Compressed sensing for fMRI at high field

Abstract

Conventional functional magnetic resonance imaging (fMRI) technique known as gradient recalled echo (GRE) echo-planar imaging (EPI) is too sensitive to image distortion and degradation caused by local magnetic field inhomogeneity at high magnetic fields. Pass-band balanced steady state free precession (bSSFP) has been proposed as an alternative high-resolution fMRI technique, however, the temporal resolution of bSSFP fMRI is lower than the typically used GRE-EPI fMRI. One potential approach to improve the temporal resolution of bSFFP fMRI is to use compressed sensing (CS). Recently, several fMRI studies have applied CS to GRE-EPI and spiral scan, although it is known that GRE-EPI generally suffers from the contribution of magnetic field inhomogeneity which can degrade the performance of CS algorithms. Although suffering from banding artifacts, bSSFP utilizes different radio frequency (RF) excitations for each K-space lines, thus may work better with CS algorithms than GRE-EPI. In this study, we tested the feasibility of a CS algorithm, called k-t FOCUSS, for both GRE and bSSFP fMRI at 9.4T using the model of rat somatosensory stimulation. Experimental results show k-t FOCUSS algorithm with sampling reduction by a factor of 4 works well for both GRE and bSSFP fMRI at high field. The combination of CS algorithm with bSSFP may be a good solution for improving the temporal resolution of fMRI at high field.