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论文类型：期刊论文

发表时间：2014-01-01

发表刊物：Journal of Computational Information Systems

收录刊物：EI、Scopus

卷号：10

期号：5

页面范围：2009-2016

ISSN号：15539105

摘要：l1-SPIRiT is an efficient reconstruction method for partially parallel magnetic resonance imaging (pMRI), which introduces wavelet-based sparsity into the SPIRiT framework. To exploit the fact that coil images are sensitivity weighted images of the original image, a joint-sparsity model is proposed in the l1-SPIRiT method. Coil images are transformed into sparse coefficients and then coefficients from different coils at the same spatial position are jointly penalized to exploit inter-coil similarities. In this work, the sparse representation is based on singular value decomposition (SVD), which preserves different levels of characteristics for the coil images in an efficient way, additionally, its adoption into the joint-sparsity model of the l1-SPIRiT method opens a new way to make use of the inter-coil structure similarities. The performance of the proposed method was tested on two datasets with various experimental settings. The experimental results indicate that, compared to the original l1-SPIRiT method, the proposed method is more robust and efficient. ? 2014 Binary Information Press.