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Abstract #3634

Learning reconstruction without ground-truth data: an unsupervised way for fast MR imaging

Jing Cheng1, Ziwen Ke1, Haifeng Wang1, Yanjie Zhu1, Leslie Ying2, Xin Liu1, Hairong Zheng1, and Dong Liang1
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2University at Buffalo, The State University of New York, Buffalo, Buffalo, NY, United States

Most deep learning methods for MR reconstruction heavily rely on the large number of training data pairs to achieve best performance. In this work, we introduce a simple but effective strategy to handle the situation where collecting lots of fully sampled rawdata is impractical. By defining a CS-based loss function, the deep networks can be trained without ground-truth images or full sampled data. In such an unsupervised way, the MR image can be reconstructed through the forward process of deep networks. This approach was evaluated on in vivo MR datasets and achieved superior performance than the conventional CS method.

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