{
  "abstract": "Photoacoustic (PA) imaging provides molecular and functional information about tissue, such as blood oxygen saturation (sO2), yet its clinical translation is hindered by inaccurate quantification. A major source of error is the spectral colouring effect, in which wavelength-dependent optical attenuation distorts the local optical fluence. Monte Carlo (MC) simulation is the gold standard for modelling light transport, but its computational demand precludes real-time use. Here, GenMC is presented, a deep generative framework based on a conditional generative adversarial network that estimates optical fluence distributions from tissue anatomy and literature-derived optical properties, with anatomical priors obtained from co-registered ultrasound images. Trained on MC-generated synthetic datasets, GenMC produces high-fidelity fluence maps in under 30 ms per frame, a four-orders-of-magnitude speed-up over conventional MC simulation, and reaches peak signal-to-noise ratios of up to 36.24 dB in vivo, outperforming UNet and Pix2Pix baselines. Validation in blood-mimicking phantoms and in 37 human volunteers spanning Fitzpatrick skin types III-V demonstrates improved accuracy, robustness, and physiological consistency of sO2 estimation. By enabling real-time, accurate, and reproducible quantification of tissue oxygenation, GenMC addresses a critical barrier to quantitative PA imaging and offers a general strategy for rapid, high-fidelity approximation of light transport in tissue.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.28261",
  "author_total": 4,
  "authors": [
    {
      "name": "Mengjie Shi"
    },
    {
      "name": "Feng He"
    },
    {
      "name": "Tom Vercauteren"
    },
    {
      "name": "Wenfeng Xia"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4sxg4q0zt1r7g.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.28261",
  "fulltext_url": null,
  "id": "4sxg4q0zt1r7g",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.28261",
  "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",
      "2204|Photoacoustic and Ultrasonic Imaging"
    ]
  ],
  "openalex": "W7214194673",
  "pdf_url": "https://arxiv.org/pdf/2609.28261",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4sxg4q0zt1r7g.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:56Z",
      "native_id": "2609.28261",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.28261"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.med-ph",
      "subfield": "biological-medical"
    }
  ],
  "title": "GenMC: Real-Time Generative Monte Carlo Surrogate for Quantitative Photoacoustic Imaging",
  "title_text": "GenMC: Real-Time Generative Monte Carlo Surrogate for Quantitative Photoacoustic Imaging",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:56Z",
  "url": "https://research.draconitic.com/paper/4sxg4q0zt1r7g",
  "venue": "arXiv",
  "versions": []
}