An Algorithm Combining Analysis-based Blind Compressed Sensing and Nonlocal Low-rank Constraints for MRI Reconstruction

Author(s): Mei Sun, Jinxu Tao*, Zhongfu Ye, Bensheng Qiu, Jinzhang Xu, Changfeng Xi.

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

Volume 15 , Issue 3 , 2019

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Graphical Abstract:


Background: In order to overcome the limitation of long scanning time, compressive sensing (CS) technology exploits the sparsity of image in some transform domain to reduce the amount of acquired data. Therefore, CS has been widely used in magnetic resonance imaging (MRI) reconstruction.

Discussion: Blind compressed sensing enables to recover the image successfully from highly under- sampled measurements, because of the data-driven adaption of the unknown transform basis priori. Moreover, analysis-based blind compressed sensing often leads to more efficient signal reconstruction with less time than synthesis-based blind compressed sensing. Recently, some experiments have shown that nonlocal low-rank property has the ability to preserve the details of the image for MRI reconstruction.

Methods: Here, we focus on analysis-based blind compressed sensing, and combine it with additional nonlocal low-rank constraint to achieve better MR images from fewer measurements. Instead of nuclear norm, we exploit non-convex Schatten p-functionals for the rank approximation.

Results & Conclusion: Simulation results indicate that the proposed approach performs better than the previous state-of-the-art algorithms.

Keywords: Blind compressed sensing, nonlocal low-rank, nonconvex optimization, low-rank approximation, Schatten p-functionals, Magnetic Resonance Imaging (MRI).

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Article Details

Year: 2019
Page: [281 - 291]
Pages: 11
DOI: 10.2174/1573405614666180130151333
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