Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning

Pisharady, Pramod Kumar and Sotiropoulos, Stamatios N. and Duarte-Carvajalino, Julio M. and Sapiro, Guillermo and Lenglet, Christophe (2017) Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning. NeuroImage . ISSN 1095-9572

[img] PDF - Repository staff only until 29 June 2018. - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Available under Licence Creative Commons Attribution Non-commercial No Derivatives.
Download (7MB)

Abstract

We present a sparse Bayesian unmixing algorithm BusineX: Bayesian Unmixing for Sparse Inference-based Estimation of Fiber Crossings (X), for estimation of white matter fiber parameters from compressed (under-sampled) diffusion MRI (dMRI) data. BusineX combines compressive sensing with linear unmixing and introduces sparsity to the previously proposed multiresolution data fusion algorithm RubiX, resulting in a method for improved reconstruction, especially from data with lower number of diffusion gradients. We formulate the estimation of fiber parameters as a sparse signal recovery problem and propose a linear unmixing framework with sparse Bayesian learning for the recovery of sparse signals, the fiber orientations and volume fractions. The data is modeled using a parametric spherical deconvolution approach and represented using a dictionary created with the exponential decay components along different possible diffusion directions. Volume fractions of fibers along these directions define the dictionary weights. The proposed sparse inference, which is based on the dictionary representation, considers the sparsity of fiber populations and exploits the spatial redundancy in data representation, thereby facilitating inference from under-sampled q-space. The algorithm improves parameter estimation from dMRI through data-dependent local learning of hyperparameters, at each voxel and for each possible fiber orientation, that moderate the strength of priors governing the parameter variances. Experimental results on synthetic and in-vivo data show improved accuracy with a lower uncertainty in fiber parameter estimates. BusineX resolves a higher number of second and third fiber crossings. For under-sampled data, the algorithm is also shown to produce more reliable estimates.

Item Type: Article
Keywords: Sparse Bayesian learning; Compressive sensing; Linear unmixing; Diffusion MRI; Fiber orientation; Sparse signal recovery
Schools/Departments: University of Nottingham, UK > Faculty of Medicine and Health Sciences > School of Medicine > Division of Clinical Neuroscience
Identification Number: 10.1016/j.neuroimage.2017.06.052
Depositing User: Eprints, Support
Date Deposited: 14 Jul 2017 10:10
Last Modified: 12 Oct 2017 23:38
URI: http://eprints.nottingham.ac.uk/id/eprint/44191

Actions (Archive Staff Only)

Edit View Edit View