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Dual‐polarity slice‐GRAPPA for concurrent ghost correction and slice separation in simultaneous multi‐slice EPI
Author(s) -
Hoge W. Scott,
Setsompop Kawin,
Polimeni Jonathan R.
Publication year - 2018
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.27113
Subject(s) - ghosting , computer science , polarity (international relations) , artificial intelligence , artifact (error) , computer vision , algorithm , imaging phantom , pattern recognition (psychology) , optics , physics , cell , genetics , biology
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 B 0 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.