Exploitation of temporal redundancy in compressed sensing reconstruction of fMRI studies with a prior-based algorithm (PICCS)

摘要: Purpose: Compressed sensing is a technique used to accelerate magnetic resonance imaging (MRI) acquisition without compromising image quality. While it has proven particularly useful in dynamic procedures such as cardiac cine, very few authors have applied functional (fMRI). The purpose of the present study was check whether prior constrained compressed (PICCS) algorithm, which based on an available image, can improve statistical maps fMRI better than other strategies that also exploit temporal redundancy. Methods: PICCS compared spatiotemporal total variation (TTV) and k-t FASTER, since they already demonstrated high performance robustness MRI applications, cine resting state fMRI, respectively. for average all undersampled data. Both TTV were solved using split Bregman formulation. K-t FASTER algorithm relies matrix completion reconstruct k-spaces. three algorithms evaluated two datasets with low signal-to-noise ratio (SNR)—BOLD contrast—acquired 7 T preclinical scanner retrospectively at various rates (i.e., acceleration factors). their terms sensitivity/specificity BOLD detection through receiver operating characteristic curves by visual inspection maps. Results: With SNR studies, performed similarly state-of-the-art provided consistent signal ROI. In scenarios factors, still higher TTV, whereas failed provide significant Conclusions: comparison between reconstructions (PICCS, FASTER) redundancy fMRI. prior-based PICCS, preserved activation noisy scenarios. potentially reach factor ×8 contrast ROI area under curve over 0.99.