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

Authors

  • Zhifeng Chen,

    1. Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
    Search for more papers by this author
  • Ling Xia,

    Corresponding author
    1. Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
    2. State Key Lab of CAD & CG, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
    Search for more papers by this author
  • Feng Liu,

    1. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia
    Search for more papers by this author
  • Qiuliang Wang,

    Corresponding author
    1. Division of Superconducting Magnet Science and Technology, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, People's Republic of China
    Search for more papers by this author
  • Yi Li,

    1. Division of Superconducting Magnet Science and Technology, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, People's Republic of China
    Search for more papers by this author
  • Xuchen Zhu,

    1. Division of Superconducting Magnet Science and Technology, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, People's Republic of China
    Search for more papers by this author
  • Feng Huang

    1. Philips Healthcare, Suzhou, Jiangsu, People's Republic of China
    Search for more papers by this author

  • Grant sponsor: National Nature Science Funds of China; Grant number: 81101031 and 51477167.

Abstract

Purpose

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.

Theory

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.

Methods

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.

Results

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.

Conclusion

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, 2016. © 2016 International Society for Magnetic Resonance in Medicine

Ancillary