Comparing image-based respiratory motion correction methods for anatomical roadmap guided cardiac electrophysiology procedures

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Ma, YingLiang, 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

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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:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	04 Jan 2023 17:30
Last Modified:	06 Feb 2025 13:46
URI:	https://ueaeprints.uea.ac.uk/id/eprint/90387
DOI:	10.1007/978-3-642-21028-0_7

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