Anatomy-Aware Cardiac Motion Estimation
This addresses the challenge of accurate cardiac function assessment for medical imaging, though it is incremental as it refines existing methods with anatomy-aware weak supervision.
The paper tackled the problem of unrealistic myocardium shapes in deep learning-based cardiac motion estimation by proposing an Anatomy-Aware Tracker (AATracker) that uses weak supervision from a VAE to preserve anatomy, resulting in significantly improved tracking performance and more realistic results on long-axis cardiac cine MRI.
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.