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; Jaswinder S.  Gill; Michael Cooklin; Mark O'Neill; Kawal S Rhode

doi:10.1007/978-3-642-54268-8_15

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, Jaswinder S. Gill, Michael Cooklin, Mark O'Neill, Kawal S Rhode

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

7 Citations (Scopus)

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.

Original language	English
Title of host publication	International Workshop on Statistical Atlases and Computational Models of the Heart
Editors	Oscar Camara, Tommaso Mansi, Mihaela Pop, Kawal Rhode, Maxime Sermesant, Alistair Young
Publisher	Springer Verlag
Pages	126-134
Number of pages	9
ISBN (Electronic)	978-3-642-54268-8
ISBN (Print)	978-3-642-54267-1
DOIs	https://doi.org/10.1007/978-3-642-54268-8_15
Publication status	Published - 2013
Event	International Workshop on Statistical Atlases and Computational Models of the Heart - Nagoya, Japan Duration: 26 Sept 2013 → 26 Sept 2013

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	8330

Conference

Conference	International Workshop on Statistical Atlases and Computational Models of the Heart
Abbreviated title	STACOM 2013
Country/Territory	Japan
City	Nagoya
Period	26/09/13 → 26/09/13

Keywords

Respiratory Motion
Respiratory Gating
Manifold Learning
Respiratory Phase
Cardiac Gating

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10.1007/978-3-642-54268-8_15

Cite this

Panayiotou, M., King, A. P., Bhatia, K. K., Housden, R. J., Ma, Y., Rinaldi, C. A., Gill, J. S., Cooklin, M., O'Neill, M., & Rhode, K. S. (2013). Extraction of cardiac and respiratory motion information from cardiac x-ray fluoroscopy images using hierarchical manifold learning. In O. Camara, T. Mansi, M. Pop, K. Rhode, M. Sermesant, & A. Young (Eds.), International Workshop on Statistical Atlases and Computational Models of the Heart (pp. 126-134). ( Lecture Notes in Computer Science; Vol. 8330). Springer Verlag. https://doi.org/10.1007/978-3-642-54268-8_15

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. et al.
International Workshop on Statistical Atlases and Computational Models of the Heart. ed. / Oscar Camara; Tommaso Mansi; Mihaela Pop; Kawal Rhode; Maxime Sermesant; Alistair Young. Springer Verlag, 2013. p. 126-134 ( Lecture Notes in Computer Science; Vol. 8330).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Panayiotou, M, King, AP, Bhatia, KK, Housden, RJ, Ma, Y, Rinaldi, CA, Gill, JS, Cooklin, M, O'Neill, M & Rhode, KS 2013, Extraction of cardiac and respiratory motion information from cardiac x-ray fluoroscopy images using hierarchical manifold learning. in O Camara, T Mansi, M Pop, K Rhode, M Sermesant & A Young (eds), International Workshop on Statistical Atlases and Computational Models of the Heart. Lecture Notes in Computer Science, vol. 8330, Springer Verlag, pp. 126-134, International Workshop on Statistical Atlases and Computational Models of the Heart, Nagoya, Japan, 26/09/13. https://doi.org/10.1007/978-3-642-54268-8_15

Panayiotou M, King AP, Bhatia KK, Housden RJ, Ma Y, Rinaldi CA et al. Extraction of cardiac and respiratory motion information from cardiac x-ray fluoroscopy images using hierarchical manifold learning. In Camara O, Mansi T, Pop M, Rhode K, Sermesant M, Young A, editors, International Workshop on Statistical Atlases and Computational Models of the Heart. Springer Verlag. 2013. p. 126-134. ( Lecture Notes in Computer Science). doi: 10.1007/978-3-642-54268-8_15

Panayiotou, Maria ; King, Andrew P ; Bhatia, Kanwal K. et al. / Extraction of cardiac and respiratory motion information from cardiac x-ray fluoroscopy images using hierarchical manifold learning. International Workshop on Statistical Atlases and Computational Models of the Heart. editor / Oscar Camara ; Tommaso Mansi ; Mihaela Pop ; Kawal Rhode ; Maxime Sermesant ; Alistair Young. Springer Verlag, 2013. pp. 126-134 ( Lecture Notes in Computer Science).

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AU - Ma, YingLiang

AU - Rinaldi, C Aldo

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AB - 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.

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KW - Respiratory Phase

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

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