Image Quantification
Baseline HRCT scans of the patients and controls obtained between 2007 and 2019 were retrieved from the radiology archive. All CT scans were performed non-contrast and volumetric thin-section using a 64-slice multiple-detector CT scanner (Aquilion, Toshiba, Tokyo, Japan). Participants were in the supine position and held a full inspiration during acquisition. Images of 1 mm thickness were presented at window settings designed for lung parenchyma evaluation (width 1,500 Hounsfield units (HU); level – 500 HU). Scans were analyzed by Myrian XP Lung 3D software (Intrasense SA, Montpellier, France) for lung volume and lobar density quantification. One operator (OC) blinded to the patients’ clinical features performed lung volume and density quantification using the Myrian toolbox. Myrian is a medical imaging and computer-aided diagnostic software program used to view, store, reproduce, and export medical images. It is used for 3D imaging involving maximum intensity projection and extremely accurate volume mapping, which utilizes dedicated segmentation algorithms to separate entire vascular pulmonary structures and normal parenchyma and quantify high precision lung parenchyma volume. The software is compliant with DICOM images and shows the 3D images in three planes (axial, coronal, and sagittal). All CT scans were pre-processed using Gaussian smoothing for noise reduction and histogram equalization for contrast enhancement. CT data of the lung was saved as DICOM format, and subsequently imported into Myrian-Xp-Live software. The system automatically identified and extracted image information of the airways, vessels and lung parenchyma, and then generated reconstructed 3D images of the lung. The right and left lungs, as well as the trachea and adjacent vasculature could be clearly observed in all directions. Segmentation of lung volume with an adaptive area was used to divide the left and right lungs. The option of manual correction was used to solve problems caused by individual anatomical variability at each segmentation step. Based on their high density, the pulmonary vessels were extracted from the segmented lung volumes. For the right and left lungs, the densities were measured automatically, with a choice of – 700 or – 900 HU as the characteristic density value of healthy lungs (Figure 1).