Dynamic spatio-temporal graph convolutional networks for cardiac motion analysis

We propose a dynamic spatio-temporal graph convolutional network (DST-GCN) approach to learn the left ventricular (LV) motion patterns from cardiac MR cine images. We represent the myocardial geometry using a graph that is constructed from sample nodes on endo-and epicardial contours. The DST-GCN follows an encoder-decoder framework. The encoder accepts a given cardiac motion represented by a sequence of ST-GCN. The decoder employs a graph-based gated recurrent unit (G-GRU) to predict future cardiac motion. We show that the DST-GCN can automatically quantify the spatio-temporal patterns in cardiac MR that characterise cardiac motion. Experiments are performed on the UK Biobank dataset. We compare four methods from two architecture variances. Experiments show that the proposed method inputting node velocities with residual connection in the decoder outperform others, and achieves a mean squared error of 0.135 pixel between the ground truth node locations and our prediction.