Computational lung modelling in respiratory medicine

Abstract

Computational modelling of the lungs is an active field of study that integrates computational advances with lung biophysics, biomechanics, physiology and medical imaging to promote individualized diagnosis, prognosis and therapy evaluation in lung diseases. The complex and hierarchical architecture of the lung offers a rich, but also challenging, research area demanding a cross-scale understanding of lung mechanics and advanced computational tools to effectively model lung biomechanics in both health and disease. Various approaches have been proposed to study different aspects of respiration, ranging from compartmental to discrete micromechanical and continuum representations of the lungs. This article reviews several developments in computational lung modelling and how they are integrated with preclinical and clinical data. We begin with a description of lung anatomy and how different tissue components across multiple length scales affect lung mechanics at the organ level. We then review common physiological and imaging data acquisition methods used to inform modelling efforts. Building on these reviews, we next present a selection of model-based paradigms that integrate data acquisitions with modelling to understand, simulate and predict lung dynamics in health and disease. Finally, we highlight possible future directions where computational modelling can improve our understanding of the structure-function relationship in the lung.

Keywords: computational modelling; lung biomechanics; lung biophysical models; lung imaging.

Figure 1.

An idealized example of developing a computational model based on animal models and its application to patient diagnosis/treatment. CT, computed tomography; P-V, pressure–volume. Images taken from [,–12] and Cancer Research UK with permission.

Figure 2.

The hierarchical organization of the human lung components. The components are AW, airway; AD, alveolar duct; A, alveolus; E1, type I epithelial cell; E2, type II epithelial cell; S, air–liquid interface; LB, lamellar bodies. The components in the alveolar wall are El, elastin; PG, proteoglycans; C, collagen; F, fibroblasts; BM, basement membranes. Images taken from [13,14] with permission. Created using BioRender.

Figure 3.

The change in alveolar and pleural pressure during respiration and how it affects air flowing in and out of the lungs. al, alveolar; pl, pleural. Images adapted from [46].

Figure 4.

Various data acquisition methods used in clinical and preclinical settings. Blue and red headers are used to separate non-invasive from invasive methods. MRI, magnetic resonance imaging; EIT, electrical impedance tomography; PET, positron emission tomography; COPD, chronic pulmonary obstruction. 18-FDG, [¹⁸F]-fluoro-2-deoxy-d-glucose; note that there is lower uptake of 18-FDG and lower radioactivity in regions with poor aeration in the PET scan. Images are taken from [,,,–60] with permission.

Figure 5.

Schematics of the steps in developing a compartmental computational model and estimating lung properties. Images taken from [6,12,112] with permission. (a) Invasive P-V measurements, (b) compartmental model and (c) model fits to experimental data. TDC, total duct capacity.

Figure 6.

Schematics of the steps in developing an image-based computational model. EIT, electrical impedance tomography; ε_v, volumetric strain. Images taken from [6,7,13,58] with permission.