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@ARTICLE{Straub:178654,
      author       = {S. Straub$^*$ and J. Stiegeler$^*$ and E. El-Sanosy$^*$ and
                      M. Bendszus and M. E. Ladd$^*$ and T. M. Schneider},
      title        = {{A} novel gradient echo data based vein segmentation
                      algorithm and its application for the detection of regional
                      cerebral differences in venous susceptibility.},
      journal      = {NeuroImage},
      volume       = {250},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {DKFZ-2022-00183},
      pages        = {118931},
      year         = {2022},
      note         = {#EA:E020# / Volume 250, 15 April 2022, 118931},
      abstract     = {Accurate segmentation of cerebral venous vasculature from
                      gradient echo data is of central importance in several areas
                      of neuroimaging such as for the susceptibility-based
                      assessment of brain oxygenation or planning of electrode
                      placement in deep brain stimulation. In this study, a vein
                      segmentation algorithm for single- and multi-echo gradient
                      echo data is proposed. First, susceptibility maps, true
                      susceptibility-weighted images, and, in the multi-echo case,
                      R2* maps were generated from the gradient echo data. These
                      maps were filtered with an inverted Hamming filter to
                      suppress background contrast as well as artifacts from field
                      inhomogeneities at the brain boundaries. A shearlet-based
                      scale-wise representation was generated to calculate a
                      vesselness function and to generate segmentations based on
                      local thresholding. The accuracy of the proposed algorithm
                      was evaluated for different echo times and image resolutions
                      using a manually generated reference segmentation and two
                      vein segmentation algorithms (Frangi vesselness-based,
                      recursive vesselness filter) as a reference with the Dice
                      and Cohen's coefficients as well as the modified Hausdorff
                      distance. The Frangi-based and recursive vesselness filter
                      methods were significantly outperformed with regard to all
                      error metrics. Applying the algorithm, susceptibility
                      differences likely related to differences in blood
                      oxygenation between superficial and deep venous territories
                      could be demonstrated.},
      keywords     = {arteries (Other) / brain vessels (Other) / magnetic
                      resonance imaging (Other) / quantitative susceptibility
                      mapping (Other) / segmentation (Other) / veins (Other)},
      cin          = {E020 / E010},
      ddc          = {610},
      cid          = {I:(DE-He78)E020-20160331 / I:(DE-He78)E010-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:35085764},
      doi          = {10.1016/j.neuroimage.2022.118931},
      url          = {https://inrepo02.dkfz.de/record/178654},
}