PEC‐GRAPPA reconstruction of simultaneous multislice EPI with slice‐dependent 2D Nyquist ghost correction

Abstract

Purpose To provide simultaneous multislice (SMS) EPI reconstruction with k‐space implementation and robust Nyquist ghost correction. Methods 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. Results 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. Conclusion 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.

Robust autocalibrated loraks for EPI ghost correction

“…Structured low-rank matrix methods for EPI ghost correction (16)(17)(18)(19)(20) can be viewed as an extension of structured low-rank matrix methods for conventional MR image reconstruction (24,(28)(29)(30)(31)(32)(33)(34), and are based on the same underlying theoretical principles. In particular, it has been shown that when MRI images have limited support, smooth phase variations, multi-channel correlations, or transform-domain sparsity, then the MRI k-space data will be linearly predictable (35), which means that convolutional Hankel-or Toeplitz-structured matrices formed from the k-space data will possess low-rank characteristics.…”

“…Nyquist ghosts are one of the most common EPI artifacts, and occur because of systematic differences between the interleaved lines of k-space that are acquired with different readout gradient polarities, and/or because of systematic differences between interleaved lines of k-space data that are acquired with different shots in a multi-shot acquisition. Despite substantial efforts over several decades to solve this problem (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), the widely-deployed modern ghost correction schemes are still prone to incomplete ghost suppression, as illustrated in Supporting Information Fig. S1.…”

“…Recently, structured low-rank matrix methods for ghost correction (16)(17)(18)(19)(20) have received increasing attention for their ability to provide excellent ghost-suppression performance without the need for additional "navigator" information (i.e., reference scans collected alongside each EPI readout that allow estimation of the systematic inconsistencies between different gradient polarities or different shots). These methods can suppress ghosts better than navigator-based methods, and eliminate the need to acquire navigators for each EPI readout.…”

k‐Space deep learning for reference‐free EPI ghost correction

“…Another class of high‐performance reference‐free EPI ghost correction methods have been proposed using low‐rank matrix completion approaches . Specifically, Lee et al used the annihilating filter‐based low‐rank Hankel matrix approach (ALOHA), where the key idea is to take advantage of the fact that the concatenated Hankel matrix, which consists of even and odd k‐space lines, has a low‐rank structure due to the multi‐channel redundancy.…”

“…17 Another class of high-performance reference-free EPI ghost correction methods have been proposed using low-rank matrix completion approaches. [19][20][21][22][23][24] Specifically, Lee et al 19 used the annihilating filter-based low-rank Hankel matrix approach (ALOHA), [25][26][27] where the key idea is to take advantage of the fact that the concatenated Hankel matrix, which consists of even and odd k-space lines, has a low-rank structure due to the multi-channel redundancy. Thus, the phase mismatch correction problem in EPI can be reformulated as a missing k-space interpolation problem for even and odd k-space data that can be solved using low-rank Hankel matrix completion.…”