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Abstract #1944

Unsupervised Dynamic Image Reconstruction using Deep Generative Adversarial Networks and Total Variation Smoothing

Elizabeth Cole1, Shreyas Vasanawala1, and John Pauly1
1Stanford University, Palo Alto, CA, United States

Deep learning (DL)-based image reconstruction methods have achieved promising results across multiple MRI applications. However, most approaches require large-scale fully-sampled ground truth data for supervised training. Acquiring fully-sampled data is often either difficult or impossible, particularly for dynamic datasets. We present a DL framework for MRI reconstruction which does not use fully-sampled data. We test the proposed method in two scenarios: retrospectively undersampled cine and prospectively undersampled abdominal DCE. Our unsupervised method can produce faster reconstructions which are non-inferior to compressed sensing. Our novel proposed method can enable accelerated imaging and accurate reconstruction in applications where fully-sampled data is unavailable.

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