Fast tracking of catheters in 2D fluoroscopic images using an integrated CPU-GPU framework
Catheter tracking has become more important in recent interventional applications for atrial fibrillation (AF) ablation procedures. It can provide real-time guidance for the physicians and be used for motion compensation by overlaying a 3D left atrium model on live 2D fluoroscopic images. To achieve that, this paper has two main contributions. We first propose a new approach to generate tracking hypotheses based on catheter electrode detection. The novelly-designed tracking hypotheses are evaluated by a Bayesian-framework that fuses learning-based detection and template matching. The second contribution is a novel integrated framework that efficiently distributes computation between a GPU (graphics processing unit) and a CPU. Our framework implements Probabilistic Boosting-Tree (PBT)-based [7] classification for object detection in 2D data on the GPU. Quantitative evaluation has been conducted on a databases of 1073 clinical fluoroscopic sequences. The new framework achieves robust performance with the median error at 0.5mm and the 95th percentile error at 1.0mm. The speed of tracking the coronary sinus (CS) catheter reaches more than 30 frames-per-second (fps) on most evaluation data. The achieved speed is faster than most real-time fluoroscopy frame rates.