title: Super-resolution reconstruction of diffusion parameters from diffusion-weighted images with different slice orientations.
abstract: OBJECTIVE
  Diffusion MRI is hampered by long acquisition times, low spatial resolution, and a low signal-to-noise ratio. Recently, methods have been proposed to improve the trade-off between spatial resolution, signal-to-noise ratio, and acquisition time of diffusion-weighted images via super-resolution reconstruction (SRR) techniques. However, during the reconstruction, these SRR methods neglect the q-space relation between the different diffusion-weighted images.
  
  
  METHODS
  An SRR method that includes a diffusion model and directly reconstructs high resolution diffusion parameters from a set of low resolution diffusion-weighted images was proposed. Our method allows an arbitrary combination of diffusion gradient directions and slice orientations for the low resolution diffusion-weighted images, optimally samples the q- and k-space, and performs motion correction with b-matrix rotation.
  
  
  RESULTS
  Experiments with synthetic data and in vivo human brain data show an increase of spatial resolution of the diffusion parameters, while preserving a high signal-to-noise ratio and low scan time. Moreover, the proposed SRR method outperforms the previous methods in terms of the root-mean-square error.
  
  
  CONCLUSIONS
  The proposed SRR method substantially increases the spatial resolution of MRI that can be obtained in a clinically feasible scan time.
authors.affiliation: iMinds-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium.
authors.forename: Gwendolyn
authors.initials: G
authors.lastname: Van Steenkiste
authors.affiliation: iMinds-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium.
authors.forename: Ben
authors.initials: B
authors.lastname: Jeurissen
authors.affiliation: iMinds-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium.
authors.forename: Jelle
authors.initials: J
authors.lastname: Veraart
authors.affiliation: iMinds-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium.|Delft Center for Systems and Control, Delft University of Technology, 2628 CD Delft, The Netherlands.
authors.forename: Arnold J
authors.initials: AJ
authors.lastname: den Dekker
authors.affiliation: Department of Radiology, University of Antwerp, Antwerp University Hospital, Belgium.
authors.forename: Paul M
authors.initials: PM
authors.lastname: Parizel
authors.affiliation: Imaging Science and Technology, Delft University of Technology, 2628 CJ Delft, The Netherlands.|BIGR, Department of Medical informatics and Radiology, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands.
authors.forename: Dirk H J
authors.initials: DH
authors.lastname: Poot
authors.affiliation: iMinds-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium.
authors.forename: Jan
authors.initials: J
authors.lastname: Sijbers
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D004058:Diffusion; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: MRI; diffusion; diffusion tensor; motion correction; signal-to-noise ratio; super-resolution