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Structured sparsity for spatially coherent fibre orientation estimation in diffusion {MRI}

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Publication date 2015
Author Auría Rasclosa Anna, Daducci Alessandro, Thiran Jean-Philippe, Wiaux Yves,
Project Advanced signal processing on the sphere for high angular resolution diffusion magnetic resonance imaging
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Original article (peer-reviewed)

Journal Neuroimage
Volume (Issue) 115
Page(s) 245 - 255
Title of proceedings Neuroimage
DOI 10.1016/j.neuroimage.2015.04.049

Open Access

URL http://infoscience.epfl.ch
Type of Open Access Repository (Green Open Access)

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.
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