{
  "abstract": "Deep-learning (DL) segmentation enables cardiac substructure (CS) delineation for advanced cardiac-sparing radiation therapy (RT). Patients presenting for RT may have implantable devices (cardioverter-defibrillators (ICDs)) and metal artifacts. Stereotactic body RT (SBRT) is used to treat ventricular tachycardia (VT), a population frequently with ICDs. We apply a DL model for RT patients with ICDs or undergoing VT-SBRT, enabling segmentation and CS-level dose analysis. CT simulation (CT-SIM) of 19 patients with ICDs and received VT-SBRT were evaluated. A validated segmentation model was retrained using a highly-curated CT-SIM cohort, supplemented with ICD images for training and validation, with a hold-out ICD test set (n=10), to predict 20 CS. Additional CT-SIM datasets included cancer-RT patients with (n=9) and without (n=10) ICDs to evaluate generalizability. The retrained ICD-supplemented model was compared against a non-ICD model using Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and Wilcoxon signed-rank test (p<0.05). Dosimetric evaluation was performed for VT-SBRT CS. For VT-SBRT, the ICD-supplemented model achieved average DSC/HD95 of 0.71(0.19)/10.7(22.5)mm respectively, outperforming (p<0.05) the non-ICD model. On CT-SIM with ICDs, average DSC/HD95 were 0.70(0.22)/7.9(5.1)mm respectively, outperforming (p<0.05) the non-ICD model. On CT-SIM without ICDs, the average DSC was 0.74(0.17) (Non-ICD, DSC, 0.75(0.16), p<0.05) and average HD95 was 5.5(3.1)mm (Non-ICD, HD95, 5.3(2.8mm)). For VT-SBRT, CS dose depended on target location, with higher relative dose delivered to the ventricles, tricuspid valve, atrioventricular node, and left coronary arteries than other CS. ICD-supplemented training improved CS segmentation despite metal artifacts without meaningfully degrading non-ICD performance, supporting segmentation and CS-level dosimetry in ICD populations.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.36367",
  "author_total": 11,
  "authors": [
    {
      "name": "Nicholas Summerfield"
    },
    {
      "name": "Dustin Jacqmin"
    },
    {
      "name": "Chase Ruff"
    },
    {
      "name": "Andrew M. Baschnagel"
    },
    {
      "name": "Adam Burr"
    },
    {
      "name": "Michael Bassetti"
    },
    {
      "name": "Ryan Kipp"
    },
    {
      "name": "Matthew Kalscheur"
    },
    {
      "name": "Patrick M. Hill"
    },
    {
      "name": "Ming Dong"
    },
    {
      "name": "Carri Glide-Hurst"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8ft8ydj4z1ymh.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.36367",
  "fulltext_url": null,
  "id": "8ft8ydj4z1ymh",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.36367",
  "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",
      "1707|Medical Image Segmentation Techniques"
    ]
  ],
  "openalex": "W7214995585",
  "pdf_url": "https://arxiv.org/pdf/2609.36367",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8ft8ydj4z1ymh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:31Z",
      "native_id": "2609.36367",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.36367"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.med-ph",
      "subfield": "biological-medical"
    }
  ],
  "title": "Cardiac segmentation for ventricular tachycardia radioablation and radiotherapy patients with cardiac implants",
  "title_text": "Cardiac segmentation for ventricular tachycardia radioablation and radiotherapy patients with cardiac implants",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:31Z",
  "url": "https://research.draconitic.com/paper/8ft8ydj4z1ymh",
  "venue": "arXiv",
  "versions": []
}