DocumentCode :
3767300
Title :
Magnetic resonance image reconstruction via Lo-norm minimization
Author :
H. Chen;J. Tao;Y. Sun;Z. Ye;B. Qiu
Author_Institution :
Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, Anhui, People´s Republic of China
fYear :
2015
Firstpage :
1
Lastpage :
6
Abstract :
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.
Publisher :
iet
Conference_Titel :
Biomedical Image and Signal Processing (ICBISP 2015), 2015 IET International Conference on
Print_ISBN :
978-1-78561-044-8
Type :
conf
DOI :
10.1049/cp.2015.0780
Filename :
7450356
Link To Document :
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