Advanced Search
Volume 37 Issue 11
Nov.  2015
Turn off MathJax
Article Contents
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

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

doi: 10.11999/JEIT150179
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
  • 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).
  • loading
  • Cands E J, Romberg J, and Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2): 489-509.
    Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
    娄静涛, 李永乐, 谭树人, 等. 基于全变分的全向图像稀疏重构算法[J]. 电子学报, 2014, 44(2): 243-249.
    Lou Jing-tao, Li Yong-le, Tan Shu-ren, et al.. Sparse reconstruction for omnidirectional image based on total variation[J]. Acta Electronica Sinica, 2014, 44(2): 243-249.
    李然, 干宗良, 崔子冠, 等. 联合时空特征的视频分块压缩感知重构[J]. 电子与信息学报, 2014, 36(2): 285-292.
    Li Ran, Gan Zong-liang, Cui Zi-guan, et al.. Block compressed sensing reconstruction of video combined with temporal-spatial characteristics[J]. Journal of Electronics Information Technology, 2014, 36(2): 285-292.
    Lustig M, Donoho D L, and Pauly J M. Sparse MRI: the application of compressed sensing for rapid MR Imaging[J]. Magnetic Resonance in Medicine, 2007, 58(6): 1182-1195.
    Hu Y and Jacob M. Higher Degree Total Variation (HDTV) regularization for image recovery[J]. IEEE Transactions on Image Processing, 2012, 21(5): 2559-2571.
    Guo W, Qin J, and Yin W. A new detail-preserving regularity scheme[J]. SIAM Journal on Imaging Sciences, 2014, 7(2): 1309-1334.
    Selesnick I W and Chen P. Total variation denoising with overlapping group sparsity[C]. IEEE International Conference Acoust, Speech, Signal Processing (ICASSP), Vancouver, Canada, 2013: 1-5.
    Zhang S, Block K T, and Frahm J. Magnetic resonance imaging in real time: advances using radial FLASH[J]. Journal of Magnetic Resonance Imaging, 2010, 31(1): 101-109.
    Huang J, Zhang S, and Metaxas D. Efficient MR image reconstruction for compressed MR imaging[J]. Medical Image Analysis, 2011, 15(5): 670-679.
    Ning B, Qu X, Guo D, et al.. Magnetic resonance image reconstruction using trained geometric directions in 2D redundant wavelets domain and non-convex optimization[J]. Magnetic Resonance Imaging, 2013, 31(9): 1611-1622.
    Qu X, Hou Y, Lam F, et al.. Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator[J]. Medical Image Analysis, 2014, 18(6): 843-856.
    Jiang M , Jin J, Liu F, et al.. Sparsity-constrained SENSE reconstruction: an efficient implementation using a fast composite splitting algorithm[J]. Magnetic Resonance Imaging, 2013, 31(7): 1218-1227.
    Figueiredo M, Bioucas-Dias J, and Nowak R. Majorization minimization algorithms for wavelet-based image restoration[J]. IEEE Transactions on Image Processing, 2007, 16(12): 2980-2991.
    He B, Liao L Z, Han D, et al.. A new inexact alternating directions method for monotone variational inequalities[J]. Mathematical Programming, 2002, 92(1): 103-118.
    Jung H, Ye J C, and Kim E Y. Improved k-t BLAST and k-t SENSE using FOCUSS[J]. Physics in Medicine and Biology, 2007, 52(11): 3201-3226.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (1663) PDF downloads(737) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return