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Abstract #0993

RED-N2N: Image reconstruction for MRI using deep CNN priors trained without ground truth

Jiaming Liu1, Cihat Eldeniz1, Yu Sun1, Weijie Gan1, Sihao Chen1, Hongyu An1, and Ulugbek S. Kamilov1
1Washington University in St. Louis, St. Louis, MO, United States

We propose a new MR image reconstruction method that systematically enforces data consistency while also exploiting deep-learning imaging priors. The prior is specified through a convolutional neural network (CNN) trained to remove undersampling artifacts from MR images without any artifact-free ground truth. The results on reconstructing free-breathing MRI data into ten respiratory phases show that the method can form high-quality 4D images from severely undersampled measurements corresponding to acquisitions of about 1 minute in length. The results also highlight the improved performance of the method compared to several popular alternatives, including compressive sensing and UNet3D.

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