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Abstract #4649

Semi-Supervised Learning for Reconstructing Under-Sampled MR Scans

Feiyu Chen1, Joseph Y Cheng2, John M Pauly1, and Shreyas S Vasanawala2

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

Supervised deep-learning approaches have been applied to MRI reconstruction, and these approaches were demonstrated to significantly improve the speed of reconstruction by parallelizing the computation and using a pre-trained neural network model. However, for many applications, ground-truth images are difficult or impossible to acquire. In this study, we propose a semi-supervised deep-learning method, which enables us to train a deep neural network for MR reconstruction without using fully-sampled images.

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