {Cohen-adad, Julien and Mcnab, Jennifer and Gagoski, Borjan and Wedeen, Van and Wald, Lawrence and Hospital, Massachusetts General and States, United}, Title = {{OHBM https://www.aievolution.com/hbm1001/index.cfm?...}}, Pages = {1--6}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Cohen-adad et al. - Unknown - OHBM httpswww.aievolution.comhbm1001index.cfm....pdf:pdf} } @Article{Nannen2003a, Author = {Nannen, Volker}, Title = {{A Short Introduction to Model Selection , Kolmogorov Complexity and Minimum Description Length ( MDL )}}, Journal = {Complexity}, Number = {Mdl}, Pages = {1--23}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Nannen - 2003 - A Short Introduction to Model Selection , Kolmogorov Complexity and Minimum Description Length ( MDL ).pdf:pdf}, year = 2003 } @Article{Masutani2003EorJRadiography, Author = {Masutani, Yoshitaka and Aoki, Shigeki and Abe, Osamu and Hayashi, Naoto and Otomo, Kuni}, Title = {\{{MR}\} diffusion tensor imaging: recent advance and new techniques for diffusion tensor visualization}, Journal = {European Journal of Radiology}, Volume = {46}, Number = {1}, Pages = {53-66}, abstract = {Recently, diffusion tensor imaging is attracting the biomedical researchers for its application in depiction of fiber tracts based on diffusion anisotropy. In this paper, we briefly describe the basic theory of diffusion tensor MR imaging, the determination process of diffusion tensor, and the basic concepts of diffusion tensor visualization techniques. Several results of clinical application in our institute are also introduced. Finally, the limitations, advantages and disadvantages of the techniques are discussed for further application of diffusion tensor visualization.}, file = {Masutani2003EorJRadiography.pdf:Masutani2003EorJRadiography.pdf:PDF}, url = {http://www.sciencedirect.com/science/article/B6T6F-481N1XP-1/2/c1ca22568a2d933c2d6c23d493b98d1b}, year = 2003 } @Article{anwander2007cbp, Author = {Anwander, A. and Tittgemeyer, M. and von Cramon, D Y and Friederici, A D and Knosche, T R}, Title = {{Connectivity-Based Parcellation of {B}roca's {A}rea}}, Journal = {Cerebral Cortex}, Volume = {17}, Number = {4}, Pages = {816}, file = {attachment\:anwander_dti_broca_parcellation_2007.pdf:attachment\:anwander_dti_broca_parcellation_2007.pdf:PDF}, publisher = {Oxford Univ Press}, year = 2007 } @Article{Hermoye2006NeuroImage, Author = {Hermoye, Laurent and Saint-Martin, Christine and Cosnard, Guy and Lee, Seung-Koo and Kim, Jinna and Nassogne, Marie-Cecile and Menten, Renaud and Clapuyt, Philippe and Donohue, Pamela K. and Hua, Kegang and Wakana, Setsu and Jiang, Hangyi and {van Zijl}, Peter C.M. and Mori, Susumu}, Title = {Pediatric diffusion tensor imaging: Normal database and observation of the white matter maturation in early childhood}, Journal = {NeuroImage}, Volume = {29}, Number = {2}, Pages = {493-504}, abstract = {Recent advances in diffusion tensor imaging (DTI) have made it possible to reveal white matter anatomy and to detect neurological abnormalities in children. However, the clinical use of this technique is hampered by the lack of a normal standard of reference. The goal of this study was to initiate the establishment of a database of DTI images in children, which can be used as a normal standard of reference for diagnosis of pediatric neurological abnormalities. Seven pediatric volunteers and 23 pediatric patients (age range: 0-54 months) referred for clinical MR examinations, but whose brains were shown to be normal, underwent anatomical and DTI acquisitions on a 1.5 T MR scanner. The white matter maturation, as observed on DTI color maps, was described and illustrated. Changes in diffusion fractional anisotropy (FA), average apparent diffusion constant (ADCave), and T2-weighted (T2W) signal intensity were quantified in 12 locations to characterize the anatomical variability of the maturation process. Almost all prominent white matter tracts could be identified from birth, although their anisotropy was often low. The evolution of FA, shape, and size of the white matter tracts comprised generally three phases: rapid changes during the first 12 months; slow modifications during the second year; and relative stability after 24 months. The time courses of FA, ADCave, and T2W signal intensity confirmed our visual observations that maturation of the white matter and the normality of its architecture can be assessed with DTI in young children. The database is available online and is expected to foster the use of this promising technique in the diagnosis of pediatric pathologies.}, file = {attachment\:Hermoye2006NeuroImage.pdf:attachment\:Hermoye2006NeuroImage.pdf:PDF}, publisher = {Elsevier}, url = {http://www.sciencedirect.com/science/article/B6WNP-4H6GPNP-1/2/36429532df681a3d26bc67f5f3f8e9d9}, year = 2006 } @Article{Lee2007, Author = {Lee, Jae-gil and Han, Jiawei}, Title = {{Trajectory Clustering : A Partition-and-Group Framework ∗}}, Journal = {Group}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Lee, Han - 2007 - Trajectory Clustering A Partition-and-Group Framework ∗.pdf:pdf}, keywords = {a number of clustering,age processing,algorithms have been,and im-,data analysis,density-based clustering,market research,mdl principle,partition-and-group framework,pattern recognition,tering,trajectory clus-}, year = 2007 } @Article{BPP+00, Author = {Basser, P. J. and Pajevic, S. and Pierpaoli, C. and Duda, J. and Aldroubi, A.}, Title = {In vivo fiber tractography using {DT}-{MRI} data.}, Journal = {Magn Reson Med}, Volume = {44}, Number = {4}, Pages = {625-32}, abstract = {Fiber tract trajectories in coherently organized brain white matter pathways were computed from in vivo diffusion tensor magnetic resonance imaging (DT-MRI) data. First, a continuous diffusion tensor field is constructed from this discrete, noisy, measured DT-MRI data. Then a Frenet equation, describing the evolution of a fiber tract, was solved. This approach was validated using synthesized, noisy DT-MRI data. Corpus callosum and pyramidal tract trajectories were constructed and found to be consistent with known anatomy. The method's reliability, however, degrades where the distribution of fiber tract directions is nonuniform. Moreover, background noise in diffusion-weighted MRIs can cause a computed trajectory to hop from tract to tract. Still, this method can provide quantitative information with which to visualize and study connectivity and continuity of neural pathways in the central and peripheral nervous systems in vivo, and holds promise for elucidating architectural features in other fibrous tissues and ordered media.}, authoraddress = {Section on Tissue Biophysics and Biomimetics, NICHD, Bethesda, Maryland 20892-5772, USA. pjbasser@helix.nih.gov}, keywords = {Artifacts ; Brain/*anatomy \& histology ; Humans ; Image Processing, Computer-Assisted ; *Magnetic Resonance Imaging/methods ; Nerve Fibers}, language = {eng}, medline-aid = {10.1002/1522-2594(200010)44:4<625::AID-MRM17>3.0.CO;2-O [pii]}, medline-crdt = {2000/10/12 11:00}, medline-da = {20001103}, medline-dcom = {20001103}, medline-edat = {2000/10/12 11:00}, medline-fau = {Basser, P J ; Pajevic, S ; Pierpaoli, C ; Duda, J ; Aldroubi, A}, medline-is = {0740-3194 (Print)}, medline-jid = {8505245}, medline-jt = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine}, medline-lr = {20061115}, medline-mhda = {2001/02/28 10:01}, medline-own = {NLM}, medline-pl = {UNITED STATES}, medline-pmid = {11025519}, medline-pst = {ppublish}, medline-pt = {Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.}, medline-sb = {IM}, medline-so = {Magn Reson Med. 2000 Oct;44(4):625-32.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=11025519}, year = 2000 } @Article{O'Donnell2007, Author = {O'Donnell, Lauren J and Westin, Carl-Fredrik and Golby, Alexandra J}, Title = {{Tract-based morphometry.}}, Journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}, Volume = {10}, Number = {Pt 2}, Pages = {161--8}, abstract = {Multisubject statistical analyses of diffusion tensor images in regions of specific white matter tracts have commonly measured only the mean value of a scalar invariant such as the fractional anisotropy (FA), ignoring the spatial variation of FA along the length of fiber tracts. We propose to instead perform tract-based morphometry (TBM), or the statistical analysis of diffusion MRI data in an anatomical tract-based coordinate system. We present a method for automatic generation of white matter tract arc length parameterizations, based on learning a fiber bundle model from tractography from multiple subjects. Our tract-based coordinate system enables TBM for the detection of white matter differences in groups of subjects. We present example TBM results from a study of interhemispheric differences in FA.}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/O'Donnell, Westin, Golby - 2007 - Tract-based morphometry..pdf:pdf}, keywords = {Algorithms,Artificial Intelligence,Brain,Brain: cytology,Cluster Analysis,Diffusion Magnetic Resonance Imaging,Diffusion Magnetic Resonance Imaging: methods,Humans,Image Enhancement,Image Enhancement: methods,Image Interpretation, Computer-Assisted,Image Interpretation, Computer-Assisted: methods,Imaging, Three-Dimensional,Imaging, Three-Dimensional: methods,Nerve Fibers, Myelinated,Nerve Fibers, Myelinated: ultrastructure,Neural Pathways,Neural Pathways: cytology,Pattern Recognition, Automated,Pattern Recognition, Automated: methods,Reproducibility of Results,Sensitivity and Specificity}, month = jan, pmid = {18044565}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19154790}, year = 2007 } @Article{Correia2009a, Author = {Correia, Stephen and Lee, Stephanie Y and Voorn, Thom and Tate, David F and Paul, Robert H and Salloway, Stephen P and Malloy, Paul F and Laidlaw, David H}, Title = {{NIH Public Access}}, Journal = {Water}, Volume = {42}, Number = {2}, Pages = {568--581}, doi = {10.1016/j.neuroimage.2008.05.022.Quantitative}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Correia et al. - 2009 - NIH Public Access.pdf:pdf}, year = 2009 } @Article{toosy2004cfs, Author = {Toosy, A. T. and Ciccarelli, O. and Parker, G.J.M. and Wheeler-Kingshott, C. A. M. and Miller, D. H. and Thompson, A. J.}, Title = {Characterizing function--structure relationships in the human visual system with functional \{{M}{RI}\} and diffusion tensor imaging}, Journal = {Neuroimage}, Volume = {21}, Number = {4}, Pages = {1452--1463}, file = {attachment\:toosy_visual_fmri_dti_2003.pdf:attachment\:toosy_visual_fmri_dti_2003.pdf:PDF}, publisher = {Elsevier}, year = 2004 } @Article{Descoteaux2007, Author = {Descoteaux, M and Angelino, E and Fitzgibbons, S and Deriche, R}, Title = {{Regularized, fast, and robust analytical q-ball imaging}}, Journal = {Magnetic Resonance in Medicine}, Volume = {vol}, Pages = {58no3pp497--510}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Descoteaux et al. - 2007 - Regularized, fast, and robust analytical q-ball imaging.pdf:pdf}, year = 2007 } @Article{Zvitia2010a, Author = {Zvitia, Orly and Mayer, Arnaldo and Shadmi, Ran and Miron, Shmuel and Greenspan, Hayit K}, Title = {{Co-registration of white matter tractographies by adaptive-mean-shift and Gaussian mixture modeling.}}, Journal = {IEEE transactions on medical imaging}, Volume = {29}, Number = {1}, Pages = {132--45}, abstract = {In this paper, we present a robust approach to the registration of white matter tractographies extracted from diffusion tensor-magnetic resonance imaging scans. The fibers are projected into a high dimensional feature space based on the sequence of their 3-D coordinates. Adaptive mean-shift clustering is applied to extract a compact set of representative fiber-modes (FM). Each FM is assigned to a multivariate Gaussian distribution according to its population thereby leading to a Gaussian mixture model (GMM) representation for the entire set of fibers. The registration between two fiber sets is treated as the alignment of two GMMs and is performed by maximizing their correlation ratio. A nine-parameters affine transform is recovered and eventually refined to a twelve-parameters affine transform using an innovative mean-shift based registration refinement scheme presented in this paper. The validation of the algorithm on synthetic intrasubject data demonstrates its robustness to interrupted and deviating fiber artifacts as well as outliers. Using real intrasubject data, a comparison is conducted to other intensity based and fiber-based registration algorithms, demonstrating competitive results. An option for tracking-in-time, on specific white matter fiber tracts, is also demonstrated on the real data.}, doi = {10.1109/TMI.2009.2029097}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Zvitia et al. - 2010 - Co-registration of white matter tractographies by adaptive-mean-shift and Gaussian mixture modeling.(2).pdf:pdf}, issn = {1558-0062}, keywords = {Algorithms,Brain,Brain: anatomy \& histology,Cluster Analysis,Diffusion Tensor Imaging,Diffusion Tensor Imaging: methods,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: methods,Models, Neurological,Normal Distribution,Reproducibility of Results}, month = jan, pmid = {19709970}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19709970}, year = 2010 } @Article{Mobbs2009, Author = {Mobbs, Dean and Yu, Rongjun and Meyer, Marcel and Passamonti, Luca and Seymour, Ben and Calder, Andrew J and Schweizer, Susanne and Frith, Chris D and Dalgleish, Tim}, Title = {{A key role for similarity in vicarious reward.}}, Journal = {Science (New York, N.Y.)}, Volume = {324}, Number = {5929}, Pages = {900}, abstract = {Humans appear to have an inherent prosocial tendency toward one another in that we often take pleasure in seeing others succeed. This fact is almost certainly exploited by game shows, yet why watching others win elicits a pleasurable vicarious rewarding feeling in the absence of personal economic gain is unclear. One explanation is that game shows use contestants who have similarities to the viewing population, thereby kindling kin-motivated responses (for example, prosocial behavior). Using a game show-inspired paradigm, we show that the interactions between the ventral striatum and anterior cingulate cortex subserve the modulation of vicarious reward by similarity, respectively. Our results support studies showing that similarity acts as a proximate neurobiological mechanism where prosocial behavior extends to unrelated strangers.}, doi = {10.1126/science.1170539}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Mobbs et al. - 2009 - A key role for similarity in vicarious reward..pdf:pdf}, issn = {1095-9203}, keywords = {Adult,Basal Ganglia,Basal Ganglia: physiology,Brain Mapping,Empathy,Female,Games, Experimental,Gyrus Cinguli,Gyrus Cinguli: physiology,Humans,Magnetic Resonance Imaging,Male,Prefrontal Cortex,Prefrontal Cortex: physiology,Reward,Self Concept,Social Behavior,Social Desirability,Young Adult}, month = may, pmid = {19443777}, url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2839480\&tool=pmcentrez\&rendertype=abstract}, year = 2009 } @Article{Nannen2003b, Author = {Nannen, Volker}, Title = {{A Short Introduction to Kolmogorov Complexity}}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Nannen - 2003 - A Short Introduction to Kolmogorov Complexity.pdf:pdf}, year = 2003 } @conference{Auerbach2004ISMRM, author = {Auerbach, E. J. and Ugurbil, K.}, journal = {Proc. Intl. Soc. Mag. Reson. Med.}, owner = {ian}, timestamp = {2009.03.04}, title = {Improvement in Diffusion MRI at 3T and Beyond with the Twice-Refocused Adiabatic Spin Echo (TRASE) Sequence}, year = 2004 } @Article{sherbondy2006mma, Author = {Sherbondy, AJ and Akers, DL and Dougherty, RF and Ben-Shachar, M. and Napel, S. and Wandell, BA}, Title = {{MetroTrac: A metropolis algorithm for probabilistic tractography}}, Journal = {Human Brain Mapping, Florence}, year = 2006 } @InProceedings{bjornemoMICCAI02, Author = {M. Bj\"ornemo and A. Brun and R. Kikinis and C.-F. Westin}, Title = {Regularized Stochastic White Matter Tractography Using Diffusion Tensor {MRI}}, BookTitle = {Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02)}, Pages = {435--442}, Address = {Tokyo, Japan}, year = 2002 } @PhdThesis{maddah_phdthesis2008, Author = {Maddah, M.}, Title = {{Quantitative Analysis of Cerebral White Matter Anatomy from Diffusion MRI}}, School = {Massachusetts Institute of Technology}, year = 2008 } @Article{iturriamedina2007cba, Author = {Iturria-Medina, Y. and Canales-Rodr{\'\i}guez, EJ and Melie-Garc{\'\i}a, L. and Vald{\'e}s-Hern{\'a}ndez, PA and Mart{\'\i}nez-Montes, E. and Alem{\'a}n-G{\'o}mez, Y. and S{\'a}nchez-Bornot, JM}, Title = {Characterizing brain anatomical connections using diffusion weighted \{{M}{RI}\} and graph theory}, Journal = {Neuroimage}, Volume = {36}, Number = {3}, Pages = {645--660}, file = {attachment\:iturria-medinaet_dti_graph_2007.pdf:attachment\:iturria-medinaet_dti_graph_2007.pdf:PDF}, publisher = {Elsevier}, year = 2007 } @Article{Harel2001, Author = {Harel, David and Koren, Yehuda}, Title = {{On Clustering Using Random Walks}}, Pages = {18--41}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Harel, Koren - 2001 - On Clustering Using Random Walks.pdf:pdf}, year = 2001 } @Article{Kim2009, Author = {Kim, M S and Han, J}, Title = {{Chronicle: A two-stage density-based clustering algorithm for dynamic networks}}, Journal = {In: Discovery Science.}, Volume = {pp}, Pages = {152--167}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Kim, Han - 2009 - Chronicle A two-stage density-based clustering algorithm for dynamic networks.pdf:pdf}, year = 2009 } @Article{Tsai2007, Author = {Tsai, Andy and Westin, Carl-fredrik and Hero, Alfred O and Willsky, Alan S}, Title = {{Fiber tract clustering on manifolds with dual rooted-graphs}}, Journal = {in CVPR}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Tsai et al. - 2007 - Fiber tract clustering on manifolds with dual rooted-graphs.pdf:pdf}, year = 2007 } @Article{Staempfli2008NeuroImage, Author = {Staempfli, P. and Reischauer, C. and Jaermann, T. and Valavanis, A. and Kollias, S. and Boesiger, P.}, Title = {Combining {fMRI} and {DTI}: A framework for exploring the limits of {fMRI}-guided {DTI} fiber tracking and for verifying {DTI}-based fiber tractography results}, Journal = {NeuroImage}, Volume = {39}, Number = {1}, Pages = {119-126}, abstract = {A powerful, non-invasive technique for estimating and visualizing white matter tracts in the human brain in vivo is white matter fiber tractography that uses magnetic resonance diffusion tensor imaging. The success of this method depends strongly on the capability of the applied tracking algorithm and the quality of the underlying data set. However, DTI-based fiber tractography still lacks standardized validation. In the present work, a combined fMRI/DTI study was performed, both to develop a setup for verifying fiber tracking results using fMRI-derived functional connections and to explore the limitations of fMRI based DTI fiber tracking. Therefore, a minor fiber bundle that features several fiber crossings and intersections was examined: The striatum and its connections to the primary motor cortex were examined by using two approaches to derive the somatotopic organization of the striatum. First, an fMRI-based somatotopic map of the striatum was reconstructed, based on fMRI activations that were provoked by unilateral motor tasks. Second, fMRI-guided DTI fiber tracking was performed to generate DTI-based somatotopic maps, using a standard line propagation and an advanced fast marching algorithm. The results show that the fiber connections reconstructed by the advanced fast marching algorithm are in good agreement with known anatomy, and that the DTI-revealed somatotopy is similar to the fMRI somatotopy. Furthermore, the study illustrates that the combination of fMRI with DTI can supply additional information in order to choose reasonable seed regions for generating functionally relevant networks and to validate reconstructed fibers.}, file = {attachment\:Staempfli2008NeuroImage.pdf:attachment\:Staempfli2008NeuroImage.pdf:PDF}, publisher = {Elsevier}, url = {http://www.sciencedirect.com/science/article/B6WNP-4PHSC6C-2/2/dbb7febf8dca292f483c25d800bdf700}, year = 2008 } @Article{Kubicki2006, Author = {Kubicki, M and Shenton, M E}, Title = {{A Method for Clustering White Matter}}, Journal = {Ajnr. American Journal Of Neuroradiology}, Number = {May}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Kubicki, Shenton - 2006 - A Method for Clustering White Matter.pdf:pdf}, year = 2006 } @Article{Reese2003MRM, Author = {Reese, T.G. and Heid, O. and Weisskoff, R.M. and Wedeen, V.J.}, Title = {Reduction of eddy-current-induced distortion in diffusion MRI using a twice-refocused spin echo}, Journal = {Magnetic Resonance in Medicine}, Volume = {49}, Number = {1}, Pages = {177-182}, abstract = {CP: Copyright © 2003 Wiley-Liss, Inc. ON: 1522-2594 PN: 0740-3194 AD: Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts; Medical Engineering Division, Siemens AG, Erlangen, Germany; Epix Medical Inc., Cambridge, Massachusetts DOI: 10.1002/mrm.10308 US: http://dx.doi.org/10.1002/mrm.10308 AB: Image distortion due to field gradient eddy currents can create image artifacts in diffusion-weighted MR images. These images, acquired by measuring the attenuation of NMR signal due to directionally dependent diffusion, have recently been shown to be useful in the diagnosis and assessment of acute stroke and in mapping of tissue structure. This work presents an improvement on the spin-echo (SE) diffusion sequence that displays less distortion and consequently improves image quality. Adding a second refocusing pulse provides better image quality with less distortion at no cost in scanning efficiency or effectiveness, and allows more flexible diffusion gradient timing. By adjusting the timing of the diffusion gradients, eddy currents with a single exponential decay constant can be nulled, and eddy currents with similar decay constants can be greatly reduced. This new sequence is demonstrated in phantom measurements and in diffusion anisotropy images of normal human brain. Magn Reson Med 49:177-182, 2003. © 2003 Wiley-Liss, Inc.}, owner = {ian}, timestamp = {2009.03.12}, year = 2003 } @Article{Hua2008NeuroImage, Author = {Hua, Kegang and Zhang, Jiangyang and Wakana, Setsu and Jiang, Hangyi and Li, Xin and Reich, Daniel S. and Calabresi, Peter A. and Pekar, James J. and {van Zijl}, Peter C.M. and Mori, Susumu}, Title = {Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification}, Journal = {NeuroImage}, Volume = {39}, Number = {1}, Pages = {336-347}, abstract = {Diffusion tensor imaging (DTI) is an exciting new MRI modality that can reveal detailed anatomy of the white matter. DTI also allows us to approximate the 3D trajectories of major white matter bundles. By combining the identified tract coordinates with various types of MR parameter maps, such as T2 and diffusion properties, we can perform tract-specific analysis of these parameters. Unfortunately, 3D tract reconstruction is marred by noise, partial volume effects, and complicated axonal structures. Furthermore, changes in diffusion anisotropy under pathological conditions could alter the results of 3D tract reconstruction. In this study, we created a white matter parcellation atlas based on probabilistic maps of 11 major white matter tracts derived from the DTI data from 28 normal subjects. Using these probabilistic maps, automated tract-specific quantification of fractional anisotropy and mean diffusivity were performed. Excellent correlation was found between the automated and the individual tractography-based results. This tool allows efficient initial screening of the status of multiple white matter tracts. }, file = {attachment\:Hua2008NeuroImage.pdf:attachment\:Hua2008NeuroImage.pdf:PDF}, publisher = {Elsevier}, url = {http://www.sciencedirect.com/science/article/B6WNP-4PF1WFR-5/2/c08a39189151d2b118cf7f8805fe8e2a}, year = 2008 } @Article{Tuch2002, Author = {Tuch, David S. and Reese, Timothy G. and Wiegell, Mette R. and Makris, Nikos and Belliveau, John W. and Wedeen, Van J.}, Title = {{High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity}}, Journal = {Magnetic Resonance in Medicine}, Volume = {48}, Number = {4}, Pages = {577--582}, abstract = {Magnetic resonance (MR) diffusion tensor imaging (DTI) can resolve the white matter fiber orientation within a voxel provided that the fibers are strongly aligned. However, a given voxel may contain a distribution of fiber orientations due to, for example, intravoxel fiber crossing. The present study sought to test whether a geodesic, high b-value diffusion gradient sampling scheme could resolve multiple fiber orientations within a single voxel. In regions of fiber crossing the diffusion signal exhibited multiple local maxima/minima as a function of diffusion gradient orientation, indicating the presence of multiple intravoxel fiber orientations. The multimodality of the observed diffusion signal precluded the standard tensor reconstruction, so instead the diffusion signal was modeled as arising from a discrete mixture of Gaussian diffusion processes in slow exchange, and the underlying mixture of tensors was solved for using a gradient descent scheme. The multitensor reconstruction resolved multiple intravoxel fiber populations corresponding to known fiber anatomy. Magn Reson Med 48:577-582, 2002. � 2002 Wiley-Liss, Inc.}, doi = {10.1002/mrm.10268}, url = {http://dx.doi.org/10.1002/mrm.10268}, year = 2002 } @Article{Kerkyacharian2007, Author = {Kerkyacharian, G and Petrushev, P and Picard, D and Willer, T}, Title = {{Needlet algorithms for estimation in inverse problems}}, Journal = {Electron. J. Stat}, Volume = {1}, Pages = {30--76}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Kerkyacharian et al. - 2007 - Needlet algorithms for estimation in inverse problems.pdf:pdf}, year = 2007 } @Article{Edition, Author = {Edition, Second}, Title = {{Statistical Pattern Stas-tical Pattern Recognit ion}}, Journal = {Pattern Recognition}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Edition - Unknown - Statistical Pattern Stas-tical Pattern Recognit ion.pdf:pdf} } @Article{Sverre2009, Author = {Sverre, Dag}, Title = {{Fast numerical computations with Cython}}, Number = {SciPy}, Pages = {15--22}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Sverre - 2009 - Fast numerical computations with Cython.pdf:pdf}, year = 2009 } @Article{Heim2007ComputationalStatisticsDataAnalysis, Author = {Heim, S. and Fahrmeir, L. and Eilers, P.H.C. and Marx, B.D.}, Title = {3D space-varying coefficient models with application to diffusion tensor imaging}, Journal = {Computational Statistics \& Data Analysis}, Volume = {51}, Number = {12}, Pages = {6212-6228}, abstract = {The present methodological development and the primary application field originate from diffusion tensor imaging (DTI), a powerful nuclear magnetic resonance technique which enables the quantification of microscopical tissue properties. The current analysis framework of separate voxelwise regressions is reformulated as a 3D space-varying coefficient model (SVCM) for the entire set of diffusion tensor images recorded on a 3D voxel grid. The SVCM unifies the three-step cascade of standard data processing (voxelwise regression, smoothing, interpolation) into one framework based on B-spline basis functions. Thereby strength is borrowed from spatially correlated voxels to gain a regularization effect right at the estimation stage. Two SVCM variants are conceptualized: a full tensor product approach and a sequential approximation, rendering the SVCM numerically and computationally feasible even for the huge dimension of the joint model in a realistic setup. A simulation study shows that both approaches outperform the standard method of voxelwise regression with subsequent regularization. Application of the fast sequential method to real DTI data demonstrates the inherent ability to increase the grid resolution by evaluating the incorporated basis functions at intermediate points. The resulting continuous regularized tensor field may serve as basis for multiple applications, yet, ameloriation of local adaptivity is desirable. }, file = {attachment\:Heim2007ComputationalStatisticsDataAnalysis.pdf:attachment\:Heim2007ComputationalStatisticsDataAnalysis.pdf:PDF}, publisher = {Elsevier}, url = {http://www.sciencedirect.com/science/article/B6V8V-4MV74WR-2/2/882882c104fa98632263c151db9fda23}, year = 2007 } @Article{Heller, Author = {Heller, Katherine A}, Title = {{Bayesian Hierarchical Clustering}}, Journal = {Neuroscience}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Heller - Unknown - Bayesian Hierarchical Clustering.pdf:pdf} } @Book{behrens2009diffusion, Author = {Behrens, T.E.J.}, Title = {{Diffusion MRI: From Quantitative Measurement to In-vivo Neuroanatomy}}, Publisher = {Academic Press}, year = 2009 } @Article{Descoteaux2007a, Author = {Descoteaux, M and Angelino, E and Fitzgibbons, S and Deriche, R}, Title = {{Regularized, fast, and robust analytical q-ball imaging}}, Journal = {Magnetic Resonance in Medicine}, Volume = {vol}, Pages = {58no3pp497--510}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Descoteaux et al. - 2007 - Regularized, fast, and robust analytical q-ball imaging.pdf:pdf}, year = 2007 } @Article{PHW03, Author = {Parker, G. J. and Haroon, H. A. and Wheeler-Kingshott, C. A.}, Title = {A framework for a streamline-based probabilistic index of connectivity ({PIC}o) using a structural interpretation of {MRI} diffusion measurements.}, Journal = {J Magn Reson Imaging}, Volume = {18}, Number = {2}, Pages = {242-54}, abstract = {PURPOSE: To establish a general methodology for quantifying streamline-based diffusion fiber tracking methods in terms of probability of connection between points and/or regions. MATERIALS AND METHODS: The commonly used streamline approach is adapted to exploit the uncertainty in the orientation of the principal direction of diffusion defined for each image voxel. Running the streamline process repeatedly using Monte Carlo methods to exploit this inherent uncertainty generates maps of connection probability. Uncertainty is defined by interpreting the shape of the diffusion orientation profile provided by the diffusion tensor in terms of the underlying microstructure. RESULTS: Two candidates for describing the uncertainty in the diffusion tensor are proposed and maps of probability of connection to chosen start points or regions are generated in a number of major tracts. CONCLUSION: The methods presented provide a generic framework for utilizing streamline methods to generate probabilistic maps of connectivity.}, authoraddress = {Imaging Science and Biomedical Engineering, University of Manchester, Manchester, UK. geoff.parker@man.ac.uk}, keywords = {Anisotropy ; Brain/*anatomy \& histology ; Diffusion ; Diffusion Magnetic Resonance Imaging/*methods ; Echo-Planar Imaging ; Humans ; Models, Statistical ; Monte Carlo Method ; *Probability ; Uncertainty}, language = {eng}, medline-aid = {10.1002/jmri.10350 [doi]}, medline-ci = {Copyright 2003 Wiley-Liss, Inc.}, medline-crdt = {2003/07/29 05:00}, medline-da = {20030728}, medline-dcom = {20040129}, medline-edat = {2003/07/29 05:00}, medline-fau = {Parker, Geoffrey J M ; Haroon, Hamied A ; Wheeler-Kingshott, Claudia A M}, medline-is = {1053-1807 (Print)}, medline-jid = {9105850}, medline-jt = {Journal of magnetic resonance imaging : JMRI}, medline-lr = {20061115}, medline-mhda = {2004/01/30 05:00}, medline-own = {NLM}, medline-pl = {United States}, medline-pmid = {12884338}, medline-pst = {ppublish}, medline-pt = {Journal Article ; Research Support, Non-U.S. Gov't}, medline-sb = {IM}, medline-so = {J Magn Reson Imaging. 2003 Aug;18(2):242-54.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=12884338}, year = 2003 } @Article{Garyfallidis2009, Author = {Garyfallidis, Eleftherios}, Title = {{Towards an accurate brain tractography using di usion weighted imaging 1 Introduction}}, Journal = {Imaging}, Number = {June}, Pages = {1--25}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Garyfallidis - 2009 - Towards an accurate brain tractography using di usion weighted imaging 1 Introduction.pdf:pdf}, year = 2009 } @Article{Liu2007NeuroImage, Author = {Liu, Tianming and Li, Hai and Wong, Kelvin and Tarokh, Ashley and Guo, Lei and Wong, Stephen T.C.}, Title = {Brain tissue segmentation based on \{{D}{TI}\} data}, Journal = {NeuroImage}, Volume = {15}, Number = {1}, Pages = {114-123}, abstract = {We present a method for automated brain tissue segmentation based on the multi-channel fusion of diffusion tensor imaging (DTI) data. The method is motivated by the evidence that independent tissue segmentation based on DTI parametric images provides complementary information of tissue contrast to the tissue segmentation based on structural MRI data. This has important applications in defining accurate tissue maps when fusing structural data with diffusion data. In the absence of structural data, tissue segmentation based on DTI data provides an alternative means to obtain brain tissue segmentation. Our approach to the tissue segmentation based on DTI data is to classify the brain into two compartments by utilizing the tissue contrast existing in a single channel. Specifically, because the apparent diffusion coefficient (ADC) values in the cerebrospinal fluid (CSF) are more than twice that of gray matter (GM) and white matter (WM), we use ADC images to distinguish CSF and non-CSF tissues. Additionally, fractional anisotropy (FA) images are used to separate WM from non-WM tissues, as highly directional white matter structures have much larger fractional anisotropy values. Moreover, other channels to separate tissue are explored, such as eigenvalues of the tensor, relative anisotropy (RA), and volume ratio (VR). We developed an approach based on the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm that combines these two-class maps to obtain a complete tissue segmentation map of CSF, GM, and WM. Evaluations are provided to demonstrate the performance of our approach. Experimental results of applying this approach to brain tissue segmentation and deformable registration of DTI data and spoiled gradient-echo (SPGR) data are also provided.}, file = {attachment\:Liu2007NeuroImage.pdf:attachment\:Liu2007NeuroImage.pdf:PDF}, publisher = {Elsevier}, url = {http://www.sciencedirect.com/science/article/B6WNP-4P61N6N-3/2/a1e3c8c3d22d6c80fa4693813e380a76}, year = 2007 } @Article{Alexander2007Neurotherapeutics, Author = {Alexander, Andrew L. and Lee, Jee Eun and Lazar, Mariana and Field, Aaron S.}, Title = {Diffusion Tensor Imaging of the Brain}, Journal = {Neurotherapeutics}, Volume = {4}, Number = {3}, Pages = {316-329}, abstract = {Diffusion tensor imaging (DTI) is a promising method for characterizing microstructural changes or differences with neuropathology and treatment. The diffusion tensor may be used to characterize the magnitude, the degree of anisotropy, and the orientation of directional diffusion. This review addresses the biological mechanisms, acquisition, and analysis of DTI measurements. The relationships between DTI measures and white matter pathologic features (e.g., ischemia, myelination, axonal damage, inflammation, and edema) are summarized. Applications of DTI to tissue characterization in neurotherapeutic applications are reviewed. The interpretations of common DTI measures (mean diffusivity, MD; fractional anisotropy, FA; radial diffusivity, $D_r$; and axial diffusivity, $D_a$) are discussed. In particular, FA is highly sensitive to microstructural changes, but not very specific to the type of changes (e.g., radial or axial). To maximize the specificity and better characterize the tissue microstructure, future studies should use multiple diffusion tensor measures (e.g., MD and FA, or $D_a$ and $D_r$).}, doi = {10.1016/j.nurt.2007.05.011}, file = {attachment\:Alexander2007Neurotherapeutics.pdf:attachment\:Alexander2007Neurotherapeutics.pdf:PDF}, year = 2007 } @Article{canalesrodriguez2009mdq, Author = {Canales-Rodr{\'\i}guez, E.J. and Melie-Garc{\'\i}a, L. and Iturria-Medina, Y. and Center, C.N.}, Title = {{Mathematical description of q-space in spherical coordinates: Exact q-ball imaging.}}, Journal = {Magnetic resonance in medicine: official journal of the Society of Magnetic Resonance in Medicine/Society of Magnetic Resonance in Medicine}, year = 2009 } @Article{Yen2009, Author = {Yen, Luh and Fouss, Francois and Decaestecker, Christine and Francq, Pascal and Saerens, Marco}, Title = {{Graph nodes clustering with the sigmoid commute-time kernel: A comparative study}}, Journal = {Data \& Knowledge Engineering}, Volume = {68}, Number = {3}, Pages = {338--361}, doi = {10.1016/j.datak.2008.10.006}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Yen et al. - 2009 - Graph nodes clustering with the sigmoid commute-time kernel A comparative study(2).pdf:pdf}, issn = {0169023X}, publisher = {Elsevier B.V.}, url = {http://linkinghub.elsevier.com/retrieve/pii/S0169023X0800147X}, year = 2009 } @Article{Hartley, Author = {Hartley, Richard and Zisserman, Andrew}, Title = {{in computervision Multiple View Geometry in Computer Vision}} } @Article{Oishi2008NeuroImage, Author = {Oishi, Kenichi and Zilles, Karl and Amunts, Katrin and Faria, Andreia and Jiang, Hangyi and Li, Xin and Akhter, Kazi and Hua, Kegang and Woods, Roger and Toga, Arthur W. and Pike, G. Bruce and Rosa-Neto, Pedro and Evans, Alan and Zhang, Jiangyang and Huang, Hao and Miller, Michael I. and {van Zijl}, Peter C. M. and Mazziotta, John and Mori, Susumu}, Title = {Human brain white matter atlas: Identification and assignment of common anatomical structures in superficial white matter}, Journal = {NeuroImage}, Volume = {in press}, abstract = {Structural delineation and assignment are the fundamental steps in understanding the anatomy of the human brain. The white matter has been structurally defined in the past only at its core regions (deep white matter). However, the most peripheral white matter areas, which are interleaved between the cortex and the deep white matter, have lacked clear anatomical definitions and parcellations. We used axonal fiber alignment information from diffusion tensor imaging (DTI) to delineate the peripheral white matter, and investigated its relationship with the cortex and the deep white matter. Using DTI data from 81 healthy subjects, we identified nine common, blade-like anatomical regions, which were further parcellated into 21 subregions based on the cortical anatomy. Four short association fiber tracts connecting adjacent gyri (U-fibers) were also identified reproducibly among the healthy population. We anticipate that this atlas will be useful resource for atlas-based white matter anatomical studies.}, file = {attachment\:Oishi2008NeuroImage.pdf:attachment\:Oishi2008NeuroImage.pdf:PDF}, year = 2008 } @Article{Jianu2009, Author = {Jianu, Radu and Demiralp, CaÄŸatay and Laidlaw, David H}, Title = {{Exploring 3D DTI fiber tracts with linked 2D representations.}}, Journal = {IEEE transactions on visualization and computer graphics}, Volume = {15}, Number = {6}, Pages = {1449--56}, abstract = {We present a visual exploration paradigm that facilitates navigation through complex fiber tracts by combining traditional 3D model viewing with lower dimensional representations. To this end, we create standard streamtube models along with two two-dimensional representations, an embedding in the plane and a hierarchical clustering tree, for a given set of fiber tracts. We then link these three representations using both interaction and color obtained by embedding fiber tracts into a perceptually uniform color space. We describe an anecdotal evaluation with neuroscientists to assess the usefulness of our method in exploring anatomical and functional structures in the brain. Expert feedback indicates that, while a standalone clinical use of the proposed method would require anatomical landmarks in the lower dimensional representations, the approach would be particularly useful in accelerating tract bundle selection. Results also suggest that combining traditional 3D model viewing with lower dimensional representations can ease navigation through the complex fiber tract models, improving exploration of the connectivity in the brain.}, doi = {10.1109/TVCG.2009.141}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Jianu, Demiralp, Laidlaw - 2009 - Exploring 3D DTI fiber tracts with linked 2D representations..pdf:pdf}, issn = {1077-2626}, keywords = {Algorithms,Brain,Brain: anatomy \& histology,Cluster Analysis,Computer Graphics,Diffusion Magnetic Resonance Imaging,Diffusion Magnetic Resonance Imaging: methods,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: methods,Imaging, Three-Dimensional,Imaging, Three-Dimensional: methods,Models, Biological,Nerve Fibers}, pmid = {19834220}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19834220}, year = 2009 } @Article{Oliphant2003, Author = {Oliphant, Travis E}, Title = {{SciPy Tutorial}}, Number = {September}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Oliphant - 2003 - SciPy Tutorial.pdf:pdf}, year = 2003 } @InProceedings{Wedeen2000, Author = {Wedeen, VJ and Reese, TG and Tuch, DS and Weigel, MR and Dou, JG and Weiskoff, RM and Chessler, D}, Title = {{Mapping fiber orientation spectra in cerebral white matter with Fourier-transform diffusion MRI}}, BookTitle = {Proc. Intl. Sot. Mag. Reson. Med}, Volume = {8}, Pages = {82}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Wedeen et al. - 2000 - Mapping fiber orientation spectra in cerebral white matter with Fourier-transform diffusion MRI.pdf:pdf}, url = {http://cds.ismrm.org/ismrm-2000/PDF1/0082.pdf}, year = 2000 } @conference{o2006high, author = {O'Donnell, L. and Westin, CF}, booktitle = {International Society of Magnetic Resonance in Medicine (ISMRM)}, organization = {Citeseer}, title = {{A high-dimensional fiber tract atlas}}, year = 2006 } @Article{Sorensen1999, Author = {Sorensen, A. Gregory and Wu, Ona and Copen, William A. and Davis, Timothy L. and Gonzalez, R. Gilberto and Koroshetz, Walter J. and Reese, Timothy G. and Rosen, Bruce R. and Wedeen, Van J. and Weisskoff, Robert M.}, Title = {{Human Acute Cerebral Ischemia: Detection of Changes in Water Diffusion Anisotropy by Using MR Imaging}}, Journal = {Radiology}, Volume = {212}, Number = {3}, Pages = {785--792}, abstract = {PURPOSE: To (a) determine the optimal choice of a scalar metric of anisotropy and (b) determine by means of magnetic resonance imaging if changes in diffusion anisotropy occurred in acute human ischemic stroke. MATERIALS AND METHODS: The full diffusion tensor over the entire brain was measured. To optimize the choice of a scalar anisotropy metric, the performances of scalar indices in simulated models and in a healthy volunteer were analyzed. The anisotropy, trace apparent diffusion coefficient (ADC), and eigenvalues of the diffusion tensor in lesions and contralateral normal brain were compared in 50 patients with stroke. RESULTS: Changes in anisotropy in patients were quantified by using fractional anisotropy because it provided the best performance in terms of contrast-to-noise ratio as a function of signal-to-noise ratio in simulations. The anisotropy of ischemic white matter decreased (P = .01). Changes in anisotropy in ischemic gray matter were not significant (P = .63). The trace ADC decreased for ischemic gray matter and white matter (P < .001). The first and second eigenvalues decreased in both ischemic gray and ischemic white matter (P < .001). The third eigenvalue decreased in ischemic gray (P = .001) and white matter (P = .03). CONCLUSION: Gray matter is mildly anisotropic in normal and early ischemic states. However, early white matter ischemia is associated with not only changes in trace ADC values but also significant changes in the anisotropy, or shape, of the water self-diffusion tensor.}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Sorensen et al. - 1999 - Human Acute Cerebral Ischemia Detection of Changes in Water Diffusion Anisotropy by Using MR Imaging.html:html}, month = sep, shorttitle = {Human Acute Cerebral Ischemia}, url = {http://radiology.rsnajnls.org/cgi/content/abstract/212/3/785}, year = 1999 } @Article{Ang2003, Author = {Ang, Y O N G T and Yengaard, J E N S R N and Akkenberg, B Ente P and Undersen, H A N S J \O rgen G G}, Title = {{STEREOLOGY OF NEURONAL CONNECTIONS ( MYELINATED FIBERS OF WHITE MATTER AND SYNAPSES OF NEOCORTEX ) IN}}, Journal = {Methods}, Pages = {171--182}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Ang et al. - 2003 - STEREOLOGY OF NEURONAL CONNECTIONS ( MYELINATED FIBERS OF WHITE MATTER AND SYNAPSES OF NEOCORTEX ) IN.pdf:pdf}, keywords = {human brain,myelinated nerve fibers,neocortex,stereology,synapse,white matter}, year = 2003 } @Article{Tournier2008, Author = {Tournier, J-Donald and Yeh, Chun-Hung and Calamante, Fernando and Cho, Kuan-Hung and Connelly, Alan and Lin, Ching-Po}, Title = {{Resolving crossing fibres using constrained spherical deconvolution: validation using diffusion-weighted imaging phantom data.}}, Journal = {NeuroImage}, Volume = {42}, Number = {2}, Pages = {617--25}, abstract = {Diffusion-weighted imaging can potentially be used to infer the connectivity of the human brain in vivo using fibre-tracking techniques, and is therefore of great interest to neuroscientists and clinicians. A key requirement for fibre tracking is the accurate estimation of white matter fibre orientations within each imaging voxel. The diffusion tensor model, which is widely used for this purpose, has been shown to be inadequate in crossing fibre regions. A number of approaches have recently been proposed to address this issue, based on high angular resolution diffusion-weighted imaging (HARDI) data. In this study, an experimental model of crossing fibres, consisting of water-filled plastic capillaries, is used to thoroughly assess three such techniques: constrained spherical deconvolution (CSD), super-resolved CSD (super-CSD) and Q-ball imaging (QBI). HARDI data were acquired over a range of crossing angles and b-values, from which fibre orientations were computed using each technique. All techniques were capable of resolving the two fibre populations down to a crossing angle of 45 degrees , and down to 30 degrees for super-CSD. A bias was observed in the fibre orientations estimated by QBI for crossing angles other than 90 degrees, consistent with previous simulation results. Finally, for a 45 degrees crossing, the minimum b-value required to resolve the fibre orientations was 4000 s/mm(2) for QBI, 2000 s/mm(2) for CSD, and 1000 s/mm(2) for super-CSD. The quality of estimation of fibre orientations may profoundly affect fibre tracking attempts, and the results presented provide important additional information regarding performance characteristics of well-known methods.}, doi = {10.1016/j.neuroimage.2008.05.002}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Tournier et al. - 2008 - Resolving crossing fibres using constrained spherical deconvolution validation using diffusion-weighted imaging phantom data..pdf:pdf}, issn = {1095-9572}, keywords = {Algorithms,Artificial Intelligence,Brain,Brain: anatomy \& histology,Diffusion Magnetic Resonance Imaging,Diffusion Magnetic Resonance Imaging: instrumentat,Diffusion Magnetic Resonance Imaging: methods,Humans,Image Enhancement,Image Enhancement: methods,Image Interpretation, Computer-Assisted,Image Interpretation, Computer-Assisted: methods,Imaging, Three-Dimensional,Imaging, Three-Dimensional: methods,Nerve Fibers, Myelinated,Nerve Fibers, Myelinated: ultrastructure,Pattern Recognition, Automated,Pattern Recognition, Automated: methods,Phantoms, Imaging,Reproducibility of Results,Sensitivity and Specificity}, pmid = {18583153}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18583153}, year = 2008 } @Article{Aganj2010, Author = {Aganj, Iman and Lenglet, Christophe and Jahanshad, Neda and Yacoub, Essa and Harel, Noam and Thompson, Paul M and Series, I M A Preprint and E, Church Street S}, Title = {{A HOUGH TRANSFORM GLOBAL PROBABILISTIC APPROACH A Hough Transform Global Probabilistic Approach to Multiple- Subject Diffusion MRI Tractography}}, Pages = {612--626}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Aganj et al. - 2010 - A HOUGH TRANSFORM GLOBAL PROBABILISTIC APPROACH A Hough Transform Global Probabilistic Approach to Multiple- Subject Diffusion MRI Tractography.pdf:pdf}, year = 2010 } @Article{Sherbondy2008JVision, Author = {Sherbondy, Anthony J. and Dougherty, Robert F. and Ben-Shachar, Michal and Napel, Sandy and Wandell, Brian A.}, Title = {{ConTrack: Finding the most likely pathways between brain regions using diffusion tractography}}, Journal = {J. Vis.}, Volume = {8}, Number = {9}, Pages = {1-16}, abstract = {Magnetic resonance diffusion-weighted imaging coupled with fiber tractography (DFT) is the only non-invasive method for measuring white matter pathways in the living human brain. DFT is often used to discover new pathways. But there are also many applications, particularly in visual neuroscience, in which we are confident that two brain regions are connected, and we wish to find the most likely pathway forming the connection. In several cases, current DFT algorithms fail to find these candidate pathways. To overcome this limitation, we have developed a probabilistic DFT algorithm (ConTrack) that identifies the most likely pathways between two regions. We introduce the algorithm in three parts: a sampler to generate a large set of potential pathways, a scoring algorithm that measures the likelihood of a pathway, and an inferential step to identify the most likely pathways connecting two regions. In a series of experiments using human data, we show that ConTrack estimates known pathways at positions that are consistent with those found using a high quality deterministic algorithm. Further we show that separating sampling and scoring enables ConTrack to identify valid pathways, known to exist, that are missed by other deterministic and probabilistic DFT algorithms.}, file = {attachment\:Sherbondy-2008-jov-8-9-15.pdf:attachment\:Sherbondy-2008-jov-8-9-15.pdf:PDF}, issn = {1534-7362}, keywords = {diffusion imaging, fiber tractography, MT+, corpus callosum, optic radiation}, month = {7}, url = {http://journalofvision.org/8/9/15/}, year = 2008 } @Article{jones1999osm, Author = {Jones, DK and Horsfield, MA and Simmons, A.}, Title = {{Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging}}, Journal = {optimization}, Volume = {525}, year = 1999 } @Article{Dauguet2007, Author = {Dauguet, Julien and Peled, Sharon and Berezovskii, Vladimir and Delzescaux, Thierry and Warfield, Simon K and Born, Richard and Westin, Carl-Fredrik}, Title = {{Comparison of fiber tracts derived from in-vivo DTI tractography with 3D histological neural tract tracer reconstruction on a macaque brain.}}, Journal = {NeuroImage}, Volume = {37}, Number = {2}, Pages = {530--8}, abstract = {Since the introduction of diffusion weighted imaging (DWI) as a method for examining neural connectivity, its accuracy has not been formally evaluated. In this study, we directly compared connections that were visualized using injected neural tract tracers (WGA-HRP) with those obtained using in-vivo diffusion tensor imaging (DTI) tractography. First, we injected the tracer at multiple sites in the brain of a macaque monkey; second, we reconstructed the histological sections of the labeled fiber tracts in 3D; third, we segmented and registered the fibers (somatosensory and motor tracts) with the anatomical in-vivo MRI from the same animal; and last, we conducted fiber tracing along the same pathways on the DTI data using a classical diffusion tracing technique with the injection sites as seeds. To evaluate the performance of DTI fiber tracing, we compared the fibers derived from the DTI tractography with those segmented from the histology. We also studied the influence of the parameters controlling the tractography by comparing Dice superimposition coefficients between histology and DTI segmentations. While there was generally good visual agreement between the two methods, our quantitative comparisons reveal certain limitations of DTI tractography, particularly for regions at remote locations from seeds. We have thus demonstrated the importance of appropriate settings for realistic tractography results.