Analysis and simulation of multimodal cardiac images to study the heart function

This thesis focuses on the analysis of the cardiac electrical and kinematic function for heart failure patients. An expected outcome is a set of computational tools that may help a clinician in understanding, diagnosing and treating patients su ering from cardiac motion asynchrony, a speci c aspect of heart failure. Understanding the inverse electro-kinematic coupling relationship is the main task of this study. With this knowledge, the widely available cardiac image sequences acquired non-invasively at clinics could be used to estimate the cardiac electrophysiology (EP) without having to perform the invasive cardiac EP mapping procedures. To this end, we use real clinical cardiac sequence and a cardiac electromechanical model to create controlled synthetic sequence so as to produce a training set in an attempt to learn the cardiac electro-kinematic relationship. Creating patient-speci c database of synthetic sequences allows us to study this relationship using a machine learning approach. A rst contribution of this work is a non-linear registration method applied and evaluated on cardiac sequences to estimate the cardiac motion. Second, a new approach in the generation of the synthetic but virtually realistic cardiac sequence which combines a biophysical model and clinical images is developed. Finally, we present the cardiac electrophysiological activation time estimation from medical images using a patient-speci c database of synthetic image sequences.