Population‐based Bayesian regularization for microstructural diffusion MRI with NODDIDA

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 param...

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Bibliographic Details
Published in:Magnetic resonance in medicine Vol. 82; no. 4; pp. 1553 - 1565
Main Authors: Mozumder, Meghdoot, Pozo, Jose M., Coelho, Santiago, Frangi, Alejandro F.
Format: Journal Article
Language:English
Published: United States Wiley Subscription Services, Inc 01.10.2019
John Wiley and Sons Inc
Subjects:
Adult
Bayes Theorem
Bayesian analysis
biophysical tissue models
Brain
Brain - cytology
Brain - diagnostic imaging
Convexity
Data acquisition
Datasets
Diffusion
Diffusion Magnetic Resonance Imaging - methods
diffusion MRI
Full Papers—Computer Processing and Modeling
Humans
Image Processing, Computer-Assisted - methods
Magnetic resonance imaging
Medical imaging
Microstructure
microstructure imaging
Middle Aged
modeling
Neurites - physiology
Neuroimaging
NMR
Nuclear magnetic resonance
Optimization
Parameter estimation
Parameter robustness
Population
Regularization
diffusion MRI
parameter estimation
microstructure imaging
modeling
biophysical tissue models
ISSN:0740-3194, 1522-2594, 1522-2594
Online Access:Get full text
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Summary: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 adults (henceforth population‐based prior). Methods We 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. Results The 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. Conclusions The use of the proposed Bayesian population‐based prior can lead to clinically feasible and robust estimation of NODDIDA parameters without changing the acquisition protocol.
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ISSN:0740-3194
1522-2594
1522-2594
DOI:10.1002/mrm.27831