Experimental validation of a variational data assimilation procedure for estimating space-dependent cardiac conductivities

Customization of mathematical and numerical models to patient-specific settings is a critical step of the translation process bringing scientific computing to the clinical activity. In cardiovascular diseases, this process is at an advanced stage. It requires image processing for patient morphology retrieval and data assimilation for the calibration of the parameters of the model. Different methods of data assimilation are available for calibrating parameters from measures and an accurate assessment of their reliability in realistic scenarios is not trivial. In this paper, we consider the estimation of cardiac space-dependent conductivities for the Monodomain modeling of the propagation of the excitation potential in the heart with a variational deterministic approach. We perform an extensive validation of our method based on experimental data obtained by fluorescence optical mapping recordings on animal models. The results demonstrate that our procedure provides reliable results when coupled with phenomenological ionic models like the Fenton–Karma and the Mitchell–Schaeffer ones. These promising results give confidence that our approach could be used in clinical scenarios for applying computational techniques to support the decision-making process of medical doctors, like, e.g., the optimal placement of pacemakers.