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@ARTICLE{Krajsek:810743,
      author       = {Krajsek, Kai and Menzel, Marion I. and Scharr, Hanno},
      title        = {{A} {R}iemannian {B}ayesian {F}ramework for {E}stimating
                      {D}iffusion {T}ensor {I}mages},
      journal      = {International journal of computer vision},
      volume       = {120},
      number       = {3},
      issn         = {1573-1405},
      address      = {Dordrecht [u.a.]},
      publisher    = {Springer Science + Business Media B.V},
      reportid     = {FZJ-2016-03335},
      pages        = {272–299},
      year         = {2016},
      abstract     = {Diffusion tensor magnetic resonance imaging (DT-MRI) is a
                      non-invasive imaging technique allowing to estimate the
                      molecular self-diffusion tensors of water within surrounding
                      tissue. Due to the low signal-to-noise ratio of magnetic
                      resonance images, reconstructed tensor images usually
                      require some sort of regularization in a post-processing
                      step. Previous approaches are either suboptimal with respect
                      to the reconstruction or regularization step. This paper
                      presents a Bayesian approach for simultaneous reconstruction
                      and regularization of DT-MR images that allows to resolve
                      the disadvantages of previous approaches. To this end,
                      estimation theoretical concepts are generalized to tensor
                      valued images that are considered as Riemannian manifolds.
                      Doing so allows us to derive a maximum a posteriori
                      estimator of the tensor image that considers both the
                      statistical characteristics of the Rician noise occurring in
                      MR images as well as the nonlinear structure of tensor
                      valued images. Experiments on synthetic data as well as real
                      DT-MRI data validate the advantage of considering both
                      statistical as well as geometrical characteristics of
                      DT-MRI.},
      cin          = {IBG-2 / IEK-8},
      ddc          = {004},
      cid          = {I:(DE-Juel1)IBG-2-20101118 / I:(DE-Juel1)IEK-8-20101013},
      pnm          = {582 - Plant Science (POF3-582) / 583 - Innovative
                      Synergisms (POF3-583)},
      pid          = {G:(DE-HGF)POF3-582 / G:(DE-HGF)POF3-583},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000382977200003},
      doi          = {10.1007/s11263-016-0909-2},
      url          = {https://juser.fz-juelich.de/record/810743},
}