Extraction of cardiac and respiratory motion information from cardiac X-ray fluoroscopy images using hierarchical manifold learning

Panayiotou, Maria, King, Andrew P., Bhatia, Kanwal K., Housden, R. James, Ma, YingLiang ORCID: https://orcid.org/0000-0001-5770-5843, Rinaldi, C. Aldo, Gill, Jas, Cooklin, Michael, O'Neill, Mark and Rhode, Kawal S. (2014) Extraction of cardiac and respiratory motion information from cardiac X-ray fluoroscopy images using hierarchical manifold learning. In: Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. Lecture Notes in Computer Science . Springer, 126–134. ISBN 978-3-642-54267-1

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Abstract

We present a novel and clinically useful method to automatically determine the regions that carry cardiac and respiratory motion information directly from standard mono-plane X-ray fluoroscopy images. We demonstrate the application of our method for the purposes of retrospective cardiac and respiratory gating of X-ray images. Validation is performed on five mono-plane imaging sequences comprising a total of 284 frames from five patients undergoing radiofrequency ablation for the treatment of atrial fibrillation. We established end-inspiration, end-expiration and systolic gating with success rates of 100%, 100% and 95.3%, respectively. This technique is useful for retrospective gating of X-ray images and, unlike many previously proposed techniques, does not require specific catheters to be visible and works without any knowledge of catheter geometry.

Item Type: Book Section
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 04 Jan 2023 17:30
Last Modified: 04 Jan 2023 17:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/90385
DOI: 10.1007/978-3-642-54268-8_15

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