Compressed sensing fMRI using gradient-recalled echo and EPI sequences

Jong Chul Ye just sent me the following:

Dear Igor,

I just wanted to let you know about our recent NeuroImage paper on in vivo compressed sensing fMRI (http://bisp.kaist.ac.kr/papers/YNIMG2014_NeuroImg.pdf). As you can see from our rat odor stimulation experiments, one of the nice things about applying CS to fMRI is that it improves sensitivity significantly. Most of researchers (including myself) who have conducted retrospective downsampling fMRI experiments have expected that CS is at most as good as the fully sampled case. However, in real in vivo experiment, the accelerated acquisition from CS improves the temporal resolution, which results in sensitivity increases. This indicates significant potentials in using CS for fMRI applications. Enjoy !

Best,

-Jong

Thanks Jong. Here is the paper:

Compressed sensing fMRI using gradient-recalled echo and EPI sequences by Xiaopeng Zong, Juyoung Lee, Alexander Poplawsky, Seong-Gi Kima, Jong Chul Ye

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 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.

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