Catheter Tracking: Filter-Based vs. Learning-Based

Abstract

Abstract. Atrial fibrillation is the most common sustained arrhythmia. One important treatment option is radio-frequency catheter ablation (RFCA) of the pulmonary veins attached to the left atrium. RFCA is usually performed under fluoroscopic (X-ray) image guidance. Overlay images computed from pre-operative 3-D volumetric data can be used to add anatomical detail otherwise not visible under X-ray. Unfortunately, current fluoro overlay images are static, i.e., they do not move synchronously with respiratory and cardiac motion. A filter-based catheter tracking approach using simultaneous biplane fluoroscopy was previously presented. It requires localization of a circumferential tracking catheter, though. Unfortunately, the initially proposed method may fail to accommodate catheters of different size. It may also detect wrong structures in the presence of high background clutter. We developed a new learning-based approach to overcome both problems. First, a 3-D model of the catheter is reconstructed. A cascade of boosted classifiers is then used to segment the circumferential mapping catheter. Finally, the 3-D motion at the site of ablation is estimated by tracking the reconstructed model in 3-D from biplane fluoroscopy. We compared our method to the previous approach using 13 clinical data sets and found that the 2-D tracking error improved from 1.0 mm to 0.8 mm. The 3-D tracking error was reduced from 0.8 mm to 0.7 mm.

Constrained 2-D/3-D Registration for Motion Compensation in AFib Ablation Procedures

“…Note, however, that by using a learning-based method we might be able to improve the tracking results. At least, this is what we observed for the biplane approach [14]. Here, the filter-based approach [16] yielded a 2-D tracking error of 1.0 mm and a 3-D tracking error of 0.8 mm.…”

“…Here, the filter-based approach [16] yielded a 2-D tracking error of 1.0 mm and a 3-D tracking error of 0.8 mm. Using a learning-based method, the errors were reduced to 0.8 mm in 2-D and to 0.7 mm in 3-D [14].…”

“…In the first step of our method, a 3-D model of the circumferential mapping catheter is generated, as proposed in [13,16,14]. A single biplane frame is required to manually initialize the model generation step.…”

“…In the first approach, a filter-based technique using a vessel enhancement filtering was used. The method was later extended by integrating a learning-based catheter segmentation [14]. However, this technique still required simultaneous biplane imaging.…”

“…In EP labs equipped with biplane C-arm systems, often only one imaging plane is used at a time to reduce the dose to the patient. In this case, the methods suggested in [13,14] are not applicable. The 2-D method described in [15] is not ideal either, as it requires re-initialization of the catheter model as soon as the C-arm view direction changes.…”