Construction of a 4D statistical atlas of the cardiac anatomy and its use in classification.

In this paper we present a novel method for building a 4D statistical atlas describing the cardiac anatomy and how the cardiac anatomy changes during the cardiac cycle. The method divides the distribution space of cardiac shapes into two subspaces. One distribution subspace accounts for changes in cardiac shape caused by inter-subject variability. The second distribution subspace accounts for changes in cardiac shape caused by deformation during the cardiac cycle (i.e. intra-subject variability). Principal component analysis (PCA) have been performed in order to calculate the most significant modes of variation of each distribution subspace. During the construction of the statistical atlas we eliminate the need for manual landmarking of the cardiac images by using a non-rigid surface registration algorithm to propagate a set of pseudo-landmarks from an automatically landmarked atlas to each frame of all the image sequences. In order to build the atlas we have used 26 cardiac image sequences from healthy volunteers. We show how the resulting statistical atlas can be used to differentiate between cardiac image sequences from patients with hypertrophic cardiomyopathy and normal subjects.