Patient-specific simulation of particle dynamics in the respiratory airways from CT-scan-reconstructed images using a continuous phase modelling framework.

Understanding particle transport and deposition in human airway bifurcations is vital for respiratory health and inhaled therapies. CT scan images of respiratory pathways provide detailed anatomical models up to 6-12 airway generations, which are useful for airflow modelling in larger airways. However, imperfections in CT imaging, particularly at branching points, complicate the simulation of airflow and particle dynamics in smaller airways. To address these challenges, we present a computationally efficient albeit patient-specific simulation framework for the transport of micron-sized particles in respiratory pathways. This framework employs a Eulerian modelling approach that incorporates CT-scan derived patient-specific geometry along with the underlying vascular structures. To address the challenges due to limited resolution and minimize entrance effects in airflow simulations, flow extensions are added at the inlet regions, preventing distortion of airflow patterns in the lung. Particles are modelled as a continuous phase by solving equations for their concentration distribution, which are coupled with the fluid flow equations to simulate particle dynamics efficiently. The results show that particle size, flow rate, and airway structure significantly influence deposition patterns: small particles (1 μm) penetrate deeply with minimal deposition, medium-sized particles (10 μm) exhibit a balance between inertial impaction and gravitational settling, and large particles (30 μm) predominantly deposit in the upper airways. Compared to the traditional particle-tracking framework, our approach reduces computational costs while effectively capturing the key effects of the flow field on particle transport in realistic airway bifurcations. This advancement enables faster and more scalable personalized simulations for respiratory health assessments, offering a more efficient alternative to resource-intensive methods, and is crucial for applications like improving aerosol drug delivery, assessing exposure to airborne pollutants, and designing preventive health strategies.