Validation of a Variational Data Assimilation Procedure for the Estimation of Cardiac Conductivities

An accurate patient-specific parameter estimation is crucial for extending computational tools from medical research to clinical practice. In cardiac electrophysiology, critical parameters are the conductivity tensors and their quantification is quite troublesome in living organisms, as witnessed by different discordant data in the literature. With this motivation, we investigate a novel variational data assimilation approach for the estimation of the cardiac conductivity parameters combining available patient-specific measures with mathematical models. By resorting to a derivative-based optimization method, we significantly improve the numerical approaches in the literature. Moreover, we present an extensive numerical simulation campaign reproducing experimental and realistic settings in presence of noisy data. We will discuss the interplay between the estimation of Monodomain and Bidomain conductivities as well as experimental validation with ex-vivo animal tissues.