Spatio-temporal air flow properties in a 3D personalised model of the human lung

TL;DR

This paper introduces a multi-scale, personalized 3D lung model combining CT-derived geometries and fluid dynamics to analyze spatio-temporal airflow and shear stresses.

Contribution

It presents a novel integrated model that captures detailed lung mechanics and airflow dynamics for personalized respiratory analysis.

Findings

Simulations reveal detailed airflow patterns and shear stress distributions.

The model successfully integrates CT data with fluid-structure interaction.

It provides a platform for studying lung ventilation in health and disease.

Abstract

We propose a multi-scale lung model to investigate spatio-temporal distributions of ventilation variables. Lung envelope and large airway geometries are derived from CT scans; smaller airways are generated using a physiologically consistent algorithm. Tissue mechanics is modeled using nonlinear elasticity under small deformations, coupled with local air pressure from fluid dynamics within the bronchial tree. Airflow accounts for inertia and static airway compliance. Simulations employ finite elements. Using this model, we explore spatio-temporal airflows and shear stresses distributions.