}, doi = {10.1016/j.neuroimage.2007.04.067}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Dauguet et al. - 2007 - Comparison of fiber tracts derived from in-vivo DTI tractography with 3D histological neural tract tracer reconstruction on a macaque brain..pdf:pdf}, issn = {1053-8119}, keywords = {Animals,Anisotropy,Brain,Brain: anatomy \& histology,Diffusion Magnetic Resonance Imaging,Image Processing, Computer-Assisted,Imaging, Three-Dimensional,Immunohistochemistry,Macaca,Nerve Fibers,Nerve Fibers: ultrastructure,Neural Pathways,Neural Pathways: cytology}, pmid = {17604650}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17604650}, year = 2007 } @InProceedings{Fillard2006ISBI, Author = {Fillard, Pierre and Arsigny, Vincent and Pennec, Xavier and Ayache, Nicholas}, Title = {The Tensor Distribution Function}, BookTitle = {Third IEEE International Symposium on Biomedical Imaging: From Nano to Macro}, Pages = {(abstract)}, Publisher = {IEEE}, abstract = {Diffusion tensor MRI is an imaging modality that is gaining importance in clinical applications. However, in a clinical environment, data have to be acquired rapidly, often at the detriment of the image quality. We propose a new variational framework that specifically targets low quality DT-MRI. The Rician nature of the noise on the images leads us to a maximum likelihood strategy to estimate the tensor field. To further reduce the noise, we optimally exploit the spatial correlation by adding to the estimation an anisotropic regularization term. This criterion is easily optimized thanks to the use of the recently introduced Log-Euclidean metrics. Results on real clinical data show promising improvements of fiber tracking in the brain and the spinal cord.}, year = 2006 } @Article{ZHK+06, Author = {Zhuang, J. and Hrabe, J. and Kangarlu, A. and Xu, D. and Bansal, R. and Branch, C. A. and Peterson, B. S.}, Title = {Correction of eddy-current distortions in diffusion tensor images using the known directions and strengths of diffusion gradients.}, Journal = {J Magn Reson Imaging}, Volume = {24}, Number = {5}, Pages = {1188-93}, abstract = {PURPOSE: To correct eddy-current artifacts in diffusion tensor (DT) images without the need to obtain auxiliary scans for the sole purpose of correction. MATERIALS AND METHODS: DT images are susceptible to distortions caused by eddy currents induced by large diffusion gradients. We propose a new postacquisition correction algorithm that does not require any auxiliary reference scans. It also avoids the problematic procedure of cross-correlating images with significantly different contrasts. A linear model is used to describe the dependence of distortion parameters (translation, scaling, and shear) on the diffusion gradients. The model is solved numerically to provide an individual correction for every diffusion-weighted (DW) image. RESULTS: The assumptions of the linear model were successfully verified in a series of experiments on a silicon oil phantom. The correction obtained for this phantom was compared with correction obtained by a previously published method. The algorithm was then shown to markedly reduce eddy-current distortions in DT images from human subjects. CONCLUSION: The proposed algorithm can accurately correct eddy-current artifacts in DT images. Its principal advantages are that only images with comparable signals and contrasts are cross-correlated, and no additional scans are required.}, authoraddress = {Magnetic Resonance Imaging Unit, Department of Psychiatry, Columbia College of Physicians and Surgeons, New York, New York, USA. jc.zhuang@gmail.com}, keywords = {*Algorithms ; Brain/*anatomy \& histology ; Diffusion Magnetic Resonance Imaging/*methods ; Echo-Planar Imaging/instrumentation/*methods ; Humans ; Image Enhancement/*methods ; Image Interpretation, Computer-Assisted/*methods ; Phantoms, Imaging ; Reproducibility of Results ; Sensitivity and Specificity}, language = {eng}, medline-aid = {10.1002/jmri.20727 [doi]}, medline-ci = {Copyright (c) 2006 Wiley-Liss, Inc.}, medline-crdt = {2006/10/07 09:00}, medline-da = {20061030}, medline-dcom = {20070130}, medline-edat = {2006/10/07 09:00}, medline-fau = {Zhuang, Jiancheng ; Hrabe, Jan ; Kangarlu, Alayar ; Xu, Dongrong ; Bansal, Ravi ; Branch, Craig A ; Peterson, Bradley S}, medline-gr = {DA017820/DA/NIDA NIH HHS/United States ; K02 MH074677-01/MH/NIMH NIH HHS/United States ; MH068318/MH/NIMH NIH HHS/United States ; MH59139/MH/NIMH NIH HHS/United States ; MH74677/MH/NIMH NIH HHS/United States ; R01 DA017820-03/DA/NIDA NIH HHS/United States ; R01 MH068318-03/MH/NIMH NIH HHS/United States}, medline-is = {1053-1807 (Print)}, medline-jid = {9105850}, medline-jt = {Journal of magnetic resonance imaging : JMRI}, medline-lr = {20081120}, medline-mhda = {2007/01/31 09:00}, medline-mid = {NIHMS44414}, medline-oid = {NLM: NIHMS44414 ; NLM: PMC2364728}, medline-own = {NLM}, medline-pl = {United States}, medline-pmc = {PMC2364728}, medline-pmid = {17024663}, medline-pst = {ppublish}, medline-pt = {Evaluation Studies ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't}, medline-sb = {IM}, medline-so = {J Magn Reson Imaging. 2006 Nov;24(5):1188-93.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=17024663}, year = 2006 } @Article{Rothwell, Author = {Rothwell, John}, Title = {{HBM2010 Program at a Glance *}}, Journal = {Program}, Pages = {2010--2010}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Rothwell - Unknown - HBM2010 Program at a Glance.pdf:pdf} } @Article{Mori2008NeuroImage, Author = {Mori, Susumu and Oishi, Kenichi and Jiang, Hangyi and Jiang, Li and Li, Xin and Akhter, Kazi and Hua, Kegang and Faria, Andreia V. and Mahmood, Asif and Woods, Roger and Toga, Arthur W. and Pike, G. Bruce and Neto, Pedro Rosa and Evans, Alan and Zhang, Jiangyang and Huang, Hao and Miller, Michael I. and {van Zijl}, Peter and Mazziotta, John}, Title = {Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template}, Journal = {NeuroImage}, Volume = {40}, Number = {2}, Pages = {570-582}, abstract = {Brain registration to a stereotaxic atlas is an effective way to report anatomic locations of interest and to perform anatomic quantification. However, existing stereotaxic atlases lack comprehensive coordinate information about white matter structures. In this paper, white matter-specific atlases in stereotaxic coordinates are introduced. As a reference template, the widely used ICBM-152 was used. The atlas contains fiber orientation maps and hand-segmented white matter parcellation maps based on diffusion tensor imaging (DTI). Registration accuracy by linear and non-linear transformation was measured, and automated template-based white matter parcellation was tested. The results showed a high correlation between the manual ROI-based and the automated approaches for normal adult populations. The atlases are freely available and believed to be a useful resource as a target template and for automated parcellation methods. }, file = {attachment\:Mori2008NeuroImage.pdf:attachment\:Mori2008NeuroImage.pdf:PDF}, publisher = {Elsevier}, url = {http://www.sciencedirect.com/science/article/B6WNP-4RH37X2-1/2/24add3aed52eb682f7064260c33384e4}, year = 2008 } @conference{weinstein1999tad, author = {Weinstein, D. and Kindlmann, G. and Lundberg, E.}, booktitle = {Proceedings of the conference on Visualization'99: celebrating ten years}, organization = {IEEE Computer Society Press Los Alamitos, CA, USA}, pages = {249--253}, title = {{Tensorlines: Advection-diffusion based propagation through diffusion tensor fields}}, year = 1999 } @Misc{tenenbaum2000ggf, Author = {Tenenbaum, J.B. and Silva, V. and Langford, J.C.}, Title = {{A global geometric framework for nonlinear dimensionality reduction}}, journal = {Science}, number = {5500}, pages = {2319--2323}, volume = {290}, year = 2000 } @Article{Loper1990, Author = {Loper, David and Annua, Benton E R Spin-up}, Title = {{Bingham statistics}}, Journal = {Statistics}, Volume = {2}, Number = {c}, Pages = {45--47}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Loper, Annua - 1990 - Bingham statistics.pdf:pdf}, year = 1990 } @Article{chamberlain2008gma, Author = {Chamberlain, S.R. and Menzies, L.A. and Fineberg, N.A. and del Campo, N. and Suckling, J. and Craig, K. and M{\"u}ller, U. and Robbins, T.W. and Bullmore, E.T. and Sahakian, B.J.}, Title = {{Grey matter abnormalities in trichotillomania: morphometric magnetic resonance imaging study}}, Journal = {The British Journal of Psychiatry}, Volume = {193}, Number = {3}, Pages = {216--221}, publisher = {RCP}, year = 2008 } @Article{Batchelor2006, Author = {Batchelor, P G and Calamante, F and Tournier, J D and Atkinson, D and Hill, D L and Connelly, A}, Title = {{Quantification of the shape of fiber tracts}}, Journal = {Magn. Reson. Med}, Volume = {55}, Pages = {894--903}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Batchelor et al. - 2006 - Quantification of the shape of fiber tracts.pdf:pdf}, year = 2006 } @Article{Qazi2009, Author = {Qazi, Arish a and Radmanesh, Alireza and O'Donnell, Lauren and Kindlmann, Gordon and Peled, Sharon and Whalen, Stephen and Westin, Carl-Fredrik and Golby, Alexandra J}, Title = {{Resolving crossings in the corticospinal tract by two-tensor streamline tractography: Method and clinical assessment using fMRI.}}, Journal = {NeuroImage}, Volume = {47 Suppl 2}, Pages = {T98--106}, abstract = {An inherent drawback of the traditional diffusion tensor model is its limited ability to provide detailed information about multidirectional fiber architecture within a voxel. This leads to erroneous fiber tractography results in locations where fiber bundles cross each other. This may lead to the inability to visualize clinically important tracts such as the lateral projections of the corticospinal tract. In this report, we present a deterministic two-tensor eXtended Streamline Tractography (XST) technique, which successfully traces through regions of crossing fibers. We evaluated the method on simulated and in vivo human brain data, comparing the results with the traditional single-tensor and with a probabilistic tractography technique. By tracing the corticospinal tract and correlating with fMRI-determined motor cortex in both healthy subjects and patients with brain tumors, we demonstrate that two-tensor deterministic streamline tractography can accurately identify fiber bundles consistent with anatomy and previously not detected by conventional single-tensor tractography. When compared to the dense connectivity maps generated by probabilistic tractography, the method is computationally efficient and generates discrete geometric pathways that are simple to visualize and clinically useful. Detection of crossing white matter pathways can improve neurosurgical visualization of functionally relevant white matter areas.}, doi = {10.1016/j.neuroimage.2008.06.034}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Qazi et al. - 2009 - Resolving crossings in the corticospinal tract by two-tensor streamline tractography Method and clinical assessment using fMRI..pdf:pdf}, issn = {1095-9572}, keywords = {Algorithms,Brain Neoplasms,Brain Neoplasms: physiopathology,Computer Simulation,Female,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Middle Aged,Models, Theoretical,Motor Cortex,Motor Cortex: pathology,Motor Cortex: physiopathology,Probability,Pyramidal Tracts,Pyramidal Tracts: pathology}, pmid = {18657622}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18657622}, year = 2009 } @Article{Neji2008, Author = {Neji, R and Fleury, G and Deux, J-f and Rahmouni, A and Bassez, G and Vignaud, A and Paragios, N and Mas, Laboratoire and Paris, Ecole Centrale and Galen, Equipe and Saclay, Inria}, Title = {{SUPPORT VECTOR DRIVEN MARKOV RANDOM FIELDS TOWARDS DTI SEGMENTATION OF THE HUMAN SKELETAL MUSCLE b b b b}}, Pages = {923--926}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Neji et al. - 2008 - SUPPORT VECTOR DRIVEN MARKOV RANDOM FIELDS TOWARDS DTI SEGMENTATION OF THE HUMAN SKELETAL MUSCLE b b b b.pdf:pdf}, year = 2008 } @Article{Okada2006, Author = {Okada, Tsutomu}, Title = {{Diffusion-Tensor Fiber Purpose : Methods : Results : Conclusion :}}, Volume = {238}, Number = {2}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Okada - 2006 - Diffusion-Tensor Fiber Purpose Methods Results Conclusion.pdf:pdf}, year = 2006 } @Article{moriBook, Author = {Mori, S. and Wakana, S. and Nagae-Poetscher, LM and Van Zijl, PCM}, Title = {{MRI atlas of human white matter}}, Journal = {American Journal of Neuroradiology}, publisher = {Am Soc Neuroradiology} } @Article{Basser1994BiophysicalJ, Author = {Basser, Peter J. and Mattiello, James and LeBihan, Denis}, Title = {{MR} Diffusion Tensor Spectroscopy and Imaging}, Journal = {Biophysical Journal}, Volume = {66}, Pages = {259-267}, abstract = {This paper describes a new {NMR} imaging modality-{MR} diffusion tensor imaging. It consists of estimating an effective diffusion tensor, $D_{\textrm{eff}}$, within a voxel, and then displaying useful quantities derived from it. We show how the phenomenon of anisotropic diffusion of water (or metabolites) in anisotropic tissues, measured noninvasively by these {NMR} methods, is exploited to determine fiber tract orientation and mean particle displacements. Once $D_{\textrm{eff}}$ is estimated from a series of {NMR} pulsed-gradient, spin-echo experiments, a tissue's three orthotropic axes can be determined. They coincide with the eigen- vectors of $D_{\textrm{eff}}$, while the effective diffusivities along these orthotropic directions are the eigenvalues of $D_{\textrm{eff}}$. Diffusion ellipsoids, constructed in each voxel from $D_{\textrm{eff}}$, depict both these orthotropic axes and the mean diffusion distances in these directions. Moreover, the three scalar invariants of $D_{\textrm{eff}}$, which are independent of the tissue's orientation in the laboratory frame of reference, reveal useful information about molecular mobility reflective of local microstructure and anatomy. Inherently, tensors (like $D_{\textrm{eff}}$) describing transport processes in anisotropic media contain new information within a macroscopic voxel that scalars (such as the apparent diffusivity, proton density, $T_1$, and $T_2$) do not.}, year = 1994 } @Article{Avants2010, Author = {Avants, Brian B and Tustison, Nick and Song, Gang}, Title = {{Advanced Normalization Tools ( ANTS )}}, Journal = {Computing}, Pages = {1--33}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Avants, Tustison, Song - 2010 - Advanced Normalization Tools ( ANTS ).pdf:pdf}, year = 2010 } @Article{Beaulieu2002NMRBiomed, Author = {Christian Beaulieu}, Title = {The basis of anisotropic water diffusion in the nervous system - a technical review}, Journal = {NMR in Biomedicine}, Volume = {15}, Number = {7-8}, Pages = {435-455}, doi = {10.1002/nbm.782}, owner = {ian}, timestamp = {2009.04.27}, url = {http://dx.doi.org/10.1002/nbm.782}, year = 2002 } @Book{Callaghan1991OUP, Author = {Callaghan, Paul T.}, Title = {Principles of Nuclear Magnetic Resonance Microscopy}, Publisher = {Oxford University Press}, owner = {ian}, timestamp = {2009.03.12}, url = {http://books.google.co.uk/books?id=yjrjT_W5hygC}, year = 1991 } @Article{ODonnell_MICCAI07, Author = {O'Donnell, L. J. and Westin, C. F. and Golby, A. J.}, Title = {Tract-based morphometry.}, Journal = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv}, Volume = {10}, Number = {Pt 2}, Pages = {161-8}, abstract = {Multisubject statistical analyses of diffusion tensor images in regions of specific white matter tracts have commonly measured only the mean value of a scalar invariant such as the fractional anisotropy (FA), ignoring the spatial variation of FA along the length of fiber tracts. We propose to instead perform tract-based morphometry (TBM), or the statistical analysis of diffusion MRI data in an anatomical tract-based coordinate system. We present a method for automatic generation of white matter tract arc length parameterizations, based on learning a fiber bundle model from tractography from multiple subjects. Our tract-based coordinate system enables TBM for the detection of white matter differences in groups of subjects. We present example TBM results from a study of interhemispheric differences in FA.}, authoraddress = {Golby Surgical Brain Mapping Laboratory, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston MA, USA. odonnell@bwh.harvard.edu}, keywords = {Algorithms ; *Artificial Intelligence ; Brain/*cytology ; Cluster Analysis ; Diffusion Magnetic Resonance Imaging/*methods ; Humans ; Image Enhancement/methods ; Image Interpretation, Computer-Assisted/*methods ; Imaging, Three-Dimensional/*methods ; Nerve Fibers, Myelinated/*ultrastructure ; Neural Pathways/cytology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity}, language = {eng}, medline-crdt = {2007/11/30 09:00}, medline-da = {20071129}, medline-dcom = {20080103}, medline-edat = {2007/11/30 09:00}, medline-fau = {O'Donnell, Lauren J ; Westin, Carl-Fredrik ; Golby, Alexandra J}, medline-gr = {P41 RR15241-01A1/RR/NCRR NIH HHS/United States ; P41RR13218/RR/NCRR NIH HHS/United States ; R01 AG20012-01/AG/NIA NIH HHS/United States ; R01MH074794/MH/NIMH NIH HHS/United States ; U41RR019703/RR/NCRR NIH HHS/United States ; U54EB005149/EB/NIBIB NIH HHS/United States}, medline-jid = {101249582}, medline-jt = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}, medline-mhda = {2008/01/04 09:00}, medline-own = {NLM}, medline-pl = {Germany}, medline-pmid = {18044565}, medline-pst = {ppublish}, medline-pt = {Journal Article ; Research Support, N.I.H., Extramural}, medline-sb = {IM}, medline-so = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. 2007;10(Pt 2):161-8.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=18044565}, year = 2007 } @Article{Intelligence2009, Author = {Intelligence, Comp}, Title = {{Spatial Filtering and Single-Trial Classification of EEG during Vowel Speech Imagery}}, Journal = {Science And Technology}, Volume = {5}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Intelligence - 2009 - Spatial Filtering and Single-Trial Classification of EEG during Vowel Speech Imagery.pdf:pdf}, keywords = {4259 nagatsuta,bci,csp,eeg,hama,imagery,japan 226-8503,mailing address,midori-ku,r2-15,spatial filter,speech,vowel,yoko-}, year = 2009 } @Article{Becher1999, Author = {Becher, B and Giacomini, P S and Pelletier, D and McCrea, E and Prat, a and Antel, J P}, Title = {{Interferon-gamma secretion by peripheral blood T-cell subsets in multiple sclerosis: correlation with disease phase and interferon-beta therapy.}}, Journal = {Annals of neurology}, Volume = {45}, Number = {2}, Pages = {247--50}, abstract = {Interferon-gamma (IFN-gamma) is implicated as a participant in the immune effector and regulatory mechanisms considered to mediate the pathogenesis of multiple sclerosis (MS). We have used an intracellular cytokine staining technique to demonstrate that the proportion of ex vivo peripheral blood CD4 and CD8 T-cell subsets expressing IFN-gamma is increased in secondary progressing (SP) MS patients, whereas the values in untreated relapsing-remitting (RR) MS patients are reduced compared with those of controls. Patients treated with interferon-beta (IFN-beta) have an even more significant reduction in the percentage of IFN-gamma-secreting cells. The finding that the number of IFN-gamma-expressing CD8 cells is increased in SPMS patients, a group with reduced functional suppressor activity, and is most significantly reduced by IFN-beta therapy, which increases suppressor activity, indicates that IFN-gamma secretion by CD8 T cells and functional suppressor defects attributed to this cell subset in MS can be dissociated.}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Becher et al. - 1999 - Interferon-gamma secretion by peripheral blood T-cell subsets in multiple sclerosis correlation with disease phase and interferon-beta therapy..pdf:pdf}, issn = {0364-5134}, keywords = {Adult,Female,Humans,Interferon-beta,Interferon-beta: therapeutic use,Interferon-gamma,Interferon-gamma: secretion,Male,Middle Aged,Multiple Sclerosis,Multiple Sclerosis: immunology,Multiple Sclerosis: therapy,T-Lymphocyte Subsets,T-Lymphocyte Subsets: immunology,T-Lymphocytes,T-Lymphocytes: immunology}, month = feb, pmid = {9989628}, url = {http://www.ncbi.nlm.nih.gov/pubmed/9989628}, year = 1999 } @Article{Corouge2004, Author = {Corouge, Isabelle and Gouttard, Sylvain and Gerig, Guido}, Title = {{Accepted for oral presentation A Statistical Shape Model of Individual Fiber Tracts Extracted from Diffusion Tensor MRI}}, Journal = {Analysis}, Volume = {3217}, Number = {Part II}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Corouge, Gouttard, Gerig - 2004 - Accepted for oral presentation A Statistical Shape Model of Individual Fiber Tracts Extracted from Diffusion Tensor MRI.pdf:pdf}, keywords = {diffusion tensor imaging,statistical shape modelling}, year = 2004 } @Article{Mangin2002, Author = {Mangin, J-F and Poupon, C and Cointepas, Y and Rivi\`{e}re, D and Papadopoulos-Orfanos, D and Clark, C a and R\'{e}gis, J and {Le Bihan}, D}, Title = {{A framework based on spin glass models for the inference of anatomical connectivity from diffusion-weighted MR data - a technical review.}}, Journal = {NMR in biomedicine}, Volume = {15}, Number = {7-8}, Pages = {481--92}, abstract = {A family of methods aiming at the reconstruction of a putative fascicle map from any diffusion-weighted dataset is proposed. This fascicle map is defined as a trade-off between local information on voxel microstructure provided by diffusion data and a priori information on the low curvature of plausible fascicles. The optimal fascicle map is the minimum energy configuration of a simulated spin glass in which each spin represents a fascicle piece. This spin glass is embedded into a simulated magnetic external field that tends to align the spins along the more probable fiber orientations according to diffusion models. A model of spin interactions related to the curvature of the underlying fascicles introduces a low bending potential constraint. Hence, the optimal configuration is a trade-off between these two kind of forces acting on the spins. Experimental results are presented for the simplest spin glass model made up of compass needles located in the center of each voxel of a tensor based acquisition.}, doi = {10.1002/nbm.780}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Mangin et al. - 2002 - A framework based on spin glass models for the inference of anatomical connectivity from diffusion-weighted MR data - a technical review..pdf:pdf}, issn = {0952-3480}, keywords = {Algorithms,Astrocytes,Astrocytes: cytology,Brain,Brain Mapping,Brain Mapping: methods,Brain: cytology,Diffusion Magnetic Resonance Imaging,Diffusion Magnetic Resonance Imaging: methods,Humans,Image Enhancement,Image Enhancement: methods,Imaging, Three-Dimensional,Imaging, Three-Dimensional: methods,Methods,Models, Biological,Nerve Fibers, Myelinated,Nerve Fibers, Myelinated: pathology,Nerve Net,Nerve Net: cytology,Neural Pathways,Neural Pathways: cytology,Pattern Recognition, Automated,Quality Control,Spin Labels}, pmid = {12489097}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12489097}, year = 2002 } @Article{Dryden2005, Author = {Dryden, Ian L.}, Title = {{Statistical analysis on high-dimensional spheres and shape spaces}}, Journal = {The Annals of Statistics}, Volume = {33}, Number = {4}, Pages = {1643--1665}, arxivid = {arXiv:math/0508279v1}, doi = {10.1214/009053605000000264}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Dryden - 2005 - Statistical analysis on high-dimensional spheres and shape spaces.pdf:pdf}, issn = {0090-5364}, keywords = {and phrases,bingham distribution,complex bingham,complex watson,di-}, month = aug, url = {http://projecteuclid.org/Dienst/getRecord?id=euclid.aos/1123250225/}, year = 2005 } @Article{Ziyan, Author = {Ziyan, U and Sabuncu, M R and O’donnell, L J and C}, Title = {{-F. Westin. Nonlinear registration of diffusion mr images based on fiber bundles}}, Journal = {In Medical Image Computing and Computer-Assisted Intervention (MICCAI ’}, Volume = {07}, Number = {volume4791}, Pages = {351--358}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Ziyan et al. - Unknown - -F. Westin. Nonlinear registration of diffusion mr images based on fiber bundles.pdf:pdf} } @Article{Orasis2007, Author = {Orasis, Projet}, Title = {{Optimization of Discrete Markov Random Fields via Dual Decomposition}}, Journal = {Computer}, Number = {April}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Orasis - 2007 - Optimization of Discrete Markov Random Fields via Dual Decomposition.pdf:pdf}, year = 2007 } @Article{Delivery, Author = {Delivery, Price and Cost, Total and Brimpari, Minodora}, Title = {{PC World UK Computer Superstore - Buy cheap c ... PC World UK Computer Superstore - Buy cheap c ...}}, Journal = {Computer}, Volume = {5610000}, Pages = {1--9}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Delivery, Cost, Brimpari - Unknown - PC World UK Computer Superstore - Buy cheap c ... PC World UK Computer Superstore - Buy cheap c ....pdf:pdf} } @Article{Engel, Author = {Engel, Klaus and Hadwiger, Markus and Kniss, Joe M and Lefohn, Aaron E and Weiskopf, Daniel}, Title = {{Real-Time Volume Graphics Real-Time Volume Graphics}}, Journal = {Notes}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Engel et al. - Unknown - Real-Time Volume Graphics Real-Time Volume Graphics.pdf:pdf} } @Article{Neji2009, Author = {Neji, Radhou\`{e}ne and Ahmed, Jean-fran\c{c}ois Deux and Nikos, Besbes and Georg, Komodakis and Mezri, Langs and Alain, Maatouk and Guillaume, Rahmouni and Gilles, Bassez and Paragios, Nikos}, Title = {{Manifold-driven Grouping of Skeletal Muscle Fibers}}, Journal = {Science}, Number = {February}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Neji et al. - 2009 - Manifold-driven Grouping of Skeletal Muscle Fibers.pdf:pdf}, year = 2009 } @Article{Ib2001, Author = {Ib, Luis}, Title = {{TUTORIAL on QUATERNIONS Part I}}, Journal = {Seminar}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Ib - 2001 - TUTORIAL on QUATERNIONS Part I.pdf:pdf}, year = 2001 } @Article{Wainwright2005, Author = {Wainwright, Martin J and Jordan, Michael I}, Title = {{A Variational Principle for Graphical Models}}, Journal = {Electrical Engineering}, Number = {March}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Wainwright, Jordan - 2005 - A Variational Principle for Graphical Models.pdf:pdf}, year = 2005 } @Article{Hosey2005MagResMed, Author = {Hosey, T. and Williams, G. and Ansorge, R.}, Title = {{Inference of multiple fiber orientations in high angular resolution diffusion imaging}}, Journal = {Magnetic Resonance in Medicine}, Volume = {54}, Number = {6}, Pages = {1480-1489}, abstract = {A method is presented that is capable of determining more than one fiber orientation within a single voxel in high angular resolution diffusion imaging (HARDI) data sets. This method is an extension of the Markov chain method recently introduced to diffusion tensor imaging (DTI) analysis, allowing the probability density function of up to 2 intra-voxel fiber orientations to be inferred. The multiple fiber architecture within a voxel is then assessed by calculating the relative probabilities of a 1 and 2 fiber model. It is demonstrated that for realistic signal to noise ratios, it is possible to accurately characterize the directions of 2 intersecting fibers using a 2 fiber model. The shortcomings of under-fitting a 2 fiber model, or over-fitting a 1 fiber model, are explored. This new algorithm enhances the tools available for fiber tracking.}, file = {attachment\:Hosey2005MagResMed.pdf:attachment\:Hosey2005MagResMed.pdf:PDF}, year = 2005 } @Article{Yen2009a, Author = {Yen, Luh and Fouss, Francois and Decaestecker, Christine and Francq, Pascal and Saerens, Marco}, Title = {{Graph nodes clustering with the sigmoid commute-time kernel: A comparative study}}, Journal = {Data \& Knowledge Engineering}, Volume = {68}, Number = {3}, Pages = {338--361}, doi = {10.1016/j.datak.2008.10.006}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Yen et al. - 2009 - Graph nodes clustering with the sigmoid commute-time kernel A comparative study(2).pdf:pdf}, issn = {0169023X}, publisher = {Elsevier B.V.}, url = {http://linkinghub.elsevier.com/retrieve/pii/S0169023X0800147X}, year = 2009 } @Article{Ziyan2007, Author = {Ziyan, Ulas and Sabuncu, Mert R. and Grimson, W. Eric. L. and Westin, Carl-Fredrik}, Title = {{A Robust Algorithm for Fiber-Bundle Atlas Construction}}, Journal = {2007 IEEE 11th International Conference on Computer Vision}, Pages = {1--8}, doi = {10.1109/ICCV.2007.4409143}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Ziyan et al. - 2007 - A Robust Algorithm for Fiber-Bundle Atlas Construction.pdf:pdf}, isbn = {978-1-4244-1630-1}, issn = {1550-5499}, month = oct, publisher = {Ieee}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4409143}, year = 2007 } @Article{Baldi2009, Author = {Baldi, P. and Kerkyacharian, G. and Marinucci, D. and Picard, D.}, Title = {{Asymptotics for spherical needlets}}, Journal = {The Annals of Statistics}, Volume = {37}, Number = {3}, Pages = {1150--1171}, arxivid = {arXiv:math/0606599v2}, doi = {10.1214/08-AOS601}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Baldi et al. - 2009 - Asymptotics for spherical needlets.pdf:pdf}, issn = {0090-5364}, keywords = {High-frequency asymptotics, spherical needlets, ra}, url = {http://projecteuclid.org/euclid.aos/1239369018}, year = 2009 } @Article{MoriNeuron2006, Author = {Mori, Susumu and Zhang, Jiangyang}, Title = {Principles of Diffusion Tensor Imaging and Its Applications to Basic Neuroscience Research}, Journal = {Neuron}, Volume = {51}, Pages = {527-39}, abstract = {The brain contains more than 100 billion neurons that communicate with each other via axons for the formation of complex neural networks. The structural mapping of such networks during health and disease states is essential for understanding brain function. However, our understanding of brain structural connectivity is surprisingly limited, due in part to the lack of noninvasive methodologies to study axonal anatomy. Diffusion tensor imaging (DTI) is a recently developed MRI technique that can measure macroscopic axonal organization in nervous system tissues. In this article, the principles of DTI methodologies are explained, and several applications introduced, including visualization of axonal tracts in myelin and axonal injuries as well as human brain and mouse embryonic development. The strengths and limitations of DTI and key areas for future research and development are also discussed.}, file = {attachment\:Mori_Neuron_2006.pdf:attachment\:Mori_Neuron_2006.pdf:PDF}, year = 2006 } @Article{Heid2000ISMRM, Author = {Heid, O.}, Title = {Eddy current-nulled diffusion weighting.}, Journal = {In: Proceedings of the 8th Annual Meeting of ISMRM, Denver}, Pages = {799}, owner = {ian}, timestamp = {2009.03.12}, year = 2000 } @Article{merboldt1992diffusion, Author = {Merboldt, K.D. and H{\\"a}nicke, W. and Bruhn, H. and Gyngell, M.L. and Frahm, J.}, Title = {{Diffusion imaging of the human brain in vivo using high-speed STEAM MRI}}, Journal = {Magnetic Resonance in Medicine}, Volume = {23}, Number = {1}, Pages = {179--192}, issn = {1522-2594}, publisher = {John Wiley \& Sons}, year = 1992 } @Article{Descoteaux2009a, Author = {Descoteaux, Maxime and Deriche, Rachid and Kn\"{o}sche, Thomas R and Anwander, Alfred}, Title = {{Deterministic and probabilistic tractography based on complex fibre orientation distributions.}}, Journal = {IEEE transactions on medical imaging}, Volume = {28}, Number = {2}, Pages = {269--86}, abstract = {We propose an integral concept for tractography to describe crossing and splitting fibre bundles based on the fibre orientation distribution function (ODF) estimated from high angular resolution diffusion imaging (HARDI). We show that in order to perform accurate probabilistic tractography, one needs to use a fibre ODF estimation and not the diffusion ODF. We use a new fibre ODF estimation obtained from a sharpening deconvolution transform (SDT) of the diffusion ODF reconstructed from q-ball imaging (QBI). This SDT provides new insight into the relationship between the HARDI signal, the diffusion ODF, and the fibre ODF. We demonstrate that the SDT agrees with classical spherical deconvolution and improves the angular resolution of QBI. Another important contribution of this paper is the development of new deterministic and new probabilistic tractography algorithms using the full multidirectional information obtained through use of the fibre ODF. An extensive comparison study is performed on human brain datasets comparing our new deterministic and probabilistic tracking algorithms in complex fibre crossing regions. Finally, as an application of our new probabilistic tracking, we quantify the reconstruction of transcallosal fibres intersecting with the corona radiata and the superior longitudinal fasciculus in a group of eight subjects. Most current diffusion tensor imaging (DTI)-based methods neglect these fibres, which might lead to incorrect interpretations of brain functions.}, doi = {10.1109/TMI.2008.2004424}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Descoteaux et al. - 2009 - Deterministic and probabilistic tractography based on complex fibre orientation distributions..pdf:pdf}, issn = {1558-0062}, keywords = {Algorithms,Brain,Brain: anatomy \& histology,Diffusion Magnetic Resonance Imaging,Diffusion Magnetic Resonance Imaging: methods,Echo-Planar Imaging,Echo-Planar Imaging: methods,Humans,Image Enhancement,Image Enhancement: methods,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: methods,Models, Neurological,Models, Statistical,Nerve Fibers,Nerve Fibers: ultrastructure,Normal Distribution,Reproducibility of Results,Sensitivity and Specificity}, month = feb, pmid = {19188114}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19188114}, year = 2009 } @InProceedings{Deriche2007ISBI, Author = {DERICHE, r. AND DESCOTEAUX, M.}, Title = {Splitting Tracking Through Crossing Fibers: Multidirectional Q-Ball Tracking-}, BookTitle = {4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI’07)}, Pages = {756–759}, abstract = {We present a new tracking algorithm based on the full multidirectional information of the diffusion orientation distribution function (ODF) estimated from Q-Ball Imaging (QBI). From the ODF, we extract all available maxima and then extend streamline (STR) tracking to allow for splitting in multiple directions (SPLIT-STR). Our new algorithm SPLIT-STR overcomes important limitations of classical diffusion tensor streamline tracking in regions of low anisotropy and regions of fiber crossings. Not only can the tracking propagate through fiber crossings but it can also deal with fibers fanning and branching. SPLIT-STR algorithm is efficient and validated on synthetic data, on a biological phantom and compared against probabilistic tensor tracking on a human brain dataset with known crossing fibers}, owner = {ian}, timestamp = {2009.03.10}, year = 2007 } @Article{Garyfallidis2009a, Author = {Garyfallidis, Eleftherios and Brett, Matthew and Nimmo-smith, Ian}, Title = {{Fast Dimensionality Reduction for Brain Tractography Clustering}}, Journal = {Sciences-New York}, Pages = {7--10}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Garyfallidis, Brett, Nimmo-smith - 2009 - Fast Dimensionality Reduction for Brain Tractography Clustering.pdf:pdf}, year = 2009 } @Article{JohansenBerg2004ProcNatAcadSci, Author = {Johansen-Berg, H and Behrens, T E and Robson, M D and Drobnjak, I and Rushworth, M F and Brady, JM and Smith, S M and Higham, D J and Matthews, P M}, Title = {Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex.}, Journal = {Proc. Natl. Acad. Sci. USA}, Volume = {101}, Number = {36}, Pages = {13335-13340}, abstract = {A fundamental issue in neuroscience is the relation between structure and function. However, gross landmarks do not correspond well to microstructural borders and cytoarchitecture cannot be visualized in a living brain used for functional studies. Here, we used diffusion-weighted and functional MRI to test structure-function relations directly. Distinct neocortical regions were defined as volumes having similar connectivity profiles and borders identified where connectivity changed. Without using prior information, we found an abrupt profile change where the border between supplementary motor area (SMA) and pre-SMA is expected. Consistent with this anatomical assignment, putative SMA and pre-SMA connected to motor and prefrontal regions, respectively. Excellent spatial correlations were found between volumes defined by using connectivity alone and volumes activated during tasks designed to involve SMA or pre-SMA selectively. This finding demonstrates a strong relationship between structure and function in medial frontal cortex and offers a strategy for testing such correspondences elsewhere in the brain.}, file = {attachment\:JohansenBerg2004ProcNatAcadSci.pdf:attachment\:JohansenBerg2004ProcNatAcadSci.pdf:PDF}, year = 2004 } @Article{Maddah_MICCA2005, Author = {Maddah, M. and Mewes, A. U. and Haker, S. and Grimson, W. E. and Warfield, S. K.}, Title = {Automated atlas-based clustering of white matter fiber tracts from {DTMRI}.}, Journal = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv}, Volume = {8}, Number = {Pt 1}, Pages = {188-95}, abstract = {A new framework is presented for clustering fiber tracts into anatomically known bundles. This work is motivated by medical applications in which variation analysis of known bundles of fiber tracts in the human brain is desired. To include the anatomical knowledge in the clustering, we invoke an atlas of fiber tracts, labeled by the number of bundles of interest. In this work, we construct such an atlas and use it to cluster all fiber tracts in the white matter. To build the atlas, we start with a set of labeled ROIs specified by an expert and extract the fiber tracts initiating from each ROI. Affine registration is used to project the extracted fiber tracts of each subject to the atlas, whereas their B-spline representation is used to efficiently compare them to the fiber tracts in the atlas and assign cluster labels. Expert visual inspection of the result confirms that the proposed method is very promising and efficient in clustering of the known bundles of fiber tracts.}, authoraddress = {Computer Science and Artificial Intelligence Laboratory, Massachussets Institute of Technology, Cambridge, MA 02139, USA. mmaddah@bwh.harvard.edu}, keywords = {Algorithms ; Anatomy, Artistic ; *Artificial Intelligence ; Brain/*cytology ; Computer Simulation ; Diffusion Magnetic Resonance Imaging/*methods ; Humans ; Image Enhancement/*methods ; Image Interpretation, Computer-Assisted/*methods ; Imaging, Three-Dimensional/methods ; Medical Illustration ; Models, Anatomic ; Nerve Fibers, Myelinated/*ultrastructure ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity}, language = {eng}, medline-crdt = {2006/05/12 09:00}, medline-da = {20060511}, medline-dcom = {20060609}, medline-edat = {2006/05/12 09:00}, medline-fau = {Maddah, Mahnaz ; Mewes, Andrea U J ; Haker, Steven ; Grimson, W Eric L ; Warfield, Simon K}, medline-gr = {1U54 EB005149/EB/NIBIB NIH HHS/United States ; P01 CA67165/CA/NCI NIH HHS/United States ; P41 RR13218/RR/NCRR NIH HHS/United States ; R01 CA109246/CA/NCI NIH HHS/United States ; R01 LM007861/LM/NLM NIH HHS/United States ; R21 MH67054/MH/NIMH NIH HHS/United States}, medline-jid = {101249582}, medline-jt = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}, medline-lr = {20071114}, medline-mhda = {2006/06/10 09:00}, medline-own = {NLM}, medline-pl = {Germany}, medline-pmid = {16685845}, medline-pst = {ppublish}, medline-pt = {Evaluation Studies ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.}, medline-sb = {IM}, medline-so = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. 2005;8(Pt 1):188-95.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=16685845}, year = 2005 } @Article{Lenglet2010a, Author = {Lenglet, Christophe and Series, I M A Preprint and Hall, Lind and E, Church Street S and Aganj, Iman and Sapiro, Guillermo}, Title = {{ODF MAXIMA EXTRACTION IN INSTITUTE FOR MATHEMATICS AND ITS APPLICATIONS ODF Maxima Extraction in Spherical Harmonic Representation via Analytical Search Space Reduction}}, Journal = {Methods}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Lenglet et al. - 2010 - ODF MAXIMA EXTRACTION IN INSTITUTE FOR MATHEMATICS AND ITS APPLICATIONS ODF Maxima Extraction in Spherical Harmonic Representation via Analytical Search Space Reduction.pdf:pdf}, year = 2010 } @Article{Com, Author = {Com, Bookboon}, Title = {{RANDOM VARIABLES I}}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Com - Unknown - RANDOM VARIABLES I.pdf:pdf} } @Article{Mai, Author = {Mai, Thanh and Ngoc, Pham and Picard, Dominique}, Title = {{Localized deconvolution on the sphere}}, Pages = {1--33}, arxivid = {arXiv:0908.1952v1}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Mai, Ngoc, Picard - Unknown - Localized deconvolution on the sphere.pdf:pdf}, keywords = {62g05 62g08 62g20 62c10,and phrases,minimax estima-,msc 2000 subject classification,second- generation wavelets,statistical inverse problems,tion} } @Article{Alexander2005NeuroImage, Author = {Alexander, Daniel C. and Barker, Gareth J.}, Title = {Optimal imaging parameters for fiber-orientation estimation in diffusion MRI}, Journal = {NeuroImage}, Volume = {27}, Pages = {357 – 367}, abstract = {This study uses Monte Carlo simulations to investigate the optimal value of the diffusion weighting factor b for estimating white-matter fiber orientations using diffusion MRI with a standard spherical sampling scheme. We devise an algorithm for determining the optimal echo time, pulse width, and pulse separation in the pulsed-gradient spinecho sequence for a specific value of b. The Monte Carlo simulations provide an estimate of the optimal value of b for recovering one and two fiber orientations. We show that the optimum is largely independent of the noise level in the measurements and the number of gradient directions and that the optimum depends only weakly on the diffusion anisotropy, the maximum gradient strength, and the spin – spin relaxation time. The optimum depends strongly on the mean diffusivity. In brain tissue, the optima we estimate are in the ranges [0.7, 1.0] \times 10^9 s m^{-2} and [2.2, 2.8] \times 10^9 s m^{-2} for the one- and two-fiber cases, respectively. The best b for estimating the fractional anisotropy is slightly higher than for estimating fiber directions in the one-fiber case and slightly lower in the two-fiber case. To estimate Tr(D) in the onefiber case, the optimal setting is higher still. Simulations suggest that a ratio of high to low b measurements of 5 to 1 is a good compromise for measuring fiber directions and size and shape indices.}, owner = {ian}, timestamp = {2009.03.04}, year = 2005 } @Article{Yeh2010, Author = {Yeh, F and Wedeen, V and Tseng, W}, Title = {{Generalized Q-Sampling Imaging.}}, Journal = {IEEE transactions on medical imaging}, Number = {c}, abstract = {Based on the Fourier transform relation between diffusion MR signals and the underlying diffusion displacement, a new relation is derived to estimate the spin distribution function (SDF) directly from diffusion MR signals. This relation leads to an imaging method called generalized q-sampling imaging (GQI), which can obtain the SDF from the shell sampling scheme used in q-ball imaging (QBI) or the grid sampling scheme used in diffusion spectrum imaging (DSI). The accuracy of GQI was evaluated by a simulation study and an in vivo experiment in comparison with QBI and DSI. The simulation results showed that the accuracy of GQI was comparable to that of QBI and DSI. The simulation study of GQI also showed that an anisotropy index, named quantitative anisotropy, was correlated with the volume fraction of the resolved fiber component. The in vivo images of GQI demonstrated that SDF patterns were similar to the ODFs reconstructed by QBI or DSI. The tractography generated from GQI was also similar to those generated from QBI and DSI. In conclusion, the proposed GQI method can be applied to grid or shell sampling schemes and can provide directional and quantitative information about the crossing fibers.}, doi = {10.1109/TMI.2010.2045126}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Yeh, Wedeen, Tseng - 2010 - Generalized Q-Sampling Imaging..pdf:pdf}, issn = {1558-0062}, month = mar, pmid = {20304721}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20304721}, year = 2010 } @Article{Cook2007, Author = {Cook, P. A. and Symms, M. and Boulby, P. A. and Alexander, D. C.}, Title = {Optimal acquisition orders of diffusion-weighted {MRI} measurements.}, Journal = {J Magn Reson Imaging}, Volume = {25}, Number = {5}, Pages = {1051-8}, abstract = {PURPOSE: To propose a new method to optimize the ordering of gradient directions in diffusion-weighted MRI so that partial scans have the best spherical coverage. MATERIALS AND METHODS: Diffusion-weighted MRI often uses a spherical sampling scheme, which acquires images sequentially with diffusion-weighting gradients in unique directions distributed isotropically on the hemisphere. If not all of the measurements can be completed, the quality of diffusion tensors fitted to the partial scan is sensitive to the order of the gradient directions in the scanner protocol. If the directions are in a random order, then a partial scan may cover some parts of the hemisphere densely but other parts sparsely and thus provide poor spherical coverage. We compare the results of ordering with previously published methods for optimizing the acquisition in simulation. RESULTS: Results show that all methods produce similar results and all improve the accuracy of the estimated diffusion tensors significantly over unordered acquisitions. CONCLUSION: The new ordering method improves the spherical coverage of partial scans and has the advantage of maintaining the optimal coverage of the complete scan.}, authoraddress = {Centre for Medical Image Computing, Department of Computer Science University College London, London, UK. p.cook@cs.ucl.ac.uk}, keywords = {Algorithms ; Anisotropy ; Brain Mapping/*methods ; Diffusion Magnetic Resonance Imaging/*methods ; Humans ; Image Enhancement/*methods ; Image Processing, Computer-Assisted}, language = {eng}, medline-aid = {10.1002/jmri.20905 [doi]}, medline-ci = {(c) 2007 Wiley-Liss, Inc.}, medline-crdt = {2007/04/26 09:00}, medline-da = {20070430}, medline-dcom = {20070628}, medline-edat = {2007/04/26 09:00}, medline-fau = {Cook, Philip A ; Symms, Mark ; Boulby, Philip A ; Alexander, Daniel C}, medline-is = {1053-1807 (Print)}, medline-jid = {9105850}, medline-jt = {Journal of magnetic resonance imaging : JMRI}, medline-mhda = {2007/06/29 09:00}, medline-own = {NLM}, medline-pl = {United States}, medline-pmid = {17457801}, medline-pst = {ppublish}, medline-pt = {Journal Article ; Research Support, Non-U.S. Gov't}, medline-sb = {IM}, medline-so = {J Magn Reson Imaging. 2007 May;25(5):1051-8.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=17457801}, year = 2007 } @Article{O'Donnell2009, Author = {O'Donnell, Lauren J and Westin, Carl-Fredrik and Golby, Alexandra J}, Title = {{Tract-based morphometry for white matter group analysis.}}, Journal = {NeuroImage}, Volume = {45}, Number = {3}, Pages = {832--44}, abstract = {We introduce an automatic method that we call tract-based morphometry, or TBM, for measurement and analysis of diffusion MRI data along white matter fiber tracts. Using subject-specific tractography bundle segmentations, we generate an arc length parameterization of the bundle with point correspondences across all fibers and all subjects, allowing tract-based measurement and analysis. In this paper we present a quantitative comparison of fiber coordinate systems from the literature and we introduce an improved optimal match method that reduces spatial distortion and improves intra- and inter-subject variability of FA measurements. We propose a method for generating arc length correspondences across hemispheres, enabling a TBM study of interhemispheric diffusion asymmetries in the arcuate fasciculus (AF) and cingulum bundle (CB). The results of this study demonstrate that TBM can detect differences that may not be found by measuring means of scalar invariants in entire tracts, such as the mean diffusivity (MD) differences found in AF. We report TBM results of higher fractional anisotropy (FA) in the left hemisphere in AF (caused primarily by lower lambda(3), the smallest eigenvalue of the diffusion tensor, in the left AF), and higher left hemisphere FA in CB (related to higher lambda(1), the largest eigenvalue of the diffusion tensor, in the left CB). By mapping the significance levels onto the tractography trajectories for each structure, we demonstrate the anatomical locations of the interhemispheric differences. The TBM approach brings analysis of DTI data into the clinically and neuroanatomically relevant framework of the tract anatomy.}, doi = {10.1016/j.neuroimage.2008.12.023}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/O'Donnell, Westin, Golby - 2009 - Tract-based morphometry for white matter group analysis..pdf:pdf}, issn = {1095-9572}, keywords = {Brain,Brain Mapping,Brain Mapping: methods,Brain: anatomy \& histology,Diffusion Magnetic Resonance Imaging,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: methods}, pmid = {19154790}, publisher = {Elsevier Inc.}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19154790}, year = 2009 } @Article{Descoteaux2007MagResMed, Author = {Descoteaux, Maxime and Angelino, Elaine and Fitzgibbons, Shaun and Deriche, Rachid}, Title = {Regularized, fast, and robust analytical Q-ball imaging}, Journal = {Magnetic Resonance in Medicine}, Volume = {58}, Number = {3}, Pages = {497-510}, abstract = {We propose a regularized, fast, and robust analytical solution for the Q-ball imaging (QBI) reconstruction of the orientation distribution function (ODF) together with its detailed validation and a discussion on its benefits over the state-of-the-art. Our analytical solution is achieved by modeling the raw high angular resolution diffusion imaging signal with a spherical harmonic basis that incorporates a regularization term based on the Laplace– Beltrami operator defined on the unit sphere. This leads to an elegant mathematical simplification of the Funk–Radon transform which approximates the ODF. We prove a new corollary of the Funk–Hecke theorem to obtain this simplification. Then, we show that the Laplace–Beltrami regularization is theoretically and practically better than Tikhonov regularization. At the cost of slightly reducing angular resolution, the Laplace–Beltrami regularization reduces ODF estimation errors and improves fiber detection while reducing angular error in the ODF maxima detected. Finally, a careful quantitative validation is performed against ground truth from synthetic data and against real data from a biological phantom and a human brain dataset. We show that our technique is also able to recover known fiber crossings in the human brain and provides the practical advantage of being up to 15 times faster than original numerical QBI method.