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@ARTICLE{Liu:1026684,
      author       = {Liu, Xin and Rüttgers, Mario and Quercia, Alessio and
                      Egele, Romain and Pfaehler, Elisabeth and Shende, Rushikesh
                      and Aach, Marcel and Schröder, Wolfgang and Balaprakash,
                      Prasanna and Lintermann, Andreas},
      title        = {{R}efining computer tomography data with super-resolution
                      networks to increase the accuracy of respiratory flow
                      simulations},
      journal      = {Future generation computer systems},
      volume       = {159},
      issn         = {0167-739X},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2024-03499},
      pages        = {474 - 488},
      year         = {2024},
      abstract     = {Accurately computing the flow in the nasal cavity with
                      computational fluid dynamics (CFD) simulations requires
                      highly resolved computational meshes based on anatomically
                      realistic geometries. Such geometries can only be obtained
                      from computer tomography (CT) data with high spatial
                      resolution, i.e., featuring a ≤ 1 mm slice thickness. In
                      practice, CT images are, however, recorded at a lower
                      resolution to not expose patients to high radiation and to
                      reduce the overall costs. To overcome this problem and to
                      provide patients with a detailed physics-based diagnosis,
                      e.g., for surgery planning, the potential of
                      super-resolution networks (SRNs) to increase the CT
                      resolution is analyzed. Therefore, an SRN is developed and
                      trained on CT data. Its predictive performance is improved
                      by an automated hyperparameter optimization technique. The
                      training time is further reduced without predictive accuracy
                      degradation by oversampling images with challenging regions.
                      The performance of the SRN is assessed by an analysis of the
                      reconstructed 3D surfaces of the human upper airway and by
                      comparing results of CFD simulations. That is, surfaces and
                      simulation results based on SRN-generated CT data at 1 mm
                      resolution are compared to those obtained from unmodified CT
                      data-sets at low (3 mm) and high (1 mm) resolution, as well
                      as from CT data interpolated to a 1 mm resolution from
                      coarse data. The findings reveal the SRN-based approach to
                      have the lowest deviations in the surfaces and CFD results
                      when compared to those based on the original high-resolution
                      data. The pressure loss between the inflow (nostrils) and
                      outflow (pharynx) regions averaged for three test patients
                      differs by only $1.3\%,$ compared to $8.7\%$ and $8.8\%$ in
                      the coarse and interpolated cases. It is concluded that the
                      SRN-based method is a promising tool to enhance
                      underresolved CT data to yield reliable numerical results of
                      respiratory flows.},
      cin          = {JSC / IAS-8 / INM-4},
      ddc          = {004},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)IAS-8-20210421 /
                      I:(DE-Juel1)INM-4-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / RAISE - Research on AI- and
                      Simulation-Based Engineering at Exascale (951733) / JLESC -
                      Joint Laboratory for Extreme Scale Computing
                      (JLESC-20150708) / HDS LEE - Helmholtz School for Data
                      Science in Life, Earth and Energy (HDS LEE)
                      (HDS-LEE-20190612)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(EU-Grant)951733 /
                      G:(DE-Juel1)JLESC-20150708 / G:(DE-Juel1)HDS-LEE-20190612},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001249701400001},
      doi          = {10.1016/j.future.2024.05.020},
      url          = {https://juser.fz-juelich.de/record/1026684},
}