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Abstract #2421

Parallel magnetic resonance imaging via dictionary learning

Shanshan Wang 1,2 , Xi Peng 1 , Jianbo Liu 1 , Yuanyuan Liu 1 , Pei Dong 2 , and Dong Liang 1

1 Paul C. Lauterbur Research Centre for Biomedical Imaging, Chinese Academy of Sciences, Shenzhen, GuangDong, China, 2 School of Information Technologies, University of Sydney, Sydney, New South Wales, Australia

This work proposes a dictionary learning (DL) based sensitivity encoding (SENSE) approach to accurately reconstruct parallel MR images. Specifically, we regularizes the targeted image with sparse representation over an adaptive learned dictionary and formulates the reconstruction as an L2-DL minimization problem. A "divide and conquer" strategy is used to solve the proposed formulation by addressing two subproblems i.e. dictionary learning and image updating. Meanwhile, k-space data is updated as well to add more fine details back. Experimental results show that the proposed method improves the reconstruction accuracy in terms of detail preserving and outperforms the state-of-the-art SparseSENSE based approach.

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