Optimizing airway wall segmentation and quantification by reducing the influence of adjacent vessels and intravascular contrast material with a modified integral-based algorithm in quantitative computed tomography

Quantification of airway wall contrast enhancement on virtual monoenergetic images from spectral computed tomography

OBJECTIVES

Quantitative computed tomography (CT) plays an increasingly important role in phenotyping airway diseases. Lung parenchyma and airway inflammation could be quantified by contrast enhancement at CT, but its investigation by multiphasic examinations is limited. We aimed to quantify lung parenchyma and airway wall attenuation in a single contrast-enhanced spectral detector CT acquisition.

METHODS

For this cross-sectional retrospective study, 234 lung-healthy patients who underwent spectral CT in four different contrast phases (non-enhanced, pulmonary arterial, systemic arterial, and venous phase) were recruited. Virtual monoenergetic images were reconstructed from 40-160 keV, on which attenuations of segmented lung parenchyma and airway walls combined for 5th-10th subsegmental generations were assessed in Hounsfield Units (HU) by an in-house software. The spectral attenuation curve slope between 40 and 100 keV (λHU) was calculated.

RESULTS

Mean lung density was higher at 40 keV compared to that at 100 keV in all groups (p < 0.001). λHU of lung attenuation was significantly higher in the systemic (1.7 HU/keV) and pulmonary arterial phase (1.3 HU/keV) compared to that in the venous phase (0.5 HU/keV) and non-enhanced (0.2 HU/keV) spectral CT (p < 0.001). Wall thickness and wall attenuation were higher at 40 keV compared to those at 100 keV for the pulmonary and systemic arterial phase (p ≤ 0.001). λHU for wall attenuation was significantly higher in the pulmonary arterial (1.8 HU/keV) and systemic arterial (2.0 HU/keV) compared to that in the venous (0.7 HU/keV) and non-enhanced (0.3 HU/keV) phase (p ≤ 0.002).

CONCLUSIONS

Spectral CT may quantify lung parenchyma and airway wall enhancement with a single contrast phase acquisition, and may separate arterial and venous enhancement. Further studies are warranted to analyze spectral CT for inflammatory airway diseases.

KEY POINTS

• Spectral CT may quantify lung parenchyma and airway wall enhancement with a single contrast phase acquisition. • Spectral CT may separate arterial and venous enhancement of lung parenchyma and airway wall. • The contrast enhancement can be quantified by calculating the spectral attenuation curve slope from virtual monoenergetic images.

µCT to quantify muco-obstructive lung disease and effects of neutrophil elastase knockout in mice

Muco-obstructive lung diseases are characterized by airway obstruction and hyperinflation, which can be quantified by imaging. Our aim was to evaluate µCT for longitudinal quantification of muco-obstructive lung disease in β-epithelial Na⁺ channel overexpressing (Scnn1b-TG) mice and of the effects of neutrophil elastase (NE) knockout on its progression. Lungs from wild-type (WT), NE^-/-, Scnn1b-TG, and Scnn1b-TG/NE^-/- mice were scanned with 9 µm resolution at 0, 5, 14 and 60 days of age, and airway and parenchymal disease was quantified. Mucus adhesion lesions (MAL) were persistently increased in Scnn1b-TG compared to WT mice from 0 days (20.25±6.50 vs. 9.60±2.07, P<0.05), and this effect was attenuated in Scnn1b-TG/NE^-/- mice (5.33±3.67, P<0.001). Airway wall area percentage (WA%) was increased in Scnn1b-TG mice compared to WT from 14 days onward (59.2±6.3% vs. 49.8±9.0%, P<0.001) but was similar in Scnn1b-TG/NE^-/- compared to WT at 60 days (46.4±9.2% vs. 45.4±11.5%, P=0.97). Air proportion (Air%) and mean linear intercept (Lm) were persistently increased in Scnn1b-TG compared to WT from 5 days on (53.9±4.5% vs. 30.0±5.5% and 78.82±8.44µm vs. 65.66±4.15µm, respectively, P<0.001), whereas in Scnn1b-TG/NE^-/- Air% and Lm were similar to WT from birth (27.7±5.5% vs.27.2±5.9%, P =0.92 and 61.48±9.20µm vs. 61.70±6.73µm, P=0.93, respectively). Our results suggest that µCT is sensitive to detect the onset and progression of muco-obstructive lung disease and effects of genetic deletion of NE on morphology of airways and lung parenchyma in Scnn1b-TG mice, and that it may serve as a sensitive endpoint for preclinical studies of novel therapeutic interventions for muco-obstructive lung diseases.