Ventilatory capacity in CLAD is driven by dysfunctional airway structure

BACKGROUND

Chronic lung allograft dysfunction (CLAD) encompasses three main phenotypes: bronchiolitis obliterans syndrome (BOS), restrictive allograft syndrome (RAS) and a Mixed phenotype combining both pathologies. How the airway structure in its entirety is affected in these phenotypes is still poorly understood.

METHODS

A detailed analysis of airway morphometry was applied to gain insights on the effects of airway remodelling on the distribution of alveolar ventilation in end-stage CLAD. Ex vivo whole lung μCT and tissue-core μCT scanning of six control, six BOS, three RAS and three Mixed explant lung grafts (9 male, 9 female, 2014-2021, Leuven, Belgium) were used for digital airway reconstruction and calculation of airway dimensions in relation to luminal obstructions.

FINDINGS

BOS and Mixed explants demonstrated airway obstructions of proximal bronchioles (starting at generation five), while RAS explants particularly had airway obstructions in the most distal bronchioles (generation >12). In BOS and Mixed explants 76% and 84% of bronchioles were obstructed, respectively, while this was 22% in RAS. Bronchiolar obstructions were mainly caused by lymphocytic inflammation of the airway wall or fibrotic remodelling, i.e. constrictive bronchiolitis. Proximal bronchiolectasis and imbalance in distal lung ventilation were present in all CLAD phenotypes and explain poor lung function and deterioration of specific lung function parameters.

INTERPRETATION

Alterations in the structure of conducting bronchioles revealed CLAD to affect alveolar ventilatory distribution in a regional fashion. The significance of various obstructions, particularly those associated with mucus, is highlighted.

FUNDING

This research was funded with the National research fund Flanders (G060322N), received by R.V.

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.

Anatomical Labeling of Human Airway Branches using a Novel Two-Step Machine Learning and Hierarchical Features

Chronic obstructive pulmonary disease (COPD) is a common inflammatory disease associated with restricted lung airflow. Quantitative computed tomography (CT)-based bronchial measures are popularly used in COPD-related studies, which require both airway segmentation and anatomical branch labeling. This paper presents an algorithm for anatomical labeling of human airway tree branches using a novel two-step machine learning and hierarchical features. Anatomical labeling of airway branches allows standardized spatial referencing of airway phenotypes in large population-based studies. State-of-the-art anatomical labeling methods are associated with mandatory manual reviewing and correction for mislabeled branches-a time-consuming process susceptible to inter-observer variability. The new method is fully automated, and it uses hierarchical branch-level features from the current as well as ancestral and descendant branches. During the first machine learning step, it differentiates candidate anatomical branches from insignificant topological branches, often, responsible for variations in airway branching patterns. The second step is designed for lung lobe-based classification of anatomical labels for valid candidate branches. The machine learning classifiers has been designed, trained, and validated using total lung capacity (TLC) CT scans (n = 350) from the Iowa cohort of the nationwide COPDGene study during their baseline visits. One hundred TLC CT scans were used for training and validation, and a different set of 250 scans were used for testing and evaluative experiments. The new method achieved labeling accuracies of 98.4, 97.2, 92.3, 93.4, and 94.1% in the right upper, right middle, right lower, left upper, and left lower lobe, respectively, and an overall accuracy of 95.9%. For five clinically significant segmental branches, the method has achieved an accuracy of 95.2%.

Geometric features of pig airways using computed tomography

Accurate knowledge of the airway geometry is needed when constructing physical models of the airway tree and for numerical modeling of flow or sound propagation in the airways. Human and animal experiments are conducted to validate these models. Many studies documented the geometric details of the human airways. However, information about the geometry of pig airways is scarcer. Earlier studies suggested that the morphology of animal airways can be significantly different from that of humans. The objective of this study is to measure the airway diameter, length and bifurcation angles in domestic pigs using computed tomography. In this study, lungs of six pigs were imaged, then segmentation software tools were used to extract the geometry of the airway lumen. The airway dimensions were measured from the resulting 3‐D models for the first 24 airway generations. Results showed that the size and morphology of the airways of the six pigs were similar. The trachea diameters were found to be comparable to the typical human adult, but the diameter, length and branching angles of other airways were noticeably different from that of humans. For example, pig airways consistently had an early branching from the trachea that feeds the top right lung lobe and precedes the main carina. This branch is absent in the human airways. The results suggested that the pig airways geometry may not be accurately approximated by human airways and this approximation may contribute to increasing the errors in computational models of the pig chest.

