title: An improved non-Cartesian partially parallel imaging by exploiting artificial sparsity.
abstract: To 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.
  
  
  
  This work is based on the GRAPPA-like PPI having an improved performance when the to-be-reconstructed image is sparse in the image domain.
  
  
  
  A 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.
  
  
  
  For 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.
  
  
  
  Taking 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.
authors: Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China.
authors: Zhifeng
authors: Z
authors: Chen
authors: 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.
authors: Ling
authors: L
authors: Xia
authors: School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia.
authors: Feng
authors: F
authors: Liu
authors: Division of Superconducting Magnet Science and Technology, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, People's Republic of China.
authors: Qiuliang
authors: Q
authors: Wang
authors: Division of Superconducting Magnet Science and Technology, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, People's Republic of China.
authors: Yi
authors: Y
authors: Li
authors: Division of Superconducting Magnet Science and Technology, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, People's Republic of China.
authors: Xuchen
authors: X
authors: Zhu
authors: Philips Healthcare, Suzhou, Jiangsu, People's Republic of China.
authors: Feng
authors: F
authors: Huang
mesh_terms: D000465:Algorithms; D016477:Artifacts; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
keywords: GROWL; artificial sparsity; fast imaging; high-pass filter; non-Cartesian partially parallel imaging; total variation