Trusting the Inverse: Reliability-Aware Mapping for Simulation-Based Microstructure Estimation in Diffusion MRI

2026-09-30

AI Summary

This study addresses the inherent ambiguity in diffusion MRI microstructure parameter estimation and the lack of voxel-wise reliability characterization by proposing a reliability-aware framework based on out-of-distribution signals, local mismatches, and parameter degeneracy. By constructing a geometrically realistic Monte Carlo dictionary constrained by electron microscopy data, this work designs a complementary three-dimensional scoring system to quantify estimation uncertainty, achieving the first effective decoupling of extrapolation errors, interpolation errors, and degeneracy while overcoming traditional point-estimation limitations. Experiments demonstrate that the reliability index correlates significantly with actual errors in synthetic data (ρ=−0.742), and over half of the voxels in in vivo rat and human brain data yield highly reliable estimates, thereby validating the effectiveness of the proposed method.

Abstract

Diffusion-weighted MRI can probe tissue microstructure non-invasively, however interpreting microstructural parameter estimates remains challenging due to the intrinsic ambiguity of the inverse problem. While simulation-based approaches can incorporate increasingly realistic tissue models, they still lack voxel-wise characterisation of the reliability and degeneracy of the inferred parameters. Here, we introduce a reliability framework for simulation-based microstructure estimation based on three complementary scores that identify distinct sources of unreliability in the estimation process: out-of-distribution signals, local signal mismatch, and parameter degeneracy. The framework was implemented using a Monte Carlo dictionary of 1,050 synthetic voxels generated from geometrically realistic substrates, with parameter ranges grounded in electron microscopy measurements of rat corpus callosum. The dictionary spans biologically plausible axon radii (0.25-0.85 $μ$m), microscopic angular spread (0-10$^\circ$), packing densities (60-92%), and intrinsic diffusivities (1.75-3.0 $μ$m$^2$/ms). On synthetic data, the resulting Reliability Index correlated with actual estimation error (Spearman $ρ= -0.742$) and distinguished between extrapolation, poor local interpolation, and parameter degeneracy. Applied to in vivo corpus callosum DW-MRI in rat (256 voxels, four animals) and human (MGH-USC HCP, 18,765 voxels, nine subjects), 91% and 73% of voxels respectively exceeded $R > 0.5$. These results demonstrate how reliability-aware analysis can support the interpretation and future development of simulation-based diffusion MRI microstructure models.

Problem

• Diffusion MRI
• microstructure estimation
• inverse problem
• parameter degeneracy
• reliability

Innovation

• Reliability-Aware Mapping
• Diffusion MRI
• Microstructure Estimation
• Parameter Degeneracy
• Monte Carlo Dictionary