Motion tracking of left myocardium in cardiac cine magnetic resonance image based on displacement flow U-Net and variational autoencoder

The high morbidity and mortality of cardiovascular diseases (CVDs) seriously affects the quality of human life. How to asses cardiac function, assist in the diagnosis and treatment of clinical CVDs and evaluate prognosis is a problem to be solved urgently. In response to this issue, based on previous work of Cardiac Cine Magnetic Resonance (CCMR) image segmentation of the left myocardium (LVM), a robust and accurate LVM motion tracking method (DispFlow_UNet_Flow) with using the displacement flow UNet (DispFlow_UNet) and biomechanics-informed variational autoencoder (VAE) is proposed in this paper. The following are the main research contents: (1) building a compressed excitation residual U-net network to accurately segment LVM, calculating the ventricular volume and myocardial mass according to the segmentation results, and then evaluating the overall cardiac function; (2) reconstructing the dense displacement field (DDF) based on the proposed motion tracking method, and obtaining the LVM dense displacement field by combining the LVM segmentation mask; (3) contrasting and evaluating the motion tracking results by using the true displacement vector field of simulated data and clinical data sets. All the results show that the tracking algorithm proposed in this paper has high precision and generalization capability.