Application of compressed sensing using chirp encoded 3D GRE and MPRAGE sequences

Kamlesh Pawar, Zhaolin Chen, Jingxin Zhang, N. Jon Shah, Gary F. Egan

Research output: Contribution to journalArticleResearchpeer-review

Abstract

An implementation of Non-Fourier chirp-encoding in 3D Gradient Recalled Echo (GRE), susceptibility-weighted imaging (SWI) and Magnetization Prepared Rapid Gradient Echo (MPRAGE) sequences is presented with compressive sensing reconstruction. 3D GRE and MPRAGE sequences were designed, in which the phase encoding (PE) direction was encoded with spatially selective chirp encoding Radio Frequency (RF) pulses, while the slice and the readout directions were Fourier encoded using gradients. During each excitation along the PE direction, a different spatially-selective RF excitation pulse was used to encode the PE direction with a complete set of unitary chirp encoding basis. Multichannel compressive sensing reconstruction on the undersampled in vivo data demonstrated that images reconstructed from chirp encoded data were able to preserve the spatial resolution better than the Fourier encoding. The mean Structural Similarity (SSIM) across five subjects at the acceleration factor of 6, for chirp encoded MPRAGE was 0.934 compared to 0.912 for Fourier encoded MPRAGE. The implementation of prospective undersampling demonstrated the feasibility of using chirp encoding in clinical practice for accelerated imaging. The minimum intensity projection of the compressive sensing (CS) reconstructed susceptibility weighted images revealed that chirp encoding is able to delineate small vessels better than the Fourier encoding with the SSIM of 0.960 for chirp encoding compared to the SSIM of 0.949 for the Fourier encoding. Improved performance of chirp encoding for CS reconstruction and SWI, along with the feasibility of implementation makes them a practical candidate for clinical MRI scans.

Original languageEnglish
Number of pages13
JournalInternational Journal of Imaging Systems and Technology
DOIs
Publication statusAccepted/In press - 2020

Keywords

  • compressed sensing
  • image reconstruction
  • MRI pulse sequence

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