Predicting heart motion from a single snapshot

Doctors need to see how different regions of the heart move through a full cycle to diagnose disease, but getting that full motion data is expensive. This method reconstructs the complete motion sequence from just a single end-diastolic image by learning region-specific motion patterns and adapting to different disease types. On three datasets, it outperformed standard generative models by preserving localized heart dynamics rather than averaging them away.