{
  "abstract": "Background: Accurate modeling of photon transport through the heterogeneous tissues of the human head is important for individualized transcranial light stimulation (tLS). However, high-precision Monte Carlo (MC) simulations can be computationally demanding, and existing general-purpose simulation tools do not provide an integrated, brain-oriented workflow. Methods: We developed TLS-Sim, a framework that integrates subject-specific head-model construction, multiple light-source configuration, GPU-accelerated MC photon simulation, visualization, and intracranial dosimetric analysis. Its central feature is a deep-learning-accelerated MC pathway, termed Flux-to-Flux, that estimates a high-photon-count energy-deposition field from a paired low-photon-count simulation. Sparse and full Monte Carlo fields were encoded using a three-dimensional variational autoencoder (VAE), and a three-dimensional velocity network was trained to transform the sparse latent representation toward the corresponding full-simulation representation. Results: We demonstrate the end-to-end toolkit and the Flux-to-Flux (F2F) pathway on a representative individualized head model evaluated across three source configurations. Relative to the sparse simulation, the learned prediction improved agreement with the full Monte Carlo reference by approximately +4 dB in signal-to-noise ratio on average, with correspondingly higher structural similarity and lower mean absolute error. Conclusions: TLS-Sim provides a modular, openly released computational framework for individualized tLS simulation and analysis. The Flux-to-Flux pathway is presented as an implemented capability that reduces the computational burden of high-photon-count simulation while improving spatial agreement with the full MC reference, most strongly at the low-fluence field boundaries where the sparse simulation is noisiest.",
  "abstract_shown": true,
  "affiliations": [
    "Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College; Chinese Institute for Brain Research, Beijing"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "name": "K. Zhang"
    },
    {
      "name": "H. Jia"
    },
    {
      "name": "Z. Li"
    },
    {
      "name": "S. Wang"
    },
    {
      "name": "Y. Zhang"
    },
    {
      "name": "F. Cong"
    },
    {
      "name": "Z. Cui"
    },
    {
      "name": "X. Li"
    },
    {
      "affs": [
        0
      ],
      "name": "C. Zhao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1whf1ae0zehcp.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.09.749356",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.09.749356v1.full",
  "id": "1whf1ae0zehcp",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.09.749356v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioengineering"
    ],
    [
      "openalex",
      "2741|Optical Imaging and Spectroscopy Techniques"
    ]
  ],
  "openalex": "W7212323462",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.09.749356v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1whf1ae0zehcp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:22Z",
      "native_id": "10.64898/2026.09.09.749356",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.09.749356v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioengineering",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "TLS-Sim: An Open-Source Toolkit for Individualized Transcranial Light Stimulation Modeling with Deep-Learning-Accelerated Simulation",
  "title_text": "TLS-Sim: An Open-Source Toolkit for Individualized Transcranial Light Stimulation Modeling with Deep-Learning-Accelerated Simulation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:22Z",
  "url": "https://research.draconitic.com/paper/1whf1ae0zehcp",
  "venue": "bioRxiv",
  "versions": []
}