Hierarchical-µGUIDE: fast and robust Bayesian hierarchical modelling using deep learning simulation-based inference

Motivation: In-vivo brain microstructure can be estimated using diffusion MRI. However, most approaches do not quantify estimates reliability, although crucial for interpreting the results, and consider every voxel independently, leading to high uncertainties. Goal(s): Our goal is to develop a new framework to efficiently estimate tissue microstructure and improve data fitting quality. Approach: We propose Hierarchical-µGUIDE, a Bayesian method that estimates posterior distributions, by combining simulation-based inference with a hierarchical structure. Results: Hierarchical-µGUIDE bypasses the high computational and time cost of conventional Bayesian approaches. Sharper microstructure parameter maps that preserve tissue heterogeneity are obtained, along with a tissue parcellation that segments an epileptic lesion. Impact: The proposed Bayesian framework improves single-subject inference for clinical diagnosis, by efficiently estimating posterior distributions, reducing estimates uncertainty, and learning a tissue parcellation. This works unlocks the possibility to apply hierarchical Bayesian methods taylored for microstructure estimation to large datasets.