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

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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	Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges
Subtitle of host publication	STACOM 2013
Editors	Oscar Camara, Tommaso Mansi, Mihaela Pop, Kawal Rhode, Maxime Sermesant, Alistair Young
Publisher	Springer
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 - 26 Sep 2014

Publication series

Name	Lecture Notes in Computer Science
Volume	8330

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

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Panayiotou, M., King, A. P., Bhatia, K. K., Housden, R. J., Ma, Y., 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.), Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges: STACOM 2013 (pp. 126–134). (Lecture Notes in Computer Science; Vol. 8330). Springer. 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. Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges: STACOM 2013. editor / Oscar Camara ; Tommaso Mansi ; Mihaela Pop ; Kawal Rhode ; Maxime Sermesant ; Alistair Young. Springer, 2014. pp. 126–134 (Lecture Notes in Computer Science).

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

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 YingLiang Ma and Rinaldi, {C. Aldo} and Jas Gill and Michael Cooklin and Mark O'Neill and Rhode, {Kawal S.}",

year = "2014",

month = sep,

day = "26",

doi = "10.1007/978-3-642-54268-8_15",

language = "English",

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series = "Lecture Notes in Computer Science",

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pages = "126–134",

editor = "Oscar Camara 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, Y, 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), Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges: STACOM 2013. Lecture Notes in Computer Science, vol. 8330, Springer, pp. 126–134. 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.

Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges: STACOM 2013. ed. / Oscar Camara; Tommaso Mansi; Mihaela Pop; Kawal Rhode; Maxime Sermesant; Alistair Young. Springer, 2014. p. 126–134 (Lecture Notes in Computer Science; Vol. 8330).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Panayiotou, Maria

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AU - Ma, YingLiang

AU - Rinaldi, C. Aldo

AU - Gill, Jas

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AU - O'Neill, Mark

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N2 - 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.

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 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, Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges: STACOM 2013. Springer. 2014. p. 126–134. (Lecture Notes in Computer Science). 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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