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Information Technology Journal

Year: 2013 | Volume: 12 | Issue: 23 | Page No.: 7709-7713
DOI: 10.3923/itj.2013.7709.7713
Compressed Sensing MRI with Walsh Transform-based Sparsity Basis
Feng Zhen, Guo He, Wang Yu-Xin, Xu Wen-Long, Jiang Ming-Feng and Liu Feng

Abstract: The choice of sparsity bases plays a crucial role to reconstruct high-quality MR images from heavily under-sampled k-space signals. Traditionally, the Wavelet transform and the Total Variation (TV) are used as the sparsity bases. In this study, a novel sparsity basis, based on a two-dimensional Walsh transform, is proposed to sparsify the MR image. The basic theory of the Walsh transform-based CS-MRI is explained and the proposed technique is validated with experiments. Three different types of MR images are used to test the proposed method performance in terms of reconstruction accuracy. The results show that the proposed Walsh transform-based sparsity basis is capable of reconstructing MRI images with a higher fidelity than the traditional Wavelet transform-based sparsity basis using a similar running time.

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How to cite this article
Feng Zhen, Guo He, Wang Yu-Xin, Xu Wen-Long, Jiang Ming-Feng and Liu Feng, 2013. Compressed Sensing MRI with Walsh Transform-based Sparsity Basis. Information Technology Journal, 12: 7709-7713.

Keywords: Compressed sensing, MRI, walsh transform and sparsity basis

REFERENCES
  • Aharon, M., M. Elad and A. Bruckstein, 2006. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process, 54: 4311-4322.
    CrossRef    

  • CCAI, 2012. Siemens open bore 1.5 Tesla MRI scanner. Cottage Centre for Advanced Imaging. http://www.cottageadvancedimaging.com/tabid/89/Default.aspx.

  • Fine, N.J., 1949. On the Walsh functions. Trans. Am. Math. Soc., 65: 372-414.
    Direct Link    

  • Huang, J., S. Zhang and D. Metaxas, 2011. Efficient MR image reconstruction for compressed MR imaging. Med. Image Anal., 15: 670-679.
    CrossRef    

  • Jiang, H., W. Deng and Z. Shen, 2012. Surveillance video processing using compressive sensing. Inverse Prob. Imaging, 6: 201-214.

  • Li, C., T. Sun, K.F. Kelly and Y. Zhang, 2012. A compressive sensing and unmixing scheme for hyperspectral data processing. IEEE Trans. Image Process., 21: 1200-1210.
    CrossRef    

  • Lustig, M., D. Donoho and J.M. Pauly, 2007. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magnet. Resonance Med., 58: 1182-1195.
    CrossRef    

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