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; Ying Liang Ma; C. Aldo Rinaldi; Jas 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, Ying Liang Ma, C. Aldo Rinaldi, Jas Gill, Michael Cooklin, Mark O'Neill, Kawal S. Rhode

Fédération d'anesthésiologie-réanimation et de médecine hyperbare, hôpital d'instruction des armées du Val-de-Grâce, 75005 Paris, France; Inserm U676, hôpital Robert-Debré, 48, boulevard Sérurier, 75019 Paris, France; Université Paris Diderot, Sorbonne Paris Cité, UMRS 676, 75013 Paris, France; Centre for the Developing Brain, Department of Perinatal Imaging and Health, King's College London, London, Royaume-Uni. Electronic address: [email protected].
Imperial College London
Department of Cardiology
GSTT Guy's and St Thomas' NHS Foundation Trust

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › 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	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Oscar Camara, Tommaso Mansi, Mihaela Pop, Kawal Rhode, Maxime Sermesant, Alistair Young
Publisher	Springer-Verlag Berlin Heidelberg
Pages	126-134
Number of pages	9
Volume	8330
ISBN (Electronic)	978-3-642-54268-8
ISBN (Print)	9783642542671
DOIs	https://doi.org/10.1007/978-3-642-54268-8_15
Publication status	Published - 2014
Event	4th International Workshop on Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, STACOM 2013, Held in Conjunction with MICCAI 2013 - Nagoya, Japan Duration: 26 Sept 2013 → 26 Sept 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8330 LNCS
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	4th International Workshop on Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, STACOM 2013, Held in Conjunction with MICCAI 2013
Country/Territory	Japan
City	Nagoya
Period	26/09/2013 → 26/09/2013

Access to Document

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

Cite this

Panayiotou, M., King, A. P., Bhatia, K. K., Housden, R. J., Ma, Y. L., Rinaldi, C. A., Gill, J., Cooklin, M., O'Neill, M., & Rhode, K. S. (2014). 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.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8330, pp. 126-134). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8330 LNCS). Springer-Verlag Berlin Heidelberg. https://doi.org/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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Oscar Camara ; Tommaso Mansi ; Mihaela Pop ; Kawal Rhode ; Maxime Sermesant ; Alistair Young. Vol. 8330 Springer-Verlag Berlin Heidelberg, 2014. pp. 126-134 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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. ",

author = "Maria Panayiotou and King, {Andrew P.} and Bhatia, {Kanwal K.} and Housden, {R. James} and Ma, {Ying Liang} and Rinaldi, {C. Aldo} and Jas Gill and Michael Cooklin and Mark O'Neill and Rhode, {Kawal S.}",

note = "{\textcopyright} Springer-Verlag Berlin Heidelberg 2014.; 4th International Workshop on Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, STACOM 2013, Held in Conjunction with MICCAI 2013 ; Conference date: 26-09-2013 Through 26-09-2013",

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editor = "Camara, {Oscar } and Tommaso Mansi and Mihaela Pop and Kawal Rhode and Maxime Sermesant and Alistair Young",

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Panayiotou, M , King, AP, Bhatia, KK, Housden, RJ , Ma, YL , Rinaldi, CA , Gill, J , Cooklin, M , O'Neill, M & Rhode, KS 2014, 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), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 8330, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8330 LNCS, Springer-Verlag Berlin Heidelberg, pp. 126-134, 4th International Workshop on Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, STACOM 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, 26/09/2013. 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.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Oscar Camara; Tommaso Mansi; Mihaela Pop; Kawal Rhode; Maxime Sermesant; Alistair Young. Vol. 8330 Springer-Verlag Berlin Heidelberg, 2014. p. 126-134 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8330 LNCS).

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

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T1 - Extraction of cardiac and respiratory motion information from cardiac X-ray fluoroscopy images using hierarchical manifold learning

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AU - King, Andrew P.

AU - Bhatia, Kanwal K.

AU - Housden, R. James

AU - Ma, Ying Liang

AU - Rinaldi, C. Aldo

AU - Gill, Jas

AU - Cooklin, Michael

AU - O'Neill, Mark

AU - Rhode, Kawal S.

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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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Panayiotou M , King AP, Bhatia KK, Housden RJ , Ma YL , 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, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8330. Springer-Verlag Berlin Heidelberg. 2014. p. 126-134. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). 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

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