Image Reconstruction Method to Separate Simultaneous Encoded Slices with In-Plane and Through-Plane Acceleration in FMRI

FMRI has been a safe medical imaging tool to study brain function by observing the spatial and temporal changes in brain metabolism in recent decades. To capture brain functionality more efficiently, efforts have been made to accelerate the number of images 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 aliased and acquired concurrently. The Through-Plane Acceleration (TPA) method is one of the SMS techniques that can reduce data acquisition time in proportion to the number of aliased images acquired per unit of time. Other image acquisition acceleration techniques, such as the In-Plane Acceleration (IPA) method, focus on reducing the total image scan time by skipping partial lines in the frequency domain (k-space), resulting in a “fold-up” artifact after inverse Fourier transform. To un-alias and un-fold the acquired images, the Sensitivity Encoding (SENSE) and the GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) techniques can be utilized but still have their drawbacks. Due to the short physical distance and high similarity in coil sensitivity information between the aliased voxels, a singular matrix problem arises in the design matrix, and the influence of the geometry factor (g-factor) increases. To manually increase the distance and the difference in coil sensitivity information between the aliased images, the Controlled Aliasing in Parallel Imaging (CAIPI) and view angle tilting (VAT) techniques achieve slice-wise image shift by applying different radiofrequency pulse sequences. In this dissertation, multi-direction image shift techniques are incorporated with the multi-coil separation of parallel encoded complex-valued slices (mSPECS) technique in a Bayesian approach. The TPA and IPA techniques are integrated with Hadamard phase encoding and a novel 2D Hadamard phase encoding technique. A bootstrapping technique and an artificial aliasing of calibration images are applied to enhance the condition of the design matrix. Through the investigation of the novel SMS techniques on both simulation and experimental fMRI dataset, our model significantly reduces total image scan time while preserving and detecting task signal effectively.