  Cardiac motion estimation is critical to the assessment of cardiac function.
Myocardium feature tracking (FT) can directly estimate cardiac motion from cine
MRI, which requires no special scanning procedure. However, current deep
learning-based FT methods may result in unrealistic myocardium shapes since the
learning is solely guided by image intensities without considering anatomy. On
the other hand, motion estimation through learning is challenging because
ground-truth motion fields are almost impossible to obtain. In this study, we
propose a novel Anatomy-Aware Tracker (AATracker) for cardiac motion estimation
that preserves anatomy by weak supervision. A convolutional variational
autoencoder (VAE) is trained to encapsulate realistic myocardium shapes. A
baseline dense motion tracker is trained to approximate the motion fields and
then refined to estimate anatomy-aware motion fields under the weak supervision
from the VAE. We evaluate the proposed method on long-axis cardiac cine MRI,
which has more complex myocardium appearances and motions than short-axis.
Compared with other methods, AATracker significantly improves the tracking
performance and provides visually more realistic tracking results,
demonstrating the effectiveness of the proposed weakly-supervision scheme in
cardiac motion estimation.
