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

Anna Auria, Alessandro Daducci, Jean-Philippe Thiran, Yves Wiaux

Research output: Contribution to journalArticle

21 Citations (Scopus)
36 Downloads (Pure)

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 also promoted 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.
Original languageEnglish
Pages (from-to)245-255
JournalNeuroImage
Volume115
Early online date2 May 2015
DOIs
Publication statusPublished - 2015

Fingerprint Dive into the research topics of 'Structured sparsity for spatially coherent fibre orientation distribution estimation in diffusion MRI'. Together they form a unique fingerprint.

  • Cite this