Ratios of peripheral-to-central airway lumen area and percentage wall area as predictors of severity of chronic obstructive pulmonary disease

Two-stage contextual transformer-based convolutional neural network for airway extraction from CT images

Accurate airway segmentation from computed tomography (CT) images is critical for planning navigation bronchoscopy and realizing a quantitative assessment of airway-related chronic obstructive pulmonary disease (COPD). Existing methods face difficulty in airway segmentation, particularly for the small branches of the airway. These difficulties arise due to the constraints of limited labeling and failure to meet clinical use requirements in COPD. We propose a two-stage framework with a novel 3D contextual transformer for segmenting the overall airway and small airway branches using CT images. The method consists of two training stages sharing the same modified 3D U-Net network. The novel 3D contextual transformer block is integrated into both the encoder and decoder path of the network to effectively capture contextual and long-range information. In the first training stage, the proposed network segments the overall airway with the overall airway mask. To improve the performance of the segmentation result, we generate the intrapulmonary airway branch label, and train the network to focus on producing small airway branches in the second training stage. Extensive experiments were performed on in-house and multiple public datasets. Quantitative and qualitative analyses demonstrate that our proposed method extracts significantly more branches and longer lengths of the airway tree while accomplishing state-of-the-art airway segmentation performance. The code is available at https://github.com/zhaozsq/airway_segmentation.

Quantitative CT parameters correlate with lung function in chronic obstructive pulmonary disease: A systematic review and meta-analysis

Objective

This study aimed to analyze the correlation between quantitative computed tomography (CT) parameters and airflow obstruction in patients with COPD.

Methods

PubMed, Embase, Cochrane and Web of Knowledge were searched by two investigators from inception to July 2022, using a combination of pertinent items to discover articles that investigated the relationship between CT measurements and lung function parameters in patients with COPD. Five reviewers independently extracted data, and evaluated it for quality and bias. The correlation coefficient was calculated, and heterogeneity was explored. The following CT measurements were extracted: percentage of lung attenuation area <-950 Hounsfield Units (HU), mean lung density, percentage of airway wall area, air trapping index, and airway wall thickness. Two airflow obstruction parameters were extracted: forced expiratory volume in the first second as a percentage of prediction (FEV₁%pred) and FEV₁ divided by forced expiratory volume lung capacity.

Results

A total of 141 studies (25,214 participants) were identified, which 64 (6,341 participants) were suitable for our meta-analysis. Results from our analysis demonstrated that there was a significant correlation between quantitative CT parameters and lung function. The absolute pooled correlation coefficients ranged from 0.26 (95% CI, 0.18 to 0.33) to 0.70 (95% CI, 0.65 to 0.75) for inspiratory CT and 0.56 (95% CI, 0.51 to 0.60) to 0.74 (95% CI, 0.68 to 0.80) for expiratory CT.

Conclusions

Results from this analysis demonstrated that quantitative CT parameters are significantly correlated with lung function in patients with COPD. With recent advances in chest CT, we can evaluate morphological features in the lungs that cannot be obtained by other clinical indices, such as pulmonary function tests. Therefore, CT can provide a quantitative method to advance the development and testing of new interventions and therapies for patients with COPD.

Feasibility of bronchial wall quantification in low- and ultralow-dose third-generation dual-source CT: An ex vivo lung study

PURPOSE

To investigate image quality and bronchial wall quantification in low- and ultralow-dose third-generation dual-source computed tomography (CT).

METHODS

A lung specimen from a formerly healthy male was scanned using third-generation dual-source CT at standard-dose (51 mAs/120 kV, CTDI_vol 3.41 mGy), low-dose (1/4th and 1/10th of standard dose), and ultralow-dose setting (1/20th). Low kV (70, 80, 90, and Sn100 kV) scanning was applied in each low/ultralow-dose setting, combined with adaptive mAs to keep a constant dose. Images were reconstructed at advanced modeled iterative reconstruction (ADMIRE) levels 1, 3, and 5 for each scan. Bronchial wall were semi-automatically measured from the lobar level to subsegmental level. Spearman correlation analysis was performed between bronchial wall quantification (wall thickness and wall area percentage) and protocol settings (dose, kV, and ADMIRE). ANOVA with a post hoc pairwise test was used to compare signal-to-noise ratio (SNR), noise and bronchial wall quantification values among standard- and low/ultralow-dose settings, and among ADMIRE levels.

RESULTS

Bronchial wall quantification had no correlation with dose level, kV, or ADMIRE level (|correlation coefficients| < 0.3). SNR and noise showed no statistically significant differences at different kV in the same ADMIRE level (1, 3, or 5) and in the same dose group (P > 0.05). Generally, there were no significant differences in bronchial wall quantification among the standard- and low/ultralow-dose settings, and among different ADMIRE levels (P > 0.05).

