Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI.

A number of methods have been proposed for resolving crossing fibers from diffusion-weighted (DW) MRI. However, other complex fiber geometries have drawn minimal attention. In this study, we focus on fiber orientation dispersion induced by within-voxel fanning. We use a multi-compartment, model-base...

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Main Authors: Sotiropoulos, SN, Behrens, T, Jbabdi, S
格式: Journal article
語言:English
出版: 2012
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author Sotiropoulos, SN
Behrens, T
Jbabdi, S
author_facet Sotiropoulos, SN
Behrens, T
Jbabdi, S
author_sort Sotiropoulos, SN
collection OXFORD
description A number of methods have been proposed for resolving crossing fibers from diffusion-weighted (DW) MRI. However, other complex fiber geometries have drawn minimal attention. In this study, we focus on fiber orientation dispersion induced by within-voxel fanning. We use a multi-compartment, model-based approach to estimate fiber dispersion. Bingham distributions are employed to represent continuous distributions of fiber orientations, centered around a main orientation, and capturing anisotropic dispersion. We evaluate the accuracy of the model for different simulated fanning geometries, under different acquisition protocols and we illustrate the high SNR and angular resolution needs. We also perform a qualitative comparison between our parametric approach and five popular non-parametric techniques that are based on orientation distribution functions (ODFs). This comparison illustrates how the same underlying geometry can be depicted by different methods. We apply the proposed model on high-quality, post-mortem macaque data and present whole-brain maps of fiber dispersion, as well as exquisite details on the local anatomy of fiber distributions in various white matter regions.
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spelling oxford-uuid:63d1cf6c-0b02-48d7-b55f-f1df7bec34d12022-03-26T18:15:20ZBall and rackets: Inferring fiber fanning from diffusion-weighted MRI.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:63d1cf6c-0b02-48d7-b55f-f1df7bec34d1EnglishSymplectic Elements at Oxford2012Sotiropoulos, SNBehrens, TJbabdi, SA number of methods have been proposed for resolving crossing fibers from diffusion-weighted (DW) MRI. However, other complex fiber geometries have drawn minimal attention. In this study, we focus on fiber orientation dispersion induced by within-voxel fanning. We use a multi-compartment, model-based approach to estimate fiber dispersion. Bingham distributions are employed to represent continuous distributions of fiber orientations, centered around a main orientation, and capturing anisotropic dispersion. We evaluate the accuracy of the model for different simulated fanning geometries, under different acquisition protocols and we illustrate the high SNR and angular resolution needs. We also perform a qualitative comparison between our parametric approach and five popular non-parametric techniques that are based on orientation distribution functions (ODFs). This comparison illustrates how the same underlying geometry can be depicted by different methods. We apply the proposed model on high-quality, post-mortem macaque data and present whole-brain maps of fiber dispersion, as well as exquisite details on the local anatomy of fiber distributions in various white matter regions.
spellingShingle Sotiropoulos, SN
Behrens, T
Jbabdi, S
Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI.
title Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI.
title_full Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI.
title_fullStr Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI.
title_full_unstemmed Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI.
title_short Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI.
title_sort ball and rackets inferring fiber fanning from diffusion weighted mri
work_keys_str_mv AT sotiropoulossn ballandracketsinferringfiberfanningfromdiffusionweightedmri
AT behrenst ballandracketsinferringfiberfanningfromdiffusionweightedmri
AT jbabdis ballandracketsinferringfiberfanningfromdiffusionweightedmri