Noninvasive Computational Imaging of Cardiac Electrophysiology for 3-D Infarct

Myocardial infarction (MI) creates electrophysiologically altered substrates that are responsible for ventricular ar rhythmias, such as tachycardia and fibrillation. The presence, size, location, and composition of infarct scar bear significant prognostic and therapeutic implications for individual subjects. We have developed a statistical physiological model-constrained framework that uses noninvasive body-surface-potential data and tomographic images to estimate subject-specific transmembrane potential (TMP) dynamics inside the 3-D myocardium. In this paper, we adapt this framework for the purpose of noninvasive imaging, detection, and quantification of 3-D scar mass for postMI patients: the framework requires no prior knowledge of MI and converges to final subject-specific TMP estimates after several passes of estimation with intermediate feedback; based on the primary features of the estimated spatiotemporal TMP dynamics, we provide 3-D imaging of scar tissue and quantitative evaluation of scar location and extent. Phantom experiments were performed on a computational model of realistic heart-torso geometry, considering 87 transmural infarct scars of different sizes and locations inside the myocardium, and 12 compact infarct scars (extent between 10% and 30%) at different transmural depths. Real data experiments were carried out on BSP and magnetic resonance imaging (MRI) data from four postMI patients, validated by gold standards and existing results. This framework shows unique advantage of noninvasive, quantitative, computational imaging of subject-specific TMP dynamics and infarct mass of the 3-D myocardium, with the potential to reflect details in the spatial structure and tissue composition/heterogeneity of 3-D infarct scar.