Interpreting optical mapping recordings in the ischemic heart: A combined experimental and computational investigation

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Abstract

The occlusion of a coronary artery results in myocardial ischemia, significantly disturbing the heart's normal electrical behavior, with potentially lethal consequences, such as sustained arrhythmias. Biologists attempt to shed light on underlying mechanisms with optical voltage mapping, a widely used technique for non-contact visualization of surface electrical activity. However, this method suffers from signal distortion due to fluorescent photon scattering within the biological tissue. The distortion effect may be more pronounced during ischemia, when a gradient of electrophysiological properties exists at the surface of the heart due to diffusion with the surrounding environment. In this paper, a combined experimental and computer simulation investigation into how photon scattering, in the presence of ischemia-induced spatial heterogeneities, distorts optical mapping recordings is performed. Dual excitation wavelength optical mapping experiments are conducted in rabbit hearts. In order to interpret experimental results a computer simulation study is performed using a 3D model of ischemic rabbit cardiac tissue combined with a model of photon diffusion to simulate optical mapping recordings. Results show that the presence of a border zone, in combination with fluorescent photon scattering, distorts the optical signal. Furthermore, changes in the illumination wavelength can alter the relative contribution of the border zone to the emitted signal. The techniques developed in this study may help with interpretation of optical mapping data in electrophysiological investigations of myocardial ischemia. © 2011 Springer-Verlag Berlin Heidelberg.

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Dutta, S., Bishop, M. J., Pathmanathan, P., Lee, P., Kohl, P., Quinn, T. A., & Rodriguez, B. (2011). Interpreting optical mapping recordings in the ischemic heart: A combined experimental and computational investigation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6666 LNCS, pp. 20–27). https://doi.org/10.1007/978-3-642-21028-0_3

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