Prospective identification of CRT super responders using a motion atlas and random projection ensemble learning

Peressutti D, Bai W, Jackson T, Sohal M, Rinaldi A, Rueckert D, King A (2015)

Publication Type: Conference contribution

Publication year: 2015

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Book Volume: 9351

Pages Range: 493-500

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Munich, DEU

ISBN: 9783319245737

DOI: 10.1007/978-3-319-24574-4_59

Abstract

Cardiac Resynchronisation Therapy (CRT) treats patients with heart failure and electrical dyssynchrony. However, many patients do not respond to therapy. We propose a novel framework for the prospective characterisation of CRT ‘super-responders’ based on motion analysis of the Left Ventricle (LV). A spatio-temporal motion atlas for the comparison of the LV motions of different subjects is built using cardiac MR imaging. Patients likely to present a super-response to the therapy are identified using a novel ensemble learning classification method based on random projections of the motion data. Preliminary results on a cohort of 23 patients show a sensitivity and specificity of 70% and 85%.

Involved external institutions

King’s College London GB United Kingdom (GB) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB)

How to cite

APA:

Peressutti, D., Bai, W., Jackson, T., Sohal, M., Rinaldi, A., Rueckert, D., & King, A. (2015). Prospective identification of CRT super responders using a motion atlas and random projection ensemble learning. In Alejandro F. Frangi, Nassir Navab, Joachim Hornegger, William M. Wells (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 493-500). Munich, DEU: Springer Verlag.

MLA:

Peressutti, Devis, et al. "Prospective identification of CRT super responders using a motion atlas and random projection ensemble learning." Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015, Munich, DEU Ed. Alejandro F. Frangi, Nassir Navab, Joachim Hornegger, William M. Wells, Springer Verlag, 2015. 493-500.

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