Article ; Online: Cryo-Balloon Catheter Localization Based on a Support-Vector-Machine Approach.
IEEE transactions on medical imaging
2016 Volume 35, Issue 8, Page(s) 1892–1902
Abstract: 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 ...
Abstract | 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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Language | English |
Publishing date | 2016-08 |
Publishing country | United States |
Document type | Journal Article |
ZDB-ID | 622531-7 |
ISSN | 1558-254X ; 0278-0062 |
ISSN (online) | 1558-254X |
ISSN | 0278-0062 |
DOI | 10.1109/TMI.2016.2537052 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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