Cryo-balloon catheter localization in fluoroscopic images

Electrophysiology (EP) studies and catheter ablation have become important treatment options for several types of cardiac arrhythmias. We present a novel image-based approach for automatic detection and 3-D reconstruction of EP catheters where the physician marks the catheter to be reconstructed by a single click in each image. The result can be used to provide 3-D information for enhanced navigation throughout EP procedures. Our approach involves two X-ray projections acquired from different angles, and it is based on two steps: First, we detect the catheter in each view after manual initialization using a graph-search method. Then, the detection results are used to reconstruct a full 3-D model of the catheter based on automatically determined point pairs for triangulation. An evaluation on 176 different clinical fluoroscopic images yielded a detection rate of 83.4%. For measuring the error, we used the coupling distance which is a more accurate quality measure than the average point-wise distance to a reference. For successful outcomes, the 2-D detection error was 1.7 mm ±1.2 mm. Using successfully detected catheters for reconstruction, we obtained a reconstruction error of 1.8 mm ±1.1 mm on phantom data. On clinical data, our method yielded a reconstruction error of 2.2 mm ±2.2 mm.

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Cryo-Balloon Catheter Localization Based on a Support-Vector-Machine Approach

Kurzendorfer

Mewes

Maier

et al. 2016

IEEE Trans. Med. Imaging

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Cryo-balloon catheters have attracted an increasing amount of interest in the medical community as they can reduce patient risk during left atrial pulmonary vein ablation procedures. As cryo-balloon catheters are not equipped with electrodes, they cannot be localized automatically by electro-anatomical mapping systems. As a consequence, X-ray fluoroscopy has remained an important means for guidance during the procedure. Most recently, image guidance methods for fluoroscopy-based procedures have been proposed, but they provide only limited support for cryo-balloon catheters and require significant user interaction. To improve this situation, we propose a novel method for automatic cryo-balloon catheter detection in fluoroscopic images by detecting the cryo-balloon catheter's built-in X-ray marker. Our approach is based on a blob detection algorithm to find possible X-ray marker candidates. Several of these candidates are then excluded using prior knowledge. For the remaining candidates, several catheter specific features are introduced. They are processed using a machine learning approach to arrive at the final X-ray marker position. Our method was evaluated on 75 biplane fluoroscopy images from 40 patients, from two sites, acquired with a biplane angiography system. The method yielded a success rate of 99.0% in plane A and 90.6% in plane B, respectively. The detection achieved an accuracy of 1.00 mm±0.82 mm in plane A and 1.13 mm±0.24 mm in plane B. The localization in 3-D was associated with an average error of 0.36 mm±0.86 mm.

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Cryo-balloon catheter localization in fluoroscopic images

Cited by 3 publications

References 17 publications

Deep learning-based framework for motion-compensated image fusion in catheterization procedures

Deep learning-based framework for motion-compensated image fusion in catheterization procedures

Electrophysiology Catheter Detection and Reconstruction From Two Views in Fluoroscopic Images

Cryo-Balloon Catheter Localization Based on a Support-Vector-Machine Approach

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