Prospective Identification of CRT Super Responders Using a Motion Atlas and Random Projection Ensemble Learning

• Published in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 pp. 493 - 500
• Main Authors: Peressutti, Devis, Bai, Wenjia, Jackson, Thomas, Sohal, Manav, Rinaldi, Aldo, Rueckert, Daniel, King, Andrew
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• ISBN: 9783319245737; 3319245732
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-24574-4_59

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%.