Blipped‐controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g‐factor penalty

Citations Over TimeTop 1% of 2011 papers

Abstract

Simultaneous multislice Echo Planar Imaging (EPI) acquisition using parallel imaging can decrease the acquisition time for diffusion imaging and allow full-brain, high-resolution functional MRI (fMRI) acquisitions at a reduced repetition time (TR). However, the unaliasing of simultaneously acquired, closely spaced slices can be difficult, leading to a high g-factor penalty. We introduce a method to create interslice image shifts in the phase encoding direction to increase the distance between aliasing pixels. The shift between the slices is induced using sign- and amplitude-modulated slice-select gradient blips simultaneous with the EPI phase encoding blips. This achieves the desired shifts but avoids an undesired "tilted voxel" blurring artifact associated with previous methods. We validate the method in 3× slice-accelerated spin-echo and gradient-echo EPI at 3 T and 7 T using 32-channel radio frequency (RF) coil brain arrays. The Monte-Carlo simulated average g-factor penalty of the 3-fold slice-accelerated acquisition with interslice shifts is <1% at 3 T (compared with 32% without slice shift). Combining 3× slice acceleration with 2× inplane acceleration, the g-factor penalty becomes 19% at 3 T and 10% at 7 T (compared with 41% and 23% without slice shift). We demonstrate the potential of the method for accelerating diffusion imaging by comparing the fiber orientation uncertainty, where the 3-fold faster acquisition showed no noticeable degradation.

Related Papers

• → Recovery of limited-extent images aliased because of spectral undersampling(2001)12 cited
• → Variable Density K-space Trajectories for Reducing Aliasing Artifacts in MRI Reconstruction(2005)1 cited
• → Stochastic Oscillation Undersampling to Reduce Frequency Aliasing Artifacts in the Magnetic Resonance Imaging(2019)
• → Sampling(2017)
• → Accelerate Single-Channel MRI by Exploiting Uniform Undersampling Aliasing Periodicity through Deep Learning(2023)