}, doi = {10.1002/mrm.21277}, file = {attachment\:Descoteaux2007MagResMed.pdf:attachment\:Descoteaux2007MagResMed.pdf:PDF}, publisher = {Wiley-Liss, Inc.}, url = {http://dx.doi.org/10.1002/mrm.21277}, year = 2007 } @TechReport{Zhuang2008Kentucky, Author = {Zhuang, Qi and Gold, Brian T. and Huang, Ruiwang and Liang, Xuwei and Cao, Ning and Zhang, Jun}, Title = {Generalized Diffusion Simulation-Based Tractography}, Institution = {Technical Report CMIDA-HiPSCCS 009-08, Department of Computer Science, University of Kentucky, KY}, abstract = {Diffusion weighted imaging ({DWI}) techniques have been used to study human brain white matter fiber structures in vivo. Commonly used standard diffusion tensor magnetic resonance imaging ({DTI}) tractography derived from the second order diffusion tensor model has limitations in its ability to resolve complex fiber tracts. We propose a new fiber tracking method based on the generalized diffusion tensor ({GDT}) model. This new method better models the anisotropic diffusion process in human brain by using the generalized diffusion simulation-based fiber tractography ({GDST}). Due to the additional information provided by {GDT}, the {GDST} method simulates the underlying physical diffusion process of the human brain more accurately than does the standard {DTI} method. The effectiveness of the new fiber tracking algorithm was demonstrated via analyses on real and synthetic {DWI} datasets. In addition, the general analytic expression of high order b matrix is derived in the case of twice refocused spin-echo ({TRSE}) pulse sequence which is used in the {DWI} data acquisition. Based on our results, we discuss the benefits of {GDT} and the second order diffusion tensor on fiber tracking.}, owner = {ian}, timestamp = {2008.10.01}, year = 2008 } @Article{Bar-Shir2008JMR, Author = {Bar-Shir, Amnon and Avram, Liat and Özarslan, Evren and Basser, Peter J. and Cohen, Yoram}, Title = {The effect of the diffusion time and pulse gradient duration ratio on the diffraction pattern and the structural information estimated from q-space diffusion MR: Experiments and simulations}, Journal = {Journal of Magnetic Resonance}, Volume = {194}, Pages = {230–236}, owner = {ian}, timestamp = {2009.03.05}, year = 2008 } @Article{king1994q, Author = {King, M.D. and Houseman, J. and Roussel, S.A. and Van Bruggen, N. and Williams, S.R. and Gadian, D.G.}, Title = {{q-Space imaging of the brain}}, Journal = {Magnetic Resonance in Medicine}, Volume = {32}, Number = {6}, Pages = {707--713}, issn = {1522-2594}, publisher = {John Wiley \& Sons}, year = 1994 } @Book{MAB04, Author = {{Matt A. Bernstein} and {Kevin F. King} and {Xiaohong Joe Zhou}}, Title = {Handbook of {MRI} {P}ulse {S}equences}, Publisher = {Elsevier Academic Press}, year = 2004 } @Article{Reese2003, Author = {Reese, T G and Heid, O and Weisskoff, R M and Wedeen, V J}, Title = {{Reduction of eddy-current-induced distortion in diffusion MRI using a twice-refocused spin echo.}}, Journal = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine}, Volume = {49}, Number = {1}, Pages = {177--82}, abstract = {Image distortion due to field gradient eddy currents can create image artifacts in diffusion-weighted MR images. These images, acquired by measuring the attenuation of NMR signal due to directionally dependent diffusion, have recently been shown to be useful in the diagnosis and assessment of acute stroke and in mapping of tissue structure. This work presents an improvement on the spin-echo (SE) diffusion sequence that displays less distortion and consequently improves image quality. Adding a second refocusing pulse provides better image quality with less distortion at no cost in scanning efficiency or effectiveness, and allows more flexible diffusion gradient timing. By adjusting the timing of the diffusion gradients, eddy currents with a single exponential decay constant can be nulled, and eddy currents with similar decay constants can be greatly reduced. This new sequence is demonstrated in phantom measurements and in diffusion anisotropy images of normal human brain.}, doi = {10.1002/mrm.10308}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Reese et al. - 2003 - Reduction of eddy-current-induced distortion in diffusion MRI using a twice-refocused spin echo..pdf:pdf}, issn = {0740-3194}, keywords = {Artifacts,Brain,Brain: anatomy \& histology,Brain: pathology,Echo-Planar Imaging,Echo-Planar Imaging: methods,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Phantoms, Imaging,Stroke,Stroke: diagnosis}, pmid = {12509835}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12509835}, year = 2003 } @Article{Yu, Author = {Yu, Hwanjo and Yang, Jiong}, Title = {{Classifying Large Data Sets Using SVMs with Hierarchical Clusters}}, Journal = {Science}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Yu, Yang - Unknown - Classifying Large Data Sets Using SVMs with Hierarchical Clusters.pdf:pdf}, keywords = {hierarchical cluster,support vector machines} } @Article{Zanche2008, Author = {Zanche, N De and Pruessmann, K P and Boesiger, P}, Title = {{Preliminary Experience with Visualization of Intracortical Fibers by Focused High-Resolution}}, Journal = {Ajnr. American Journal Of Neuroradiology}, doi = {10.3174/ajnr.A0742}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Zanche, Pruessmann, Boesiger - 2008 - Preliminary Experience with Visualization of Intracortical Fibers by Focused High-Resolution.pdf:pdf}, year = 2008 } @Article{DavisTMI02, Author = {Davies, R. H. and Twining, C. J. and Cootes, T. F. and Waterton, J. C. and Taylor, C. J.}, Title = {A minimum description length approach to statistical shape modeling.}, Journal = {IEEE Trans Med Imaging}, Volume = {21}, Number = {5}, Pages = {525-37}, abstract = {We describe a method for automatically building statistical shape models from a training set of example boundaries/surfaces. These models show considerable promise as a basis for segmenting and interpreting images. One of the drawbacks of the approach is, however, the need to establish a set of dense correspondences between all members of a set of training shapes. Often this is achieved by locating a set of "landmarks" manually on each training image, which is time consuming and subjective in two dimensions and almost impossible in three dimensions. We describe how shape models can be built automatically by posing the correspondence problem as one of finding the parameterization for each shape in the training set. We select the set of parameterizations that build the "best" model. We define "best" as that which minimizes the description length of the training set, arguing that this leads to models with good compactness, specificity and generalization ability. We show how a set of shape parameterizations can be represented and manipulated in order to build a minimum description length model. Results are given for several different training sets of two-dimensional boundaries, showing that the proposed method constructs better models than other approaches including manual landmarking-the current gold standard. We also show that the method can be extended straightforwardly to three dimensions.}, authoraddress = {Division of Imaging Science and Biomedical Engineering, University of Manchester, UK. rhodri.h.davies@stud.man.ac.uk}, keywords = {*Algorithms ; Animals ; *Artificial Intelligence ; Brain/anatomy \& histology ; Brain Ischemia/diagnosis ; Cartilage, Articular/anatomy \& histology ; Hand/anatomy \& histology ; Heart Ventricles ; Hip/radiography/ultrasonography ; Hip Prosthesis ; Humans ; Image Enhancement/*methods ; Image Interpretation, Computer-Assisted/*methods ; Information Theory ; Kidney/anatomy \& histology ; Knee ; Magnetic Resonance Imaging ; *Models, Statistical ; Multivariate Analysis ; Normal Distribution ; Pattern Recognition, Automated ; Quality Control ; Rats ; Rats, Inbred F344 ; Rats, Sprague-Dawley ; Sensitivity and Specificity ; Stochastic Processes}, language = {eng}, medline-aid = {10.1109/TMI.2002.1009388 [doi]}, medline-crdt = {2002/06/20 10:00}, medline-da = {20020619}, medline-dcom = {20021227}, medline-edat = {2002/06/20 10:00}, medline-fau = {Davies, Rhodri H ; Twining, Carole J ; Cootes, Tim F ; Waterton, John C ; Taylor, Chris J}, medline-is = {0278-0062 (Print)}, medline-jid = {8310780}, medline-jt = {IEEE transactions on medical imaging}, medline-lr = {20061115}, medline-mhda = {2002/12/28 04:00}, medline-own = {NLM}, medline-pl = {United States}, medline-pmid = {12071623}, medline-pst = {ppublish}, medline-pt = {Comparative Study ; Journal Article ; Research Support, Non-U.S. Gov't}, medline-sb = {IM}, medline-so = {IEEE Trans Med Imaging. 2002 May;21(5):525-37.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=12071623}, year = 2002 } @Article{Papadakis2000, Author = {Papadakis, N G and Murrills, C D and Hall, L D and Huang, C L and {Adrian Carpenter}, T}, Title = {{Minimal gradient encoding for robust estimation of diffusion anisotropy.}}, Journal = {Magnetic resonance imaging}, Volume = {18}, Number = {6}, Pages = {671--9}, abstract = {This study has investigated the relationship between the noise sensitivity of measurement by magnetic resonance imaging (MRI) of the diffusion tensor (D) of water and the number N of diffusion-weighting (DW) gradient directions, using computer simulations of strongly anisotropic fibers with variable orientation. The DW directions uniformly sampled the diffusion ellipsoid surface. It is shown that the variation of the signal-to-noise ratio (SNR) of three ideally rotationally invariant scalars of D due to variable fiber orientation provides an objective quantitative measure for the diffusion ellipsoid sampling efficiency, which is independent of the SNR value of the baseline signal obtained without DW; the SNR variation decreased asymptotically with increasing N. The minimum number N(0) of DW directions, which minimized the SNR variation of the three scalars of D was determined, thereby achieving the most efficient ellipsoid sampling. The resulting time efficient diffusion tensor imaging (DTI) protocols provide robust estimation of diffusion anisotropy in the presence of noise and can improve the repeatability/reliability of DTI experiments when there is high variability in the orientation of similar anisotropic structures, as for example, in studies which require repeated measurement of one individual, intersubject comparisons or multicenter studies.}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Papadakis et al. - 2000 - Minimal gradient encoding for robust estimation of diffusion anisotropy..pdf:pdf}, issn = {0730-725X}, keywords = {Anisotropy,Computer Simulation,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Models, Theoretical,Statistics as Topic}, month = jul, pmid = {10930776}, url = {http://www.ncbi.nlm.nih.gov/pubmed/10930776}, year = 2000 } @Article{roberts2005fdi, Author = {Roberts, T. P. L. and Liu, F. and Kassner, A. and Mori, S. and Guha, A.}, Title = {{Fiber Density Index Correlates with Reduced Fractional Anisotropy in White Matter of Patients with Glioblastoma}}, Journal = {American Journal of Neuroradiology}, Volume = {26}, Number = {9}, Pages = {2183--2186}, file = {attachment\:roberts_FA_glioblastoma_2005.pdf:attachment\:roberts_FA_glioblastoma_2005.pdf:PDF}, publisher = {Am Soc Neuroradiology}, year = 2005 } @Article{Baas2008, Author = {Baas, Matthias}, Title = {{Python Computer Graphics Kit}}, Journal = {Interface}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Baas - 2008 - Python Computer Graphics Kit.pdf:pdf}, year = 2008 } @Article{Odonnell_MICCAI05, Author = {O'Donnell, L. and Westin, C. F.}, Title = {White matter tract clustering and correspondence in populations.}, Journal = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv}, Volume = {8}, Number = {Pt 1}, Pages = {140-7}, abstract = {We present a novel method for finding white matter fiber correspondences and clusters across a population of brains. Our input is a collection of paths from tractography in every brain. Using spectral methods we embed each path as a vector in a high dimensional space. We create the embedding space so that it is common across all brains, consequently similar paths in all brains will map to points near each other in the space. By performing clustering in this space we are able to find matching fiber tract clusters in all brains. In addition, we automatically obtain correspondence of tractographic paths across brains: by selecting one or several paths of interest in one brain, the most similar paths in all brains are obtained as the nearest points in the high-dimensional space.}, authoraddress = {MIT Computer Science and Artificial Intelligence Lab, Cambridge MA, USA. lauren@csail.mit.edu}, keywords = {Algorithms ; *Artificial Intelligence ; Brain/*anatomy \& histology ; Cluster Analysis ; Diffusion Magnetic Resonance Imaging/*methods ; Humans ; Image Enhancement/methods ; Image Interpretation, Computer-Assisted/*methods ; Imaging, Three-Dimensional/*methods ; Nerve Fibers, Myelinated/*ultrastructure ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity}, language = {eng}, medline-crdt = {2006/05/12 09:00}, medline-da = {20060511}, medline-dcom = {20060609}, medline-edat = {2006/05/12 09:00}, medline-fau = {O'Donnell, Lauren ; Westin, Carl-Fredrik}, medline-gr = {1-R01-NS051826-01/NS/NINDS NIH HHS/United States ; P41-RR13218/RR/NCRR NIH HHS/United States ; U24 RR021382/RR/NCRR NIH HHS/United States ; U54 EB005149/EB/NIBIB NIH HHS/United States}, medline-jid = {101249582}, medline-jt = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}, medline-lr = {20071114}, medline-mhda = {2006/06/10 09:00}, medline-own = {NLM}, medline-pl = {Germany}, medline-pmid = {16685839}, medline-pst = {ppublish}, medline-pt = {Comparative Study ; Evaluation Studies ; Journal Article ; Research Support, N.I.H., Extramural}, medline-sb = {IM}, medline-so = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. 2005;8(Pt 1):140-7.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=16685839}, year = 2005 } @Article{Sorland2002MagResChem, Author = {Sørland, Geir Humborstad and Aksnes, Dagfinn}, Title = {Artefacts and pitfalls in diffusion measurements by NMR}, Journal = {Magnetic Resonance in Chemistry}, Volume = {40}, Number = {13}, Pages = {S139-S146}, abstract = {When applying pulsed field gradient (PFG) NMR experiments to determine the molecular mobility characterized by the diffusion coefficient, it is crucial to have control over all experimental parameters that may affect the performance of the diffusion experiment. This could be diffusion measurement in the presence of magnetic field transients, internal magnetic field gradients, either constant or spatially varying, convection, mechanical vibrations, or in the presence of physical restrictions affecting the diffusion propagator. The effect of these parameters on the diffusion experiment is discussed and visualized. It is also outlined how to minimize their influence on the measured diffusivity that is extracted from the PFG-NMR experiment. For an expanded and more general treatment we refer to the excellent reviews by Dr William S. Price (Concepts Magn. Reson. 1997; 9: 299; 1998; 10: 197) and the references therein.}, doi = {10.1002/mrc.1112}, owner = {ian}, timestamp = {2009.03.12}, url = {http://dx.doi.org/10.1002/mrc.1112}, year = 2002 } @Article{ZiyanMICCAI07, Author = {Ziyan, U. and Sabuncu, M. R. and O'Donnell, L. J. and Westin, C. F.}, Title = {Nonlinear registration of diffusion {MR} images based on fiber bundles.}, Journal = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv}, Volume = {10}, Number = {Pt 1}, Pages = {351-8}, abstract = {In this paper, we explore the use of fiber bundles extracted from diffusion MR images for a nonlinear registration algorithm. We employ a white matter atlas to automatically label major fiber bundles and to establish correspondence between subjects. We propose a polyaffine framework to calculate a smooth and invertible nonlinear warp field based on these correspondences, and derive an analytical solution for the reorientation of the tensor fields under the polyaffine transformation. We demonstrate our algorithm on a group of subjects and show that it performs comparable to a higher dimensional nonrigid registration algorithm.}, authoraddress = {MIT Computer Science and Artificial Intelligence Lab, Cambridge MA, USA. ulas@mit.edu}, keywords = {*Algorithms ; *Artificial Intelligence ; Brain/*anatomy \& histology ; Diffusion Magnetic Resonance Imaging/*methods ; Image Enhancement/*methods ; Image Interpretation, Computer-Assisted/*methods ; Imaging, Three-Dimensional/*methods ; Nerve Fibers, Myelinated/*ultrastructure ; Nonlinear Dynamics ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity}, language = {eng}, medline-crdt = {2007/12/07 09:00}, medline-da = {20071204}, medline-dcom = {20080103}, medline-edat = {2007/12/07 09:00}, medline-fau = {Ziyan, Ulas ; Sabuncu, Mert R ; O'Donnell, Lauren J ; Westin, Carl-Fredrik}, medline-gr = {P41-RR13218/RR/NCRR NIH HHS/United States ; P41-RR15241/RR/NCRR NIH HHS/United States ; R01-AG20012/AG/NIA NIH HHS/United States ; R01-MH074794/MH/NIMH NIH HHS/United States ; U54-EB005149/EB/NIBIB NIH HHS/United States}, medline-jid = {101249582}, medline-jt = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}, medline-mhda = {2008/01/04 09:00}, medline-own = {NLM}, medline-pl = {Germany}, medline-pmid = {18051078}, medline-pst = {ppublish}, medline-pt = {Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't}, medline-sb = {IM}, medline-so = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. 2007;10(Pt 1):351-8.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=18051078}, year = 2007 } @Article{Aganj, Author = {Aganj, I and Lenglet, C and Keriven, R and Sapiro, G and Harel, N and Thompson, P}, Title = {{A Hough Transform Global Approach to Diffusion MRI Tractography}}, Journal = {Methods}, Pages = {4--4}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Aganj et al. - Unknown - A Hough Transform Global Approach to Diffusion MRI Tractography.pdf:pdf} } @Article{Pedersen2008, Author = {Pedersen, Michael Syskind and Baxter, Bill and Rish\o j, Christian and Theobald, Douglas L and Larsen, Jan and Strimmer, Korbinian and Christiansen, Lars and Hansen, Kai and Wilkinson, Leland and He, Liguo and Thibaut, Loic and Bar, Miguel}, Title = {{The Matrix Cookbook [}}, Journal = {Matrix}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Pedersen et al. - 2008 - The Matrix Cookbook.pdf:pdf}, keywords = {acknowledgements,and suggestions,bill baxter,christian rish\o j,contributions,derivative of,derivative of inverse matrix,determinant,differentiate a matrix,douglas l,esben,matrix algebra,matrix identities,matrix relations,thank the following for,theobald,we would like to}, year = 2008 } @Article{denislebihan2006aap, Author = {Denis Le Bihan, MD and Poupon, C. and Amadon, A. and Lethimonnier, F.}, Title = {{Artifacts and pitfalls in diffusion MRI}}, Journal = {Journal of Magnetic Resonance Imaging}, Volume = {24}, Pages = {478--488}, year = 2006 } @Article{bernstein2005handbook, Author = {Bernstein, M.A. and King, K.E. and Zhou, X.J. and Fong, W.}, Title = {{Handbook of MRI pulse sequences}}, Journal = {Medical Physics}, Volume = {32}, Pages = {1452}, year = 2005 } @Article{BJ02, Author = {Basser, P. J. and Jones, D. K.}, Title = {Diffusion-tensor {MRI}: theory, experimental design and data analysis - a technical review.}, Journal = {NMR Biomed}, Volume = {15}, Number = {7-8}, Pages = {456-67}, abstract = {This article treats the theoretical underpinnings of diffusion-tensor magnetic resonance imaging (DT-MRI), as well as experimental design and data analysis issues. We review the mathematical model underlying DT-MRI, discuss the quantitative parameters that are derived from the measured effective diffusion tensor, and describe artifacts that arise in typical DT-MRI acquisitions. We also discuss difficulties in identifying appropriate models to describe water diffusion in heterogeneous tissues, as well as in interpreting experimental data obtained in such issues. Finally, we describe new statistical methods that have been developed to analyse DT-MRI data, and their potential uses in clinical and multi-site studies.}, authoraddress = {Section on Tissue Biophysics and Biomimetics, NICHD, National Institutes of Health, Bethesda, MD 20892, USA.}, keywords = {Anisotropy ; Artifacts ; Brain/cytology/metabolism ; Diffusion ; Diffusion Magnetic Resonance Imaging/instrumentation/*methods ; Image Enhancement/*methods ; *Models, Biological ; Models, Chemical ; Nerve Fibers/chemistry/*metabolism/*pathology ; Neural Pathways/chemistry/cytology/metabolism ; Research Design ; Water/chemistry}, language = {eng}, medline-aid = {10.1002/nbm.783 [doi]}, medline-ci = {Copyright 2002 John Wiley & Sons, Ltd.}, medline-da = {20021218}, medline-dcom = {20030701}, medline-edat = {2002/12/19 04:00}, medline-fau = {Basser, Peter J ; Jones, Derek K}, medline-is = {0952-3480 (Print)}, medline-jid = {8915233}, medline-jt = {NMR in biomedicine}, medline-lr = {20061115}, medline-mhda = {2003/07/02 05:00}, medline-own = {NLM}, medline-pl = {England}, medline-pmid = {12489095}, medline-pst = {ppublish}, medline-pt = {Journal Article ; Research Support, Non-U.S. Gov't ; Review}, medline-pubm = {Print}, medline-rf = {107}, medline-rn = {7732-18-5 (Water)}, medline-sb = {IM}, medline-so = {NMR Biomed. 2002 Nov-Dec;15(7-8):456-67.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks\&dbfrom=pubmed\&retmode=ref\&id=12489095}, year = 2002 } @Article{Maaten2008a, Author = {Maaten, Laurens Van Der and Hinton, Geoffrey}, Title = {{Visualizing Data using t-SNE}}, Journal = {Journal of Machine Learning Research}, Volume = {9}, Pages = {2579--2605}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Maaten, Hinton - 2008 - Visualizing Data using t-SNE.pdf:pdf}, keywords = {dimensionality reduction,embedding algorithms,manifold learning,multidimensional scaling,visualization}, year = 2008 } @Article{Parker2005PhilTransRoySoc, Author = {Parker, G J and Alexander, D C}, Title = {Probabilistic anatomical connectivity derived from the microscopic persistent angular structure of cerebral tissue}, Journal = {Philos Trans R Soc Lond B Biol Sci.}, Volume = {360}, Number = {1457}, Pages = {893-902}, abstract = {Recently developed methods to extract the persistent angular structure (PAS) of axonal fibre bundles from diffusion-weighted magnetic resonance imaging (MRI) data are applied to drive probabilistic fibre tracking, designed to provide estimates of anatomical cerebral connectivity. The behaviour of the PAS function in the presence of realistic data noise is modelled for a range of single and multiple fibre configurations. This allows probability density functions (PDFs) to be generated that are parametrized according to the anisotropy of individual fibre populations. The PDFs are incorporated in a probabilistic fibre-tracking method to allow the estimation of whole-brain maps of anatomical connection probability. These methods are applied in two exemplar experiments in the corticospinal tract to show that it is possible to connect the entire primary motor cortex (M1) when tracing from the cerebral peduncles, and that the reverse experiment of tracking from M1 successfully identifies high probability connection via the pyramidal tracts. Using the extracted PAS in probabilistic fibre tracking allows higher specificity and sensitivity than previously reported fibre tracking using diffusion-weighted MRI in the corticospinal tract.}, file = {attachment\:Parker2005PhilTransRoySoc.pdf:attachment\:Parker2005PhilTransRoySoc.pdf:PDF}, year = 2005 } @Article{Kreher2008ISMRM, Author = {Kreher, B. W. and Mader, I. and Kiselev, V. G.}, Title = {Gibbs Tracking: A Novel Approach for the Reconstruction of Neuronal Pathways}, Journal = {Proc. Intl. Soc. Mag. Reson. Med.}, Volume = {16}, Pages = {425}, abstract = {Fibre tractography based on diffusion weighted MRI is a powerful method to extract the anatomical connectivity in white matter in vivo. The main idea of the currently available methods of fibre tracking is the reconstruction of long neuronal pathways in small successive steps by following the local, voxel-defined fibre direction. Starting from local information on the diffusivity, long-distance connections are determined. This method is inherently prone to instability, since a mistake at a single crossing affects radically the final result. In this paper we present a method based on a new principle. Instead of walking successively through the volume all neuronal pathways and the totality of the signal is taken into account at the same time. This novel approach is capable to reconstruct crossing and spreading fibre configuration.}, file = {attachment\:Kreher2008ISMRM.pdf:attachment\:Kreher2008ISMRM.pdf:PDF}, year = 2008 } @Article{Bea02, Author = {Beaulieu, C.}, Title = {The basis of anisotropic water diffusion in the nervous system - a technical review.}, Journal = {NMR Biomed}, Volume = {15}, Number = {7-8}, Pages = {435-55}, abstract = {Anisotropic water diffusion in neural fibres such as nerve, white matter in spinal cord, or white matter in brain forms the basis for the utilization of diffusion tensor imaging (DTI) to track fibre pathways. The fact that water diffusion is sensitive to the underlying tissue microstructure provides a unique method of assessing the orientation and integrity of these neural fibres, which may be useful in assessing a number of neurological disorders. The purpose of this review is to characterize the relationship of nuclear magnetic resonance measurements of water diffusion and its anisotropy (i.e. directional dependence) with the underlying microstructure of neural fibres. The emphasis of the review will be on model neurological systems both in vitro and in vivo. A systematic discussion of the possible sources of anisotropy and their evaluation will be presented followed by an overview of various studies of restricted diffusion and compartmentation as they relate to anisotropy. Pertinent pathological models, developmental studies and theoretical analyses provide further insight into the basis of anisotropic diffusion and its potential utility in the nervous system.}, authoraddress = {Department of Biomedical Engineering, Faculty of Medicine, University of Alberta, Edmonton, Canada. christian.beaulieu@ualberta.ca}, keywords = {*Anisotropy ; Brain/metabolism/pathology ; Brain Chemistry ; Diffusion ; Diffusion Magnetic Resonance Imaging/*methods ; Models, Biological ; Nerve Fibers/chemistry/metabolism/pathology ; Nervous System/chemistry/*metabolism/*pathology ; Nervous System Diseases/metabolism/pathology ; Spinal Cord/chemistry/cytology/metabolism ; Water/*chemistry}, language = {eng}, medline-aid = {10.1002/nbm.782 [doi]}, medline-ci = {Copyright 2002 John Wiley & Sons, Ltd.}, medline-da = {20021218}, medline-dcom = {20030701}, medline-edat = {2002/12/19 04:00}, medline-fau = {Beaulieu, Christian}, medline-is = {0952-3480 (Print)}, medline-jid = {8915233}, medline-jt = {NMR in biomedicine}, medline-lr = {20061115}, medline-mhda = {2003/07/02 05:00}, medline-own = {NLM}, medline-pl = {England}, medline-pmid = {12489094}, medline-pst = {ppublish}, medline-pt = {Journal Article ; Research Support, Non-U.S. Gov't ; Review}, medline-pubm = {Print}, medline-rf = {131}, medline-rn = {7732-18-5 (Water)}, medline-sb = {IM}, medline-so = {NMR Biomed. 2002 Nov-Dec;15(7-8):435-55.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks\&dbfrom=pubmed\&retmode=ref\&id=12489094}, year = 2002 } @Article{Sciences2009, Author = {Sciences, Cognition Brain}, Title = {{Michaelmas Term 2008}}, Journal = {Sciences-New York}, Pages = {9469--9469}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Sciences - 2009 - Michaelmas Term 2008.pdf:pdf}, year = 2009 } @Article{Zvitia2010, Author = {Zvitia, Orly and Mayer, Arnaldo and Shadmi, Ran and Miron, Shmuel and Greenspan, Hayit K}, Title = {{Co-registration of white matter tractographies by adaptive-mean-shift and Gaussian mixture modeling.}}, Journal = {IEEE transactions on medical imaging}, Volume = {29}, Number = {1}, Pages = {132--45}, abstract = {In this paper, we present a robust approach to the registration of white matter tractographies extracted from diffusion tensor-magnetic resonance imaging scans. The fibers are projected into a high dimensional feature space based on the sequence of their 3-D coordinates. Adaptive mean-shift clustering is applied to extract a compact set of representative fiber-modes (FM). Each FM is assigned to a multivariate Gaussian distribution according to its population thereby leading to a Gaussian mixture model (GMM) representation for the entire set of fibers. The registration between two fiber sets is treated as the alignment of two GMMs and is performed by maximizing their correlation ratio. A nine-parameters affine transform is recovered and eventually refined to a twelve-parameters affine transform using an innovative mean-shift based registration refinement scheme presented in this paper. The validation of the algorithm on synthetic intrasubject data demonstrates its robustness to interrupted and deviating fiber artifacts as well as outliers. Using real intrasubject data, a comparison is conducted to other intensity based and fiber-based registration algorithms, demonstrating competitive results. An option for tracking-in-time, on specific white matter fiber tracts, is also demonstrated on the real data.}, doi = {10.1109/TMI.2009.2029097}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Zvitia et al. - 2010 - Co-registration of white matter tractographies by adaptive-mean-shift and Gaussian mixture modeling..pdf:pdf}, issn = {1558-0062}, keywords = {Algorithms,Brain,Brain: anatomy \& histology,Cluster Analysis,Diffusion Tensor Imaging,Diffusion Tensor Imaging: methods,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: methods,Models, Neurological,Normal Distribution,Reproducibility of Results}, month = jan, pmid = {19709970}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19709970}, year = 2010 } @Article{Martinez2007, Author = {Martinez, Aleix M}, Title = {{Spherical-Homoscedastic Distributions : The Equivalency of Spherical and Normal Distributions in Classification}}, Journal = {Journal of Machine Learning Research}, Volume = {8}, Pages = {1583--1623}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Martinez - 2007 - Spherical-Homoscedastic Distributions The Equivalency of Spherical and Normal Distributions in Classification.pdf:pdf}, keywords = {computer vision,directional data,linear and non-linear classifiers,norm normalization,normal distributions,spherical distributions}, year = 2007 } @Article{Tu2007TransMedIm, Author = {Tu, Zhuowen and Narr, Katherine L. and Doll´ar, Piotr and Dinov, Ivo and Thompson, Paul M. and Toga, Arthur W.}, Title = {Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models}, Journal = {Transactions on Medical Imaging}, Volume = {in press}, abstract = {In this paper, a hybrid discriminative/generative model for brain anatomical structure segmentation is proposed. The learning aspect of the approach is emphasized. In the discriminative appearance models, various cues such as intensity and curvatures are combined to locally capture the complex appearances of different anatomical structures. A probabilistic boosting tree (PBT) framework is adopted to learn multi-class discriminative models that combine hundreds of features across different scales. On the generative side, Principal Component Analysis (PCA) shape models are used to capture the global shape information about each anatomical structure. The parameters to combine the discriminative appearance and generative shape models are also automatically learned. Thus low-level and highlevel information is learned and integrated in a hybrid model. Segmentations are obtained by minimizing an energy function associated with the proposed hybrid model. Finally, a gridface structure is designed to explicitly represent the 3D region topology. This representation handles an arbitrary number of regions and facilitates fast surface evolution. Our system was trained and tested on a set of 3D MRI volumes and the results obtained are encouraging.}, file = {attachment\:Tu2007TransMedIm.pdf:attachment\:Tu2007TransMedIm.pdf:PDF}, year = 2007 } @Article{Duru2010, Author = {Duru, Dilek G\"{o}ksel and Ozkan, Mehmed}, Title = {{Determination of neural fiber connections based on data structure algorithm.}}, Journal = {Computational intelligence and neuroscience}, Volume = {2010}, Pages = {251928}, abstract = {The brain activity during perception or cognition is mostly examined by functional magnetic resonance imaging (fMRI). However, the cause of the detected activity relies on the anatomy. Diffusion tensor magnetic resonance imaging (DTMRI) as a noninvasive modality providing in vivo anatomical information allows determining neural fiber connections which leads to brain mapping. Still a complete map of fiber paths representing the human brain is missing in literature. One of the main drawbacks of reliable fiber mapping is the correct detection of the orientation of multiple fibers within a single imaging voxel. In this study a method based on linear data structures is proposed to define the fiber paths regarding their diffusivity. Another advantage of the proposed method is that the analysis is applied on entire brain diffusion tensor data. The implementation results are promising, so that the method will be developed as a rapid fiber tractography algorithm for the clinical use as future study.}, doi = {10.1155/2010/251928}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Duru, Ozkan - 2010 - Determination of neural fiber connections based on data structure algorithm..pdf:pdf}, issn = {1687-5273}, keywords = {Algorithms,Brain,Brain: anatomy \& histology,Diffusion Tensor Imaging,Diffusion Tensor Imaging: methods,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: methods,Linear Models,Neural Pathways,Neural Pathways: anatomy \& histology,Uncertainty}, month = jan, pmid = {20069047}, url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2801001\&tool=pmcentrez\&rendertype=abstract}, year = 2010 } @Article{Tang1997, Author = {Tang, Y and Nyengaard, J R}, Title = {{A stereological method for estimating the total length and size of myelin fibers in human brain white matter.}}, Journal = {Journal of neuroscience methods}, Volume = {73}, Number = {2}, Pages = {193--200}, abstract = {A practically unbiased stereological method to obtain estimates of the volume and total length of nerve fibers in brain white matter is described. The sampling scheme is designed so that the majority of brain white matter is left intact, thus providing the possibility for resampling and further analysis. Uniform sampling of one complete hemispherical white matter is performed. The volume fraction of nerve fibers in white matter is estimated by point counting. The total length of nerve fibers was estimated from the product of the volume of white matter, obtained with the Cavalieri principle, and the fiber length density, obtained from the isotropic, uniform random sections which were ensured by the isector. The size of nerve fibers was derived by measuring the profile diameter perpendicular to its longest axis. The influence of the postmortem fixation delay on nerve fiber parameters was investigated in one dog and one pig. The criteria for identification of nerve fiber profiles at light microscopy were evaluated using electron microscopy.}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Tang, Nyengaard - 1997 - A stereological method for estimating the total length and size of myelin fibers in human brain white matter..pdf:pdf}, issn = {0165-0270}, keywords = {Adolescent,Adult,Animals,Brain,Brain: ultrastructure,Dogs,Female,Humans,Middle Aged,Models, Neurological,Nerve Fibers, Myelinated,Nerve Fibers, Myelinated: ultrastructure,Neurosciences,Neurosciences: methods,Swine}, month = may, pmid = {9196291}, url = {http://www.ncbi.nlm.nih.gov/pubmed/9196291}, year = 1997 } @Article{margolis5nal, Author = {Margolis, G. and Pickett, JP}, Title = {{New applications of the Luxol fast blue myelin stain.}}, Journal = {Laboratory investigation; a journal of technical methods and pathology}, Volume = {5}, Number = {6}, Pages = {459} } @Article{Ghosh2008, Author = {Ghosh, Aurobrata and Tsigaridas, Elias and Descoteaux, Maxime and Comon, Pierre and Mourrain, Bernard and Deriche, Rachid}, Title = {{A polynomial based approach to extract the maxima of an antipodally symmetric spherical function and its application to extract fiber directions from the Orientation Distribution Function in Diffusion MRI}}, Journal = {Tensor}, Pages = {237--248}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Ghosh et al. - 2008 - A polynomial based approach to extract the maxima of an antipodally symmetric spherical function and its application to extract fiber directions from the Orientation Distribution Function in Diffusion MRI.pdf:pdf}, year = 2008 } @Article{HTJ+03, Author = {Hagmann, P. and Thiran, J. P. and Jonasson, L. and Vandergheynst, P. and Clarke, S. and Maeder, P. and Meuli, R.}, Title = {D{TI} mapping of human brain connectivity: statistical fibre tracking and virtual dissection.}, Journal = {Neuroimage}, Volume = {19}, Number = {3}, Pages = {545-54}, abstract = {Several approaches have been used to trace axonal trajectories from diffusion MRI data. If such techniques were first developed in a deterministic framework reducing the diffusion information to one single main direction, more recent approaches emerged that were statistical in nature and that took into account the whole diffusion information. Based on diffusion tensor MRI data coming from normal brains, this paper presents how brain connectivity could be modelled globally by means of a random walk algorithm. The mass of connections thus generated was then virtually dissected to uncover different tracts. Corticospinal, corticobulbar, and corticothalamic tracts, the corpus callosum, the limbic system, several cortical association bundles, the cerebellar peduncles, and the medial lemniscus were all investigated. The results were then displayed in the form of an in vivo brain connectivity atlas. The connectivity pattern and the individual fibre tracts were then compared to known anatomical data; a good matching was found.}, authoraddress = {Signal Processing Institute, Swiss Federal Institute of Technology, 1015 Lausanne, Switzerland. patric.hagmann@epfl.ch}, keywords = {Algorithms ; Axons/physiology ; Brain/*anatomy \& histology ; *Brain Mapping ; Cerebellum/anatomy \& histology/physiology ; Cerebral Cortex/anatomy \& histology/physiology ; Computer Graphics ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging ; Models, Neurological ; Nerve Fibers/*physiology ; Neural Pathways/*anatomy \& histology ; Pyramidal Tracts/anatomy \& histology/physiology ; Thalamus/anatomy \& histology/physiology}, language = {eng}, medline-aid = {S1053811903001423 [pii]}, medline-crdt = {2003/07/26 05:00}, medline-da = {20030725}, medline-dcom = {20030909}, medline-edat = {2003/07/26 05:00}, medline-fau = {Hagmann, P ; Thiran, J-P ; Jonasson, L ; Vandergheynst, P ; Clarke, S ; Maeder, P ; Meuli, R}, medline-is = {1053-8119 (Print)}, medline-jid = {9215515}, medline-jt = {NeuroImage}, medline-lr = {20041117}, medline-mhda = {2003/09/10 05:00}, medline-own = {NLM}, medline-pl = {United States}, medline-pmid = {12880786}, medline-pst = {ppublish}, medline-pt = {Clinical Trial ; Journal Article}, medline-sb = {IM}, medline-so = {Neuroimage. 2003 Jul;19(3):545-54.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=12880786}, year = 2003 } @Article{Wassermann2004, Author = {Wassermann, Demian and Deriche, Rachid}, Title = {{Simultaneous Manifold Learning and Clustering : Grouping White Matter Fiber Tracts Using a Volumetric White Matter Atlas}}, Journal = {International Journal of Computer Vision}, Pages = {1--8}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Wassermann, Deriche - 2004 - Simultaneous Manifold Learning and Clustering Grouping White Matter Fiber Tracts Using a Volumetric White Matter Atlas.pdf:pdf}, year = 2004 } @Article{BKK+04, Author = {Bodammer, N. and Kaufmann, J. and Kanowski, M. and Tempelmann, C.}, Title = {Eddy current correction in diffusion-weighted imaging using pairs of images acquired with opposite diffusion gradient polarity.}, Journal = {Magn Reson Med}, Volume = {51}, Number = {1}, Pages = {188-93}, abstract = {In echo-planar-based diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI), the evaluation of diffusion parameters such as apparent diffusion coefficients and anisotropy indices is affected by image distortions that arise from residual eddy currents produced by the diffusion-sensitizing gradients. Correction methods that coregister diffusion-weighted and non-diffusion-weighted images suffer from the different contrast properties inherent in these image types. Here, a postprocessing correction scheme is introduced that makes use of the inverse characteristics of distortions generated by gradients with reversed polarity. In this approach, only diffusion-weighted images with identical contrast are included for correction. That is, non-diffusion-weighted images are not needed as a reference for registration. Furthermore, the acquisition of an additional dataset with moderate diffusion-weighting as suggested by Haselgrove and Moore (Magn Reson Med 1996;36:960-964) is not required. With phantom data it is shown that the theoretically expected symmetry of distortions is preserved in the images to a very high degree, demonstrating the practicality of the new method. Results from human brain images are also presented.}, authoraddress = {Department of Neurology II, Otto von Guericke University Magdeburg, Germany. bodammer@neuro2.med.uni-magdeburg.de}, keywords = {Algorithms ; Brain/*anatomy \& histology ; Diffusion Magnetic Resonance Imaging/*methods ; Humans ; *Image Processing, Computer-Assisted ; Phantoms, Imaging}, language = {eng}, medline-aid = {10.1002/mrm.10690 [doi]}, medline-ci = {Copyright 2003 Wiley-Liss, Inc.}, medline-crdt = {2004/01/06 05:00}, medline-da = {20040105}, medline-dcom = {20040507}, medline-edat = {2004/01/06 05:00}, medline-fau = {Bodammer, Nils ; Kaufmann, Jorn ; Kanowski, Martin ; Tempelmann, Claus}, medline-is = {0740-3194 (Print)}, medline-jid = {8505245}, medline-jt = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine}, medline-lr = {20061115}, medline-mhda = {2004/05/08 05:00}, medline-own = {NLM}, medline-pl = {United States}, medline-pmid = {14705060}, medline-pst = {ppublish}, medline-pt = {Journal Article ; Research Support, Non-U.S. Gov't}, medline-sb = {IM}, medline-so = {Magn Reson Med. 2004 Jan;51(1):188-93.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=14705060}, year = 2004 } @Article{Correia2009b, Author = {Correia, Stephen and Lee, Stephanie Y and Voorn, Thom and Tate, David F and Paul, Robert H and Salloway, Stephen P and Malloy, Paul F and Laidlaw, David H}, Title = {{NIH Public Access}}, Journal = {Water}, Volume = {42}, Number = {2}, Pages = {568--581}, doi = {10.1016/j.neuroimage.2008.05.022.Quantitative}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Correia et al. - 2009 - NIH Public Access.pdf:pdf}, year = 2009 } @Article{Wang1999, Author = {Wang, Y and Berg, P and Scherg, M}, Title = {{Common spatial subspace decomposition applied to analysis of brain responses under multiple task conditions: a simulation study.}}, Journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, Volume = {110}, Number = {4}, Pages = {604--14}, abstract = {A method, called common spatial subspace decomposition, is presented which can extract signal components specific to one condition from multiple magnetoencephalography/electroencephalography data sets of multiple task conditions. Signal matrices or covariance matrices are decomposed using spatial factors common to multiple conditions. The spatial factors and corresponding spatial filters are then dissociated into specific and common parts, according to the common spatial subspace which exists among the data sets. Finally, the specific signal components are extracted using the corresponding spatial filters and spatial factors. The relationship between this decomposition and spatio-temporal source models is described in this paper. Computer simulations suggest that this method can facilitate the analysis of brain responses under multiple task conditions and merits further application.}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Wang, Berg, Scherg - 1999 - Common spatial subspace decomposition applied to analysis of brain responses under multiple task conditions a simulation study..pdf:pdf}, issn = {1388-2457}, keywords = {Brain,Brain Mapping,Brain: physiology,Computer Simulation,Humans,Models, Neurological,Task Performance and Analysis}, month = apr, pmid = {10378728}, url = {http://www.ncbi.nlm.nih.gov/pubmed/10378728}, year = 1999 } @Article{Zhai2003, Author = {Zhai, Guihua and Lin, Weili and Wilber, Kathy P and Gerig, Guido and Gilmore, John H}, Title = {{Comparisons of regional white matter diffusion in healthy neonates and adults performed with a 3.0-T head-only MR imaging unit.}}, Journal = {Radiology}, Volume = {229}, Number = {3}, Pages = {673--81}, abstract = {PURPOSE: To evaluate the normal brains of adults and neonates for regional and age-related differences in apparent diffusion coefficient (ADC) and fractional anisotropy (FA). MATERIALS AND METHODS: Eight healthy adults and 20 healthy neonates were examined with a 3.0-T head-only magnetic resonance (MR) imaging unit by using a single-shot diffusion-tensor sequence. Trace ADC maps, FA maps, directional maps of the putative directions of white matter (WM) tracts, and fiber-tracking maps were obtained. Regions of interest-eight in WM and one in gray matter (GM)-were predefined for the ADC and FA measurements. The Student t test was used to compare FA and ADC between adults and neonates, whereas the Tukey multiple-comparison test was used to compare FA and ADC in different brain regions in the adult and neonate groups. RESULTS: A global elevation in ADC (P <.001) in both GM and WM and a reduction in FA (P <.001) in WM were observed in neonates as compared with these values in adults. In addition, significant regional variations in FA and ADC were observed in both groups. Regional variations in FA and ADC were less remarkable in adults, whereas neonates had consistently higher FA values and lower ADC values in the central WM as compared with these values in the peripheral WM. Fiber tracking revealed only major WM tracts in the neonates but fibers extending to the peripheral WM in the adults. CONCLUSION: There were regional differences in FA and ADC values in the neonates; such variations were less remarkable in the adults.}, doi = {10.1148/radiol.2293021462}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Zhai et al. - 2003 - Comparisons of regional white matter diffusion in healthy neonates and adults performed with a 3.0-T head-only MR imaging unit..pdf:pdf}, issn = {0033-8419}, keywords = {Adult,Age Factors,Brain,Brain: anatomy \& histology,Diffusion Magnetic Resonance Imaging,Diffusion Magnetic Resonance Imaging: instrumentat,Humans,Infant, Newborn,ROC Curve}, month = dec, pmid = {14657305}, url = {http://www.ncbi.nlm.nih.gov/pubmed/14657305}, year = 2003 } @Article{Fillard2009, Author = {Fillard, P. and Poupon, C. and Mangin, J.F.}, Title = {{Spin Tracking: A Novel Global Tractography Algorithm}}, Journal = {NeuroImage}, Volume = {47}, Pages = {S127--S127}, doi = {10.1016/S1053-8119(09)71230-3}, issn = {10538119}, url = {http://dx.doi.org/10.1016/S1053-8119(09)71230-3}, year = 2009 } @Article{Behrens2003NatureNeuroscience, Author = {Behrens, T E J and Johansen-Berg, H and Woolrich, M W and Wheeler-Kingshott, C A M and Boulby, P A and Barker, G J and Sillery, E L and Sheehan, K and Ciccarellu, O and Thompson, A J and Brady, J M and Matthews, P M}, Title = {Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging}, Journal = {Nature Neuroscience}, Volume = {6}, Number = {7}, Pages = {750-757}, abstract = {Evidence concerning anatomical connectivities in the human brain is sparse and based largely on limited post-mortem observations. Diffusion tensor imaging has previously been used to define large white-matter tracts in the living human brain, but this technique has had limited success in tracing pathways into gray matter. Here we identified specific connections between human thalamus and cortex using a novel probabilistic tractography algorithm with diffusion imaging data. Classification of thalamic gray matter based on cortical connectivity patterns revealed distinct subregions whose locations correspond to nuclei described previously in histological studies. The connections that we found between thalamus and cortex were similar to those reported for non-human primates and were reproducible between individuals. Our results provide the first quantitative demonstration of reliable inference of anatomical connectivity between human gray matter structures using diffusion data and the first connectivity-based segmentation of gray matter.}, file = {attachment\:Behrens2003NatureNeuroscience.pdf:attachment\:Behrens2003NatureNeuroscience.pdf:PDF}, publisher = {Nature Publishing Group}, year = 2003 } @Article{Joya, Author = {Joy, Kenneth I}, Title = {{Numerical Methods for Particle Tracing in Vector Fields}}, Journal = {Science}, Pages = {1--7}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Joy - Unknown - Numerical Methods for Particle Tracing in Vector Fields.pdf:pdf} } @Article{Blankertz2008, Author = {Blankertz, Benjamin and Tomioka, Ryota and Lemm, Steven and Kawanabe, Motoaki and M\"{u}ller, Klaus-robert}, Title = {{Optimizing Spatial Filters for Robust EEG Single-Trial Analysis}}, Journal = {Signal Processing}, Volume = {XX}, Pages = {1--12}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Blankertz et al. - 2008 - Optimizing Spatial Filters for Robust EEG Single-Trial Analysis.pdf:pdf}, year = 2008 } @Article{WirestamMRM2006, Author = {Wirestam, R. and Bibic, A. and Latt, J. and Brockstedt, S. and Stahlberg, F.}, Title = {{Denoising of complex MRI data by wavelet-domain filtering: Application to high-b-value diffusion-weighted imaging}}, Journal = {Magnetic Resonance in Medicine}, Volume = {56}, Number = {5}, publisher = {Wiley Subscription Services, Inc., A Wiley Company Hoboken}, year = 2006 } @Article{Lenglet2010, Author = {Lenglet, Christophe and Series, I M A Preprint and Hall, Lind and E, Church Street S and Aganj, Iman and Sapiro, Guillermo}, Title = {{ODF MAXIMA EXTRACTION IN INSTITUTE FOR MATHEMATICS AND ITS APPLICATIONS ODF Maxima Extraction in Spherical Harmonic Representation via Analytical Search Space Reduction}}, Journal = {Methods}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Lenglet et al. - 2010 - ODF MAXIMA EXTRACTION IN INSTITUTE FOR MATHEMATICS AND ITS APPLICATIONS ODF Maxima Extraction in Spherical Harmonic Representation via Analytical Search Space Reduction.pdf:pdf}, year = 2010 } @Article{Bai2009, Author = {Bai, Y}, Title = {{Correcting for Motion between Acquisitions in Diffusion MR Imaging}}, Journal = {Chart}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Bai - 2009 - Correcting for Motion between Acquisitions in Diffusion MR Imaging.pdf:pdf}, year = 2009 } @Book{mcrobbie2006mpp, Author = {McRobbie, D.W. and Moore, E.A. and Graves, M.J.