A Hough Transform Global Probabilistic Approach to Multiple-subject Diffusion MRI Tractography

Overview

Journal Med Image Anal

Publisher Elsevier

Specialty Radiology

Date 2011 Mar 8

PMID 21376655

Citations 68

Authors

Iman Aganj

Christophe Lenglet

Neda Jahanshad

Essa Yacoub

Noam Harel

Paul M Thompson

Guillermo Sapiro

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

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Main Paths of Brain Fibers in Diffusion Images Mixed with a Noise to Improve Performance of Tractography Algorithm-Evaluation in Phantom.

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Multi-Head Graph Convolutional Network for Structural Connectome Classification.

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Automatic Geometry-based Estimation of the Locus Coeruleus Region on T-Weighted Magnetic Resonance Images.

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