Dynamic structural-image-guided noninvasive volumetric cardiac electrophysiological mapping

Body surface potential (BSP) has been used as the single data source in inverse electrocardiography (IECG). The lack of volumetric spatial resolutions in BSP, however, hinders the noninvasive mapping of volumetric cardiac transmembrane potentials (TMPs). Tomographic image sequence, which contains temporally sparse but spatially dense cardiac kinematic measures, becomes ideal dynamic complements to BSPs through cardiac electromechanical (EM) coupling. In this paper, we present a model-constrained Bayesian framework to integrate tomographic image sequence and BSP maps (BSPM) for volumetric cardiac TMP mapping. A priori physiological knowledge is incorporated via stochastic modeling of the cardiac electrophysiological system with unknown systematic errors. With this system as a platform for data integration, adaptive data assimilation is used to estimate patient specific TMPs from BSPMs under the guidance of tomographic images. In this way, complementary images are integrated in accord with their respective merits and limitations. Phantom and real data experiments exhibit notable improvements and practicability of the presented framework.