Abstract

A central problem in biomechanical studies of personalized human left ventricular (LV) modelling is estimating the material properties from in-vivo clinical MRI measurements in a time frame suitable for use in the clinic. Understanding these properties can provide insight into heart function or dysfunction and help inform personalised treatment. However, finding a solution to the differential equations which describe the myocardium through numerical integration can be computationally expensive. To circumvent this issue, we use the concept of statistical emulation to infer the myocardium properties in a viable clinical time frame using in-vivo MRI data. Emulation methods avoid computationally expensive simulations from the LV model by replacing it with a surrogate model inferred from simulations generated before the arrival of a patient, vastly improving efficiency at the clinic. In the talk I will compare and contrast various emulation strategies, discuss uncertainty quantification and (it time permits) discuss an extension of this framework to fluid dynamics in the pulmonary blood circulation system for prognostication of pulmonary hypertension.