Patient-specific model adaptation and validation requires a comparison of simulations with measured patient data. For patients suffering from atrial fibrillation, such data is mainly available as intracardiac catheter signals. In this work, we demonstrate the simulation of clinically relevant catheter data as measured using circular mapping catheters (such as Lasso or Orbiter) and coronary sinus catheters using atrial simulations on a realistic geometry. Four circular catheters are modeled using a projection technique for two distinct types of application. We show that in sinus rhythm, the choice of a distinct electrophysiological model does not impair the signal quality. Finally, we compare simulated potentials to a real clinical measurement. In the future, with patient- specific models available, such comparisons can constitute an important interface for personalizing cardiac models.
Intracardiac catheter recordings are available in common clinical practice. They can therefore be employed to adapt and validate atrial computer models of individual patients. Hence, their information content needs to be analyzed quantitatively. During treatment of atrial arrhythmia such as atrial flutter or fibrillation, the location of ectopic foci in the pulmonary veins is of special interest. In this study, virtual catheter signals are extracted from an atrial simulation on a realistic geometry with normal sinus rhythm as well as ectopic stimuli in all four pulmonary veins. Using a simplified Pan-Tompkins algorithm, the activation times are determined. Based on the analysis of the activation sequence in a circular mapping catheter simulated on the posterior left atrial wall, all four ectopic foci can clearly be associated with the pulmonary vein they came from. For a catheter on the anterior wall, this is possible for three of the four ectopic beats. Despite the knowledge gathered for the personalization of patient models, such simulations may help cardiologists to better classify measured signals.
Student Theses (1)
D. Straub. Segmentation of the knee using quantitative magnetic resonance imaging. Institut für Biomedizinische Technik, Karlsruher Institut für Technologie (KIT). Diplomarbeit. 2009