Emulation strategies for Bayesian inference of regional left ventricle material parameters

This paper presents a Bayesian surrogate-modeling framework for inferring regional material parameters in patient-specific left ventricle biomechanical models. The approach partitions the left ventricle into five physiological zones derived from American Heart Association guidelines and applies the Holzapfel-Ogden constitutive model to characterize myocardial tissue properties. By leveraging cardiac magnetic resonance imaging data, the method enables personalized assessment of regional myocardial dysfunction that was previously limited by homogeneous material assumptions.