}, Title = {{MRI from Picture to Proton}}, Publisher = {Cambridge University Press}, year = 2006 } @Article{Tang1997a, Author = {Tang, Y and Nyengaard, J R}, Title = {{A stereological method for estimating the total length and size of myelin fibers in human brain white matter.}}, Journal = {Journal of neuroscience methods}, Volume = {73}, Number = {2}, Pages = {193--200}, abstract = {A practically unbiased stereological method to obtain estimates of the volume and total length of nerve fibers in brain white matter is described. The sampling scheme is designed so that the majority of brain white matter is left intact, thus providing the possibility for resampling and further analysis. Uniform sampling of one complete hemispherical white matter is performed. The volume fraction of nerve fibers in white matter is estimated by point counting. The total length of nerve fibers was estimated from the product of the volume of white matter, obtained with the Cavalieri principle, and the fiber length density, obtained from the isotropic, uniform random sections which were ensured by the isector. The size of nerve fibers was derived by measuring the profile diameter perpendicular to its longest axis. The influence of the postmortem fixation delay on nerve fiber parameters was investigated in one dog and one pig. The criteria for identification of nerve fiber profiles at light microscopy were evaluated using electron microscopy.}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Tang, Nyengaard - 1997 - A stereological method for estimating the total length and size of myelin fibers in human brain white matter..pdf:pdf}, issn = {0165-0270}, keywords = {Adolescent,Adult,Animals,Brain,Brain: ultrastructure,Dogs,Female,Humans,Middle Aged,Models, Neurological,Nerve Fibers, Myelinated,Nerve Fibers, Myelinated: ultrastructure,Neurosciences,Neurosciences: methods,Swine}, month = may, pmid = {9196291}, url = {http://www.ncbi.nlm.nih.gov/pubmed/9196291}, year = 1997 } @Article{Bullmore2009, Author = {Bullmore, E and Sporns, O}, Title = {{Complex brain networks: graph theoretical analysis of structural and functional systems}}, Journal = {Nature Reviews Neuroscience}, Volume = {10}, Number = {3}, Pages = {186--198}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Bullmore, Sporns - 2009 - Complex brain networks graph theoretical analysis of structural and functional systems.pdf:pdf}, year = 2009 } @Article{Pajevic1999, Author = {Pajevic, Sinisa and Pierpaoli, Carlo}, Title = {{Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: Application to white matter fiber tract mapping in the human brain}}, Journal = {Magnetic Resonance in Medicine}, Volume = {42}, Number = {3}, abstract = {This paper investigates the use of color to represent the directional information contained in the diffusion tensor. Ideally, one wants to take into account both the properties of human color vision and of the given display hardware to produce a representation in which differences in the orientation of anisotropic structures are proportional to the perceived differences in color. It is argued here that such a goal cannot be achieved in general and therefore, empirical or heuristic schemes, which avoid some of the common artifacts of previously proposed approaches, are implemented. Directionally encoded color (DEC) maps of the human brain obtained using these schemes clearly show the main association, projection, and commissural white matter pathways. In the brainstem, motor and sensory pathways are easily identified and can be differentiated from the transverse pontine fibers and the cerebellar peduncles. DEC maps obtained from diffusion tensor imaging data provide a simple and effective way to visualize fiber direction, useful for investigating the structural anatomy of different organs. Magn Reson Med 42:526-540, 1999. © 1999 Wiley-Liss, Inc.}, doi = {10.1002/(SICI)1522-2594(199909)42:3<526::AID-MRM15>3.0.CO;2-J}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Pajevic, Pierpaoli - 1999 - Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data Application to white matter fiber tract mapping in the human brain.pdf:pdf}, url = {http://www3.interscience.wiley.com/journal/63500786/abstract}, year = 1999 } @Article{DauguetNeuroImage2007, Author = {Dauguet, J. and Peled, S. and Berezovskii, V. and Delzescaux, T. and Warfield, S. K. and Born, R. and Westin, C. F.}, Title = {Comparison of fiber tracts derived from in-vivo {DTI} tractography with 3{D} histological neural tract tracer reconstruction on a macaque brain.}, Journal = {Neuroimage}, Volume = {37}, Number = {2}, Pages = {530-8}, abstract = {Since the introduction of diffusion weighted imaging (DWI) as a method for examining neural connectivity, its accuracy has not been formally evaluated. In this study, we directly compared connections that were visualized using injected neural tract tracers (WGA-HRP) with those obtained using in-vivo diffusion tensor imaging (DTI) tractography. First, we injected the tracer at multiple sites in the brain of a macaque monkey; second, we reconstructed the histological sections of the labeled fiber tracts in 3D; third, we segmented and registered the fibers (somatosensory and motor tracts) with the anatomical in-vivo MRI from the same animal; and last, we conducted fiber tracing along the same pathways on the DTI data using a classical diffusion tracing technique with the injection sites as seeds. To evaluate the performance of DTI fiber tracing, we compared the fibers derived from the DTI tractography with those segmented from the histology. We also studied the influence of the parameters controlling the tractography by comparing Dice superimposition coefficients between histology and DTI segmentations. While there was generally good visual agreement between the two methods, our quantitative comparisons reveal certain limitations of DTI tractography, particularly for regions at remote locations from seeds. We have thus demonstrated the importance of appropriate settings for realistic tractography results.}, authoraddress = {Computational Radiology Laboratory, Children's Hospital, Harvard Medical School, Boston, USA. dauguet@bwh.harvard.edu}, keywords = {Animals ; Anisotropy ; Brain/*anatomy \& histology ; *Diffusion Magnetic Resonance Imaging ; Image Processing, Computer-Assisted ; *Imaging, Three-Dimensional ; Immunohistochemistry ; Macaca ; Nerve Fibers/ultrastructure ; Neural Pathways/*cytology}, language = {eng}, medline-aid = {S1053-8119(07)00328-X [pii] ; 10.1016/j.neuroimage.2007.04.067 [doi]}, medline-crdt = {2007/07/03 09:00}, medline-da = {20070730}, medline-dcom = {20071012}, medline-dep = {20070524}, medline-edat = {2007/07/03 09:00}, medline-fau = {Dauguet, Julien ; Peled, Sharon ; Berezovskii, Vladimir ; Delzescaux, Thierry ; Warfield, Simon K ; Born, Richard ; Westin, Carl-Fredrik}, medline-gr = {P01 HD18655/HD/NICHD NIH HHS/United States ; P30-EY12196/EY/NEI NIH HHS/United States ; P41 RR013218/RR/NCRR NIH HHS/United States ; R01 HL074942/HL/NHLBI NIH HHS/United States ; R01 RR021885/RR/NCRR NIH HHS/United States ; R01-MH50747/MH/NIMH NIH HHS/United States ; R21 MH067054/MH/NIMH NIH HHS/United States ; U41 RR019703/RR/NCRR NIH HHS/United States ; U54 EB005149/EB/NIBIB NIH HHS/United States}, medline-is = {1053-8119 (Print)}, medline-jid = {9215515}, medline-jt = {NeuroImage}, medline-lr = {20071203}, medline-mhda = {2007/10/13 09:00}, medline-own = {NLM}, medline-phst = {2007/01/25 [received] ; 2007/04/05 [revised] ; 2007/04/10 [accepted] ; 2007/05/24 [aheadofprint]}, medline-pl = {United States}, medline-pmid = {17604650}, medline-pst = {ppublish}, medline-pt = {Comparative Study ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.}, medline-sb = {IM}, medline-so = {Neuroimage. 2007 Aug 15;37(2):530-8. Epub 2007 May 24.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=17604650}, year = 2007 } @Article{Jonasson2007, Author = {Jonasson, Lisa and Bresson, Xavier and Thiran, Jean-Philippe and Wedeen, Van J and Hagmann, Patric}, Title = {{Representing diffusion MRI in 5-D simplifies regularization and segmentation of white matter tracts.}}, Journal = {IEEE transactions on medical imaging}, Volume = {26}, Number = {11}, Pages = {1547--54}, abstract = {We present a new five-dimensional (5-D) space representation of diffusion magnetic resonance imaging (dMRI) of high angular resolution. This 5-D space is basically a non-Euclidean space of position and orientation in which crossing fiber tracts can be clearly disentangled, that cannot be separated in three-dimensional position space. This new representation provides many possibilities for processing and analysis since classical methods for scalar images can be extended to higher dimensions even if the spaces are not Euclidean. In this paper, we show examples of how regularization and segmentation of dMRI is simplified with this new representation. The regularization is used with the purpose of denoising and but also to facilitate the segmentation task by using several scales, each scale representing a different level of resolution. We implement in five dimensions the Chan-Vese method combined with active contours without edges for the segmentation and the total variation functional for the regularization. The purpose of this paper is to explore the possibility of segmenting white matter structures directly as entirely separated bundles in this 5-D space. We will present results from a synthetic model and results on real data of a human brain acquired with diffusion spectrum magnetic resonance imaging (MRI), one of the dMRI of high angular resolution available. These results will lead us to the conclusion that this new high-dimensional representation indeed simplifies the problem of segmentation and regularization.}, doi = {10.1109/TMI.2007.899168}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Jonasson et al. - 2007 - Representing diffusion MRI in 5-D simplifies regularization and segmentation of white matter tracts..pdf:pdf}, issn = {0278-0062}, keywords = {Algorithms,Artificial Intelligence,Brain,Brain: anatomy \& histology,Diffusion Magnetic Resonance Imaging,Diffusion Magnetic Resonance Imaging: methods,Humans,Image Enhancement,Image Enhancement: methods,Image Interpretation, Computer-Assisted,Image Interpretation, Computer-Assisted: methods,Imaging, Three-Dimensional,Imaging, Three-Dimensional: methods,Nerve Fibers, Myelinated,Nerve Fibers, Myelinated: ultrastructure,Pattern Recognition, Automated,Pattern Recognition, Automated: methods,Reproducibility of Results,Sensitivity and Specificity}, month = nov, pmid = {18041269}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18041269}, year = 2007 } @Article{Frenkel2003, Author = {Frenkel, Max and Basri, Ronen}, Title = {{Using the Fast Marching Method}}, Pages = {35--51}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Frenkel, Basri - 2003 - Using the Fast Marching Method.pdf:pdf}, year = 2003 } @Article{Laidlaw, Author = {Laidlaw, David H}, Title = {{Similarity Coloring of DTI Fiber Tracts}}, Journal = {Science}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Laidlaw - Unknown - Similarity Coloring of DTI Fiber Tracts.pdf:pdf} } @Article{Parker2004BJR, Author = {Parker, G J M}, Title = {{Analysis of MR diffusion weighted images}}, Journal = {Br J Radiol}, Volume = {77}, Number = {suppl_2}, Pages = {S176-185}, abstract = {Diffusion-weighted MR images provide information that is present in no other imaging modality. Whilst some of this information may be appreciated visually in diffusion weighted images, much of it may be extracted only with the aid of data post-processing. This review summarizes the methods available for interpreting diffusion weighted imaging (DWI) information using the diffusion tensor and other models of the DWI signal. This is followed by an overview of methods that allow the estimation of fibre tract orientation and that provide estimates of the routes and degree of anatomical cerebral white matter connectivity. }, doi = {10.1259/bjr/81090732}, eprint = {http://bjr.birjournals.org/cgi/reprint/77/suppl_2/S176.pdf}, file = {attachment\:Parker2004BJR.pdf:attachment\:Parker2004BJR.pdf:PDF}, url = {http://bjr.birjournals.org/cgi/content/abstract/77/suppl_2/S176}, year = 2004 } @Article{Prentice1984, Author = {Prentice, Michael J.}, Title = {{A distribution-free method of interval estimation for unsigned directional data}}, Journal = {Biometrika}, Volume = {71}, Number = {1}, Pages = {147--154}, doi = {10.1093/biomet/71.1.147}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Prentice - 1984 - A distribution-free method of interval estimation for unsigned directional data.pdf:pdf}, issn = {0006-3444}, url = {http://biomet.oxfordjournals.org/cgi/doi/10.1093/biomet/71.1.147}, year = 1984 } @Article{MelieGarcia2008NeuroImage, Author = {Melie-Garcia, Lester and Canales-Rodriguez, Erick J. and Aleman-Gomez, Yasser and Lin, Ching-Po and Iturria-Medina, Yasser and Valdes-Hernandez, Pedro A. }, Title = {A bayesian framework to identify principal intravoxel diffusion profiles based on diffusion-weighted \{{M}{R}\} imaging}, Journal = {NeuroImage}, Volume = {42}, Number = {2}, Pages = {750-770}, abstract = {In this paper we introduce a new method to characterize the intravoxel anisotropy based on diffusion-weighted imaging (DWI). The proposed solution, under a fully Bayesian formalism, deals with the problem of joint Bayesian Model selection and parameter estimation to reconstruct the principal diffusion profiles or primary fiber orientations in a voxel. We develop an efficient stochastic algorithm based on the reversible jump Markov chain Monte Carlo (RJMCMC) method in order to perform the Bayesian computation. RJMCMC is a good choice for this problem because of its ability to jump between models of different dimensionality. This methodology provides posterior estimates of the parameters of interest (fiber orientation, diffusivities etc) unconditional of the model assumed. It also gives an empirical posterior distribution of the number of primary nerve fiber orientations given the DWI data. Different probability maps can be assessed using this methodology: 1) the intravoxel fiber orientation map (or orientational distribution function) that gives the probability of finding a fiber in a particular spatial orientation; 2) a three-dimensional map of the probability of finding a particular number of fibers in each voxel; 3) a three-dimensional MaxPro (maximum probability) map that provides the most probable number of fibers for each voxel. In order to study the performance and reliability of the presented approach, we tested it on synthetic data; an ex-vivo phantom of intersecting capillaries; and DWI data from a human subject.}, file = {attachment\:MelieGarcia2008NeuroImage.pdf:attachment\:MelieGarcia2008NeuroImage.pdf:PDF}, publisher = {Elsevier}, url = {http://www.sciencedirect.com/science/article/B6WNP-4SD6SK8-3/2/8c1ea05184c975fa63eb37b877737d9f}, year = 2008 } @Article{Dougherty2005, Author = {Dougherty, Robert F and Ben-Shachar, Michal and Bammer, Roland and Brewer, Alyssa a and Wandell, Brian a}, Title = {{Functional organization of human occipital-callosal fiber tracts.}}, Journal = {Proceedings of the National Academy of Sciences of the United States of America}, Volume = {102}, Number = {20}, Pages = {7350--5}, abstract = {Diffusion tensor imaging (DTI) and fiber tracking (FT) were used to measure the occipital lobe fiber tracts connecting the two hemispheres in individual human subjects. These tracts are important for normal vision. Also, damage to portions of these tracts is associated with alexia. To assess the reliability of the DTI-FT measurements, occipital-callosal projections were estimated from each subject's left and right hemispheres independently. The left and right estimates converged onto the same positions within the splenium. We further characterized the properties of the estimated occipital-callosal fiber tracts by combining them with functional MRI. We used functional MRI to identify visual field maps in cortex and labeled fibers by the cortical functional response at the fiber endpoint. This labeling reveals a regular organization of the fibers within the splenium. The dorsal visual maps (dorsal V3, V3A, V3B, V7) send projections through a large band in the middle of the splenium, whereas ventral visual maps (ventral V3, V4) send projections through the inferior-anterior corner of the splenium. The agreement between the independent left/right estimates, further supported by previous descriptions of homologous tracts in macaque, validates the DTI-FT methods. However, a principal limitation of these methods is low sensitivity: a large number of fiber tracts that connect homotopic regions of ventral and lateral visual cortex were undetected. We conclude that most of the estimated tracts are real and can be localized with a precision of 1-2 mm, but many tracts are missed because of data and algorithm limitations.}, doi = {10.1073/pnas.0500003102}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Dougherty et al. - 2005 - Functional organization of human occipital-callosal fiber tracts..pdf:pdf}, issn = {0027-8424}, keywords = {Adult,Algorithms,Brain Mapping,Corpus Callosum,Corpus Callosum: cytology,Echo-Planar Imaging,Echo-Planar Imaging: methods,Female,Humans,Magnetic Resonance Imaging,Male,Middle Aged,Occipital Lobe,Occipital Lobe: cytology,Visual Fields,Visual Fields: physiology}, month = may, pmid = {15883384}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15883384}, year = 2005 } @Article{Behrens2007NeuroImage, Author = {Behrens, T.E.J. and Johansen-Berg, H. and Jbabdi, S. and Rushworth, M.F.S. and Woolrich, M.W.}, Title = {Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?}, Journal = {NeuroImage}, Volume = {34}, Number = {1}, Pages = {144-155}, abstract = {We present a direct extension of probabilistic diffusion tractography to the case of multiple fibre orientations. Using automatic relevance determination, we are able to perform online selection of the number of fibre orientations supported by the data at each voxel, simplifying the problem of tracking in a multi-orientation field. We then apply the identical probabilistic algorithm to tractography in the multi- and single-fibre cases in a number of example systems which have previously been tracked successfully or unsuccessfully with single-fibre tractography. We show that multi-fibre tractography offers significant advantages in sensitivity when tracking non-dominant fibre populations, but does not dramatically change tractography results for the dominant pathways.}, file = {attachment\:Behrens2007NeuroImage.pdf:attachment\:Behrens2007NeuroImage.pdf:PDF}, publisher = {Elsevier}, url = {http://www.sciencedirect.com/science/article/B6WNP-4M6SBH3-4/2/043728426dfb426bd39df3b8d3751bed}, year = 2007 } @Article{Catani2002NeuroImage, Author = {Catani, Marco and Howard, Robert J. and Pajevic, Sinisa and Jones, Derek K.}, Title = {Virtual {in vivo} interactive dissection of white matter fasciculi in the human brain }, Journal = {NeuroImage}, Volume = {17}, Pages = {77-94}, abstract = {This work reports the use of diffusion tensor magnetic resonance tractography to visualize the three-dimensional (3D) structure of the major white matter fasciculi within living human brain. Specifically, we applied this technique to visualize in vivo (i) the superior longitudinal (arcuate) fasciculus, (ii) the inferior longitudinal fasciculus, (iii) the superior fronto-occipital (subcallosal) fasciculus, (iv) the inferior frontooccipital fasciculus, (v) the uncinate fasciculus, (vi) the cingulum, (vii) the anterior commissure, (viii) the corpus callosum, (ix) the internal capsule, and (x) the fornix. These fasciculi were first isolated and were then interactively displayed as a 3D-rendered object. The virtual tract maps obtained in vivo using this approach were faithful to the classical descriptions of white matter anatomy that have previously been documented in postmortem studies. Since we have been able to interactively delineate and visualize white matter fasciculi over their entire length in vivo, in a manner that has only previously been possible by histological means, “virtual in vivo interactive dissection” (VIVID) adds a new dimension to anatomical descriptions of the living human brain.}, doi = {10.1006/nimg.2002.1136}, file = {attachment\:Catani2002NeuroImage.pdf:attachment\:Catani2002NeuroImage.pdf:PDF}, publisher = {Elsevier}, year = 2002 } @Article{Marinucci2008a, Author = {Marinucci, D and Pietrobon, D and Balbi, A and Baldi, P and Cabella, P and Kerkyacharian, G and Natoli, P and Picard, D and Vittorio, N}, Title = {{Spherical Needlets for CMB Data Analysis}}, Volume = {000}, Number = {February}, arxivid = {arXiv:0707.0844v1}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Marinucci et al. - 2008 - Spherical Needlets for CMB Data Analysis.pdf:pdf}, year = 2008 } @Article{DoughertyPNAS2005, Author = {Dougherty, R. F. and Ben-Shachar, M. and Bammer, R. and Brewer, A. A. and Wandell, B. A.}, Title = {Functional organization of human occipital-callosal fiber tracts.}, Journal = {Proc Natl Acad Sci U S A}, Volume = {102}, Number = {20}, Pages = {7350-5}, abstract = {Diffusion tensor imaging (DTI) and fiber tracking (FT) were used to measure the occipital lobe fiber tracts connecting the two hemispheres in individual human subjects. These tracts are important for normal vision. Also, damage to portions of these tracts is associated with alexia. To assess the reliability of the DTI-FT measurements, occipital-callosal projections were estimated from each subject's left and right hemispheres independently. The left and right estimates converged onto the same positions within the splenium. We further characterized the properties of the estimated occipital-callosal fiber tracts by combining them with functional MRI. We used functional MRI to identify visual field maps in cortex and labeled fibers by the cortical functional response at the fiber endpoint. This labeling reveals a regular organization of the fibers within the splenium. The dorsal visual maps (dorsal V3, V3A, V3B, V7) send projections through a large band in the middle of the splenium, whereas ventral visual maps (ventral V3, V4) send projections through the inferior-anterior corner of the splenium. The agreement between the independent left/right estimates, further supported by previous descriptions of homologous tracts in macaque, validates the DTI-FT methods. However, a principal limitation of these methods is low sensitivity: a large number of fiber tracts that connect homotopic regions of ventral and lateral visual cortex were undetected. We conclude that most of the estimated tracts are real and can be localized with a precision of 1-2 mm, but many tracts are missed because of data and algorithm limitations.}, authoraddress = {Stanford Institute for Reading and Learning, Department of Psychology, Stanford University, Stanford, CA 94305, USA. bobd@stanford.edu}, keywords = {Adult ; Algorithms ; *Brain Mapping ; Corpus Callosum/*cytology ; Echo-Planar Imaging/methods ; Female ; Humans ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Occipital Lobe/*cytology ; Visual Fields/physiology}, language = {eng}, medline-aid = {0500003102 [pii] ; 10.1073/pnas.0500003102 [doi]}, medline-crdt = {2005/05/11 09:00}, medline-da = {20050518}, medline-dcom = {20050713}, medline-dep = {20050509}, medline-edat = {2005/05/11 09:00}, medline-fau = {Dougherty, Robert F ; Ben-Shachar, Michal ; Bammer, Roland ; Brewer, Alyssa A ; Wandell, Brian A}, medline-gr = {EY-015000/EY/NEI NIH HHS/United States ; EY-03164/EY/NEI NIH HHS/United States}, medline-is = {0027-8424 (Print)}, medline-jid = {7505876}, medline-jt = {Proceedings of the National Academy of Sciences of the United States of America}, medline-lr = {20081120}, medline-mhda = {2005/07/14 09:00}, medline-oid = {NLM: PMC1129102}, medline-own = {NLM}, medline-phst = {2005/05/09 [aheadofprint]}, medline-pl = {United States}, medline-pmc = {PMC1129102}, medline-pmid = {15883384}, medline-pst = {ppublish}, medline-pt = {Comparative Study ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, P.H.S.}, medline-sb = {IM}, medline-so = {Proc Natl Acad Sci U S A. 2005 May 17;102(20):7350-5. Epub 2005 May 9.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=15883384}, year = 2005 } @Article{ValentinaTomassini09192007, Author = {Tomassini, Valentina and Jbabdi, Saad and Klein, Johannes C. and Behrens, Timothy E. J. and Pozzilli, Carlo and Matthews, Paul M. and Rushworth, Matthew F. S. and Johansen-Berg, Heidi}, Title = {Diffusion-Weighted Imaging Tractography-Based Parcellation of the Human Lateral Premotor Cortex Identifies Dorsal and Ventral Subregions with Anatomical and Functional Specializations}, Journal = {J. Neurosci.}, Volume = {27}, Number = {38}, Pages = {10259-10269}, abstract = {Lateral premotor cortex (PM) in the macaque monkey can be segregated into structurally and functionally distinct subregions, including a major division between dorsal (PMd) and ventral (PMv) parts, which have distinct cytoarchitecture, function, and patterns of connectivity with both frontal and parietal cortical areas. The borders of their subregions are less well defined in the human brain. Here we use diffusion tractography to identify a reproducible border between dorsal and ventral subregions of human precentral gyrus. We derive connectivity fingerprints for the two subregions and demonstrate that each has a distinctive pattern of connectivity with frontal cortex and lateral parietal cortex, suggesting that these areas correspond to human PMd and PMv. Although putative human PMd has a high probability of connection with the superior parietal lobule, dorsal prefrontal cortex, and cingulate cortex, human PMv has a higher probability of connection with the anterior inferior parietal lobule and ventral prefrontal cortex. Finally, we assess the correspondence between our PMd/PMv border and local sulcal and functional anatomy. The location of the border falls at the level of the gyral branch that divides the inferior precentral sulcus from the superior precentral sulcus and corresponded closely to the location of a functional border defined using previous functional magnetic resonance imaging studies.}, doi = {10.1523/JNEUROSCI.2144-07.2007}, eprint = {http://www.jneurosci.org/cgi/reprint/27/38/10259.pdf}, file = {attachment\:tomassini_parcellation_2007.pdf:attachment\:tomassini_parcellation_2007.pdf:PDF}, url = {http://www.jneurosci.org/cgi/content/abstract/27/38/10259}, year = 2007 } @Article{Behrens2003MRM, Author = {Behrens, T. E. J. and Woolrich, M. W. and Jenkinson, M. and Johansen-Berg, H. and Nunes, R. G. and Clare, S. and Matthews, P. M. and Brady, J. M. and Smith, S. M.}, Title = {Characterization and propagation of uncertainty in diffusion-weighted \{{M}{R}\} imaging}, Journal = {Magnetic Resonance in Medicine}, Volume = {50}, Pages = {1077-1088}, abstract = {A fully probabilistic framework is presented for estimating local probability density functions on parameters of interest in a model of diffusion. This technique is applied to the estimation of parameters in the diffusion tensor model, and also to a simple partial volume model of diffusion. In both cases the parameters of interest include parameters defining local fiber direction. A technique is then presented for using these density functions to estimate global connectivity (i.e., the probability of the existence of a connection through the data field, between any two distant points), allowing for the quantification of belief in tractography results. This technique is then applied to the estimation of the cortical connectivity of the human thalamus. The resulting connectivity distributions correspond well with predictions from invasive tracer methods in nonhuman primate.}, file = {attachment\:Behrens2003MRM.pdf:attachment\:Behrens2003MRM.pdf:PDF}, publisher = {Wiley-Liss}, year = 2003 } @Article{ODonnell_MICCAI06, Author = {O'Donnell, L. and Westin, C. F.}, Title = {High-dimensional white matter atlas generation and group analysis.}, Journal = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv}, Volume = {9}, Number = {Pt 2}, Pages = {243-51}, abstract = {We present a two-step process including white matter atlas generation and automatic segmentation. Our atlas generation method is based on population fiber clustering. We produce an atlas which contains high-dimensional descriptors of fiber bundles as well as anatomical label information. We use the atlas to automatically segment tractography in the white matter of novel subjects and we present quantitative results (FA measurements) in segmented white matter regions from a small population. We demonstrate reproducibility of these measurements across scans. In addition, we introduce the idea of using clustering for automatic matching of anatomical structures across hemispheres.}, authoraddress = {Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge MA, USA. lauren@csail.mit.edu}, keywords = {Algorithms ; Anatomy, Artistic/methods ; *Artificial Intelligence ; Brain/*anatomy \& histology ; Cluster Analysis ; Computer Simulation ; Diffusion Magnetic Resonance Imaging/*methods ; Humans ; Image Enhancement/methods ; Image Interpretation, Computer-Assisted/*methods ; Imaging, Three-Dimensional/methods ; Medical Illustration ; Models, Anatomic ; Nerve Fibers, Myelinated/*ultrastructure ; Neural Pathways/*anatomy \& histology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity}, language = {eng}, medline-crdt = {2007/03/16 09:00}, medline-da = {20070314}, medline-dcom = {20070406}, medline-edat = {2007/03/16 09:00}, medline-fau = {O'Donnell, Lauren ; Westin, Carl-Fredrik}, medline-gr = {P41 RR15241-01A1/RR/NCRR NIH HHS/United States ; P41-RR13218/RR/NCRR NIH HHS/United States ; R01 AG20012-01/AG/NIA NIH HHS/United States ; R01 MH 50747/MH/NIMH NIH HHS/United States ; U24-RR021382/RR/NCRR NIH HHS/United States ; U54-EB005149/EB/NIBIB NIH HHS/United States}, medline-jid = {101249582}, medline-jt = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}, medline-lr = {20071203}, medline-mhda = {2007/04/07 09:00}, medline-own = {NLM}, medline-pl = {Germany}, medline-pmid = {17354778}, medline-pst = {ppublish}, medline-pt = {Evaluation Studies ; Journal Article ; Research Support, N.I.H., Extramural}, medline-sb = {IM}, medline-so = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. 2006;9(Pt 2):243-51.}, medline-stat = {MEDLINE}, url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=17354778}, year = 2006 } @Article{Correia2009, Author = {Correia, Marta Morgado}, Title = {{Development of Methods for the Acquisition and Analysis of Diffusion Weighted MRI Data}}, Journal = {Brain}, Number = {June}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Correia - 2009 - Development of Methods for the Acquisition and Analysis of Diffusion Weighted MRI Data.pdf:pdf}, year = 2009 } @Article{jbabdi2007bfg, Author = {Jbabdi, S. and Woolrich, MW and Andersson, JLR and Behrens, TEJ}, Title = {{A Bayesian framework for global tractography}}, Journal = {Neuroimage}, Volume = {37}, Number = {1}, Pages = {116--129}, publisher = {Elsevier}, year = 2007 } @Article{Jian2007aNeuroImage, Author = {Jian, Bing and Vemuri, Baba C. and Ozarslan, Evren and Carney, Paul R. and Mareci, Thomas H.}, Title = {A novel tensor distribution model for the diffusion-weighted \{{M}{R}\} signal}, Journal = {NeuroImage}, Volume = {37}, Number = {1}, Pages = {164-176}, abstract = {Diffusion MRI is a non-invasive imaging technique that allows the measurement of water molecule diffusion through tissue in vivo. The directional features of water diffusion allow one to infer the connectivity patterns prevalent in tissue and possibly track changes in this connectivity over time for various clinical applications. In this paper, we present a novel statistical model for diffusion-weighted MR signal attenuation which postulates that the water molecule diffusion can be characterized by a continuous mixture of diffusion tensors. An interesting observation is that this continuous mixture and the MR signal attenuation are related through the Laplace transform of a probability distribution over symmetric positive definite matrices. We then show that when the mixing distribution is a Wishart distribution, the resulting closed form of the Laplace transform leads to a Rigaut-type asymptotic fractal expression, which has been phenomenologically used in the past to explain the MR signal decay but never with a rigorous mathematical justification until now. Our model not only includes the traditional diffusion tensor model as a special instance in the limiting case, but also can be adjusted to describe complex tissue structure involving multiple fiber populations. Using this new model in conjunction with a spherical deconvolution approach, we present an efficient scheme for estimating the water molecule displacement probability functions on a voxel-by-voxel basis. Experimental results on both simulations and real data are presented to demonstrate the robustness and accuracy of the proposed algorithms.}, file = {attachment\:Jian2007aNeuroImage.pdf:attachment\:Jian2007aNeuroImage.pdf:PDF}, url = {http://www.sciencedirect.com/science/article/B6WNP-4NMSRV9-3/2/b4bc62020864c9b5767ce1e87874128a}, year = 2007 } @Article{Chen2006, Author = {Chen, Bin and Guo, Hua and Song, Allen W}, Title = {{Correction for direction-dependent distortions in diffusion tensor imaging using matched magnetic field maps.}}, Journal = {NeuroImage}, Volume = {30}, Number = {1}, Pages = {121--9}, abstract = {Diffusion tensor imaging (DTI) has seen increased usage in clinical and basic science research in the past decade. By assessing the water diffusion anisotropy within biological tissues, e.g. brain, researchers can infer different fiber structures important for neural pathways. A typical DTI data set contains at least one base image and six diffusion-weighted images along non-collinear encoding directions. The resultant images can then be combined to derive the three principal axes of the diffusion tensor and their respective cross terms, which can in turn be used to compute fractional anisotropy (FA) maps, apparent diffusion coefficient (ADC) maps, and to construct axonal fibers. The above operations all assume that DTI images along different diffusion-weighting directions for the same brain register to each other without spatial distortions. This assumption is generally false, as the large diffusion-weighting gradients would usually induce eddy currents to generate diffusion-weighting direction-dependent field gradients, leading to mis-registration within the DTI data set. Traditional methods for correcting magnetic field-induced distortions do not usually take into account these direction-dependent eddy currents unique for DTI, and they are usually time-consuming because multiple phase images need to be acquired. In this report, we describe our theory and implementation of an efficient and effective method to correct for the main field and eddy current-induced direction-dependent distortions for DTI images under a unified framework to facilitate the daily practice of DTI acquisitions.}, doi = {10.1016/j.neuroimage.2005.09.008}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Chen, Guo, Song - 2006 - Correction for direction-dependent distortions in diffusion tensor imaging using matched magnetic field maps..pdf:pdf}, issn = {1053-8119}, keywords = {Anisotropy,Artifacts,Brain,Brain Mapping,Brain: anatomy \& histology,Diffusion Magnetic Resonance Imaging,Diffusion Magnetic Resonance Imaging: statistics \&,Echo-Planar Imaging,Echo-Planar Imaging: statistics \& numerical data,Humans,Image Enhancement,Image Enhancement: methods,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: statistics \& ,Mathematical Computing,Nerve Fibers,Nerve Fibers: ultrasonography,Neural Pathways,Neural Pathways: anatomy \& histology,Phantoms, Imaging}, month = mar, pmid = {16242966}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16242966}, year = 2006 } @Article{Corouge2006, Author = {Corouge, Isabelle and Fletcher, P Thomas and Joshi, Sarang and Gouttard, Sylvain and Gerig, Guido}, Title = {{Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.}}, Journal = {Medical image analysis}, Volume = {10}, Number = {5}, Pages = {786--98}, abstract = {Quantitative diffusion tensor imaging (DTI) has become the major imaging modality to study properties of white matter and the geometry of fiber tracts of the human brain. Clinical studies mostly focus on regional statistics of fractional anisotropy (FA) and mean diffusivity (MD) derived from tensors. Existing analysis techniques do not sufficiently take into account that the measurements are tensors, and thus require proper interpolation and statistics of tensors, and that regions of interest are fiber tracts with complex spatial geometry. We propose a new framework for quantitative tract-oriented DTI analysis that systematically includes tensor interpolation and averaging, using nonlinear Riemannian symmetric space. A new measure of tensor anisotropy, called geodesic anisotropy (GA) is applied and compared with FA. As a result, tracts of interest are represented by the geometry of the medial spine attributed with tensor statistics (average and variance) calculated within cross-sections. Feasibility of our approach is demonstrated on various fiber tracts of a single data set. A validation study, based on six repeated scans of the same subject, assesses the reproducibility of this new DTI data analysis framework.}, doi = {10.1016/j.media.2006.07.003}, file = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Corouge et al. - 2006 - Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis..pdf:pdf}, issn = {1361-8415}, keywords = {Algorithms,Artificial Intelligence,Brain,Brain: cytology,Computer Simulation,Diffusion Magnetic Resonance Imaging,Diffusion Magnetic Resonance Imaging: methods,Feasibility Studies,Humans,Image Enhancement,Image Enhancement: methods,Image Interpretation, Computer-Assisted,Image Interpretation, Computer-Assisted: methods,Imaging, Three-Dimensional,Imaging, Three-Dimensional: methods,Information Storage and Retrieval,Information Storage and Retrieval: methods,Models, Neurological,Models, Statistical,Neural Pathways,Neural Pathways: cytology,Pattern Recognition, Automated,Pattern Recognition, Automated: methods,Reproducibility of Results,Sensitivity and Specificity}, pmid = {16926104}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16926104}, year = 2006 } dipy-0.5.0/doc/documentation.rst000066400000000000000000000005111152576264200166400ustar00rootroot00000000000000.. _documentation: Documentation =================== Contents: .. toctree:: :maxdepth: 2 introduction mission installation examples_index faq developers cite devel/index theory/index reference/index Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` dipy-0.5.0/doc/examples/000077500000000000000000000000001152576264200150565ustar00rootroot00000000000000dipy-0.5.0/doc/examples/.gitignore000066400000000000000000000000361152576264200170450ustar00rootroot00000000000000gqs_tracks.npy ten_tracks.npy dipy-0.5.0/doc/examples/README000066400000000000000000000004211152576264200157330ustar00rootroot00000000000000Examples -------- These are the dipy examples. They are built as docs in the dipy ``examples_built`` directory in the documentation. If you add an example (yes please!), please remember to add it also to the ``examples_index.rst`` file listing in the ``doc`` directory. dipy-0.5.0/doc/examples/aniso_vox_2_isotropic.py000066400000000000000000000015011152576264200217460ustar00rootroot00000000000000 """ =============================== Anisotropic Voxels to Isotropic =============================== Overview ======== """ import nibabel as nib """ resample """ from dipy.align.aniso2iso import resample from dipy.data import get_data """ replace with your nifti filename """ fimg=get_data('aniso_vox') img=nib.load(fimg) data=img.get_data() data.shape """ (58, 58, 24) """ affine=img.get_affine() zooms=img.get_header().get_zooms()[:3] zooms """ (4.0, 4.0, 5.0) """ new_zooms=(3.,3.,3.) new_zooms """ (3.0, 3.0, 3.0) """ data2,affine2=resample(data,affine,zooms,new_zooms) data2.shape """ (77, 77, 40) Save the result as a nifti """ img2=nib.Nifti1Image(data2,affine2) nib.save(img2,'iso_vox.nii.gz') """ Or as analyze format """ img3=nib.Spm2AnalyzeImage(data2,affine2) nib.save(img3,'iso_vox.img') dipy-0.5.0/doc/examples/file_formats.py000066400000000000000000000022241152576264200201020ustar00rootroot00000000000000""" ===================== File Format Friendly ===================== Overview ======== Read :ref:`faq` """ import numpy as np from dipy.data import get_data from nibabel import trackvis """ read trackvis """ fname=get_data('fornix') print(fname) streams,hdr=trackvis.read(fname) tracks=[s[0] for s in streams] """ quick way use numpy.save """ tracks_np=np.array(tracks,dtype=np.object) np.save('fornix.npy',tracks_np) """ it is good practice to remove what is not necessary any more """ del tracks_np tracks2=list(np.load('fornix.npy')) """ huge datasets use dipy.io.dpy * direct indexing from the disk * memory usage always low * extendable """ from dipy.io.dpy import Dpy dpw=Dpy('fornix.dpy','w') """ write many tracks at once """ dpw.write_tracks(tracks2) """ write one track """ dpw.write_track(tracks2[0]*6) """ or one track each time """ for t in tracks: dpw.write_track(t*3) dpw.close() """ read tracks directly from the disk using their indices """ dpr=Dpy('fornix.dpy','r') some_tracks=dpr.read_tracksi([0,10,20,30,100]) dpr.close() """ Number of tracks in before and after """ print(len(tracks)) print(len(some_tracks)) dipy-0.5.0/doc/examples/find_correspondence.py000066400000000000000000000061571152576264200214520ustar00rootroot00000000000000""" ========================================== Find correspondence between tractographies ========================================== First import the necessary modules numpy is for numerical computation """ import numpy as np """ dipy.tracking.distances is for tractography distances """ from dipy.tracking.distances import mam_distances """ dipy.data is for getting some small datasets used in examples and tests. """ from dipy.data import get_skeleton """ ``get_skeleton`` provides two skeletons 'C1' and 'C3' previously generated from Local Skeleton Clustering (LSC) """ C1=get_skeleton('C1') C3=get_skeleton('C3') """ We create a diagram with the two skeletons offset [100,0,0] apart """ from dipy.viz import fvtk r=fvtk.ren() T1=[] for c in C1: T1.append(C1[c]['most']) fvtk.add(r,fvtk.line(T1,fvtk.gray)) T3=[] for c in C3: T3.append(C3[c]['most']) T3s=[t+ np.array([100,0,0]) for t in T3] fvtk.add(r,fvtk.line(T3s,fvtk.gray)) # To show now use: #fvtk.show(r) """ For each track in T1 find the minimum average distance to all the tracks in T3 and put information about it in ``track2track``. """ indices=range(len(T1)) track2track=[] mam_threshold=6. for i in indices: rt=[mam_distances(T1[i],t,'avg') for t in T3] rt=np.array(rt) if rt.min()< mam_threshold: track2track.append(np.array([i,rt.argmin(),rt.min()])) track2track=np.array(track2track) np.set_printoptions(2) """ When a track in T3 is simultaneously the nearest track to more than one track in T1 we identify the track in T1 that has the best correspondence and remove the other. """ good_correspondence=[] for i in track2track[:,1]: check= np.where(track2track[:,1]==i)[0] if len(check) == 1: good_correspondence.append(check[0]) elif len(check)>=2: #print check,check[np.argmin(track2track[check][:,2])] good_correspondence.append(check[np.argmin(track2track[check][:,2])]) #good_correspondence.append()) #print goo_correspondenced good_correspondence=list(set(good_correspondence)) track2track=track2track[good_correspondence,:] print 'With mam_threshold %f we find %d correspondence pairs' % (mam_threshold, np.size(track2track,0)) # If you did an fvtk.show(r) before, you'll need to clear the figure #fvtk.clear(r) """ Now plot the corresponding tracks in the same colours .. figure:: find_corr1000000.png :align: center **Showing correspondence between these two modest tractographies**. The labels on the corresponding tracks are the indices of the first tractography on the left. """ for row in track2track: color=np.random.rand(3) T=[T1[int(row[0])],T3s[int(row[1])]] fvtk.add(r,fvtk.line(T,color,linewidth=5)) pos1=T1[int(row[0])][0] pos3=T3s[int(row[1])][0] fvtk.add(r,fvtk.label(r,str(int(row[0])),tuple(pos1),(5,5,5))) fvtk.add(r,fvtk.label(r,str(int(row[0])),tuple(pos3),(5,5,5))) # To see in an interactive window: #fvtk.show(r,png_magnify=1,size=(600,600)) # To make the illustration print('Saving illustration as find_corr1000000.png') fvtk.record(r,n_frames=1,out_path='find_corr',size=(600,600)) dipy-0.5.0/doc/examples/nii_2_tracks.py000066400000000000000000000224441152576264200200050ustar00rootroot00000000000000""" =============================== From raw data to tractographies =============================== Overview ======== **This example gives a tour of some simple features of dipy.** First import the necessary modules ---------------------------------- ``numpy`` is for numerical computation """ import numpy as np """ ``nibabel`` is for data formats """ import nibabel as nib """ ``dipy.reconst`` is for the reconstruction algorithms which we use to create directionality models for a voxel from the raw data. """ import dipy.reconst.gqi as gqi import dipy.reconst.dti as dti """ ``dipy.tracking`` is for tractography algorithms which create sets of tracks by integrating directionality models across voxels. """ from dipy.tracking.propagation import EuDX """ ``dipy.data`` is for small datasets we use in tests and examples. """ from dipy.data import get_data """ Isotropic voxel sizes required ------------------------------ ``dipy`` requires its datasets to have isotropic voxel size. If you have datasets with anisotropic voxel size then you need to resample with isotropic voxel size. We have provided an algorithm for this. You can have a look at the example ``resample_aniso_2_iso.py`` Accessing the necessary datasets -------------------------------- ``get_data`` provides data for a small region of interest from a real diffusion weighted MR dataset acquired with 102 gradients (including one for b=0). In order to make this work with your data you should comment out the line below and add the paths for your nifti file (``*.nii`` or ``*.nii.gz``) and your ``*.bvec`` and ``*.bval files``. If you are not using nifti files or you don't know how to create the ``*.bvec`` and ``*.bval`` files from your raw dicom (``*.dcm``) data then you can either try recent module nibabel.nicom """ try: from nibabel.nicom.dicomreaders import read_mosaic_dir except: print('nicom for dicom is not installed') """ or to convert the dicom files to nii, bvec and bval files using ``dcm2nii``. """ fimg,fbvals,fbvecs=get_data('small_101D') """ **Load the nifti file found at path fimg as an Nifti1Image.** """ img=nib.load(fimg) """ **Read the datasets from the Nifti1Image.** """ data=img.get_data() print('data.shape (%d,%d,%d,%d)' % data.shape) """ This produces the output:: data.shape (6,10,10,102) As you would expect, the raw diffusion weighted MR data is 4-dimensional as we have one 3-d volume (6 by 10 by 10) for each gradient direction. **Read the affine matrix** which gives the mapping between volume indices (voxel coordinates) and world coordinates. """ affine=img.get_affine() """ **Read the b-values** which are a function of the strength, duration, temporal spacing and timing parameters of the specific paradigm used in the scanner, one per gradient direction. """ bvals=np.loadtxt(fbvals) """ **Read the b-vectors**, the unit gradient directions. """ gradients=np.loadtxt(fbvecs).T """ Calculating models and parameters of directionality --------------------------------------------------- We are now set up with all the data and parameters to start calculating directional models for voxels and their associated parameters, e.g. anisotropy. **Calculate the Single Tensor Model (STM).** """ ten=dti.Tensor(data,bvals,gradients,thresh=50) """ **Calculate Fractional Anisotropy (FA) from STM** """ FA=ten.fa() print('FA.shape (%d,%d,%d)' % FA.shape) """ As expected the FA is a 3-d array with one value per voxel:: FA.shape (6,10,10) Generate a tractography ----------------------- Here we use the Euler Delta Crossings (EuDX) algorithm. The main input parameters of ``EuDX`` are * an anisotropic scalar metric e.g. FA * the indices for the peaks on the sampling sphere. Other important options are * the number of random seeds where the track propagation is initiated, * a stopping criterion, for example a low threshold for anisotropy. For instance if we are using *Fractional Anisotropy (FA)* a typical threshold value might be ``a_low=.2`` """ eu=EuDX(a=FA,ind=ten.ind(),seeds=10000,a_low=.2) """ EuDX returns a generator class which yields a further track each time this class is called. In this way we can generate millions of tracks without using a substantial amount of memory. For an example of what to do when you want to generate millions of tracks with minimum memory usage have a look at ``save_dpy.py`` in the ``examples`` directory. However, in the current example that we only have 10000 seeds, and we can load all tracks in a list using list comprehension([]) without having to worry about memory. """ ten_tracks=[track for track in eu] """ In dipy we usually represent tractography as a list of tracks. Every track is a numpy array of shape (N,3) where N is the number of points in the track. """ print ('The number of FA tracks is %d' % len(ten_tracks)) print ('The number of points in ten_tracks[130] is %d' % len(ten_tracks[130])) print ('The points in ten_tracks[130] are:') print ten_tracks[130] """ As we use random seeding for the tractography the results will differ when repeated, however one run gave us the following information:: The number of FA tracks is 8280 The number of points in ten_track[130] is 7 The points in ten_tracks[130] are: [[ 1.73680878 5.08249903 4.48492956] [ 1.45797026 4.76981783 4.21201992] [ 1.14244306 4.46308756 3.97461915] [ 0.84001541 4.14648438 3.73316503] [ 0.53758776 3.82988143 3.49171114] [ 0.22055824 3.52935386 3.24845099] [ 0.22055824 3.52935386 3.24845099]] Another way to represent tractography is as a numpy array of numpy objects. This way has an additional advantage that it can be saved very easily using numpy utilities. In theory, in a list it is faster to append an element, and in an array is faster to access. In other words both representations have different pros and cons. Other representations are possible too e.g. graphtheoretic etc. """ ten_tracks_asobj=np.array(ten_tracks,dtype=np.object) np.save('ten_tracks.npy',ten_tracks_asobj) print('FA tracks saved in ten_tracks.npy') """ Crossings and Generalized Q-Sampling ------------------------------------ You probably have heard about the problem of crossings in diffusion MRI. The single tensor model cannot detect a simple crossing of two fibres. However with *Generalized Q-Sampling (GQS)* this is possible even up to a quadruple crossing or higher depending on the resolution of your datasets. Resolution will typically depend on signal-to-noise ratio and voxel-size. """ gqs=gqi.GeneralizedQSampling(data,bvals,gradients) """ A useful metric derived from GQS is *Quantitative Anisotropy* (QA). """ QA=gqs.qa() print('QA.shape (%d,%d,%d,%d)' % QA.shape) """ QA is a 4-d array with up to 5 peak QA values for each voxel:: QA.shape (6,10,10,5) QA array is significantly different in shape from the FA array, however it too can be directly input to the EuDX class: """ eu2=EuDX(a=QA,ind=gqs.ind(),seeds=10000,a_low=.0239) """ This shows one of the advantages of our EuDX algorithm: it can be used with a wide range of model-based methods, such as - Single Tensor - Multiple Tensor - Stick & Ball - Higher Order Tensor and model-free methods such as - DSI - QBall - GQI *etc.