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Delineating white matter structure in diffusion tensor MRI with anisotropy creasesG. Kindlmann, Xavier Tricoche, C.-F. WestinMedical Image Analysis Volume 11, Number 5, Pages 492-502 October, 2007
AbstractGeometric 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.
ReferenceKindlmann G, Tricoche X, Westin CF. Delineating white matter structure in diffusion tensor MRI with anisotropy creases. Medical Image Analysis 2007;11(5):492-502.Bibtex entry
@ARTICLE{kindlmannMIA07,
author = {Gordon Kindlmann and Xavier Tricoche and Carl-Fredrik
Westin},
title = {Delineating white matter structure in diffusion tensor
{MRI} with anisotropy creases},
journal = {Medical Image Analysis},
year = {2007},
volume = {11},
pages = {492--502},
number = {5},
month = {October},
doi = "10.1016/j.media.2007.07.005"}
GrantsNIH T32-EB002177 (NIBIB), NIH U41-RR019703 (IGT), NIH P41-RR13218 (NAC), NIH P41-RR12553 (CIBC), NIH R01-MH050740, NIH R01-MH074794Copyright Information© Elsevier. Copyrights to this PDF document are held by Elsevier B.V.. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the Elsevier Publishing. This material is presented electronically to ensure timely dissemination of scholarly and technical work. Certain rights are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the author and/or copyright holder. |
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