A voxel image-based pulmonary airflow simulation method with an automatic detection algorithm for airway outlets.

Investigations of pulmonary airflows in respiratory systems are important for the diagnostics and treatment of pulmonary diseases. For accurate prediction of the flow field in an airway, a numerical simulation must be conducted using the true geometry from computed tomography (CT) data. Flow simulation is still a difficult task because of the mesh generation process and preprocessing setup procedures. In this study, we developed a voxel image-based simulation method using an automatic detection algorithm for airway outlets to simplify the simulation process and improve its applicability in the medical field. Our approach is based on the lattice Boltzmann method with a topology analysis algorithm, which can preserve all raw information from the original CT images and give an accurate flow field inside the airways. Our method can reproduce the essential flow features inside airways, is highly efficient, and decreases the overall processing time. Thus, it has a great potential for future real-time airflow analyses to provide airflow information to medical experts. HIGHLIGHTS: This paper proposed a voxel image-based simulation method with a novel automatic outlet-selecting algorithm to calculate the velocity and pressure of physiological flows in multi-generation-branched airways. Our approach simplifies the simulation process by automatically applying the boundary conditions to large numbers of outlets and minimizes the time-consuming mesh generation process. Our proposed method has considerable potential for real-time simulations improving the applicability to patient-specific medical diagnostics and treatments.