Using Reactive Machine AI to Derive Quantitative Lung CT Metrics of COPD, ILD, and COVID-19 Pneumonia


This chapter will first review the basic structure of the human lung and use this information to explore the different QCT lung metrics that can be obtained from a single chest CT scan obtained at total lung capacity (TLC). A chest CT scan obtained at TLC and analyzed using lung CT AI can detect and assess lung density changes that occur in patients with emphysema from COPD, pulmonary inflammation and fibrosis in ILD, and acute viral pneumonia from COVID-19. The changes in lung density that result from these different diseases reflect important structural changes in the lung tissue that correlate with other measures of lung disease, such as clinical symptoms, exercise limitations, and pulmonary function testing. The successful application of lung CT AI to the assessment of diffuse lung diseases depends on the following four important steps: (1) quantitative chest CT protocol to acquire high-quality 3D CT images of both lungs, (2) segment the lungs from the rest of the thoracic anatomy, (3) extract quantitative CT metrics from the lung CT images and, (4) use the extracted QCT metrics to detect and assess normal and diseased lung tissue ( Fig. 5.1 ).

Fig. 5.1, Flow diagram outlining the important steps in a Reactive AI algorithm to assess a TLC lung CT scan for evidence of normal and diseased lung tissue.

Normal Lung Structure

The human lung is the largest visceral organ in the human body with a volume between 4 and 6 liters in normal adults. The lung is comprised mainly of air and water. The lung has high intrinsic contrast for x-ray CT imaging because the lung is 80% air and 20% water with HU values of −1000 HU and 0 HU, respectively. The high intrinsic contrast in the lung between water density and air density enables high-quality CT images of normal lung tissue, airways, and blood vessels.

There are 23 generations of airways from the trachea to the alveoli. The trachea is the first airway generation. There are two functional compartments of the lung airways: conducting airways, airway generations 1 to 16, airway generations 17 to 23, and gas diffusion ( Fig. 5.2 ). The conducting airways transport air from the largest conducting airway, the trachea, to the smallest conducting airway the terminal bronchiole, generation 16. The terminal bronchial conducts air to the lung acinus, which is the largest gas exchanging unit of the lung. The structure of the acinus includes several generations of respiratory bronchioles, alveolar ducts, and alveoli ( Fig. 5.3 ). The lung is designed to provide a very efficient transfer of oxygen and carbon dioxide gases. The oxygen molecules in inspired air are transferred from the alveolar spaces to the red blood cells, and carbon dioxide is transferred from red blood cells to the alveolar air spaces. The essential structural features of the lung include the high surface to volume ratio of the lung structure, as well as the thin alveolar walls that enable a very efficient exchange of oxygen from the alveolar lumen into the capillary lumen within the wall of the alveolus, and the efficient exchange of carbon dioxide from the red blood cells in the capillary lumen into the alveolar space. The surface area of the alveolar walls in a human lung is the size of a tennis court, 140 m 2 , but are folded into a very compact space 6 liters in size. The normal thickness of the alveolar wall is about 2 microns.

Fig. 5.2, Different generations of conducting and diffusion airways of the human lung.

Fig. 5.3, Terminal bronchiole, respiratory bronchioles, alveolar ducts, and alveoli that make up the pulmonary acinus of the lung as described in the text. The pulmonary acinus is the largest gas exchanging unit of the human lung.

QCT Scanning Protocol and Lung Segmentation

The first step in lung CT AI of diffuse lung disease is to obtain a quality 3D chest CT scan using an appropriate QCT scanning protocol that we described in detail in Chapter 3 . In this chapter, we will discuss using a single TLC CT scan to assess normal and diseased lung structure. In Chapter 6 , we will discuss how to obtain functional lung information by using both a TLC chest CT scan and an FRC/RV chest CT scan.

