2 research outputs found
Compressed sensing fMRI using gradient-recalled echo and EPI sequences
Compressed sensing (CS)may be useful for accelerating data acquisitions in high-resolution fMRI. However, due
to the inherent slow temporal dynamics of the hemodynamic signals and concerns of potential statistical power
loss, the CS approach for fMRI (CS–fMRI) has not been extensively investigated. To evaluate the utility of CS in
fMRI application, we systematically investigated the properties of CS–fMRI using computer simulations and
in vivo experiments of rat forepaw sensory and odor stimulations with gradient-recalled echo (GRE) and echo
planar imaging (EPI) sequences. Various undersampling patterns along the phase-encoding directionwere studied
and k–t FOCUSSwas used as the CS reconstruction algorithm,which exploits the temporal redundancy of images.
Functional sensitivity, specificity, and time courses were compared between fully-sampled and CS–fMRI
with reduction factors of 2 and 4. CS–fMRI with GRE, but not with EPI, improves the statistical sensitivity for
activation detection over the fully sampled data when the ratio of the fMRI signal change to noise is low. CS improves
the temporal resolution and reduces temporal noise correlations. While CS reduces the functional response
amplitudes, the noise variance is also reduced to make the overall activation detection more sensitive.
Consequently, CS is a valuable fMRI acceleration approach, especially for GRE fMRI studies.
© 2014 Elsevier Inc. All rights reserved.119201sciescopu