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Abstract #1950

Weakly Supervised MR Image Reconstruction using Untrained Neural Networks

Beliz Gunel1, Morteza Mardani1, Akshay Chaudhari2, Shreyas Vasanawala2, and John Pauly1
1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States

Untrained neural networks such as ConvDecoder have emerged as a compelling MR image reconstruction method. Although ConvDecoder does not require any training data, it requires tens of minutes to reconstruct a single MR slice at inference time, making the method impractical for clinical deployment. In this work, we propose using ConvDecoder to construct "weak labels" from undersampled MR scans at training time. Using limited supervised pairs and constructed weakly supervised pairs, we train an unrolled neural network that gives strong reconstruction performance with fast inference time, significantly improving over supervised and self-training baselines in the low data regime.

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