Quantitative Analysis of Airway Tree in Low-dose Chest CT with a New Model-based Iterative Reconstruction Algorithm: Comparison to Adaptive Statistical Iterative Reconstruction in Routine-dose CT

Factors Associated with Age-Related Changes in Non-Smoking Urban Men and Women in China Determined by Low-Dose Computed Tomography Imaging

Background

Respiratory function usually worsens in the elderly with aging. This study aimed to retrospectively investigate tracheal changes caused by “normal aging” through use of low-dose CT (LDCT) in non-smoking asymptomatic urban residents and the related factors influencing tracheal changes.

Material/Methods

A total of 733 Chinese subjects who underwent LDCT were recruited. The trachea shape, width, and calcification degree of the tracheal wall were measured and compared between males and females and among different age groups. The effects of age, sex, trachea morphology, BMI, BP, GLU, TC, TG, HDL, and LDL on the width and calcification of tracheal wall were analyzed by multiple linear regression.

Results

Significant sex differences in trachea shape were found, as type II and type I were found mainly in the males and females, respectively. The values of anterior-posterior inner diameter (AP), left-right inner diameter (LR), width, and calcification score of tracheae in the males were higher than that in the females. In both males and females, trachea AP, wall width, and calcification scores increased with age, but this trend was not observed in tracheal LR. Age, sex, and trachea shape had significant effects on the width and calcification scores of tracheal walls, and trachea calcification was one of the factors influencing tracheal wall width.

Conclusions

Tracheal aging can be evaluated by measuring trachea shape, thickness, and the degree of calcification of the tracheal wall by LDCT, while sex and age should be taken into consideration comprehensively for judging normal trachea aging. In addition, obesity may aggravate trachea aging.

Automated labeling of the airway tree in terms of lobes based on deep learning of bifurcation point detection

This paper presents an automatic lobe-based labeling of airway tree method, which can detect the bifurcation points for reconstructing and labeling the airway tree from a computed tomography image. A deep learning-based network structure is designed to identify the four key bifurcation points. Then, based on the detected bifurcation points, the entire airway tree is reconstructed by a new region-growing method. Finally, with the basic airway tree anatomy and topology knowledge, individual branches of the airway tree are classified into different categories in terms of pulmonary lobes. There are several advantages in our method such as the detection of the bifurcation points does not depend on the segmentation of airway tree and only four bifurcation points need to be manually labeled for each sample to prepare the training dataset. The segmentation of airway tree is guided by the detected points, which overcomes the difficulty of manual seed selection of conventional region-growing algorithm. In addition, the bifurcation points can help analyze the tree structure, which provides a basis for effective airway tree labeling. Experimental results show that our method is fast, stable, and the accuracy of our method is 97.85%, which is higher than that of the traditional skeleton-based method. Graphical Abstract The pipeline of our proposed lobe-based airway tree labeling method. Given a raw CT volume, a neural network structure is designed to predict major bifurcation points of airway tree. Based on the detected points, airway tree is reconstructed and labeled in terms of lobes.

Effect of iterative reconstruction techniques on image quality in low radiation dose chest CT: a phantom study

PURPOSE

We aimed to evaluate the quality of chest computed tomography (CT) images obtained with low-dose CT using three iterative reconstruction (IR) algorithms.

METHODS

Two 64-detector spiral CT scanners (HDCT and iCT) were used to scan a chest phantom containing 6 ground-glass nodules (GGNs) at 11 radiation dose levels. CT images were reconstructed by filtered back projection or three IR algorithms. Reconstructed images were analyzed for CT values, average noise, contrast-to-noise ratio (CNR) values, subjective image noise, and diagnostic acceptability of the GGNs. Repeated-measures analysis of variance was used for statistical analyses.

RESULTS

Average noise decreased and CNR increased with increasing radiation dose when the same reconstruction algorithm was applied. Average image noise was significantly lower when reconstructed with MBIR than with iDOSE4 at the same low radiation doses. The two radiologists showed good interobserver consistency in image quality with kappa 0.83. A significant relationship was found between image noise and diagnostic acceptability of the GGNs.

CONCLUSION

Three IR algorithms are able to reduce the image noise and improve the image quality of low-dose CT. In the same radiation dose, the low-dose CT image quality reconstructed with MBIR algorithms is better than that of other IR algorithms.

Reproducibility of an airway tapering measurement in computed tomography with application to bronchiectasis

We propose a pipeline to acquire a scalar tapering measurement from the carina to the most distal point of an individual airway visible on computed tomography (CT). We show the applicability of using tapering measurements on clinically acquired data by quantifying the reproducibility of the tapering measure. We generate a spline from the centerline of an airway to measure the area and arclength at contiguous intervals. The tapering measurement is the gradient of the linear regression between area in log space and arclength. The reproducibility of the measure was assessed by analyzing different radiation doses, voxel sizes, and reconstruction kernel on single timepoint and longitudinal CT scans and by evaluating the effect of airway bifurcations. Using 74 airways from 10 CT scans, we show a statistical difference, p = 3.4 × 10 - 4 , in tapering between healthy airways ( n = 35 ) and those affected by bronchiectasis ( n = 39 ). The difference between the mean of the two populations is 0.011    mm - 1 , and the difference between the medians of the two populations was 0.006    mm - 1 . The tapering measurement retained a 95% confidence interval of ± 0.005    mm - 1 in a simulated 25 mAs scan and retained a 95% confidence of ± 0.005    mm - 1 on simulated CTs up to 1.5 times the original voxel size. We have established an estimate of the precision of the tapering measurement and estimated the effect on precision of the simulated voxel size and CT scan dose. We recommend that the scanner calibration be undertaken with the phantoms as described, on the specific CT scanner, radiation dose, and reconstruction algorithm that are to be used in any quantitative studies.