Impact of the parallel imaging reconstruction algorithm on brain activity detection in fMRI

PMRI is a fast imaging technique that allows reconstruction of full FoV images based on undersampled k-space data acquired using multiple reciever coils with complementary sensitivity profiles. It enables the acquisition of highly resolved images either in space or in time, which is of particular interest in applications like neuroimaging. These improvements are counterbalanced by a degraded SNR and the presence of artifacts that depend on the reconstruction algorithm. To improve the performance of the widely used SENSE algorithm, regularization in the wavelet domain has been efficiently investigated. In this paper, we illustrate the gain induced by such a regularization in terms of statistical impact on fMRI data analysis using a fast-event related protocol. Our results show that the reconstruction algorithm has a dramatic impact on the statistical sensitivity in fMRI and that the proposed method outperforms classical SENSE reconstruction at the subject-level using different acquisition setups.