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

Maria Panayiotou, Andrew P. King, Kanwal K. Bhatia, R. James Housden, YingLiang Ma, C. Aldo Rinaldi, Jas Gill, Michael Cooklin, Mark O'Neill, Kawal S. Rhode

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationStatistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges
Subtitle of host publicationSTACOM 2013
EditorsOscar Camara, Tommaso Mansi, Mihaela Pop, Kawal Rhode, Maxime Sermesant, Alistair Young
Number of pages9
ISBN (Electronic)978-3-642-54268-8
ISBN (Print)978-3-642-54267-1
Publication statusPublished - 26 Sep 2014

Publication series

NameLecture Notes in Computer Science

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