Abstract

Motivation: Analytical biophysical diffusion MRI (dMRI) models fail to capture the full complexity of diffusion processes. Goal(s): We propose a Monte Carlo (MC) simulation framework enabling the numerical implementation of biophysical models with unprecedented fidelity to histology. Approach: Our framework enables simulating diffusion within cancer environments reconstructed from histology. It provides paired examples of dMRI signals and histological properties, which can be used to build numerical microstructure parameter estimators. Results: Our approach enables more accurate estimation of key properties such as cell size compared to fitting of classical multi-compartment analytical models. Impact: We propose a Monte Carlo (MC) simulation framework enabling the implementation of biophysicalmodels with unprecedented fidelity to histology. The framework improves microstructure inference compared to standard analytical fitting, and may provide more robust biomarkers in diseases such ascancer.