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

Zhifeng Chen1, Ling Xia1,2, Feng Liu3

  • 1Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China.

Magnetic Resonance in Medicine
|August 9, 2016
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Summary

Artificial sparsity enhances non-Cartesian partially parallel imaging (PPI) performance. The artificial sparsity-enhanced GROWL (ARTS-GROWL) method significantly reduces reconstruction errors and improves image quality in magnetic resonance imaging.

Keywords:
GROWLartificial sparsityfast imaginghigh-pass filternon-Cartesian partially parallel imagingtotal variation

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Area of Science:

  • Magnetic Resonance Imaging
  • Image Reconstruction
  • Medical Physics

Background:

  • Partially parallel imaging (PPI) accelerates data acquisition in MRI.
  • Non-Cartesian trajectories offer advantages but pose reconstruction challenges.
  • Generalized autocalibrating partially parallel acquisitions (GRAPPA) is a common PPI technique.

Purpose of the Study:

  • To improve non-Cartesian PPI performance by introducing artificial sparsity.
  • To demonstrate the efficacy of artificial sparsity using the GROWL (GRAPPA operator for wider band lines) method as an example.
  • To develop a systematic scheme for generating artificial sparsity in non-Cartesian imaging.

Main Methods:

  • Proposed a three-step scheme: 1) generating synthetic k-space data with artificial sparsity, 2) applying GROWL to this synthetic data, and 3) reconstructing the final image.
  • Developed artificial sparsity-enhanced GROWL (ARTS-GROWL) for non-Cartesian PPI.
  • Validated the method using both simulation and in vivo data.

Main Results:

  • ARTS-GROWL significantly reduced reconstruction errors compared to conventional GROWL for tested acceleration factors.
  • Experimental results demonstrated improved signal-to-noise ratio (SNR) with ARTS-GROWL.
  • Normalized root-mean-square error (NRMSE) was reduced by the artificial sparsity approach.

Conclusions:

  • Artificial sparsity is an effective strategy for enhancing non-Cartesian PPI.
  • The ARTS-GROWL method offers a practical approach to improving image reconstruction quality in accelerated MRI.
  • This technique holds promise for faster and higher-quality MRI scans.