Abstract
PurposeInformation 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 adults (henceforth population‐based prior).MethodsWe reformulate the problem as a Bayesian maximum a posteriori estimation, which includes as a special case previous approach using non‐informative uniform priors. A population‐based prior is estimated from 35 subjects of the MGH Adult Diffusion data (Human Connectome Project), acquired with an extensive acquisition protocol including high b‐values. The accuracy and robustness of different approaches with and without the population‐based prior is tested on subsets of the MGH dataset, and an independent dataset from a clinically comparable scanner, with only clinically plausible dMRI measurements.ResultsThe population‐based prior produced substantially more accurate and robust parameter estimates, compared to the conventional uniform priors, for clinically feasible protocols, without introducing any evident bias.ConclusionsThe use of the proposed Bayesian population‐based prior can lead to clinically feasible and robust estimation of NODDIDA parameters without changing the acquisition protocol.
Highlights
Diffusion magnetic resonance imaging allows in vivo and noninvasive mapping of water molecules’ diffusive movement in biological tissues
In order to illustrate the presence of a degenerated estimation in NODDIDA parameter and to explore in detail how this affects different estimation strategies, we considered two random voxels selected one from the corpus callosum (CC) and another from the posterior limb of the internal capsule (PLIC), from subject MGH_1001
We introduced a novel framework of NODDIDA estimation, which allowed the use of a population‐based prior information in a Bayesian formulation
Summary
Diffusion magnetic resonance imaging (dMRI) allows in vivo and noninvasive mapping of water molecules’ diffusive movement in biological tissues. The most common of them is the diffusion tensor imaging,[2] which, despite its simplicity, can still provide meaningful biomarkers that are widely used as indications of microstructural tissue changes.[3] Micro‐structural models,[4] instead, derive the dMRI signal from a physical model of the tissue microstructure (e.g.5-7). This allows capturing more specific information of individual tissue constituents
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