Generation of Pig Airways using Rules Developed from the Measurements of Physical Airways

BACKGROUND

A method for generating bronchial tree would be helpful when constructing models of the tree for benchtop experiments as well as for numerical modeling of flow or sound propagation in the airways. Early studies documented the geometric details of the human airways that were used to develop methods for generating human airway tree. However, methods for generating animal airway tree are scarcer. Earlier studies suggested that the morphology of animal airways can be significantly different from that of humans. Hence, using algorithms for the human airways may not be accurate in generating models of animal airway geometry.

OBJECTIVE

The objective of this study is to develop an algorithm for generating pig airway tree based on the geometric details extracted from the physical measurements.

METHODS

In the current study, measured values of branch diameters, lengths and bifurcation angles and rotation of bifurcating planes were used to develop an algorithm that is capable of generating a realistic pig airway tree.

RESULTS

The generation relations between parent and daughter branches were found to follow certain trends. The diameters and the length of different branches were dependent on airway generations while the bifurcation angles were primarily dependent on bifurcation plane rotations. These relations were sufficient to develop rules for generating a model of the pig large airways.

CONCLUSION

The results suggested that the airway tree generated from the algorithm can provide an approximate geometric model of pig airways for computational and benchtop studies.

A tree-matching algorithm: Application to airways in CT images of subjects with the acute respiratory distress syndrome

To match anatomical trees such as airways, we propose a graph-based strategy combined with an appropriate distance function. The strategy was devised to cope with topological and geometrical differences that may arise between trees corresponding to the same subject, but extracted from images acquired in different conditions. The proposed distance function, called father/family distance, combines topological and geometrical information in a single measure, by calculating a sum of path-to-path distances between sub-trees of limited extent. To use it successfully, the branches of these sub-trees need to be brought closer, which is obtained by successively translating the roots of these sub-trees prior to their actual matching. The work herein presented contributes to a study of the acute respiratory distress syndrome, where a series of pulmonary CT images from the same subject is acquired at varying settings (pressure and volume) of the mechanical ventilation. The method was evaluated on 45 combinations of synthetic trees, as well as on 15 pairs of real airway trees: nine corresponding to end-expiration and end-inspiration with the same pressure, and six corresponding to end-inspiration with significantly different pressures. It achieved a high rate of successful matches with respect to a hand-made reference containing a total of 2391 matches in real data: sensitivity of 94.3% and precision of 92.8%, when using the basic parameter settings of the algorithm.

Accurate airway centerline extraction based on topological thinning using graph-theoretic analysis

The quantitative analysis of the airway tree is of critical importance in the CT-based diagnosis and treatment of popular pulmonary diseases. The extraction of airway centerline is a precursor to identify airway hierarchical structure, measure geometrical parameters, and guide visualized detection. Traditional methods suffer from extra branches and circles due to incomplete segmentation results, which induce false analysis in applications. This paper proposed an automatic and robust centerline extraction method for airway tree. First, the centerline is located based on the topological thinning method; border voxels are deleted symmetrically to preserve topological and geometrical properties iteratively. Second, the structural information is generated using graph-theoretic analysis. Then inaccurate circles are removed with a distance weighting strategy, and extra branches are pruned according to clinical anatomic knowledge. The centerline region without false appendices is eventually determined after the described phases. Experimental results show that the proposed method identifies more than 96% branches and keep consistency across different cases and achieves superior circle-free structure and centrality.