Large-domain histology-based diffusion MRI simulation via independent local simulations

Abstract

Diffusion MRI simulations based on realistic tissue microstructure provide a means to validate biophysical models and optimize acquisition protocols, but their computational cost restricts most studies to domains far smaller than a clinical voxel. The objective of this study was to develop an automated and scalable framework that converts whole-slide histology into diffusion MRI simulations at clinically relevant spatial scales while remaining feasible on standard workstation hardware. We present an end-to-end pipeline integrating two-dimensional whole-slide cell segmentation, mesh generation, and finite element Bloch-Torrey simulation. To enable simulations at large spatial scales without prohibitive memory growth, we introduce a subdomain tiling strategy in which the tissue domain is partitioned into extended subdomains simulated independently under no-flux boundary conditions. Signals are aggregated only from the central regions of each subdomain to minimize boundary artifacts. For an 800 {micro}m x 800 {micro}m histology-based domain, the aggregated signal differed by 0.07% from the corresponding full-domain finite element simulation while reducing wall-clock time from several days to hours and maintaining bounded memory usage independent of global domain size. When applied to a 2016 {micro}m x 2016 {micro}m heterogeneous region approximating the in-plane dimensions of a clinical voxel, the apparent diffusion coefficient obtained from the full domain differed from values computed in smaller dense and sparse subregions, demonstrating the influence of structural heterogeneity at clinically relevant scales on derived diffusion metrics. The proposed framework establishes an automated and memory-stable approach for generating diffusion MRI simulations directly from routine histology.