Replayable Monte Carlo MRI: From Persistent Tissue Microphysics to Queryable Virtual Phantoms

Abstract

Multiphysics Monte Carlo simulation is the reference method for diffusion MRI in idealized tissue: walkers diffuse through a numerical substrate with relaxation, permeability, susceptibility and magnetization transfer, and accumulate the signal of a prescribed acquisition. Yet walker histories are massive, petabytes for a tractography-scale phantom, so current simulators treat them as transient. They are regenerated for every new acquisition, field strength or relaxation rate, although identical for all of them. I propose to keep these histories instead as a replay pack, compressed into persistent, shareable tissue microphysics, so that any such query is a cheap inner product against the pack. By storing each path as a Brownian bridge cut to the band a scanner class can play, transform coding its coefficients and contracting them into time segments, a replay pack is up to 5000 times smaller than the histories it keeps, while staying within the Monte-Carlo floor for every waveform of that class. It matches analytical solutions in pores, and MC/DC and MCMRSimulator on realistic axon meshes. At the scale of the field's largest benchmark, I re-simulate DiSCo, the billion-walker Monte Carlo tractography phantom, as a multiphysics replay phantom. It reproduces the published protocol's signal to one percent, and any waveform, relaxation or susceptibility up to 100 ms is a query of the same replay pack. I release every replay pack of this paper on the SubstrateCommons Hugging Face Hub and deploy replayable DiSCo as a public Hugging Face Space (https://huggingface.co/spaces/rfick/disco-replay), where anyone can choose an acquisition type (PGSE, OGSE, PGSTE or tensor encoding) and the tissue's parameters, and within two minutes receive their multiphysics Monte Carlo diffusion-weighted image volumes. With replay, a Monte Carlo simulation becomes an instrument anyone can query, at scale.