* We designed the algorithm this way so we that we can compare directly tractographies generated from the same dataset with very different models and/or choices of threshold. Now we look at the QA tractography: """ gqs_tracks=[track for track in eu2] print('The number of QA tracks is %d' % len(gqs_tracks)) """ with output:: The number of QA tracks is 14022 Note the difference between the number of gqs_tracks and ten_tracks. There are more with QA than with FA. This is because of the presence of crossings which GQI can detect but STM cannot. When the underlying directionality model supports crossings then distinct tracks will be propagated from a seed towards the different directions in equal abundance. In ``dipy`` it is very easy to count the number of crossings in a voxel, volume or region of interest """ gqs_tracks_asobj=np.array(gqs_tracks,dtype=np.object) np.save('gqs_tracks.npy',gqs_tracks_asobj) print('QA tracks saved in gqs_tracks.npy') """ **This is the end of this very simple example** You can reload the saved tracks using ``np.load`` from your current directory. You can optionaly install ``python-vtk`` and visualize the tracks using ``fvtk``: """ from dipy.viz import fvtk r=fvtk.ren() fvtk.add(r,fvtk.line(ten_tracks,fvtk.red,opacity=0.05)) gqs_tracks2=[t+np.array([10,0,0]) for t in gqs_tracks] fvtk.add(r,fvtk.line(gqs_tracks2,fvtk.green,opacity=0.05)) """ Press 's' to save this screenshot when you have displayed it with ``fvtk.show``. Or you can even record a video using ``fvtk.record``. You would show the figure with something like:: fvtk.show(r,png_magnify=1,size=(600,600)) To record a video of 50 frames of png, something like:: fvtk.record(r,cam_pos=(0,40,-40),cam_focal=(5,0,0),n_frames=50,magnification=1,out_path='nii_2_tracks',size=(600,600),bgr_color=(0,0,0)) .. figure:: nii_2_tracks1000000.png :align: center **Same region of interest with different underlying voxel representations generates different tractographies**. """ # Here's how we make the figure. print('Saving illustration as nii_2_tracks1000000.png') fvtk.record(r,n_frames=1,out_path='nii_2_tracks',size=(600,600)) dipy-0.5.0/doc/examples/tractography_clustering.py000066400000000000000000000072231152576264200224020ustar00rootroot00000000000000""" ============================= Tractography Clustering ============================= Overview ======== **This example gives a tour of clustering related features of dipy.** First import the necessary modules ---------------------------------- ``numpy`` is for numerical computation """ import numpy as np import time from nibabel import trackvis as tv from dipy.tracking import metrics as tm from dipy.tracking import distances as td from dipy.io import pickles as pkl from dipy.viz import fvtk #fname='/home/user/Data_Backup/Data/PBC/pbc2009icdm/brain1/brain1_scan1_fiber_track_mni.trk' #fname='/home/user/Data/PBC/pbc2009icdm/brain1/brain1_scan1_fiber_track_mni.trk' from dipy.data import get_data fname=get_data('fornix') print(fname) """ Load Trackvis file for *Fornix*: """ streams,hdr=tv.read(fname) """ Copy tracks: """ T=[i[0] for i in streams] #T=T[:1000] """ Downsample tracks to just 3 points: """ tracks=[tm.downsample(t,3) for t in T] """ Delete unnecessary data: """ del streams,hdr """ Perform Local Skeleton Clustering (LSC) with a 5mm threshold: """ now=time.clock() C=td.local_skeleton_clustering(tracks,d_thr=5) print('Done in %.2f s' % (time.clock()-now,)) """ Reduce the number of points for faster visualization using the ``approx_polygon_track`` algorithm which retains points depending on how much they are need to define the shape of the track: """ T=[td.approx_polygon_track(t) for t in T] """ Show the initial *Fornix* dataset: """ r=fvtk.ren() fvtk.add(r,fvtk.line(T,fvtk.white,opacity=1)) #fvtk.show(r) fvtk.record(r,n_frames=1,out_path='fornix_initial',size=(600,600)) """ .. figure:: fornix_initial1000000.png :align: center **Initial Fornix dataset**. """ """ Show the *Fornix* after clustering (with random bundle colors): """ fvtk.clear(r) colors=np.zeros((len(T),3)) for c in C: color=np.random.rand(1,3) for i in C[c]['indices']: colors[i]=color fvtk.add(r,fvtk.line(T,colors,opacity=1)) #fvtk.show(r) fvtk.record(r,n_frames=1,out_path='fornix_clust',size=(600,600)) """ .. figure:: fornix_clust1000000.png :align: center **Showing the different clusters with random colors**. """ """ Calculate some statistics about the clusters """ lens=[len(C[c]['indices']) for c in C] print('max %d min %d' %(max(lens), min(lens))) print('singletons %d ' % lens.count(1)) print('doubletons %d' % lens.count(2)) print('tripletons %d' % lens.count(3)) """ Find and display the skeleton of most representative tracks in each cluster: """ skeleton=[] fvtk.clear(r) for c in C: bundle=[T[i] for i in C[c]['indices']] si,s=td.most_similar_track_mam(bundle,'avg') skeleton.append(bundle[si]) fvtk.label(r,text=str(len(bundle)),pos=(bundle[si][-1]),scale=(2,2,2)) fvtk.add(r,fvtk.line(skeleton,colors,opacity=1)) #fvtk.show(r) fvtk.record(r,n_frames=1,out_path='fornix_most',size=(600,600)) """ .. figure:: fornix_most1000000.png :align: center **Showing skeleton with the most representative tracks as the skeletal representation**. The numbers are depicting the number of tracks in each cluster. This is a very compact way to see the underlying structures an alternative would be to draw the representative tracks with different widths. """ """ Save the skeleton information in the dictionary. Now try to play with different thresholds LSC and check the different results. Try it with your datasets and gives us some feedback. """ for (i,c) in enumerate(C): C[c]['most']=skeleton[i] for c in C: print('Keys in bundle %d' % c) print(C[c].keys()) print('Shape of skeletal track (%d, %d) ' % C[c]['most'].shape) pkl.save_pickle('skeleton_fornix.pkl',C) dipy-0.5.0/doc/examples/visualize_crossings.py000066400000000000000000000160471152576264200215450ustar00rootroot00000000000000 """ ==================== Visualize Crossings ==================== Overview ======== **This example visualizes the crossings structure of a few voxels.** First import the necessary modules ---------------------------------- ``numpy`` is for numerical computation """ import numpy as np """ ``nibabel`` is for data formats """ import nibabel as nib """ ``dipy.reconst`` is for the reconstruction algorithms which we use to create directionality models for a voxel from the raw data. """ import dipy.reconst.gqi as gqi """ ``dipy.data`` is for small datasets we use in tests and examples. """ from dipy.data import get_data """ Isotropic voxel sizes required ------------------------------ ``dipy`` requires its datasets to have isotropic voxel size. If you have datasets with anisotropic voxel size then you need to resample with isotropic voxel size. We have provided an algorithm for this. You can have a look at the example ``resample_aniso_2_iso.py`` Accessing the necessary datasets -------------------------------- ``get_data`` provides data for a small region of interest from a real diffusion weighted MR dataset acquired with 102 gradients (including one for b=0). In order to make this work with your data you should comment out the line below and add the paths for your nifti file (``*.nii`` or ``*.nii.gz``) and your ``*.bvec`` and ``*.bval files``. If you are not using nifti files or you don't know how to create the ``*.bvec`` and ``*.bval`` files from your raw dicom (``*.dcm``) data then you can either try the example called ``dcm_2_tracks.py`` or use mricron_ to convert the dicom files to nii, bvec and bval files using ``dcm2nii``. """ fimg,fbvals,fbvecs=get_data('small_101D') """ **Load the nifti file found at path fimg as an Nifti1Image.** """ img=nib.load(fimg) """ **Read the datasets from the Nifti1Image.** """ data=img.get_data() print('data.shape (%d,%d,%d,%d)' % data.shape) """ This produces the output:: data.shape (6,10,10,102) As you would expect, the raw diffusion weighted MR data is 4-dimensional as we have one 3-d volume (6 by 10 by 10) for each gradient direction. **Read the affine matrix** which gives the mapping between volume indices (voxel coordinates) and world coordinates. """ affine=img.get_affine() """ **Read the b-values** which are a function of the strength, duration, temporal spacing and timing parameters of the specific paradigm used in the scanner, one per gradient direction. """ bvals=np.loadtxt(fbvals) """ **Read the b-vectors**, the unit gradient directions. """ gradients=np.loadtxt(fbvecs).T """ Crossings and Generalized Q-Sampling ------------------------------------ You probably have heard about the problem of crossings in diffusion MRI. The single tensor model cannot detect a simple crossing of two fibres. However with *Generalized Q-Sampling (GQS)* this is possible even up to a quadruple crossing or higher depending on the resolution of your datasets. Resolution will typically depend on signal-to-noise ratio and voxel-size. """ gqs=gqi.GeneralizedQSampling(data,bvals,gradients) """ A useful metric derived from GQS is *Quantitative Anisotropy* (QA). """ QA=gqs.qa() print('QA.shape (%d,%d,%d,%d)' % QA.shape) """ QA is a 4-d array with up to 5 peak QA values for each voxel:: QA.shape (6,10,10,5) The QA array is significantly different in shape from the FA array, however it too can be directly input to the EuDX class: We explore the voxel [0,0,0]. """ qa=QA[0,0,0] """ ``qa`` is the quantitative anisotropy metric """ IN=gqs.ind() ind=IN[0,0,0] """ ``ind`` holds the indices of the vertices of (up to 5) gqi odf local maxima """ print 'quantitative anisotropy metric =', qa print 'indices of local gqi odf maxima =', ind """ There are approximately equal maxima in the directions of vertices 117 and 1. To find out where these are we need to work with the symmetric 362 vertex sphere on which the reconstruction was performed. """ from dipy.data import get_sphere verts, faces = get_sphere('symmetric362') from dipy.viz import fvtk r=fvtk.ren() print 'Vertex 117 is', verts[117] print 'Vertex 1 is', verts[1] print 'The number of local maxima is', np.sum(ind>0) """ - Vertex 117 is [ 0.54813892 0.76257497 0.34354511] - Vertex 1 is [ 0.0566983 0.17449942 0.98302352] - The number of local maxima is 2 """ summary = [] for i, index in enumerate(np.ndindex(QA.shape[:3])): if QA[index][0] > .0239: summary.append([index, np.sum(IN[index]>0), QA[index]]) #print i, index, np.sum(IN[index]>0), QA[index] print "There are %d suprathreshold voxels" % len(summary) maxcounts = np.zeros(10,'int') for voxel, count, indices in summary: maxcounts[count]+=1 #print maxcounts[maxcounts>0] """ We are using a fairly low threshold of 0.0239 and all 600 voxels are suprathreshold. maxcounts[maxcounts>0] = [ 0 405 152 30 10], so there are - 405 voxels with a single maximum (no crossing), - 152 with 2 maxima, - 30 voxels with 3 maxima, - 10 voxels with 4 maxima, - and 3 voxels with (at least) 5 maxima. We locate 3 contiguous voxels [3,8,4], [3,8,5], and [3,8,6] which have respectively 1, 2, and 3 crossings. ``fvtk.crossing`` is a helper function which we use to graph the orientations of the maxima of all the voxels in our dataset. We use 3 different colourings and offset the graphs to display them in one diagram. The colourings are: - all blue, with the 3 voxels used above ([3,8,4], [3,8,5], and [3,8,6]) marked in blue, indigo, and red. - the Boys' colour map (see ``colormap.boys2rgb.py``) - the orientation colour map (see ``colormap.orient2rgb.py`` with red: left-right; green: anteroposterior; blue: superior-inferior. """ #3,8,4 no crossing no_cross=fvtk.crossing(QA[3,8,4],IN[3,8,4],verts,1) #3,8,5 crossing cross=fvtk.crossing(QA[3,8,5],IN[3,8,5],verts,1) #3,8,6 double crossing dcross=fvtk.crossing(QA[3,8,6],IN[3,8,6],verts,1) all,allo=fvtk.crossing(QA,IN,verts,1,True) fvtk.add(r,fvtk.line(all,fvtk.azure,linewidth=1.)) no_cross_shift=[c+np.array([3,8,4]) for c in no_cross] cross_shift=[c+np.array([3,8,5]) for c in cross] dcross_shift=[c+np.array([3,8,6]) for c in dcross] fvtk.add(r,fvtk.line(no_cross_shift,fvtk.blue,linewidth=5.)) fvtk.add(r,fvtk.line(cross_shift,fvtk.indigo,linewidth=5.)) fvtk.add(r,fvtk.line(dcross_shift,fvtk.red,linewidth=5.)) from dipy.viz import colormap as cm all_shift=[c+np.array([10,0,0]) for c in all] all_shift2=[c+np.array([20,0,0]) for c in all] colors=np.zeros((len(all),3)) colors2=np.zeros((len(all),3)) for (i,a) in enumerate(all): #print a[0] colors[i]=cm.boys2rgb(allo[i]) colors2[i]=cm.orient2rgb(allo[i]) fvtk.add(r,fvtk.line(all_shift,colors,linewidth=1.)) fvtk.add(r,fvtk.line(all_shift2,colors2,linewidth=2.)) """ .. figure:: visualize_cross1000000.png :align: center **The crossings of a region of interest shown with one color, or boy2rgb or standard orient2rgb colormap**. """ # To show the figure # fvtk.show(r,size=(800,800)) # Here's how we make the illustration. print('Saving illustration as visualize_cross1000000.png') fvtk.record(r,n_frames=1,out_path='visualize_cross',size=(600,600)) dipy-0.5.0/doc/examples_built/000077500000000000000000000000001152576264200162555ustar00rootroot00000000000000dipy-0.5.0/doc/examples_built/.gitignore000066400000000000000000000001451152576264200202450ustar00rootroot00000000000000# Ignore everything in this directory apart from gitignore and the README file * !.gitignore !README dipy-0.5.0/doc/examples_built/README000066400000000000000000000012301152576264200171310ustar00rootroot00000000000000Examples built README --------------------- This directory is a build-time container for the examples. The /tools/make_examples.py script takes the examples in /doc/examples, compiles the examples to rst files, builds all the graphics, and puts the result into this directory, along with copies of the .py files. The contents of this directory then gets included because the /doc/example_index.rst file points to this directory and the built .rst files. Please don't put anything in this directory other than the .gitignore (ignore everything) and this README. If you need something in here, please copy it from the make_examples.py script. dipy-0.5.0/doc/examples_index.rst000066400000000000000000000006731152576264200170050ustar00rootroot00000000000000.. _examples: .. In order to build the examples, you'll need (on Debian) sudo apt-get install python-tables python-matplotib python-vtk ======== Examples ======== .. toctree:: :maxdepth: 2 note_about_examples examples_built/nii_2_tracks examples_built/find_correspondence examples_built/aniso_vox_2_isotropic examples_built/visualize_crossings examples_built/tractography_clustering examples_built/file_formats dipy-0.5.0/doc/faq.rst000066400000000000000000000201341152576264200145410ustar00rootroot00000000000000.. _faq: ========================== Frequently Asked Questions ========================== ----------- Theoretical ----------- 1. **What is a b-value?** The b-value $b$ or *diffusion weighting* is a function of the strength, duration and temporal spacing and timing parameters of the specific paradigm. This function is derived from the Bloch-Torrey equations. In the case of the classical Stejskal-Tanner pulsed gradient spin-echo (PGSE) sequence, at the time of readout $b=\gamma^{2}G^{2}\delta^{2}\left(\Delta-\frac{\delta}{3}\right)$ where $\gamma$ is the gyromagnetic radio, $\delta$ denotes the pulse width, $G$ is the gradient amplitude and $\Delta$ the centre to centre spacing. $\gamma$ is a constant, but we can change the other three parameters and in that way control the b-value. 2. **What is q-space?** Q-space is the space of one or more 3D spin displacement wave vectors $\mathbf{q}$ as shown in equation \ref{eq:fourier}. The vector $\mathbf{q}$ parametrises the space of diffusion gradients. It is related to the applied magnetic gradient $\mathbf{g}$ by the formula $\mathbf{q}=(2\pi)^{-1}\gamma\delta\mathbf{g}$. Every single vector $\mathbf{q}$ has the same orientation as the direction of diffusion gradient $\mathbf{g}$ and length proportional to the strength $g$ of the gradient field. Every single point in q-space corresponds to a possible 3D volume of the MR signal for a specific gradient direction and strength. Therefore if, for example, we have programmed the scanner to apply 60 gradient directions then our data should have 60 diffusion volumes with each volume obtained for a specific gradient. A Diffusion Weighted Image (DWI) is the volume acquired from only one direction gradient. 3. **What DWI stands for?** Diffusion Weighted Imaging (DWI) is MRI imaging designed to be sensitive to diffusion. A diffusion weighted image is a volume of voxel data gathered by applying only one gradient direction using a diffusion sequence. We expect that the signal in any voxel should be low if there is greater mobility of water molecules along the specified gradient direction and it should be high if there is less movement in that direction. Yes, it is counterintuitive but correct! However greater mobility gives greater opportunity for the proton spins to be dephased producing a smaller RF signal. 4. **Why dMRI and not DTI?** Diffusion MRI (dMRI or dwMRI) are prefered terms if you want to speak about diffusion weighted MRI in general. DTI (diffusion tensor imaging) is just one of the many ways you can reconstruct the voxel from your measured signal. There are plenty of others for example DSI, GQI, QBI etc. 5. **What is the recommended practice for registration of diffusion datasets?** Registration can be tricky. But this is what usually works for us for normal healthy adult subjects. We register the FA (fractional anisotropy) images to the FMRIB_FA_1mm template which is in MNI space using ``flirt`` and ``fnirth`` from FSL. Then we can apply the warping displacements in any other scalar volumes that we have to register that scalar volume into the MNI space. We need the corresponding inverse displacements to map a tractography into MNI space. 6. **What is the difference between Image coordinates and World coordinates?** Image coordinates have positive integer values and represent the centres $(i, j, k)$ of the voxels. There is an affine transform (stored in the nifti file) that takes the image coordinates and transforms them to millimeter (mm) in real world space. World coordinates have floating point precision and your dataset have 3 real dimensions e.g. $(x, y, z)$. 7. **Why 'tracks' and not 'tracts'?** Tractography is only an approximation or simulation - if you prefer - of the real tracts (brain neural fiber pathways or brain nerves). Therefore we prefer to call these simulated tracts as tracks (trajectories or curves represented as sequences of points joined by line segments) so that others will be clear that they are not the real tracts (fibers) but only an estimate or suggestion. We hope that in the future tractography could reach a point that what you see on your screen is a very faithful representation of what is actually in the white matter of the brain. However the field is not yet at this level of detail. 8. **Why use 'deterministic' and not 'probabilistic' tractography?** We wanted to create at the outset a tractographic method which will help us and you to get closer to datasets in a very efficient way. Therefore, we created first the ``EuDX`` (Euler Delta Crossings) algorithm which is a tracking method which can work both with model or model-free input and resolve also crossing fibers with a high order of crossings. Also it is very fast to calculate (~2 minutes for 1 million tracks ). We hope that at a later stage we will be able to incorporate and test more methods e.g. probabilistic, global and graph-theoretic. 9. **We made the mistake in our lab of generating datasets with nonisotropic voxel sizes wusehat do we do?** You need to resample your raw data to an isotropic size. Have a look at the module ``dipy.align.noniso2iso``. (We think it is a mistake to acquire nonisotropic data because the directional resolution of the data will depend on the orientation of the gradient with respect to the voxels, being lower when aligned with a longer voxel dimension.) 10. **Why nonisotropic voxel sizes are a bad idea in diffusion?** If for example you have $2 \times 2 \times 4 \textrm{mm}^3$ voxels, the last dimension will be averaged over the double distance and less detail will be captured compared to the other two dimensions. Furthermore, with very nonisotropic voxels the uncertainty on orientation estimates will depend on the position of the subject in the scanner. --------- Practical --------- 1. **Why python and not matlab or some other language?** python is free, batteries included, very well designed, painless to read and easy to use. There is nothing else like it. Give it a go. Once with python always with python. 2. **Isn't python slow?** True, some times python can be slow if you are using for example multiple nested for loops. In that case we use cython which takes execution up to C speed. 3. **What numerical libraries do you use in python?** The best ever designed numerical library numpy. 2. **Which python console do your recommend?** ``ipython`` 3. **What do you use for visualization?** We use ``fosvtk(fvtk)`` which depends in turn on ``python-vtk``:: from dipy.viz import fvtk 4. **What about interactive visualization?** There is already interaction in the ``fvtk`` module but we have started a new project only for visualization which we plan to integrate in ``dipy`` in the near future for more information have a look at http://fos.me 5. **Which file formats do you support?** Nifti (.nii), Dicom (Siemens(read-only)), Trackvis (.trk), Dipy (.dpy), Numpy (.npy, ,npz), text and any other formats supported by nibabel and pydicom. You can also read/save in Matlab version v4 (Level 1.0), v6 and v7 to 7.2 using scipy.io.loadmat. For higher versions >= 7.3 you can use pytables or any other python to hdf5 library e.g. h5py . For object serialization you can used dipy.io.pickles function load_pickle, save_pickle. 6. **What is dpy**? ``dpy`` is an ``hdf5`` file format which we use in dipy to store tractography and other information. This allows us to store huge tractographies and load different parts of the datasets directly from the disk as if it were in memory. 7. **Which python editor should I use?** Any text editor would do the job but we prefer the following Aptana, Emacs, Vim and Eclipse (with PyDev). 8. **I have problems reading my dicom files using nibabel, what should I do?** Use Chris Roden's dcm2nii to transform them to nifti files. http://www.cabiatl.com/mricro/mricron/dcm2nii.html Or you can make your own reader using pydicom http://code.google.com/p/pydicom/ and then use nibabel to store the data as niftis. \dipy-0.5.0/doc/glossary.rst000066400000000000000000000070731152576264200156440ustar00rootroot00000000000000========== Glossary ========== .. glossary:: Affine matrix A matrix implementing an :term:`affine transformation` in :term:`homogenous coordinates`. For a 3 dimensional transform, the matrix is shape 4 by 4. Affine transformation See `wikipedia affine`_ definition. An affine transformation is a :term:`linear transformation` followed by a translation. Axis angle A representation of rotation. See: `wikipedia axis angle`_ . From Euler's rotation theorem we know that any rotation or sequence of rotations can be represented by a single rotation about an axis. The axis $\boldsymbol{\hat{u}}$ is a :term:`unit vector`. The angle is $\theta$. The :term:`rotation vector` is a more compact representation of $\theta$ and $\boldsymbol{\hat{u}}$. Euclidean norm Also called Euclidean length, or L2 norm. The Euclidean norm $\|\mathbf{x}\|$ of a vector $\mathbf{x}$ is given by: .. math:: \|\mathbf{x}\| := \sqrt{x_1^2 + \cdots + x_n^2} Pure Pythagoras. Euler angles See: `wikipedia Euler angles`_ and `Mathworld Euler angles`_. Gimbal lock See :ref:`gimbal-lock` Homogenous coordinates See `wikipedia homogenous coordinates`_ Linear transformation A linear transformation is one that preserves lines - that is, if any three points are on a line before transformation, they are also on a line after transformation. See `wikipedia linear transform`_. Rotation, scaling and shear are linear transformations. Quaternion See: `wikipedia quaternion`_. An extension of the complex numbers that can represent a rotation. Quaternions have 4 values, $w, x, y, z$. $w$ is the *real* part of the quaternion and the vector $x, y, z$ is the *vector* part of the quaternion. Quaternions are less intuitive to visualize than :term:`Euler angles` but do not suffer from :term:`gimbal lock` and are often used for rapid interpolation of rotations. Reflection A transformation that can be thought of as transforming an object to its mirror image. The mirror in the transformation is a plane. A plan can be defined with a point and a vector normal to the plane. See `wikipedia reflection`_. Rotation matrix See `wikipedia rotation matrix`_. A rotation matrix is a matrix implementing a rotation. Rotation matrices are square and orthogonal. That means, that the rotation matrix $R$ has columns and rows that are :term:`unit vector`, and where $R^T R = I$ ($R^T$ is the transpose and $I$ is the identity matrix). Therefore $R^T = R^{-1}$ ($R^{-1}$ is the inverse). Rotation matrices also have a determinant of $1$. Rotation vector A representation of an :term:`axis angle` rotation. The angle $\theta$ and unit vector axis $\boldsymbol{\hat{u}}$ are stored in a *rotation vector* $\boldsymbol{u}$, such that: .. math:: \theta = \|\boldsymbol{u}\| \, \boldsymbol{\hat{u}} = \frac{\boldsymbol{u}}{\|\boldsymbol{u}\|} where $\|\boldsymbol{u}\|$ is the :term:`Euclidean norm` of $\boldsymbol{u}$ Shear matrix Square matrix that results in shearing transforms - see `wikipedia shear matrix`_. Unit vector A vector $\boldsymbol{\hat{u}}$ with a :term:`Euclidean norm` of 1. Normalized vector is a synonym. The "hat" over the $\boldsymbol{\hat{u}}$ is a convention to express the fact that it is a unit vector. dipy-0.5.0/doc/index.rst000066400000000000000000000055011152576264200151020ustar00rootroot00000000000000.. _home: #### Dipy #### Dipy_ is an *international*, **free** and **open soure** software project for **diffusion** *magnetic resonance imaging* **analysis**. Depends on a few standard libraries: python_ (the core language), numpy_ (for numerical computation), scipy_ (for more specific mathematical operations), cython_ (for extra speed) and nibabel_ (for file formats). Optionally, it can use python-vtk_ (for visualisation), pytables_ (for handling large datasets), matplotlib_ (for scientific plotting), and ipython_ (for interaction with the code and its results). Dipy is multiplatform and will run under any standard operating systems such as *Windows*, *Linux*, *Mac OS X*. Just some of our **state-of-the-art** applications are: - Reconstruction algorithms e.g. GQI, DTI - Tractography generation algorithms e.g. EuDX - Intelligent downsampling of tracks - Ultra fast tractography clustering - Resampling datasets with anisotropic voxels to isotropic - Visualizing multiple brains simultaneously - Finding track correspondence between different brains - Warping tractographies into another space e.g. MNI space - Reading many different file formats e.g. Trackvis or Nifti - Dealing with huge tractographies without memory restrictions - Playing with datasets interactively without storing - And much more and even more to come in next releases **Join in the fun** and enjoy the `video `_ we made for the Summer Exhibition in London for the celebration of the 350 years of the Royal Society. An Example ~~~~~~~~~~~~~ Here is a tiny usage example for dipy :: >>> import numpy as np >>> from dipy.reconst.dti import Tensor >>> from dipy.data import get_data >>> fimg,fbval,fbvec=get_data('small_101D') >>> import nibabel as nib >>> img=nib.load(fimg) >>> data=img.get_data() >>> bvals=np.loadtxt(fbvals) >>> gradients=np.loadtxt(fbvecs).T >>> ten=dti.Tensor(data,bvals,gradients,thresh=50) >>> FA=ten.fa() >>> MASK = FA < 0.2 In this code snippet we loaded a small diffusion dataset with their data, b-vectors and b-values, calculated the Tensors and fractional anisotropy (FA) and then created a mask to remove the regions with low anisotropy. :ref:`Download ` dipy and try it for yourself. A skeleton ~~~~~~~~~~~~~ .. figure:: _static/simplified_tractography.png :align: center **This is a depiction of a tractography skeleton created using dipy**. Using skeletal tracks we can very easily have a fast visual description of our datasets. If you want to learn more how you can create these with your datasets read the examples in our :ref:`documentation` . .. We need the following toctree directive to include the documentation .. in the document hierarchy - see http://sphinx.pocoo.org/concepts.html .. toctree:: :hidden: documentation dipy-0.5.0/doc/installation.rst000066400000000000000000000213201152576264200164710ustar00rootroot00000000000000.. _installation: ############ Installation ############ dipy_ is in active development at the moment. You can install it from our latest release, but you may find that the release has got well behind the current development - at least - we hope so - if we're developing fast enough! .. _python-versions: *********************** Note on python versions *********************** Sorry, but dipy_ does not yet work with python 3 - so all the instructions following instructions apply to python 2.5 or python 2.6 or python 2.7. On OSX we always use the python binaries available from the python.org downloads, and not the python that comes with the OSX system. If you don't have the python.org python you need to go to http://python.org/downloads, then download and install the python version you want (2.7 or 2.6 or 2.5). Check that you have this version on your path (perhaps after ``. ~/.bash_profile``) with ``which python``. This should show something like:: /Library/Frameworks/Python.framework/Versions/2.6/bin/python We've compiled dipy against this python, and all our testing on OSX too. ******************** Installing a release ******************** If you are on Debian or Ubuntu Linux we recommend you try :ref:`install-packages` first. Otherwise please try `install-easy-install`. .. _install-easy-install: Using easy_install ================== See first the :ref:`python-versions`. In case ``easy_install`` is not installed then please install setuptools_ or distribute_. Please install numpy_ and scipy_ using their respective binary installers if you haven't already. For windows you can use pythonxy_ to get numpy and scipy and lots of other useful python packages. This is quite a big package but will install lots of python stuff that is useful for your scientific endeavors. When you have numpy and scipy installed then try :: easy_install dipy This command should work under Linux, Mac OS X and Windows. Then from any python console or script try :: >>> import dipy Does it work? For any problems/suggestions please let us know by sending us an e-mail to the `nipy mailing list`_ with the subject line starting with ``[dipy]``. By the way, you might be tempted to try and run the tests after installing with easy_install. Unfortunately this doesn't work because of a problem with easy_install. To run the tests you need to install from source on windows or mac, or via a package on Linux (source also works on Linux). .. _install-packages: Using packages ============== Windows ------- Download and install numpy_ and scipy_ - or install pythonxy_. Install nibabel_ from `nibabel pypi`_ using the ``.exe`` installer. Install dipy from `dipy pypi`_ using the ``.exe`` installer for your version of python. Then from any python console or script try :: >>> import dipy OSX --- Download and install numpy_ and scipy_ using the OSX binary packages for your distribution of python. Install nibabel_ from `nibabel pypi`_ using the ``.mpkg`` installer for your version of python. Install dipy from `dipy pypi`_ using the ``.mpkg`` installer for your version of python. Then from any python console or script try :: >>> import dipy Linux ----- For Debian and Ubuntu, set up the NeuroDebian_ repositories - see `NeuroDebian how to`_. Then:: sudo apt-get install python-dipy We hope to get packages for the other Linux distributions, but for now, please try :ref:`install-easy-install` instead. ********************** Installing from source ********************** Getting the source ================== You can get the released source zip file or ``tar.gz`` archive from `dipy pypi`_. If you want the latest development source as an archive, go to the `dipy github`_ page, and click on the Download button. More likely you will want to get the source repository to be able to follow the latest changes. In that case, see :ref:`following-latest`. After you've unpacked the archive or cloned the repository, you will have a new directory, containing the dipy ``setup.py`` file, among others. We'll call this directory - that contains the ``setup.py`` file - the *dipy source root directory*. Sometimes we'll also call it the ```` directory. Building and installing ======================= Windows ------- pythonxy_ is probably the easiest way to install the dependencies that you need. Otherwise you will need python_ (obviously). You'll need to install the mingw_ compiler suite if you don't have a c compiler on your machine. We suggest you run the mingw_ automated installer, and install the developer tools, including msys_. Don't forget to put the mingw ``bin`` directory on your path so python can find the compiler. Install numpy_, scipy_, nibabel_ and cython_ from their respective binary installers. All of these come with pythonxy_ . You can also install them from their Windows binary installers. You'll find these by following the links from their home pages. Start a command shell like ``cmd`` or Powershell_ and change directory into the *dipy source root directory*. To install into your system:: python setup.py install --compiler=mingw32 To install inplace - so that dipy is running out of the source code directory:: python setup.py develop (this is the mode we recommend for following the latest source code). If you get an error with ``python setup.py develop`` make sure you have installed `setuptools`_. If you get an error saying "unable to find vcvarsall.bat" then you need to create a file called "pydistutils.cfg" in notepad and give it the contents :: [build] compiler=mingw32 Save this into your system python ``distutils`` directory as ``distutils.cfg``. This will be something like ``C:\Python26\Lib\distutils\distutils.cfg``. OSX --- See the :ref:`python-versions` for which python you need. Make sure you have Xcode_ installed. Download and install numpy_ and scipy_ from their respective download sites. Chose the version for your versions of OSX and python. Install cython_. This is probably most easily done with:: sudo easy_install cython Install nibabel_ :: sudo easy_install nibabel From here follow the :ref:`install-source-nix` instructions. Ubuntu/Debian ------------- :: sudo apt-get install python-dev python-setuptools sudo apt-get install python-numpy python-scipy then:: sudo easy_install cython sudo easy_install nibabel (we need the latest version of these two - hence ``easy_install`` rather than ``apt-get``). You might want the optional packages too (highly recommended):: sudo apt-get install ipython python-tables python-vtk python-matplotlib Now follow :ref:`install-source-nix`. Fedora / Mandriva maybe Redhat ------------------------------ Making this up, but:: yum install gcc-c++ yum install python-devel yum install python-setuptools yum install numpy scipy Then:: sudo easy_install cython sudo easy_install nibabel Options:: yum install ipython yum install python-matplotlib python-vtk python-tables Now follow :ref:`install-source-nix`. .. _install-source-nix: Install from source for unices (e.g Linux, OSX) ----------------------------------------------- Change directory into the *dipy source root directory* . To install for the system:: python setup.py install To build in the source tree so you can run the code in the source tree (recommended for following the latest source) either: * option 1 - using ``setup.py develop``:: python setup.py develop * option 2 - putting dipy into your search path manually. This is more long-winded but a bit easier to understand what's going on:: python setup.py build_ext --inplace and then symlink the ``/dipy`` directory into a directory on your python path (``>>> import sys; print sys.path``) or add the *dipy source root directory* into your ``PYTHONPATH`` environment variable. Search google for ``PYTHONPATH`` for details or see `python module path`_ for an introduction. When adding dipy_ to the ``PYTHONPATH``, we usually add the ``PYTHONPATH`` at the end of ``~/.bashrc`` or (OSX) ``~/.bash_profile`` so we don't need to retype it every time. This should look something like:: export PYTHONPATH=/home/user_dir/Devel/dipy:/home/user_dir/Devel/nibabel After changing the ``~/.bashrc`` or (OSX) ``~/.bash_profile`` try:: source ~/.bashrc or:: source ~/.bash_profile so that you can have immediate access to dipy_ without needing to restart your terminal. If you want to run the tests:: sudo easy_install nose Then (in python or ipython_):: >>> import dipy >>> dipy.test() You can also run the examples in ``/doc``. To build the documentation you will need:: sudo easy_install -U sphinx Then change directory to ```` and:: make html to make the html documentation. dipy-0.5.0/doc/introduction.rst000066400000000000000000000027601152576264200165200ustar00rootroot00000000000000.. _introduction: =============== What is dipy? =============== * **a python package** for analyzing ``diffusion data`` * a software library or **API** * a **module** and a lightweight **toolkit** * **a platform** to develop and test **old/new** algorithms * part of a **bigger plan** - the nipy_ suite * a new easy way to do diffusion **research** * **quick**, **scriptable** and **readable** * a project to contribute and **share** your code * always **free** * always **open source** * **A new** *window* into the ``brain``. Want to know more? Read our :ref:`documentation`, :ref:`installation` guidelines and try the :ref:`examples`. Didn't find what you are looking for? Then try :ref:`faq` and then if this doesn't help either send an e-mail to our e-mail list nipy-devel@neuroimaging.scipy.org with subject starting with ``[dipy]``. We would love to help :-) .. figure:: _static/three_brains_golden_new_small.png :align: center **This is a depiction of track correspondence between three different brains**. A few tracks were selected on the red brain and their corresponding tracks were found automatically on the cyan and blue brains [1]. If you want to learn more how you can create these with your datasets read the examples in our :ref:`documentation` . [1] Garyfallidis E, Brett M, Tsiaras V, Vogiatzis G, Nimmo-Smith I (2010), *“Identification of corresponding tracks in diffusion MRI tractographiesâ€* Proc. Intl. Soc. Mag. Reson. Med. 18 dipy-0.5.0/doc/links_names.inc000066400000000000000000000154201152576264200162400ustar00rootroot00000000000000.. This (-*- rst -*-) format file contains commonly used link targets and name substitutions. It may be included in many files, therefore it should only contain link targets and name substitutions. Try grepping for "^\.\. _" to find plausible candidates for this list. .. NOTE: reST targets are __not_case_sensitive__, so only one target definition is needed for nipy, NIPY, Nipy, etc... .. _nipy: http://nipy.org .. _`Brain Imaging Center`: http://bic.berkeley.edu/ .. _dipy: http://dipy.org .. _`dipy github`: http://github.com/Garyfallidis/dipy .. _dipy pypi: http://pypi.python.org/pypi/dipy .. _nibabel: http://nipy.sourceforge.net/nibabel .. _nibabel pypi: http://pypi.python.org/pypi/nibabel .. _nipy development guidelines: http://nipy.sourceforge.net/devel .. Packaging .. _neurodebian: http://neuro.debian.net .. _neurodebian how to: http://neuro.debian.net/#how-to-use-this-repository .. Documentation tools .. _graphviz: http://www.graphviz.org/ .. _Sphinx: http://sphinx.pocoo.org/ .. _`Sphinx reST`: http://sphinx.pocoo.org/rest.html .. _reST: http://docutils.sourceforge.net/rst.html .. _docutils: http://docutils.sourceforge.net .. Licenses .. _GPL: http://www.gnu.org/licenses/gpl.html .. _BSD: http://www.opensource.org/licenses/bsd-license.php .. _LGPL: http://www.gnu.org/copyleft/lesser.html .. Working process .. _pynifti: http://niftilib.sourceforge.net/pynifti/ .. _nifticlibs: http://nifti.nimh.nih.gov .. _nifti: http://nifti.nimh.nih.gov .. _`nipy launchpad`: https://launchpad.net/nipy .. _launchpad: https://launchpad.net/ .. _`nipy trunk`: https://code.launchpad.net/~nipy-developers/nipy/trunk .. _`nipy mailing list`: http://projects.scipy.org/mailman/listinfo/nipy-devel .. _`nipy bugs`: https://bugs.launchpad.net/nipy .. _pep8: http://www.python.org/dev/peps/pep-0008/ .. _`numpy coding style`: http://scipy.org/scipy/numpy/wiki/CodingStyleGuidelines .. _python module path: http://docs.python.org/tutorial/modules.html#the-module-search-path .. Code support stuff .. _pychecker: http://pychecker.sourceforge.net/ .. _pylint: http://www.logilab.org/project/pylint .. _pyflakes: http://divmod.org/trac/wiki/DivmodPyflakes .. _virtualenv: http://pypi.python.org/pypi/virtualenv .. _git: http://git.or.cz/ .. _github: http://github.com .. _flymake: http://flymake.sourceforge.net/ .. _rope: http://rope.sourceforge.net/ .. _pymacs: http://pymacs.progiciels-bpi.ca/pymacs.html .. _ropemacs: http://rope.sourceforge.net/ropemacs.html .. _ECB: http://ecb.sourceforge.net/ .. _emacs_python_mode: http://www.emacswiki.org/cgi-bin/wiki/PythonMode .. _doctest-mode: http://www.cis.upenn.edu/~edloper/projects/doctestmode/ .. _bazaar: http://bazaar-vcs.org/ .. _nose: http://somethingaboutorange.com/mrl/projects/nose .. _`python coverage tester`: http://nedbatchelder.com/code/modules/coverage.html .. _cython: http://cython.org .. Other python projects .. _numpy: http://numpy.scipy.org .. _scipy: http://www.scipy.org .. _ipython: http://ipython.scipy.org .. _`ipython manual`: http://ipython.scipy.org/doc/manual/html .. _matplotlib: http://matplotlib.sourceforge.net .. _pythonxy: http://www.pythonxy.com .. _ETS: http://code.enthought.com/projects/tool-suite.php .. _`Enthought Tool Suite`: http://code.enthought.com/projects/tool-suite.php .. _python: http://www.python.org .. _mayavi: http://mayavi.sourceforge.net/ .. _sympy: http://code.google.com/p/sympy/ .. _setuptools: http://pypi.python.org/pypi/setuptools .. _pytables: http://www.pytables.org .. _python-vtk: http://www.vtk.org .. Python imaging projects .. _PyMVPA: http://www.pymvpa.org .. _BrainVISA: http://brainvisa.info .. _anatomist: http://brainvisa.info .. _pydicom: http://code.google.com/p/pydicom/ .. Not so python imaging projects .. _matlab: http://www.mathworks.com .. _spm: http://www.fil.ion.ucl.ac.uk/spm .. _spm8: http://www.fil.ion.ucl.ac.uk/spm/software/spm8 .. _eeglab: http://sccn.ucsd.edu/eeglab .. _AFNI: http://afni.nimh.nih.gov/afni .. _FSL: http://www.fmrib.ox.ac.uk/fsl .. _FreeSurfer: http://surfer.nmr.mgh.harvard.edu .. _voxbo: http://www.voxbo.org .. _mricron: http://www.cabiatl.com/mricro/mricron .. _slicer: http://www.slicer.org/ .. File formats .. _DICOM: http://medical.nema.org/ .. _`wikipedia DICOM`: http://en.wikipedia.org/wiki/Digital_Imaging_and_Communications_in_Medicine .. _GDCM: http://sourceforge.net/apps/mediawiki/gdcm .. _`DICOM specs`: ftp://medical.nema.org/medical/dicom/2009/ .. _`DICOM object definitions`: ftp://medical.nema.org/medical/dicom/2009/09_03pu3.pdf .. _dcm2nii: http://www.cabiatl.com/mricro/mricron/dcm2nii.html .. _`mricron install`: http://www.cabiatl.com/mricro/mricron/install.html .. _dicom2nrrd: http://www.slicer.org/slicerWiki/index.php/Modules:DicomToNRRD-3.4 .. _Nrrd: http://teem.sourceforge.net/nrrd/format.html .. General software .. _gcc: http://gcc.gnu.org .. _xcode: http://developer.apple.com/TOOLS/xcode .. _mingw: http://www.mingw.org/wiki/Getting_Started .. _mingw distutils bug: http://bugs.python.org/issue2698 .. _cygwin: http://cygwin.com .. _macports: http://www.macports.org/ .. _VTK: http://www.vtk.org/ .. _ITK: http://www.itk.org/ .. _swig: http://www.swig.org .. Functional imaging labs .. _`functional imaging laboratory`: http://www.fil.ion.ucl.ac.uk .. _FMRIB: http://www.fmrib.ox.ac.uk .. Other organizations .. _enthought: .. _kitware: http://www.kitware.com .. _nitrc: http://www.nitrc.org .. General information links .. _`wikipedia FMRI`: http://en.wikipedia.org/wiki/Functional_magnetic_resonance_imaging .. _`wikipedia PET`: http://en.wikipedia.org/wiki/Positron_emission_tomography .. Mathematical methods .. _`wikipedia ICA`: http://en.wikipedia.org/wiki/Independent_component_analysis .. _`wikipedia PCA`: http://en.wikipedia.org/wiki/Principal_component_analysis .. Mathematical ideas .. _`wikipedia spherical coordinate system`: http://en.wikipedia.org/wiki/Spherical_coordinate_system .. _`mathworld spherical coordinate system`: http://mathworld.wolfram.com/SphericalCoordinates.html .. _`wikipedia affine`: http://en.wikipedia.org/wiki/Affine_transformation .. _`wikipedia linear transform`: http://en.wikipedia.org/wiki/Linear_transformation .. _`wikipedia rotation matrix`: http://en.wikipedia.org/wiki/Rotation_matrix .. _`wikipedia homogenous coordinates`: http://en.wikipedia.org/wiki/Homogeneous_coordinates .. _`wikipedia axis angle`: http://en.wikipedia.org/wiki/Axis_angle .. _`wikipedia Euler angles`: http://en.wikipedia.org/wiki/Euler_angles .. _`Mathworld Euler angles`: http://mathworld.wolfram.com/EulerAngles.html .. _`wikipedia quaternion`: http://en.wikipedia.org/wiki/Quaternion .. _`wikipedia shear matrix`: http://en.wikipedia.org/wiki/Shear_matrix .. _`wikipedia reflection`: http://en.wikipedia.org/wiki/Reflection_(mathematics) .. _`wikipedia direction cosine`: http://en.wikipedia.org/wiki/Direction_cosine .. vim:syntax=rst dipy-0.5.0/doc/make.bat000066400000000000000000000056131152576264200146520ustar00rootroot00000000000000@ECHO OFF REM Command file for Sphinx documentation set SPHINXBUILD=sphinx-build set ALLSPHINXOPTS=-d _build/doctrees %SPHINXOPTS% . if NOT "%PAPER%" == "" ( set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% ) if "%1" == "" goto help if "%1" == "help" ( :help echo.Please use `make ^` where ^ is one of echo. html to make standalone HTML files echo. dirhtml to make HTML files named index.html in directories echo. pickle to make pickle files echo. json to make JSON files echo. htmlhelp to make HTML files and a HTML help project echo. qthelp to make HTML files and a qthelp project echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter echo. changes to make an overview over all changed/added/deprecated items echo. linkcheck to check all external links for integrity echo. doctest to run all doctests embedded in the documentation if enabled goto end ) if "%1" == "clean" ( for /d %%i in (_build\*) do rmdir /q /s %%i del /q /s _build\* goto end ) if "%1" == "html" ( %SPHINXBUILD% -b html %ALLSPHINXOPTS% _build/html echo. echo.Build finished. The HTML pages are in _build/html. goto end ) if "%1" == "dirhtml" ( %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% _build/dirhtml echo. echo.Build finished. The HTML pages are in _build/dirhtml. goto end ) if "%1" == "pickle" ( %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% _build/pickle echo. echo.Build finished; now you can process the pickle files. goto end ) if "%1" == "json" ( %SPHINXBUILD% -b json %ALLSPHINXOPTS% _build/json echo. echo.Build finished; now you can process the JSON files. goto end ) if "%1" == "htmlhelp" ( %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% _build/htmlhelp echo. echo.Build finished; now you can run HTML Help Workshop with the ^ .hhp project file in _build/htmlhelp. goto end ) if "%1" == "qthelp" ( %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% _build/qthelp echo. echo.Build finished; now you can run "qcollectiongenerator" with the ^ .qhcp project file in _build/qthelp, like this: echo.^> qcollectiongenerator _build\qthelp\dipy.qhcp echo.To view the help file: echo.