A Deep Learning Approach to Multi-Fiber Parameter Estimation and Uncertainty Quantification in Diffusion MRI

TL;DR

This paper presents a deep learning-based method for efficient multi-fiber parameter estimation and uncertainty quantification in diffusion MRI, addressing challenges like low SNR and complex models, and demonstrating improved accuracy and scalability.

Contribution

Introduces a novel sequential deep learning approach for multi-fiber diffusion MRI parameter inference that decomposes complex tasks into manageable subproblems, enabling scalable and uncertainty-aware estimation.

Findings

Estimates of extracellular diffusivity are highly uncertain under typical acquisition schemes.

Intra-cellular volume fraction can be estimated with relatively high precision.

Method outperforms standard alternatives in simulation and real data analysis.

Abstract

Diffusion MRI (dMRI) is the primary imaging modality used to study brain microstructure in vivo. Reliable and computationally efficient parameter inference for common dMRI biophysical models is a challenging inverse problem, due to factors such as variable dimensionalities (reflecting the unknown number of distinct white matter fiber populations in a voxel), low signal-to-noise ratios, and non-linear forward models. These challenges have led many existing methods to use biologically implausible simplified models to stabilize estimation, for instance, assuming shared microstructure across all fiber populations within a voxel. In this work, we introduce a novel sequential method for multi-fiber parameter inference that decomposes the task into a series of manageable subproblems. These subproblems are solved using deep neural networks tailored to problem-specific structure and symmetry, and trained via simulation. The resulting inference procedure is largely amortized, enabling scalable parameter estimation and uncertainty quantification across all model parameters. Simulation studies and real imaging data analysis using the Human Connectome Project (HCP) demonstrate the advantages of our method over standard alternatives. In the case of the standard model of diffusion, our results show that under HCP-like acquisition schemes, estimates for extra-cellular parallel diffusivity are highly uncertain, while those for the intra-cellular volume fraction can be estimated with relatively high precision.