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

Overview

1. National Heart & Lung Institute, Imperial College London, ICTEM Building, Du Cane Road, London, W12 0NN, United Kingdom
2. School of Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’ Hospital, Westminster Bridge Road, London, SE1 7EH, United Kingdom
3. Cardiac Rhythm Management, Medtronic, London, United Kingdom
4. Gottfried Schatz Research Center Biophysics, Medical University of Graz, Neue Stiftingtalstraße 6, Graz, 8010, Austria
5. BioTechMed-Graz, Graz, Austria
6. Department of Biomedical Engineering, The University of Utah, 201 Presidents Circle, Salt Lake City, 84112, Utah, United States
7. Scientific Computing and Imaging Institute, The University of Utah, 72 South Central Campus Drive, Salt Lake City, 84112, Utah, United States
8. Faculty of Medicine and Health, The University of Sydney, Science Rd, Camperdown, Sydney, 2050, New South Wales, Australia
9. Department of Cardiology, Royal Prince Alfred Hospital, 50 Missenden Road, Camperdown, Sydney, 2050, New South Wales, Australia
10. British Heart Foundation Centre of Research Excellence, Imperial College London, ICTEM Building, Du Cane Road, London, W12 0NN, United Kingdom
11. School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom
12. The Alan Turing Institute, British Library, 96 Euston Road, London, NW1 2DB, United Kingdom

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.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

No file of the authors' code could be read here: it is described below, and read at its source.

Code availability

Anatomical model generation and post-processing was performed using Simpleware software (Version vX-2025.06; Synopsys, Inc., Sunnyvale, USA), available under academic license (synopsys.com/simpleware.html). The open-source tool TotalSegmentator (github.com/wasserth/TotalSegmentator) and the open-source application ParaView (paraview.org) were also used. Gaussian process emulators, Sobol’ sensitivity analysis, and Bayesian history matching were implemented using Python scripts from the open-source GPErks project (github.com/stelong/GPErks). The patient-specific modelling pipeline, implemented in Python, will be made publicly available in the CEMRG GitHub repository (github.com/CEMRG-publications) upon acceptance of the manuscript. Computational electrophysiology simulations were performed using the Cardiac Arrhythmia Research Package (CARPentry), available under academic license (carpentry.medunigraz.at). However, these can also be performed using openCARP(opencarp.org), which is fully open-source.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

• 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
• 0 scripts, each with its path and the digest of its content;
• no match between paragraphs and code yet;
• neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

• zenodo:7253863, at Zenodo; found in “Data availability”

Data availability

Four patient-specific finite element meshes are publicly available through the repository Zenodo (zenodo.org/records/7253863). Data for the healthy controls will be made available subject to reasonable request and institutional governance.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, journal, dates, 18 authors, 2 funders, 93 references.

Cite

This paper

Cicci, L., Qian, S., Rodero, C., Strocchi, M., Corrado, C., Campos, F. O., Malik, S., Lee, A. W., Qayyum, A., Gillette, K., Isbister, J. C., Sy, R. W., Lee, M., Noseda, M., Wilkinson, R. D., Plank, G., Bishop, M., & Niederer, S. A. (2026). Personalising cardiac electrophysiology models from CT and ECG for 3D activation imaging and tissue characterisation. Research Square (preprint). https://doi.org/10.21203/rs.3.rs-8873590/v1

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1371/journal.pcbi.1014555 [code]

Body surface potential driven personalisation of electrophysiological digital twins in hypertrophic cardiomyopathy.

Journal: PLoS computational biology

In common: other, 14 references

[2] doi:10.1371/journal.pcbi.1014325 [code]

Assessing the importance of sex and disease-specific anatomy in electrophysiology and mechanical simulations with a newly developed public virtual cohort of four-chamber heart models.

Journal: PLoS computational biology

In common: 7 references

[3] doi:10.21203/rs.3.rs-9303831/v1

Probabilistic Calibration of a Closed-loop Cardiac Electromechanical Model with Application to Cardiac Resynchronization Therapy

Journal: Research Square (preprint)

In common: 4 references

[4] doi:10.3389/fphys.2026.1827577 [code]

The all-or-none repolarization in cardiac ventricular myocytes: an <i>in silico</i> characterization of a relevant biomarker of ventricular action potential.

Journal: Frontiers in physiology

In common: 2 references

[5] doi:10.1111/psyp.70385 [code]

Exploring EEG and ECG in Music Listening: A Scoping Review.

Journal: Psychophysiology

In common: other, 1 reference

[6] doi:10.1038/s41540-026-00730-2 [code]

Sometimes extracellular recordings fail for good reasons.

Journal: NPJ systems biology and applications

In common: other, 1 reference

[7] doi:10.1038/s41467-026-74694-6 [code]

Semi-supervised Omics Factor Analysis (SOFA) disentangles known and latent sources of variation in multi-omic data.

Journal: Nature communications

In common: 1 reference

[8] doi:10.1371/journal.pcbi.1014590 [code]

Real-time GPU-accelerated coupled cardiac system: Integrating bidirectional interactions between living optogenetic monolayers and computational simulations.

Journal: PLoS computational biology

In common: 1 reference

[9] doi:10.1371/journal.pcbi.1014532 [code]

High reelin expression may explain why a subgroup of entorhinal cortex neurons functions as an initial nucleation site of Alzheimer's disease.

Journal: PLoS computational biology

In common: 1 reference

[10] doi:10.1371/journal.pone.0357448 [code]

Anatomy-aware, label-informed approach improves image registration for challenging datasets.

Journal: PloS one

In common: 1 reference

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.