A Monte Carlo simulation framework for histology-informed diffusion MRI cancer characterisation and microstructural parameter estimation

Diffusion Magnetic Resonance Imaging (dMRI) simulations in geometries mimicking the complexity of human tissues at the microscopic scale enable the development of innovative biomarkers with unprecedented fidelity to histology. To date, approaches of this kind have focussed heavily on brain imaging. Nevertheless, simulation-informed dMRI has huge potential also in other applications, as for example in body cancer imaging, where new non-invasive biomarkers are still sought. This article fills this gap by introducing a Monte Carlo diffusion simulation framework informed by histology, for enhanced body dMR microstructural imaging -- the Histo-Sim approach. We generate dictionaries of synthetic dMRI signals with coupled tissue properties from virtual cancer environments, reconstructed from hematoxylin-eosin stains of human liver biopsies. These enable the data-driven estimation of innovative microstructural tissue properties, such as the intrinsic extra-cellular diffusivity, or cell size (CS) distribution moments. We compare Histo-Sim to metrics from well-established analytical multi-compartment models in silico, on fixed mouse tissues scanned ex vivo (kidneys, spleens, and breast tumours) and in cancer patients in vivo. Results suggest that Histo-Sim is feasible in clinical settings, and that it delivers metrics that more accurately reflect the underlying histology as compared to analytical models. In conclusion, Histo-Sim offers histologically-meaningful tissue descriptors that may increase the specificity of dMRI towards cancer, and thus play a crucial role in precision oncology.