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Abstract #0272

Learning data consistency for MR dynamic imaging

Jing Cheng1, Wenqi Huang1, Zhuoxu Cui1, Ziwen Ke1, Leslie Ying2, Haifeng Wang1, Yanjie Zhu1, 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

Existing deep learning-based methods for MR reconstruction employ deep networks to exploit the prior information and integrate the prior knowledge into the reconstruction under the explicit constraint of data consistency, without considering the real distribution of the noise. In this work, we propose a new DL-based approach termed Learned DC that implicitly learns the data consistency with deep networks, corresponding to the actual probability distribution of system noise. We evaluated the proposed approach with highly undersampled dynamic cardiac cine data. Experimental results demonstrate the superior performance of the Learned DC.

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