Echocardiography to magnetic resonance image registration for use in image-guided cardiac catheterization procedures

Ma, Ying Liang ORCID: https://orcid.org/0000-0001-5770-5843, Penney, Graeme P., Rinaldi, C. Aldo, Cooklin, Mike, Razavi, Reza and Rhode, Kawal S. (2009) Echocardiography to magnetic resonance image registration for use in image-guided cardiac catheterization procedures. Physics in Medicine and Biology, 54 (16). ISSN 0031-9155

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Abstract

We present a robust method to register three-dimensional echocardiography (echo) images to magnetic resonance images (MRI) based on anatomical features, which is designed to be used in the registration pipeline for overlaying MRI-derived roadmaps onto two-dimensional live x-ray images during cardiac catheterization procedures. The features used in image registration are the endocardial surface of the left ventricle and the centre line of the descending aorta. The MR-derived left ventricle surface is generated using a fully automated algorithm, and the echo-derived left ventricle surface is produced using a semi-automatic segmentation method provided by the QLab software (Philips Healthcare) that it is routinely used in clinical practice. We test our method on data from six volunteers and four patients. We validated registration accuracy using two methods: the first calculated a root mean square distance error using expert identified anatomical landmarks, and the second method used catheters as landmarks in two clinical electrophysiology procedures. Results show a mean error of 4.1 mm, which is acceptable for our clinical application, and no failed registrations were observed. In addition, our algorithm works on clinical data, is fast and only requires a small amount of manual input, and so it is applicable for use during cardiac catheterization procedures.

Item Type:	Article
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 01:41
URI:	https://ueaeprints.uea.ac.uk/id/eprint/90406
DOI:	10.1088/0031-9155/54/16/013

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