Dual-polarity slice-GRAPPA for concurrent ghost correction and slice separation in simultaneous multi-slice EPI

Magn Reson Med. 2018 Oct;80(4):1364-1375. doi: 10.1002/mrm.27113. Epub 2018 Feb 9.

Abstract

Purpose: A ghost correction strategy for Simultaneous Multi-Slice (SMS) EPI methods that provides improved ghosting artifact reduction compared to conventional methods is presented. Conventional Nyquist ghost correction methods for SMS-EPI rely on navigator data that contain phase errors from all slices in the simultaneously acquired slice-group. These navigator data may contain spatially nonlinear phase differences near regions of B0 inhomogeneity, which violates the linear model employed by most EPI ghost correction algorithms, resulting in poor reconstructions.

Methods: Dual-Polarity GRAPPA (DPG) was previously shown to accurately model and correct both spatially nonlinear and 2D phase errors in conventional single-slice EPI data. Here, an extension we call Dual-Polarity slice-GRAPPA (DPsG) is adapted to the slice-GRAPPA method and applied to SMS-EPI data for slice separation and ghost correction concurrently-eliminating the need for a separate ghost correction step while also providing improved slice-specific EPI phase error correction.

Results: Images from in vivo SMS-EPI data reconstructed using DPsG in place of conventional Nyquist ghost correction and slice-GRAPPA are presented. DPsG is shown to reduce ghosting artifacts and provide improved temporal SNR compared to the conventional reconstruction.

Conclusion: The proposed use of DPsG for SMS-EPI reconstruction can provide images with lower artifact levels, higher image fidelity, and improved time-series stability compared to conventional reconstruction methods.

Keywords: MultiBand EPI; blipped-CAIPI; nyquist ghost correction; parallel imaging.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Brain / diagnostic imaging
  • Echo-Planar Imaging / methods*
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Linear Models
  • Nonlinear Dynamics
  • Signal Processing, Computer-Assisted
  • Signal-To-Noise Ratio