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

Jonathan Stéphano, Michaël Brunengo, Riccardo Di Dio, Thomas Laporte, Benjamin Mauroy

TL;DR

The paper addresses the challenge of capturing spatially resolved ventilation dynamics in the human lung, which is difficult with models that assume uniform airway pathways. It proposes a hybrid multiscale framework that fuses CT-derived 3D geometries of the lungs and large airways with an algorithmically generated small-airway network, solving coupled tissue mechanics and airway flow using a nonlinear incompressible Navier–Stokes formulation with boundary conditions driven by thoracic pressures. Key contributions include a unified geometry/mesh pipeline, a generalized resistance matrix for asymmetric branching, and explicit mapping from tissue deformation to local airway pressures to produce distributions of wall shear stress and regional flows, demonstrated on a rest-ventilation scenario with thousands of airways. The framework offers a platform to study mucus transport and airway mechanics in a spatially resolved manner, with potential pathways toward experimental validation and clinical translation, such as validation through imaging modalities and application to chest physiotherapy planning.

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.

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

TL;DR

The paper addresses the challenge of capturing spatially resolved ventilation dynamics in the human lung, which is difficult with models that assume uniform airway pathways. It proposes a hybrid multiscale framework that fuses CT-derived 3D geometries of the lungs and large airways with an algorithmically generated small-airway network, solving coupled tissue mechanics and airway flow using a nonlinear incompressible Navier–Stokes formulation with boundary conditions driven by thoracic pressures. Key contributions include a unified geometry/mesh pipeline, a generalized resistance matrix for asymmetric branching, and explicit mapping from tissue deformation to local airway pressures to produce distributions of wall shear stress and regional flows, demonstrated on a rest-ventilation scenario with thousands of airways. The framework offers a platform to study mucus transport and airway mechanics in a spatially resolved manner, with potential pathways toward experimental validation and clinical translation, such as validation through imaging modalities and application to chest physiotherapy planning.

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.
Paper Structure (5 sections, 4 equations, 2 figures)

This paper contains 5 sections, 4 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Methodology
  3. 3D geometrical models
  4. Small airways models
  5. Results and conclusions

Figures (2)

  • Figure 1: Example of lung model. a: Large airways reconstructed from ct-scans. b: Airways in gray are reconstructed from CT scans and include 10 terminal openings $(O_i)_{i=1..10}$. Each opening connects to a green airway subtree generated by our algorithm. c: Size statistics for airways generated by the reconstruction algorithm (green in panel a). Top: airways daughter-to-mother diameter ratio distribution. Bottom: airways length-to-diameter ratio distribution. Both distributions are consistent with observed data. d: Reconstructed 3D model: large airways (gray scale: air velocity), left and right lungs (colors: index of $(A_i)_{i=1..10}$ decomposition), and their idealized thoracic envelope.
  • Figure 2: Lung model simulation at rest. a: Top: Applied thoracic pressure over time. Bottom : Corresponding airflow–volume loop. Hydrodynamic resistance: $1.09 \times 10^5$ Pa·m$^{-3}$·s ($1.09$ cmH$_2$O·L$^{-1}$·s); compliance: $0.475 \times 10^{-6}$ m$^3$·Pa$^{-1}$ ($0.0475$ L·cmH$_2$O$^{-1}$). b: Front view of the lung model, color-coded for local airflow. The complex pattern arises from the heterogeneous hydrodynamic resistance of the paths feeding each peripheral region $B_{i,k}$. c: Mean shear rates over one cycle in the reconstructed airways (rear view). d: Shear rate distributions at peak flow. Top: Shear rate distribution vs. airway radius. Lower right: Overall shear rate distribution at peak inspiration (red) and expiration (blue). Lower left: Time-varying hydrodynamic resistance of the ten subtrees.