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

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 d...

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Main Authors:	Canales-Rodríguez, Erick J., Daducci, Alessandro, Sotiropoulos, Stamatios N., Caruyer, Emmanuel, Aja-Fernández, Santiago, Radua, Joaquim, Yurramendi Mendizabal, Jesús M., Iturria-Medina, Yasser, Melie-García, Lester, Alemán-Gómez, Yasser, Thiran, Jean-Philippe, Sarró, Salvador, Pomarol-Clotet, Edith, Salvador, Raymond
Format:	Online
Language:	English
Published:	Public Library of Science 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4607500/

id	pubmed-4607500
recordtype	oai_dc
spelling	pubmed-46075002015-10-29 Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization Canales-Rodríguez, Erick J. Daducci, Alessandro Sotiropoulos, Stamatios N. Caruyer, Emmanuel Aja-Fernández, Santiago Radua, Joaquim Yurramendi Mendizabal, Jesús M. Iturria-Medina, Yasser Melie-García, Lester Alemán-Gómez, Yasser Thiran, Jean-Philippe Sarró, Salvador Pomarol-Clotet, Edith Salvador, Raymond Research Article 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. Public Library of Science 2015-10-15 /pmc/articles/PMC4607500/ /pubmed/26470024 http://dx.doi.org/10.1371/journal.pone.0138910 Text en © 2015 Canales-Rodríguez et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Canales-Rodríguez, Erick J. Daducci, Alessandro Sotiropoulos, Stamatios N. Caruyer, Emmanuel Aja-Fernández, Santiago Radua, Joaquim Yurramendi Mendizabal, Jesús M. Iturria-Medina, Yasser Melie-García, Lester Alemán-Gómez, Yasser Thiran, Jean-Philippe Sarró, Salvador Pomarol-Clotet, Edith Salvador, Raymond
spellingShingle	Canales-Rodríguez, Erick J. Daducci, Alessandro Sotiropoulos, Stamatios N. Caruyer, Emmanuel Aja-Fernández, Santiago Radua, Joaquim Yurramendi Mendizabal, Jesús M. Iturria-Medina, Yasser Melie-García, Lester Alemán-Gómez, Yasser Thiran, Jean-Philippe Sarró, Salvador Pomarol-Clotet, Edith Salvador, Raymond Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization
author_facet	Canales-Rodríguez, Erick J. Daducci, Alessandro Sotiropoulos, Stamatios N. Caruyer, Emmanuel Aja-Fernández, Santiago Radua, Joaquim Yurramendi Mendizabal, Jesús M. Iturria-Medina, Yasser Melie-García, Lester Alemán-Gómez, Yasser Thiran, Jean-Philippe Sarró, Salvador Pomarol-Clotet, Edith Salvador, Raymond
author_sort	Canales-Rodríguez, Erick J.
title	Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization
title_short	Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization
title_full	Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization
title_fullStr	Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization
title_full_unstemmed	Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization
title_sort	spherical deconvolution of multichannel diffusion mri data with non-gaussian noise models and spatial regularization
description	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.
publisher	Public Library of Science
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4607500/
_version_	1613488811520557056

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

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