Prospective identification of CRT super responders using a motion atlas and random projection ensemble learning - Peressutti, Devis; Bai, Wenjia; Jackson, Thomas; Sohal, Manav; Rinaldi, Aldo; Rueckert, Daniel; King, Andrew (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015)
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%.
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