A Bayesian approach to 2D acceleration for studying activation detection rate for simultaneously encoded slice...

Functional Magnetic Resonance Imaging (fMRI) enables researchers to study brain function and advance understanding in human sciences. Spatial and temporal changes in brain metabolism as by detecting the Blood Oxygen Level Dependent (BOLD) contrast signal are represented in the frequency domain of an image, known as k-space. Traditional MRI methodologies require full k-space information, which follows a unique data acquisition sequence to reconstruct the complete image. This process presents a time-consuming obstacle for medical imaging techniques. Our study proposes a novel image reconstruction method to enhance the efficiency of data acquisition while maintaining high accuracy in activation detection. The through-plane and in-plane acceleration techniques are combined to accelerate image acquisition along two dimensions. Multiple image-shift strategies and a 2D Hadamard encoding scheme are used to increase encoding diversity and reduce slice leakage. By applying our approach to both simulated and experimental fMRI data, we successfully reduced total scan time while achieving a higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in regions of interest (ROI). Compared with conventional reconstruction strategies, the proposed method demonstrates potential improvements in activation detection under specific acceleration and encoding conditions, while also providing voxel-wise estimates through a Bayesian framework.