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@ARTICLE{Dorn:141170,
      author       = {S. Dorn$^*$ and S. Chen and S. Sawall$^*$ and J. Maier$^*$
                      and M. Knaup$^*$ and M. Uhrig$^*$ and H.-P. Schlemmer$^*$
                      and A. Maier and M. Lell and M. Kachelriess$^*$},
      title        = {{T}owards context-sensitive {CT} imaging - organ-specific
                      image formation for single ({SECT}) and dual energy computed
                      tomography ({DECT}).},
      journal      = {Medical physics},
      volume       = {45},
      number       = {10},
      issn         = {0094-2405},
      address      = {College Park, Md.},
      publisher    = {AAPM},
      reportid     = {DKFZ-2018-01697},
      pages        = {4541 - 4557},
      year         = {2018},
      abstract     = {The purpose of this study was to establish a novel paradigm
                      to facilitate radiologists' workflow - combining mutually
                      exclusive CT image properties that emerge from different
                      reconstructions, display settings and organ-dependent
                      spectral evaluation methods into a single context-sensitive
                      imaging by exploiting prior anatomical information.The CT
                      dataset is segmented and classified into different organs,
                      for example, the liver, left and right kidney, spleen,
                      aorta, and left and right lung as well as into the tissue
                      types bone, fat, soft tissue, and vessels using a cascaded
                      three-dimensional fully convolutional neural network (CNN)
                      consisting of two successive 3D U-nets. The binary organ and
                      tissue masks are transformed to tissue-related weighting
                      coefficients that are used to allow individual
                      organ-specific parameter settings in each anatomical region.
                      Exploiting the prior knowledge, we develop a novel paradigm
                      of a context-sensitive (CS) CT imaging consisting of a
                      prior-based spatial resolution (CSR), display (CSD), and
                      dual energy evaluation (CSDE). The CSR locally emphasizes
                      desired image properties. On a per-voxel basis, the
                      reconstruction most suitable for the organ, tissue type, and
                      clinical indication is chosen automatically. Furthermore, an
                      organ-specific windowing and display method is introduced
                      that aims at providing superior image visualization. The
                      CSDE analysis allows to simultaneously evaluate multiple
                      organs and to show organ-specific DE overlays wherever
                      appropriate. The ROIs that are required for a
                      patient-specific calibration of the algorithms are
                      automatically placed into the corresponding anatomical
                      structures. The DE applications are selected and only
                      applied to the specific organs based on the prior knowledge.
                      The approach is evaluated using patient data acquired with a
                      dual source CT system. The final CS images simultaneously
                      link the indication-specific advantages of different
                      parameter settings and result in images combining
                      tissue-related desired image properties.A comparison with
                      conventionally reconstructed images reveals an improved
                      spatial resolution in highly attenuating objects and in air
                      while the compound image maintains a low noise level in soft
                      tissue. Furthermore, the tissue-related weighting
                      coefficients allow for the combination of varying settings
                      into one novel image display. We are, in principle, able to
                      automate and standardize the spectral analysis of the DE
                      data using prior anatomical information. Each tissue type is
                      evaluated with its corresponding DE application
                      simultaneously.This work provides a proof of concept of CS
                      imaging. Since radiologists are not aware of the presented
                      method and the tool is not yet implemented in everyday
                      clinical practice, a comprehensive clinical evaluation in a
                      large cohort might be topic of future research. Nonetheless,
                      the presented method has potential to facilitate workflow in
                      clinical routine and could potentially improve diagnostic
                      accuracy by improving sensitivity for incidental findings.
                      It is a potential step toward the presentation of evermore
                      increasingly complex information in CT and toward improving
                      the radiologists workflow significantly since dealing with
                      multiple CT reconstructions may no longer be necessary. The
                      method can be readily generalized to multienergy data and
                      also to other modalities.},
      cin          = {E020 / E010 / E025},
      ddc          = {610},
      cid          = {I:(DE-He78)E020-20160331 / I:(DE-He78)E010-20160331 /
                      I:(DE-He78)E025-20160331},
      pnm          = {315 - Imaging and radiooncology (POF3-315)},
      pid          = {G:(DE-HGF)POF3-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30098038},
      doi          = {10.1002/mp.13127},
      url          = {https://inrepo02.dkfz.de/record/141170},
}