Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications

Abstract

The frenetic development of imaging technology-both hardware and software-provides exceptional potential for investigation of the lung. In the last two decades, CT was exploited for detailed characterization of pulmonary structures and description of respiratory disease. The introduction of volumetric acquisition allowed increasingly sophisticated analysis of CT data by means of computerized algorithm, namely quantitative CT (QCT). Hundreds of thousands of CTs have been analysed for characterization of focal and diffuse disease of the lung. Several QCT metrics were developed and tested against clinical, functional and prognostic descriptors. Computer-aided detection of nodules, textural analysis of focal lesions, densitometric analysis and airway segmentation in obstructive pulmonary disease and textural analysis in interstitial lung disease are the major chapters of this discipline. The validation of QCT metrics for specific clinical and investigational needs prompted the translation of such metrics from research field to patient care. The present review summarizes the state of the art of QCT in both focal and diffuse lung disease, including a dedicated discussion about application of QCT metrics as parameters for clinical care and outcomes in clinical trials.

Figure 1.

(a–i) CAD segmentation of subsolid nodule during a 4-year active surveillance. The semi-automatic segmentation of pulmonary nodule provides several metrics that can be used for standardized characterization and management. Noteworthy, the longitudinal assessment of subsolid nodule takes advantage of volumetric measurement of density, which was proposed for optimal stratification of nodule growth, also known as mass doubling time. From right to left, the same non-solid nodule segmented at baseline (a–c), after 2 years (middle column: d–f), and after 4 years (g–i) with progressive increase in growth rate according to MDT. For each time point the segmentation is rendered in axial (a, d and f), coronal (b, e and h) and sagittal plane (c, f and i). The MDT of this non-solid nodule rose from 1562 days at the 2-year LDCT to 350 days at the 4-year LDCT, reflecting a progressive increase in growth rate. CAD, computer-aided detection; LDCT, low-dose CT; MDT, mass doubling time.

Figure 2.

(a–d) Density histogram for computation of parenchymal metrics. Axial CT images of a patient with upper lobe predominant emphysema (a, b). The density histogram (c) summarizes the distribution of parenchymal density (white line: both lungs). Dedicated representation of individual lobar density histograms provides quantitative differences between lobes (red line: right upper lobe; green line: right lower lobe) for objective assessment of emphysema heterogeneity and selection of the most appropriate treatment (e.g. endobronchial valves vs endobronchial coils). Numeric output is automatically computed by the software (d) and displayed according to whole lung characteristics or according to the selected lobes (e.g. right and left lung or individual lobes). Quantitative metrics that can be extracted from the histogram include lung volume, mean lung density, weight, density percentiles (in the present table, the lowest 15th percentile is reported, P15), density mode (the most represented density value within the entire lung, Pk) and volume of lung parenchyma with density below a predefined threshold (in the present table, the relative volume of lung with density < −950 HU is reported as a measure of emphysema, LAV, low attenuation volume). HU, Hounsfield unit; MLD, mean lung density; Vol. volume; Wt., weight.

Figure 3. (a-d) Parametric response mapping for topographic densitometric categorization of parenchyma into normal lung, air trapping and emphysema in COPD. Volumetric inspiratory (a) and expiratory (b) CTs are warped together for quantitative analysis of densitometric clusters by means of parametric response map (c): Insp > −950 HU and Exp > −856 HU (normal lung), Insp > −950 and Exp < −856 HU (SAD) and Insp < −950 and Exp −856HU (emphysema). The native CT data set (d) is overlaid by colour-coded volumetric representation of the densitometric categories that allow topographic description of pulmonary disease (green, normal lung; yellow, air trapping; red, emphysema). The present COPD case shows the coexistence of air trapping and emphysema, which are objectively apportioned by the b-phase densitometric quantification with evidence of airway predominant disease. COPD, chronic obstructive pulmonary disease; HU, Hounsfield unit; SAD, small airway disease.

Figure 4.

(a–d) Direct and indirect quantification of airway. Airway segmentation from trachea to intrapulmonary bronchi and stretched view of a selected airway in the right lower lobe (a); oblique axial reformatting of bronchial structure with direct automatic segmentation lumen diameter (LD: 4.6 mm), wall thickness (WT: 1.0 mm) and relative surface of wall compared to total airway surface (wall area, WA%: 52.1). Volumetric reconstruction of airway segmentation (c) for automatic direct measurement of all airways for indirect calculation of Pi10 by means of regression line derived from plotting of internal perimeter and square root of wall area of airways with internal perimeter <20 mm (d).

Figure 5.

(a–c) Asthma phenotypes according to QCT cluster. Volumetric model of segmented airway and specific quantitative analysis of the right upper lobe apical segmental bronchus (RB1) on cross-section (insets). The airway metrics were normalized to the body surface area for definition of QCT clusters of asthma according to wall volume (WV) and lumen volume (LV). The three clusters are defined as follows: (a) cluster 1 with increased WV and LV, decreased percentage WV and severe air trapping; (b) cluster 2 with minor central airway remodelling, moderate air trapping and low response to bronchodilator; (c) cluster 3 reduced WV and LV, increased WV percentage and severe air trapping on CT. Figure reproduced under a Creative Commons license (CC BY) from Gupta S. et al, J Allergy Clin Immunol. 2014 Mar;133 :729–38.e18. https://doi.org/10.1016/j.jaci.2013.09.039. LV, lumen volume; QCT, quantitative CT; WV, wall volume.

Figure 6.

(a–j) Longitudinal quantitative analysis of fibrotic interstitial lung disease. Automatic volumetric segmentation of parenchymal abnormalities in a patient with idiopathic pulmonary fibrosis at baseline (top row: a−e) and 1-year follow-up (bottom row: f−j). The colour-coded overlay on native high resolution computed tomography (HRCT) images shows the distribution of parenchymal abnormalities on axial, coronal and sagittal reconstruction at baseline (a−c) and 1 year (f−h). The data are also provided in a volumetric model that shows both lungs with colour-coded characterization of parenchyma volume. Furthermore, a synthetic 2D graph (the so-called Glyph) is built that provides comprehensive display of abnormal parenchyma and its distribution between lobes (baseline Glyph in e, 1-year Glyph in j). 2D, two-dimensional.