A comprehensive and biophysically detailed computational model of the whole human heart electromechanics
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Abstract (arXiv)
While ventricular electromechanics is extensively studied, four-chamber heart models have only been addressed recently; most of these works however neglect atrial contraction. Indeed, as atria are characterized by a complex physiology influenced by the ventricular function, developing computational models able to capture the physiological atrial function and atrioventricular interaction is very challenging. In this paper, we propose a biophysically detailed electromechanical model of the whole human heart that considers both atrial and ventricular contraction. Our model includes: i) an anatomically accurate whole-heart geometry; ii) a comprehensive myocardial fiber architecture; iii) a biophysically detailed microscale model for the active force generation; iv) a 0D closed-loop model of the circulatory system; v) the fundamental interactions among the different core models; vi) specific constitutive laws and model parameters for each cardiac region. Concerning the numerical discretization, we propose an efficient segregated-intergrid-staggered scheme and we employ recently developed stabilization techniques that are crucial to obtain a stable formulation in a four-chamber scenario. We are able to reproduce the healthy cardiac function for all the heart chambers, in terms of pressure-volume loops, time evolution of pressures, volumes and fluxes, and three-dimensional cardiac deformation, with unprecedented matching (to the best of our knowledge) with the expected physiology. We also show the importance of considering atrial contraction, fibers-stretch-rate feedback and suitable stabilization techniques, by comparing the results obtained with and without these features in the model. The proposed model represents the state-of-the-art electromechanical model of the iHEART ERC project and is a fundamental step toward the building of physics-based digital twins of the human heart.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jul 2022.
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Citing papers per year: 2023: 2; 2024: 3; 2025: 1; 2026: 2.
8 papers.
- 2023Branched Latent Neural Mapscited by 3
- 2024Liquid Fourier Latent Dynamics Networks for fast GPU-based numerical simulations in computational cardiologycited by 3
- 2025Full-field surrogate modeling of cardiac function encoding geometric variabilitycited by 3
- 2024Neural active manifolds: nonlinear dimensionality reduction for uncertainty quantificationcited by 2
- 2026Personalized 4D Whole-Heart Mesh Reconstruction from Cine MRI via Multi-Scale Temporal Modeling and Differentiable Contour Renderingcited by 1
- 2023Real-time whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equationscited by 0
- 2024An Optimization Framework to Personalize Passive Cardiac Mechanicscited by 0
- 2026Latent Dynamics Graph Convolutional Networks for model order reduction of parameterized time-dependent PDEscited by 0
- 2026Personalized 4D Whole-Heart Mesh Reconstruction from Cine MRI via Multi-Scale Temporal Modeling and Differentiable Contour Rendering2 Jul 2026
- 2026Latent Dynamics Graph Convolutional Networks for model order reduction of parameterized time-dependent PDEs16 Jan 2026
- 2025Full-field surrogate modeling of cardiac function encoding geometric variability29 Apr 2025
- 2024Liquid Fourier Latent Dynamics Networks for fast GPU-based numerical simulations in computational cardiology19 Aug 2024
- 2024Neural active manifolds: nonlinear dimensionality reduction for uncertainty quantification7 Aug 2024
- 2024An Optimization Framework to Personalize Passive Cardiac Mechanics3 Apr 2024
- 2023Branched Latent Neural Maps4 Aug 2023
- 2023Real-time whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations8 Jun 2023