Noninvasive Imaging of Cardiac Electrophysiology in Pathological Hearts

Noninvasive imaging of cardiac electrical activity is important to both basic cardiovascular research and clinical treatment. It offers the capability to translate body surface electrical signals into cardiac electrical activities and provide direct information on the electrical status of the heart. This dissertation research is aimed to investigate a novel physical-model-based cardiac electrical imaging technique (CEI) under different pathological conditions, for the purpose of further developing it into a clinical useful tool. The CEI technique is adapted to image myocardial infarction and atrial arrhythmias. For the imaging of myocardial infarction, the computer simulation was performed by using a cellular automaton heart model with simulated myocardial infarctions. The simulation results demonstrate that CEI can quantify myocardial infarction and offer the potential to distinguish between epicardial and endocardial infarctions. Furthermore, the CEI approach was adapted to image atrial electrical activities. A frequency-based CEI technique has been proposed to incorporated spectral analysis with the electrical source imaging technique to localize high-frequency drivers during atrial fibrillation (AF). The imaging results were compared with clinical electrophysiological mappings and shown good consistency. The CEI technique was also applied to image atrial excitations in subjects with normal atrial activation and atrial flutter. The results from patients with atrial flutter demonstrated that CEI is also capable of imaging reentrant pattern. The performance of CEI was also experimentally evaluated in in situ swine heart with induced ventricular tachycardia. The consistency between the non-invasively imaged electrical activities and computer simulation or the directly measured counterparts from clinical/animal study implies that CEI is capable of localizing electrically-abnormal substrate, extracting the spectral features during AF, reconstructing the global patterns of atrial and ventricular activation sequences, localizing the arrhythmogenic foci, and imaging dynamically changing arrhythmia on a beat-to-beat basis. The promising results presented in this dissertation study suggest that the cardiac electrical imaging technique has the potential to assist in the diagnosis and treatment of cardiovascular diseases. The present dissertation research takes an important step towards further translating this technique into clinical assistive tool by extending the application to hearts with electrophysiological abnormal substrates, adapting it to image atrial electrical activities, and further evaluating the performance in a clinical setting on human subjects