Interslice leakage artifact reduction technique for simultaneous multislice acquisitions

Abstract

Purpose Controlled aliasing techniques for simultaneously acquired EPI slices have been shown to significantly increase the temporal efficiency for both diffusion-weighted imaging (DWI) and fMRI studies. The “slice-GRAPPA” (SG) method has been widely used to reconstruct such data. We investigate robust optimization techniques for SG to ensure image reconstruction accuracy through a reduction of leakage artifacts. Methods Split slice-GRAPPA (SP-SG) is proposed as an alternative kernel optimization method. The performance of SP-SG is compared to standard SG using data collected on a spherical phantom and in-vivo on two subjects at 3T. Slice accelerated and non-accelerated data were collected for a spin-echo diffusion weighted acquisition. Signal leakage metrics and time-series SNR were used to quantify the performance of the kernel fitting approaches. Results The SP-SG optimization strategy significantly reduces leakage artifacts for both phantom and in-vivo acquisitions. In addition, a significant boost in time-series SNR for in-vivo diffusion weighted acquisitions with in-plane 2× and slice 3× accelerations was observed with the SP-SG approach. Conclusion By minimizing the influence of leakage artifacts during the training of slice-GRAPPA kernels, we have significantly improved reconstruction accuracy. Our robust kernel fitting strategy should enable better reconstruction accuracy and higher slice-acceleration across many applications.

Accurate modeling of temporal correlations in rapidly sampled fMRI time series

“…As the TR was reduced with increasing MB factor, the flip angle was optimized to match the Ernst angle based on a grey matter (GM) T1 value of 1,000 ms at 3 T (Weiskopf et al, 2013). All multiband RF excitations were performed with MB RF Phase Scramble selected (Wong 2012) and the data were reconstructed using the MB LeakBlock Kernel option (Cauley et al, 2014), which has been shown to suppress residual aliasing of BOLD signal across slices in fMRI (Risk, Kociuba, & Rowe, 2018; Todd et al, 2016). The same echo‐time was chosen for all the protocols (TE = 30.2 ms).…”

Theoretical and experimental evaluation of multi-band EPI for high-resolution whole brain pCASL Imaging

“…Slices within each band can be collected in an ascending, descending or interleaved fashion, and slices across bands are acquired simultaneously in a spatially interleaved fashion. The applied MB-EPI method employed a blipped-CAIPI approach (Setsompop et al, 2012b) and aliasing constrained slice-GRAPPA algorithm to reduce leakage contamination (Cauley et al, 2013). In addition, a phase-scrambling strategy was applied for the MB RF pulses to reduce RF pulse peak power and/or duration (Goelman, 1997, Wong, 2012).…”

“…While an important initial finding, these studies did not explore the potential of MB-EPI on ASL imaging in high-resolution whole brain applications where the slice dependent effects become pronounced. On the other hand, MB-EPI introduces confounding factors that may reduce the reliability of CBF estimation, including spatially varying noise amplification as characterized by geometry factor (g-factor) due to slice-GRAPPA (Generalized Autocalibrating Partially Parallel Acquisition) reconstruction (Robson et al, 2008) and leakage contamination resulting from imperfect anti-aliasing of simultaneously acquired multiple slices (Cauley et al, 2013, Xu et al, 2013). These potential confounding effects and their slice dependence were not directly and systematically investigated in the previous studies.…”

Recent advances in parallel imaging for MRI

“…For slice-GRAPPA, the L-factor can be calculated analytically from the GRAPPA weights. Split slice-GRAPPA uses the L-factor to optimize the weight calibration to trade off slice leakage and in-plane artifacts [111]. …”