{
  "abstract": "Patient-specific biomechanical models of the left ventricle can relate cardiac magnetic resonance imaging to regional myocardial material properties, but existing emulator-based studies typically treat the myocardium as mechanically homogeneous, limiting representation of localised dysfunction. We propose a Bayesian surrogate-modelling framework for inferring regional Holzapfel-Ogden material parameters in a left ventricle partitioned into five physiological zones derived from the American Heart Association 17-segment model. Eight emulator strategies spanning single- versus multi-output, local versus global, and Gaussian-process- versus neural-network-based architectures were screened using parameter point-estimation accuracy; the three retained models were evaluated using empirical marginal credible-interval coverage and posterior contraction. We found that models with comparable point accuracy nevertheless differed markedly in uncertainty. A multi-output variational Gaussian process provided the most favourable balance across these criteria and was retained for the subsequent analyses. In synthetic local and global stiffening scenarios, maximum a posteriori estimates generally distinguished stiffened from baseline zones, but the nonlinear-stiffening parameters were more difficult to identify from end-diastolic observations than the stiffness-magnitude parameters. A healthy-volunteer analysis demonstrates feasibility with an incomplete observation vector and jointly inferred strain-noise scales. These results suggest that the proposed framework provides a computationally feasible, uncertainty-aware approach to regional left ventricle parameter inference.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.20372",
  "author_total": 5,
  "authors": [
    {
      "name": "Hongjin Ren"
    },
    {
      "name": "Vinny Davies"
    },
    {
      "name": "Hao Gao"
    },
    {
      "name": "Mu Niu"
    },
    {
      "name": "Benn Macdonald"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fee1cyfc2fzgc.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.20372",
  "fulltext_url": null,
  "id": "fee1cyfc2fzgc",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.20372",
  "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",
      "stat.AP"
    ],
    [
      "openalex",
      "2204|Elasticity and Material Modeling"
    ]
  ],
  "openalex": "W7213583445",
  "pdf_url": "https://arxiv.org/pdf/2609.20372",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-17",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fee1cyfc2fzgc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:29Z",
      "native_id": "2609.20372",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.20372"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Emulation strategies for Bayesian inference of regional left ventricle material parameters",
  "title_text": "Emulation strategies for Bayesian inference of regional left ventricle material parameters",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:29Z",
  "url": "https://research.draconitic.com/paper/fee1cyfc2fzgc",
  "venue": "arXiv",
  "versions": []
}