SMS MUSSELS: A navigator‐free reconstruction for simultaneous multi‐slice‐accelerated multi‐shot diffusion weighted imaging

Abstract

Purpose To introduce a novel reconstruction method for simultaneous multi‐slice (SMS)‐accelerated multi‐shot diffusion weighted imaging (ms‐DWI). Methods SMS acceleration using blipped‐CAIPI schemes have been proposed to speed up the acquisition of ms‐DWIs. The reconstruction of the data requires (a) phase compensation to combine data from different shots and (b) slice unfolding to separate the data of different slices. The traditional approaches first estimate the phase maps corresponding to each shot and slice which are then employed to iteratively recover the slice unfolded DWIs without phase artifacts. In contrast, the proposed reconstruction directly recovers the slice‐unfolded k‐space data of the multiple shots for each slice in a single‐step recovery scheme. The proposed method is enabled by the low‐rank property inherent in the k‐space samples of ms‐DW acquisition. This enabled to formulate a joint recovery scheme that simultaneously (a) unfolds the k‐space data of each slice using a SENSE‐based scheme and (b) recover the missing k‐space samples in each slice of the multi‐shot acquisition employing a structured low‐rank matrix completion. Additional smoothness regularization is also utilized for higher acceleration factors. The proposed joint recovery is tested on simulated and in vivo data and compared to similar un‐navigated methods. Results Our experiments show effective slice unfolding and successful recovery of DWIs with minimal phase artifacts using the proposed method. The performance is comparable to existing methods at low acceleration factors and better than existing methods for higher acceleration factors. Conclusions For the slice accelerations considered in this study, the proposed method can successfully recover DWIs from SMS‐accelerated ms‐DWI acquisitions.

Highly efficient MRI through multi-shot echo planar imaging

“…In previous work 13-15 , we incorporated virtual coil (VC) concept 16 and simultaneous multi-slice (SMS) acquisition 17,18 into structured low-rank model and deep learning-based reconstructions to further accelerate msEPI. Similar works were reported by other groups 19,20 .…”

“…In previous work 13-15 , we incorporated virtual coil (VC) concept 16 and simultaneous multi-slice (SMS) acquisition 17,18 into structured low-rank model and deep learning-based reconstructions to further accelerate msEPI. Similar works were reported by other groups 19,20 .In this study, we propose an efficient Blip Up-Down Acquisition (BUDA), where a 2-shot EPI sampling was performed with interleaved blip-up and -down acquisitions, and then combined with B0 forward-modeling and structured low-rank reconstruction to yield distortion-free images. With BUDA, the required number of shots was reduced to two, which improved the sampling efficiency of msEPI.…”

Robust autocalibrated structured low‐rank EPI ghost correction

“…While this paper focused on EPI ghost correction for standard single-slice excitation, we believe that the extension of these ideas to simultaneous multi-slice EPI acquisitions (similar to Refs. [9,20,55,56]) is a very promising research direction.…”

Improved MUSSELS reconstruction for high‐resolution multi‐shot diffusion weighted imaging

“…SLRMC is now a widely employed MRI reconstruction technique which has been applied to solve several MR reconstruction problems, 11,15,[27][28][29][30][31][32][33][34][35] in addition to phase correction problems for EPI ghost correction. 9,26,36 While SLRMC efficiently exploits the compactness in the Fourier samples to formulate novel recovery priors for several of the abovementioned problems, the computational complexity of these methods can be challenging in some applications, especially those involving concatenated Hankel matrices.…”