Abstract
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.