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Panayiotou, Maria, Rhode, Kawal S., King, Andrew P., Ma, YingLiang 
ORCID: https://orcid.org/0000-0001-5770-5843, Cooklin, Michael, O'Neill, Mark, Gill, Jaswinder, Rinaldi, C. A. and Housden, R. James
(2015)
Image-based view-angle independent cardiorespiratory motion gating and coronary sinus catheter tracking for x-ray-guided cardiac electrophysiology procedures.
Physics in Medicine and Biology, 60 (20).
ISSN 0031-9155
Abstract
Determination of the cardiorespiratory phase of the heart has numerous applications during cardiac imaging. In this article we propose a novel view-angle independent near-real time cardiorespiratory motion gating and coronary sinus (CS) catheter tracking technique for x-ray fluoroscopy images that are used to guide cardiac electrophysiology procedures. The method is based on learning CS catheter motion using principal component analysis and then applying the derived motion model to unseen images taken at arbitrary projections, using the epipolar constraint. This method is also able to track the CS catheter throughout the x-ray images in any arbitrary subsequent view. We also demonstrate the clinical application of our model on rotational angiography sequences. We validated our technique in normal and very low dose phantom and clinical datasets. For the normal dose clinical images we established average systole, end-expiration and end-inspiration gating success rates of 100%, 85.7%, and 92.3%, respectively. For very low dose applications, the technique was able to track the CS catheter with median errors not exceeding 1 mm for all tracked electrodes. Average gating success rates of 80.3%, 71.4%, and 69.2% were established for the application of the technique on clinical datasets, even with a dose reduction of more than 10 times. In rotational sequences at normal dose, CS tracking median errors were within 1.2 mm for all electrodes, and the gating success rate was 100%, for view angles from RAO 90° to LAO 90°. This view-angle independent technique can extract clinically useful cardiorespiratory motion information using x-ray doses significantly lower than those currently used in clinical practice.
| Item Type: | Article |
|---|---|
| Additional Information: | Acknowledgments: The authors acknowledge financial support from the Department of Health via the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London and King's College Hospital NHS Foundation Trust. This work is funded by EPSRC programme grant EP/H046410/1. The authors would like to thank Philips Healthcare, Best, The Netherlands, for providing technical help with the prototype interventional guidance platform that was based on the commercial EP Navigator software. |
| 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 |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 04 Jan 2023 17:33 |
| Last Modified: | 10 Dec 2024 01:40 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/90389 |
| DOI: | 10.1088/0031-9155/60/20/8087 |
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