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The accurate quantification of left ventricular (LV) deformation/strain showssignificant promise for quantitatively assessing cardiac function for use indiagnosis and therapy planning (Jasaityte et al., 2013). However, accurateestimation of the displacement of myocardial tissue and hence LV strain hasbeen challenging due to a variety of issues, including those related toderiving tracking tokens from images and following tissue locations over theentire cardiac cycle. In this work, we propose a point matching scheme wherecorrespondences are modeled as flow through a graphical network. Myocardialsurface points are set up as nodes in the network and edges define neighborhoodrelationships temporally. The novelty lies in the constraints that are imposedon the matching scheme, which render the correspondences one-to-one through theentire cardiac cycle, and not just two consecutive frames. The constraints alsoencourage motion to be cyclic, which is an important characteristic of LVmotion. We validate our method by applying it to the estimation of quantitativeLV displacement and strain estimation using 8 synthetic and 8 open-chestedcanine 4D echocardiographic image sequences, the latter with sonomicrometriccrystals implanted on the LV wall. We were able to achieve excellent trackingaccuracy on the synthetic dataset and observed a good correlation withcrystal-based strains on the in-vivo data.

Flow Network Tracking for Spatiotemporal and Periodic Point Matching: Applied to Cardiac Motion Analysis