Improved l1-SPIRiT using 3D walsh transform-based sparsity basis |
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Authors: | Zhen Feng Feng Liu Mingfeng Jiang Stuart Crozier He Guo Yuxin Wang |
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Institution: | 1. School of Software Technology, Dalian University of Technology, Dalian 116620, P. R. China;2. School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, QLD 4072, Australia;3. School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, P. R. China;4. School of Computer Science and Technology, Dalian University of Technology, Dalian 116624, P. R. China |
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Abstract: | l1-SPIRiT is a fast magnetic resonance imaging (MRI) method which combines parallel imaging (PI) with compressed sensing (CS) by performing a joint l1-norm and l2-norm optimization procedure. The original l1-SPIRiT method uses two-dimensional (2D) Wavelet transform to exploit the intra-coil data redundancies and a joint sparsity model to exploit the inter-coil data redundancies. In this work, we propose to stack all the coil images into a three-dimensional (3D) matrix, and then a novel 3D Walsh transform-based sparsity basis is applied to simultaneously reduce the intra-coil and inter-coil data redundancies. Both the 2D Wavelet transform-based and the proposed 3D Walsh transform-based sparsity bases were investigated in the l1-SPIRiT method. The experimental results show that the proposed 3D Walsh transform-based l1-SPIRiT method outperformed the original l1-SPIRiT in terms of image quality and computational efficiency. |
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Keywords: | MRI Compressed Sensing l1-SPIRiT Walsh transform |
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