Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization.

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Version: Final published version
Serval ID
serval:BIB_2F61B1520724
Type
Article: article from journal or magazin.
Collection
Publications
Title
Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization.
Journal
PLoS One
Author(s)
Canales-Rodríguez E.J., Daducci A., Sotiropoulos S.N., Caruyer E., Aja-Fernández S., Radua J., Yurramendi Mendizabal J.M., Iturria-Medina Y., Melie-García L., Alemán-Gómez Y., Thiran J.P., Sarró S., Pomarol-Clotet E., Salvador R.
ISSN
1932-6203 (Electronic)
ISSN-L
1932-6203
Publication state
Published
Issued date
2015
Peer-reviewed
Oui
Volume
10
Number
10
Pages
e0138910
Language
english
Notes
Publication types: Journal ArticlePublication Status: epublish
Abstract
Spherical deconvolution (SD) methods are widely used to estimate the intra-voxel white-matter fiber orientations from diffusion MRI data. However, while some of these methods assume a zero-mean Gaussian distribution for the underlying noise, its real distribution is known to be non-Gaussian and to depend on many factors such as the number of coils and the methodology used to combine multichannel MRI signals. Indeed, the two prevailing methods for multichannel signal combination lead to noise patterns better described by Rician and noncentral Chi distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to Rician and noncentral Chi likelihood models. To quantify the benefits of using proper noise models, RUMBA-SD was compared with dRL-SD, a well-established method based on the RL algorithm for Gaussian noise. Another aim of the study was to quantify the impact of including a total variation (TV) spatial regularization term in the estimation framework. To do this, we developed TV spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The evaluation was performed by comparing various quality metrics on 132 three-dimensional synthetic phantoms involving different inter-fiber angles and volume fractions, which were contaminated with noise mimicking patterns generated by data processing in multichannel scanners. The results demonstrate that the inclusion of proper likelihood models leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and to better detect non-dominant fibers. The inclusion of TV regularization dramatically improved the resolution power of both techniques. The above findings were also verified in human brain data.
Pubmed
Web of science
Open Access
Yes
Create date
17/11/2015 17:30
Last modification date
20/08/2019 13:13
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