Under-sampling the k-space data and reconstructing images with sparsity constraint is one efficient way to accelerate magnetic resonance imaging However, achieving high acceleration factor is challenging since image structures may be lost or blurred when the sampled information is not sufficient. In this work, we propose a new approach to reconstruct magnetic resonance images by learning the prior knowledge from multi-contrast images with graph-based sparsifying transform. To incorporate extra information from multi-contrast image, registration is included in a bi-level optimization frame as well as the sparse reconstruction. Experiment results demonstrate that the proposed method outperforms the state-of-art with high accelerating factor.
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