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Ma, YingLiang 
ORCID: https://orcid.org/0000-0001-5770-5843, King, Andy P., Gogin, Nicolas, Gijsbers, Geert, Rinaldi, C. Aldo, Gill, Jaswinder, Razavi, Reza and Rhode, Kawal S.
(2011)
Comparing image-based respiratory motion correction methods for anatomical roadmap guided cardiac electrophysiology procedures.
In:
Functional Imaging and Modeling of the Heart.
Lecture Notes in Computer Science
.
Springer, 55–62.
ISBN 978-3-642-21027-3
Abstract
X-ray fluoroscopically guided cardiac electrophysiological procedures are routinely carried out for diagnosis and treatment of cardiac arrhythmias. X-ray images have poor soft tissue contrast and, for this reason, overlay of static 3D roadmaps derived from pre-procedural volumetric data can be used to add anatomical information. However, the registration between the 3D roadmap and the 2D X-ray data can be compromised by patient respiratory motion. Three methods were evaluated to correct for respiratory motion using features in the X-ray image data. The first method is based on tracking either the diaphragm or the heart border using the image intensity in a region of interest. The second method detects the tracheal bifurcation using the generalized Hough transform and a 3D model derived from pre-operative image data. The third method is based on tracking the coronary sinus (CS) catheter. All three methods were applied to X-ray images from 18 patients undergoing radiofrequency ablation for the treatment of atrial fibrillation. The 2D target registration errors (TRE) at the pulmonary veins were calculated to validate the methods. A TRE of 1.6 mm ± 0.8 mm was achieved for the diaphragm tracking; 1.7 mm ± 0.9 mm for heart border tracking; 1.9 mm ± 1.0 mm for trachea tracking and 1.8 mm ± 0.9 mm for CS catheter tracking. We also present a comparison between our techniques with other published image-based motion correction strategies.
| 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/90387 |
| DOI: | 10.1007/978-3-642-21028-0_7 |
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