Strain-Based Regional Nonlinear Cardiac Material Properties Estimation from Medical Images

Abstract

Abstract. Model personalization is essential for model-based surgical planning and treatment assessment. As alteration in material elasticity is a fundamental cause to various cardiac pathologies, estimation of material properties is important to model personalization. Although the myocardium is heterogeneous, hyperelastic, and orthotropic, existing image-based estimation frameworks treat the tissue as either heterogeneous but linear, or hyperelastic but homogeneous. In view of these, we present a physiology-based framework for estimating regional, hyperelastic, and orthotropic material properties. A cardiac physiological model is adopted to describe the macroscopic cardiac physiology. By using a strainbased objective function which properly reflects the change of material constants, the regional material properties of a hyperelastic and orthotropic constitutive law are estimated using derivative-free optimization. Experiments were performed on synthetic and real data to show the characteristics of the framework.

Quantifying the Uncertainty in Model Parameters Using Gaussian Process-Based Markov Chain Monte Carlo: An Application to Cardiac Electrophysiological Models

“…Most existing works on parameter estimation use deterministic methods to find an optimal value of the model parameter so that model outputs best fit the measurement data [18,15,5]. However, significant uncertainty can exist in the estimated parameter values due to the uncertainty in available data.…”

Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning

“…There are many optimization methods developed in the past few decades. Derivative free methods, such as the Subplex method (Wong et al, 2015 ), Bound Optimization BY Quadratic Approximation (BOBYQA) (Wong et al, 2012 ), New Unconstrained Optimization Algorithm (NEWUOA) (Sermesant et al, 2012 ), and hybrid particle swarm method (Mineroff et al, 2019 ), have been used in estimating cardiac model parameters. Derivative-based variational data assimilation approaches have also been applied to estimate cardiac conductivities in ventricular tissue (Yang and Veneziani, 2015 ; Barone et al, 2020b ) and heterogeneous elastic material properties in personalized cardiac mechanic model (Balaban et al, 2018 ).…”

“…There are two general approaches to this inverse problem: deterministic optimization and probabilistic inference. In deterministic optimization, we seek a single optimal value of the unknown model parameter that will minimize the mismatch between the model output and the measurement data (Sermesant et al, 2012 ; Wong et al, 2012 , 2015 ; Yang and Veneziani, 2015 ; Balaban et al, 2018 ; Mineroff et al, 2019 ; Barone et al, 2020a , b ). These estimates, however, do not take into account the uncertainty in the measurement data, nor can they offer insights into the presence of non-unique solutions that can match the same data.…”

A clinically applicable strategy to estimate the in vivo distribution of mechanical material properties of the right ventricular wall

“…Although the majority of studies to-date have focused on the LV, and thus, only included the LV geometry in the forward model similarly to the previous References 13-19, some work has considered ventricular interaction and characterized properties for more than just the LV. [20][21][22] For example, the work by Wong et al 20 used a bi-ventricle model that included both the LV and RV combined, and characterized material properties for the LV, RV, and a predefined infarcted region. The work of Rama and Skatulla 22 established a computationally efficient strategy to characterize mechanical properties of the heart wall and considered examples of a single ventricle (i.e., only LV) and bi-ventricle system, although all examples used artificially generated geometries (i.e., geometries were not derived directly from the segmentation of actual human imaging data).…”