UF Mathematics Colloquium Abstract:

Personalized noninvasive imaging of subject-specific cardiac electrical activity can guide and improve preventive diagnosis and treatment of cardiac arrhythmia. Compared to body surface potential (BSP) recordings and electrophysiological information reconstructed on heart surfaces, volumetric myocardial transmembrane potential (TMP) dynamics is of greater clinical importance in exhibiting arrhythmic details and arrythmogenic substrates inside the myocardium. We present a physiological-model-constrained statistical framework that uses noninvasive BSP measurements and tomographic images of individual subjects to reconstruct subject-specific volumetric TMP dynamics and/or tissue excitability inside the 3D myocardium. General knowledge of volumetric TMP activity is incorporated through cardiac electrophysiological models so as to constrain TMP reconstruction. A maximum a posteriori estimation framework is then developed to use stochastic state space system and nonlinear data assimilation to estimate volumetric myocardial TMP dynamics and/or tissue excitability from personal BSP data. Robustness of the presented framework to practical model and data errors is evaluated. Computational phantom experiments on a variety of cardiac pathologies demonstrate the potential of this framework in identifying local arrhythmic details inside the myocardium; particularly, comparison with classical regularization-based approaches shows that this framework is able to improve the accuracy of epicardial potential reconstruction. Real-data experiments on post myocardial infarction patients further present the unique ability of this framework in providing noninvasive imaging of volumetric cardiac electrical dynamics and tissue property for individual subjects, as well as the advantage in noninvasive imaging and quantitation of myocardial infarction.