{
  "abstract": "Cardiac model personalisation requires inferring mechanical parameters that are not directly measurable in vivo. Ultrafast ultrasound shear wave elastography (SWE) enables non-invasive tracking of myocardial stiffness dynamics over the cardiac cycle, providing a target for personalisation. However, mapping these observations to subject specific model parameters remains ill-posed, as multiple parameter sets can reproduce the same stiffness dynamics. We formulate SWE-informed personalisation as a statistical inference problem using simulation-based inference (SBI). Using a subject-adapted 0D cardiovascular model and neural posterior estimation, we estimate model-conditional posterior distributions over active stiffness scale k0, contraction rate kATP, and relaxation rate kSR, conditioned on SWE-derived curve features and subject specific context. Among six healthy volunteers, four passed objective prior-support diagnostics and were retained for quantitative posterior analysis. Curve-level RMSE against the observed SWE target decreased from 12.61 $\\pm$ 5.55 kPa for the prior predictive median to 1.14 $\\pm$ 0.38 kPa for the posterior predictive median, an 89.7 $\\pm$ 4.2% reduction. Posterior analysis revealed parameter-specific uncertainty, k0-kATP compensation, weaker constraint of kSR, and the importance of prior-predictive diagnostics for assessing whether each subject is represented within the modelled SWE feature space. These results support SBI for uncertainty aware SWE-based personalisation, while identifying prior support and forward-model adequacy as key diagnostics.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Statistical Atlases and Computational Modelling of the Heart (STACOM), MICCAI 2026., Sep 2026, Strasbourg, France",
  "arxiv": "2609.35214",
  "author_total": 4,
  "authors": [
    {
      "name": "Camilla Ferrario"
    },
    {
      "name": "Maelys Venet"
    },
    {
      "name": "Olivier Villemain"
    },
    {
      "name": "Maxime Sermesant"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/aqb66cwgn8dfc.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.35214",
  "fulltext_url": null,
  "id": "aqb66cwgn8dfc",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.35214",
  "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.LG"
    ],
    [
      "arxiv",
      "physics.med-ph"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "2741|Ultrasound Imaging and Elastography"
    ]
  ],
  "openalex": "W7214709284",
  "pdf_url": "https://arxiv.org/pdf/2609.35214",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/aqb66cwgn8dfc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:12Z",
      "native_id": "2609.35214",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.35214"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.med-ph",
      "subfield": "biological-medical"
    }
  ],
  "title": "Uncertainty Quantification in Cardiac Model Personalisation from Ultrafast Ultrasound",
  "title_text": "Uncertainty Quantification in Cardiac Model Personalisation from Ultrafast Ultrasound",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:21Z",
  "url": "https://research.draconitic.com/paper/aqb66cwgn8dfc",
  "venue": "arXiv",
  "versions": []
}