Chinese Journal of Magnetic Resonance >

2015 , Vol. 32 >Issue 4: 584 - 595

DOI: https://doi.org/10.11938/cjmr20150404

A New Method for Evaluation of Random Undersampling Matrix in Compressed Sensing-MRI

  • XIAO Sa1 ,
  • 2 ,
  • LV Zhi-cheng1 ,
  • SUN Xian-ping1 ,
  • YE Chao-hui1 ,
  • ZHOU Xin1*
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  • 1. State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Key Laboratory of Magnetic Resonance in Biological Systems, National Center for Magnetic Resonance in Wuhan (Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences), Wuhan 430071, China; 2. University of Chinese Academy of Sciences, Beijing 100049, China
*Corresponding author： ZHOU Xin, Tel: +86-27-87198802, E-mail: xinzhou@wipm.ac.cn.

Received date: 2015-02-15

  Revised date: 2015-10-22

  Online published: 2015-12-05

Supported by

国家自然科学基金资助项目(81227902, 11174327)

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Abstract

In compressed sensing magnetic resonance imaging (CS-MRI), the quality of reconstructed image is largely determined by the random undersampling matrix. It is a common practice to select the random undersampling matrix though computation of the point spread function (PSF) and the maximal artifacts possible. In this paper, we proposed to use two novel statistical parameters, mean value (MV) and standard deviation (SD), to guide the selection of random undersampling matrix. The two parameters evaluate the average amplitude and fluctuation of the possible artifacts, respectively. Experiments on mice brain and human brain were used to compare image quality of CS reconstructions of MRI data acquired with random undersampling matrices determined by different criteria. It was shown that reconstruction with MV had better performance when the sampling ratio is above four times of sparsity. It is concluded that better CS-MRI reconstruction quality can be achieved with reasonable selection of sampling ratio guided by prior knowledge of sparsity and MV as random undersampling matrix evaluation parameter.

Key words： MRI; compressed sensing; random undersampling matrix; point spread function

Cite this article

XIAO Sa1 , 2 , LV Zhi-cheng1 , SUN Xian-ping1 , YE Chao-hui1 , ZHOU Xin1* . A New Method for Evaluation of Random Undersampling Matrix in Compressed Sensing-MRI
[J]. Chinese Journal of Magnetic Resonance, 2015 , 32(4) : 584 -595 . DOI: 10.11938/cjmr20150404

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