Optimizing Image Reconstruction in fMRI using a Reduced Field-of-View and (k, t)-Space undersampling

Motivation: By employing the PS-Sparse reconstruction, the 3D reduced field-of-view imaging with (k, t)-space undersampling (k-t 3D-rFOVI) can acquire fMRI data with high temporal and spatial resolutions. Goal(s): This study aims to improve the data quality of k-t 3D-rFOVI-based fMRI by optimizing two key parameters in PS-Sparse reconstruction: the model order L and the sparsity regularization parameter λ. Approach: k-t 3D-rFOVI images were reconstructed with various L and λ values and compared with the images with full k-space sampling (ground truth). Results: PS-Sparse reconstruction with L/λ = 16/0 provided the best accuracy in recovering brain activation from the fMRI data. Impact: The optimal parameters determined in this study improved the performance of PS-Sparse reconstruction on image quality, allowing accurate detection of brain activations in fMRI studies using k-t 3D-rFOVI.