%0 Electronic Article
%A H. Chen
%+ [Dept. of Electron. Eng. & Inf. Sci., Univ. of Sci. & Technol. of China, Hefei, Dept. of Electron. Eng. [amp ] Inf. Sci., Univ. of Sci. [amp ] Technol. of China, Hefei]
%A J. Tao
%+ [Dept. of Electron. Eng. & Inf. Sci., Univ. of Sci. & Technol. of China, Hefei, Dept. of Electron. Eng. [amp ] Inf. Sci., Univ. of Sci. [amp ] Technol. of China, Hefei]
%A Y. Sun
%+ [Dept. of Electron. Eng. & Inf. Sci., Univ. of Sci. & Technol. of China, Hefei, Dept. of Electron. Eng. [amp ] Inf. Sci., Univ. of Sci. [amp ] Technol. of China, Hefei]
%A Z. Ye
%+ [Dept. of Electron. Eng. & Inf. Sci., Univ. of Sci. & Technol. of China, Hefei, Dept. of Electron. Eng. [amp ] Inf. Sci., Univ. of Sci. [amp ] Technol. of China, Hefei]
%A B. Qiu
%+ [Dept. of Electron. Eng. & Inf. Sci., Univ. of Sci. & Technol. of China, Hefei, Dept. of Electron. Eng. [amp ] Inf. Sci., Univ. of Sci. [amp ] Technol. of China, Hefei]
%K MRI reconstruction
%K L1-norm-based technique
%K sample condition
%K alternating direction method
%K L0-norm minimization
%K total variation
%K magnetic resonance image
%K compressed sensing
%K discrete gradient-magnitude image transform
%K augment Lagrange function
%K iterative algorithm
%K signal acquisition time reduction
%X In recent years, Compressed Sensing (CS) has been applied to under-sampling Magnetic Resonance Imaging (MRI) for significantly reducing signal acquisition time. Total Variation (TV), the L1-norm of the discrete gradient-magnitude transform of image, is widely used as the regularization in the CS inspired MRI. Although the classic L1-norm based techniques achieve impressive results, they inherently require a degree of over-sampling to achieve exact reconstruction. In this paper, an iterative algorithm based on the L0-norm is proposed. The proposed method uses the Alternating Direction Method (ADM) to solve the unconstrained Augment Lagrange problem. The problem is first reformulated as the famous Augment Lagrange Function, and then alternatively minimized by ADM. Numerical comparison indicates that the proposed method can obviously improve the reconstruction quality, especially in highly under sample condition.
%T Magnetic resonance image reconstruction via L0-norm minimization
%B IET Conference Proceedings
%D January 2015
%P 6 .-6 .
%I Institution of Engineering and Technology
%U https://digital-library.theiet.org/;jsessionid=1usxbawlc6as4.x-iet-live-01content/conferences/10.1049/cp.2015.0780
%G EN