In Vivo Cardiac Biomechanical Model Parameter Estimation from Ultrafast Shear Wave Elastography

In Vivo Cardiac Biomechanical Model Parameter Estimation from Ultrafast Shear Wave Elastography | Research Square Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article In Vivo Cardiac Biomechanical Model Parameter Estimation from Ultrafast Shear Wave Elastography Camilla Ferrario, Maelys Venet, Olivier Villemain, Maxime Sermesant This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8744899/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Quantifying myocardial stiffness (MS) over the full cardiac cycle offers a direct window into tissue structure and mechanics, yet translating time-resolved shear wave elastography (SWE) into intrinsic material properties remains a complex inverse problem. While SWE provides detailed stiffness quantification, these measurements combine passive structural properties with active contractility under subject-specific loading conditions. In this work, we present a computational framework to decompose these contributions using a closed-loop reduced-order electromechanical model. We formulate the personalization process as a sequential inverse problem: first, a passive hyperelastic baseline is identified from the late-diastolic window; second, active contractile parameters are estimated by fitting the full-cycle waveform using a regularized covariance matrix adaptation evolution strategy (CMA-ES). We validated this framework on a cohort of healthy pediatric volunteers (n=20). The personalized models reproduced subject-specific stiffness trajectories with high fidelity (median cross-correlation 0.997, nRMSE 1.02). Uncertainty analysis confirmed the practical identifiability of the active parameters, with stable convergence across random initializations. This study establishes a computationally efficient, robust pipeline for converting non-invasive SWE assessments into interpretable mechanical signatures, paving the way for physics-informed phenotyping in clinical cohorts. Cardiac mechanics reduced-order electromechanics (0D) ultrafast ultrasound imaging myocardial stiffness patient-specific modeling Bayesian calibration Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Jun, 2026 Reviews received at journal 17 Jun, 2026 Reviewers agreed at journal 21 May, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviews received at journal 23 Apr, 2026 Reviews received at journal 19 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviewers invited by journal 23 Feb, 2026 Editor assigned by journal 04 Feb, 2026 Submission checks completed at journal 03 Feb, 2026 First submitted to journal 30 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information

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