title: Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI.
abstract: BACKGROUND
  Parallel magnetic resonance imaging (MRI) is a fast imaging technique that helps acquiring highly resolved images in space/time. Its performance depends on the reconstruction algorithm, which can proceed either in the k-space or in the image domain.
  
  
  OBJECTIVE
  To improve the performance of the widely used SENSE algorithm, 2D regularization in the wavelet domain has been investigated. In this paper, we first extend this approach to 3D-wavelet representations and the 3D sparsity-promoting regularization term, in order to address reconstruction artifacts that propagate across adjacent slices. The resulting optimality criterion is convex but nonsmooth, and we resort to the parallel proximal algorithm to minimize it. Second, to account for temporal correlation between successive scans in functional MRI (fMRI), we extend our first contribution to 3D + t acquisition schemes by incorporating a prior along the time axis into the objective function.
  
  
  RESULTS
  Our first method (3D-UWR-SENSE) is validated on T1-MRI anatomical data for gray/white matter segmentation. The second method (4D-UWR-SENSE) is validated for detecting evoked activity during a fast event-related functional MRI protocol.
  
  
  CONCLUSIONS
  We show that our algorithm outperforms the SENSE reconstruction at the subject and group levels (15 subjects) for different contrasts of interest (motor or computation tasks) and two parallel acceleration factors (R = 2 and R = 4) on 2 × 2 × 3 MM(3) echo planar imaging (EPI) images.
authors.affiliation: IRIT-INP-ENSEEIHT, University of Toulouse, Toulouse, France, lotfi.chaari@enseeiht.fr.
authors.forename: Lotfi
authors.initials: L
authors.lastname: Chaari
authors.forename: Philippe
authors.initials: P
authors.lastname: Ciuciu
authors.forename: Sébastien
authors.initials: S
authors.lastname: Mériaux
authors.forename: Jean-Christophe
authors.initials: JC
authors.lastname: Pesquet
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods; D044962:Data Compression / Q000379:methods; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D062211:Spatio-Temporal Analysis; D058067:Wavelet Analysis