Robust autocalibrated loraks for EPI ghost correction

Self Cite

Purpose: We propose and evaluate a new structured low-rank method for echoplanar imaging (EPI) ghost correction called Robust Autocalibrated LORAKS (RAC-LORAKS). The method can be used to suppress EPI ghosts arising from the differences between different readout gradient polarities and/or the differences between different shots. It does not require conventional EPI navigator signals, and is robust to imperfect autocalibration data. Methods: Autocalibrated LORAKS is a previous structured low-rank method for EPI ghost correction that uses GRAPPA-type autocalibration data to enable highquality ghost correction. This method works well when the autocalibration data are pristine, but performance degrades substantially when the autocalibration information is imperfect. RAC-LORAKS generalizes Autocalibrated LORAKS in two ways. First, it does not completely trust the information from autocalibration data, and instead considers the autocalibration and EPI data simultaneously when estimating low-rank matrix structure. Second, it uses complementary information from the autocalibration data to improve EPI reconstruction in a multi-contrast joint reconstruction framework. RAC-LORAKS is evaluated using simulations and in vivo data, including comparisons to state-of-the-art methods. Results: RAC-LORAKS is demonstrated to have good ghost elimination performance compared to state-of-the-art methods in several complicated EPI acquisition scenarios (including gradient-echo brain imaging, diffusion-encoded brain imaging, and cardiac imaging). Conclusions: RAC-LORAKS provides effective suppression of EPI ghosts and is robust to imperfect autocalibration data.

Section: Introductionmentioning

confidence: 99%

Robust autocalibrated structured low‐rank EPI ghost correction

Lobos

Hoge

Javed

et al. 2020

Self Cite

“…19,20 These methods can also be adapted for Nyquist ghost elimination in EPI reconstruction by treating EPI data from different readout polarities/shots as from different virtual channels. 19,[21][22][23] The central k-space of multiple slices can also be recovered jointly by extending the existing lowrank matrix completion methods with tensorial expressions and then used as calibration data for subsequent parallel imaging reconstruction. 12 The multislice nature of 2D acquisition allows for different slices having complementary sampling pattern and enables missing k-space sample estimation from adjacent slices.…”

Section: Introductionmentioning

confidence: 99%

“…The transform sparsity can also be formulated as annihilating filter‐based low‐rank Hankel matrix completion and used for accelerated MR parameter mapping 19,20 . These methods can also be adapted for Nyquist ghost elimination in EPI reconstruction by treating EPI data from different readout polarities/shots as from different virtual channels 19,21‐23 . The central k‐space of multiple slices can also be recovered jointly by extending the existing low‐rank matrix completion methods with tensorial expressions and then used as calibration data for subsequent parallel imaging reconstruction 12 …”

Section: Introductionmentioning

confidence: 99%

Calibrationless parallel imaging reconstruction for multislice MR data using low‐rank tensor completion

Liu

Zhao

et al. 2020

Purpose To provide joint calibrationless parallel imaging reconstruction of highly accelerated multislice 2D MR k‐space data. Methods Adjacent image slices in multislice MR data have similar coil sensitivity maps, spatial support, and image content. Such similarities can be utilized to improve image quality by reconstructing multiple slices jointly with low‐rank tensor completion. Specifically, the multichannel k‐space data from multiple slices are constructed into a block‐wise Hankel tensor and iteratively updated by promoting tensor low‐rankness through higher‐order SVD. This multislice block‐wise Hankel tensor completion was implemented for 2D spiral and Cartesian k‐space undersampling where sampling patterns vary between adjacent slices. The approach was evaluated with human brain MR data and compared to the traditional single‐slice simultaneous autocalibrating and k‐space estimation reconstruction. Results The proposed multislice block‐wise Hankel tensor completion approach robustly reconstructed highly undersampled multislice 2D spiral and Cartesian data. It produced substantially lower level of artifacts compared to the traditional single‐slice simultaneous autocalibrating and k‐space estimation reconstruction. Quantitative evaluation using error maps and root mean square error demonstrated its significantly improved performance in terms of residual artifacts and root mean square error. Conclusion Our proposed multislice block‐wise Hankel tensor completion method exploits the similar coil sensitivity and image content within multislice MR data through a tensor completion framework. It offers a new and effective approach to acquire and reconstruct highly undersampled multislice MR data in a calibrationless manner.

“…Moreover, the 2D intershot phase variations within the single‐band multi‐shot EPI reference scan due to the physiological and hardware factors can introduce extra artifacts, further degrading the coil sensitivity calibration. Such degraded coil sensitivity calibration will undermine the subsequent SMS reconstruction with increased artifacts or decreased SNR …”

Section: Introductionmentioning

confidence: 99%

“…Such degraded coil sensitivity calibration will undermine the subsequent SMS reconstruction with increased artifacts or decreased SNR. 1,[18][19][20] Recently, the PEC-SENSE approach has been expanded to SMS EPI reconstruction for Nyquist ghost correction using slice-dependent 2D phase error maps.1, 21 Additionally, virtual coil simultaneous autocalibrating-k-space estimation (VC-SAKE) is used to remove both the residual Nyquist ghosts and the intershot phase variations in reference EPI scan. 1,21 VC-SAKE significantly improves the coil sensitivity estimation.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Lyu

Barth

et al. 2018

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.