An electromechanics-driven fluid dynamics model for the simulation of the whole human heart
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Abstract (arXiv)
We introduce a multiphysics and geometric multiscale computational model, suitable to describe the hemodynamics of the whole human heart, driven by a four-chamber electromechanical model. We first present a study on the calibration of the biophysically detailed RDQ20 activation model (Regazzoni et al., 2020) that is able to reproduce the physiological range of hemodynamic biomarkers. Then, we demonstrate that the ability of the force generation model to reproduce certain microscale mechanisms, such as the dependence of force on fiber shortening velocity, is crucial to capture the overall physiological mechanical and fluid dynamics macroscale behavior. This motivates the need for using multiscale models with high biophysical fidelity, even when the outputs of interest are relative to the macroscale. We show that the use of a high-fidelity electromechanical model, combined with a detailed calibration process, allows us to achieve remarkable biophysical fidelity in terms of both mechanical and hemodynamic quantities. Indeed, our electromechanical-driven CFD simulations - carried out on an anatomically accurate geometry of the whole heart - provide results that match the cardiac physiology both qualitatively (in terms of flow patterns) and quantitatively (when comparing in silico results with biomarkers acquired in vivo). We consider the pathological case of left bundle branch block, and we investigate the consequences that an electrical abnormality has on cardiac hemodynamics thanks to our multiphysics integrated model. The computational model that we propose can faithfully predict a delay and an increasing wall shear stress in the left ventricle in the pathological condition. The interaction of different physical processes in an integrated framework allows us to faithfully describe and model this pathology, by capturing and reproducing the intrinsic multiphysics nature of the human heart.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jan 2023.
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Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.
- 2024Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiologycited by 3
- 2025A Critical Assessment of Pattern Comparisons Between POD and Autoencoders in Intraventricular Flowscited by 0
- 2026Flow-based conditional cardiac anatomy generation for virtual cohortscited by 0
- 2026Flow-based conditional cardiac anatomy generation for virtual cohorts10 Aug 2026
- 2025A Critical Assessment of Pattern Comparisons Between POD and Autoencoders in Intraventricular Flows22 Dec 2025
- 2024Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology13 Aug 2024