A CAIPI Approach to Decrease Geometry Factor for Simultaneous Multi-Slice Technique in FMRI

Functional Magnetic Resonance Imaging (fMRI) enables researchers to study brain functions and advance understanding in human sciences. By detecting the Blood Oxygen Level Dependent (BOLD) contrast signal, spatial and temporal changes in brain metabolism are represented in the frequency domain, 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. Therefore, the primary focus of our study is to propose a novel imaging reconstruction method that improves the efficiency of the data acquisition process while maintaining high accuracy in activation detection. In our approach, we introduce a novel two-dimensional acceleration method to expedite the imaging acquisition process. Multiple imaging shift techniques and a new two-dimensional Hadamard aliasing pattern were incorporated to reduce the dependency on aliased voxels and increase the diversity of the acquired information. By applying our approach to both simulated and experimental fMRI data, we successfully reduced the total scan time while achieving higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in regions of interest (ROI). Moreover, compared with traditional imaging reconstruction techniques, our method significantly improves the activation detection rate.