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

(Left) Different possible curves are tested. (Middle) The highest-score curve is selected. (Right) The process is repeated for all the seed points. [Display omitted] ► Global probabilistic tractography based on the Hough transform is introduced. ► Local minima are avoided by an exhaustive search at...

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Bibliographic Details
Published in:Medical image analysis 2011-08, Vol.15 (4), p.414-425
Main Authors: Aganj, Iman, Lenglet, Christophe, Jahanshad, Neda, Yacoub, Essa, Harel, Noam, Thompson, Paul M., Sapiro, Guillermo
Format: Article
Language:English
Subjects:
Algorithms
Animals
Brain - anatomy & histology
Data Interpretation, Statistical
Diffusion Tensor Imaging - methods
Diffusion-weighted magnetic resonance imaging (DWI)
Haplorhini
Hough transform
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Nerve Fibers, Myelinated - ultrastructure
Orientation distribution function (ODF)
Pattern Recognition, Automated - methods
Population studies
Reproducibility of Results
Sensitivity and Specificity
Tractography
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Summary:(Left) Different possible curves are tested. (Middle) The highest-score curve is selected. (Right) The process is repeated for all the seed points. [Display omitted] ► Global probabilistic tractography based on the Hough transform is introduced. ► Local minima are avoided by an exhaustive search at the desired resolution. ► The technique is easily extended to multiple subjects. ► Results are presented on simulated and real 1.5T, 4T, and 7T HARDI datasets. 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.
ISSN:1361-8415
1361-8423
DOI:10.1016/j.media.2011.01.003