Voxelwise regularisation of high angular resolution diffusion imaging data

Abstract

The problem of noise suppression in high angular resolution diffusion MRI data is approached through direct regularisation of the apparent diffusion coefficient profiles. The proposed algorithm is derived in a Bayesian framework in the style of the traditional techniques for image restoration using Markov random field models. In a novel departure from the classical approach, a Markov random field model is applied within each voxel across gradient directions, thus smoothing the image data without inducing additional spatial dependencies that would render region-of-interest statistical testing of diffusion characteristics invalid. The anisotropic smoothing algorithm exploits the heterogeneous distribution of gradient directions and their antipodal pairs on the sphere and, in application to both simulated and experimental high angular resolution imaging datasets, is demonstrated to be superior to the isotropic Markov random field variant and the maximum likelihood estimator.