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Ma, YingLiang 
ORCID: https://orcid.org/0000-0001-5770-5843, Gogin, Nicolas, Cathier, Pascal, Housden, R. James, Gijsbers, Geert, Cooklin, Michael, O'Neill, Mark, Gill, Jaswinder, Rinaldi, C. Aldo, Razavi, Reza and Rhode, Kawal S.
(2013)
Real-time x-ray fluoroscopy-based catheter detection and tracking for cardiac electrophysiology interventions.
Medical Physics, 40 (7).
ISSN 0094-2405
Abstract
Purpose: X-ray fluoroscopically guided cardiac electrophysiology (EP) procedures are commonly carried out to treat patients with arrhythmias. X-ray images have poor soft tissue contrast and, for this reason, overlay of a three-dimensional (3D) roadmap derived from preprocedural volumetric images can be used to add anatomical information. It is useful to know the position of the catheter electrodes relative to the cardiac anatomy, for example, to record ablation therapy locations during atrial fibrillation therapy. Also, the electrode positions of the coronary sinus (CS) catheter or lasso catheter can be used for road map motion correction. Methods: In this paper, the authors present a novel unified computational framework for image-based catheter detection and tracking without any user interaction. The proposed framework includes fast blob detection, shape-constrained searching and model-based detection. In addition, catheter tracking methods were designed based on the customized catheter models input from the detection method. Three real-time detection and tracking methods are derived from the computational framework to detect or track the three most common types of catheters in EP procedures: the ablation catheter, the CS catheter, and the lasso catheter. Since the proposed methods use the same blob detection method to extract key information from x-ray images, the ablation, CS, and lasso catheters can be detected and tracked simultaneously in real-time. Results: The catheter detection methods were tested on 105 different clinical fluoroscopy sequences taken from 31 clinical procedures. Two-dimensional (2D) detection errors of 0.50 ± 0.29, 0.92 ± 0.61, and 0.63 ± 0.45 mm as well as success rates of 99.4%, 97.2%, and 88.9% were achieved for the CS catheter, ablation catheter, and lasso catheter, respectively. With the tracking method, accuracies were increased to 0.45 ± 0.28, 0.64 ± 0.37, and 0.53 ± 0.38 mm and success rates increased to 100%, 99.2%, and 96.5% for the CS, ablation, and lasso catheters, respectively. Subjective clinical evaluation by three experienced electrophysiologists showed that the detection and tracking results were clinically acceptable. Conclusions: The proposed detection and tracking methods are automatic and can detect and track CS, ablation, and lasso catheters simultaneously and in real-time. The accuracy of the proposed methods is sub-mm and the methods are robust toward low-dose x-ray fluoroscopic images, which are mainly used during EP procedures to maintain low radiation dose.
| Item Type: | Article |
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| Additional Information: | ACKNOWLEDGMENTS: This work was supported by funding from Philips Healthcare, Best, The Netherlands, and the KCL Centre of Excellence in Medical Engineering funded by the Wellcome Trust and the EPSRC under Grant No. WT 088641/Z/09/Z. Support was also provided by the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust and King's College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health. © 2013 The Authors. Published by American Association of Physicists in Medicine and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Science > Research Groups > Data Science and AI |
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| Depositing User: | LivePure Connector |
| Date Deposited: | 04 Jan 2023 09:33 |
| Last Modified: | 10 Dec 2024 01:40 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/90377 |
| DOI: | 10.1118/1.4808114 |
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