Recently, exploiting low rank property of the data accomplished by the non-convex optimization has
shown great potential to decrease measurements for compressed sensing. In this paper, the low rank regularization
is adopted to gradient similarity minimization, and applied for highly undersampled magnetic resonance imaging
(MRI) reconstruction, termed gradient-based low rank MRI reconstruction (GLRMRI). In the proposed method,
by incorporating the spatially adaptive iterative singular-value thresholding (SAIST) to optimize our gradient
scheme, the deterministic annealing iterates the procedure e±ciently and superior reconstruction performance
is achieved. Extensive experimental results have consistently demonstrated that GLRMRI recovers both real-
valued MR images and complex-valued MR data accurately, especially in the edge preserving perspective, and
outperforms the current state-of-the-art approaches in terms of higher peak signal to noise ratio (PSNR) and lower
high-frequency error norm (HFEN) values.
XU Xiaoling (徐晓玲), LIU Yiling (刘沂玲), LIU Qiegen (刘且根),LU Hongyang (卢红阳), ZHANG Minghui (张明辉)
. Gradient-Based Low Rank Method for Highly Undersampled Magnetic Resonance Imaging Reconstruction[J]. Journal of Shanghai Jiaotong University(Science), 2018
, 23(3)
: 384
.
DOI: 10.1007/s12204-018-1927-8
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