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@ARTICLE{Rttgers:1043189,
      author       = {Rüttgers, Mario and Waldmann, Moritz and Hübenthal,
                      Fabian and Vogt, Klaus and Tsubokura, Makoto and Lee,
                      Sangseung and Lintermann, Andreas},
      title        = {{T}owards a widespread usage of computational fluid
                      dynamics simulations for automated virtual nasal surgery
                      planning},
      journal      = {Future generation computer systems},
      volume       = {174},
      issn         = {0167-739X},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2025-02797},
      pages        = {107935},
      year         = {2025},
      abstract     = {Efficient computational approaches are crucial for
                      advancing computational fluid dynamics (CFD)-based automated
                      planning in nasal surgeries, such as septoplasties and
                      turbinectomies. This study introduces a hybrid
                      lattice-Boltzmann and level-set method to address the
                      trade-off between computational cost and automation. By
                      interpolating geometry variations in discrete steps between
                      pre-surgical and target states, the approach achieves
                      computational efficiency with only 21 surface variations per
                      intervention. Previous methods rely on more costly coupling
                      strategies, such as reinforcement learning or thermal
                      modeling, which may still be appropriate for complex
                      planning scenarios involving multiple intervention sites or
                      thermal flow analysis. In contrast, the presented method
                      reduces complexity while retaining key predictive
                      capabilities, making it particularly suitable for
                      widespread, time-sensitive clinical use focused on a single
                      surgical intervention. Fluid mechanical metrics, including
                      pressure loss and volume flow rate balance, are evaluated
                      alongside tissue removal volume to recommend optimized
                      surgical plans. Case studies on three patients demonstrate
                      tissue savings of $12–25\%$ without compromising key flow
                      parameters. Additionally, a non-linear regression model
                      trained on as few as 11 CFD simulations predicts pressure
                      loss and flow rates with errors below $4\%,$ and reduces
                      computational costs by $50\%.$ The proposed framework
                      represents a significant step toward making CFD-based
                      virtual nasal surgery planning more accessible and
                      practical.},
      cin          = {JSC},
      ddc          = {004},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / HANAMI - Hpc
                      AlliaNce for Applications and supercoMputing Innovation: the
                      Europe - Japan collaboration (101136269)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)101136269},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001512546300001},
      doi          = {10.1016/j.future.2025.107935},
      url          = {https://juser.fz-juelich.de/record/1043189},
}