An improved non‐Cartesian partially parallel imaging by exploiting artificial sparsity

Zhifeng Chen; Ling Xia; Feng Liu; Qiuliang Wang; Yi Li; Xuchen Zhu; Feng Huang

doi:10.1002/mrm.26360

journal article Aug 08, 2016

An improved non‐Cartesian partially parallel imaging by exploiting artificial sparsity

Zhifeng Chen Ling Xia

Feng Liu

Qiuliang Wang Yi Li

Xuchen Zhu Feng Huang

Magnetic Resonance in Medicine Vol. 78 No. 1 pp. 271-279 · Wiley

View at Publisher Save 10.1002/mrm.26360

Abstract

PurposeTo improve the performance of non‐Cartesian partially parallel imaging (PPI) by exploiting artificial sparsity, the generalized autocalibrating partially parallel acquisitions (GRAPPA) operator for wider band lines (GROWL) is taken as a specific example for explanation.TheoryThis work is based on the GRAPPA‐like PPI having an improved performance when the to‐be‐reconstructed image is sparse in the image domain.MethodsA systematic scheme is proposed to artificially generate the sparse image for non‐Cartesian trajectory. Using GROWL as a specific non‐Cartesian PPI method, artificial sparsity‐enhanced GROWL (ARTS‐GROWL) is used to demonstrate the efficiency of the proposed scheme. The ARTS‐GROWL consists of three steps: 1) generating synthetic k‐space data corresponding to an image with smaller support, that is, artificial sparsity; 2) applying GROWL to the synthetic k‐space data from previous step; and 3) recovering the final image from the reconstruction with the processed data.ResultsFor simulation and in vivo data, the experiments demonstrate that the proposed ARTS‐GROWL significantly reduces the reconstruction errors compared with the conventional GROWL technique for the tested acceleration factors.ConclusionTaking ARTS‐GROWL, for instance, experimental results indicate that artificial sparsity improved the signal‐to‐noise ratio and normalized root‐mean‐square error of non‐Cartesian PPI. Magn Reson Med 78:271–279, 2017. © 2016 International Society for Magnetic Resonance in Medicine

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Details

Published: Aug 08, 2016
Vol/Issue: 78(1)
Pages: 271-279
License: View

Authors

Z

Zhifeng Chen

Department of Biomedical Engineering Zhejiang University Hangzhou Zhejiang People's Republic of China

L

Ling Xia

Department of Biomedical Engineering Zhejiang University Hangzhou Zhejiang People's Republic of China; State Key Lab of CAD & CG Zhejiang University Hangzhou Zhejiang People's Republic of China

F

Feng Liu

School of Information Technology and Electrical Engineering The University of Queensland Brisbane QLD Australia

Q

Qiuliang Wang

Division of Superconducting Magnet Science and Technology, Institute of Electrical Engineering, Chinese Academy of Sciences Beijing People's Republic of China

Y

Yi Li

Division of Superconducting Magnet Science and Technology, Institute of Electrical Engineering, Chinese Academy of Sciences Beijing People's Republic of China

X

Xuchen Zhu

Division of Superconducting Magnet Science and Technology, Institute of Electrical Engineering, Chinese Academy of Sciences Beijing People's Republic of China

F

Feng Huang

Philips Healthcare Suzhou Jiangsu People's Republic of China

Funding

ARC Discovery

National Nature Science Funds of China Award: 81101031

Cite This Article

Zhifeng Chen, Ling Xia, Feng Liu, et al. (2016). An improved non‐Cartesian partially parallel imaging by exploiting artificial sparsity. Magnetic Resonance in Medicine, 78(1), 271-279. https://doi.org/10.1002/mrm.26360

An improved non‐Cartesian partially parallel imaging by exploiting artificial sparsity

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