Examining the airflow in the nasal cavity is important for understanding nasal obstruction, air conditioning in the nose, and physical aspects of olfaction. Despite this importance, the influence of the nasal geometry on the airflow is poorly understood. This is in part because direct experimental measurements of the flow are impossible. An alternative is to simulate the airflow using computational fluid dynamics. However, because the nasal cavity is a complex structure with a characteristic length scale of only 3 mm, small variations in the geometry can potentially have a large impact on the measured flow.
In our paper published in Biomedical Physics & Engineering Express, we present the first validated algorithm for reconstructing nasal geometries from CT scans. Our algorithm is based on a radiodensity threshold to separate the air-filled nasal cavity from the surrounding soft tissue. We determine the best parameters and the sensitivity of the resulting reconstruction using a so-called CT phantom, which is a plastic object of known geometry. Moreover, we use our algorithm in a proof-of-principle study to analyze 3 human nasal geometries. Our study indicates that choosing the correct threshold value is crucial since the measured resistance can vary significantly depending on this value.
Our algorithm can be automated, enabling the analysis of many nasal cavities to provide a good statistical picture of the airflow. For instance, such large-scale clinical trials could improve our understanding of nasal obstruction and how to handle it surgically in the future.