Unsupervised motion-compensation of multi-slice cardiac perfusion MRI.

This paper presents a novel method for registration of single and multi-slice cardiac perfusion MRI. Utilising off-line computer intensive analyses of variance and clustering in an annotated training set, the presented method is capable of providing registration without any manual interaction in less than a second per frame. Changes in image intensity during the bolus passage are modelled by a slice-coupled active appearance model, which is augmented with a cluster analysis of the training set. Landmark correspondences are optimised using the MDL framework due to Davies et al. Image search is verified and stabilised using perfusion specific prior models of pose and shape estimated from training data. Qualitative and quantitative validation of the method is carried out using 2000 clinical quality, short-axis, perfusion MR slice images, acquired from 10 freely breathing patients with acute myocardial infarction. Despite evident perfusion deficits and varying image quality in the limited training set, a leave-one-out cross-validation of the method showed a mean point to curve distance of 1.25+/-0.36 pixels for the left and right ventricle combined. We conclude that this learning-based method holds great promise for the automation of cardiac perfusion investigations, due to its accuracy, robustness and generalisation ability.