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@ARTICLE{Lindemeyer:861302,
      author       = {Lindemeyer, J. and Oros-Peusquens, A.-M. and Shah, N. J.},
      title        = {{Q}uality-based {U}nw{R}ap of {SU}bdivided {L}arge {A}rrays
                      ({URSULA}) for high-resolution {MRI} data},
      journal      = {Medical image analysis},
      volume       = {52},
      issn         = {1361-8415},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2019-01797},
      pages        = {13 - 23},
      year         = {2019},
      abstract     = {In Magnetic Resonance Imaging, mapping of the static
                      magnetic field and the magnetic susceptibility is based on
                      multidimensional phase measurements. Phase data are
                      ambiguous and have to be unwrapped to their true range in
                      order to exhibit a correct representation of underlying
                      features. High-resolution imaging at ultra-high fields,
                      where susceptibility and phase contrast are natural tools,
                      can generate large datasets, which tend to dramatically
                      increase computing time demands for spatial unwrapping
                      algorithms. This article describes a novel method, URSULA,
                      which introduces an artificial volume compartmentalisation
                      that allows large-scale unwrapping problems to be broken
                      down, making URSULA ideally suited for computational
                      parallelisation. In the presented study, URSULA is
                      illustrated with a quality-guided unwrapping approach.
                      Validation is performed on numerical data and an application
                      on a high-resolution measurement, at the clinical field
                      strength of 3T is demonstrated. In conclusion, URSULA allows
                      for a reduction of the problem size, a substantial speed-up
                      and for handling large data sets without sacrificing the
                      overall accuracy of the resulting phase information.},
      cin          = {INM-4 / INM-11 / JARA-BRAIN},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-4-20090406 / I:(DE-Juel1)INM-11-20170113 /
                      $I:(DE-82)080010_20140620$},
      pnm          = {573 - Neuroimaging (POF3-573)},
      pid          = {G:(DE-HGF)POF3-573},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30468969},
      UT           = {WOS:000457512600002},
      doi          = {10.1016/j.media.2018.11.004},
      url          = {https://juser.fz-juelich.de/record/861302},
}