Real-time respiratory motion correction for cardiac electrophysiology procedures using image-based coronary sinus catheter tracking

Ma, YingLiang ORCID: https://orcid.org/0000-0001-5770-5843, King, Andy P., Gogin, Nicolas, Rinaldi, C. Aldo, Gill, Jaswinder, Razavi, Reza and Rhode, Kawal S. (2010) Real-time respiratory motion correction for cardiac electrophysiology procedures using image-based coronary sinus catheter tracking. In: Medical Image Computing and Computer-Assisted Intervention. Lecture Notes in Computer Science . Springer, 391–399. ISBN 978-3-642-15704-2

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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. We propose a novel method to correct for respiratory motion using real-time image-based coronary sinus (CS) catheter tracking. The first step of the proposed technique is to use a blob detection method to detect all possible catheter electrodes in the X-ray data. We then compute a cost function to select one CS catheter from all catheter-like objects. For correcting respiratory motion, we apply a low pass filter to the 2D motion of the CS catheter and update the 3D roadmap using this filtered motion. We tested our CS catheter tracking method on 1048 fluoroscopy frames from 15 patients and achieved a success rate of 99.3% and an average 2D tracking error of 0.4 mm ± 0.2 mm. We also validated our respiratory motion correction strategy by computing the 2D target registration error (TRE) at the pulmonary veins and achieved a TRE of 1.6 mm ± 0.9 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:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	05 Jan 2023 10:30
Last Modified:	10 Dec 2024 01:13
URI:	https://ueaeprints.uea.ac.uk/id/eprint/90401
DOI:	10.1007/978-3-642-15705-9_48

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