International journal of computer assisted radiology and surgery · 2021
Pre-surgery planning tool for estimation of resection volume to improve nasal breathing based on lattice Boltzmann fluid flow simulations.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
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История · было → стало
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Berger M, Pillei M, Giotakis A, Mehrle A, Recheis W, Kral F, Kraxner M, Riechelmann H, Freysinger W
Аннотация
PURPOSE: State-of-the-art medical examination techniques (e.g., rhinomanometry and endoscopy) do not always lead to satisfactory postoperative outcome. A fully automatized optimization tool based on patient computer tomography (CT) data to calculate local pressure gradient regions to reshape pathological nasal cavity geometry is proposed.
METHODS: Five anonymous pre- and postoperative CT datasets with nasal septum deviations were used to simulate the airflow through the nasal cavity with lattice Boltzmann (LB) simulations. Pressure gradient regions were detected by a streamline analysis. After shape optimization, the volumetric difference between the two shapes of the nasal cavity yields the estimated resection volume.
RESULTS: At LB rhinomanometry boundary conditions (bilateral flow rate of 600 ml/s), the preliminary study shows a critical pressure gradient of -1.1 Pa/mm as optimization criterion. The maximum coronal airflow ΔA := cross-section ratio [Formula: see text] found close to the nostrils is 1.15. For the patients a pressure drop ratio ΔΠ := (pre-surgery - virtual surgery)/(pre-surgery - post-surgery) between nostril and nasopharynx of 1.25, 1.72, -1.85, 0.79 and 1.02 is calculated.
CONCLUSIONS: LB fluid mechanics optimization of the nasal cavity can yield results similar to surgery for air-flow cross section and pressure drop between nostril and nasopharynx. The optimization is numerically stable in all five cases of the presented study. A limitation of this study is that anatomical constraints (e.g. mucosa) have not been considered.
METHODS: Five anonymous pre- and postoperative CT datasets with nasal septum deviations were used to simulate the airflow through the nasal cavity with lattice Boltzmann (LB) simulations. Pressure gradient regions were detected by a streamline analysis. After shape optimization, the volumetric difference between the two shapes of the nasal cavity yields the estimated resection volume.
RESULTS: At LB rhinomanometry boundary conditions (bilateral flow rate of 600 ml/s), the preliminary study shows a critical pressure gradient of -1.1 Pa/mm as optimization criterion. The maximum coronal airflow ΔA := cross-section ratio [Formula: see text] found close to the nostrils is 1.15. For the patients a pressure drop ratio ΔΠ := (pre-surgery - virtual surgery)/(pre-surgery - post-surgery) between nostril and nasopharynx of 1.25, 1.72, -1.85, 0.79 and 1.02 is calculated.
CONCLUSIONS: LB fluid mechanics optimization of the nasal cavity can yield results similar to surgery for air-flow cross section and pressure drop between nostril and nasopharynx. The optimization is numerically stable in all five cases of the presented study. A limitation of this study is that anatomical constraints (e.g. mucosa) have not been considered.