{
  "abstract": "To enable accurate and rapid photon control point and proton beamlet dose calculation in the DoseRAD2026 challenge, we present BEAM3R, a dose estimation framework operating in beam's-eye-view (BEV). Our core innovation combines a Mamba-3 state-space depth-sequence core with physics-based transport conditioning to model long-range depth transport without expensive 3D convolutions. BEAM3R shares a 2D CNN encoder-decoder architecture for photon and proton dose tasks, processing per-plane BEV slices. Proton beamlets are conditioned on water equivalent thickness and remaining range, encoding the parameters determining Bragg peak position. Photon models use a bidirectional Mamba-3 core to capture dose contributions from materials downstream of the calculation point, while the proton model uses a forward core with learned energy-prefix tokens and a Bragg-peak refinement module. To reduce interpolation artifacts and support high spatial resolution, we introduce axial grid alignment of BEV lattices with CT slices and an implicit super-resolution representation via sub-pixel phase packing, evaluated by a differentiable Triton-accelerated resampler that reconstructs packed cubic B-spline coefficients directly in CT space. For MRI-based tasks, synthetic CTs (sCT) are generated by a patch-based conditional GAN with a SwinUNETR backbone. On the preliminary DoseRAD2026 test set, CT-to-photon and CT-to-proton models achieved 1%/1 mm local gamma pass rates of 96.8% and 96.0%, with stratified plan-level MAEs of 0.0041 and 0.0079. Substituting sCT reduced gamma pass rates to 89.7% for photon and 75.4% proton plan level doses, with stratified plan-level MAEs of 0.0093 and 0.0336. Standardised runtimes were 23.4 s and 18.4 s for CT-to-photon and CT-to-proton prediction, increasing to 39.7 s and 42.8 s for the corresponding MRI-based pipelines.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.04747",
  "author_total": 5,
  "authors": [
    {
      "name": "Chen Cheng"
    },
    {
      "name": "Michael Ferraro"
    },
    {
      "name": "James Grover"
    },
    {
      "name": "David E J Waddington"
    },
    {
      "name": "Emily Hewson"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fb7n1m4rfgtt7.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.04747",
  "fulltext_url": null,
  "id": "fb7n1m4rfgtt7",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.04747",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CV"
    ],
    [
      "arxiv",
      "physics.med-ph"
    ],
    [
      "openalex",
      "3108|Advanced Radiotherapy Techniques"
    ]
  ],
  "openalex": "W7211904661",
  "pdf_url": "https://arxiv.org/pdf/2609.04747",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fb7n1m4rfgtt7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:14Z",
      "native_id": "2609.04747",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.04747"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.med-ph",
      "subfield": "biological-medical"
    }
  ],
  "title": "BEAM3R: Beam's-eye-view architecture with Mamba-3 for implicit dose reconstruction",
  "title_text": "BEAM3R: Beam's-eye-view architecture with Mamba-3 for implicit dose reconstruction",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:14Z",
  "url": "https://research.draconitic.com/paper/fb7n1m4rfgtt7",
  "venue": "arXiv",
  "versions": []
}