Increasing Accuracy of Optimal Surfaces Using Min-Marginal Energies

CT-based airway changes after smoking cessation in the general population

PURPOSE

Previous research has demonstrated improvements in CT-derived bronchial parameters in the first years after smoking cessation. This study investigates the association between longer smoking cessation duration and bronchial parameters in lung-healthy and lung-unhealthy ex-smokers from the general population.

MATERIALS AND METHODS

We conducted a cross-sectional analysis using low-dose CT scans of ex-smokers from the general population with at least 10 pack-years from the ImaLife study, a sub study within the Lifelines cohort. Participants ⩾45 years who completed a lung-function test were recruited for low-dose CT imaging. We divided them into lung-healthy and lung-unhealthy based on spirometry, self-reported diagnosis and imaging signs of respiratory disease. Bronchial parameters Pi10, wall thickness, luminal area and wall area percent (WAP) were obtained using a previously validated method. Multivariable linear regression (MLR) was used to evaluate the independent associations between smoking cessation duration and bronchial parameters, adjusting for sex, age, height, weight, and pack-years.

RESULTS

The study included 1,869 ex-smokers; 1,421 (76 %) were classified as lung-healthy (58 % men, mean age 64.2 ± 9.8 years, pack-years 16.5 [12.5-23.3], smoking cessation duration 20.0 [14.0-29.0] years) and 448 (24 %) as unhealthy (56 % men, mean age 66.1 ± 10.5 years, pack-years 18.2 [13.4-25.2], smoking cessation duration 20.0 [13.8-29.0] years). In the lung-unhealthy group, individuals with a longer duration of smoking cessation had a lower WAP compared to those with a shorter cessation duration (-0.528 % per 10 years, p = 0.005). In contrast, in MLR no significant associations were observed for the lung-healthy group..

CONCLUSIONS

In individuals with respiratory conditions, longer smoking cessation duration is related to a decrease in wall area percent of the bronchial walls. The results suggest the potential for improvements in airway health when people quit smoking, warranting further investigation with longitudinal studies.

Low-Dose CT-derived Bronchial Parameters in Individuals with Healthy Lungs

Background CT-derived bronchial parameters have been linked to chronic obstructive pulmonary disease and asthma severity, but little is known about these parameters in healthy individuals. Purpose To investigate the distribution of bronchial parameters at low-dose CT in individuals with healthy lungs from a Dutch general population. Materials and Methods In this prospective study, low-dose chest CT performed between May 2017 and October 2022 were obtained from participants who had completed the second-round assessment of the prospective, longitudinal Imaging in Lifelines study. Participants were aged at least 45 years, and those with abnormal spirometry, self-reported respiratory disease, or signs of lung disease at CT were excluded. Airway lumens and walls were segmented automatically. The square root of the bronchial wall area of a hypothetical airway with an internal perimeter of 10 mm (Pi10), luminal area (LA), wall thickness (WT), and wall area percentage were calculated. Associations between sex, age, height, weight, smoking status, and bronchial parameters were assessed using univariable and multivariable analyses. Results The study sample was composed of 8869 participants with healthy lungs (mean age, 60.9 years ± 10.4 [SD]; 4841 [54.6%] female participants), including 3672 (41.4%) never-smokers and 1197 (13.5%) individuals who currently smoke. Bronchial parameters for male participants were higher than those for female participants (Pi10, slope [β] range = 3.49-3.66 mm; LA, β range = 25.40-29.76 mm2; WT, β range = 0.98-1.03 mm; all P < .001). Increasing age correlated with higher Pi10, LA, and WT (r2 range = 0.06-0.09, 0.02-0.01, and 0.02-0.07, respectively; all P < .001). Never-smoking individuals had the lowest Pi10 followed by formerly smoking and currently smoking individuals (3.62 mm ± 0.13, 3.68 mm ± 0.14, and 3.70 mm ± 0.14, respectively; all P < .001). In multivariable regression models, age, sex, height, weight, and smoking history explained up to 46% of the variation in bronchial parameters. Conclusion In healthy individuals, bronchial parameters differed by sex, height, weight, and smoking history; male sex and increasing age were associated with wider lumens and thicker walls. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Emrich and Varga-Szemes in this issue.

