Clustering of Aortic Vortex Flow in Cardiac 4D PC-MRI Data

Abstract

This paper presents a method for clustering aortic vortical blood flow using a reliable dissimilarity measure combined with a clustering technique. Current medical studies investigate specific properties of aberrant blood flow patterns such as vortices, since a correlation to the genesis and evolution of various cardiovascular diseases is assumed. The classification requires a precise definition of spatio-temporal vortex entities, which is performed manually. This task is time-consuming for larger studies and error-prone due to inter-observer variability. In contrast, our method allows an automatic and reliable vortex clustering that facilitates the vortex classification. We introduce an efficient calculation of a dissimilarity measure that groups spatio-temporally adjacent vortices. We combine our dissimilarity measure with the most commonly used clustering techniques. Each combination was applied to 15 4D PCMRI datasets. The clustering results were qualitatively compared to a manually generated ground truth of two domain experts.