Learning-Based Hypothesis Fusion for Robust Catheter Tracking in 2D X-Ray Fluoroscopy

Abstract

Catheter tracking has become more and more important in recent interventional applications. It provides real time navigation for the physicians and can be used to control a motion compensated fluoro overlay reference image for other means of guidance, e.g. involving a 3D anatomical model. Tracking the coronary sinus (CS) catheter is effective to compensate respiratory and cardiac motion for 3D overlay navigation to assist positioning the ablation catheter in Atrial Fibrillation (Afib) treatments. During interventions, the CS catheter performs rapid motion and non-rigid deformation due to the beating heart and respiration. In this paper, we model the CS catheter as a set of electrodes. Novelly designed hypotheses generated by a number of learning-based detectors are fused. Robust hypothesis matching through a Bayesian framework is then used to select the best hypothesis for each frame. As a result, our tracking method achieves very high robustness against challenging scenarios such as low SNR, occlusion, foreshortening, non-rigid deformation, as well as the catheter moving in and out of ROI. Quantitative evaluation has been conducted on a database of 13221 frames from 1073 sequences. Our approach obtains 0.50mm median error and 0.76mm mean error. 97.8% of evaluated data have errors less than 2.00mm. The speed of our tracking algorithm reaches 5 frames-per-second on most data sets. Our approach is not limited to the catheters inside the CS but can be extended to track other types of catheters, such as ablation catheters or circumferential mapping catheters.