Atrial arrhythmias like atrial fibrillation and atrial flutter are a major health challenge in developed countries. Radiofrequency ablation performed via intracardiac catheters is a curative therapy for these reentrant arrhythmias. However, the optimal location of ablation lesions is not straightforward to determine, particularly for complex activation patterns. Thus, a clinical need for tools to intuitively visualize complex activation patterns and to provide a platform to evaluate different ablation strategies in dry runs is apparent. Here, we present a virtual reality system that allows to interactively simulate atrial excitation propagation and place ablation lesions. Our software builds on the IMHOTEP framework for the Unity3D engine and implements a multithreaded model-view-controller design pattern. Excitation propagation is computed using a fast marching approach considering refractoriness. Interactive rewind and playback is supported through a combination of the flyweight pattern for simulation data with complete snapshots for key frames. The system was evaluated in a user study using the HTC ViveTM headset including two controllers. For high fidelity virtual reality interaction, a minimum frame rate of 60 per second is required. In a biatrial anatomical model comprising 36,059 nodes (Figure 1), even complex activation patterns with multiple wavefronts could be simulated and rendered down to 2x slow motion (1 sec activation sequence displayed during 2 sec wall time) on a desktop machine. Results of the user study suggest added value regarding the comprehension of arrhythmias and ablation options and very good intuitiveness of the user interface requiring almost no teach-in. The virtual reality tool is ready to be used for educational purposes and prepared to import personalized models supporting diagnosis and therapy planning for atrial arrhythmias in the future.
Student Theses (1)
M. Pfeiffer. Investigation of the contrast source inversion method for microwave brain tomoghraphy. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT). Masterarbeit. 2016
In this work, the Contrast Source Inversion algorithm is investigated, with the goal of re-constructing two-dimensional slice images of the complex permittivity of the human brain for stroke diagnosis. The algorithm is explained in detail. Enhancements of the method, such as a multiplicative total variation constraint and frequency hopping are discussed. Multiple numerical models of varying levels of abstraction and their reconstructions are presented. The work is concluded by a discussion of the strengths and shortcomings of the algorithm within the context of stroke diagnosis.