CONCLUSION

The combined use of low/ultralow-dose scanning and ADMIRE does not influence bronchial wall quantification compared to standard-dose CT. This specimen study suggests the potential that an ultralow-dose scan can be used for bronchial wall quantification.

Airway quantification using adaptive statistical iterative reconstruction-V on wide-detector low-dose CT: a validation study on lung specimen

PURPOSE

To evaluate the accuracy of airway quantification of adaptive statistical iterative reconstruction (ASIR)-V on low-dose CT using a human lung specimen.

METHOD

A lung specimen was scanned on Revolution CT with low-dose settings (20 mAs, 40 mAs and 60 mAs/100 kV) and standard-dose setting (100 mAs/120 kV). CT images were reconstructed using lung kernel with eleven ASIR-V levels from 0 to 100% with 10% interval. ASIR-V level from 0 to 100% with 10% interval was reconstructed on lung kernel. Wall area percentage (%WA) and wall thickness (WT) were measured.

RESULTS

Radiation dose of 20 mAs, 40 mAs and 60 mAs low-dose settings reduced by 87.6%, 75.2% and 62.8% compared to that on standard dose, respectively. Low-dose settings significantly decreased image SNR (p < 0.05) and increased noise (p < 0.001). ASIR-V level exponentially improved image SNR and linearly decreased image noise (all p < 0.001). The mean airway measurement ratios of low-dose to standard-dose were within 2% variation for %WA and within 3% variation for WT. Most %WA and WT values showed no obvious correlation with ASIR-V levels.

CONCLUSION

ASIR-V showed to improve image quality in low radiation dose. However, low-dose settings and ASIR-V strength did not significantly influence airway quantification values, although variation in measurements slightly increased with dose reduction.

Airway wall thickening on CT: Relation to smoking status and severity of COPD

Airway wall thickening in cigarette smokers is thought to be a result of inflammatory changes and airway remodeling. This study investigates if CT-derived airway wall thickening associates to disease severity in smokers with and without COPD and if airway wall thickening is reversible by smoking cessation. We examined 2000 smokers and 46 never-smokers who returned for a 5-year follow-up visit in the COPDGene-study. Multivariable regression analyses were performed at visit 1 to associate airway wall thickness (expressed as Pi10) with percent predicted forced expiratory volume in 1 s (FEV1%-predicted), 6-min walking distance (6MWD), and St. George Respiratory Questionnaire (SGRQ). Longitudinal analyses were performed to assess the effect of smoking cessation on Pi10 using linear mixed models. A higher Pi10 was significantly associated with worse FEV1%-predicted, 6MWD, and SGRQ in all GOLD-stages. Longitudinal analyses showed that subjects that quit smoking significantly decreased in Pi10 (ΔPi10 = -0.18 mm, p < 0.001). Subjects that started smoking had a significant increase in Pi10 (ΔPi10 = 0.14 mm, p < 0.001). Pi10 is a clinically relevant biomarker of smoking-related airway injury in smokers with and without COPD. The change in Pi10 with change in smoking status suggests that it can quantify a reversible component of smoking-related airway inflammation.

Performance of quantitative CT parameters in assessment of disease severity in COPD: A prospective study

BACKGROUND

Both emphysematous destruction of lung parenchyma and airway remodeling is thought to contribute to airflow limitation in cases of chronic obstructive pulmonary disease (COPD).

OBJECTIVE

To evaluate the value of quantitative computed tomography (QCT) parameters of emphysema and airway disease with disease severity in patients with COPD.

MATERIALS AND METHODS

We prospectively studied 50 patients with COPD, which included nonsmokers and patients with different degrees of cumulative smoking exposure. Three QCT parameters namely LAA% (low attenuation area percentage), WA% (Wall area percentage), and pi10 were calculated as per the standard technique. Forced expiratory volume in 1 s (FEV1), BODE score, and MMRC dyspnea scale were used as measures of disease severity.

RESULTS

FEV1 was inversely and significantly associated with all three QCT parameters. Receiver operated characteristic curves in prediction of GOLD class 3 COPD yielded cut-off values of 12.2, 61.45, and 3.5 for LAA%, WA%, and pi10, respectively, with high sensitivities and specificities. In multiple linear regression model, however, only LAA% proved to be significantly associated with FEV1, BODE, and dyspnea.

CONCLUSION

QCT indices of both emphysema and airway disease influence FEV1, dyspnea, and BODE score in patients with COPD. Emphysema, however, appears to be more closely related to disease severity.