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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Constrained 2-D/3-D registration for motion compensation in AFib ablation procedures
IPCAI'11 Proceedings of the Second international conference on Information processing in computer-assisted interventions
Combined cardiac and respiratory motion compensation for atrial fibrillation ablation procedures
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
STACOM'12 Proceedings of the third international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
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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.