MDCT-based longitudinal automated airway and air trapping analysis in school-age children with mild cystic fibrosis lung disease.

Objectives: Quantitative computed tomography (QCT) offers some promising markers to quantify cystic fibrosis (CF)-lung disease. Air trapping may precede irreversible bronchiectasis; therefore, the temporal interdependencies of functional and structural lung disease need to be further investigated. We aim to quantify airway dimensions and air trapping on chest CT of school-age children with mild CF-lung disease over two years. Methods: Fully-automatic software analyzed 144 serial spirometer-controlled chest CT scans of 36 children (median 12.1 (10.2-13.8) years) with mild CF-lung disease (median ppFEV1 98.5 (90.8-103.3) %) at baseline, 3, 12 and 24 months. The airway wall percentage (WP5-10), bronchiectasis index (BEI), as well as severe air trapping (A3) were calculated for the total lung and separately for all lobes. Mixed linear models were calculated, considering the lobar distribution of WP5-10, BEI and A3 cross-sectionally and longitudinally. Results: WP5-10 remained stable (P = 0.248), and BEI changed from 0.41 (0.28-0.7) to 0.54 (0.36-0.88) (P = 0.156) and A3 from 2.26% to 4.35% (P = 0.086) showing variability over two years. ppFEV1 was also stable (P = 0.276). A robust mixed linear model showed a cross-sectional, regional association between WP5-10 and A3 at each timepoint (P < 0.001). Further, BEI showed no cross-sectional, but another mixed model showed short-term longitudinal interdependencies with air trapping (P = 0.003). Conclusions: Robust linear/beta mixed models can still reveal interdependencies in medical data with high variability that remain hidden with simpler statistical methods. We could demonstrate cross-sectional, regional interdependencies between wall thickening and air trapping. Further, we show short-term regional interdependencies between air trapping and an increase in bronchiectasis. The data indicate that regional air trapping may precede the development of bronchiectasis. Quantitative CT may capture subtle disease progression and identify regional and temporal interdependencies of distinct manifestations of CF-lung disease.

Validation of automated lobe segmentation on paired inspiratory-expiratory chest CT in 8-14 year-old children with cystic fibrosis.

OBJECTIVES: Densitometry on paired inspiratory and expiratory multidetector computed tomography (MDCT) for the quantification of air trapping is an important approach to assess functional changes in airways diseases such as cystic fibrosis (CF). For a regional analysis of functional deficits, an accurate lobe segmentation algorithm applicable to inspiratory and expiratory scans is beneficial. MATERIALS AND METHODS: We developed a fully automated lobe segmentation algorithm, and subsequently validated automatically generated lobe masks (ALM) against manually corrected lobe masks (MLM). Paired inspiratory and expiratory CTs from 16 children with CF (mean age 11.1±2.4) acquired at 4 time-points (baseline, 3mon, 12mon, 24mon) with 2 kernels (B30f, B60f) were segmented, resulting in 256 ALM. After manual correction spatial overlap (Dice index) and mean differences in lung volume and air trapping were calculated for ALM vs. MLM. RESULTS: The mean overlap calculated with Dice index between ALM and MLM was 0.98±0.02 on inspiratory, and 0.86±0.07 on expiratory CT. If 6 lobes were segmented (lingula treated as separate lobe), the mean overlap was 0.97±0.02 on inspiratory, and 0.83±0.08 on expiratory CT. The mean differences in lobar volumes calculated in accordance with the approach of Bland and Altman were generally low, ranging on inspiratory CT from 5.7±52.23cm3 for the right upper lobe to 17.41±14.92cm3 for the right lower lobe. Higher differences were noted on expiratory CT. The mean differences for air trapping were even lower, ranging from 0±0.01 for the right upper lobe to 0.03±0.03 for the left lower lobe. CONCLUSIONS: Automatic lobe segmentation delivers excellent results for inspiratory and good results for expiratory CT. It may become an important component for lobe-based quantification of functional deficits in cystic fibrosis lung disease, reducing necessity for user-interaction in CT post-processing.

Imaging of the pediatric airway.

Airway compromise can be fixed, dynamic (with varying degrees of collapse during the respiratory cycle), or exhibit both components. The location of the abnormality can be classified as extrinsic (located outside but exerting mass effect on the airway) or intrinsic (intramural and/or intraluminal). The etiologies of airway compromise are categorized as: congenital, infectious, inflammatory, traumatic, vascular, or neoplastic (1). The role of imaging of the airway is to determine the presence, nature and anatomic level of airway compromise, categorize it as intrinsic or extrinsic, provide a differential diagnosis, and guide further imaging or management (1). The differential diagnosis of a lesion takes into account the patient's age and gender, location of the lesion, clinical presentation, and imaging appearance.