This work demonstrates the use of recurrent generative spatiotemporal autoencoders to predict up to fifteen future frames of abdominal DCE-MRI video data, starting with only three ground truth input frames for context. The objective is to predict what healthy patient video data and organ-specific contrast curves look like, to expedite anomaly detection and enable pulse sequence optimization. The model in this study shows promise; it was able to learn contrast changes without losing structural resolution during training time, and lays the foundation for future work.
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