Two-dimensional-NGC-SENSE-GRAPPA for fast, ghosting-robust reconstruction of in-plane and slice-accelerated blipped-CAIPI echo planar imaging

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Abstract

Purpose

Ghosting-robust reconstruction of blipped-CAIPI echo planar imaging simultaneous multislice data with low computational load.

Methods

To date, Slice-GRAPPA, with “odd–even” kernels that improve ghosting performance, has been the framework of choice for such reconstructions due to its predecessor SENSE-GRAPPA being deemed unsuitable for blipped-CAIPI data. Modifications to SENSE-GRAPPA are used to restore CAIPI compatibility and to make it robust against ghosting. Two implementations are tested, one where slices and in-plane unaliasing are dealt in the same serial manner as in Slice-GRAPPA [referred to as one-dimensional (1D)-NGC-SENSE-GRAPPA, where NGC stands for Nyquist Ghost Corrected] and one where both are unaliased in a single step (2D-NGC-SENSE-GRAPPA), which is analytically and experimentally shown to be computationally cheaper.

Results

The 1D-NGC-SENSE-GRAPPA and odd-even Slice-GRAPPA perform identically, whereas 2D-NGC-SENSE-GRAPPA shows reduced error propagation, less residual ghosting when reliable reference data were available. When the latter was not the case, error propagation was increased.

Conclusion

Unlike Slice-GRAPPA, SENSE-GRAPPA operates fully within the GRAPPA framework, for which improved reconstructions (e.g., iterative, nonlinear) have been developed over the past decade. It could, therefore, bring benefit to the reconstruction of SMS data as an attractive alternative to Slice-GRAPPA. Magn Reson Med 77:998–1009, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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