  Reliable motion estimation and strain analysis using 3D+time echocardiography
(4DE) for localization and characterization of myocardial injury is valuable
for early detection and targeted interventions. However, motion estimation is
difficult due to the low-SNR that stems from the inherent image properties of
4DE, and intelligent regularization is critical for producing reliable motion
estimates. In this work, we incorporated the notion of domain adaptation into a
supervised neural network regularization framework. We first propose an
unsupervised autoencoder network with biomechanical constraints for learning a
latent representation that is shown to have more physiologically plausible
displacements. We extended this framework to include a supervised loss term on
synthetic data and showed the effects of biomechanical constraints on the
network's ability for domain adaptation. We validated both the autoencoder and
semi-supervised regularization method on in vivo data with implanted
sonomicrometers. Finally, we showed the ability of our semi-supervised learning
regularization approach to identify infarcted regions using estimated regional
strain maps with good agreement to manually traced infarct regions from
postmortem excised hearts.
