In silico mechanistic assessment of imaging-based measures of cardiac (patho) physiology

To contribute on personalising medicine, understanding the mechanisms underlying cardiac dysfunction is key for accurate personalization of clinical decision-making. This dissertation focuses on unravelling mechanisms of imaging-based observations measured in the presence of cardiac pathologies. These physiological mechanisms have direct clinical benefits on clinical conditions like aortic regurgitation, pulmonary hypertension and intense exercise. The computational CircAdapt model was used to simulate human physiology of the heart and circulation and explore those mechanisms. We also present a novel hypothesis on the right ventricular dysfunction induced by extreme intensity exercise, which offers an explanation for the disproportionately low exercise tolerance in a number of cardiovascular diseases.