{
  "abstract": "Introduction: Moderate coronary stenosis (CAD-RADS 3) is a diagnostic gray zone in coronary CT angiography (CCTA). We evaluated whether super-resolution deep learning reconstruction (SR-DLR), particularly with a 1024 matrix, modifies CAD-RADS classification and inter-reader agreement. Materials and methods: Thirty-nine CCTA examinations from two centers, each containing at least one 50–69% stenosis identified on the routine normal-resolution deep learning reconstruction (NR-DLR), were reconstructed with NR-DLR and SR-DLR (512 and 1024 matrices). Five experienced readers blindly assigned CAD-RADS categories. Agreement was assessed with Gwet’s AC2 and paired reclassification with exact McNemar tests. Subgroup analyses (plaque composition, initial image quality) were exploratory.\n\nResults: Forty-six lesions were analyzed (19 calcified, 16 mixed, 10 non-calcified, one in-stent). Agreement remained excellent (AC2 0.89 [0.86–0.92], 0.87 [0.83–0.90] and 0.84 [0.81–0.88] for NR-DLR, SR-DLR-512 and SR-DLR-1024). Consensus CAD-RADS 2 rose from 6.5% to 21.7% with SR-DLR-1024 (p = 0.016; 10 lesions downgraded, 2 upgraded), predominantly in lesions with non-calcified components. Per-reader CAD-RADS 2 rates ranged from 13% to 30%, increasing significantly for two of five readers (p = 0.004).\n\nConclusion: SR-DLR-1024 modified CAD-RADS classification in about one fifth of intermediate lesions while preserving excellent agreement. Without an invasive reference standard, these reclassifications reflect a change in reader interpretation rather than a proven gain in accuracy.",
  "abstract_shown": true,
  "affiliations": [
    "Pole d’imagerie médicale, Hôpitaux universitaire de Strasbourg, Strasbourg, France",
    "ICube – UMR 7357, CNRS, Université de Strasbourg, Strasbourg, France",
    "Faculté de médecine, maïeutique et des sciences de la santé, Université de Strasbourg, Strasbourg, France",
    "Department of Diagnostic Radiology, Graduate School of Biomedical Health Science, Hiroshima University, Japan",
    "Canon Inc., Shimomaruko, Tokyo, 146-8501, Japan"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
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        0,
        1,
        2
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      "name": "Jean Philippe Dillenseger"
    },
    {
      "affs": [
        3
      ],
      "name": "Fuminari Tatsugami"
    },
    {
      "affs": [
        0
      ],
      "name": "Aissam Labani"
    },
    {
      "affs": [
        3
      ],
      "name": "Wataru Fukumoto"
    },
    {
      "affs": [
        0
      ],
      "name": "Soraya El Ghannudi"
    },
    {
      "affs": [
        4
      ],
      "name": "Akira Taniguchi"
    },
    {
      "affs": [
        4
      ],
      "name": "Karim Haioun"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Catherine Roy"
    },
    {
      "affs": [
        3
      ],
      "name": "Kazuo Awai"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Mickaël Ohana"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cwd3qex504h35.bib",
  "cited_by": 0,
  "doi": "10.1016/j.ejro.2026.100821",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13734965/",
  "id": "cwd3qex504h35",
  "issns": [
    "2352-0477"
  ],
  "landing_url": "https://doi.org/10.1016/j.ejro.2026.100821",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Article"
    ],
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      "openalex",
      "2741|Cardiac Imaging and Diagnostics"
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  "openalex": "W7223694364",
  "pdf_url": "https://www.ejropen.com/article/S2352-0477(26)00098-5/pdf",
  "pmcid": "PMC13734965",
  "pmid": "42990970",
  "published": "2026-10-26",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/cwd3qex504h35.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:20:45Z",
      "native_id": "PMC13734965",
      "source": "pmc",
      "url": "https://doi.org/10.1016/j.ejro.2026.100821"
    }
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  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Cardiac Imaging and Diagnostics",
      "subfield": "imaging"
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  ],
  "title": "Impact of super resolution deep learning reconstruction with a 1024 matrix on CAD-RADS classification of intermediate coronary stenosis: A bicentric retrospective study",
  "title_text": "Impact of super resolution deep learning reconstruction with a 1024 matrix on CAD-RADS classification of intermediate coronary stenosis: A bicentric retrospective study",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:20:45Z",
  "url": "https://research.draconitic.com/paper/cwd3qex504h35",
  "venue": "European Journal of Radiology Open",
  "versions": []
}