Personalising cardiac electrophysiology models from CT and ECG for 3D activation imaging and tissue characterisation

Personalising cardiac electrophysiology models from CT and ECG for 3D activation imaging and tissue characterisation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); 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 Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Personalising cardiac electrophysiology models from CT and ECG for 3D activation imaging and tissue characterisation Ludovica Cicci, Shuang Qian, Cristobal Rodero, Marina Strocchi, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8873590/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Background: Electrocardiographic imaging maps cardiac electrical activity non-invasively but is restricted to the epicardium. Computational electrophysiology models can predict 3D activation and tissue properties but require extensive parameter calibration. Methods: We introduce an unbiased workflow combining sensitivity analysis with emulator-based Bayesian history matching to calibrate over 100 organ- and tissue-scale parameters. The framework incorporates CT-scan images and 12-lead ECGs with a multi-scale electrophysiology model to generate personalised ventricular simulations. Results: The framework was tested on seven subjects (four with synthetic and three with clinical ECGs), with validation performed using high-density body surface potentials from a 252-electrode vest for the clinical cases. Calibrated models reproduced individual ECG morphologies and showed strong agreement with independent measurements (Pearson's correlation coefficient: 0.80±0.04). Conclusions: The study links non-invasive data with high-fidelity simulations to estimate spatially-varying properties, supporting personalised cardiac modelling for clinical use. Health sciences/Cardiology Biological sciences/Computational biology and bioinformatics Health sciences/Biomarkers Full Text Additional Declarations There is NO Competing Interest. Supplementary Files LCicciNCMSupplements.pdf Supplementary Information for ``Personalising cardiac electrophysiology models from CT and ECG for 3D activation imaging and tissue characterisation" Cite Share Download PDF Status: Under Review Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board 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 {"props":{"pageProps":{"initialData":{"identity":"rs-8873590","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":600640747,"identity":"12eaa1dd-17eb-4363-a2d5-1aa53e8359f4","order_by":0,"name":"Ludovica 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Computational electrophysiology models can predict 3D activation and tissue properties but require extensive parameter calibration.\r\n\r\nMethods: We introduce an unbiased workflow combining sensitivity analysis with emulator-based Bayesian history matching to calibrate over 100 organ- and tissue-scale parameters. The framework incorporates CT-scan images and 12-lead ECGs with a multi-scale electrophysiology model to generate personalised ventricular simulations.\r\n\r\nResults: The framework was tested on seven subjects (four with synthetic and three with clinical ECGs), with validation performed using high-density body surface potentials from a 252-electrode vest for the clinical cases. 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