From image to personalized cardiac simulation: encoding anatomical structures into a model-based segmentation framework

Whole organ scale patient specific biophysical simulations contribute to the understanding, diagnosis and treatment of complex diseases such as cardiac arrhythmia. However, many individual steps are required to bridge the gap from an anatomical scan to a personalized biophysical model. In biophysical modeling, differential equations are solved on spatial domains represented by volumetric meshes of high resolution and in model-based segmentation, surface or volume meshes represent the patient's geometry. We simplify the personalization process by representing the simulation mesh and additional relevant structures relative to the segmentation mesh. Using a surface correspondence preserving model-based segmentation algorithm, we facilitate the integration of anatomical information into biophysical models avoiding a complex processing pipeline. In a simulation study, we observe surface correspondence of up to 1.6 mm accuracy for the four heart chambers. We compare isotropic and anisotropic atrial excitation propagation in a personalized simulation.