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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