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Parallel MRI is a fast imaging technique that enables the acquisition ofhighly resolved images in space or/and in time. The performance of parallelimaging strongly 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 beendeeply investigated. In this paper, we extend this approach using 3D-waveletrepresentations in order to handle all slices together and addressreconstruction artifacts which propagate across adjacent slices. The gaininduced by such extension (3D-Unconstrained Wavelet Regularized -SENSE:3D-UWR-SENSE) is validated on anatomical image reconstruction where no temporalacquisition is considered. Another important extension accounts for temporalcorrelations that exist between successive scans in functional MRI (fMRI). Inaddition to the case of 2D+t acquisition schemes addressed by some othermethods like kt-FOCUSS, our approach allows us to deal with 3D+t acquisitionschemes which are widely used in neuroimaging. The resulting 3D-UWR-SENSE and4D-UWR-SENSE reconstruction schemes are fully unsupervised in the sense thatall regularization parameters are estimated in the maximum likelihood sense ona reference scan. The gain induced by such extensions is illustrated on bothanatomical and functional image reconstruction, and also measured in terms ofstatistical sensitivity for the 4D-UWR-SENSE approach during a fastevent-related fMRI protocol. Our 4D-UWR-SENSE algorithm outperforms the SENSEreconstruction at the subject and group levels (15 subjects) for differentcontrasts of interest (eg, motor or computation tasks) and using differentparallel acceleration factors (R=2 and R=4) on 2x2x3mm3 EPI images.

Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI