Population-based Bayesian regularization for microstructural diffusion MRI with NODDIDA.

ABSTRACT:

Purpose

Information on the brain microstructure can be probed by Diffusion Magnetic Resonance Imaging (dMRI). Neurite Orientation Dispersion and Density Imaging with Diffusivities Assessment (NODDIDA) is one of the simplest microstructural model proposed. However, the estimation of the NODDIDA parameters from clinically plausible dMRI acquisition is ill-posed, and different parameter sets can describe the same measurements equally well. A few approaches to resolve this problem focused on developing better optimization strategies for this non-convex optimization. However, this fundamentally does not resolve ill-posedness. This article introduces a Bayesian estimation framework, which is regularized through knowledge from an extensive dMRI measurement set on a population of healthy a

SUBMITTER: Mozumder M 

PROVIDER: S-EPMC6771666 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature