Juerg Tschirren

Intrathoracic airway trees: segmentation and airway morphology analysis from low-dose CT scans

The segmentation of the human airway tree from volumetric computed tomography (CT) images builds an important step for many clinical applications and for physiological studies. Previously proposed algorithms suffer from one or several problems: leaking into the surrounding lung parenchyma, the need for the user to manually adjust parameters, excessive runtime. Low-dose CT scans are increasingly utilized in lung screening studies, but segmenting them with traditional airway segmentation algorithms often yields less than satisfying results. In this paper, a new airway segmentation method based on fuzzy connectivity is presented. Small adaptive regions of interest are used that follow the airway branches as they are segmented. This has several advantages. It makes it possible to detect leaks early and avoid them, the segmentation algorithm can automatically adapt to changing image parameters, and the computing time is kept within moderate values. The new method is robust in the sense that it works on various types of scans (low-dose and regular dose, normal subjects and diseased subjects) without the need for the user to manually adjust any parameters. Comparison with a commonly used region-grow segmentation algorithm shows that the newly proposed method retrieves a significantly higher count of airway branches. A method that conducts accurate cross-sectional airway measurements on airways is presented as an additional processing step. Measurements are conducted in the original gray-level volume. Validation on a phantom shows that subvoxel accuracy is achieved for all airway sizes and airway orientations.

Characterization of the interstitial lung diseases via density-based and texture-based analysis of computed tomography images of lung structure and function1 ☆

RATIONALE AND OBJECTIVES Efforts to establish a quantitative approach to the computed tomography (CT)-based character ization of the lung parenchyma in interstitial lung disease (including emphysema) has been sought. The accuracy of these tools must be site independent. Multi-detector row CT has remained the gold standard for imaging the lung, and it provides the ability to image both lung structure as well as lung function. MATERIAL AND METHODS Imaging is via multi-detector row CT and protocols include careful control of lung volume during scanning. Characterization includes not only anatomic-based measures but also functional measures including regional parameters derived from measures of pulmonary blood flow and ventilation. Image processing includes the automated detection of the lungs, lobes, and airways. The airways provide the road map to the lung parenchyma. Software automatically detects the airways, the airway centerlines, and the branch points, and then automatically labels the airway tree segments with a standardized set of labels, allowing for intersubject as well intrasubject comparisons across time. By warping all lungs to a common atlas, the atlas provides the range of normality for the various parameters provided by CT imaging. RESULTS Imaged density and textural changes mark underlying structural changes at the most peripheral regions of the lung. Additionally, texture-based alterations in the parameters of blood flow may provide early evidence of pathologic processes. Imaging of stable xenon gas provides a regional measure of ventilation which, when coupled with measures of flow, provide for a textural analysis regional of ventilation-perfusion matching. CONCLUSION With the improved resolution and speed of CT imaging, the patchy nature of regional parenchymal pathology can be imaged as texture of structure and function. With careful control of imaging protocols and the use of objective image analysis methods it is possible to provide site-independent tools for the assessment of interstitial lung disease. There remains a need to validate these methods, which requires interdisciplinary and cross-institutional efforts to gather appropriate data bases of images along with a consensus on appropriate ground truths associated with the images. Furthermore, there is the growing need for scanner manufacturers to focus on not just visually pleasing images, but on quantitatifiably accurate images.

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.

Segmentation, Skeletonization, and Branchpoint Matching - A Fully Automated Quantitative Evaluation of Human Intrathoracic Airway Trees

Modern multislice X-ray CT scanners provide high-resolution volumetric image data containing a wealth of structural and functional information. The size of the volumes makes it more and more difficult for human observers to visually evaluate their contents. Similar to other areas of medical image analysis, highly automated extraction and quantitative assessment of volumetric data is increasingly important in pulmonary physiology, diagnosis, and treatment. We present a method for a fully automated segmentation of a human airway tree, its skeletonization, identification of airway branches and branchpoints, as well as a method for matching the airway trees, branches, and branchpoints for the same subject over time and across subjects. The validation of our method shows a high correlation between the automatically obtained results and reference data provided by human observers.

Automated analysis of Doppler ultrasound velocity flow diagrams

A highly automated method for the identification and quantization of maximum blood velocity curves from Doppler ultrasound flow diagrams is presented. The method uses an image processing scheme to analyze video-recorded image sequences of flow diagrams. The sequences are acquired, a sequence of images relating to chronological cardiac cycles is extracted, and a maximum blood velocity envelope is determined and quantified. The results are verified against hand-traced reference curves. Excellent correlation of r = 0.99 is achieved.

Quantitative analysis of intrathoracic airway trees: methods and validation.

A method for quantitative assessment of tree structures is reported allowing evaluation of airway or vascular tree morphology and its associated function. Our skeletonization and branch-point identification method provides a basis for tree quantification or tree matching, tree-branch diameter measurement in any orientation, and labeling individual branch segments. All main components of our method were specifically developed to deal with imaging artifacts typically present in volumetric medical image data. The proposed method has been tested in 343 computer phantom instances subjected to changes of its orientation as well as in a repeatedly CT-scanned rubber plastic phantom width sub-voxel accuracy and high reproducibility. Application to 35 human in vivo trees yielded reliable and well-positioned centerlines and branch-points.

Automated Nomenclature Labeling of the Bronchial Tree in 3D-CT Lung Images

A nomenclature labeling algorithm for the human bronchial tree down to sub-lobar segments is proposed, as a means of inter and intra subject comparisons for the evaluation of lung structure and function. The algorithm is a weighted maximum clique search of an association graph between a reference tree and an object tree. The adjacency between nodes in the association graph is defined so as to reflect the consistency between the bronchial name in the reference tree and the node connectivity in the object tree. Nodes in the association graph are weighted according to the similarity between two tree nodes in the respective trees. This algorithm is robust to various branching patterns and false branches that arise during segmentation processing. Experiments have been performed for nine airway trees extracted automatically from clinical 3D-CT data, where approximately 250 branches were contained. Of these, 95 % were accurately named.

Effect of Emphysema on CT Scan Measures of Airway Dimensions in Smokers

BACKGROUND In CT scans of smokers with COPD, the subsegmental airway wall area percent (WA%) is greater and more strongly correlated with FEV1 % predicted than WA% obtained in the segmental airways. Because emphysema is linked to loss of airway tethering and may limit airway expansion, increases in WA% may be related to emphysema and not solely to remodeling. We aimed to first determine whether the stronger association of subsegmental vs segmental WA% with FEV1 % predicted is mitigated by emphysema and, second, to assess the relationships among emphysema, WA%, and total bronchial area (TBA). METHODS We analyzed CT scan segmental and subsegmental WA% (WA% = 100 × wall area/TBA) of six bronchial paths and corresponding lobar emphysema, lung function, and clinical data in 983 smokers with COPD. RESULTS Compared with segmental WA%, the subsegmental WA% had a greater effect on FEV1% predicted (-0.8% to -1.7% vs -1.9% to -2.6% per 1-unit increase in WA%, respectively; P < .05 for most bronchial paths). After adjusting for emphysema, the association between subsegmental WA% and FEV1 % predicted was weakened in two bronchial paths. Increases in WA% between bronchial segments correlated directly with emphysema in all bronchial paths (P < .05). In multivariate regression models, emphysema was directly related to subsegmental WA% in most bronchial paths and inversely related to subsegmental TBA in all bronchial paths. CONCLUSION The greater effect of subsegmental WA% on airflow obstruction is mitigated by emphysema. Part of the emphysema effect might be due to loss of airway tethering, leading to a reduction in TBA and an increase in WA%.

Association Between Expiratory Central Airway Collapse and Respiratory Outcomes Among Smokers

IMPORTANCE Central airway collapse greater than 50% of luminal area during exhalation (expiratory central airway collapse [ECAC]) is associated with cigarette smoking and chronic obstructive pulmonary disease (COPD). However, its prevalence and clinical significance are unknown. OBJECTIVE To determine whether ECAC is associated with respiratory morbidity in smokers independent of underlying lung disease. DESIGN, SETTING, AND PARTICIPANTS Analysis of paired inspiratory-expiratory computed tomography images from a large multicenter study (COPDGene) of current and former smokers from 21 clinical centers across the United States. Participants were enrolled from January 2008 to June 2011 and followed up longitudinally until October 2014. Images were initially screened using a quantitative method to detect at least a 30% reduction in minor axis tracheal diameter from inspiration to end-expiration. From this sample of screen-positive scans, cross-sectional area of the trachea was measured manually at 3 predetermined levels (aortic arch, carina, and bronchus intermedius) to confirm ECAC (>50% reduction in cross-sectional area). EXPOSURES Expiratory central airway collapse. MAIN OUTCOMES AND MEASURES The primary outcome was baseline respiratory quality of life (St Georges Respiratory Questionnaire [SGRQ] scale 0 to 100; 100 represents worst health status; minimum clinically important difference [MCID], 4 units). Secondary outcomes were baseline measures of dyspnea (modified Medical Research Council [mMRC] scale 0 to 4; 4 represents worse dyspnea; MCID, 0.7 units), baseline 6-minute walk distance (MCID, 30 m), and exacerbation frequency (events per 100 person-years) on longitudinal follow-up. RESULTS The study included 8820 participants with and without COPD (mean age, 59.7 [SD, 6.9] years; 4667 [56.7%] men; 4559 [51.7%] active smokers). The prevalence of ECAC was 5% (443 cases). Patients with ECAC compared with those without ECAC had worse SGRQ scores (30.9 vs 26.5 units; P < .001; absolute difference, 4.4 [95% CI, 2.2-6.6]) and mMRC scale scores (median, 2 [interquartile range [IQR], 0-3]) vs 1 [IQR, 0-3]; P < .001]), but no significant difference in 6-minute walk distance (399 vs 417 m; absolute difference, 18 m [95% CI, 6-30]; P = .30), after adjustment for age, sex, race, body mass index, forced expiratory volume in the first second, pack-years of smoking, and emphysema. On follow-up (median, 4.3 [IQR, 3.2-4.9] years), participants with ECAC had increased frequency of total exacerbations (58 vs 35 events per 100 person-years; incidence rate ratio [IRR], 1.49 [95% CI, 1.29-1.72]; P < .001) and severe exacerbations requiring hospitalization (17 vs 10 events per 100 person-years; IRR, 1.83 [95% CI, 1.51-2.21]; P < .001). CONCLUSIONS AND RELEVANCE In a cross-sectional analysis of current and former smokers, the presence of ECAC was associated with worse respiratory quality of life. Further studies are needed to assess long-term associations with clinical outcomes.

Comparison of airway diameter measurements from an anthropomorphic airway tree phantom using hyperpolarized 3He MRI and high-resolution computed tomography

An anthropomorphic airway tree phantom was imaged with both hyperpolarized (HP) 3He MRI using a dynamic projection scan and computed tomography (CT). Airway diameter measurements from the HP 3He MR images obtained using a newly developed model‐based algorithm were compared against their corresponding CT values quantified with a well‐established method. Of the 45 airway segments that could be evaluated with CT, only 14 airway segments (31%) could be evaluated using HP 3He MRI. No airway segments smaller than ∼4 mm in diameter and distal to the fourth generation were adequate for analysis in MRI. For the 14 airway segments measured, only two airway segments yielded a non‐equivalent comparison between the two imaging modalities, while eight more had inconclusive comparison results, leaving only four airway segments (29%) that satisfied the designed equivalence criteria. Some of the potential problems in airway diameter quantification described in the formulation of the model‐based algorithm were observed in this study. These results suggest that dynamic projection HP 3He MRI may have limited utility for measuring airway segment diameters, particularly those of the central airways. Magn Reson Med 58:636–642, 2007.