^> assistant -collectionFile _build\qthelp\dipy.ghc goto end ) if "%1" == "latex" ( %SPHINXBUILD% -b latex %ALLSPHINXOPTS% _build/latex echo. echo.Build finished; the LaTeX files are in _build/latex. goto end ) if "%1" == "changes" ( %SPHINXBUILD% -b changes %ALLSPHINXOPTS% _build/changes echo. echo.The overview file is in _build/changes. goto end ) if "%1" == "linkcheck" ( %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% _build/linkcheck echo. echo.Link check complete; look for any errors in the above output ^ or in _build/linkcheck/output.txt. goto end ) if "%1" == "doctest" ( %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% _build/doctest echo. echo.Testing of doctests in the sources finished, look at the ^ results in _build/doctest/output.txt. goto end ) :end dipy-0.5.0/doc/mission.rst000066400000000000000000000010251152576264200154510ustar00rootroot00000000000000.. _mission: =================== Mission statement =================== Mission of Statement The purpose of dipy is to make it **easier to do better diffusion MR imaging research**. Following up with the nipy mission statement we aim to build software that is * **clearly written** * **clearly explained** * **a good fit for the underlying ideas** * **a natural home for collaboration** We hope that, if we fail to do this, you will let us know and we will try and make it better. See also :ref:`introduction` dipy-0.5.0/doc/note_about_examples.rst000066400000000000000000000015441152576264200200330ustar00rootroot00000000000000========================= A note about the examples ========================= The examples here are some uses of the analysis and visualization functionality of dipy_, with example data from actual neuroscience experiments, or with synthetic data, which is generated as part of the example. All the examples presented in the documentation are generated from *fully functioning* python scripts, which are available as part of the source distribution in the doc/examples folder. If you want to replicate a particular analysis or visualization, simply copy the relevant ".py" script from the source distribution, edit out the body of the text of the example (which appear as blocks of text between triple quotes '"""') and alter it to your purpose. Thanks to the developers of PyMVPA_ for designing the software which enables us to provide these documented examples! dipy-0.5.0/doc/reference/000077500000000000000000000000001152576264200151765ustar00rootroot00000000000000dipy-0.5.0/doc/reference/align.rst000066400000000000000000000001661152576264200170250ustar00rootroot00000000000000=========== dipy align =========== .. currentmodule:: dipy.align .. autosummary:: :toctree: aniso2iso dipy-0.5.0/doc/reference/boots.rst000066400000000000000000000002131152576264200170520ustar00rootroot00000000000000=================== dipy bootstrapping =================== .. currentmodule:: dipy.boots .. autosummary:: :toctree: resampling dipy-0.5.0/doc/reference/core.rst000066400000000000000000000003651152576264200166640ustar00rootroot00000000000000=========== dipy core =========== .. automodule:: dipy.core :members: .. currentmodule:: dipy.core .. autosummary:: :toctree: geometry graph meshes onetime profile rng triangle_subdivide sphere_stats dipy-0.5.0/doc/reference/data.rst000066400000000000000000000001071152576264200166370ustar00rootroot00000000000000========= dipy data ========= .. automodule:: dipy.data :members: dipy-0.5.0/doc/reference/dipy.align.aniso2iso.rst000066400000000000000000000001521152576264200216710ustar00rootroot00000000000000:mod:`dipy.align.aniso2iso` ========================= .. automodule:: dipy.align.aniso2iso :members: dipy-0.5.0/doc/reference/dipy.boots.resampling.rst000066400000000000000000000001541152576264200221620ustar00rootroot00000000000000:mod:`dipy.boots.resampling` ========================= .. automodule:: dipy.boots.resampling :members: dipy-0.5.0/doc/reference/dipy.core.geometry.rst000066400000000000000000000001461152576264200214570ustar00rootroot00000000000000:mod:`dipy.core.geometry` ========================= .. automodule:: dipy.core.geometry :members: dipy-0.5.0/doc/reference/dipy.core.graph.rst000066400000000000000000000001401152576264200207170ustar00rootroot00000000000000:mod:`dipy.core.graph` ========================= .. automodule:: dipy.core.graph :members: dipy-0.5.0/doc/reference/dipy.core.meshes.rst000066400000000000000000000001421152576264200211040ustar00rootroot00000000000000:mod:`dipy.core.meshes` ========================= .. automodule:: dipy.core.meshes :members: dipy-0.5.0/doc/reference/dipy.core.onetime.rst000066400000000000000000000001441152576264200212620ustar00rootroot00000000000000:mod:`dipy.core.onetime` ========================= .. automodule:: dipy.core.onetime :members: dipy-0.5.0/doc/reference/dipy.core.profile.rst000066400000000000000000000001441152576264200212620ustar00rootroot00000000000000:mod:`dipy.core.profile` ========================= .. automodule:: dipy.core.profile :members: dipy-0.5.0/doc/reference/dipy.core.rng.rst000066400000000000000000000001341152576264200204070ustar00rootroot00000000000000:mod:`dipy.core.rng` ========================= .. automodule:: dipy.core.rng :members: dipy-0.5.0/doc/reference/dipy.core.sphere_stats.rst000066400000000000000000000001621152576264200223260ustar00rootroot00000000000000:mod:`dipy.core.sphere_stats` ============================= .. automodule:: dipy.core.sphere_stats :members: dipy-0.5.0/doc/reference/dipy.core.triangle_subdivide.rst000066400000000000000000000001761152576264200234720ustar00rootroot00000000000000:mod:`dipy.core.triangle_subdivide` ========================= .. automodule:: dipy.core.triangle_subdivide :members: dipy-0.5.0/doc/reference/dipy.external.fsl.rst000066400000000000000000000001441152576264200213000ustar00rootroot00000000000000:mod:`dipy.external.fsl` ========================= .. automodule:: dipy.external.fsl :members: dipy-0.5.0/doc/reference/dipy.io.bvectxt.rst000066400000000000000000000001351152576264200207600ustar00rootroot00000000000000:mod:`dipy.io.bvectxt` ====================== .. automodule:: dipy.io.bvectxt :members: dipy-0.5.0/doc/reference/dipy.io.dpy.rst000066400000000000000000000001241152576264200200730ustar00rootroot00000000000000:mod:`dipy.io.dpy` ===================== .. automodule:: dipy.io.dpy :members: dipy-0.5.0/doc/reference/dipy.io.pickles.rst000066400000000000000000000001341152576264200207320ustar00rootroot00000000000000:mod:`dipy.io.pickles` ===================== .. automodule:: dipy.io.pickles :members: dipy-0.5.0/doc/reference/dipy.reconst.dandelion.rst000066400000000000000000000001621152576264200223040ustar00rootroot00000000000000:mod:`dipy.reconst.dandelion` ============================= .. automodule:: dipy.reconst.dandelion :members: dipy-0.5.0/doc/reference/dipy.reconst.dti.rst000066400000000000000000000001421152576264200211250ustar00rootroot00000000000000:mod:`dipy.reconst.dti` ========================= .. automodule:: dipy.reconst.dti :members: dipy-0.5.0/doc/reference/dipy.reconst.gqi.rst000066400000000000000000000001421152576264200211250ustar00rootroot00000000000000:mod:`dipy.reconst.gqi` ========================= .. automodule:: dipy.reconst.gqi :members: dipy-0.5.0/doc/reference/dipy.reconst.maskedview.rst000066400000000000000000000001601152576264200225040ustar00rootroot00000000000000:mod:`dipy.reconst.maskedview` ========================= .. automodule:: dipy.reconst.maskedview :members: dipy-0.5.0/doc/reference/dipy.reconst.modelarray.rst000066400000000000000000000001651152576264200225110ustar00rootroot00000000000000:mod:`dipy.reconst.modelarray` ============================== .. automodule:: dipy.reconst.modelarray :members: dipy-0.5.0/doc/reference/dipy.reconst.qball.rst000066400000000000000000000001461152576264200214440ustar00rootroot00000000000000:mod:`dipy.reconst.qball` ========================= .. automodule:: dipy.reconst.qball :members: dipy-0.5.0/doc/reference/dipy.tracking.distances.rst000066400000000000000000000001651152576264200224540ustar00rootroot00000000000000:mod:`dipy.tracking.distances` ============================== .. automodule:: dipy.tracking.distances :members: dipy-0.5.0/doc/reference/dipy.tracking.learning.rst000066400000000000000000000001621152576264200222730ustar00rootroot00000000000000:mod:`dipy.tracking.learning` ============================= .. automodule:: dipy.tracking.learning :members: dipy-0.5.0/doc/reference/dipy.tracking.metrics.rst000066400000000000000000000001541152576264200221430ustar00rootroot00000000000000:mod:`dipy.tracking.metrics` ========================= .. automodule:: dipy.tracking.metrics :members: dipy-0.5.0/doc/reference/dipy.tracking.propagation.rst000066400000000000000000000001731152576264200230210ustar00rootroot00000000000000:mod:`dipy.tracking.propagation` ================================ .. automodule:: dipy.tracking.propagation :members: dipy-0.5.0/doc/reference/dipy.tracking.propspeed.rst000066400000000000000000000001651152576264200225000ustar00rootroot00000000000000:mod:`dipy.tracking.propspeed` ============================== .. automodule:: dipy.tracking.propspeed :members: dipy-0.5.0/doc/reference/dipy.tracking.vox2track.rst000066400000000000000000000001651152576264200224220ustar00rootroot00000000000000:mod:`dipy.tracking.vox2track` ============================== .. automodule:: dipy.tracking.vox2track :members: dipy-0.5.0/doc/reference/dipy.viz.fvtk.rst000066400000000000000000000001301152576264200204470ustar00rootroot00000000000000:mod:`dipy.viz.fvtk` ===================== .. automodule:: dipy.viz.fvtk :members: dipy-0.5.0/doc/reference/external.rst000066400000000000000000000002421152576264200175500ustar00rootroot00000000000000############# dipy external ############# .. automodule:: dipy.external :members: .. currentmodule:: dipy.external .. autosummary:: :toctree: fsl dipy-0.5.0/doc/reference/index.rst000066400000000000000000000004411152576264200170360ustar00rootroot00000000000000.. _reference: ================ dipy reference ================ :Release: |version| :Date: |today| This reference manual details functions, modules, and objects included in dipy. .. toctree:: core align boots data external io reconst tracking viz dipy-0.5.0/doc/reference/io.rst000066400000000000000000000001711152576264200163360ustar00rootroot00000000000000========= dipy io ========= .. currentmodule:: dipy.io .. autosummary:: :toctree: pickles bvectxt dpy dipy-0.5.0/doc/reference/reconst.rst000066400000000000000000000002611152576264200174040ustar00rootroot00000000000000============== dipy reconst ============== .. currentmodule:: dipy.reconst .. autosummary:: :toctree: dti gqi maskedview modelarray recspeed dipy-0.5.0/doc/reference/tracking.rst000066400000000000000000000003021152576264200175250ustar00rootroot00000000000000============== dipy tracking ============== .. currentmodule:: dipy.tracking .. autosummary:: :toctree: distances learning metrics propagation propspeed vox2trackdipy-0.5.0/doc/reference/viz.rst000066400000000000000000000001501152576264200165340ustar00rootroot00000000000000=========== dipy viz =========== .. currentmodule:: dipy.viz .. autosummary:: :toctree: fvtkdipy-0.5.0/doc/sphinxext/000077500000000000000000000000001152576264200152725ustar00rootroot00000000000000dipy-0.5.0/doc/sphinxext/docscrape.py000066400000000000000000000357051152576264200176210ustar00rootroot00000000000000"""Extract reference documentation from the NumPy source tree. """ import inspect import textwrap import re import pydoc from StringIO import StringIO from warnings import warn class Reader(object): """A line-based string reader. """ def __init__(self, data): """ Parameters ---------- data : str String with lines separated by '\n'. """ if isinstance(data,list): self._str = data else: self._str = data.split('\n') # store string as list of lines self.reset() def __getitem__(self, n): return self._str[n] def reset(self): self._l = 0 # current line nr def read(self): if not self.eof(): out = self[self._l] self._l += 1 return out else: return '' def seek_next_non_empty_line(self): for l in self[self._l:]: if l.strip(): break else: self._l += 1 def eof(self): return self._l >= len(self._str) def read_to_condition(self, condition_func): start = self._l for line in self[start:]: if condition_func(line): return self[start:self._l] self._l += 1 if self.eof(): return self[start:self._l+1] return [] def read_to_next_empty_line(self): self.seek_next_non_empty_line() def is_empty(line): return not line.strip() return self.read_to_condition(is_empty) def read_to_next_unindented_line(self): def is_unindented(line): return (line.strip() and (len(line.lstrip()) == len(line))) return self.read_to_condition(is_unindented) def peek(self,n=0): if self._l + n < len(self._str): return self[self._l + n] else: return '' def is_empty(self): return not ''.join(self._str).strip() class NumpyDocString(object): def __init__(self, docstring, config={}): docstring = textwrap.dedent(docstring).split('\n') self._doc = Reader(docstring) self._parsed_data = { 'Signature': '', 'Summary': [''], 'Extended Summary': [], 'Parameters': [], 'Returns': [], 'Raises': [], 'Warns': [], 'Other Parameters': [], 'Attributes': [], 'Methods': [], 'See Also': [], 'Notes': [], 'Warnings': [], 'References': '', 'Examples': '', 'index': {} } self._parse() def __getitem__(self,key): return self._parsed_data[key] def __setitem__(self,key,val): if not self._parsed_data.has_key(key): warn("Unknown section %s" % key) else: self._parsed_data[key] = val def _is_at_section(self): self._doc.seek_next_non_empty_line() if self._doc.eof(): return False l1 = self._doc.peek().strip() # e.g. Parameters if l1.startswith('.. index::'): return True l2 = self._doc.peek(1).strip() # ---------- or ========== return l2.startswith('-'*len(l1)) or l2.startswith('='*len(l1)) def _strip(self,doc): i = 0 j = 0 for i,line in enumerate(doc): if line.strip(): break for j,line in enumerate(doc[::-1]): if line.strip(): break return doc[i:len(doc)-j] def _read_to_next_section(self): section = self._doc.read_to_next_empty_line() while not self._is_at_section() and not self._doc.eof(): if not self._doc.peek(-1).strip(): # previous line was empty section += [''] section += self._doc.read_to_next_empty_line() return section def _read_sections(self): while not self._doc.eof(): data = self._read_to_next_section() name = data[0].strip() if name.startswith('..'): # index section yield name, data[1:] elif len(data) < 2: yield StopIteration else: yield name, self._strip(data[2:]) def _parse_param_list(self,content): r = Reader(content) params = [] while not r.eof(): header = r.read().strip() if ' : ' in header: arg_name, arg_type = header.split(' : ')[:2] else: arg_name, arg_type = header, '' desc = r.read_to_next_unindented_line() desc = dedent_lines(desc) params.append((arg_name,arg_type,desc)) return params _name_rgx = re.compile(r"^\s*(:(?P\w+):`(?P[a-zA-Z0-9_.-]+)`|" r" (?P[a-zA-Z0-9_.-]+))\s*", re.X) def _parse_see_also(self, content): """ func_name : Descriptive text continued text another_func_name : Descriptive text func_name1, func_name2, :meth:`func_name`, func_name3 """ items = [] def parse_item_name(text): """Match ':role:`name`' or 'name'""" m = self._name_rgx.match(text) if m: g = m.groups() if g[1] is None: return g[3], None else: return g[2], g[1] raise ValueError("%s is not a item name" % text) def push_item(name, rest): if not name: return name, role = parse_item_name(name) items.append((name, list(rest), role)) del rest[:] current_func = None rest = [] for line in content: if not line.strip(): continue m = self._name_rgx.match(line) if m and line[m.end():].strip().startswith(':'): push_item(current_func, rest) current_func, line = line[:m.end()], line[m.end():] rest = [line.split(':', 1)[1].strip()] if not rest[0]: rest = [] elif not line.startswith(' '): push_item(current_func, rest) current_func = None if ',' in line: for func in line.split(','): if func.strip(): push_item(func, []) elif line.strip(): current_func = line elif current_func is not None: rest.append(line.strip()) push_item(current_func, rest) return items def _parse_index(self, section, content): """ .. index: default :refguide: something, else, and more """ def strip_each_in(lst): return [s.strip() for s in lst] out = {} section = section.split('::') if len(section) > 1: out['default'] = strip_each_in(section[1].split(','))[0] for line in content: line = line.split(':') if len(line) > 2: out[line[1]] = strip_each_in(line[2].split(',')) return out def _parse_summary(self): """Grab signature (if given) and summary""" if self._is_at_section(): return summary = self._doc.read_to_next_empty_line() summary_str = " ".join([s.strip() for s in summary]).strip() if re.compile('^([\w., ]+=)?\s*[\w\.]+\(.*\)$').match(summary_str): self['Signature'] = summary_str if not self._is_at_section(): self['Summary'] = self._doc.read_to_next_empty_line() else: self['Summary'] = summary if not self._is_at_section(): self['Extended Summary'] = self._read_to_next_section() def _parse(self): self._doc.reset() self._parse_summary() for (section,content) in self._read_sections(): if not section.startswith('..'): section = ' '.join([s.capitalize() for s in section.split(' ')]) if section in ('Parameters', 'Returns', 'Raises', 'Warns', 'Other Parameters', 'Attributes', 'Methods'): self[section] = self._parse_param_list(content) elif section.startswith('.. index::'): self['index'] = self._parse_index(section, content) elif section == 'See Also': self['See Also'] = self._parse_see_also(content) else: self[section] = content # string conversion routines def _str_header(self, name, symbol='-'): return [name, len(name)*symbol] def _str_indent(self, doc, indent=4): out = [] for line in doc: out += [' '*indent + line] return out def _str_signature(self): if self['Signature']: return [self['Signature'].replace('*','\*')] + [''] else: return [''] def _str_summary(self): if self['Summary']: return self['Summary'] + [''] else: return [] def _str_extended_summary(self): if self['Extended Summary']: return self['Extended Summary'] + [''] else: return [] def _str_param_list(self, name): out = [] if self[name]: out += self._str_header(name) for param,param_type,desc in self[name]: out += ['%s : %s' % (param, param_type)] out += self._str_indent(desc) out += [''] return out def _str_section(self, name): out = [] if self[name]: out += self._str_header(name) out += self[name] out += [''] return out def _str_see_also(self, func_role): if not self['See Also']: return [] out = [] out += self._str_header("See Also") last_had_desc = True for func, desc, role in self['See Also']: if role: link = ':%s:`%s`' % (role, func) elif func_role: link = ':%s:`%s`' % (func_role, func) else: link = "`%s`_" % func if desc or last_had_desc: out += [''] out += [link] else: out[-1] += ", %s" % link if desc: out += self._str_indent([' '.join(desc)]) last_had_desc = True else: last_had_desc = False out += [''] return out def _str_index(self): idx = self['index'] out = [] out += ['.. index:: %s' % idx.get('default','')] for section, references in idx.iteritems(): if section == 'default': continue out += [' :%s: %s' % (section, ', '.join(references))] return out def __str__(self, func_role=''): out = [] out += self._str_signature() out += self._str_summary() out += self._str_extended_summary() for param_list in ('Parameters', 'Returns', 'Other Parameters', 'Raises', 'Warns'): out += self._str_param_list(param_list) out += self._str_section('Warnings') out += self._str_see_also(func_role) for s in ('Notes','References','Examples'): out += self._str_section(s) for param_list in ('Attributes', 'Methods'): out += self._str_param_list(param_list) out += self._str_index() return '\n'.join(out) def indent(str,indent=4): indent_str = ' '*indent if str is None: return indent_str lines = str.split('\n') return '\n'.join(indent_str + l for l in lines) def dedent_lines(lines): """Deindent a list of lines maximally""" return textwrap.dedent("\n".join(lines)).split("\n") def header(text, style='-'): return text + '\n' + style*len(text) + '\n' class FunctionDoc(NumpyDocString): def __init__(self, func, role='func', doc=None, config={}): self._f = func self._role = role # e.g. "func" or "meth" if doc is None: if func is None: raise ValueError("No function or docstring given") doc = inspect.getdoc(func) or '' NumpyDocString.__init__(self, doc) if not self['Signature'] and func is not None: func, func_name = self.get_func() try: # try to read signature argspec = inspect.getargspec(func) argspec = inspect.formatargspec(*argspec) argspec = argspec.replace('*','\*') signature = '%s%s' % (func_name, argspec) except TypeError, e: signature = '%s()' % func_name self['Signature'] = signature def get_func(self): func_name = getattr(self._f, '__name__', self.__class__.__name__) if inspect.isclass(self._f): func = getattr(self._f, '__call__', self._f.__init__) else: func = self._f return func, func_name def __str__(self): out = '' func, func_name = self.get_func() signature = self['Signature'].replace('*', '\*') roles = {'func': 'function', 'meth': 'method'} if self._role: if not roles.has_key(self._role): print "Warning: invalid role %s" % self._role out += '.. %s:: %s\n \n\n' % (roles.get(self._role,''), func_name) out += super(FunctionDoc, self).__str__(func_role=self._role) return out class ClassDoc(NumpyDocString): def __init__(self, cls, doc=None, modulename='', func_doc=FunctionDoc, config={}): if not inspect.isclass(cls) and cls is not None: raise ValueError("Expected a class or None, but got %r" % cls) self._cls = cls if modulename and not modulename.endswith('.'): modulename += '.' self._mod = modulename if doc is None: if cls is None: raise ValueError("No class or documentation string given") doc = pydoc.getdoc(cls) NumpyDocString.__init__(self, doc) if config.get('show_class_members', True): if not self['Methods']: self['Methods'] = [(name, '', '') for name in sorted(self.methods)] if not self['Attributes']: self['Attributes'] = [(name, '', '') for name in sorted(self.properties)] @property def methods(self): if self._cls is None: return [] return [name for name,func in inspect.getmembers(self._cls) if not name.startswith('_') and callable(func)] @property def properties(self): if self._cls is None: return [] return [name for name,func in inspect.getmembers(self._cls) if not name.startswith('_') and func is None] dipy-0.5.0/doc/sphinxext/docscrape_sphinx.py000066400000000000000000000171171152576264200212070ustar00rootroot00000000000000import re, inspect, textwrap, pydoc import sphinx from docscrape import NumpyDocString, FunctionDoc, ClassDoc class SphinxDocString(NumpyDocString): def __init__(self, docstring, config={}): self.use_plots = config.get('use_plots', False) NumpyDocString.__init__(self, docstring, config=config) # string conversion routines def _str_header(self, name, symbol='`'): return ['.. rubric:: ' + name, ''] def _str_field_list(self, name): return [':' + name + ':'] def _str_indent(self, doc, indent=4): out = [] for line in doc: out += [' '*indent + line] return out def _str_signature(self): return [''] if self['Signature']: return ['``%s``' % self['Signature']] + [''] else: return [''] def _str_summary(self): return self['Summary'] + [''] def _str_extended_summary(self): return self['Extended Summary'] + [''] def _str_param_list(self, name): out = [] if self[name]: out += self._str_field_list(name) out += [''] for param,param_type,desc in self[name]: out += self._str_indent(['**%s** : %s' % (param.strip(), param_type)]) out += [''] out += self._str_indent(desc,8) out += [''] return out @property def _obj(self): if hasattr(self, '_cls'): return self._cls elif hasattr(self, '_f'): return self._f return None def _str_member_list(self, name): """ Generate a member listing, autosummary:: table where possible, and a table where not. """ out = [] if self[name]: out += ['.. rubric:: %s' % name, ''] prefix = getattr(self, '_name', '') if prefix: prefix = '~%s.' % prefix autosum = [] others = [] for param, param_type, desc in self[name]: param = param.strip() if not self._obj or hasattr(self._obj, param): autosum += [" %s%s" % (prefix, param)] else: others.append((param, param_type, desc)) if autosum: out += ['.. autosummary::', ' :toctree:', ''] out += autosum if others: maxlen_0 = max([len(x[0]) for x in others]) maxlen_1 = max([len(x[1]) for x in others]) hdr = "="*maxlen_0 + " " + "="*maxlen_1 + " " + "="*10 fmt = '%%%ds %%%ds ' % (maxlen_0, maxlen_1) n_indent = maxlen_0 + maxlen_1 + 4 out += [hdr] for param, param_type, desc in others: out += [fmt % (param.strip(), param_type)] out += self._str_indent(desc, n_indent) out += [hdr] out += [''] return out def _str_section(self, name): out = [] if self[name]: out += self._str_header(name) out += [''] content = textwrap.dedent("\n".join(self[name])).split("\n") out += content out += [''] return out def _str_see_also(self, func_role): out = [] if self['See Also']: see_also = super(SphinxDocString, self)._str_see_also(func_role) out = ['.. seealso::', ''] out += self._str_indent(see_also[2:]) return out def _str_warnings(self): out = [] if self['Warnings']: out = ['.. warning::', ''] out += self._str_indent(self['Warnings']) return out def _str_index(self): idx = self['index'] out = [] if len(idx) == 0: return out out += ['.. index:: %s' % idx.get('default','')] for section, references in idx.iteritems(): if section == 'default': continue elif section == 'refguide': out += [' single: %s' % (', '.join(references))] else: out += [' %s: %s' % (section, ','.join(references))] return out def _str_references(self): out = [] if self['References']: out += self._str_header('References') if isinstance(self['References'], str): self['References'] = [self['References']] out.extend(self['References']) out += [''] # Latex collects all references to a separate bibliography, # so we need to insert links to it if sphinx.__version__ >= "0.6": out += ['.. only:: latex',''] else: out += ['.. latexonly::',''] items = [] for line in self['References']: m = re.match(r'.. \[([a-z0-9._-]+)\]', line, re.I) if m: items.append(m.group(1)) out += [' ' + ", ".join(["[%s]_" % item for item in items]), ''] return out def _str_examples(self): examples_str = "\n".join(self['Examples']) if (self.use_plots and 'import matplotlib' in examples_str and 'plot::' not in examples_str): out = [] out += self._str_header('Examples') out += ['.. plot::', ''] out += self._str_indent(self['Examples']) out += [''] return out else: return self._str_section('Examples') def __str__(self, indent=0, func_role="obj"): out = [] out += self._str_signature() out += self._str_index() + [''] out += self._str_summary() out += self._str_extended_summary() for param_list in ('Parameters', 'Returns', 'Other Parameters', 'Raises', 'Warns'): out += self._str_param_list(param_list) out += self._str_warnings() out += self._str_see_also(func_role) out += self._str_section('Notes') out += self._str_references() out += self._str_examples() for param_list in ('Attributes', 'Methods'): out += self._str_member_list(param_list) out = self._str_indent(out,indent) return '\n'.join(out) class SphinxFunctionDoc(SphinxDocString, FunctionDoc): def __init__(self, obj, doc=None, config={}): self.use_plots = config.get('use_plots', False) FunctionDoc.__init__(self, obj, doc=doc, config=config) class SphinxClassDoc(SphinxDocString, ClassDoc): def __init__(self, obj, doc=None, func_doc=None, config={}): self.use_plots = config.get('use_plots', False) ClassDoc.__init__(self, obj, doc=doc, func_doc=None, config=config) class SphinxObjDoc(SphinxDocString): def __init__(self, obj, doc=None, config={}): self._f = obj SphinxDocString.__init__(self, doc, config=config) def get_doc_object(obj, what=None, doc=None, config={}): if what is None: if inspect.isclass(obj): what = 'class' elif inspect.ismodule(obj): what = 'module' elif callable(obj): what = 'function' else: what = 'object' if what == 'class': return SphinxClassDoc(obj, func_doc=SphinxFunctionDoc, doc=doc, config=config) elif what in ('function', 'method'): return SphinxFunctionDoc(obj, doc=doc, config=config) else: if doc is None: doc = pydoc.getdoc(obj) return SphinxObjDoc(obj, doc, config=config) dipy-0.5.0/doc/sphinxext/math_dollar.py000066400000000000000000000037731152576264200201440ustar00rootroot00000000000000import re def dollars_to_math(source): r""" Replace dollar signs with backticks. More precisely, do a regular expression search. Replace a plain dollar sign ($) by a backtick (`). Replace an escaped dollar sign (\$) by a dollar sign ($). Don't change a dollar sign preceded or followed by a backtick (`$ or $`), because of strings like "``$HOME``". Don't make any changes on lines starting with spaces, because those are indented and hence part of a block of code or examples. This also doesn't replaces dollar signs enclosed in curly braces, to avoid nested math environments, such as :: $f(n) = 0 \text{ if $n$ is prime}$ Thus the above line would get changed to `f(n) = 0 \text{ if $n$ is prime}` """ s = "\n".join(source) if s.find("$") == -1: return # This searches for "$blah$" inside a pair of curly braces -- # don't change these, since they're probably coming from a nested # math environment. So for each match, we replace it with a temporary # string, and later on we substitute the original back. global _data _data = {} def repl(matchobj): global _data s = matchobj.group(0) t = "___XXX_REPL_%d___" % len(_data) _data[t] = s return t s = re.sub(r"({[^{}$]*\$[^{}$]*\$[^{}]*})", repl, s) # matches $...$ dollars = re.compile(r"(? vpAglXŒ`3€IDATxÚìÝw|ÕúÇñÏœ™Ý’B!„Ы€ bA ˆ¢н\ìX¯ríõZ~нlˆ (Hé„ @$!„’33¿? 7\M.e’ð¼_/_áìÎÎ~g³BöÉ9ç1¼}B!„B!„B4åw!„B!„B!ª2)° !„B!„Bq¤À&„B!„B!Ä!›B!„B!„‡@ lB!„B!„B)° !„B!„Bq¤À&„B!„B!Ä!›B!„B!„‡@ lB!„B!„B)° !„B!„Bq¤À&„B!„B!Ä!›B!„B!„‡@ lB!„B!„B)° !„B!„Bq¤À&„B!„B!Ä!›B!„B!„‡@ lB!„ÿ£åË—/_¾ Ã0 ÿ¯õêÕ«W¯žß¯†B!ıK lB!„ÿ£U«V­ZµÊïбcÇŽ;úB!„âØ%6!„Bˆÿ‘ß6Ó4MÓ„›o¾ùæ›oöûÕB!„8vÞ>~B!„{ÁyçwÞyçÁÌ™3gΜYv¿RJ)Ÿ|òÉ'Ÿ|ƒ 4hß©…B!Ž]2ƒM!„¢’(.....†‹.ºè¢‹.úsa­Ô[o½õÖ[oIaM!„¢²›B!„ÏB¡P(‚þýû÷ïߦL™2eÊ”?÷òË/¿üòËpà 7Üpà ~§B!„¥¤À&„Bá­µÖ.»ì²Ë.» &L˜0a„?÷ä“O>ùä“pçwÞyç~§B!„$6!„Bˆ£ÌqÇq`ðàÁƒ†1cÆŒ3æÏÇ=øàƒ>ø <ôÐC=ôß©…B!DE¤ÉB!ÄQ⺮ëºpÍ5×\sÍ5eM þèöÛo¿ýöÛá•W^yå•WüN-„B!þŽØ„B!Ž°ÒŸ¶nºé¦›nº Þ}÷Ýwß}÷ÏÇ]wÝu×]w]Ùý†a†áwz!„Bñw¤À&„Bq„ÝvÛm·Ýv¼öÚk¯½öÚŸï8pàÀaäÈ‘#GŽ¥”R²‘‡B!D•!?º !„B!÷Þ{ï½÷Þ[qa­oß¾}ûö-[**…5!„BˆªI~„B!„8̆>|øpxá…^xá…?ßß³gÏž={ÂW_}õÕW_eY–eùZq(<×s=œgiíw!„G›,B!„8Lž~úé§Ÿ~ºâ®ŸÝºuëÖ­üøã?þø#DDDDDDøZq(¶ÎÌOËÜKŠ×ÌýµìösÏ=á„K/õ;Bˆ£E~W*„Bqˆ^z饗^z©âÂÚ)§œrÊ)§À?üðÃ?HaMˆª,?¿°0'Vd~»4vïn›»ÈïTB!ü&6!„BˆÿÑ믿þúë¯Ã°aÆ öçû;tèСC‡²kQQQQQQ~§BŒÂ¼â¬‚X—’µfùrØúm~Lf&YéÁO %ª^¯¦Ï@¡Y”¾ë(Z]|káVà\æ#3Ø„â˜!KD…B!Ò{ï½÷Þ{ïÁ7Þxã7šjݺuëÖ­aÆŒ3fÌ€:uêÔ©SÇïÔBˆ2m»8ÖoùaeSØüzî„´÷Áz_µ°Š¡nnÜI½!á­Úõëå€Ñ‚T•éqÙƒÖfC¨Yɺ’qй uZ¯ü¾!„G‹Ì`B!„8@#GŽ9r$ÜtÓM7ÝtÓŸ kM›6mÚ´)L™2eÊ”)RX¢*(mJno­·z5¤ÿœ½vÍ“à¥z[Ý<¨suÍ×êÕ†úÖqêÏ5ÍÜX@êç%ÝÓ)°Uº !ıGf° !„BüQ£F5 ®¸âŠ+®¸Çq§ìþ 4hÐfÍš5kÖ,hذaÆ ýN-„(Oi·ÏŒ9“×Â:²‡¦&€}н®¤b_¯Ù v&$ÝXû·–´ÂÝ¿?oZƒŒ^«º 3õ>œl5_tö`¿¯V!ÄÑ"¿[B!„¨À„ &L˜ƒ 4hП k¥6oÞ¼yófhÔ¨Q£FÀ0 Ã0ÿׇ~øá‡öûU¢jÚæíŒÉÌ„9y+ëMª +ó7',œá7Ÿ †ß4,:n54>»Þ¢æ›¼°VÊçå»É ¾äfó¿¯V!ÄÑ&KD…B!*PÚÄ@k­µö; 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(Nm,3) split_thrs : sequence of 3 floats with the squared distances approx_tracks: bool if True return an approximation of the initial tracks info: bool print some information Returns -------- C : dict a tree graph containing the clusters atracks : sequence of approximated tracks the approximation preserves initial shape. ''' ''' t1=time.clock() print 'Reducing to 3-point approximate tracks...' tracks3=[tm.downsample(t,3) for t in tracks] t2=time.clock() print 'Done in ', t2-t1, 'secs' print 'Reducing to n-point approximate tracks...' atracks=[pf.approx_polygon_track(t) for t in tracks] t3=time.clock() print 'Done in ', t3-t2, 'secs' print('Starting larch_preprocessing...') C=pf.larch_preproc(tracks3,split_thrs,info) t4=time.clock() print 'Done in ', t4-t3, 'secs' print('Finding most similar tracks in every cluster ...') for c in C: local_tracks=[atracks[i] for i in C[c]['indices']] #identify the most similar track in the cluster C[c] and return the index of #the track and the distances of this track with all other tracks msi,distances=pf.most_similar_track_mam(local_tracks,metric='avg') C[c]['repz']=atracks[C[c]['indices'][msi]] C[c]['repz_dists']=distances print 'Done in ', time.clock()-t4 if ret_atracks: return C,atracks else: return C ''' return def detect_corresponding_tracks(indices,tracks1,tracks2): ''' Detect corresponding tracks from 1 to 2 Parameters ---------- indices : sequence of indices of tracks1 that are to be detected in tracks2 tracks1 : sequence of tracks as arrays, shape (N1,3) .. (Nm,3) tracks2 : sequence of tracks as arrays, shape (M1,3) .. (Mm,3) Returns ------- track2track : array of int showing the correspondance ''' li=len(indices) track2track=np.zeros((li,3)) cnt=0 for i in indices: rt=[pf.zhang_distances(tracks1[i],t,'avg') for t in tracks2] rt=np.array(rt) track2track[cnt-1]=np.array([cnt,i,rt.argmin()]) cnt+=1 return track2track.astype(int) def detect_corresponding_tracks_extended(indices,tracks1,indices2,tracks2): ''' Detect corresponding tracks from 1 to 2 Parameters: ---------------- indices: sequence of indices of tracks1 that are to be detected in tracks2 tracks1: sequence of tracks as arrays, shape (N1,3) .. (Nm,3) indices2: sequence of indices of tracks2 in the initial brain tracks2: sequence of tracks as arrays, shape (M1,3) .. (Mm,3) Returns: ----------- track2track: array of int showing the correspondance ''' li=len(indices) track2track=np.zeros((li,3)) cnt=0 for i in indices: rt=[pf.zhang_distances(tracks1[i],t,'avg') for t in tracks2] rt=np.array(rt) track2track[cnt-1]=np.array([cnt,i,indices2[rt.argmin()]]) cnt+=1 return track2track.astype(int) def rm_far_ends(ref,tracks,dist=25): ''' rm tracks with far endpoints Parameters ---------- ref : array, shape (N,3) xyz points of the reference track tracks : sequence of tracks as arrays, shape (N1,3) .. (Nm,3) dist : float endpoint distance threshold Returns ------- tracksr : sequence reduced tracks indices : sequence indices of tracks ''' indices=[i for (i,t) in enumerate(tracks) if tm.max_end_distances(t,ref) <= dist] tracksr=[tracks[i] for i in indices] return tracksr,indices def rm_far_tracks(ref,tracks,dist=25,down=False): ''' Remove tracks which are far away using as a distance metric the average euclidean distance of the following three points start point, midpoint and end point. Parameters ---------- ref : array, shape (N,3) xyz points of the reference track tracks : sequence of tracks as arrays, shape (N1,3) .. (Nm,3) dist : float average distance threshold down: bool {True, False} if down = True then ref and tracks are already downsampled if down = False then downsample them Returns ------- tracksr : sequence reduced tracks indices : sequence indices of tracks ''' if down==False: tracksd=[tm.downsample(t,3) for t in tracks] refd=tm.downsample(ref,3) indices=[i for (i,t) in enumerate(tracksd) if np.mean(np.sqrt(np.sum((t-refd)**2,axis=1))) <= dist] tracksr=[tracks[i] for i in indices] return tracksr, indices if down==True: indices=[i for (i,t) in enumerate(tracks) if np.mean(np.sqrt(np.sum((t-ref)**2,axis=1))) <= dist] tracksr=[tracks[i] for i in indices] return tracksr,indices def missing_tracks(indices1,indices2): ''' Missing tracks in bundle1 but not bundle2 Parameters: ------------------ indices1: sequence of indices of tracks in bundle1 indices2: sequence of indices of tracks in bundle2 Returns: ----------- indices: sequence of indices of tracks in bundle1 absent from bundle2 Example: ------------- >>> tracksar,indar=rm_far_tracks(ref,tracksa,dist=20) >>> fornix_ind=G[5]['indices'] >>> len(missing_tracks(fornix_ind, indar)) = 5 >>> tracksar,indar=rm_far_tracks(ref,tracksa,dist=25) >>> fornix_ind=G[5]['indices'] >>> len(missing_tracks(fornix_ind, indar)) = 0 ''' return list(set(indices1).difference(set(indices2))) def skeletal_tracks(tracks,rand_selected=1000,ball_radius=5,neighb_no=50): ''' Filter out unnescessary tracks and keep only a few good ones. Aka the balls along a track method. Parameters: ---------------- tracks: sequence of tracks rand_selected: int number of initially selected fibers ball_radius: float balls along tracks radii neighb_no: int lowest threshold for the number of tracks included Returns: ----------- reps: sequence of indices of representative aka skeletal tracks. They should be <= rand_selected ''' trackno=len(tracks) #select 1000 random tracks random_indices=(trackno*np.random.rand(rand_selected)).astype(int) tracks3points=[tm.downsample(t,3) for t in tracks] #store representative tracks representative=[] representative_indices=[] #store indices of already visited tracks i.e. which already have a representative track visited=[] import time t1=time.clock() # for every index of the possible representative tracks for (i,t) in enumerate(random_indices): #if track is not already classified if i not in visited: print(i,t) #rm far tracks tracksr,indices=rm_far_tracks(tracks3points[t],tracks3points,dist=25,down=True) cnt_neighb=0 just_visited=[] #for every possible neighbour track tr with index tri for tri in indices: cnt_intersected_balls=0 #for every point of the possible representative track for p in tracks[t]: #if you intersect the sphere surrounding the point of the random track increase a counter if tm.inside_sphere(tracks[tri],p,ball_radius): cnt_intersected_balls+=1 #if all spheres are covered then accept this track as your neighbour if cnt_intersected_balls ==len(tracks[t]): cnt_neighb+=1 just_visited.append(tri) #if the number of possible neighbours is above threshold then accept track[t] as a representative fiber if cnt_neighb>=neighb_no: representative.append(t) visited=visited+just_visited print 'Time:',time.clock()-t1 return representative def detect_corpus_callosum(tracks,plane=91,ysize=217,zsize=181,width=1.0,use_atlas=0,use_preselected_tracks=0,ball_radius=5): ''' Detect corpus callosum in a mni registered dataset of shape (181,217,181) Parameters: ---------------- tracks: sequence of tracks Returns: ---------- cc_indices: sequence with the indices of the corpus_callosum tracks left_indices: sequence with the indices of the rest of the brain ''' cc=[] #for every track for (i,t) in enumerate(tracks): #for every index of any point in the track for pi in range(len(t)-1): #if track segment is cutting the plane (assuming the plane is at the x-axis X=plane) if (t[pi][0] <= plane and t[pi+1][0] >= plane) or (t[pi+1][0] <= plane and t[pi][0] >= plane) : v=t[pi+1]-t[pi] k=(plane-t[pi][0])/v[0] hit=k*v+t[pi] #report the index of the track and the point of intersection with the plane cc.append((i,hit)) #indices cc_i=[c[0] for c in cc] print 'Number of tracks cutting plane Before',len(cc_i) #hit points cc_p=np.array([c[1] for c in cc]) p_neighb=len(cc_p)*[0] cnt=0 #imaging processing from now on im=np.zeros((ysize,zsize)) im2=np.zeros((ysize,zsize)) im_track={} cnt=0 for p in cc_p: p1=int(round(p[1])) p2=int(round(p[2])) im[p1,p2]=1 im2[p1,p2]=im2[p1,p2]+1 try: im_track[(p1,p2)]=im_track[(p1,p2)]+[cc_i[cnt]] except: im_track[(p1,p2)]=[cc_i[cnt]] cnt+=1 #create a cross structure cross=np.array([[0,1,0],[1,1,1],[0,1,0]]) im=(255*im).astype('uint8') im2=(np.interp(im2,[0,im2.max()],[0,255])).astype('uint8') #erosion img=nd.binary_erosion(im,structure=cross) #and another one erosion #img=nd.binary_erosion(img,structure=cross) #im2g=nd.grey_erosion(im2,structure=cross) #im2g2=nd.grey_erosion(im2g,structure=cross) indg2=np.where(im2==im2.max()) p1max=indg2[0][0] p2max=indg2[1][0] #label objects imgl=nd.label(img) no_labels=imgl[1] imgl=imgl[0] #find the biggest objects the second biggest should be the cc the biggest should be the background ''' find_big=np.zeros(no_labels) for i in range(no_labels): ind=np.where(imgl==i) find_big[i]=len(ind[0]) print find_big find_bigi=np.argsort(find_big) ''' cc_label=imgl[p1max,p2max] imgl2=np.zeros((ysize,zsize)) #cc is found and copied to a new image here #imgl2[imgl==int(find_bigi[-2])]=1 imgl2[imgl==int(cc_label)]=1 imgl2=imgl2.astype('uint8') #now do another dilation to recover some cc shape from the previous erosion imgl2d=nd.binary_dilation(imgl2,structure=cross) #and another one #imgl2d=nd.binary_dilation(imgl2d,structure=cross) imgl2d=imgl2d.astype('uint8') #get the tracks back cc_indices=[] indcc=np.where(imgl2d>0) for i in range(len(indcc[0])): p1=indcc[0][i] p2=indcc[1][i] cc_indices=cc_indices+im_track[(p1,p2)] print 'After', len(cc_indices) #export also the rest of the brain indices=range(len(tracks)) left=set(indices).difference(set(cc_indices)) left_indices=[l for l in left] #return im,im2,imgl2d,cc_indices,left_indices return cc_indices,left_indices def track_indices_for_a_value_in_atlas(atlas,value,tes,tracks): ind=np.where(atlas==value) indices=set([]) for i in range(len(ind[0])): try: tmp=tes[(ind[0][i], ind[1][i], ind[2][i])] indices=indices.union(set(tmp)) except: pass #bundle=[tracks[i] for i in list(indices)] #return bundle,list(indices) return list(indices) def relabel_by_atlas_value_and_mam(atlas_tracks,atlas,tes,tracks,tracksd,zhang_thr): emi=emi_atlas() brain_relabeled={} for e in range(1,9): #from emi: print emi[e]['bundle_name'] indices=emi[e]['init_ref']+emi[e]['selected_ref']+emi[e]['apr_ref'] tmp=detect_corresponding_tracks(indices,atlas_tracks,tracks) corresponding_indices=tmp[:,2] corresponding_indices=list(set(corresponding_indices)) value_indices=[] for value in emi[e]['value']: value_indices+=track_indices_for_a_value_in_atlas(atlas,value,tes,tracks) value_indices=list(set(value_indices)) print 'len corr_ind',len(corresponding_indices) #check if value_indices do not have anything in common with corresponding_indices and expand if list(set(value_indices).intersection(set(corresponding_indices)))==[]: #value_indices=corresponding_indices print 'len corr_ind',len(corresponding_indices) for ci in corresponding_indices: print 'koukou',ci ref=tracksd[ci] brain_rf, ind_fr = rm_far_tracks(ref,tracksd,dist=10,down=True) value_indices+=ind_fr value_indices=list(set(value_indices)) print 'len vi',len(value_indices) value_indices_new=[] #reduce value_indices which are far from every corresponding fiber for vi in value_indices: dist=[] for ci in corresponding_indices: dist.append(pf.zhang_distances(tracks[vi],tracks[ci],'avg')) for d in dist: if d <= zhang_thr[e-1]: value_indices_new.append(vi) value_indices=list(set(value_indices_new)) #store value indices brain_relabeled[e]={} brain_relabeled[e]['value_indices']=value_indices brain_relabeled[e]['corresponding_indices']=corresponding_indices brain_relabeled[e]['color']=emi[e]['color'] brain_relabeled[e]['bundle_name']=emi[e]['bundle_name'][0] return brain_relabeled def threshold_hitdata(hitdata, divergence_threshold=0.25, fibre_weight=0.8): ''' [1] Removes hits in hitdata which have divergence above threshold. [2] Removes fibres in hitdata whose fraction of remaining hits is below the required weight. Parameters: ---------------- ref: array, shape (N,5) xyzrf hit data from cut_planes divergence_threshold: float if radial coefficient of divergence is above this then drop the hit fibre_weight: float the number of remaing hits on a fibre as a fraction of len(trackdata), which is the maximum number possible Returns: ----------- reduced_hitdata: array, shape (M, 5) light_weight_fibres: list of integer track indices ''' # first pass: remove hits with r>divergence_threshold firstpass = [[[x,y,z,r,f] for (x,y,z,r,f) in plane if r<=divergence_threshold] for plane in hitdata] # second pass: find fibres hit weights fibrecounts = {} for l in [[f,r] for (x,y,z,r,f) in itertools.chain(*firstpass)]: f = l[0].astype('int') try: fibrecounts[f] += 1 except: fibrecounts[f] = 1 weight_thresh = len(hitdata)*fibre_weight heavy_weight_fibres = [f for f in fibrecounts.keys() if fibrecounts[f]>=weight_thresh] # third pass reduced_hitdata = [np.array([[x,y,z,r,f] for (x,y,z,r,f) in plane if fibrecounts[f.astype('int')] >= weight_thresh]) for plane in firstpass] return reduced_hitdata, heavy_weight_fibres def neck_finder(hitdata, ref): ''' To identify regions of concentration of fibres related by hitdata to a reference fibre ''' #typically len(hitdata) = len(ref)-2 at present, though it should ideally be # len(ref)-1 which is the number of segments in ref # We will assume that hitdata[i] relates to the segment from ref[i] to ref[i+1] #xyz=[] #rcd=[] #fibres=[] weighted_mean_rcd = [] unweighted_mean_rcd = [] weighted_mean_dist = [] unweighted_mean_dist = [] hitcount = [] for (p, plane) in enumerate(hitdata): xyz = plane[:,:3] rcd =plane[:,3] fibres = plane[:,4] hitcount +=[len(plane)] radial_distances=np.sqrt(np.diag(np.inner(xyz-ref[p],xyz-ref[p]))) unweighted_mean_rcd += [np.average(1-rcd)] weighted_mean_rcd += [np.average(1-rcd, weights=np.exp(-radial_distances))] unweighted_mean_dist += [np.average(np.exp(-radial_distances))] weighted_mean_dist += [np.average(np.exp(-radial_distances), weights=1-rcd)] return np.array(hitcount), np.array(unweighted_mean_rcd), np.array(weighted_mean_rcd), \ np.array(unweighted_mean_dist), np.array(weighted_mean_dist) def max_concentration(plane_hits,ref): ''' calculates the log determinant of the concentration matrix for the hits in planehits ''' dispersions = [np.prod(np.sort(npla.eigvals(np.cov(p[:,0:3].T)))[1:2]) for p in plane_hits] index = np.argmin(dispersions) log_max_concentration = -np.log2(dispersions[index]) centre = ref[index+1] return index, centre, log_max_concentration def refconc(brain, ref, divergence_threshold=0.3, fibre_weight=0.7): ''' given a reference fibre locates the parallel fibres in brain (tracks) with threshold_hitdata applied to cut_planes output then follows with concentration to locate the locus of a neck ''' hitdata = pf.cut_plane(brain, ref) reduced_hitdata, heavy_weight_fibres = threshold_hitdata(hitdata, divergence_threshold, fibre_weight) #index, centre, log_max_concentration = max_concentration(reduced_hitdata, ref) index=None centre=None log_max_concentration=None return heavy_weight_fibres, index, centre def bundle_from_refs(brain,braind, refs, divergence_threshold=0.3, fibre_weight=0.7,far_thresh=25,zhang_thresh=15, end_thresh=10): ''' ''' bundle = set([]) centres = [] indices = [] for ref in refs: refd=tm.downsample(ref,3) brain_rf, ind_fr = rm_far_tracks(refd,braind,dist=far_thresh,down=True) brain_rf=[brain[i] for i in ind_fr] #brain_rf,ind_fr = rm_far_tracks(ref,brain,dist=far_thresh,down=False) heavy_weight_fibres, index, centre = refconc(brain_rf, ref, divergence_threshold, fibre_weight) heavy_weight_fibres_z = [i for i in heavy_weight_fibres if pf.zhang_distances(ref,brain_rf[i],'avg')end_thresh] hwfind = set([ind_fr[i] for i in heavy_weight_fibres_z]) bundle = bundle.union(hwfind) bundle_med = [] for i in bundle: minmaxdist = 0. for ref in refs: minmaxdist=min(minmaxdist,tm.max_end_distances(brain[i],ref)) if minmaxdist<=end_thresh: bundle_med.append(i) #centres.append(centre) #indices.append(index) #return list(bundle), centres, indices return bundle_med class FACT_Delta(): ''' Generates tracks with termination criteria defined by a delta function [1]_ and it has similarities with FACT algorithm [2]_. Can be used with any reconstruction method as DTI,DSI,QBI,GQI which can calculate an orientation distribution function and find the local peaks of that function. For example a single tensor model can give you only one peak a dual tensor model 2 peaks and quantitative anisotropy method as used in GQI can give you 3,4,5 or even more peaks. The parameters of the delta function are checking thresholds for the direction propagation magnitude and the angle of propagation. A specific number of seeds is defined randomly and then the tracks are generated for that seed if the delta function returns true. Trilinear interpolation is being used for defining the weights of the propagation. References ---------- .. [1] Yeh. et al. Generalized Q-Sampling Imaging, TMI 2010. .. [2] Mori et al. