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

Iman Aganj; Christophe Lenglet; Neda Jahanshad; Essa Yacoub; Noam Harel; Paul M. Thompson; Guillermo Sapiro

doi:10.1016/j.media.2011.01.003

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

Iman Aganj, Christophe Lenglet, Neda Jahanshad, Essa Yacoub, Noam Harel, Paul M. Thompson, Guillermo Sapiro

Radiology

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114 Scopus citations

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.

Original language	English (US)
Pages (from-to)	414-425
Number of pages	12
Journal	Medical Image Analysis
Volume	15
Issue number	4
DOIs	https://doi.org/10.1016/j.media.2011.01.003
State	Published - Aug 2011

Bibliographical note

Funding Information:
This work was partly supported by NIH (P41 RR008079, P30 NS057091, R01 HD050735, R01 EB007813, R01 MH060662, R01 EB008432, R01 EB008645, CON000000004051–3014, CON000000015793–3014, NLM T15 LM07356), NSF, ONR, NGA, ARO, DARPA, and the University of Minnesota Institute for Translational Neuroscience. Computing resources were provided by the University of Minnesota Supercomputing Institute, and the Laboratory of Neuro Imaging (UCLA). We would like to thank Jennifer Campbell of McGill University, Katie L. McMahon and Greig I. de Zubicaray of the Centre for Magnetic Resonance, University of Queensland, and Margaret J. Wright of Queensland Institute of Medical Research for providing us with additional HARDI data.

Keywords

Diffusion-weighted magnetic resonance imaging (DWI)
Hough transform
Orientation distribution function (ODF)
Population studies
Tractography

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10.1016/j.media.2011.01.003

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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.",

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author = "Iman Aganj and Christophe Lenglet and Neda Jahanshad and Essa Yacoub and Noam Harel and Thompson, {Paul M.} and Guillermo Sapiro",

note = "Funding Information: This work was partly supported by NIH (P41 RR008079, P30 NS057091, R01 HD050735, R01 EB007813, R01 MH060662, R01 EB008432, R01 EB008645, CON000000004051–3014, CON000000015793–3014, NLM T15 LM07356), NSF, ONR, NGA, ARO, DARPA, and the University of Minnesota Institute for Translational Neuroscience. Computing resources were provided by the University of Minnesota Supercomputing Institute, and the Laboratory of Neuro Imaging (UCLA). We would like to thank Jennifer Campbell of McGill University, Katie L. McMahon and Greig I. de Zubicaray of the Centre for Magnetic Resonance, University of Queensland, and Margaret J. Wright of Queensland Institute of Medical Research for providing us with additional HARDI data. ",

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AU - Harel, Noam

AU - Thompson, Paul M.

AU - Sapiro, Guillermo

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

Abstract

Bibliographical note

Keywords

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