Feng Zhen
School of Software Technology, Dalian University of Technology, 116620, Dalian, China
Guo He
School of Software Technology, Dalian University of Technology, 116620, Dalian, China
Wang Yu-Xin
School of Computer Science and Technology, Dalian University of Technology, 116624, Dalian, China
Xu Wen-Long
Department of Biomedical Engineering, China Jiliang University, 310018, Hangzhou, China
Jiang Ming-Feng
School of Information Science and Technology, Zhejiang Sci-Tech University, 310018, Hangzhou, China
Liu Feng
School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, QLD 4072, Australia
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.
DOI: 10.3923/itj.2013.7709.7713
URL: https://scialert.net/abstract/?doi=itj.2013.7709.7713
DOI: 10.3923/itj.2013.7709.7713
URL: https://scialert.net/abstract/?doi=itj.2013.7709.7713
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