MICCAI 2013 - LNCS 8149-8151

Fast Data-Driven Calibration of a Cardiac Electrophysiology Model from Images and ECG

Oliver Zettinig^{1, 2}, Tommaso Mansi¹, Bogdan Georgescu¹, Elham Kayvanpour³, Farbod Sedaghat-Hamedani³, Ali Amr³, Jan Haas³, Henning Steen³, Benjamin Meder³, Hugo Katus³, Nassir Navab², Ali Kamen¹, and Dorin Comaniciu¹

¹Siemens Corporation, Corporate Technology, Imaging and Computer Vision, Princeton, NJ, USA

²Computer Aided Medical Procedures, Technische Universität München, Germany

³University Hospital Heidelberg, Department of Internal Medicine III - Cardiology, Angiology and Pneumology, Heidelberg, Germany

Abstract. Recent advances in computational electrophysiology (EP) models make them attractive for clinical use. We propose a novel data-driven approach to calibrate an EP model from standard 12-lead electrocardiograms (ECG), which are in contrast to invasive or dense body surface measurements widely available in clinical routine. With focus on cardiac depolarization, we first propose an efficient forward model of ECG by coupling a mono-domain, Lattice-Boltzmann model of cardiac EP to a boundary element formulation of body surface potentials. We then estimate a polynomial regression to predict myocardium, left ventricle and right ventricle endocardium electrical diffusion from QRS duration and ECG electrical axis. Training was performed on 4,200 ECG simulations, calculated in 3s each, using different diffusion parameters on 13 patient geometries. This allowed quantifying diffusion uncertainty for given ECG parameters due to the ill-posed nature of the ECG problem. We show that our method is able to predict myocardium diffusion within the uncertainty range, yielding a prediction error of less than 5ms for QRS duration and 2° for electrical axis. Prediction results compared favorably with those obtained with a standard optimization procedure, while being 60 times faster. Our data-driven model can thus constitute an efficient preliminary step prior to more refined EP personalization.

LNCS 8149, p. 1 ff.

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