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 ).

Box 8.1
Steps to Build Patient-Specific Lung Physiome (LP) Model

  • 1.

    Acquire high-quality 3D chest CT scans of the thorax and segment the lungs from the rest of the thoracic anatomy

  • 2.

    Segment the airway tree and pulmonary arteries and veins from the lung CT images

  • 3.

    Generate a 3D finite element mesh of the lungs that is bounded by the 3D lung CT volume

  • 4.

    Place the airway tree into the 3D finite element mesh of the lung and attach the airway tree to the 3D finite element mesh

  • 5.

    Place the extra-acinar pulmonary arteries and veins into the 3D finite element mesh

  • 6.

    Place the intra-acinar pulmonary arteries and veins into the 3D finite element mesh

  • 7.

    Compute known biophysical properties of the lung and include them in the LP model

Generating the 3D Finite Element Mesh of the Lung

The method of developing a finite element model of the lung begins by geometrically fitting a volumetric finite element mesh, tetrahedrons, or some other 3D polygon, to the 3D volume of the lungs obtained from a 3D lung CT scan. The volume mesh is then filled with a grid of uniformly spaced points with each point representing a pulmonary acinus; recall there are about 32,000 pulmonary acini in an adult lung. The acinus grid is uniformly spaced assuming that the lung tissue is uniformly expanded at total lung capacity (TLC). This is a reasonable assumption for an upright human lung where maximal expansion can be attained, however, this is less likely in a supine human lung.

Generating the Airway Tree Within the 3D Mesh of the Lung

An initial 1D finite element mesh is placed along the centerlines of the segmented airways obtained from a 3D chest CT scan and acts as an initial condition for the algorithm. Additional new 1D airway branches are generated at the end of a previous branch by directing a branch toward the center of mass of a subset of the acinus grid points where points in any current subset are those that are closest to the parent branch. This process continues until each acinus grid point is supplied by a single terminal model airway. This models the actual lung anatomy where the terminal bronchiole, approximately airway generation 16, supplies a single lung acinus. Tawhai’s finite element lung model generates a subject-specific airway tree for the larger airways that are visible on the 3D lung CT and a shape constraint of the subject-specific airway tree using the surface of the 3D lung CT. The algorithmically generated airways cannot exactly match the individual’s airway tree beyond those identified by the 3D lung CT; however, the averaged airway geometry of the model is consistent with measured human airway morphometry. Because the anatomically structured airway model is generated within the volumetric finite element model of the lung, the modeled airways are connected to the volumetric finite element model of the lung, and as the lung deforms, so will the airways. This coupling of airway and lung tissue function is a strength of Tawhai’s finite element lung model.

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