{
  "abstract": "Objectives: This study aimed to evaluate the ability of VoxelMap, a deep learning framework for 3D respiratory motion estimation, to generate volumetric images in real-time from 2D projections acquired for two deformable lung phantoms on a clinical interventional C-arm angiography system. Materials and Methods: VoxelMap was evaluated using the PixelPrint4D and Casper deformable physical thoracic respiratory phantoms. Four-dimensional cone-beam CT (4D CBCT) images acquired on a clinical fixed-room C-arm angiography system were used for network training and volumetric ground truth. Prediction accuracy was evaluated on withheld respiratory phases using structural similarity index measure (SSIM) and peak signalto-noise ratio (PSNR). Model inference time was measured to assess suitability for continuous real-time processing. Deep ensembles were also used to assess model uncertainty. Correlation between ensemble uncertainty and prediction error was evaluated for both phantoms. Results: VoxelMap achieved high prediction accuracy across withheld respiratory phases in both physical phantoms. SSIM values exceeded 0.92 in all experiments, with a higher performance observed for the Casper phantom (0.969-0.973) than for the PixelPrint4D phantom (0.927-0.943). Model inference times were less than 15 ms for both phantoms which is compatible with real-time processing. Ensemble uncertainty was positively correlated with prediction error in both phantoms, indicating that higher uncertainty was associated with less accurate volumetric predictions. Conclusions: Our study demonstrated that VoxelMap generates accurate volumetric deformations of respiratory anatomy from projections acquired on a clinical interventional C-arm system, with computational inference times compatible with continuous processing. By deriving volumetric information directly from two-dimensional fluoroscopic projections, VoxelMap has the potential to augment continuous fluoroscopic guidance with continuously updated volumetric information. Ensemble uncertainty may provide a potentially useful indicator of prediction reliability. Further studies using clinically representative training data and patient studies is required to establish clinical utility. Our code is available at https://github.com/Image-X-Institute/Voxelmap.",
  "abstract_shown": true,
  "affiliations": [
    "University of Sydney"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "A. S. Lin"
    },
    {
      "name": "A. Biju"
    },
    {
      "name": "A. R. Goode"
    },
    {
      "name": "J. Y. Im"
    },
    {
      "name": "P. B. Noel"
    },
    {
      "name": "M. Bazalova-Carter"
    },
    {
      "name": "T. Reynolds"
    },
    {
      "affs": [
        0
      ],
      "name": "N. Hindley"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9qv1yjhfvwrzv.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.28.26366767",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.28.26366767v1.full",
  "id": "9qv1yjhfvwrzv",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.28.26366767v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "radiology and imaging"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.28.26366767v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9qv1yjhfvwrzv.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T00:20:24Z",
      "native_id": "10.64898/2026.10.28.26366767",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.28.26366767v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:radiology and imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Real-time Volumetric Imaging for Minimally-Invasive Thoracic Interventions: A Deformable Phantom Study",
  "title_text": "Real-time Volumetric Imaging for Minimally-Invasive Thoracic Interventions: A Deformable Phantom Study",
  "type": "preprint",
  "updated_at": "2026-11-04T00:20:58Z",
  "url": "https://research.draconitic.com/paper/9qv1yjhfvwrzv",
  "venue": "medRxiv",
  "versions": []
}