The second step is to automatically and consistently segment the lungs from the rest of the chest anatomy using a validated software program for this purpose. The computer algorithms that do this are quite sophisticated and use reactive AI, limited memory AI, or a combination of these AI levels. These AI algorithms are designed to run automatically and are very efficient. Fig. 5.4 shows an axial, sagittal, and coronal chest CT image from a normal patient. Lower density is represented by darker gray colors and higher dense tissues with lighter gray colors. The low density of normal lung tissue reflects the fact that the normal lung is 80% air and 20% soft tissue (10% blood and 10% tissue). The solid cylindrical branching soft tissue density structures in the lung are the arteries and veins. The hollow cylindrical branching air-containing structures are the airways. Fig. 5.5 shows a 3D lung CT image after the image segmentation software has processed the original chest CT images.

Fig. 5.4, (A) axial, (B) coronal, and (C) sagittal 2D planar images of the thorax at the level of the carina at the bifurcation of the trachea (arrows) . These images were displayed using a WW of 1500 HU and a WL of −500 HU. These WW and WL settings optimize the chest CT images for displaying the lung tissue for visual assessment.

Fig. 5.5, 3D semitransparent surface rendering of the lungs with only the trachea and lung tissue remaining. The rest of the chest anatomy (e.g., chest wall, spine, heart, aorta) has been removed. The large central pulmonary artery and veins have also been removed. The lung voxel histograms that we discuss in this chapter are derived from the lung portrayed here with the central airways and central pulmonary vessels removed. The airways and central pulmonary vessels are usually assessed separately. The central airways are discussed toward the end of Chapter 6 and the central pulmonary vessels are discussed in Chapter 8.

The third step of lung CT AI to assess diffuse lung disease is to extract quantitative CT features from the segmented lung CT images. This can be used in step four to detect and assess normal and diseased lung tissue. In this chapter, we will discuss straightforward reactive lung CT AI methods to derive CT features from the lung voxel histogram ( Fig. 5.6 ). The fourth step of lung CT AI is to use the CT features derived in step three to assess the presence and extent of diffuse lung disease. Normal lung density, decreased lung density from emphysema, and increased lung density from pulmonary fibrosis and pneumonia can be assessed using Reactive Lung CT AI.

Fig. 5.6, TLC lung CT voxel histogram plot from a normal patient. The mean lung density is −860 HU in this normal patient.

Chronic Obstructive Pulmonary Disease (COPD) Induced Changes in Lung Structure

COPD first produces narrowing and destruction of small conducting airways before emphysema develops in the human lung. The narrowing and destruction of small conducting airways increase the resistance to airflow in COPD. The progression of COPD can then produce emphysema in the lung. “Emphysema is defined as a condition of the lung characterized by abnormal, permanent enlargement of airspaces distal to the terminal bronchiole, accompanied by the destruction of alveolar walls, and without obvious lung fibrosis”. This enlargement of alveoli and the destruction of alveolar walls effectively reduces the available surface area for gas exchange per unit volume of lung tissue; therefore the efficiency of gas exchange in the lung is decreased in emphysema. The destruction of tissue in emphysema will decrease the density of lung tissue, and this can be detected and assessed using lung CT AI.

Quantitative CT Metrics of Lung Density in COPD

3D CT images of the lungs can be analyzed by looking at the location and individual values of the lung CT voxels in the lung. The simplest approach is to assess the lung CT voxel histogram of both lungs. The spatial information is lost in this approach if the voxel histograms of both lungs are combined. Fig. 5.6 shows the voxel histogram plot of normal lungs. Fig. 5.7 shows the voxel histogram plot of emphysematous lungs.

Fig. 5.7, TLC lung CT voxel histogram from a patient with severe emphysema. The mean lung density is −890 HU and the LAA −950 is 39%. A vertical red line marks the −950 HU value.

Assessing features of the lung CT voxel histogram was one of the earliest methods of quantitatively assessing normal and diseased lung tissue. The disadvantage of combining the CT lung voxels from both lungs is that the spatial information is lost. This can be overcome by assessing the CT voxel histogram at smaller scales. This has been done for individual lungs, lung lobes, sublobar segments, and individual voxels. The voxel-level completely preserves the spatial and CT voxel value information for each voxel, and can then be further processed with more powerful lung CT AI methods; more on this in Chapter 6 . The following discussion will elaborate on different quantitative lung metrics that can be derived from the lung CT voxel histogram from a single TLC lung CT scan, and how they can be used to detect and assess normal and diseased lung tissue.

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