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 1999. ''' def __init__(self,qa,ind,seeds_no=1000,odf_vertices=None,qa_thr=0.0239,step_sz=0.5,ang_thr=60.): ''' Parameters ---------- qa: array, shape(x,y,z,Np), magnitude of the peak (QA) or shape(x,y,z) a scalar volume like FA. ind: array, shape(x,y,z,Np), indices of orientations of the QA peaks found at odf_vertices used in QA or, shape(x,y,z), ind seeds_no: number of random seeds odf_vertices: sphere points which define a discrete representation of orientations for the peaks, the same for all voxels qa_thr: float, threshold for QA(typical 0.023) or FA(typical 0.2) step_sz: float, propagation step ang_thr: float, if turning angle is smaller than this threshold then tracking stops. Returns ------- tracks: sequence of arrays ''' if len(qa.shape)==3: qa.shape=qa.shape+(1,) ind.shape=ind.shape+(1,) #store number of maximum peacks self.Np=qa.shape[-1] x,y,z,g=qa.shape tlist=[] if odf_vertices==None: eds=np.load(os.path.join(os.path.dirname(__file__),'matrices',\ 'evenly_distributed_sphere_362.npz')) odf_vertices=eds['vertices'] self.seed_list=[] for i in range(seeds_no): rx=(x-1)*np.random.rand() ry=(y-1)*np.random.rand() rz=(z-1)*np.random.rand() seed=np.array([rx,ry,rz]) #print 'init seed', seed #self.seed_list.append(seed.copy()) track=self.propagation(seed.copy(),qa,ind,odf_vertices,qa_thr,ang_thr,step_sz) if track == None: pass else: self.seed_list.append(seed.copy()) tlist.append(track) self.tracks=tlist def trilinear_interpolation(self,X): ''' Parameters ---------- X: array, shape(3,), a point Returns -------- W: array, shape(8,2) weights, think of them like the 8 subvolumes of a unit cube surrounding the seed. IN: array, shape(8,2), the corners of the unit cube ''' Xf=np.floor(X) #d holds the distance from the (floor) corner of the voxel d=X-Xf #nd holds the distance from the opposite corner nd = 1-d #filling the weights W=np.array([[ nd[0] * nd[1] * nd[2] ], [ d[0] * nd[1] * nd[2] ], [ nd[0] * d[1] * nd[2] ], [ nd[0] * nd[1] * d[2] ], [ d[0] * d[1] * nd[2] ], [ nd[0] * d[1] * d[2] ], [ d[0] * nd[1] * d[2] ], [ d[0] * d[1] * d[2] ]]) IN=np.array([[ Xf[0] , Xf[1] , Xf[2] ], [ Xf[0]+1 , Xf[1] , Xf[2] ], [ Xf[0] , Xf[1]+1, Xf[2] ], [ Xf[0] , Xf[1] , Xf[2]+1 ], [ Xf[0]+1 , Xf[1]+1, Xf[2] ], [ Xf[0] , Xf[1]+1, Xf[2]+1 ], [ Xf[0]+1 , Xf[1] , Xf[2]+1 ], [ Xf[0]+1 , Xf[1]+1, Xf[2]+1 ]]) return W,IN.astype(np.int) def nearest_direction(self,dx,qa,ind,odf_vertices,qa_thr=0.0245,ang_thr=60.): ''' Give the nearest direction to a point Parameters ---------- dx: array, shape(3,), as float, moving direction of the current tracking qa: array, shape(Np,), float, quantitative anisotropy matrix, where Np the number of peaks, found using self.Np ind: array, shape(Np,), float, index of the track orientation odf_vertices: array, shape(N,3), float, odf sampling directions qa_thr: float, threshold for QA, we want everything higher than this threshold ang_thr: float, theshold, we only select fiber orientation with this range Returns -------- delta: bool, delta funtion, if 1 we give it weighting if it is 0 we don't give any weighting direction: array, shape(3,), the fiber orientation to be consider in the interpolation ''' max_dot=0 max_doti=0 angl = np.cos((np.pi*ang_thr)/180.) if qa[0] <= qa_thr: return False, np.array([0,0,0]) for i in range(self.Np): if qa[i]<= qa_thr: break curr_dot = np.abs(np.dot(dx, odf_vertices[ind[i]])) if curr_dot > max_dot: max_dot = curr_dot max_doti = i if max_dot < angl : return False, np.array([0,0,0]) if np.dot(dx,odf_vertices[ind[max_doti]]) < 0: return True, - odf_vertices[ind[max_doti]] else: return True, odf_vertices[ind[max_doti]] def propagation_direction(self,point,dx,qa,ind,odf_vertices,qa_thr,ang_thr): ''' Find where you are moving next ''' total_w = 0 # total weighting new_direction = np.array([0,0,0]) w,index=self.trilinear_interpolation(point) #print w[0],w[1],w[2],w[3],w[4],w[5],w[6],w[7] #print index #check if you are outside of the volume for i in range(3): if index[7][i] >= qa.shape[i] or index[0][i] < 0: return False, np.array([0,0,0]) #calculate qa & ind of each of the 8 corners for m in range(8): x,y,z = index[m] qa_tmp = qa[x,y,z] ind_tmp = ind[x,y,z] #print qa_tmp[0]#,qa_tmp[1],qa_tmp[2],qa_tmp[3],qa_tmp[4] delta,direction = self.nearest_direction(dx,qa_tmp,ind_tmp,odf_vertices,qa_thr,ang_thr) #print delta, direction if not delta: continue total_w += w[m] new_direction = new_direction + w[m][0]*direction if total_w < .5: # termination criteria return False, np.array([0,0,0]) return True, new_direction/np.sqrt(np.sum(new_direction**2)) def initial_direction(self,seed,qa,ind,odf_vertices,qa_thr): ''' First direction that we get from a seeding point ''' #very tricky/cool addition/flooring that helps create a valid #neighborhood (grid) for the trilinear interpolation to run smoothly #seed+=0.5 point=np.floor(seed+.5) x,y,z = point qa_tmp=qa[x,y,z,0]#maximum qa ind_tmp=ind[x,y,z,0]#corresponing orientation indices for max qa if qa_tmp < qa_thr: return False, np.array([0,0,0]) else: return True, odf_vertices[ind_tmp] def propagation(self,seed,qa,ind,odf_vertices,qa_thr,ang_thr,step_sz): ''' Parameters ---------- seed: array, shape(3,), point where the tracking starts qa: array, shape(Np,), float, quantitative anisotropy matrix, where Np the number of peaks, found using self.Np ind: array, shape(Np,), float, index of the track orientation Returns ------- d: bool, delta function result idirection: array, shape(3,), index of the direction of the propagation ''' point_bak=seed.copy() point=seed.copy() #d is the delta function d,idirection=self.initial_direction(seed,qa,ind,odf_vertices,qa_thr) #print('FD',idirection[0],idirection[1],idirection[2]) #print d if not d: return None dx = idirection #point = seed-0.5 track = [] track.append(point.copy()) #track towards one direction while d: d,dx = self.propagation_direction(point,dx,qa,ind,\ odf_vertices,qa_thr,ang_thr) if not d: break point = point + step_sz*dx track.append(point) d = True dx = - idirection point=point_bak.copy() #point = seed #track towards the opposite direction while d: d,dx = self.propagation_direction(point,dx,qa,ind,\ odf_vertices,qa_thr,ang_thr) if not d: break point = point + step_sz*dx track.insert(0,point.copy()) return np.array(track) dipy-0.5.0/scratch/odf.py000066400000000000000000000052271152576264200152520ustar00rootroot00000000000000import numpy as np from enthought.mayavi import mlab import Image def disp_odf(sph_map, theta_res=64, phi_res=32, colormap='RGB', colors=256): pi = np.pi sin = np.sin cos = np.cos theta, phi = np.mgrid[0:2*pi:theta_res*1j, 0:pi:phi_res*1j] x = sin(phi)*cos(theta) y = sin(phi)*sin(theta) z = cos(phi) nvox = np.prod(sph_map.shape) x_cen, y_cen, z_cen = _3grid(sph_map.shape) odf_values = sph_map.evaluate_at(theta, phi) max_value = odf_values.max() mlab.figure() for ii in range(nvox): odf_ii = odf_values.reshape(nvox, theta_res, phi_res)[ii,:,:] odf_ii /= max_value * 2 if colormap == 'RGB': rgb = np.r_['-1,3,0', x*odf_ii, y*odf_ii, z*odf_ii] rgb = np.abs(rgb*255/rgb.max()).astype('uint8') odf_im = Image.fromarray(rgb, mode='RGB') odf_im = odf_im.convert('P', palette=Image.ADAPTIVE, colors=colors) lut = np.empty((colors,4),'uint8') lut[:,3] = 255 lut[:,0:3] = np.reshape(odf_im.getpalette(),(colors,3)) oo = mlab.mesh(x*odf_ii + x_cen.flat[ii], y*odf_ii + y_cen.flat[ii], z*odf_ii + z_cen.flat[ii], scalars=np.int16(odf_im)) oo.module_manager.scalar_lut_manager.lut.table=lut else: oo = mlab.mesh(x*odf_ii + x_cen.flat[ii], y*odf_ii + y_cen.flat[ii], z*odf_ii + z_cen.flat[ii], scalars=odf_ii, colormap=colormap) def _3grid(shape): if len(shape) > 3: raise ValueError('cannot display 4d image') elif len(shape) < 3: d = [1, 1, 1] d[0:len(shape)] = shape else: d = shape return np.mgrid[0:d[0], 0:d[1], 0:d[2]] if __name__ == '__main__': import dipy.core.qball as qball from dipy.io.bvectxt import read_bvec_file filename='/Users/bagrata/HARDI/E1322S8I1.nii.gz' grad_table_filename='/Users/bagrata/HARDI/E1322S8I1.bvec' from nipy import load_image, save_image grad_table, b_values = read_bvec_file(grad_table_filename) img = load_image(filename) print 'input dimensions: ' print img.ndim print 'image size: ' print img.shape print 'image affine: ' print img.affine print 'images has pixels with size: ' print np.dot(img.affine, np.eye(img.ndim+1)).diagonal()[0:3] data = np.asarray(img) theta, phi = np.mgrid[0:2*np.pi:64*1j, 0:np.pi:32*1j] odf_i = qball.ODF(data[188:192,188:192,22:24,:],4,grad_table,b_values) disp_odf(odf_i[0:1,0:2,0:2]) dipy-0.5.0/scratch/sphplot.py000066400000000000000000000017071152576264200161720ustar00rootroot00000000000000import numpy as np from dipy.viz import fos import dipy.core.geometry as geometry import matplotlib.pyplot as mplp def plot_sphere(v,key): r = fos.ren() fos.add(r,fos.point(v,fos.green, point_radius= 0.01)) fos.show(r, title=key, size=(1000,1000)) def plot_lambert(v,key,centre=np.array([0,0])): lamb = geometry.lambert_equal_area_projection_cart(*v.T).T (y1,y2) = lamb radius = np.sum(lamb**2,axis=0) < 1 #print inner #print y1[inner] #print y1[-inner] fig = mplp.figure(facecolor='w') current = fig.add_subplot(111) current.patch.set_color('k') current.plot(y1[radius],y2[radius],'.g') current.plot(y1[-radius],y2[-radius],'.r') current.plot([0.],[0.],'ob') #current.patches.Circle(*centre, radius=50, color='w', fill=True, alpha=0.7) current.axes.set_aspect(aspect = 'equal', adjustable = 'box') current.title.set_text(key) fig.show() fig.waitforbuttonpress() mplp.close() dipy-0.5.0/scratch/twoD.py000066400000000000000000000012101152576264200154030ustar00rootroot00000000000000import pylab as pl import numpy as np def imshow(array, cmap='gray',interpolation='nearest', alpha=1.0, vmin=None, vmax=None, origin=None, extent=None): """ Wrapper for pylab.imshow that displays array values as well coordinate values with mouse over. """ pl.imshow(array.T, cmap=cmap, interpolation=interpolation, alpha=alpha, vmin=vmin, vmax=vmax, origin=origin, extent=extent) ax = pl.gca() ax.format_coord = __report_pixel def __report_pixel(x, y): x = np.round(x) y = np.round(y) v = pl.gca().get_images()[0].get_array()[y, x] return "x = %d y = %d v = %5.3f" % (x, y, v) dipy-0.5.0/scratch/very_scratch/000077500000000000000000000000001152576264200166165ustar00rootroot00000000000000dipy-0.5.0/scratch/very_scratch/bingham.py000066400000000000000000000126031152576264200205770ustar00rootroot00000000000000import sympy from scipy.integrate import quad, dblquad from scipy.optimize import fmin_powell import numpy as np import scipy as sc ''' def integrand(t,n,x): return np.exp(-x*t) / t**n def expint(n,x): return quad(integrand, 1, np.Inf, args=(n, x))[0] vec_expint = np.vectorize(expint) print vec_expint(3,np.arange(1.0,4.0,0.5)) ''' #array([ 0.1097, 0.0567, 0.0301, 0.0163, 0.0089, 0.0049]) ''' print sc.special.expn(3,np.arange(1.0,4.0,0.5)) ''' #array([ 0.1097, 0.0567, 0.0301, 0.0163, 0.0089, 0.0049]) ''' result = quad(lambda x: expint(3, x), 0, np.inf) print result ''' #(0.33333333324560266, 2.8548934485373678e-09) ''' I3 = 1.0/3.0 print I3 #0.333333333333 ''' def bingham_kernel(k1,k2,theta,phi): return np.exp(((k1*np.cos(phi)**2+k2*np.sin(phi)**2)*np.sin(theta)**2)/4*np.pi) def d(k1,k2): #print (k1,k2) return dblquad(lambda theta, phi: bingham_kernel(k1,k2,theta,phi), 0, np.pi, lambda phi: 0, lambda phi: 2*np.pi)[0] print d(-6.999, -3.345) #K1,K2,t1,t2,ph,th=sympy.symbols('K1,K2,t1,t2,ph,th') N = 100 def F((k1,k2),(t1,t2,N)): val = -N*4*np.pi - N*np.log(d(k1,k2)) + k1*t1 + k2*t2 print (-val,k1,k2) return -val min = fmin_powell(F,(-1,-1), ((-3.345, -6.999, 1000),)) print min #d = sympy.integrate(sympy.exp((k1*sympy.cos(phi)**2+k2*sympy.sin(phi)**2)*sympy.sin(theta)**2)/(4*sympy.pi),(phi,0,2*sympy.pi),(theta,0,sympy.pi)) ''' def I(n): return dblquad(lambda t, x: np.exp(-x*t)/t**n, 0, np.Inf, lambda x: 1, lambda x: np.Inf) print I(4) #(0.25000000000435768, 1.0518245707751597e-09) print I(3) #(0.33333333325010883, 2.8604069919261191e-09) print I(2) #(0.49999999999857514, 1.8855523253868967e-09) k1,k2,phi,theta=sympy.symbols('k1,k2,phi,theta') d = sympy.integrate(sympy.exp((k1*sympy.cos(phi)**2+k2*sympy.sin(phi)**2)*sympy.sin(theta)**2)/(4*sympy.pi),(phi,0,2*sympy.pi),(theta,0,sympy.pi)) from scipy.integrate import quad from math import pi d = sympy.integrate(sympy.exp((k1*sympy.cos(phi)**2+k2*sympy.sin(phi)**2)*sympy.sin(theta)**2)/(4*sympy.pi),(phi,0,2*sympy.pi),(theta,0,sympy.pi)) ''' ''' Table C.3: Maximum likelihood estimators of k1,k2 in the Bingham distribution for given eigenvalues w1,w2. Data from Mardia and Zemroch (1977). Upper (lower) number is k1(k2) w1 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 w2 0.02 -25.55 -25.55 0.04 -25.56 -13.11 -13.09 -13.11 0.06 -25.58 -13.14 -9.043 -8.996 -9.019 -9.043 0.08 -25.6 -13.16 -9.065 -7.035 -6.977 -6.999 -7.020 -7.035 0.10 -25.62 -13.18 -9.080 -7.042 -5.797 -5.760 -5.777 -5.791 -5.798 -5.797 0.12 -25.63 -13.19 -9.087 -7.041 -5.789 -4.917 -4.923 -4.934 -4.941 -4.941 -4.933 -4.917 0.14 -25.64 -13.20 -9.087 -7.033 -5.773 -4.896 -4.231 -4.295 -4.301 -4.301 -4.294 -4.279 -4.258 -4.231 0.16 -25.65 -13.20 -9.081 -7.019 -5.752 -4.868 -4.198 -3.659 -3.796 -3.796 -3.790 -3.777 -3.756 -3.729 -3.697 -3.659 0.18 -25.65 -13.19 -9.068 -6.999 -5.726 -4.836 -4.160 -3.616 -3.160 -3.381 -3.375 -3.363 -3.345 -3.319 -3.287 -3.249 -3.207 -3.160 0.20 -25.64 -13.18 -9.05 -6.974 -5.694 -4.799 -4.118 -3.570 -3.109 -2.709 -3.025 -3.014 -2.997 -2.973 -2.942 -2.905 -2.863 -2.816 -2.765 -2.709 0.22 -25.63 -13.17 -9.027 -6.944 -5.658 -4.757 -4.071 -3.518 -3.053 -2.649 -2.289 -2.712 -2.695 -2.673 -2.644 -2.609 -2.568 -2.521 -2.470 -2.414 -2.354 -2.289 0.24 -25.61 -23.14 -8.999 -6.910 -5.618 -4.711 -4.021 -3.463 -2.993 -2.584 -2.220 -1.888 -2.431 -2.410 -2.382 -2.349 -2.309 -2.263 -2.212 -2.157 -2.097 -2.032 -1.963 -1.888 0.26 -25.59 -13.12 -8.966 -6.870 -5.573 -4.661 -3.965 -3.403 -2.928 -2.515 -2.146 -1.809 -1.497 -2.175 -2.149 -2.117 -2.078 -2.034 -1.984 -1.929 -1.869 -1.805 -1.735 -1.661 -1.582 -1.497 0.28 -25.57 -13.09 -8.928 -6.827 -5.523 -4.606 -3.906 -3.338 -2.859 -2.441 -2.066 -1.724 -1.406 -1.106 -1.939 -1.908 -1.871 -1.828 -1.779 -1.725 -1.665 -1.601 -1.532 -1.458 -1.378 -1.294 -1.203 -1.106 0.30 -25.54 -13.05 -8.886 -6.778 -5.469 -4.547 -3.842 -3.269 -2.785 -2.361 -1.981 -1.634 -1.309 -1.002 -0.708 -1.718 -1.682 -1.641 -1.596 -1.540 -1.481 -1.417 -1.348 -1.274 -1.195 -1.110 -1.020 -0.923 -0.819 -0.708 0.32 -25.50 -13.01 -8.839 -6.725 -5.411 -4.484 -3.773 -3.195 -2.706 -2.277 -1.891 -1.537 -1.206 -0.891 -0.588 -0.292 -1.510 -1.470 -1.423 -1.371 -1.313 -1.250 -1.181 -1.108 -1.028 -0.944 -0.853 -0.756 -0.653 -0.541 -0.421 -0.292 0.34 -25.46 -12.96 -8.788 -6.668 -5.348 -4.415 -3.699 -3.116 -2.621 -2.186 -1.794 -1.433 -1.094 -0.771 -0.459 -0.152 -1.312 -1.267 -1.216 -1.159 -1.096 -1.028 -0.955 -0.876 -0.791 -0.701 -0.604 -0.500 -0.389 -0.269 -0.140 0.000 0.36 -25.42 -12.91 -8.731 -6.606 -5.280 -4.342 -3.620 -3.032 -2.531 -2.089 -1.690 -1.322 -0.974 -0.642 -1.123 -1.073 -1.017 -9.555 -0.887 -0.814 -0.736 -0.651 -0.561 -0.464 -0.360 -0.249 -0.129 0.000 0.38 -25.37 -12.86 -8.670 -6.539 -5.207 -4.263 -3.536 -2.941 -2.434 -1.986 -1.579 -1.202 -0.940 -0.885 -0.824 -0.757 -0.684 -0.606 -0.522 -0.432 -0.335 -0.231 -0.120 0.000 0.40 -25.31 -12.80 -8.604 -6.466 -5.126 -4.179 -3.446 -2.845 -2.330 -1.874 -0.762 -0.702 -0.636 -0.564 -0.486 -0.402 -0.312 -0.215 -0.111 -0.000 0.42 -25.5 -12.73 -8.532 -6.388 -5.045 -4.089 -3.349 -2.741 -0.589 -0.523 -0.452 -0.374 -0.290 -0.200 -0.104 0.000 0.44 -25.19 -12.66 -8.454 -6.305 -4.955 -3.992 -0.418 -0.347 -0.270 -0.186 -0.097 0.000 0.46 -25.12 -12.58 -8.371 -6.215 -0.250 -0.173 -0.090 0.000 Taken from http://magician.ucsd.edu/Essentials/WebBookse115.html#x136-237000C.2a ''' dipy-0.5.0/scratch/very_scratch/check_flipping.py000066400000000000000000000027701152576264200221430ustar00rootroot00000000000000import numpy as np from dipy.viz import fos from dipy.core import track_performance as pf tracks=[np.array([[0,0,0],[1,0,0,],[2,0,0]]), np.array([[3,0,0],[3.5,1,0],[4,2,0]]), np.array([[3.2,0,0],[3.7,1,0],[4.4,2,0]]), np.array([[3.4,0,0],[3.9,1,0],[4.6,2,0]]), np.array([[0,0.2,0],[1,0.2,0],[2,0.2,0]]), np.array([[2,0.2,0],[1,0.2,0],[0,0.2,0]]), np.array([[0,0,0],[0,1,0],[0,2,0]]), np.array([[0.2,0,0],[0.2,1,0],[0.2,2,0]]), np.array([[-0.2,0,0],[-0.2,1,0],[-0.2,2,0]]), np.array([[0,1.5,0],[1,1.5,0,],[6,1.5,0]]), np.array([[0,1.8,0],[1,1.8,0,],[6,1.8,0]]), np.array([[0,0,0],[2,2,0],[4,4,0]])] tracks=[t.astype(np.float32) for t in tracks] C=pf.larch_3split(tracks,None,0.5) r=fos.ren() fos.add(r,fos.line(tracks,fos.red)) #fos.show(r) for c in C: color=np.random.rand(3) for i in C[c]['indices']: fos.add(r,fos.line(tracks[i]+np.array([8.,0.,0.]),color)) fos.add(r,fos.line(tracks[i]+np.array([16.,0.,0.]),color)) fos.add(r,fos.line(C[c]['rep3']/C[c]['N']+np.array([16.,0.,0.]),fos.white)) fos.show(r) ''' print len(C) C=pf.larch_3merge(C,0.5) print len(C) for c in C: color=np.random.rand(3) for i in C[c]['indices']: fos.add(r,fos.line(tracks[i]+np.array([14.,0.,0.]),color)) #fos.show(r) for c in C: fos.add(r,fos.line(C[c]['rep3']/C[c]['N']+np.array([14.,0.,0.]),fos.white)) fos.show(r) ''' dipy-0.5.0/scratch/very_scratch/dcm2FAasnii.py000066400000000000000000000006261152576264200212540ustar00rootroot00000000000000import numpy as np import nibabel as ni from nibabel.dicom import dicomreaders as dcm from dipy.core import stensor as sten dname='/home/eg309/Data/Eleftherios/Series_003_CBU_DTI_64D_iso_1000' faname='/tmp/FA.nii' data,affine,bvals,gradients=dcm.read_mosaic_dwi_dir(dname) stl=sten.STensorL(bvals,gradients) stl.fit(data) stl.tensors FA=stl.fa img=ni.Nifti1Image(FA,affine) ni.save(img,faname) dipy-0.5.0/scratch/very_scratch/dcm2S0asnii.py000066400000000000000000000021751152576264200212510ustar00rootroot00000000000000import numpy as np import nibabel as ni from nibabel.dicom import dicomreaders as dcm import dipy.core.generalized_q_sampling as gq dname='/home/eg01/Data_Backup/Data/Frank_Eleftherios/frank/20100511_m030y_cbu100624/08_ep2d_advdiff_101dir_DSI' #dname ='/home/eg309/Data/Eleftherios/Series_003_CBU_DTI_64D_iso_1000' S0name='/tmp/S0.nii' #smallname='/tmp/small_volume2.5_steam_4000.nii' smallname='/tmp/small_64D.nii' smallname_grad = '/tmp/small_64D.gradients' smallname_bvals = '/tmp/small_64D.bvals' #read diffusion dicoms data,affine,bvals,gradients=dcm.read_mosaic_dir(dname) print data.shape #calculate QA #gqs = gq.GeneralizedQSampling(data,bvals,gradients) #gqs.QA[0] #S0 = data[:,:,:,0] ''' #save the structural volume #img=ni.Nifti1Image(S0,affine) #ni.save(img,S0name) #save the small roi volume #small= data[35:55,55:75,20:30,:] small= data[54:64,54:64,30:40,:] naffine = np.dot(affine, np.array([[1,0,0,54],[0,1,0,54],[0,0,1,30],[0,0,0,1]])) imgsmall=ni.Nifti1Image(small,naffine) ni.save(imgsmall,smallname) #save b-values and b-vecs np.save(smallname_grad,gradients) np.save(smallname_bvals,bvals) ''' dipy-0.5.0/scratch/very_scratch/eddy_currents.py000066400000000000000000000024021152576264200220400ustar00rootroot00000000000000import numpy as np import dipy as dp import nibabel as ni dname = '/home/eg01/Data_Backup/Data/Eleftherios/CBU090133_METHODS/20090227_145404/Series_003_CBU_DTI_64D_iso_1000' #dname = '/home/eg01/Data_Backup/Data/Frank_Eleftherios/frank/20100511_m030y_cbu100624/08_ep2d_advdiff_101dir_DSI' data,affine,bvals,gradients=dp.load_dcm_dir(dname) ''' rot=np.array([[1,0,0,0], [0,np.cos(np.pi/2),-np.sin(np.pi/2),0], [0,np.sin(np.pi/2), np.cos(np.pi/2),0], [0,0,0,1]]) from scipy.ndimage import affine_transform as aff naffine=np.dot(affine,rot) ''' data[:,:,:,1] source=ni.Nifti1Image(data[:,:,:,1],affine) target=ni.Nifti1Image(data[:,:,:,0],affine) #similarity 'cc', 'cr', 'crl1', 'mi', je', 'ce', 'nmi', 'smi'. 'cr' similarity='cr' #interp 'pv', 'tri' interp = 'tri' #subsampling None or sequence (3,) subsampling=None #search 'affine', 'rigid', 'similarity' or ['rigid','affine'] search='affine' #optimizer 'simplex', 'powell', 'steepest', 'cg', 'bfgs' or #sequence of optimizers optimizer= 'powell' T=dp.volume_register(source,target,similarity,\ interp,subsampling,search,) sourceT=dp.volume_transform(source, T.inv(), reference=target) s=source.get_data() t=target.get_data() sT=sourceT.get_data() dipy-0.5.0/scratch/very_scratch/ellipse.py000066400000000000000000000012131152576264200206220ustar00rootroot00000000000000import sympy import numpy as np import scipy as sc from numpy.random import random_sample as random def random_uniform_in_disc(): # returns a tuple which is uniform in the disc theta = 2*np.pi*random() r2 = random() r = np.sqrt(r2) return np.array((r*np.sin(theta),r*np.cos(theta))) def random_uniform_in_ellipse(a=1,b=1): x = a*random_uniform_in_disc()[0] y = b*np.sqrt(1-(x/a)**2)*(1-2*random()) return np.array((x,y)) import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(111) sample = np.array([random_uniform_in_ellipse(a=2,b=1) for i in np.arange(10000)]) ax.scatter(*sample.T) plt.show() dipy-0.5.0/scratch/very_scratch/gen_iter.py000066400000000000000000000017301152576264200207650ustar00rootroot00000000000000class Reverse: "Iterator for looping over a sequence backwards" def __init__(self, data): self.data = data self.index = len(data) def __iter__(self): return self def next(self): if self.index == 0: raise StopIteration self.index = self.index - 1 return self.data[self.index] class ReverseGen: 'Iterator class using generator' def __init__(self, data): self.data = data def __iter__(self): for index in range(len(self.data)-1, -1, -1): yield self.data[index] rev = Reverse('golf') iter(rev) print('class') for char in rev: print char def reverse(data): for index in range(len(data)-1, -1, -1): yield data[index] print('generator') for char in reverse('golf'): print char print('class generator') revgen = ReverseGen('golf') iter(rev) for char in revgen: print char dipy-0.5.0/scratch/very_scratch/get_vertices.py000066400000000000000000000042721152576264200216600ustar00rootroot00000000000000sphere_dic = {'fy362': {'filepath' : '/home/ian/Devel/dipy/dipy/core/matrices/evenly_distributed_sphere_362.npz', 'object': 'npz', 'vertices': 'vertices', 'omit': 0, 'hemi': False}, 'fy642': {'filepath' : '/home/ian/Devel/dipy/dipy/core/matrices/evenly_distributed_sphere_642.npz', 'object': 'npz', 'vertices': 'odf_vertices', 'omit': 0, 'hemi': False}, 'siem64': {'filepath':'/home/ian/Devel/dipy/dipy/core/tests/data/small_64D.gradients.npy', 'object': 'npy', 'omit': 1, 'hemi': True}, 'create2': {}, 'create3': {}, 'create4': {}, 'create5': {}, 'create6': {}, 'create7': {}, 'create8': {}, 'create9': {}, 'marta200': {'filepath': '/home/ian/Data/Spheres/200.npy', 'object': 'npy', 'omit': 0, 'hemi': True}, 'dsi102': {'filepath': '/home/ian/Data/Frank_Eleftherios/frank/20100511_m030y_cbu100624/08_ep2d_advdiff_101dir_DSI', 'object': 'dicom', 'omit': 1, 'hemi': True}} import numpy as np from dipy.core.triangle_subdivide import create_unit_sphere from dipy.io import dicomreaders as dcm def get_vertex_set(key): if key[:6] == 'create': number = eval(key[6:]) vertices, edges, faces = create_unit_sphere(number) omit = 0 else: entry = sphere_dic[key] if entry.has_key('omit'): omit = entry['omit'] else: omit = 0 filepath = entry['filepath'] if entry['object'] == 'npz': filearray = np.load(filepath) vertices = filearray[entry['vertices']] elif sphere_dic[key]['object'] == 'npy': vertices = np.load(filepath) elif entry['object'] == 'dicom': data,affine,bvals,gradients=dcm.read_mosaic_dir(filepath) #print (bvals.shape, gradients.shape) grad3 = np.vstack((bvals,bvals,bvals)).transpose() #print grad3.shape #vertices = grad3*gradients vertices = gradients if omit > 0: vertices = vertices[omit:,:] if entry['hemi']: vertices = np.vstack([vertices, -vertices]) return vertices[omit:,:] dipy-0.5.0/scratch/very_scratch/gqsampling_stats.py000066400000000000000000000317231152576264200225560ustar00rootroot00000000000000import os import numpy as np from nose.tools import assert_true, assert_false, assert_equal, assert_raises from numpy.testing import assert_array_equal, assert_array_almost_equal import time import dipy.core.reconstruction_performance as rp from os.path import join as opj import nibabel as ni import dipy.core.generalized_q_sampling as gq from dipy.testing import parametric #import dipy.core.track_propagation as tp import dipy.core.dti as dt import dipy.core.meshes as meshes @parametric def test_gqiodf(): #read bvals,gradients and data bvals=np.load(opj(os.path.dirname(__file__), \ 'data','small_64D.bvals.npy')) gradients=np.load(opj(os.path.dirname(__file__), \ 'data','small_64D.gradients.npy')) img =ni.load(os.path.join(os.path.dirname(__file__),\ 'data','small_64D.nii')) data=img.get_data() #print(bvals.shape) #print(gradients.shape) #print(data.shape) t1=time.clock() gqs = gq.GeneralizedQSampling(data,bvals,gradients) ten = dt.Tensor(data,bvals,gradients,thresh=50) fa=ten.fa() x,y,z,a,b=ten.evecs.shape evecs=ten.evecs xyz=x*y*z evecs = evecs.reshape(xyz,3,3) #vs = np.sign(evecs[:,2,:]) #print vs.shape #print np.hstack((vs,vs,vs)).reshape(1000,3,3).shape #evecs = np.hstack((vs,vs,vs)).reshape(1000,3,3) #print evecs.shape evals=ten.evals evals = evals.reshape(xyz,3) #print evals.shape t2=time.clock() #print('GQS in %d' %(t2-t1)) eds=np.load(opj(os.path.dirname(__file__),\ '..','matrices',\ 'evenly_distributed_sphere_362.npz')) odf_vertices=eds['vertices'] odf_faces=eds['faces'] #Yeh et.al, IEEE TMI, 2010 #calculate the odf using GQI scaling=np.sqrt(bvals*0.01506) # 0.01506 = 6*D where D is the free #water diffusion coefficient #l_values sqrt(6 D tau) D free water #diffusion coefficiet and tau included in the b-value tmp=np.tile(scaling,(3,1)) b_vector=gradients.T*tmp Lambda = 1.2 # smoothing parameter - diffusion sampling length q2odf_params=np.sinc(np.dot(b_vector.T, odf_vertices.T) * Lambda/np.pi) #implements equation no. 9 from Yeh et.al. S=data.copy() x,y,z,g=S.shape S=S.reshape(x*y*z,g) QA = np.zeros((x*y*z,5)) IN = np.zeros((x*y*z,5)) fwd = 0 #Calculate Quantitative Anisotropy and find the peaks and the indices #for every voxel summary = {} summary['vertices'] = odf_vertices v = odf_vertices.shape[0] summary['faces'] = odf_faces f = odf_faces.shape[0] ''' If e = number_of_edges the Euler formula says f-e+v = 2 for a mesh on a sphere Here, assuming we have a healthy triangulation every face is a triangle, all 3 of whose edges should belong to exactly two faces = so 2*e = 3*f to avoid division we test whether 2*f - 3*f + 2*v == 4 or equivalently 2*v - f == 4 ''' yield assert_equal(2*v-f, 4,'Direct Euler test fails') yield assert_true(meshes.euler_characteristic_check(odf_vertices, odf_faces,chi=2),'euler_characteristic_check fails') coarse = meshes.coarseness(odf_faces) print 'coarseness: ', coarse for (i,s) in enumerate(S): #print 'Volume %d' % i istr = str(i) summary[istr] = {} odf = Q2odf(s,q2odf_params) peaks,inds=rp.peak_finding(odf,odf_faces) fwd=max(np.max(odf),fwd) peaks = peaks - np.min(odf) l=min(len(peaks),5) QA[i][:l] = peaks[:l] IN[i][:l] = inds[:l] summary[istr]['odf'] = odf summary[istr]['peaks'] = peaks summary[istr]['inds'] = inds summary[istr]['evecs'] = evecs[i,:,:] summary[istr]['evals'] = evals[i,:] QA/=fwd QA=QA.reshape(x,y,z,5) IN=IN.reshape(x,y,z,5) #print('Old %d secs' %(time.clock() - t2)) #yield assert_equal((gqs.QA-QA).max(),0.,'Frank QA different than our QA') #yield assert_equal((gqs.QA.shape),QA.shape, 'Frank QA shape is different') #yield assert_equal((gqs.QA-QA).max(), 0.) #import dipy.core.track_propagation as tp #tp.FACT_Delta(QA,IN) #return tp.FACT_Delta(QA,IN,seeds_no=10000).tracks peaks_1 = [i for i in range(1000) if len(summary[str(i)]['inds'])==1] peaks_2 = [i for i in range(1000) if len(summary[str(i)]['inds'])==2] peaks_3 = [i for i in range(1000) if len(summary[str(i)]['inds'])==3] # correct numbers of voxels with respectively 1,2,3 ODF/QA peaks yield assert_array_equal((len(peaks_1),len(peaks_2),len(peaks_3)), (790,196,14), 'error in numbers of QA/ODF peaks') # correct indices of odf directions for voxels 0,10,44 # with respectively 1,2,3 ODF/QA peaks yield assert_array_equal(summary['0']['inds'],[116], 'wrong peak indices for voxel 0') yield assert_array_equal(summary['10']['inds'],[105, 78], 'wrong peak indices for voxel 10') yield assert_array_equal(summary['44']['inds'],[95, 84, 108], 'wrong peak indices for voxel 44') yield assert_equal(np.argmax(summary['0']['odf']), 116) yield assert_equal(np.argmax(summary['10']['odf']), 105) yield assert_equal(np.argmax(summary['44']['odf']), 95) pole_1 = summary['vertices'][116] #print 'pole_1', pole_1 pole_2 = summary['vertices'][105] #print 'pole_2', pole_2 pole_3 = summary['vertices'][95] #print 'pole_3', pole_3 vertices = summary['vertices'] width = 0.02#0.3 #0.05 ''' print 'pole_1 equator contains:', len([i for i,v in enumerate(vertices) if np.abs(np.dot(v,pole_1)) < width]) print 'pole_2 equator contains:', len([i for i,v in enumerate(vertices) if np.abs(np.dot(v,pole_2)) < width]) print 'pole_3 equator contains:', len([i for i,v in enumerate(vertices) if np.abs(np.dot(v,pole_3)) < width]) ''' #print 'pole_1 equator contains:', len(meshes.equatorial_vertices(vertices,pole_1,width)) #print 'pole_2 equator contains:', len(meshes.equatorial_vertices(vertices,pole_2,width)) #print 'pole_3 equator contains:', len(meshes'equatorial_vertices(vertices,pole_3,width)) #print triple_odf_maxima(vertices,summary['0']['odf'],width) #print triple_odf_maxima(vertices,summary['10']['odf'],width) #print triple_odf_maxima(vertices,summary['44']['odf'],width) #print summary['0']['evals'] ''' pole=np.array([0,0,1]) from dipy.viz import fos r=fos.ren() fos.add(r,fos.point(pole,fos.green)) for i,ev in enumerate(vertices): if np.abs(np.dot(ev,pole)) 1- width] def patch_maximum(vertices, odf, pole, width): eqvert = patch_vertices(vertices, pole, width) ''' need to test for whether eqvert is empty or not ''' if len(eqvert) == 0: print 'empty cone around pole', pole, 'with width', width return Null, Null eqvals = [odf[i] for i in eqvert] eqargmax = np.argmax(eqvals) eqvertmax = eqvert[eqargmax] eqvalmax = eqvals[eqargmax] return eqvertmax, eqvalmax def triple_odf_maxima(vertices, odf, width): indmax1 = np.argmax([odf[i] for i,v in enumerate(vertices)]) odfmax1 = odf[indmax1] indmax2, odfmax2 = equatorial_maximum(vertices, odf, vertices[indmax1], width) cross12 = np.cross(vertices[indmax1],vertices[indmax2]) indmax3, odfmax3 = patch_maximum(vertices, odf, cross12, width) return [(indmax1, odfmax1),(indmax2, odfmax2),(indmax3, odfmax3)] @parametric def test_gqi_small(): #read bvals,gradients and data bvals=np.load(opj(os.path.dirname(__file__), \ 'data','small_64D.bvals.npy')) gradients=np.load(opj(os.path.dirname(__file__), \ 'data','small_64D.gradients.npy')) img =ni.load(os.path.join(os.path.dirname(__file__),\ 'data','small_64D.nii')) data=img.get_data() print(bvals.shape) print(gradients.shape) print(data.shape) t1=time.clock() gqs = gq.GeneralizedQSampling(data,bvals,gradients) t2=time.clock() print('GQS in %d' %(t2-t1)) eds=np.load(opj(os.path.dirname(__file__),\ '..','matrices',\ 'evenly_distributed_sphere_362.npz')) odf_vertices=eds['vertices'] odf_faces=eds['faces'] #Yeh et.al, IEEE TMI, 2010 #calculate the odf using GQI scaling=np.sqrt(bvals*0.01506) # 0.01506 = 6*D where D is the free #water diffusion coefficient #l_values sqrt(6 D tau) D free water #diffusion coefficiet and tau included in the b-value tmp=np.tile(scaling,(3,1)) b_vector=gradients.T*tmp Lambda = 1.2 # smoothing parameter - diffusion sampling length q2odf_params=np.sinc(np.dot(b_vector.T, odf_vertices.T) * Lambda/np.pi) #implements equation no. 9 from Yeh et.al. S=data.copy() x,y,z,g=S.shape S=S.reshape(x*y*z,g) QA = np.zeros((x*y*z,5)) IN = np.zeros((x*y*z,5)) fwd = 0 #Calculate Quantitative Anisotropy and find the peaks and the indices #for every voxel for (i,s) in enumerate(S): odf = Q2odf(s,q2odf_params) peaks,inds=rp.peak_finding(odf,odf_faces) fwd=max(np.max(odf),fwd) peaks = peaks - np.min(odf) l=min(len(peaks),5) QA[i][:l] = peaks[:l] IN[i][:l] = inds[:l] QA/=fwd QA=QA.reshape(x,y,z,5) IN=IN.reshape(x,y,z,5) print('Old %d secs' %(time.clock() - t2)) yield assert_equal((gqs.QA-QA).max(),0.,'Frank QA different than dipy QA') yield assert_equal((gqs.QA.shape),QA.shape, 'Frank QA shape is different') yield assert_equal(len(tp.FACT_Delta(QA,IN,seeds_no=100).tracks),100,'FACT_Delta is not generating the right number of tracks for this dataset') def Q2odf(s,q2odf_params): odf=np.dot(s,q2odf_params) return odf def peak_finding(odf,odf_faces): #proton density already include from the scaling b_table[0][0] and s[0] #find local maxima peak=odf.copy() # where the smallest odf values in the vertices of a face remove the # two smallest vertices for face in odf_faces: i, j, k = face check=np.array([odf[i],odf[j],odf[k]]) zeroing=check.argsort() peak[face[zeroing[0]]]=0 peak[face[zeroing[1]]]=0 #for later testing expecting peak.max 794595.94774980657 and #np.where(peak>0) (array([166, 347]),) #we just need the first half of peak peak=peak[0:len(peak)/2] #find local maxima and give fiber orientation (inds) and magnitute #peaks in a descending order inds=np.where(peak>0)[0] pinds=np.argsort(peak[inds]) peaks=peak[inds[pinds]][::-1] return peaks, inds[pinds][::-1] if __name__ == "__main__": T=test_gqiodf() dipy-0.5.0/scratch/very_scratch/joint_hist.py000066400000000000000000000250751152576264200213530ustar00rootroot00000000000000#Calculate joint histogram and related metrics from math import sin,cos,pi import numpy as np from scipy.ndimage import affine_transform, geometric_transform from scipy.ndimage.interpolation import rotate,shift,zoom from scipy.optimize import fmin as fmin_simplex, fmin_powell, fmin_cg from scipy.optimize import leastsq from dipy.core import geometry as gm import pylab def affine_transform2d(I,M): ''' Inspired by the work of Alexis Roche and the independent work of D. Kroon Parameters ---------- I: array, shape(N,M), 2d image M: inverse transformation matrix 3x3, array, shape (3,3) mode: 0: linear interpolation and outside pixels set to nearest pixel Returns ------- Iout: array, shape(N,M), transformed image ''' #the transpose is for contiguous C arrays (default) #I=I.T #create all x,y indices xy=np.array([(i,j) for (i,j) in np.ndindex(I.shape)]) #image center is now our origin (0,0) mean=np.array(I.shape)/2. mean=mean.reshape(1,2) xyd=xy-mean #transformed coordinates lxy = mean.T + np.dot(M[:2,:2],xyd.T) + M[:2,2].reshape(2,1) lxy=lxy.T #neighborh pixels for linear interp bas0=np.floor(lxy) bas1=bas0+1 #linear interp. constants com=lxy-bas0 perc0=(1-com[:,0])*(1-com[:,1]) perc1=(1-com[:,0])*com[:,1] perc2=com[:,0]*(1-com[:,1]) perc3=com[:,0]*com[:,1] #create final image Iout=np.zeros(I.shape) #zeroing indices outside boundaries check_xbas0=np.where(np.bitwise_or(bas0[:,0]<0,bas0[:,0]>=I.shape[0])) check_ybas0=np.where(np.bitwise_or(bas0[:,1]<0,bas0[:,1]>=I.shape[1])) bas0[check_xbas0,0]=0 bas0[check_ybas0,1]=0 check_xbas1=np.where(np.bitwise_or(bas1[:,0]<0,bas1[:,0]>=I.shape[0])) check_ybas1=np.where(np.bitwise_or(bas1[:,1]<0,bas1[:,1]>=I.shape[1])) bas1[check_xbas1,0]=0 bas1[check_ybas1,1]=0 #hold shape Ish=I.shape[0] #ravel image Ione=I.ravel() #new intensities xyz0=Ione[(bas0[:,0]+bas0[:,1]*Ish).astype('int')] xyz1=Ione[(bas0[:,0]+bas1[:,1]*Ish).astype('int')] xyz2=Ione[(bas1[:,0]+bas0[:,1]*Ish).astype('int')] xyz3=Ione[(bas1[:,0]+bas1[:,1]*Ish).astype('int')] #kill mirroring #xyz0[np.bitwise_or(check_xbas0,check_ybas0)]=0 #xyz1[np.bitwise_or(check_xbas0,check_ybas1)]=0 #xyz2[np.bitwise_or(check_xbas1,check_ybas0)]=0 #xyz3[np.bitwise_or(check_xbas1,check_ybas1)]=0 #apply recalculated intensities Iout=xyz0*perc0+xyz1*perc1+xyz2*perc2+xyz3*perc3 return Iout.reshape(I.shape) def joint_histogram(A,B,binA,binB): ''' Calculate joint histogram and individual histograms for A and B ndarrays Parameters ---------- A, B: ndarrays binA, binB: 1d arrays with the bins Returns ------- JH: joint histogram HA: histogram for A HB: histogram for B Example ------- >>> A=np.array([[1,.5,.2,0,0],[.5,1,.5,0,0],[.2,.5,1,0,0],[0,0,0,0,0],[0,0,0,0,0]]) >>> B=np.array([[0,0,0,0,0],[0,1,.5,.2,0],[0,.5,1,.5,0],[0,.2,.5,1,0],[0,0,0,0,0]]) >>> bin_A=np.array([-np.Inf,.1,.35,.75,np.Inf]) >>> bin_B=np.array([-np.Inf,.1,.35,.75,np.Inf]) >>> JH,HA,HB=joint_histogram(A,B,bin_A,bin_B) ''' A=A.ravel() B=B.ravel() A2=A.copy() B2=B.copy() #assign bins for i in range(1,len(binA)): Ai=np.where(np.bitwise_and(A>binA[i-1],A<=binA[i])) A2[Ai]=i-1 for i in range(1,len(binB)): Bi=np.where(np.bitwise_and(B>binB[i-1],B<=binB[i])) B2[Bi]=i-1 JH=np.zeros((len(binA)-1,len(binB)-1)) #calculate joint histogram for i in range(len(A)): JH[A2[i],B2[i]]+=1 #calculate histogram for A HA=np.zeros(len(binA)-1) for i in range(len(A)): HA[A2[i]]+=1 #calculate histogram for B HB=np.zeros(len(binB)-1) for i in range(len(B)): HB[B2[i]]+=1 return JH,HA,HB def mutual_information(A,B,binA,binB): ''' Calculate mutual information for A and B ''' JH,HA,HB=joint_histogram(A,B,binA,binB) N=float(len(A.ravel())) MI=np.zeros(JH.shape) #print N for i in range(JH.shape[0]): for j in range(JH.shape[1]): Pij= JH[i,j]/N Pi = HA[i]/N Pj= HB[j]/N #print i,j, Pij, Pi, Pj, JH[i,j], HA[i], HB[j] MI[i,j]=Pij*np.log2(Pij/(Pi*Pj)) MI[np.isnan(MI)]=0 return MI.sum() def apply_mapping(A,T,order=0,map_type='affine2d'): ''' Apply mapping ''' if map_type=='affine2d': #create the different components #translation[2], scale[2], rotation[1], shear[2] if len(T)==7: tc1,tc2,sc1,sc2,rc,sch1,sch2=T if len(T)==5: tc1,tc2,sc1,sc2,rc=T sch1,sch2=(0,0) if len(T)==4: tc1,tc2,rc,sc=T sc1,sc2,sch1,sch2=(sc,sc,1,1) if len(T)==3: tc1,tc2,rc=T sc1,sc2,sch1,sch2=(1,1,0,0) #translation TC=np.matrix([[1,0,tc1], [0,1,tc2], [0,0, 1]]) #scaling SC=np.matrix([[sc1, 0, 0], [0, sc2, 0], [0, 0, 1]]) #rotation RC=np.matrix([[cos(rc), sin(rc), 0], [-sin(rc), cos(rc), 0], [0 , 0, 1]]) #shear SHC=np.matrix([[1, sch1,0], [sch2, 1,0], [0, 0,1]]) #apply #M=TC*SC*RC*SHC if len(T)==3: M=TC*RC if len(T)==4: M=TC*SC*RC if len(T)==5: M=TC*SC*RC if len(T)==7: M=TC*SC*RC*SHC M=np.array(M) AT=affine_transform2d(A,M) return AT def objective_mi(T,A,B,binA,binB,order=0,map_type='affine2d'): ''' Objective function for mutual information ''' AT=apply_mapping(A,T,order=0,map_type=map_type) #AT=np.round(AT) AT=AT.T NegMI= -mutual_information(AT,B,binA,binB) print '====',T,'====> - MI : ',NegMI #pylab.imshow(AT) #raw_input('Press Enter...') #pylab.imshow(np.hstack((A,B,AT))) #raw_input('Press Enter...') return NegMI def objective_sd(T,A,B,order=0,map_type='affine2d'): AT=apply_mapping(A,T,order=0,map_type=map_type) AT=AT.T if AT.sum()==0: SD=10**15 else: SD= np.sum((AT-B)**2)/np.prod(AT.shape) print '====',T,'====> SD : ',SD #pylab.imshow(np.hstack((A,B,AT))) #raw_input('Press Enter...') return SD def register(A,B,guess,metric='sd',binA=None,binB=None,xtol=0.1,ftol=0.01,order=0,map_type='affine2d'): ''' Register source A to target B using modified powell's method Powell's method tries to minimize the objective function ''' if metric=='mi': finalT=fmin_powell(objective_mi,x0=guess,args=(A,B,binA,binB,order,map_type),xtol=xtol,ftol=ftol) #finalT=leastsq(func=objective_mi,x0=np.array(guess),args=(A,B,binA,binB,order,map_type)) if metric=='sd': finalT=fmin_powell(objective_sd,x0=guess,args=(A,B,order,map_type),xtol=xtol,ftol=ftol) #finalT=leastsq(func=objective_sd,x0=np.array(guess),args=(A,B,order,map_type)) return finalT def evaluate(A,B,guess,metric='sd',binA=None,binB=None,xtol=0.1,ftol=0.01,order=0,map_type='affine2d'): #tc1,tc2,sc1,sc2,rc=T tc1=np.linspace(-50,50,20) tc2=np.linspace(-50,50,20) sc1=np.linspace(-1.2,1.2,10) sc2=np.linspace(-1.2,1.2,10) rc=np.linspace(0,np.pi,8) f_min=np.inf T_final=[] ''' for c1 in tc1: for c2 in tc2: for s1 in sc1: for s2 in sc2: for r in rc: T=[c1,c2,s1,s2,r] f=objective_sd(T,A,B,order=0,map_type='affine2d') if f0)[0]: del C[k[i]] return C def most(C): for c in C: pass # pf.most_similar_track_mam() T=pkl.load_pickle(fname) print 'Reducing the number of points...' T=[pf.approx_polygon_track(t) for t in T] print 'Reducing further to tracks with 3 pts...' T2=[tm.downsample(t,3) for t in T] print 'LARCH ...' print 'Splitting ...' t=time.clock() C=pf.larch_3split(T2,None,5.) print time.clock()-t, len(C) for c in C: print c, C[c]['rep3']/C[c]['N'] r=show_rep3(C) print 'Merging ...' t=time.clock() C=merge(C,5.) print time.clock()-t, len(C) for c in C: print c, C[c]['rep3']/C[c]['N'] show_rep3(C,r,fos.red) ''' #print 'Showing initial dataset.' r=fos.ren() #fos.add(r,fos.line(T,fos.white,opacity=1)) #fos.show(r) print 'Showing dataset after clustering.' #fos.clear(r) colors=np.zeros((len(T),3)) for c in C: color=np.random.rand(1,3) for i in C[c]['indices']: colors[i]=color fos.add(r,fos.line(T,colors,opacity=1)) fos.show(r) print 'Some statistics about the clusters' print 'Number of clusters',len(C.keys()) lens=[len(C[c]['indices']) for c in C] print 'max ',max(lens), 'min ',min(lens) print 'singletons ',lens.count(1) print 'doubletons ',lens.count(2) print 'tripletons ',lens.count(3) print 'Showing dataset after merging.' fos.clear(r) T=[t + np.array([120,0,0]) for t in T] colors=np.zeros((len(T),3)) for c in C2: color=np.random.rand(1,3) for i in C2[c]['indices']: colors[i]=color fos.add(r,fos.line(T,colors,opacity=1)) fos.show(r) print 'Some statistics about the clusters' print 'Number of clusters',len(C.keys()) lens=[len(C2[c]['indices']) for c in C] print 'max ',max(lens), 'min ',min(lens) print 'singletons ',lens.count(1) print 'doubletons ',lens.count(2) print 'tripletons ',lens.count(3) ''' dipy-0.5.0/scratch/very_scratch/pf_script.py000066400000000000000000000007201152576264200211600ustar00rootroot00000000000000import numpy as np from dipy.core.reconstruction_performance import peak_finding from dipy.core.reconstruction_performance import pf_bago from dipy.core.triangle_subdivide import create_unit_sphere, remove_half_sphere v, e, t = create_unit_sphere(5) vH, eH, tH = remove_half_sphere(v, e, t) odf = np.random.random(len(vH)) pB, iB = pf_bago(odf, eH) bO = np.r_[odf, odf] faces = e[t,0] faces = faces/2 + (faces % 2)*len(odf) p, i = peak_finding(bO, faces) dipy-0.5.0/scratch/very_scratch/registration_example.py000066400000000000000000000170651152576264200234260ustar00rootroot00000000000000import os import numpy as np import dipy as dp import nibabel as ni import resources import time from subprocess import Popen,PIPE #Registration options #similarity 'cc', 'cr', 'crl1', 'mi', je', 'ce', 'nmi', 'smi'. 'cr' similarity='cr' #interp 'pv', 'tri' interp = 'tri' #subsampling None or sequence (3,) subsampling=[1,1,1] #search 'affine', 'rigid', 'similarity' or ['rigid','affine'] search='affine' #optimizer 'simplex', 'powell', 'steepest', 'cg', 'bfgs' or #sequence of optimizers optimizer= 'powell' def eddy_current_correction(data,affine,target=None,target_affine=None): result=[] no_dirs=data.shape[-1] if target==None and target_affine==None: target=ni.Nifti1Image(data[:,:,:,0],affine) else: target=ni.Nifti1Image(target,target_affine) for i in range(1,no_dirs): source=ni.Nifti1Image(data[:,:,:,i],affine) T=dp.volume_register(source,target,similarity,\ interp,subsampling,search,optimizer) sourceT=dp.volume_transform(source, T.inv(), reference=target) print i, sourceT.get_data().shape, sourceT.get_affine().shape result.append(sourceT) result.insert(0,target) print 'no of images',len(result) return ni.concat_images(result) def register_source_2_target(source_data,source_affine,target_data,target_affine): #subsampling=target_data.shape[:3] target=ni.Nifti1Image(target_data,target_affine) source=ni.Nifti1Image(source_data,source_affine) T=dp.volume_register(source,target,similarity,\ interp,subsampling,search,optimizer) sourceT=dp.volume_transform(source, T.inv(), reference=target) return sourceT def save_volumes_as_mosaic(fname,volume_list): import Image vols=[] for vol in volume_list: vol=np.rollaxis(vol,2,1) sh=vol.shape arr=vol.reshape(sh[0],sh[1]*sh[2]) arr=np.interp(arr,[arr.min(),arr.max()],[0,255]) arr=arr.astype('ubyte') print 'arr.shape',arr.shape vols.append(arr) mosaic=np.concatenate(vols) Image.fromarray(mosaic).save(fname) def haircut_dwi_reference(nii,nii_hair): cmd='bet '+nii+' '+ nii_hair + ' -f .2 -g 0' print cmd p = Popen(cmd, shell=True,stdout=PIPE,stderr=PIPE) sto=p.stdout.readlines() ste=p.stderr.readlines() print sto print ste def register_FA_same_subj_diff_sessions(dname_grid,dname_shell): print('create temporary directory') tmp_dir='/tmp' print('load dicom data') data_gr,affine_gr,bvals_gr,gradients_gr=dp.load_dcm_dir(dname_grid) data_sh,affine_sh,bvals_sh,gradients_sh=dp.load_dcm_dir(dname_shell) print('save DWI reference as nifti') tmp_grid=os.path.join(tmp_dir,os.path.basename(dname_grid)+'_ref.nii') tmp_shell=os.path.join(tmp_dir,os.path.basename(dname_shell)+'_ref.nii') ni.save(ni.Nifti1Image(data_gr[...,0],affine_gr),tmp_grid) ni.save(ni.Nifti1Image(data_sh[...,0],affine_sh),tmp_shell) print('prepare filenames for haircut (bet)') tmp_grid_bet=os.path.join(os.path.dirname(tmp_grid),\ os.path.splitext(os.path.basename(dname_grid))[0]+\ '_ref_bet.nii.gz') tmp_shell_bet=os.path.join(os.path.dirname(tmp_shell),\ os.path.splitext(os.path.basename(dname_shell))[0]+\ '_ref_bet.nii.gz') print('bet is running') haircut_dwi_reference(tmp_grid,tmp_grid_bet) haircut_dwi_reference(tmp_shell,tmp_shell_bet) print('load nii.gz reference (s0) volumes') img_gr_bet=ni.load(tmp_grid_bet) img_sh_bet=ni.load(tmp_shell_bet) print('register the shell reference to the grid reference') source=img_sh_bet target=img_gr_bet T=dp.volume_register(source,target,similarity,\ interp,subsampling,search,optimizer) print('apply the inverse of the transformation matrix') sourceT=dp.volume_transform(source, T.inv(), reference=target) #ni.save(sourceT,'/tmp/result.nii.gz') print('calculate FA for grid and shell data') FA_grid=dp.Tensor( data_gr,bvals_gr,gradients_gr,thresh=50).FA FA_shell=dp.Tensor(data_sh,bvals_sh,gradients_sh,thresh=50).FA print('create an FA nibabel image for shell') FA_shell_img=ni.Nifti1Image(FA_shell,affine_sh) print('transform FA_shell') FA_shell_imgT=dp.volume_transform(FA_shell_img,T.inv(),reference=target) return ni.Nifti1Image(FA_grid,affine_gr),FA_shell_imgT def flirt(in_nii, ref_nii,out_nii,transf_mat): cmd='flirt -in ' + in_nii + ' -ref ' + ref_nii + ' -out ' \ + out_nii +' -dof 6 -omat ' + transf_mat print(cmd) pipe(cmd) def flirt_apply_transform(in_nii, target_nii, out_nii, transf_mat): cmd='flirt -in ' + in_nii + ' -ref ' + target_nii + ' -out ' \ + out_nii +' -init ' + transf_mat +' -applyxfm' print(cmd) pipe(cmd) def test_registration(): S012='/tmp/compare_12_with_32_Verio_directly/18620_0004.nii_S0.nii.gz' S032='/tmp/compare_12_with_32_Verio_directly/18620_0006.nii_S0.nii.gz' S012T='/tmp/compare_12_with_32_Verio_directly/S0_reg.nii.gz' MP='/tmp/compare_12_with_32_Verio_directly/MPRAGE.nii' D114=resources.get_paths('DTI STEAM 114 Trio')[2] data,affine,bvals,gradients=dp.load_dcm_dir(D114) D114i=ni.Nifti1Image(data[...,0],affine) D101=resources.get_paths('DSI STEAM 101 Trio')[2] data,affine,bvals,gradients=dp.load_dcm_dir(D101) D101i=ni.Nifti1Image(data[...,0],affine) ni.save(D101i,'/tmp/compare_12_with_32_Verio_directly/S0_101_reg.nii.gz') #source=ni.load(S012) source=D114i #target=D101i #target=ni.load(S032) target=ni.load(MP) target._data=np.squeeze(target._data) #target._affine= np.dot(np.diag([-1, -1, 1, 1]), target._affine) similarity='cr' interp = 'tri' subsampling=None search='affine' optimizer= 'powell' T=dp.volume_register(source,target,similarity,\ interp,subsampling,search,optimizer) print('Transformation matrix') print(T.inv()) sourceT=dp.volume_transform(source,T.inv(),reference=target,interp_order=0) sourceTd=sourceT.get_data() sourceTd[sourceTd<0]=0 sourceT._data=sourceTd ni.save(sourceT,S012T) sourced=source.get_data() targetd=target.get_data() sourceTd=sourceT.get_data() print 'source info',sourced.min(), sourced.max() print 'target info',targetd.min(), targetd.max() print 'sourceT info',sourceTd.min(), sourceTd.max() #save_volumes_as_mosaic('/tmp/mosaic_S0_MP_cr_pv_powell.png',\ # [sourced,sourceTd,targetd]) # RAS to LPS np.dot(np.diag([-1, -1, 1, 1]), A) # LPS to RAS if __name__ == '__main__': ''' print('Goal is to compare FA of grid versus shell acquisitions using STEAM') print('find filenames for grid and shell data') dname_grid=resources.get_paths('DSI STEAM 101 Trio')[2] dname_shell=resources.get_paths('DTI STEAM 114 Trio')[2] #print('find filenames for T1') #fname_T1=resources.get_paths('MPRAGE nifti Trio')[2] FA_grid_img,FA_shell_imgT=register_FA_same_subj_diff_sessions(dname_grid,dname_shell) #FA_shell_data=FA_shell_imgT.get_data() #FA_shell_data[FA_shell_data<0]=0 print('tile volumes') save_volumes_as_mosaic('/tmp/mosaic_fa.png',\ [FA_grid_img.get_data(),FA_shell_imgT.get_data()]) ''' dipy-0.5.0/scratch/very_scratch/simulation_comparison_dsi_gqi.py000066400000000000000000000022471152576264200253120ustar00rootroot00000000000000import numpy as np import dipy as dp import dipy.io.pickles as pkl import scipy as sp fname='/home/ian/Data/SimData/results_SNR030_1fibre' #fname='/home/eg01/Data_Backup/Data/Marta/DSI/SimData/results_SNR030_isotropic' ''' file has one row for every voxel, every voxel is repeating 1000 times with the same noise level , then we have 100 different directions. 1000 * 100 is the number of all rows. ''' marta_table_fname='/home/ian/Data/SimData/Dir_and_bvals_DSI_marta.txt' sim_data=np.loadtxt(fname) #bvalsf='/home/eg01/Data_Backup/Data/Marta/DSI/SimData/bvals101D_float.txt' b_vals_dirs=np.loadtxt(marta_table_fname) bvals=b_vals_dirs[:,0]*1000 gradients=b_vals_dirs[:,1:] gq = dp.GeneralizedQSampling(sim_data,bvals,gradients) tn = dp.Tensor(sim_data,bvals,gradients) #''' gqfile = '/home/ian/Data/SimData/gq_SNR030_1fibre.pkl' pkl.save_pickle(gqfile,gq) tnfile = '/home/ian/Data/SimData/tn_SNR030_1fibre.pkl' pkl.save_pickle(tnfile,tn) ''' print tn.evals.shape print tn.evecs.shape evals=tn.evals[0] evecs=tn.evecs[0] print evecs.shape first_directions = tn.evecs[:,:,0] first1000 = first_directions[:1000,:] cross = np.dot(first1000.T,first1000) np.linalg.eig(cross) ''' dipy-0.5.0/scratch/very_scratch/simulation_comparisons.py000066400000000000000000000305731152576264200240010ustar00rootroot00000000000000import nibabel import os import numpy as np import dipy as dp import dipy.core.generalized_q_sampling as dgqs import dipy.io.pickles as pkl import scipy as sp from matplotlib.mlab import find import dipy.core.sphere_plots as splots import dipy.core.sphere_stats as sphats import dipy.core.geometry as geometry import get_vertices as gv #old SimData files ''' results_SNR030_1fibre results_SNR030_1fibre+iso results_SNR030_2fibres_15deg results_SNR030_2fibres_30deg results_SNR030_2fibres_60deg results_SNR030_2fibres_90deg results_SNR030_2fibres+iso_15deg results_SNR030_2fibres+iso_30deg results_SNR030_2fibres+iso_60deg results_SNR030_2fibres+iso_90deg results_SNR030_isotropic ''' #fname='/home/ian/Data/SimData/results_SNR030_1fibre' ''' file has one row for every voxel, every voxel is repeating 1000 times with the same noise level , then we have 100 different directions. 1000 * 100 is the number of all rows. The 100 conditions are given by 10 polar angles (in degrees) 0, 20, 40, 60, 80, 80, 60, 40, 20 and 0, and each of these with longitude angle 0, 40, 80, 120, 160, 200, 240, 280, 320, 360. ''' #new complete SimVoxels files simdata = ['fibres_2_SNR_80_angle_90_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_60_angle_60_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_40_angle_30_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_40_angle_60_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_20_angle_15_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_2_SNR_100_angle_90_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_20_angle_30_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_2_SNR_40_angle_15_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_2_SNR_60_angle_15_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_2_SNR_100_angle_90_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_1_SNR_60_angle_00_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_2_SNR_80_angle_30_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_100_angle_15_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_100_angle_60_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_2_SNR_80_angle_60_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_60_angle_30_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_2_SNR_40_angle_60_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_2_SNR_80_angle_30_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_2_SNR_20_angle_30_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_60_angle_60_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_1_SNR_100_angle_00_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_1_SNR_100_angle_00_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_20_angle_15_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_1_SNR_20_angle_00_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_2_SNR_40_angle_15_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_20_angle_60_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_80_angle_15_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_1_SNR_80_angle_00_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_2_SNR_20_angle_90_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_2_SNR_60_angle_90_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_100_angle_30_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_80_angle_90_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_2_SNR_60_angle_15_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_20_angle_60_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_2_SNR_100_angle_15_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_1_SNR_20_angle_00_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_80_angle_60_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_1_SNR_80_angle_00_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_100_angle_30_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_1_SNR_40_angle_00_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_1_SNR_60_angle_00_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_40_angle_30_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_2_SNR_60_angle_30_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_40_angle_90_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_60_angle_90_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_2_SNR_80_angle_15_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_1_SNR_40_angle_00_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_100_angle_60_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00', 'fibres_2_SNR_40_angle_90_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7', 'fibres_2_SNR_20_angle_90_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00'] simdir = '/home/ian/Data/SimVoxels/' def gq_tn_calc_save(): for simfile in simdata: dataname = simfile print dataname sim_data=np.loadtxt(simdir+dataname) marta_table_fname='/home/ian/Data/SimData/Dir_and_bvals_DSI_marta.txt' b_vals_dirs=np.loadtxt(marta_table_fname) bvals=b_vals_dirs[:,0]*1000 gradients=b_vals_dirs[:,1:] gq = dp.GeneralizedQSampling(sim_data,bvals,gradients) gqfile = simdir+'gq/'+dataname+'.pkl' pkl.save_pickle(gqfile,gq) ''' gq.IN gq.__doc__ gq.glob_norm_param gq.QA gq.__init__ gq.odf gq.__class__ gq.__module__ gq.q2odf_params ''' tn = dp.Tensor(sim_data,bvals,gradients) tnfile = simdir+'tn/'+dataname+'.pkl' pkl.save_pickle(tnfile,tn) ''' tn.ADC tn.__init__ tn._getevals tn.B tn.__module__ tn._getevecs tn.D tn.__new__ tn._getndim tn.FA tn.__reduce__ tn._getshape tn.IN tn.__reduce_ex__ tn._setevals tn.MD tn.__repr__ tn._setevecs tn.__class__ tn.__setattr__ tn.adc tn.__delattr__ tn.__sizeof__ tn.evals tn.__dict__ tn.__str__ tn.evecs tn.__doc__ tn.__subclasshook__ tn.fa tn.__format__ tn.__weakref__ tn.md tn.__getattribute__ tn._evals tn.ndim tn.__getitem__ tn._evecs tn.shape tn.__hash__ tn._getD ''' ''' file has one row for every voxel, every voxel is repeating 1000 times with the same noise level , then we have 100 different directions. 100 * 1000 is the number of all rows. At the moment this module is hardwired to the use of the EDS362 spherical mesh. I am assumung (needs testing) that directions 181 to 361 are the antipodal partners of directions 0 to 180. So when counting the number of different vertices that occur as maximal directions we wll map the indices modulo 181. ''' def analyze_maxima(indices, max_dirs, subsets): '''This calculates the eigenstats for each of the replicated batches of the simulation data ''' results = [] for direction in subsets: batch = max_dirs[direction,:,:] index_variety = np.array([len(set(np.remainder(indices[direction,:],181)))]) #normed_centroid, polar_centroid, centre, b1 = sphats.eigenstats(batch) centre, b1 = sphats.eigenstats(batch) # make azimuth be in range (0,360) rather than (-180,180) centre[1] += 360*(centre[1] < 0) #results.append(np.concatenate((normed_centroid, polar_centroid, centre, b1, index_variety))) results.append(np.concatenate((centre, b1, index_variety))) return results #dt_first_directions = tn.evecs[:,:,0].reshape((100,1000,3)) # these are the principal directions for the full set of simulations #gq_tn_calc_save() eds=np.load(os.path.join(os.path.dirname(dp.