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 direction were studied and k-t FOCUSS was 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.
Neuroimaging Laboratory, Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15203, USA; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Xiaopeng
X
Zong
Bio-Imaging & Signal Processing Lab., Korea Advanced Institute of Science & Technology (KAIST), 373-1 Guseong-Dong, Yuseong-Gu, Daejon 305-701, Republic of Korea.
Juyoung
J
Lee
Neuroimaging Laboratory, Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15203, USA.
Alexander
A
John Poplawsky
Neuroimaging Laboratory, Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15203, USA; Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Sungkyunkwan University, Suwon 440-746, Republic of Korea; Department of Biological Science, Sungkyunkwan University, Suwon 440-746, Republic of Korea.
Seong-Gi
SG
Kim
Bio-Imaging & Signal Processing Lab., Korea Advanced Institute of Science & Technology (KAIST), 373-1 Guseong-Dong, Yuseong-Gu, Daejon 305-701, Republic of Korea.
Jong Chul
JC
Ye
D000465:Algorithms; D000818:Animals; D001931:Brain Mapping / Q000379:methods*; D044962:Data Compression / Q000379:methods*; D017352:Echo-Planar Imaging / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D064847:Multimodal Imaging / Q000379:methods; D051381:Rats; D017207:Rats, Sprague-Dawley; D015203:Reproducibility of Results; D012677:Sensation / Q000502:physiology*; D012680:Sensitivity and Specificity; D013003:Somatosensory Cortex / Q000502:physiology*
BOLD; CBV; Compressed sensing; High magnetic field; Olfactory bulb; Somatosensory cortex; fMRI; k–t FOCUSS