Efficient Quantification of Left Ventricular Function During the Full Cardiac Cycle Using a Characteristic Deformation Model

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• Brian Hong (None / Other)
• Thursday 06 June 2019, 12:00-12:30
• Seminar Room 1, Newton Institute.

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FHTW01 - Uncertainty quantification for cardiac models

Heart failure is a significant source of morbidity and the prevalence of heart failure continues to rise. Quantification of cardiac function beyond standard clinical indices is essential to improving heart failure diagnosis specificity. Patient-specific computational models of the heart offer detailed descriptions of cardiac function suitable for this purpose. Such models are typically constructed using 0D “varying elastance” or 3D Finite element method (FEM) approaches. While both methods have been successfully applied to many patient-specific applications, each has limitations. Varying elastance models are limited by their simplified representation of the myocardium while FEM models have a high computational cost that is restrictive in applications that require the simulation of many cardiac cycles. As an alternative to these approaches, we describe a computationally efficient method for simulating the dynamics of the left ventricle (LV) in three dimensions using characteristic deformation modes (CDM). In the CDM -LV model, LV motion is represented as a combination of a limited number of deformation modes, chosen to represent observed cardiac motions. A variational approach is used to incorporate a mechanical model of the myocardium. Passive stress is governed by a transversely isotropic elastic model. Active stress acts in the fiber direction and incorporates length-tension and force-velocity properties of cardiac muscle.

We apply this model to quantify LV function in two cases.  First, we quantify the passive stiffness of a mouse heart. The stiffness parameters of the mouse LV calculated with the CDM model are similar to those identified using a FEM approach. Second, we quantify LV function during the full cardiac cycle from 3D echocardiogram data. We estimate parameters for the myocardial passive stiffness and active contractile function using a bounded quasi-newton numerical optimization algorithm. We demonstrate that this method is capable of recapitulating the observed aggregated motion of the LV and provides reasonable estimates for the mechanical parameters. The problem of estimating LV functional parameters has numerous sources of uncertainty. Errors arise from the limitations of the imaging method, insufficiency of the data to fully characterize the mechanical system, and from simplifications present in the mathematical model. We present a preliminary analysis of the uncertainty resulting from these three sources. Overall, this approach provides reasonable estimates for the mechanical parameters that determine LV function on a clinically relevant time-scale.

This talk is part of the Isaac Newton Institute Seminar Series series.

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