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Abstract #4874

Deep Predictive Modeling of Dynamic Contrast-Enhanced MRI Data

Jiacheng Jason He1, Christopher Sandino1, Shreyas Vasanawala1,2, and Joseph Cheng2

1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States

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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