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| 001 | 1043189 | ||
| 005 | 20250804202237.0 | ||
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| 100 | 1 | _ | |a Rüttgers, Mario |0 P:(DE-Juel1)177985 |b 0 |u fzj |
| 245 | _ | _ | |a Towards a widespread usage of computational fluid dynamics simulations for automated virtual nasal surgery planning |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2025 |b Elsevier Science |
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| 520 | _ | _ | |a 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. |
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| 700 | 1 | _ | |a Tsubokura, Makoto |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Lee, Sangseung |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Lintermann, Andreas |0 P:(DE-Juel1)165948 |b 6 |e Corresponding author |
| 773 | _ | _ | |a 10.1016/j.future.2025.107935 |g Vol. 174, p. 107935 - |0 PERI:(DE-600)2020551-X |p 107935 |t Future generation computer systems |v 174 |y 2026 |x 0167-739X |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1043189/files/1-s2.0-S0167739X25002304-main.pdf |y OpenAccess |
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