  Personalized cardiac mechanics modeling is a powerful tool for understanding
the biomechanics of cardiac function in health and disease and assisting in
treatment planning. However, current models are limited to using medical images
acquired at a single cardiac phase, often limiting their applicability for
processing dynamic image acquisitions. This study introduces an inverse finite
element analysis (iFEA) framework to estimate the passive mechanical properties
of cardiac tissue using time-dependent medical image data. The iFEA framework
relies on a novel nested optimization scheme, in which the outer iterations
utilize a traditional optimization method to best approximate material
parameters that fit image data, while the inner iterations employ an augmented
Sellier's algorithm to estimate the stress-free reference configuration. With a
focus on characterizing the passive mechanical behavior, the framework employs
structurally based anisotropic hyperelastic constitutive models and
physiologically relevant boundary conditions to simulate myocardial mechanics.
We use a stabilized variational multiscale formulation for solving the
governing nonlinear elastodynamics equations, verified for cardiac mechanics
applications. The framework is tested in myocardium models of biventricle and
left atrium derived from cardiac phase-resolved computed tomographic (CT)
images of a healthy subject and three patients with hypertrophic obstructive
cardiomyopathy (HOCM). The impact of the choice of optimization methods and
other numerical settings, including fiber direction parameters, mesh size,
initial parameters for optimization, and perturbations to optimal material
parameters, is assessed using a rigorous sensitivity analysis. The performance
of the current iFEA is compared against an assumed power-law-based
pressure-volume relation, typically used for single-phase image acquisition.
