Lung CT AI Enables Advanced Computer Modeling of Lung Physiome Structure and Function

Virtual Physiological Human and a Lung Physiome Model

The International Union of Physiological Physiome project was the foundation for the Virtual Physiological Human (VPH) initiative and the human physiome. The term physiome describes the physiology of the whole organism.

The concept of computational physiology and the human physiome is to have mathematicians and bioengineers, working together with physiologists and molecular biologists, link together the different scales of human biology quantitative models beginning with genomic and proteomic databases, and linking these to higher levels of organization at the cell, tissue, organ, and whole organism level. The mathematical and engineering tools needed to develop quantitative models of physiological dynamics and functional behavior of the intact organism need to account for inhomogeneous, anisotropic, and nonlinear behavior of biological materials.

A complete computational model of lung function will need to span multiple spatial and temporal scales (multiscale model). This is necessary to understand how dynamic molecular interactions at small spatial dimensions link to whole lung function at large spatial dimensions. A multiscale model will use a computationally efficient strategy to capture the important functions for each spatial and temporal scale.

Physical forces acting on the surface of the lung through coupling with the chest wall are transmitted to the level of the gas exchange tissue, pulmonary acinus, where this force holds the blood vessels and airways open. The lung surface forces are further transmitted to the level of cells and molecules within the lung tissue where the local stress produced by the lung surface force modulates local cellular and molecular functions.

The stretching of lung tissue produces secretion of surfactant from the type II alveolar epithelial cells that line the pulmonary acinus along with the type I alveolar epithelial cells. The release of surfactant reduces the surface tension of the air-tissue surface of the pulmonary acinus, which decreases the lung surface forces needed to keep the acinar lumen from collapsing and, in this way, alters global lung mechanics.

Pulmonary airway antagonists, such as inhaled allergens (e.g., pollen), act at the cellular level by inducing airway smooth muscle contraction. This results in a subsequent larger-scale narrowing of airway lumens. The narrowed airway lumens in turn produce increases in airway resistance. The increase in airway resistance then produces an even larger scale decrease in whole lung ventilation.

The obstruction of a pulmonary artery lumen by acute thromboemboli, blood clot, induces local disruption of pulmonary artery blood flow that alters the shear stress of endothelial cells on a small scale in the area of the blood clot where pulmonary blood flow has decreased. This decrease in shear stress on the endothelial cells activates the release of nitric oxide by the endothelium, which is a potent vasodilator. The nitric oxide dilates on a larger scale the pulmonary vessels. The dilation of the pulmonary vessels alters on an even larger scale whole lung blood flow.

This chapter will discuss a sophisticated human lung physiome model that includes patient-specific 3D lung CT images as the structural input to a patient-specific multiscale lung model that predicts whole lung physiology of the patient. In previous chapters we have seen how different lung CT AI can assess lung density for the presence of emphysema and pulmonary fibrosis. We have discussed how combining information from inspiratory and expiratory chest CT scans can be used to assess ventilation at different scales in the lung (e.g., whole lung, lobe, voxel). We have also seen how powerful limited memory AI algorithms can be used to detect and assess different texture patterns produced by diffuse lung disease and to assess whether a lung nodule is benign or malignant. In this chapter, we will see how the 3D lung CT structure of the lung including airways, pulmonary arteries, and veins can be used to construct a patient-specific multiscale finite element model of the lung that can predict hypoxemic risk due to acute pulmonary emboli.

Tawhai et al. published details of their lung physiome/VPH model. We will refer to this model as the LP model in this chapter. The LP model builds a complete model of lung structure and how this structure interacts with lung function across a wide range of spatial scales, physical functions, and their integration. The robustness of the LP model is applicable to a wide range of physiological and pathophysiological areas of interest.

The LP model is applicable not only to the risk of gas exchange impairment elevating right ventricular pressure in patients with acute pulmonary embolism but also to the mechanics of airway hyperresponsiveness at the scale of airway smooth muscle to the scale of whole lungs. The LP model can also assess the effects of normal aging on tissue mechanics and optimize methods of mechanical ventilation of the lungs.

Finite Element Model of Lung Structure and Function

There are several major steps in building the LP model of the lung. High-quality 3D chest CT scan is acquired of the thorax and the lungs are segmented from the rest of the thoracic anatomy. The airway tree and pulmonary arteries and veins are segmented from the lung CT images. A 3D finite element mesh of the lungs is generated and bounded by the 3D lung CT volume. The airways are placed into the 3D finite element model and attached to the 3D finite element mesh of the lungs. The extra-acinar and intra-acinar pulmonary arteries and veins are placed into the model. The model then computes known biophysical properties of the lung and includes them in the LP model ( Box 8.1 ).