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Updated: Sep 10, 2025
Author Spotlight: Advancing Human Cardiac Anatomy Through Multi-Scale Analysis of Hearts
Published on: June 28, 2024
Integrating Imaging and Invasive Pressure Data into a Multiscale Whole-Heart Model
Marina Strocchi1,2, Christoph M Augustin3, Matthias A F Gsell3,4
1National Heart and Lung Institute, Imperial College London, London W12 0NN, UK.
Insights
We developed a systematic method to calibrate a whole-heart electromechanics model using clinical data. This validated model accurately replicates heart motion and response to pacing, aiding cardiovascular disease treatment decisions.
Area of Science:
- Biomedical Engineering
- Computational Biology
- Cardiovascular Physiology
Background:
- Cardiovascular diseases are a leading cause of mortality, with treatment decisions hindered by complex clinical data integration.
- Personalized physics-based models offer potential but face calibration and validation challenges.
Purpose of the Study:
- To present a novel, systematic calibration method for a whole-heart, multi-scale, electromechanics model.
- To validate the model's ability to replicate cardiac function and response to therapy using clinical data.
Main Methods:
- Employed emulators, sensitivity analysis, and history matching for systematic model calibration.
- Utilized ECG-gated CT and invasive LV pressure data to calibrate 25 model parameters.
- Validated against CT-derived motion, chamber configurations, and hemodynamic response to biventricular pacing.
Main Results:
- Achieved precise calibration, fitting key cardiac features within 0.8-10.8% of target values and 1.4 standard deviations.
- Demonstrated accurate replication of atrioventricular plane displacement and end-diastolic/end-systolic configurations.
- Successfully simulated the hemodynamic response to biventricular pacing, closely matching clinical measurements.
Conclusions:
- The developed systematic calibration method enables robust integration of clinical data into whole-heart electromechanics models.
- The validated model accurately captures local heart motion and therapeutic responses, showing promise for clinical decision support in cardiovascular disease management.
Abstract:
Cardiovascular diseases are the leading cause of death. Clinical data used to decide treatment are hard to integrate and interpret, making optimal treatment selection difficult. Personalized models can be used to integrate clinical data into a physics and physiology-constrained framework, but their clinical application faces limitations due to complex calibration and validation. In this study, we present a novel systematic calibration method for a whole-heart, multiscale, electromechanics model using emulators, sensitivity analysis, and history matching. Using cardiac motion derived from ECG-gated computed tomography (CT) and invasive left ventricular (LV) pressure data, we calibrated 25 model parameters to match the LV end-diastolic (ED) and peak pressure, ED and end-systolic (ES) volumes (EDV and ESV), right ventricle EDV, and the left atrium EDV, ESV, and the maximum volume during venous return. After calibration, all features were fit within [0.8, 10.8]% of the mean target value, and fell within 1.4 experimental standard deviations from the target values. We validated the model by comparing CT-derived and simulated atrioventricular plane displacement (AVPD) (8.2 versus 8.1 mm) and the ED and ES configurations against the CT images. The model replicated the measured acute hemodynamic response to biventricular (BIV) pacing (simulated: 222 mmHg/s versus clinical: 213±65 mmHg/s). This study provides a systematic method to integrate clinical data into a whole-heart, multiscale electromechanics framework. The validation shows that the model replicates local heart motion and response to therapy, demonstrating potential in assisting clinical decision-making.
