Image-Based Predictive Modeling of Heart Mechanics

Abstract

Personalized biophysical modeling of the heart is a useful approach for noninvasively analyzing and predicting in vivo cardiac mechanics. Three main developments support this style of analysis: state-of-the-art cardiac imaging technologies, modern computational infrastructure, and advanced mathematical modeling techniques. In vivo measurements of cardiac structure and function can be integrated using sophisticated computational methods to investigate mechanisms of myocardial function and dysfunction, and can aid in clinical diagnosis and developing personalized treatment. In this article, we review the state-of-the-art in cardiac imaging modalities, model-based interpretation of 3D images of cardiac structure and function, and recent advances in modeling that allow personalized predictions of heart mechanics. We discuss how using such image-based modeling frameworks can increase the understanding of the fundamental biophysics behind cardiac mechanics, and assist with diagnosis, surgical guidance, and treatment planning. Addressing the challenges in this field will require a coordinated effort from both the clinical-imaging and modeling communities. We also discuss future directions that can be taken to bridge the gap between basic science and clinical translation.

Modelling mitral valvular dynamics–current trend and future directions

“…Despite the strong coupling between the MV and LV, few modelling studies have integrated the MV and LV simultaneously into a single model, particularly with FSI . This is because integrated MV‐LV approach faces a number of challenges: the geometries of the MV and sub‐valvular apparatus are complex; MV dynamics is affected by chordae that are connected to the papillary muscles, which are embedded in the LV modelling of a contracting LV itself is nontrivial; the mitral annulus is nonplanar and dynamic; MV leaflets have large deformation strongly coupled with ventricular flow; and parameter inference is difficult because of the lack of data from in vivo measurements. …”

Advances in computational modelling for personalised medicine after myocardial infarction

“…Cardiac models have been developed over the past decades, ranging from single myocyte models,19 to two-dimensional approximation,20 three-dimensional models21 and multiscale/physics systems 18. A biomechanical cardiac model encompasses various components to capture ventricular dynamics,7 including geometrical representation (numerical mesh), mathematical representation (ie, finite element methods), boundary conditions (motion constraint imposed by surrounding tissue and organs, blood pressure and flow rates), material properties (myocardial passive stiffness and contractility) and model output analysis (figure 2). The development of personalised heart models is complex and involves multidisciplinary involvement and collaboration (figure 3).…”

“…The next step is volumetric meshing, where the LV wall is divided into polyhedrons as small representative solids. Different methods are being developed for cardiac geometry reconstruction including user iterative interventions for reconstruction7 or by warping idealised ventricular geometry, for example, an ellipsoid, into patient data 22…”

“…Predicting myocardial systolic stress also requires further parameterisation of the active contraction model, which usually complements a myocardial passive response model 7. Most of myocardial active models are based on ‘the sliding theory ’ at cellular level and upscaled to tissue level (table 1).…”

Cardiac image modelling: Breadth and depth in heart disease

“…Finite element analysis of cardiac mechanics can incorporate realistic non-linear, anisotropic material properties, active force generation, resulting in realistic simulations of cardiac mechanics (Chabiniok et al, 2016; Wang et al, 2015). The cardiac cycle is typically simulated in five phases: 1) passive diastolic filling, 2) isovolumic contraction, 3) ejection, 4) isovolumic relaxation and 5) early filling, though by coupling the biventricular model to a closed-loop model of the pulmonary and systemic circulations and atria, it is possible for the model itself to generate the entire cycle (Kerckhoffs et al, 2007).…”