Structured sparsity for spatially coherent fibre orientation estimation in diffusion MRI.

Details

Serval ID
serval:BIB_446E8E533FAA
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Structured sparsity for spatially coherent fibre orientation estimation in diffusion MRI.
Journal
Neuroimage
Author(s)
Auría A., Daducci A., Thiran J.P., Wiaux Y.
ISSN
1095-9572 (Electronic)
ISSN-L
1053-8119
Publication state
Published
Issued date
2015
Peer-reviewed
Oui
Volume
115
Pages
245-255
Language
english
Abstract
We propose a novel formulation to solve the problem of intra-voxel reconstruction of the fibre orientation distribution function (FOD) in each voxel of the white matter of the brain from diffusion MRI data. The majority of the state-of-the-art methods in the field perform the reconstruction on a voxel-by-voxel level, promoting sparsity of the orientation distribution. Recent methods have proposed a global denoising of the diffusion data using spatial information prior to reconstruction, while others promote spatial regularisation through an additional empirical prior on the diffusion image at each q-space point. Our approach reconciles voxelwise sparsity and spatial regularisation and defines a spatially structured FOD sparsity prior, where the structure originates from the spatial coherence of the fibre orientation between neighbour voxels. The method is shown, through both simulated and real data, to enable accurate FOD reconstruction from a much lower number of q-space samples than the state of the art, typically 15 samples, even for quite adverse noise conditions.
Pubmed
Web of science
Create date
06/07/2015 12:39
Last modification date
20/08/2019 13:48
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