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Abstract #3617

Unsupervised Deep Learning Reconstruction Using the MR Imaging Model

Peizhou Huang1, Chaoyi Zhang2, Hongyu Li2, Sunil Kumar Gaire2, Ruiying Liu2, Xiaoliang Zhang1, Xiaojuan Li3, Liang Dong4, and Leslie Ying1,2
1Biomedical Engineering, State University of New York at Buffalo, Buffalo, NY, United States, 2Electrical Engineering, State University of New York at Buffalo, Buffalo, NY, United States, 3Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, United States, 4Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, CAS, Shenzhen, China

Deep learning has been applied to MRI image reconstruction successfully. Most existing works require labeled ground-truth images to learn network parameters for image reconstruction, which is not practical in some MR applications where acquisition of fully sampled images takes too long. In this abstract, we propose a novel unsupervised deep neural network for reconstruction from undersampled data. The proposed network, named URED-net, is built upon conventional ADMM algorithm for compressed sensing reconstruction, but incorporating noise2noise, an unsupervised deep denoising network. The experimental results demonstrate proposed URED-net is superior to the standard noise2noise network with and without ground-truth images for training.

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