Data-driven computational models of heart anatomy, mechanics and hemodynamics: An integrated framework

Cardiac therapies aim to correct pathological blood flow. Patient-specific therapy planning is challenging due to the large variability in disease cause, location and severity. A predictive framework is therefore needed to assess the optimal treatment for a patient in terms of maximizing effectiveness (blood flow velocity, vorticity, cardiac output, etc.) and minimizing the risk of complications. Multi-physics fluid structure interaction models have been proposed to investigate the function of the bi-ventricular myocardium. Yet, no or only partial patient-specific data have been used due to the lack of efficient methods for accurate modeling of patient´s cardiac anatomy from images. Current imaging technologies enabling assessment of cardiac anatomy, dynamics and tissue structure, we propose an integrated framework for multi-physics heart modeling based on imaging data. Our approach relies on efficient machine learning methods to estimate an accurate and comprehensive model of patient´s anatomy from MRI. A computational model of cardiac electrophysiology and biomechanics is then solved on patient´s anatomy.