Pre-surgery planning tool for estimation of resection volume to improve nasal breathing based on lattice Boltzmann fluid flow simulations

Abstract

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 $$\frac{\mathrm{virtual surgery }}{\mathrm{post}-\mathrm{surgery}}$$ virtual surgery post - surgery 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.