Electromechanical models of the ventricles

Abstract

Computational modeling has traditionally played an important role in dissecting the mechanisms for cardiac dysfunction. Ventricular electromechanical models, likely the most sophisticated virtual organs to date, integrate detailed information across the spatial scales of cardiac electrophysiology and mechanics and are capable of capturing the emergent behavior and the interaction between electrical activation and mechanical contraction of the heart. The goal of this review is to provide an overview of the latest advancements in multiscale electromechanical modeling of the ventricles. We first detail the general framework of multiscale ventricular electromechanical modeling and describe the state of the art in computational techniques and experimental validation approaches. The powerful utility of ventricular electromechanical models in providing a better understanding of cardiac function is then demonstrated by reviewing the latest insights obtained by these models, focusing primarily on the mechanisms by which mechanoelectric coupling contributes to ventricular arrythmogenesis, the relationship between electrical activation and mechanical contraction in the normal heart, and the mechanisms of mechanical dyssynchrony and resynchronization in the failing heart. Computational modeling of cardiac electromechanics will continue to complement basic science research and clinical cardiology and holds promise to become an important clinical tool aiding the diagnosis and treatment of cardiac disease.

Fig. 1.

Electromechanical modeling of the ventricles. Overall approach to electromechanical modeling. Schematics of ionic and myofilament models, the combination of which constitutes the cell electromechanical model, are also shown.

Fig. 2.

Reconstruction of infarcted and failing canine hearts from magnetic resonance images. Modified with permission (35).

Fig. 3.

Computational meshes and fiber and sheet structure. A: computational mesh of the infarcted canine ventricles for the electrical component of the electromechanics model. A, inset: zoomed-in image of the mesh, showing the mesh details. B: computational meshes for the mechanics component. C: fiber and sheet orientations obtained from diffusion tensor magnetic resonance images. Fiber orientation is for the infarcted canine heart. Sheet orientation is shown on the endocardium in diastole. D: human ventricles with rule-based fiber orientation. Modified with permission (5, 22, 35). D, A–F: transmural changes in fiber angle for different regions in the ventricle.

Fig. 4.

Mapping of electromechanical wave (EW) imaging. Experimental (top) and simulated (bottom) interframe strain distribution associated with EW for left ventricular base (LVb) pacing (A), left ventricular apex (LVa) pacing (B), and right ventricular apex (RVa) pacing (C) are shown. Comparative graphs between experimental and simulated interframe strain traces at the septum (black) and lateral wall (blue) for LVb pacing (D), LVa pacing (E), and RVa pacing (F) are shown. Experimental and simulated electromechanical activation isochrones and simulated electrical activation isochrones during the 3 pacing protocols (G) are shown. Modified with permission (25).