  A number of reconstruction methods have been proposed recently for
accelerated functional Magnetic Resonance Imaging (fMRI) data collection.
However, existing methods suffer with the challenge of greater artifacts at
high acceleration factors. This paper addresses the issue of accelerating fMRI
collection via undersampled k-space measurements combined with the proposed
Double Temporal Sparsity based Reconstruction (DTSR) method with the l1 -l1
norm constraint. The robustness of the proposed DTSR method has been thoroughly
evaluated both at the subject level and at the group level on real fMRI data.
Results are presented at various acceleration factors. Quantitative analysis in
terms of Peak Signal-to-Noise Ratio (PSNR) and other metrics, and qualitative
analysis in terms of reproducibility of brain Resting State Networks (RSNs)
demonstrate that the proposed method is accurate and robust. In addition, the
proposed DTSR method preserves brain networks that are important for studying
fMRI data. Compared to the existing accelerated fMRI reconstruction methods,
the DTSR method shows promising potential with an improvement of 10-12dB in
PSNR with acceleration factors upto 3.5. Simulation results on real data
demonstrate that DTSR method can be used to acquire accelerated fMRI with
accurate detection of RSNs.
