The proposed correction method can robustly eliminate Nyquist ghost while preserving the image SNR. This approach requires no additional calibration data beyond standard coil sensitivity maps and can be readily applied to all EPI applications. Magn Reson Med 79:943-951, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

The proposed procedure of virtual coil SAKE calibration and PEC-SENSE reconstruction substantially reduces all ghost-related artifacts originating either directly from SMS EPI data or indirectly from EPI-based coil sensitivity maps. It is computationally efficient, and generally applicable to all SMS EPI-based applications.

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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.

The proposed method provides robust correction of both Nyquist ghost and geometric distortion in EPI, and it is particularly suitable for dynamic EPI applications. Magn Reson Med 77:1749-1761, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

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Magnetic resonance fingerprinting (MRF) is a new technique for simultaneously quantifying multiple MR parameters using one temporally resolved MR scan. But its brute-force dictionary generating and searching (DGS) process causes a huge disk space demand and computational burden, prohibiting it from a practical multiple slice high-definition imaging. The purpose of this paper was to provide a fast and space efficient DGS algorithm for MRF. Based on an empirical analysis of properties of the distance function of the acquired MRF signal and the pre-defined MRF dictionary entries, we proposed a parameter separable MRF DGS method, which breaks the multiplicative computation complexity into an additive one and enabling a resolution scalable multi-resolution DGS process, which was dubbed as MRF ZOOM. The evaluation results showed that MRF ZOOM was hundreds or thousands of times faster than the original bruteforce DGS method. The acceleration was even higher when considering the time difference for generating the dictionary. Using a high precision quantification, MRF can find the right parameter values for a 64x64 imaging slice in 117 secs. Our data also showed that spatial constraints can be used to further speed up MRF ZOOM.3

PROPELLER technique is widely used in MRI examinations for being motion insensitive, but it prolongs scan time and is restricted mainly to T2 contrast. Parallel imaging can accelerate PROPELLER and enable more flexible contrasts. Here, we propose a multi-step joint-blade (MJB) SENSE reconstruction to reduce the noise amplification in parallel imaging accelerated PROPELLER. MJB SENSE utilizes the fact that PROPELLER blades contain sharable information and blade-combined images can serve as regularization references. It consists of three steps. First, conventional blade-combined images are obtained using the conventional simple single-blade (SSB) SENSE, which reconstructs each blade separately. Second, the blade-combined images are employed as regularization for blade-wise noise reduction. Last, with virtual high-frequency data resampled from the previous step, all blades are jointly reconstructed to form the final images. Simulations were performed to evaluate the proposed MJB SENSE for noise reduction and motion correction. MJB SENSE was also applied to both T2-weighted and T1-weighted in vivo brain data. Compared to SSB SENSE, MJB SENSE greatly reduced the noise amplification at various acceleration factors, leading to increased image SNR in all simulation and in vivo experiments, including T1-weighted imaging with short echo trains. Furthermore, it preserved motion correction capability and was computationally efficient.