A Bayesian CAIPIVAT approach with through-plane acceleration to enhance efficiency of simultaneously encoded slice acquisition in FMRI

Abstract

FMRI has been a safe medical imaging tool to study brain function by demonstrating the spatial and temporal changes in brain metabolism in recent decades. To capture brain functionality more efficiently, efforts focus on accelerating image acquisition acquired per unit of time that create each volume image without losing full anatomical structure. The Simultaneous Multi-Slice (SMS) technique provides a reconstruction method where multiple slices are acquired and aliased concurrently. Traditional imaging techniques such as SENSE and GRAPPA can reconstruct an image from less measured data but have their drawbacks. The Controlled Aliasing in Parallel Imaging (CAIPI) and view angle tilting (VAT) techniques achieve slice-wise image shift to decrease the influence of the geometry factor (g-factor) of coil sensitivities and prevent the singular problem of the design matrix. In this paper, a Bayesian CAIPIVAT approach for multi-coil separation of parallel encoded complex-valued slices (mSPECS-CAIPIVAT) with a novel SMS approach is presented and combined with the Hadamard phase-encoding method for image separation. Our proposed approach was applied to simulation and experimental studies showing a decrease in the influence of the g-factor while increasing the brain activation detection rate. The signal-to-noise ratio and the contrast-to-noise ratio are also improved by our approach.