{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.01758",
  "author_total": 1,
  "authors": [
    {
      "name": "Rutger H. J. Fick"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e4yrxzkdk0gpe.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.01758",
  "fulltext_url": null,
  "id": "e4yrxzkdk0gpe",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.01758",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.med-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.01758",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/e4yrxzkdk0gpe.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:20:59Z",
      "native_id": "2611.01758",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.01758"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.med-ph",
      "subfield": "biological-medical"
    }
  ],
  "title": "Replayable Monte Carlo MRI: From Persistent Tissue Microphysics to Queryable Virtual Phantoms",
  "title_text": "Replayable Monte Carlo MRI: From Persistent Tissue Microphysics to Queryable Virtual Phantoms",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:18Z",
  "url": "https://research.draconitic.com/paper/e4yrxzkdk0gpe",
  "venue": "arXiv",
  "versions": []
}