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@ARTICLE{Waldmann:904122,
      author       = {Waldmann, Moritz and Grosch, Alice and Witzler, Christian
                      and Lehner, Matthias and Benda, Odo and Koch, Walter and
                      Vogt, Klaus and Kohn, Christopher and Schröder, Wolfgang
                      and Göbbert, Jens Henrik and Lintermann, Andreas},
      title        = {{A}n effective simulation- and measurement-based workflow
                      for enhanced diagnostics in rhinology},
      journal      = {Medical $\&$ biological engineering $\&$ computing},
      volume       = {60},
      number       = {2},
      issn         = {0140-0118},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {FZJ-2021-05692},
      pages        = {365-391},
      year         = {2022},
      abstract     = {Physics-based analyses have the potential to consolidate
                      and substantiate medical diagnoses in rhinology. Such
                      methods are frequently subject to intense investigations in
                      research. However, they are not used in clinical
                      applications, yet. One issue preventing their direct
                      integration is that these methods are commonly developed as
                      isolated solutions which do not consider the whole chain of
                      data processing from initial medical to higher valued data.
                      This manuscript presents a workflow that incorporates the
                      whole data processing pipeline based on a environment.
                      Therefore, medical image data are fully automatically
                      pre-processed by machine learning algorithms. The resulting
                      geometries employed for the simulations on high-performance
                      computing systems reach an accuracy of up to $99.5\%$
                      compared to manually segmented geometries. Additionally, the
                      user is enabled to upload and visualize 4-phase
                      rhinomanometry data. Subsequent analysis and visualization
                      of the simulation outcome extend the results of standardized
                      diagnostic methods by a physically sound interpretation.
                      Along with a detailed presentation of the methodologies, the
                      capabilities of the workflow are demonstrated by evaluating
                      an exemplary medical case. The pipeline output is compared
                      to 4-phase rhinomanometry data. The comparison underlines
                      the functionality of the pipeline. However, it also
                      illustrates the influence of mucosa swelling on the
                      simulation.},
      cin          = {JSC},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / 5112 - Cross-Domain
                      Algorithms, Tools, Methods Labs (ATMLs) and Research Groups
                      (POF4-511) / Analysis of Respiratory and Cerebrospinal Flows
                      by a Coupled Lattice-Boltzmann Method and Machine Learning
                      Approach $(jhpc54_20190501)$ / Rhinodiagnost
                      $(jhpc54_20180501)$},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-HGF)POF4-5112 /
                      $G:(DE-Juel1)jhpc54_20190501$ /
                      $G:(DE-Juel1)jhpc54_20180501$},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {34950998},
      UT           = {WOS:000733700900001},
      doi          = {10.1007/s11517-021-02446-3},
      url          = {https://juser.fz-juelich.de/record/904122},
}