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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