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Parallel MRI is a fast imaging technique that enables the acquisition ofhighly resolved images in space. It relies on $k$-space undersampling andmultiple receiver coils with complementary sensitivity profiles in order toreconstruct a full Field-Of-View (FOV) image. The performance of parallelimaging mainly depends on the reconstruction algorithm, which can proceedeither in the original $k$-space (GRAPPA, SMASH) or in the image domain(SENSE-like methods). To improve the performance of the widely used SENSEalgorithm, 2D- or slice-specific regularization in the wavelet domain has beenefficiently investigated. In this paper, we extend this approach using3D-wavelet representations in order to handle all slices together and addressreconstruction artifacts which propagate across adjacent slices. The extensionalso accounts for temporal correlations that exist between successive scans infunctional MRI (fMRI). The proposed 4D reconstruction scheme is fully\emph{unsupervised} in the sense that all regularization parameters areestimated in the maximum likelihood sense on a reference scan. The gain inducedby such extensions is first illustrated on EPI image reconstruction but alsomeasured in terms of statistical sensitivity during a fast event-related fMRIprotocol. The proposed 4D-UWR-SENSE algorithm outperforms the SENSEreconstruction at the subject and group-levels (15 subjects) for differentcontrasts of interest and using different parallel acceleration factors on$2\times2\times3$mm$^3$ EPI images.

4D Wavelet-Based Regularization for Parallel MRI Reconstruction: Impact on Subject and Group-Levels Statistical Sensitivity in fMRI