This abstract presents a novel method for registration of cardiac perfusion MRI sequences. By performing complex analyses of variance and clustering in an annotated training set off-line, our method provides real-time segmentation in an on-line setting. This renders the method feasible for live motion-compensation in MR scanners. Changes in image intensity during the bolus passage are modelled by an Active Appearance Model augmented with a cluster analysis of the training set. Preliminary validation carried out using five subjects showed acceptable segmentation accuracy produced very rapidly (below 40 ms per image)