{
  "abstract": "Background: Coronary artery calcium (CAC) predicts cardiovascular risk. Dual-energy (DE) chest radiography may enable accessible opportunistic CAC assessment by separating calcifications from overlapping bone. Purpose: To evaluate multi-task deep learning for CAC localization and CT-referenced score prediction and assess projection, image representation, and exposure effects. Methods: This retrospective simulation study used 550 calcium-scoring CT exams with reference CAC masks and Agatston scores. Dual-layer DE radiographs were simulated with a physics-based detector model. A modified nnU-Net jointly predicted CAC localization and scores from continuous probability maps. Comparisons included AP, 45°, and lateral views; lateral single-energy (SE), DE bone, and high-pass filtered DE bone images; and dose-matched models at 70, 50, and 24 mAs. Patient-level splitting reserved 10% for independent testing; five-fold models provided averaged inference. Metrics included Dice and AUC at CAC thresholds >0, >100, >400, and >1000, with bootstrap uncertainty and paired DeLong tests with Holm-Bonferroni correction. Results: Lateral views achieved Dice 0.34 versus 0.23 (AP) and 0.22 (45°), with AUCs for CAC >100 of 0.86, 0.81, and 0.77, respectively; AUC differences were not significant after correction. Lateral DE bone outperformed SE (Dice: 0.33 vs. 0.19; AUC for CAC >100: 0.86 vs. 0.64), with significant AUC advantages at >100, >400, and >1000. High-pass filtering added little classification benefit. Dice decreased with exposure, while AUC differences across doses were not significant. Conclusions: Simulation results support lateral dual-layer DE bone imaging for opportunistic CAC assessment, with significant advantages over SE. Physical and clinical validation is needed.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.28448",
  "author_total": 8,
  "authors": [
    {
      "name": "Linxi Shi"
    },
    {
      "name": "Akyl Swaby"
    },
    {
      "name": "Kaitlin Hellier"
    },
    {
      "name": "Praveen Bharathi"
    },
    {
      "name": "Scott Hsieh"
    },
    {
      "name": "Dominik Fleischmann"
    },
    {
      "name": "Shiva Abbaszadeh"
    },
    {
      "name": "Adam S. Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ephhr2ng1ktc0.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.28448",
  "fulltext_url": null,
  "id": "ephhr2ng1ktc0",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.28448",
  "language": null,
  "licence": "CC-BY-NC-SA-4.0",
  "licence_label": "CC BY-NC-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.med-ph"
    ],
    [
      "openalex",
      "2741|Cardiac Imaging and Diagnostics"
    ]
  ],
  "openalex": "W7223139853",
  "pdf_url": "https://arxiv.org/pdf/2610.28448",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ephhr2ng1ktc0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:04Z",
      "native_id": "2610.28448",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.28448"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.med-ph",
      "subfield": "biological-medical"
    }
  ],
  "title": "Toward opportunistic coronary artery calcium assessment from dual-layer dual-energy chest radiography using deep learning: a simulation study",
  "title_text": "Toward opportunistic coronary artery calcium assessment from dual-layer dual-energy chest radiography using deep learning: a simulation study",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:04Z",
  "url": "https://research.draconitic.com/paper/ephhr2ng1ktc0",
  "venue": "arXiv",
  "versions": []
}