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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