Relationship between the airway wall area and asthma control score in moderate persistent asthma.

OBJECTIVES: To assess the association between airway wall area and clinical asthma control, assessed by the Asthma Control Test (ACT). METHODS: This cross-sectional study evaluated 96 adults for asthma control ["at least well controlled" (ACT ≥ 20; n = 52) or "not well controlled" (ACT < 20; n = 44) and airway dimensions: luminal area (LA), wall area (WA) and WA%], obtained using automated dedicated software measurements from volumetric CT images. Results were analysed for segmental bronchi, subsegmental bronchi in the right upper lobe and basilar segments, both uncorrected and corrected for body surface area (BSA). RESULTS: For all bronchi corrected for BSA, there was no correlation between airway wall area and ACT score. There was a weak but statistically significant correlation between uncorrected WA and ACT score (r = -0.203; P = 0.047); WA values were numerically higher in the "not well-controlled" versus the "at least well-controlled asthma" subgroups. For sub-segmental bronchi, there was a correlation between the ACT score and both WA/BSA (r = -0.204; P = 0.047) and WA (r = -0.249; P = 0.014), and for upper lobe bronchi, between the ACT score and WA (r = -0.207; P = 0.044). CONCLUSION: We demonstrated a correlation between subsegmental bronchial airway measurements and clinical control of asthma; this is probably a reflection of airway remodelling and structural changes in chronic poorly controlled asthma. KEY POINTS: • Volumetric computed tomography offers new insights into bronchial morphology. • The relationship between current asthma control and airway wall abnormalities is assessed. • Some relationships between airway wall area and clinical control were demonstrated. • We observed less shape variation of bronchi in "not well-controlled" asthma patients.

Volumetric quantification of airway wall in CT via collision-free active surface model: application to asthma assessment.

Emerging idea in asthma phenotyping, incorporating local morphometric information on the airway wall thickness would be able to better account for the process of airway remodeling as indicator of pathology or therapeutic impact. It is thus important that such information be provided uniformly along the airway tree, not on a sparse (cross-section) sampling basis. The volumetric segmentation of the airway wall from CT data is the issue addressed in this paper by exploiting a patient-specific surface active model. An original aspect taken into account in the proposed deformable model is the management of auto-collisions for this complex morphology. The analysis of several solutions ended up with the design of a motion vector field specific to the patient geometry to guide the deformation. The segmentation result, presented as two embedded inner/outer surfaces of the wall, allows the quantification of the tissue thickness based on a locally-defined measure sensitive to even small surface irregularities. The method is validated with respect to several ground truth simulations of pulmonary CT data with different airway geometries and acquisition protocols showing accuracy within the CT resolution range. Results from an ongoing clinical study on moderate and severe asthma are presented and discussed.

Extraction of airways from CT (EXACT'09).

This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate fifteen different airway tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented airway tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of twenty chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.