Reproducibility of a combined artificial intelligence and optimal-surface graph-cut method to automate bronchial parameter extraction

OBJECTIVES

Computed tomography (CT)-based bronchial parameters correlate with disease status. Segmentation and measurement of the bronchial lumen and walls usually require significant manpower. We evaluate the reproducibility of a deep learning and optimal-surface graph-cut method to automatically segment the airway lumen and wall, and calculate bronchial parameters.

METHODS

A deep-learning airway segmentation model was newly trained on 24 Imaging in Lifelines (ImaLife) low-dose chest CT scans. This model was combined with an optimal-surface graph-cut for airway wall segmentation. These tools were used to calculate bronchial parameters in CT scans of 188 ImaLife participants with two scans an average of 3 months apart. Bronchial parameters were compared for reproducibility assessment, assuming no change between scans.

RESULTS

Of 376 CT scans, 374 (99%) were successfully measured. Segmented airway trees contained a mean of 10 generations and 250 branches. The coefficient of determination (R2) for the luminal area (LA) ranged from 0.93 at the trachea to 0.68 at the 6th generation, decreasing to 0.51 at the 8th generation. Corresponding values for Wall Area Percentage (WAP) were 0.86, 0.67, and 0.42, respectively. Bland-Altman analysis of LA and WAP per generation demonstrated mean differences close to 0; limits of agreement (LoA) were narrow for WAP and Pi10 (± 3.7% of mean) and wider for LA (± 16.4-22.8% for 2-6th generations). From the 7th generation onwards, there was a sharp decrease in reproducibility and a widening LoA.

CONCLUSION

The outlined approach for automatic bronchial parameter measurement on low-dose chest CT scans is a reliable way to assess the airway tree down to the 6th generation.

STATEMENT ON CLINICAL RELEVANCE

This reliable and fully automatic pipeline for bronchial parameter measurement on low-dose CT scans has potential applications in screening for early disease and clinical tasks such as virtual bronchoscopy or surgical planning, while also enabling the exploration of bronchial parameters in large datasets.

KEY POINTS

• Deep learning combined with optimal-surface graph-cut provides accurate airway lumen and wall segmentations on low-dose CT scans. • Analysis of repeat scans showed that the automated tools had moderate-to-good reproducibility of bronchial measurements down to the 6th generation airway. • Automated measurement of bronchial parameters enables the assessment of large datasets with less man-hours.

Automated Artery Localization and Vessel Wall Segmentation using Tracklet Refinement and Polar Conversion

Quantitative analysis of blood vessel wall structures is important to study atherosclerotic diseases and assess cardiovascular event risks. To achieve this, accurate identification of vessel luminal and outer wall contours is needed. Computer-assisted tools exist, but manual preprocessing steps, such as region of interest identification and/or boundary initialization, are still needed. In addition, prior knowledge of the ring shape of vessel walls has not been fully explored in designing segmentation methods. In this work, a fully automated artery localization and vessel wall segmentation system is proposed. A tracklet refinement algorithm was adapted to robustly identify the artery of interest from a neural network-based artery centerline identification architecture. Image patches were extracted from the centerlines and converted in a polar coordinate system for vessel wall segmentation. The segmentation method used 3D polar information and overcame problems such as contour discontinuity, complex vessel geometry, and interference from neighboring vessels. Verified by a large (>32000 images) carotid artery dataset collected from multiple sites, the proposed system was shown to better automatically segment the vessel wall than traditional vessel wall segmentation methods or standard convolutional neural network approaches. In addition, a segmentation uncertainty score was estimated to effectively identify slices likely to have errors and prompt manual confirmation of the segmentation. This robust vessel wall segmentation system has applications in different vascular beds and will facilitate vessel wall feature extraction and cardiovascular risk assessment.