The Study of Compressed Sensing MR Image Reconstruction Algorithm Based on the Extension of Total Variation Method

Jiang Ming-feng; Liu Yuan; Xu Wen-long; Feng Jie; Wang Ya-ming

doi:10.11999/JEIT150179

Volume 37 Issue 11

Nov. 2015

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Jiang Ming-feng, Liu Yuan, Xu Wen-long, Feng Jie, Wang Ya-ming. The Study of Compressed Sensing MR Image Reconstruction Algorithm Based on the Extension of Total Variation Method[J]. Journal of Electronics & Information Technology, 2015, 37(11): 2608-2612. doi: 10.11999/JEIT150179

Citation:

Jiang Ming-feng, Liu Yuan, Xu Wen-long, Feng Jie, Wang Ya-ming. The Study of Compressed Sensing MR Image Reconstruction Algorithm Based on the Extension of Total Variation Method[J]. Journal of Electronics & Information Technology, 2015, 37(11): 2608-2612. doi: 10.11999/JEIT150179

Citation:

Jiang Ming-feng, Liu Yuan, Xu Wen-long, Feng Jie, Wang Ya-ming. The Study of Compressed Sensing MR Image Reconstruction Algorithm Based on the Extension of Total Variation Method[J]. Journal of Electronics & Information Technology, 2015, 37(11): 2608-2612. doi: 10.11999/JEIT150179

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The Study of Compressed Sensing MR Image Reconstruction Algorithm Based on the Extension of Total Variation Method

doi: 10.11999/JEIT150179

1.
(School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China)
2.
(School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China)

Funds:

The National Natural Science Foundation of China (61272311)

Received Date: 2015-02-02
Rev Recd Date: 2015-06-01
Publish Date: 2015-11-19

Abstract

Abstract

The Total Variation (TV) method is often used to reconstruct the Compressed Sensing Magnetic Resonance Imaging (CS-MRI), however, it can generate the stair effect in the reconstructed MR image. In this paper, there types of TV extension based methods, i.e. High Degree Total Variation (HDTV), Total Generalize Variation (TGV) and Group-Sparsity Total Variation (GSTV), are proposed to implement the sparse reconstruction of MR image. In addition, the shift-invariant discrete wavelet transform are integrated into these TV extension based methods as the sparsifying transform. The Fast Composite Splitting Algorithm (FCSA) is adopted to solve the convex optimization problem of CS-MRI reconstruction. And the Two different types of MR images with radial sampling trajectory are used to validate the reconstruction performance of CS-MRI by using the TV extension methods. The experiment results show that the TV extension based models can overcome the shortcomings of TV based model. Moreover, compared with HDTV and TGV methods, the GSTV method can obviously improve the reconstruction quality with higher Signal-to-Noise Ratio (SNR).
- Magnetic Resonance Imaging (MRI),
- Compressed Sensing(CS),
- Total Variation (TV) extension method,
- Group-sparsity

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References(18)

References

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