A Hough transform global probabilistic approach to multiple-subject diffusion MRI tractography

被引:103
作者
Aganj, Iman [1 ]
Lenglet, Christophe [1 ,2 ]
Jahanshad, Neda [3 ]
Yacoub, Essa [2 ]
Harel, Noam [2 ]
Thompson, Paul M. [3 ]
Sapiro, Guillermo [1 ]
机构
[1] Univ Minnesota, Dept Elect & Comp Engn, Minneapolis, MN 55455 USA
[2] Univ Minnesota, Ctr Magnet Resonance Res, Minneapolis, MN 55455 USA
[3] Univ Calif Los Angeles, Lab Neuro Imaging, Sch Med, Los Angeles, CA 90095 USA
关键词
Tractography; Diffusion-weighted magnetic resonance imaging (DWI); Hough transform; Orientation distribution function (ODF); Population studies; WHITE-MATTER TRACTOGRAPHY; FIBER TRACKING; ANATOMICAL CONNECTIVITY; BRAIN CONNECTIVITY; FRONT EVOLUTION; TENSOR; RECONSTRUCTION; BOOTSTRAP; FRAMEWORK;
D O I
10.1016/j.media.2011.01.003
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A global probabilistic fiber tracking approach based on the voting process provided by the Hough transform is introduced in this work. The proposed framework tests candidate 3D curves in the volume, assigning to each one a score computed from the diffusion images, and then selects the curves with the highest scores as the potential anatomical connections. The algorithm avoids local minima by performing an exhaustive search at the desired resolution. The technique is easily extended to multiple subjects, considering a single representative volume where the registered high-angular resolution diffusion images (HARDI) from all the subjects are non-linearly combined, thereby obtaining population-representative tracts. The tractography algorithm is run only once for the multiple subjects, and no tract alignment is necessary. We present experimental results on HARDI volumes, ranging from simulated and 1.5T physical phantoms to 7T and 4T human brain and 7T monkey brain datasets. (C) 2011 Elsevier B.V. All rights reserved.
引用
收藏
页码:414 / 425
页数:12
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