Laboratory of Biomedical Imaging and Signal Processing, the University of Hong Kong, Hong Kong SAR, People's Republic of China.
Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong SAR, People's Republic of China.
Magn Reson Med. 2019 Mar;81(3):1924-1934. doi: 10.1002/mrm.27546. Epub 2018 Oct 12.
To provide simultaneous multislice (SMS) EPI reconstruction with k-space implementation and robust Nyquist ghost correction.
2D phase error correction SENSE (PEC-SENSE) was recently developed for Nyquist ghost correction in SMS EPI reconstruction for which virtual coil simultaneous autocalibration and k-space estimation (VC-SAKE) was used to remove slice-dependent Nyquist ghosts and intershot 2D phase variations in multi-shot EPI reference scan. However, masking coil sensitivity maps to exclude background region in PEC-SENSE and manually selecting slice-wise target ranks in VC-SAKE are cumbersome procedures in practice. To avoid masking, the concept of PEC-SENSE is extended to k-space implementation and termed as PEC-GRAPPA. Furthermore, a singular value shrinkage scheme is incorporated in VC-SAKE to circumvent the empirical slice-wise target rank selection. PEC-GRAPPA was evaluated and compared to PEC-SENSE with/without masking and 1D linear phase correction GRAPPA.
PEC-GRAPPA robustly reconstructed SMS EPI images from 7T phantom and human brain data, effectively removing the phase error-induced artifacts. The resulting residual artifact level and temporal SNR were comparable to those by PEC-SENSE with careful tuning. PEC-GRAPPA outperformed PEC-SENSE without masking and 1D linear phase correction GRAPPA.
Our proposed PEC-GRAPPA approach effectively removes the artifacts caused by Nyquist ghosts in SMS EPI without cumbersome tuning. This approach provides a robust and practical implementation of SMS EPI reconstruction in k-space with slice-dependent 2D Nyquist ghost correction.
提供基于 k 空间实现和稳健奈奎斯特鬼影校正的同时多层（SMS）EPI 重建。
最近开发了用于 SMS EPI 重建中奈奎斯特鬼影校正的二维相位误差校正 SENSE（PEC-SENSE），为此，使用虚拟线圈同时自动校准和 k 空间估计（VC-SAKE）来去除多shot EPI 参考扫描中切片相关的奈奎斯特鬼影和 shot 间 2D 相位变化。然而，在实践中，PEC-SENSE 中的屏蔽线圈灵敏度图以排除背景区域和 VC-SAKE 中的手动选择切片级目标秩是繁琐的过程。为了避免屏蔽，PEC-SENSE 的概念扩展到 k 空间实现，并称为 PEC-GRAPPA。此外，在 VC-SAKE 中合并了奇异值收缩方案以避免经验性的切片级目标秩选择。评估了 PEC-GRAPPA，并与具有/不具有屏蔽和 1D 线性相位校正 GRAPPA 的 PEC-SENSE 进行了比较。
PEC-GRAPPA 从 7T 幻影和人脑数据中稳健地重建了 SMS EPI 图像，有效地去除了相位误差引起的伪影。所得残余伪影水平和时间 SNR 与经过仔细调整的 PEC-SENSE 相当。PEC-GRAPPA 优于无屏蔽和 1D 线性相位校正 GRAPPA 的 PEC-SENSE。
我们提出的 PEC-GRAPPA 方法有效地去除了 SMS EPI 中奈奎斯特鬼影引起的伪影，而无需繁琐的调整。该方法为基于 k 空间的 SMS EPI 重建提供了一种稳健且实用的基于切片的 2D 奈奎斯特鬼影校正方法。
