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Compressed Sensing MRI Using Sparsity Averaging and FISTA

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Abstract

Magnetic resonance imaging (MRI) is widely adopted for clinical diagnosis due to its non-invasively detection. However, acquisition of full k-space data limits its imaging speed. Compressed sensing (CS) provides a new technique to significantly reduce the measurements with high-quality MR image reconstruction. The sparsity of the MR images is one of the crucial bases of CS-MRI. In this paper, we present to use sparsity averaging prior for CS-MRI reconstruction in the basis of that MR images have average sparsity over multiple wavelet frames. The problem is solved using a Fast Iterative Shrinkage Thresholding Algorithm (FISTA), each iteration of which includes a shrinkage step. The performance of the proposed method is evaluated for several types of MR images. The experiment results illustrate that our approach exhibits a better performance than those methods that using redundant frame or a single orthonormal basis to promote sparsity.

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Acknowledgements

This study was supported by the Fundamental Research Funds for the Central Universities, China (No. 41112414), and the National Natural Science Foundation of China (Nos. 31470714, 31370710, 61661010).

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, 150040, Heilongjiang Province, China

    Jian-ping Huang, Liang-kuan Zhu & Wen-long Song

  2. College of Computer Science and Technology, Guizhou University, Huaxi District, Guiyang, 550025, China

    Li-hui Wang

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  1. Jian-ping Huang
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  2. Liang-kuan Zhu
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Correspondence to Jian-ping Huang.

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Huang, Jp., Zhu, Lk., Wang, Lh. et al. Compressed Sensing MRI Using Sparsity Averaging and FISTA. Appl Magn Reson 48, 749–760 (2017). https://doi.org/10.1007/s00723-017-0910-0

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