Unsupervised Deep Learning Reconstruction Using the MR Imaging Model

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.