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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Hierarchical Bayesian Modelling Improves Microstructural Parameter Mapping in Diffusion and Exchange MRI Data.

Elizabeth Powell, Mark Maskery, Hedley C A Emsley

Biorxiv : the Preprint Server for Biology

|September 15, 2025

Summary

This summary is machine-generated.

Hierarchical Bayesian modelling (HBM) enhances MRI microstructure modelling by improving accuracy and precision, especially in noisy data. This method offers better parameter map quality and reveals details obscured by noise, like white matter lesions.

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Area of Science:

Neuroimaging
Biophysics
Computational Biology

Background:

Microstructure modelling in MRI quantifies tissue features using mathematical models, typically fitted voxel-by-voxel with least-squares (LSQ) minimization.
LSQ methods are susceptible to noise, leading to inaccurate parameter maps.
Hierarchical Bayesian modelling (HBM) offers a potential solution but has been limited to simpler models.

Purpose of the Study:

To demonstrate and evaluate a generalized HBM framework for complex diffusion MRI microstructure models.
To assess the performance of HBM compared to LSQ minimization for diffusion kurtosis imaging and blood-brain barrier filter exchange imaging.
To investigate HBM's ability to improve parameter estimation in the presence of noise and resolve subtle microstructural variations.

Main Methods:

Developed a generalized HBM approach utilizing a Markov chain Monte Carlo algorithm for parameter estimation with flexible parameter constraints.
Applied the HBM framework to simulated and human data for diffusion kurtosis imaging and blood-brain barrier filter exchange imaging.
Compared HBM results against traditional LSQ minimization techniques.

Main Results:

HBM significantly improved accuracy, precision, contrast-to-noise ratio, and overall parameter map quality compared to LSQ in both simulated and human data.
HBM successfully resolved local parameter variations in white matter lesions of cerebral small vessel disease subjects, which were obscured in LSQ maps.
Noise sensitivity assessments showed HBM maintained superior performance even at low signal-to-noise ratios.

Conclusions:

The generalized HBM framework effectively enhances parameter estimation for complex diffusion MRI microstructure models.
HBM provides more robust and accurate microstructural quantification than LSQ, particularly in noisy imaging conditions.
This approach has the potential to improve diagnostic capabilities by revealing subtle tissue alterations, such as those in white matter lesions.