__file__),'core','matrices','evenly_distributed_sphere_362.npz')) odf_vertices=eds['vertices'] def run_comparisons(sample_data=35): for simfile in [simdata[sample_data]]: dataname = simfile print dataname sim_data=np.loadtxt(simdir+dataname) # gqfile = simdir+'gq/'+dataname+'.pkl' # gq = pkl.load_pickle(gqfile) tnfile = simdir+'tn/'+dataname+'.pkl' tn = pkl.load_pickle(tnfile) dt_first_directions_in=odf_vertices[tn.IN] dt_indices = tn.IN.reshape((100,1000)) dt_results = analyze_maxima(dt_indices, dt_first_directions_in.reshape((100,1000,3)),range(10,91)) # gq_indices = np.array(gq.IN[:,0],dtype='int').reshape((100,1000)) # gq_first_directions_in=odf_vertices[np.array(gq.IN[:,0],dtype='int')] #print gq_first_directions_in.shape # gq_results = analyze_maxima(gq_indices, gq_first_directions_in.reshape((100,1000,3)),range(100)) #for gqi see example dicoms_2_tracks gq.IN[:,0] np.set_printoptions(precision=6, suppress=True, linewidth=200, threshold=5000) out = open('/home/ian/Data/SimVoxels/Out/'+'***_'+dataname,'w') # results = np.hstack((np.vstack(dt_results), np.vstack(gq_results))) results = np.vstack(dt_results) print >> out, results[:,:] out.close() #up = dt_batch[:,2]>= 0 #splots.plot_sphere(dt_batch[up], 'batch '+str(direction)) #splots.plot_lambert(dt_batch[up],'batch '+str(direction), centre) #spread = gq.q2odf_params e,v = np.linalg.eigh(np.dot(spread,spread.transpose())) effective_dimension = len(find(np.cumsum(e) > 0.05*np.sum(e))) #95% #rotated = np.dot(dt_batch,evecs) #rot_evals, rot_evecs = np.linalg.eig(np.dot(rotated.T,rotated)/rotated.shape[0]) #eval_order = np.argsort(rot_evals) #rotated = rotated[:,eval_order] #up = rotated[:,2]>= 0 #splot.plot_sphere(rotated[up],'first1000') #splot.plot_lambert(rotated[up],'batch '+str(direction)) def run_gq_sims(sample_data=[35]): for simfile in [simdata[sample] for sample in sample_data]: dataname = simfile print dataname sim_data=np.loadtxt(simdir+dataname) marta_table_fname='/home/ian/Data/SimData/Dir_and_bvals_DSI_marta.txt' b_vals_dirs=np.loadtxt(marta_table_fname) bvals=b_vals_dirs[:,0]*1000 gradients=b_vals_dirs[:,1:] for j in range(10): s = sim_data[10000+j,:] gqs = dp.GeneralizedQSampling(s.reshape((1,102)),bvals,gradients,Lambda=7) t0, t1, t2, npa = gqs.npa(s, width = 5) print t0, t1, t2, npa ''' for (i,o) in enumerate(gqs.odf(s)): print i,o for (i,o) in enumerate(gqs.odf_vertices): print i,o ''' #o = gqs.odf(s) #v = gqs.odf_vertices #pole = v[t0[0]] #eqv = dgqs.equatorial_zone_vertices(v, pole, 5) #print 'Number of equatorial vertices: ', len(eqv) #print np.max(o[eqv]),np.min(o[eqv]) #cos_e_pole = [np.dot(pole.T, v[i]) for i in eqv] #print np.min(cos1), np.max(cos1) #print 'equatorial max in equatorial vertices:', t1[0] in eqv #x = np.cross(v[t0[0]],v[t1[0]]) #x = x/np.sqrt(np.sum(x**2)) #print x #ptchv = dgqs.patch_vertices(v, x, 5) #print len(ptchv) #eqp = eqv[np.argmin([np.abs(np.dot(v[t1[0]].T,v[p])) for p in eqv])] #print (eqp, o[eqp]) #print t2[0] in ptchv, t2[0] in eqv #print np.dot(pole.T, v[t1[0]]), np.dot(pole.T, v[t2[0]]) #print ptchv[np.argmin([o[v] for v in ptchv])] #gq_indices = np.array(gq.IN[:,0],dtype='int').reshape((100,1000)) #gq_first_directions_in=odf_vertices[np.array(gq.IN[:,0],dtype='int')] #print gq_first_directions_in.shape #gq_results = analyze_maxima(gq_indices, gq_first_directions_in.reshape((100,1000,3)),range(100)) #for gqi see example dicoms_2_tracks gq.IN[:,0] #np.set_printoptions(precision=6, suppress=True, linewidth=200, threshold=5000) #out = open('/home/ian/Data/SimVoxels/Out/'+'+++_'+dataname,'w') #results = np.hstack((np.vstack(dt_results), np.vstack(gq_results))) #results = np.vstack(dt_results) #print >> out, results[:,:] #out.close() #run_comparisons() run_gq_sims() dipy-0.5.0/scratch/very_scratch/simulation_dsi.py000066400000000000000000000130221152576264200222110ustar00rootroot00000000000000import numpy as np import dipy as dp import pyglet from pyglet.gl import * #from delaunay.core import Triangulation #http://flub.stuffwillmade.org/delny/ try: # Try and create a window with multisampling (antialiasing) config = Config(sample_buffers=1, samples=4, depth_size=24, double_buffer=True,vsync=False) window = pyglet.window.Window(resizable=True, config=config) except pyglet.window.NoSuchConfigException: # Fall back to no multisampling for old hardware window = pyglet.window.Window(resizable=True) #fps_display = pyglet.clock.ClockDisplay() @window.event def on_resize(width, height): # Override the default on_resize handler to create a 3D projection print('%d width, %d height' % (width,height)) glViewport(0, 0, width, height) glMatrixMode(GL_PROJECTION) glLoadIdentity() gluPerspective(60., width / float(height), .1, 1000.) glMatrixMode(GL_MODELVIEW) #window.flip() return pyglet.event.EVENT_HANDLED def update(dt): global rx, ry, rz #rx += dt * 5 #ry += dt * 80 #rz += dt * 30 #rx %= 360 #ry %= 360 #rz %= 360 pass pyglet.clock.schedule(update) #pyglet.clock.schedule_interval(update,1/100.) @window.event def on_draw(): global surf for i in range(0,900,3): if np.random.rand()>0.5: surf.vertex_list.vertices[i]+=0.001*np.random.rand() surf.vertex_list.vertices[i+1]+=0.001*np.random.rand() surf.vertex_list.vertices[i+2]+=0.001*np.random.rand() else: surf.vertex_list.vertices[i]-=0.001*np.random.rand() surf.vertex_list.vertices[i+1]-=0.001*np.random.rand() surf.vertex_list.vertices[i+2]-=0.001*np.random.rand() glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT) glLoadIdentity() #fps_display.draw() #glScalef(3,1,1) glTranslatef(0, 0, -4) glRotatef(rx, 0, 0, 1) glRotatef(ry, 0, 1, 0) glRotatef(rx, 1, 0, 0) batch.draw() #pyglet.image.get_buffer_manager().get_color_buffer().save('/tmp/test.png') print pyglet.clock.get_fps() #window.clear() #fps_display.draw() def setup(): # One-time GL setup glClearColor(1, 1, 1, 1) #glClearColor(0,0,0,0) glColor3f(1, 0, 0) glEnable(GL_DEPTH_TEST) #glEnable(GL_CULL_FACE) # Uncomment this line for a wireframe view glPolygonMode(GL_FRONT_AND_BACK, GL_LINE) glLineWidth(3.) # Simple light setup. On Windows GL_LIGHT0 is enabled by default, # but this is not the case on Linux or Mac, so remember to always # include it. glEnable(GL_LIGHTING) glEnable(GL_LIGHT0) glEnable(GL_LIGHT1) # Define a simple function to create ctypes arrays of floats: def vec(*args): return (GLfloat * len(args))(*args) glLightfv(GL_LIGHT0, GL_POSITION, vec(.5, .5, 1, 0)) glLightfv(GL_LIGHT0, GL_SPECULAR, vec(.5, .5, 1, 1)) glLightfv(GL_LIGHT0, GL_DIFFUSE, vec(1, 1, 1, 1)) glLightfv(GL_LIGHT1, GL_POSITION, vec(1, 0, .5, 0)) glLightfv(GL_LIGHT1, GL_DIFFUSE, vec(.5, .0, 0, 1)) glLightfv(GL_LIGHT1, GL_SPECULAR, vec(1, 0, 0, 1)) glMaterialfv(GL_FRONT_AND_BACK, GL_AMBIENT_AND_DIFFUSE, vec(0.5, 0, 0.3, 0.5)) glMaterialfv(GL_FRONT_AND_BACK, GL_SPECULAR, vec(1, 1, 1, 0.5)) glMaterialf(GL_FRONT_AND_BACK, GL_SHININESS, 50) class Surface(object): def __init__(self, vertices,faces,batch,group=None): inds=faces.ravel().tolist() verx=vertices.ravel().tolist() normals=np.zeros((len(vertices),3)) p=vertices l=faces trinormals=np.cross(p[l[:,0]]-p[l[:,1]],p[l[:,1]]-p[l[:,2]],axisa=1,axisb=1) for (i,lp) in enumerate(faces): normals[lp]+=trinormals[i] div=np.sqrt(np.sum(normals**2,axis=1)) div=div.reshape(len(div),1) normals=(normals/div) #normals=vertices/np.linalg.norm(vertices) norms=np.array(normals).ravel().tolist() self.vertex_list = batch.add_indexed(len(vertices),\ GL_TRIANGLES,\ group,\ inds,\ ('v3d/static',verx),\ ('n3d/static',norms)) def delete(self): self.vertex_list.delete() fname='/home/eg01/Data_Backup/Data/Marta/DSI/SimData/results_SNR030_1fibre' #fname='/home/eg01/Data_Backup/Data/Marta/DSI/SimData/results_SNR030_isotropic' marta_table_fname='/home/eg01/Data_Backup/Data/Marta/DSI/SimData/Dir_and_bvals_DSI_marta.txt' sim_data=np.loadtxt(fname) #bvalsf='/home/eg01/Data_Backup/Data/Marta/DSI/SimData/bvals101D_float.txt' dname = '/home/eg01/Data_Backup/Data/Frank_Eleftherios/frank/20100511_m030y_cbu100624/08_ep2d_advdiff_101dir_DSI' #real_data,affine,bvals,gradients=dp.load_dcm_dir(dname) b_vals_dirs=np.loadtxt(marta_table_fname) bvals=b_vals_dirs[:,0]*1000 gradients=b_vals_dirs[:,1:] sim_data=sim_data gq = dp.GeneralizedQSampling(sim_data,bvals,gradients) tn = dp.Tensor(sim_data,bvals,gradients) evals=tn.evals[0] evecs=tn.evecs[0] setup() batch = pyglet.graphics.Batch() eds=np.load('/home/eg01/Devel/dipy/dipy/core/matrices/evenly_distributed_sphere_362.npz') vertices=eds['vertices'] faces=eds['faces'] surf = Surface(vertices,faces, batch=batch) rx = ry = rz = 0 print('Application Starting Now...') pyglet.app.run() dipy-0.5.0/scratch/very_scratch/spherical_statistics.py000066400000000000000000000131071152576264200234160ustar00rootroot00000000000000import numpy as np import dipy.core.meshes as meshes import get_vertices as gv from dipy.core.triangle_subdivide import create_unit_sphere #from dipy.viz import fos #from dipy.io import dicomreaders as dcm #import dipy.core.geometry as geometry #import matplotlib.pyplot as mplp import dipy.core.sphere_plots as splot # set up a dictionary of sphere points that are in use EITHER as a set # directions for diffusion weighted acquisitions OR as a set of # evaluation points for an ODF (orientation distribution function. sphere_dic = {'fy362': {'filepath' : '/home/ian/Devel/dipy/dipy/core/matrices/evenly_distributed_sphere_362.npz', 'object': 'npz', 'vertices': 'vertices', 'omit': 0, 'hemi': False}, 'fy642': {'filepath' : '/home/ian/Devel/dipy/dipy/core/matrices/evenly_distributed_sphere_642.npz', 'object': 'npz', 'vertices': 'odf_vertices', 'omit': 0, 'hemi': False}, 'siem64': {'filepath':'/home/ian/Devel/dipy/dipy/core/tests/data/small_64D.gradients.npy', 'object': 'npy', 'omit': 1, 'hemi': True}, 'create2': {}, 'create3': {}, 'create4': {}, 'create5': {}, 'create6': {}, 'create7': {}, 'create8': {}, 'create9': {}, 'marta200': {'filepath': '/home/ian/Data/Spheres/200.npy', 'object': 'npy', 'omit': 0, 'hemi': True}, 'dsi101': {'filepath': '/home/ian/Data/Frank_Eleftherios/frank/20100511_m030y_cbu100624/08_ep2d_advdiff_101dir_DSI', 'object': 'dicom', 'omit': 0, 'hemi': True}} def plot_sphere(v,key): r = fos.ren() fos.add(r,fos.point(v,fos.green, point_radius= 0.01)) fos.show(r, title=key, size=(1000,1000)) def plot_lambert(v,key): lamb = geometry.lambert_equal_area_projection_cart(*v.T).T (y1,y2) = lamb radius = np.sum(lamb**2,axis=0) < 1 #print inner #print y1[inner] #print y1[-inner] figure = mplp.figure(facecolor='w') current = figure.add_subplot(111) current.patch.set_color('k') current.plot(y1[radius],y2[radius],'.g') current.plot(y1[-radius],y2[-radius],'.r') current.axes.set_aspect(aspect = 'equal', adjustable = 'box') figure.show() figure.waitforbuttonpress() mplp.close() def get_vertex_set(key): if key[:6] == 'create': number = eval(key[6:]) vertices, edges, faces = create_unit_sphere(number) omit = 0 else: entry = sphere_dic[key] #print entry if entry.has_key('omit'): omit = entry['omit'] else: omit = 0 filepath = entry['filepath'] if entry['object'] == 'npz': filearray = np.load(filepath) vertices = filearray[entry['vertices']] elif sphere_dic[key]['object'] == 'npy': vertices = np.load(filepath) elif entry['object'] == 'dicom': data,affine,bvals,gradients=dcm.read_mosaic_dir(filepath) #print (bvals.shape, gradients.shape) grad3 = np.vstack((bvals,bvals,bvals)).transpose() #print grad3.shape #vertices = grad3*gradients vertices = gradients if omit > 0: vertices = vertices[omit:,:] if entry['hemi']: vertices = np.vstack([vertices, -vertices]) print key, ': number of vertices = ', vertices.shape[0], '(drop ',omit,')' return vertices[omit:,:] xup=np.array([ 1,0,0]) xdn=np.array([-1,0,0]) yup=np.array([0, 1,0]) ydn=np.array([0,-1,0]) zup=np.array([0,0, 1]) zdn=np.array([0,0,-1]) #for key in sphere_dic: #for key in ['siem64']: for key in ['fy642']: v = gv.get_vertex_set(key) splot.plot_sphere(v,key) splot.plot_lambert(v,key,centre=np.array([0.,0.])) equat, polar = meshes.spherical_statistics(v,north=xup,width=0.2) l = 2.*len(v) equat = equat/l polar = polar/l print '%6.3f %6.3f %6.3f %6.3f' % (equat.min(), equat.mean(), equat.max(), np.sqrt(equat.var())) print '%6.3f %6.3f %6.3f %6.3f' % (polar.min(), polar.mean(), polar.max(), np.sqrt(polar.var())) def spherical_statistics(vertices, north=np.array([0,0,1]), width=0.02): ''' function to evaluate a spherical triangulation by looking at the variability of numbers of vertices in 'vertices' in equatorial bands of width 'width' orthogonal to each point in 'vertices' ''' equatorial_counts = np.array([len(equatorial_zone_vertices(vertices, pole, width=width)) for pole in vertices if np.dot(pole,north) >= 0]) #equatorial_counts = np.bincount(equatorial_counts) #args = np.where(equatorial_counts>0) #print zip(list(args[0]), equatorial_counts[args]) polar_counts = np.array([len(polar_zone_vertices(vertices, pole, width=width)) for pole in vertices if np.dot(pole,north) >= 0]) #unique_counts = np.sort(np.array(list(set(equatorial_counts)))) #polar_counts = np.bincount(polar_counts) #counts_tokens = [(uc, bin_counts[uc]) for uc in bin_counts if ] #args = np.where(polar_counts>0) #print '(number, frequency):', zip(unique_counts,tokens) #print '(number, frequency):', counts_tokens #print zip(args, bin_counts[args]) #print zip(list(args[0]), polar_counts[args]) return equatorial_counts, polar_counts def spherical_proportion(zone_width): # assuming radius is 1: (2*np.pi*zone_width)/(4*np.pi) # 0 <= zone_width <= 2 return zone_width/2. def angle_for_zone(zone_width): return np.arcsin(zone_width/2.) def coarseness(faces): faces = np.asarray(faces) coarseness = 0.0 for face in faces: a, b, c = face coarse = np.max(coarse, geom.circumradius(a,b,c)) return coarse dipy-0.5.0/scratch/very_scratch/tractography_clustering_new_fos.py000066400000000000000000000053411152576264200256610ustar00rootroot00000000000000import time import numpy as np from nibabel import trackvis as tv from dipy.core import track_metrics as tm from dipy.core import track_performance as pf from fos.core.scene import Scene from fos.core.actors import Actor from fos.core.plots import Plot from fos.core.tracks import Tracks #fname='/home/eg01/Data_Backup/Data/PBC/pbc2009icdm/brain1/brain1_scan1_fiber_track_mni.trk' fname='/home/eg01/Data_Backup/Data/PBC/pbc2009icdm/brain2/brain2_scan1_fiber_track_mni.trk' #fname='/home/eg309/Data/PBC/pbc2009icdm/brain1/brain1_scan1_fiber_track_mni.trk' opacity=0.5 print 'Loading file...' streams,hdr=tv.read(fname) print 'Copying tracks...' T=[i[0] for i in streams] T=T[:len(T)/5] #T=T[:1000] print 'Representing tracks using only 3 pts...' tracks=[tm.downsample(t,3) for t in T] print 'Deleting unnecessary data...' del streams,hdr print 'Local Skeleton Clustering...' now=time.clock() C=pf.local_skeleton_clustering(tracks,d_thr=20) print 'Done in', time.clock()-now,'s.' print 'Reducing the number of points...' T=[pf.approx_polygon_track(t) for t in T] print 'Showing initial dataset.' #r=fos.ren() #fos.add(r,fos.line(T,fos.white,opacity=0.1)) #fos.show(r) data=T colors =[np.tile(np.array([1,1,1,opacity],'f'),(len(t),1)) for t in T] t=Tracks(data,colors,line_width=1.) t.position=(-100,0,0) print 'Showing dataset after clustering.' print 'Calculating skeletal track for every bundle.' skeletals=[] colors2 = len(data)*[None] colors_sk = []#len(C.keys())*[None] for c in C: color=np.random.rand(3) r,g,b = color bundle=[] for i in C[c]['indices']: colors2[i]=np.tile(np.array([r,g,b,opacity],'f'),(len(data[i]),1)) bundle.append(data[i]) bi=pf.most_similar_track_mam(bundle)[0] C[c]['skeletal']=bundle[bi] if len(C[c]['indices'])>100 and tm.length(bundle[bi])>30.: colors_sk.append( np.tile(np.array([r,g,b,opacity],'f'),(len(bundle[bi]),1)) ) skeletals.append(bundle[bi]) print 'len_data', len(data) print 'len_skeletals', len(skeletals) print 'len_colors2', len(colors2) print 'len_colors_sk', len(colors_sk) t2=Tracks(data,colors2,line_width=1.) t2.position=(100,0,0) sk=Tracks(skeletals,colors_sk,line_width=3.) sk.position=(0,0,0) slot={0:{'actor':t,'slot':(0, 800000)}, 1:{'actor':t2,'slot':(0, 800000)}, 2:{'actor':sk,'slot':(0, 800000)}} Scene(Plot(slot)).run() print 'Some statistics about the clusters' lens=[len(C[c]['indices']) for c in C] print 'max ',max(lens), 'min ',min(lens) print 'singletons ',lens.count(1) print 'doubletons ',lens.count(2) print 'tripletons ',lens.count(3) ''' Next Level 12: cluster0=[T[t] for t in C[0]['indices']] 13: pf.most_similar_track_mam(cluster0) ''' dipy-0.5.0/scratch/very_scratch/tractography_clustering_using_larch.py000066400000000000000000000026241152576264200265200ustar00rootroot00000000000000import time import os import numpy as np from nibabel import trackvis as tv from dipy.viz import fos from dipy.io import pickles as pkl from dipy.core import track_learning as tl from dipy.core import track_performance as pf from dipy.core import track_metrics as tm fname='/home/eg01/Data/PBC/pbc2009icdm/brain1/brain1_scan1_fiber_track_mni.trk' C_fname='/tmp/larch_tree.pkl' appr_fname='/tmp/larch_tracks.trk' print 'Loading trackvis file...' streams,hdr=tv.read(fname) print 'Copying tracks...' tracks=[i[0] for i in streams] #tracks=tracks[:1000] #print 'Deleting unnecessary data...' del streams#,hdr if not os.path.isfile(C_fname): print 'Starting LARCH ...' tim=time.clock() C,atracks=tl.larch(tracks,[50.**2,20.**2,5.**2],True,True) #tracks=[tm.downsample(t,3) for t in tracks] #C=pf.local_skeleton_clustering(tracks,20.) print 'Done in total of ',time.clock()-tim,'seconds.' print 'Saving result...' pkl.save_pickle(C_fname,C) streams=[(i,None,None)for i in atracks] tv.write(appr_fname,streams,hdr) else: print 'Loading result...' C=pkl.load_pickle(C_fname) skel=[] for c in C: skel.append(C[c]['repz']) print 'Showing dataset after clustering...' r=fos.ren() fos.clear(r) colors=np.zeros((len(skel),3)) for (i,s) in enumerate(skel): color=np.random.rand(1,3) colors[i]=color fos.add(r,fos.line(skel,colors,opacity=1)) fos.show(r) dipy-0.5.0/scratch/very_scratch/warptalk.py000066400000000000000000000274131152576264200210240ustar00rootroot00000000000000import numpy as np import nibabel as nib import numpy.linalg as npl from dipy.io.dpy import Dpy def flirt2aff(mat, in_img, ref_img): """ Transform from `in_img` voxels to `ref_img` voxels given `matfile` Parameters ---------- matfile : (4,4) array contents (as array) of output ``-omat`` transformation file from flirt in_img : img image passed (as filename) to flirt as ``-in`` image ref_img : img image passed (as filename) to flirt as ``-ref`` image Returns ------- aff : (4,4) array Transform from voxel coordinates in ``in_img`` to voxel coordinates in ``ref_img`` """ in_hdr = in_img.get_header() ref_hdr = ref_img.get_header() # get_zooms gets the positive voxel sizes as returned in the header in_zoomer = np.diag(in_hdr.get_zooms() + (1,)) ref_zoomer = np.diag(ref_hdr.get_zooms() + (1,)) # The in_img voxels to ref_img voxels as recorded in the current affines current_in2ref = np.dot(ref_img.get_affine(), in_img.get_affine()) if npl.det(current_in2ref) < 0: raise ValueError('Negative determinant to current affine mapping - bailing out') return np.dot(npl.inv(ref_zoomer), np.dot(mat, in_zoomer)) def flirt2aff_files(matfile, in_fname, ref_fname): """ Map from `in_fname` image voxels to `ref_fname` voxels given `matfile` Parameters ---------- matfile : str filename of output ``-omat`` transformation file from flirt in_fname : str filename for image passed to flirt as ``-in`` image ref_fname : str filename for image passed to flirt as ``-ref`` image Returns ------- aff : (4,4) array Transform from voxel coordinates in image for ``in_fname`` to voxel coordinates in image for ``ref_fname`` """ mat = np.loadtxt(matfile) in_img = nib.load(in_fname) ref_img = nib.load(ref_fname) return flirt2aff(mat, in_img, ref_img) #d101='/home/eg309/Data/TEST_MR10032/subj_10/101/' d101='/home/eg309/Data/PROC_MR10032/subj_10/101/' ffa=d101+'1312211075232351192010092912092080924175865ep2dadvdiffDSI10125x25x25STs005a001_bet_FA.nii.gz' fdis=d101+'1312211075232351192010092912092080924175865ep2dadvdiffDSI10125x25x25STs005a001_nonlin_displacements.nii.gz' ffareg=d101+'1312211075232351192010092912092080924175865ep2dadvdiffDSI10125x25x25STs005a001_bet_FA_reg.nii.gz' flirtaff=d101+'1312211075232351192010092912092080924175865ep2dadvdiffDSI10125x25x25STs005a001_affine_transf.mat' ftrack=d101+'1312211075232351192010092912092080924175865ep2dadvdiffDSI10125x25x25STs005a001_QA_native.dpy' froi='/home/eg309/Data/PROC_MR10032/NIFTI_ROIs/AnatomicalROIs/ROI01_GCC.nii' froi2='/home/eg309/Data/PROC_MR10032/NIFTI_ROIs/AnatomicalROIs/ROI02_BCC.nii' #froi3='/home/eg309/Data/PROC_MR10032/NIFTI_ROIs/AnatomicalROIs/ROI03_SCC.nii' froi3='/home/eg309/Downloads/SCC_analyze.nii' ref_fname = '/usr/share/fsl/data/standard/FMRIB58_FA_1mm.nii.gz' dpr=Dpy(ftrack,'r') print dpr.track_no T=dpr.read_indexed([0,1,2,3,2000,1000000]) for t in T: print t.shape dpr.close() track=T[4] im2im = flirt2aff_files(flirtaff, ffa, ref_fname) #ref_name to be replaced by ffareg print im2im from dipy.core.track_metrics import length print len(track) print length(track) #ntrack=np.dot(im2im[:3,:3],track.T)+im2im[:3,[3]] ntrack=np.dot(track,im2im[:3,:3].T)+im2im[:3,3] print length(ntrack) #print length(ntrack.T) print length(ntrack)/length(track) #print npl.det(im2im)**(1/3.) disimg=nib.load(fdis) ddata=disimg.get_data() daff=disimg.get_affine() from scipy.ndimage.interpolation import map_coordinates as mc di=ddata[:,:,:,0] dj=ddata[:,:,:,1] dk=ddata[:,:,:,2] mci=mc(di,ntrack.T) mcj=mc(dj,ntrack.T) mck=mc(dk,ntrack.T) wtrack=ntrack+np.vstack((mci,mcj,mck)).T np.set_printoptions(2) print np.hstack((wtrack,ntrack)) print length(wtrack),length(ntrack),length(track) imgroi=nib.load(froi) roidata=imgroi.get_data() roiaff=imgroi.get_affine() roiaff=daff I=np.array(np.where(roidata>0)).T wI=np.dot(roiaff[:3,:3],I.T).T+roiaff[:3,3] print wI.shape wI=wI.astype('f4') imgroi2=nib.load(froi2) roidata2=imgroi2.get_data() roiaff2=imgroi2.get_affine() roiaff2=daff I2=np.array(np.where(roidata2>0)).T wI2=np.dot(roiaff2[:3,:3],I2.T).T+roiaff2[:3,3] print wI2.shape wI2=wI2.astype('f4') imgroi3=nib.load(froi3) roidata3=imgroi3.get_data() roiaff3=imgroi3.get_affine() roiaff3=daff I3=np.array(np.where(roidata3>0)).T wI3=np.dot(roiaff3[:3,:3],I3.T).T+roiaff3[:3,3] print wI3.shape wI3=wI3.astype('f4') dpr=Dpy(ftrack,'r') print dpr.track_no from time import time t1=time() iT=np.random.randint(0,dpr.track_no,10*10**2) T=dpr.read_indexed(iT) dpr.close() t2=time() print t2-t1,len(T) Tfinal=[] ''' for (i,track) in enumerate(T): print i ntrack=np.dot(track,im2im[:3,:3].T)+im2im[:3,3] mci=mc(di,ntrack.T) mcj=mc(dj,ntrack.T) mck=mc(dk,ntrack.T) wtrack=ntrack+np.vstack((mci,mcj,mck)).T Tfinal.append(np.dot(wtrack,daff[:3,:3].T)+daff[:3,3]) ''' lengths=[len(t) for t in T] lengths.insert(0,0) offsets=np.cumsum(lengths) caboodle=np.concatenate(T,axis=0) ntrack=np.dot(caboodle,im2im[:3,:3].T)+im2im[:3,3] mci=mc(di,ntrack.T,order=1) mcj=mc(dj,ntrack.T,order=1) mck=mc(dk,ntrack.T,order=1) wtrack=ntrack+np.vstack((mci,mcj,mck)).T caboodlew=np.dot(wtrack,daff[:3,:3].T)+daff[:3,3] #caboodlew=np.dot(wtrack,roiaff[:3,:3].T)+roiaff[:3,3] Tfinal=[] for i in range(len(offsets)-1): s=offsets[i] e=offsets[i+1] Tfinal.append(caboodlew[s:e]) #ref_fname = '/usr/share/fsl/data/standard/FMRIB58_FA_1mm.nii.gz' ref_fname = '/usr/share/fsl/data/standard/FMRIB58_FA-skeleton_1mm.nii.gz' imgref=nib.load(ref_fname) refdata=imgref.get_data() refaff=imgref.get_affine() ''' refI=np.array(np.where(refdata>5000)).T wrefI=np.dot(refaff[:3,:3],refI.T).T+refaff[:3,3] print wrefI.shape wrefI=wrefI.astype('f4') ''' from dipy.viz import fos froi='/home/eg309/Data/ICBM_Wmpm/ICBM_WMPM.nii' def get_roi(froi,no): imgroi=nib.load(froi) roidata=imgroi.get_data() roiaff=imgroi.get_affine() I=np.array(np.where(roidata==no)).T wI=np.dot(roiaff[:3,:3],I.T).T+roiaff[:3,3] wI=wI.astype('f4') return wI from dipy.viz import fos r=fos.ren() #fos.add(r,fos.point(wI,fos.blue)) #fos.add(r,fos.point(wI2,fos.yellow)) #fos.add(r,fos.point(wI3,fos.green)) #fos.add(r,fos.point(wrefI,fos.cyan)) #fos.add(r,fos.point(wrefI,fos.yellow)) fos.add(r,fos.point(get_roi(froi,3),fos.blue)) fos.add(r,fos.point(get_roi(froi,4),fos.yellow)) fos.add(r,fos.point(get_roi(froi,5),fos.green)) fos.add(r,fos.line(Tfinal,fos.red)) fos.show(r) print roiaff print roiaff2 print roiaff3 print daff ##load roi image #roiimg=ni.load(froi) #roidata=roiimg.get_data() #roiaff=roiimg.get_affine() #print 'roiaff',roiaff,roidata.shape # ##load FA image #faimg=ni.load(ffa) #data=faimg.get_data() #aff=faimg.get_affine() ##aff[0,:]=-aff[0,:] ##aff[0,0]=-aff[0,0] ##aff=np.array([[2.5,0,0,-2.5*48],[0,2.5,0,-2.5*39],[0,0,2.5,-2.5*23],[0,0,0,1]]) # #print 'aff',aff, data.shape # ##cube = np.array([v for v in np.ndindex(5,5,5)]).T + np.array([[47,47,27]]).T #cube = np.array([v for v in np.ndindex(data.shape[0],data.shape[1],data.shape[2])]).T # ##from image space(image coordinates) to native space (world coordinates) #cube_native = np.dot(aff[:3,:3],cube)+aff[:3,[3]] ##print cube_native.T # ##load flirt affine #laff=np.loadtxt(flirtaff) ##laff[0,:]=-laff[0,:] ##laff=np.linalg.inv(laff) ##laff[:3,3]=0 #print 'laff',laff ##print 'inverting laff' # # ##from native space(world coordinates) to mni space(world coordinates) #cube_mni = np.dot(laff[:3,:3],cube_native)+laff[:3,[3]] ##print cube_mni.T # #dis=ni.load(fdis) #disdata=dis.get_data() #mniaff=dis.get_affine() #print 'mniaff',mniaff # ##invert disaff #mniaffinv= np.linalg.inv(mniaff) ##from mni space(world coordinates) to image mni space (image coordinates) #cube_mni_grid = np.dot(mniaffinv[:3,:3],cube_mni)+mniaffinv[:3,[3]] #print cube_mni_grid.shape # #cube_mni_grid_nearest=np.round(cube_mni_grid).astype(np.int) # #print np.max(cube_mni_grid[0,:]) #print np.max(cube_mni_grid[1,:]) #print np.max(cube_mni_grid[2,:]) # #print np.max(cube_mni_grid_nearest[0,:]) #print np.max(cube_mni_grid_nearest[1,:]) #print np.max(cube_mni_grid_nearest[2,:]) # #d0,d1,d2,junk = disdata.shape # #cube_mni_grid_nearest[np.where(cube_mni_grid_nearest<0)]=0 #cube_mni_grid_nearest[np.where(cube_mni_grid_nearest>181)]=0 # #n0=cube_mni_grid_nearest[0,:] #n1=cube_mni_grid_nearest[1,:] #n2=cube_mni_grid_nearest[2,:] ''' n0 = np.min(np.max(cube_mni_grid_nearest[0,:],0),d0) n1 = np.min(np.max(cube_mni_grid_nearest[1,:],0),d1) n2 = np.min(np.max(cube_mni_grid_nearest[2,:],0),d2) ''' #cube_mni_data=np.zeros(disdata.shape[:-1],dtype=np.float32) #cube_mni_data[n0,n1,n2]=1 ''' D=disdata[n0,n1,n2] ''' #from dipy.viz import fos #r=fos.ren() ##fos.add(r,fos.point(cube.T,fos.red)) ##fos.add(r,fos.point(cube_native.T,fos.yellow)) #fos.add(r,fos.point(cube_mni.T,fos.green)) #fos.add(r,fos.sphere(np.array([0,0,0]),10)) # ##fos.add(r,fos.point(cube_mni_grid_nearest.T,fos.red)) ###fos.add(r,fos.point(cube.T,fos.green)) ###fos.add(r,fos.point(cube_mni_grid.T,fos.red)) ###fos.add(r,fos.point(cube.T,fos.yellow)) #fos.show(r) # #def map_to_index(grid,shape): # x=grid[0,:] # y=grid[1,:] # z=grid[2,:] # xmin=x.min() # ymin=y.min() # zmin=z.min() # xmax=x.max() # ymax=y.max() # zmax=z.max() # i=(x-xmin)/(xmax-xmin)*shape[0] # j=(y-ymin)/(ymax-ymin)*shape[1] # k=(z-zmin)/(zmax-zmin)*shape[2] # return i,j,k # #i,j,k=map_to_index(cube_mni_grid,(182,218,182)) # #from scipy.ndimage import map_coordinates #FA_MNI_IMG = map_coordinates(data,np.c_[i, j, k].T) #from dipy.viz import fos #r=fos.ren() #fos.add(r,fos.point(cube_mni.T,fos.blue)) #fos.add(r,fos.point(cube_native.T,fos.green)) #fos.add(r,fos.point(cube_mni_grid.T,fos.red)) #fos.add(r,fos.point(cube.T,fos.yellow)) #fos.show(r) ###corner = cube[:,:].astype(np.int).T #print corner ###print data[corner[:,0:27],corner[:,0:27],corner[:,0:27]] #def func(x,y): # return (x+y)*np.exp(-5.*(x**2+y**2)) # #def map_to_index(x,y,bounds,N,M): # xmin,xmax,ymin,ymax=bounds # i1=(x-xmin)/(xmax-xmin)*N # i2=(y-ymin)/(ymax-ymin)*M # return i1,i2 # #x,y=np.mgrid[-1:1:10j,-1:1:10j] #fvals=func(x,y) # #xn,yn=np.mgrid[-1:1:100j,-1:1:100j] #i1,i2 = map_to_index(xn,yn,[-1,1,-1,1],*x.shape) # #from scipy.ndimage import map_coordinates # #fn = map_coordinates(fvals,[i1,i2]) #true = func(xn,yn) def test_flirt2aff(): from os.path import join as pjoin from nose.tools import assert_true import scipy.ndimage as ndi import nibabel as nib ''' matfile = pjoin('fa_data', '1312211075232351192010092912092080924175865ep2dadvdiffDSI10125x25x25STs005a001_affine_transf.mat') in_fname = pjoin('fa_data', '1312211075232351192010092912092080924175865ep2dadvdiffDSI10125x25x25STs005a001_bet_FA.nii.gz') ''' matfile=flirtaff in_fname = ffa ref_fname = '/usr/share/fsl/data/standard/FMRIB58_FA_1mm.nii.gz' res = flirt2aff_files(matfile, in_fname, ref_fname) mat = np.loadtxt(matfile) in_img = nib.load(in_fname) ref_img = nib.load(ref_fname) assert_true(np.all(res == flirt2aff(mat, in_img, ref_img))) # mm to mm transform mm_in2mm_ref = np.dot(ref_img.get_affine(), np.dot(res, npl.inv(in_img.get_affine()))) # make new in image thus transformed in_data = in_img.get_data() ires = npl.inv(res) in_data[np.isnan(in_data)] = 0 resliced_data = ndi.affine_transform(in_data, ires[:3,:3], ires[:3,3], ref_img.shape) resliced_img = nib.Nifti1Image(resliced_data, ref_img.get_affine()) nib.save(resliced_img, 'test.nii') dipy-0.5.0/setup.py000077500000000000000000000124721152576264200142160ustar00rootroot00000000000000#!/usr/bin/env python ''' Installation script for dipy package ''' import os import sys from os.path import join as pjoin from glob import glob # BEFORE importing distutils, remove MANIFEST. distutils doesn't properly # update it when the contents of directories change. if os.path.exists('MANIFEST'): os.remove('MANIFEST') import numpy as np # For some commands, use setuptools if len(set(('develop', 'bdist_egg', 'bdist_rpm', 'bdist', 'bdist_dumb', 'bdist_wininst', 'install_egg_info', 'egg_info', 'easy_install', )).intersection(sys.argv)) > 0: # setup_egg imports setuptools setup, thus monkeypatching distutils. from setup_egg import extra_setuptools_args # Import distutils _after_ potential setuptools import above, and after removing # MANIFEST from distutils.core import setup from distutils.extension import Extension # extra_setuptools_args can be defined from the line above, but it can # also be defined here because setup.py has been exec'ed from # setup_egg.py. if not 'extra_setuptools_args' in globals(): extra_setuptools_args = dict() # Import build helpers try: from nisext.sexts import package_check, get_comrec_build except ImportError: raise RuntimeError('Need nisext package from nibabel installation' ' - please install nibabel first') cmdclass = {'build_py': get_comrec_build('dipy')} # Get version and release info, which is all stored in dipy/info.py ver_file = os.path.join('dipy', 'info.py') execfile(ver_file) # We're running via setuptools - specify exta setuptools stuff if 'setuptools' in sys.modules: extra_setuptools_args['extras_require'] = dict( doc=['Sphinx>=1.0'], test=['nose>=0.10.1'], ) # I removed numpy and scipy from install requires because easy_install seems # to want to fetch these if they are already installed, meaning of course # that there's a long fragile and unnecessary compile before the install # finishes. extra_setuptools_args['install_requires'] = [ 'nibabel>=' + NIBABEL_MIN_VERSION, ] # Do our own install time dependency checking. The dependency checks in # setuptools above go into the egg so are useful for easy_install. These checks # below run whenever we run setup.py - so - install or build via setup.py package_check('numpy', NUMPY_MIN_VERSION) package_check('scipy', SCIPY_MIN_VERSION) package_check('nibabel', NIBABEL_MIN_VERSION) # Cython is a build dependency def _cython_version(pkg_name): from Cython.Compiler.Version import version return version package_check('cython', CYTHON_MIN_VERSION, version_getter=_cython_version) # we use cython to compile the modules from Cython.Distutils import build_ext cmdclass['build_ext'] = build_ext EXTS = [] for modulename, other_sources in ( ('dipy.reconst.recspeed', []), ('dipy.tracking.distances', []), ('dipy.tracking.vox2track', []), ('dipy.tracking.propspeed', [])): pyx_src = pjoin(*modulename.split('.')) + '.pyx' EXTS.append(Extension(modulename,[pyx_src] + other_sources, include_dirs = [np.get_include()])) def main(**extra_args): setup(name=NAME, maintainer=MAINTAINER, maintainer_email=MAINTAINER_EMAIL, description=DESCRIPTION, long_description=LONG_DESCRIPTION, url=URL, download_url=DOWNLOAD_URL, license=LICENSE, classifiers=CLASSIFIERS, author=AUTHOR, author_email=AUTHOR_EMAIL, platforms=PLATFORMS, version=VERSION, requires=REQUIRES, provides=PROVIDES, packages = ['dipy', 'dipy.align', 'dipy.core', 'dipy.core.tests', 'dipy.tracking', 'dipy.tracking.tests', 'dipy.reconst', 'dipy.reconst.tests', 'dipy.io', 'dipy.io.tests', 'dipy.viz', 'dipy.viz.tests', 'dipy.testing', 'dipy.boots', 'dipy.data', 'dipy.utils', 'dipy.utils.tests', 'dipy.external', 'dipy.external.tests'], ext_modules = EXTS, # The package_data spec has no effect for me (on python 2.6) -- even # changing to data_files doesn't get this stuff included in the source # distribution -- not sure if it has something to do with the magic # above, but distutils is surely the worst piece of code in all of # python -- duplicating things into MANIFEST.in but this is admittedly # only a workaround to get things started -- not a solution package_data = {'dipy': [pjoin('data', '*') ]}, data_files=[('share/doc/dipy/examples', glob(pjoin('doc','examples','*.py')))], scripts = glob(pjoin('scripts', '*')), cmdclass = cmdclass, **extra_args ) #simple way to test what setup will do #python setup.py install --prefix=/tmp if __name__ == "__main__": main(**extra_setuptools_args) dipy-0.5.0/setup_egg.py000066400000000000000000000016111152576264200150260ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: """Wrapper to run setup.py using setuptools.""" # Deal with setuptools monkeypatching bug for Pyrex import sys from os.path import dirname, join as pjoin sys.path.insert(0, pjoin(dirname(__file__), 'fake_pyrex')) from setuptools import setup ################################################################################ # Call the setup.py script, injecting the setuptools-specific arguments. extra_setuptools_args = dict( tests_require=['nose'], test_suite='nose.collector', zip_safe=False, ) if __name__ == '__main__': execfile('setup.py', dict(__name__='__main__', extra_setuptools_args=extra_setuptools_args)) dipy-0.5.0/tools/000077500000000000000000000000001152576264200136335ustar00rootroot00000000000000dipy-0.5.0/tools/build_release000077500000000000000000000012561152576264200163640ustar00rootroot00000000000000#!/usr/bin/env python """dipy release build script. """ import os from toollib import (c, get_dipydir, compile_tree, cd, pjoin, remove_tree) # Get main dipy dir, this will raise if it doesn't pass some checks dipydir = get_dipydir() cd(dipydir) # Load release info execfile(pjoin('dipy','info.py')) # Check that everything compiles compile_tree() # Cleanup for d in ['build','dist',pjoin('doc','_build'),pjoin('doc','dist')]: if os.path.isdir(d): remove_tree(d) # Build source and binary distros c('./setup.py sdist --formats=gztar,zip') # Build eggs for version in ['2.5', '2.6', '2.7']: cmd='python'+version+' ./setup_egg.py bdist_egg' stat = os.system(cmd) dipy-0.5.0/tools/doc_mod.py000077500000000000000000000013051152576264200156130ustar00rootroot00000000000000#!/usr/bin/env python """ Make documentation for module Depends on some guessed filepaths Filepaths guessed by importing """ import sys from os.path import join as pjoin, dirname, abspath ROOT_DIR = abspath(pjoin(dirname(__file__), '..')) DOC_SDIR = pjoin(ROOT_DIR, 'doc', 'reference') TEMPLATE = \ """:mod:`%s` ========================= .. automodule:: %s :members: """ def main(): try: mod_name = sys.argv[1] except IndexError: raise OSError('Need module import as input') out_fname = pjoin(DOC_SDIR, mod_name + '.rst') open(out_fname, 'wt').write(TEMPLATE % (mod_name, mod_name)) if __name__ == '__main__': main() dipy-0.5.0/tools/ex2rst000077500000000000000000000222371152576264200150160ustar00rootroot00000000000000#!/usr/bin/env python # # Note before note: dipy copied this file from nitime who ... # Note: this file is copied (possibly with minor modifications) from the # sources of the PyMVPA project - http://pymvpa.org. It remains licensed as # the rest of PyMVPA (MIT license as of October 2010). # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Helper to automagically generate ReST versions of examples""" __docformat__ = 'restructuredtext' import os import sys import re import glob from optparse import OptionParser def auto_image(line): """Automatically replace generic image markers with ones that have full size (width/height) info, plus a :target: link to the original png, to be used in the html docs. """ img_re = re.compile(r'(\s*)\.\. image::\s*(.*)$') m = img_re.match(line) if m is None: # Not an image declaration, leave the line alone and return unmodified return line # Match means it's an image spec, we rewrite it with extra tags ini_space = m.group(1) lines = [line, ini_space + ' :width: 500\n', #ini_space + ' :height: 350\n' ] fspec = m.group(2) if fspec.endswith('.*'): fspec = fspec.replace('.*', '.png') fspec = fspec.replace('fig/', '../_images/') lines.append(ini_space + (' :target: %s\n' % fspec) ) lines.append('\n') return ''.join(lines) def exfile2rst(filename): """Open a Python script and convert it into an ReST string. """ # output string s = '' # open source file xfile = open(filename) # parser status vars inheader = True indocs = False doc2code = False code2doc = False # an empty line found in the example enables the check for a potentially # indented docstring starting on the next line (as an attempt to exclude # function or class docstrings) last_line_empty = False # indentation of indented docstring, which is removed from the RsT output # since we typically do not want an indentation there. indent_level = 0 for line in xfile: # skip header if inheader and \ not (line.startswith('"""') or line.startswith("'''")): continue # determine end of header if inheader and (line.startswith('"""') or line.startswith("'''")): inheader = False # strip comments and remove trailing whitespace if not indocs and last_line_empty: # first remove leading whitespace and store indent level cleanline = line[:line.find('#')].lstrip() indent_level = len(line) - len(cleanline) - 1 cleanline = cleanline.rstrip() else: cleanline = line[:line.find('#')].rstrip() if not indocs and line == '\n': last_line_empty = True else: last_line_empty = False # if we have something that should go into the text if indocs \ or (cleanline.startswith('"""') or cleanline.startswith("'''")): proc_line = None # handle doc start if not indocs: # guarenteed to start with """ if len(cleanline) > 3 \ and (cleanline.endswith('"""') \ or cleanline.endswith("'''")): # single line doc code2doc = True doc2code = True proc_line = cleanline[3:-3] else: # must be start of multiline block indocs = True code2doc = True # rescue what is left on the line proc_line = cleanline[3:] # strip """ else: # we are already in the docs # handle doc end if cleanline.endswith('"""') or cleanline.endswith("'''"): indocs = False doc2code = True # rescue what is left on the line proc_line = cleanline[:-3] # reset the indentation indent_level = 0 else: # has to be documentation # if the indentation is whitespace remove it, other wise # keep it (accounts for some variation in docstring # styles real_indent = \ indent_level - len(line[:indent_level].lstrip()) proc_line = line[real_indent:] if code2doc: code2doc = False s += '\n' proc_line = auto_image(proc_line) if proc_line: s += proc_line.rstrip() + '\n' else: if doc2code: doc2code = False s += '\n::\n' # has to be code s += ' %s' % line xfile.close() return s def exfile2rstfile(filename, opts): """ """ # doc filename dfilename = os.path.basename(filename[:-3]) + '.rst' # open dest file dfile = open(os.path.join(opts.outdir, os.path.basename(dfilename)), 'w') # place header dfile.write('.. AUTO-GENERATED FILE -- DO NOT EDIT!\n\n') # place cross-ref target dfile.write('.. _example_' + dfilename[:-4] + ':\n\n') # write converted ReST dfile.write(exfile2rst(filename)) if opts.sourceref: # write post example see also box msg = """ .. admonition:: Example source code You can download :download:`the full source code of this example <%s>`. This same script is also included in the %s source distribution under the :file:`doc/examples/` directory. """ % (filename, opts.project) dfile.write(msg) dfile.close() def main(): parser = OptionParser( \ usage="%prog [options] [...]", \ version="%prog 0.1", description="""\ %prog converts Python scripts into restructered text (ReST) format suitable for integration into the Sphinx documentation framework. Its key feature is that it extracts stand-alone (unassigned) single, or multiline triple-quote docstrings and moves them out of the code listing so that they are rendered as regular ReST, while at the same time maintaining their position relative to the listing. The detection of such docstrings is exclusively done by parsing the raw code so it is never actually imported into a running Python session. Docstrings have to be written using triple quotes (both forms " and ' are possible). It is recommend that such docstrings are preceded and followed by an empty line. Intended docstring can make use of the full linewidth from the second docstring line on. If the indentation of multiline docstring is maintained for all lines, the respective indentation is removed in the ReST output. The parser algorithm automatically excludes file headers and starts with the first (module-level) docstring instead. """ ) #' # define options parser.add_option('--verbose', action='store_true', dest='verbose', default=False, help='print status messages') parser.add_option('-x', '--exclude', action='append', dest='excluded', help="""\ Use this option to exclude single files from the to be parsed files. This is especially useful to exclude files when parsing complete directories. This option can be specified multiple times. """) parser.add_option('-o', '--outdir', action='store', dest='outdir', type='string', default=None, help="""\ Target directory to write the ReST output to. This is a required option. """) parser.add_option('--no-sourceref', action='store_false', default=True, dest='sourceref', help="""\ If specified, the source reference section will be suppressed. """) parser.add_option('--project', type='string', action='store', default='', dest='project', help="""\ Name of the project that contains the examples. This name is used in the 'seealso' source references. Default: '' """) # parse options (opts, args) = parser.parse_args() # read sys.argv[1:] by default # check for required options if opts.outdir is None: print('Required option -o, --outdir not specified.') sys.exit(1) # build up list of things to parse toparse = [] for t in args: # expand dirs if os.path.isdir(t): # add all python files in that dir toparse += glob.glob(os.path.join(t, '*.py')) else: toparse.append(t) # filter parse list if not opts.excluded is None: toparse = [t for t in toparse if not t in opts.excluded] toparse_list = toparse toparse = set(toparse) if len(toparse) != len(toparse_list): print('Ignoring duplicate parse targets.') if not os.path.exists(opts.outdir): os.mkdir(opts.outdir) # finally process all examples for t in toparse: exfile2rstfile(t, opts) if __name__ == '__main__': main() dipy-0.5.0/tools/gitwash_dumper.py000077500000000000000000000113651152576264200172400ustar00rootroot00000000000000#!/usr/bin/env python ''' Checkout gitwash repo into directory and do search replace on name ''' import os from os.path import join as pjoin import shutil import sys import re import glob import fnmatch import tempfile from subprocess import call verbose = False def clone_repo(url, branch): cwd = os.getcwd() tmpdir = tempfile.mkdtemp() try: cmd = 'git clone %s %s' % (url, tmpdir) call(cmd, shell=True) os.chdir(tmpdir) cmd = 'git checkout %s' % branch call(cmd, shell=True) except: shutil.rmtree(tmpdir) raise finally: os.chdir(cwd) return tmpdir def cp_files(in_path, globs, out_path): try: os.makedirs(out_path) except OSError: pass out_fnames = [] for in_glob in globs: in_glob_path = pjoin(in_path, in_glob) for in_fname in glob.glob(in_glob_path): out_fname = in_fname.replace(in_path, out_path) pth, _ = os.path.split(out_fname) if not os.path.isdir(pth): os.makedirs(pth) shutil.copyfile(in_fname, out_fname) out_fnames.append(out_fname) return out_fnames def filename_search_replace(sr_pairs, filename, backup=False): ''' Search and replace for expressions in files ''' in_txt = open(filename, 'rt').read(-1) out_txt = in_txt[:] for in_exp, out_exp in sr_pairs: in_exp = re.compile(in_exp) out_txt = in_exp.sub(out_exp, out_txt) if in_txt == out_txt: return False open(filename, 'wt').write(out_txt) if backup: open(filename + '.bak', 'wt').write(in_txt) return True def copy_replace(replace_pairs, out_path, repo_url, repo_branch = 'master', cp_globs=('*',), rep_globs=('*',), renames = ()): repo_path = clone_repo(repo_url, repo_branch) try: out_fnames = cp_files(repo_path, cp_globs, out_path) finally: shutil.rmtree(repo_path) renames = [(re.compile(in_exp), out_exp) for in_exp, out_exp in renames] fnames = [] for rep_glob in rep_globs: fnames += fnmatch.filter(out_fnames, rep_glob) if verbose: print '\n'.join(fnames) for fname in fnames: filename_search_replace(replace_pairs, fname, False) for in_exp, out_exp in renames: new_fname, n = in_exp.subn(out_exp, fname) if n: os.rename(fname, new_fname) break USAGE = ''' If not set with options, the repository name is the same as the If not set with options, the main github user is the same as the repository name.''' GITWASH_CENTRAL = 'git://github.com/matthew-brett/gitwash.git' GITWASH_BRANCH = 'master' if __name__ == '__main__': from optparse import OptionParser parser = OptionParser() parser.set_usage(parser.get_usage().strip() + USAGE) parser.add_option("--repo-name", dest="repo_name", help="repository name - e.g. nitime", metavar="REPO_NAME") parser.add_option("--github-user", dest="main_gh_user", help="github username for main repo - e.g fperez", metavar="MAIN_GH_USER") parser.add_option("--gitwash-url", dest="gitwash_url", help="URL to gitwash repository - default %s" % GITWASH_CENTRAL, default=GITWASH_CENTRAL, metavar="GITWASH_URL") parser.add_option("--gitwash-branch", dest="gitwash_branch", help="branch in gitwash repository - default %s" % GITWASH_BRANCH, default=GITWASH_BRANCH, metavar="GITWASH_BRANCH") parser.add_option("--source-suffix", dest="source_suffix", help="suffix of ReST source files - default '.rst'", default='.rst', metavar="SOURCE_SUFFIX") (options, args) = parser.parse_args() if len(args) < 2: parser.print_help() sys.exit() out_path, project_name = args if options.repo_name is None: options.repo_name = project_name if options.main_gh_user is None: options.main_gh_user = options.repo_name copy_replace((('PROJECTNAME', project_name), ('REPONAME', options.repo_name), ('MAIN_GH_USER', options.main_gh_user)), out_path, options.gitwash_url, options.gitwash_branch, cp_globs=(pjoin('gitwash', '*'),), rep_globs=('*.rst',), renames=(('\.rst$', options.source_suffix),)) dipy-0.5.0/tools/make_examples.py000077500000000000000000000056721152576264200170350ustar00rootroot00000000000000#!/usr/bin/env python """Run the py->rst conversion and run all examples. Steps are: analyze example index file for example py filenames check for any filenames in example directory not included do py to rst conversion, writing into build directory run """ #----------------------------------------------------------------------------- # Library imports #----------------------------------------------------------------------------- # Stdlib imports import os from os.path import join as pjoin, abspath, splitext import shutil from subprocess import check_call from glob import glob # Third-party imports # We must configure the mpl backend before making any further mpl imports import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from matplotlib._pylab_helpers import Gcf #----------------------------------------------------------------------------- # Function defintions #----------------------------------------------------------------------------- # These global variables let show() be called by the scripts in the usual # manner, but when generating examples, we override it to write the figures to # files with a known name (derived from the script name) plus a counter figure_basename = None # We must change the show command to save instead def show(): allfm = Gcf.get_all_fig_managers() for fcount, fm in enumerate(allfm): fm.canvas.figure.savefig('%s_%02i.png' % (figure_basename, fcount+1)) _mpl_show = plt.show plt.show = show #----------------------------------------------------------------------------- # Main script #----------------------------------------------------------------------------- # Where things are EG_INDEX_FNAME = abspath('examples_index.rst') EG_SRC_DIR = abspath('examples') # Work in examples directory os.chdir('examples_built') if not os.getcwd().endswith('doc/examples_built'): raise OSError('This must be run from the doc directory') # Copy the py files; check they are in the examples list and warn if not eg_index_contents = open(EG_INDEX_FNAME, 'rt').read() pyfilelist = [fname for fname in os.listdir(EG_SRC_DIR) if fname.endswith('.py')] for fname in pyfilelist: shutil.copyfile(pjoin(EG_SRC_DIR, fname), fname) froot, _ = splitext(fname) if froot not in eg_index_contents: print 'Example %s not in index file %s' % (EG_SRC_DIR, EG_INDEX_FNAME) # Run the conversion from .py to rst file check_call('../../tools/ex2rst --project dipy --outdir . .', shell=True) # Execute each python script in the directory. if not os.path.isdir('fig'): os.mkdir('fig') for script in glob('*.py'): figure_basename = os.path.join('fig', os.path.splitext(script)[0]) execfile(script) plt.close('all') # clean up stray images, pickles, npy files, etc for globber in ('*.nii.gz', '*.dpy', '*.npy', '*.pkl', '*.mat', '*.img', '*.hdr'): for fname in glob(globber): os.unlink(fname) dipy-0.5.0/tools/pack_examples.py000077500000000000000000000021741152576264200170300ustar00rootroot00000000000000#!/usr/bin/env python """ Script to pack built examples into suitably named archive Usage %s output_dir [doc_dir] """ import os from os.path import join as pjoin import sys import shutil import tarfile import dipy __doc__ = __doc__ % sys.argv[0] EG_BUILT_SDIR = 'examples_built' dpv = 'dipy-' + dipy.__version__ archive_name = dpv + '-doc-examples.tar.gz' try: out_root = sys.argv[1] except IndexError: print __doc__ sys.exit(1) try: os.mkdir(out_root) except OSError: pass try: doc_dir = sys.argv[2] except IndexError: doc_dir = os.getcwd() archive_fname = os.path.join(out_root, archive_name) eg_built_dir = pjoin(doc_dir, EG_BUILT_SDIR) eg_out_base = pjoin(out_root, dpv, 'doc') eg_out_dir = pjoin(eg_out_base, EG_BUILT_SDIR) if os.path.isdir(eg_out_dir): shutil.rmtree(eg_out_dir) def ignorandi(src, names): return [name for name in names if name == 'README' or name == '.gitignore'] shutil.copytree(eg_built_dir, eg_out_dir, ignore=ignorandi) os.chdir(out_root) tar = tarfile.open(archive_fname, 'w|gz') tar.add(dpv) tar.close() shutil.rmtree(pjoin(out_root, dpv)) print("Written " + archive_fname) dipy-0.5.0/tools/release000077500000000000000000000023711152576264200152040ustar00rootroot00000000000000#!/usr/bin/env python """dipy release script. This should only be run at real release time. """ from os.path import join as pjoin from toollib import get_dipydir, cd, c # Get main dipy dir, this will raise if it doesn't pass some checks dipydir = get_dipydir() tooldir = pjoin(dipydir,'tools') distdir = pjoin(dipydir,'dist') #### Where I keep static backups of each release ###nibbackupdir = os.path.expanduser('~/dipy/backup') # Start in main dipy dir cd(dipydir) # Load release info execfile(pjoin('dipy','info.py')) print print "Releasing dipy" print "=================" print print 'Source dipy directory:', dipydir print # Perform local backup, go to tools dir to run it. cd(tooldir) # c('./make_tarball.py') # c('mv dipy-*.tgz %s' % nibbackupdir) # Build release files c('./build_release %s' % dipydir) # Register with the Python Package Index (PyPI) print "Registering with PyPI..." cd(dipydir) c('./setup.py register') # Upload all files c('./setup.py sdist --formats=gztar,zip upload') c('./setup.py bdist_egg upload') cd(distdir) #print "Uploading distribution files..." #c('scp * dipy@dipy.scipy.org:www/dist/') # print "Uploading backup files..." # cd(nibbackupdir) # c('scp `ls -1tr *tgz | tail -1` dipy@dipy.scipy.org:www/backup/') print "Done!" dipy-0.5.0/tools/toollib.py000066400000000000000000000024641152576264200156570ustar00rootroot00000000000000"""Various utilities common to nibabel release and maintenance tools. """ # Library imports import os import sys from distutils.dir_util import remove_tree # Useful shorthands pjoin = os.path.join cd = os.chdir # Utility functions def c(cmd): """Run system command, raise SystemExit if it returns an error.""" print "$",cmd stat = os.system(cmd) #stat = 0 # Uncomment this and comment previous to run in debug mode if stat: raise SystemExit("Command %s failed with code: %s" % (cmd, stat)) def get_dipydir(): """Get dipy directory from command line, or assume it's the one above.""" # Initialize arguments and check location try: dipydir = sys.argv[1] except IndexError: dipydir = '..' dipydir = os.path.abspath(dipydir) cd(dipydir) if not os.path.isdir('dipy') and os.path.isfile('setup.py'): raise SystemExit('Invalid dipy directory: %s' % dipydir) return dipydir # import compileall and then get dir os.path.split def compile_tree(): """Compile all Python files below current directory.""" stat = os.system('python -m compileall .') if stat: msg = '*** ERROR: Some Python files in tree do NOT compile! ***\n' msg += 'See messages above for the actual file that produced it.\n' raise SystemExit(msg)
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