Tools
Tools
Ma, YingLiang 
ORCID: https://orcid.org/0000-0001-5770-5843, Gao, Gang, Gijsbers, Geert, Rinaldi, C. Aldo, Gill, Jaswinder, Razavi, Reza and Rhode, Kawal S.
(2011)
Image-based automatic ablation point tagging system with motion correction for cardiac ablation procedures.
In:
Information Processing in Computer-Assisted Interventions.
Lecture Notes in Computer Science
.
Springer, 145–155.
ISBN 978-3-642-21503-2
Abstract
X-ray fluoroscopically guided cardiac ablation procedures are commonly carried out for the treatment of cardiac arrhythmias, such as atrial fibrillation (AF). X-ray images have poor soft tissue contrast and, for this reason, overlay of a 3D roadmap derived from pre-procedural volumetric image data can be used to add anatomical information. It is a requirement to determine and record the 3D positions of the ablation catheter tip in the 3D road map during AF ablation. This feature can be used as a guidance and post-procedure analysis tool. The 3D positions of the catheter tip can be calculated from biplane X-ray images and mapped to the 3D roadmap. However, the registration between the 3D roadmap and the 2D X-ray data can be compromised by patient respiratory and cardiac motions. As the coronary sinus (CS) catheter is not routinely altered during the procedure, tracking the CS catheter in real-time can be used as means of motion correction to improve the accuracy of registration between live X-ray images and a 3D roadmap. To achieve a fast and automatic ablation point tagging system from biplane images, we developed a novel tracking method for real-time simultaneous detection of the ablation catheter and the CS catheter from fluoroscopic X-ray images. We tested our tracking method on 1083 fluoroscopy frames from 16 patients and achieved a success rate of 97.5% and an average 2D tracking error of 0.5 mm ± 0.3 mm. In order to achieve tagging using a mono-plane X-ray image system, we proposed a novel motion gating method to select a pair of images from two short image sequences acquired from two different views. Both respiratory and cardiac motion phases are matched by selecting the pair of images with the minimum reconstruction error of the CS catheter electrodes. Finally, the 3D position of the ablation catheter tip was calculated using the epipolar constraint from the multiview images. We validated our automatic ablation point tagging strategy by computing the reconstruction error of the ablation catheter tip and achieved an error of 1.1 mm ± 0.5 mm.
| Item Type: | Book Section |
|---|---|
| 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 17:30 |
| Last Modified: | 10 Dec 2024 01:13 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/90388 |
| DOI: | 10.1007/978-3-642-21504-9_14 |
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