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Background: Although lobar patterns of emphysema heterogeneity are indicative of optimal target sites for lung volume reduction (LVR) strategies, the presence of segmental, or sublobar, heterogeneity is often underappreciated., Objective: The aim of this study was to understand lobar and segmental patterns of emphysema heterogeneity, which may more precisely indicate optimal target sites for LVR procedures., Methods: Patterns of emphysema heterogeneity were evaluated in a representative cohort of 150 severe (GOLD stage III/IV) chronic obstructive pulmonary disease (COPD) patients from the COPDGene study. High-resolution computerized tomography analysis software was used to measure tissue destruction throughout the lungs to compute heterogeneity (≥15% difference in tissue destruction) between (inter-) and within (intra-) lobes for each patient. Emphysema tissue destruction was characterized segmentally to define patterns of heterogeneity., Results: Segmental tissue destruction revealed interlobar heterogeneity in the left lung (57%) and right lung (52%). Intralobar heterogeneity was observed in at least one lobe of all patients. No patient presented true homogeneity at a segmental level. There was true homogeneity across both lungs in 3% of the cohort when defining heterogeneity as ≥30% difference in tissue destruction., Conclusion: Many LVR technologies for treatment of emphysema have focused on interlobar heterogeneity and target an entire lobe per procedure. Our observations suggest that a high proportion of patients with emphysema are affected by interlobar as well as intralobar heterogeneity. These findings prompt the need for a segmental approach to LVR in the majority of patients to treat only the most diseased segments and preserve healthier ones., (© 2015 The Author(s) Published by S. Karger AG, Basel.)

A method for computationally efficient skeletonization of three-dimensional tubular structures is reported. The method is specifically targeting skeletonization of vascular and airway tree structures in medical images but it is general and applicable to many other skeletonization tasks. The developed approach builds on the following novel concepts and properties: fast curve-thinning algorithm to increase computational speed, endpoint re-checking to avoid generation of spurious side branches, depth-and-length sensitive pruning, and exact tree-branch partitioning allowing branch volume and surface measurements. The method was validated in computer and physical phantoms and in vivo CT scans of human lungs. The validation studies demonstrated sub-voxel accuracy of branch point positioning, insensitivity to changes of object orientation, and high reproducibility of derived quantitative indices of the tubular structures offering a significant improvement over previously reported methods (p<<0.001).

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.

Matching of corresponding branchpoints between two human airway trees, as well as assigning anatomical names to the segments and branchpoints of the human airway tree, are of significant interest for clinical applications and physiological studies. In the past, these tasks were often performed manually due to the lack of automated algorithms that can tolerate false branches and anatomical variability typical for in vivo trees. In this paper, we present algorithms that perform both matching of branchpoints and anatomical labeling of in vivo trees without any human intervention and within a short computing time. No hand-pruning of false branches is required. The results from the automated methods show a high degree of accuracy when validated against reference data provided by human experts. 92.9% of the verifiable branchpoint matches found by the computer agree with experts' results. For anatomical labeling, 97.1% of the automatically assigned segment labels were found to be correct.

The interpretation of experimental results from functional medical imaging is complicated by intersubject and interspecies differences in airway geometry. The application of computational models in understanding the significance of these differences requires methods for generation of subject-specific geometric models of the bronchial airway tree. In the current study, curvilinear airway centerline and diameter models have been fitted to human and ovine bronchial trees using detailed data segmented from multidetector row X-ray-computed tomography scans. The trees have been extended to model the entire conducting airway system by using a volume-filling algorithm to generate airway centerline locations within detailed volume descriptions of the lungs or lobes. Analysis of the geometry of the scan-based and model-based airways has verified their consistency with measures from previous anatomic studies and has provided new anatomic data for the ovine bronchial tree. With the use of an identical parameter set, the volume-filling algorithm has produced airway trees with branching asymmetry appropriate for the human and ovine lung, demonstrating the dependence of the method on the shape of the lung or lobe volume. The modeling approach that has been developed can be applied to any level of detail of the airway tree and into any volume shape for the lung; hence it can be used directly for different individuals or animals and for any number of scan-based airways. The resulting models are subject-specific computational meshes with anatomically consistent geometry, suitable for application in simulation studies.

Defining the healthy human airway is important in enhancing our understanding of pulmonary disease states such as inflammation and cancer. The structure of the human airway, both static and dynamic, can be assessed using multi-detector CT (MDCT) scanning. This modality also allows for the evaluation of structures outside of the airway. The airway wall can be directly visualized using CCD chip high-resolution color optical imaging through endoscopy allowing bronchial wall evaluation by traditional biopsy methods, as well as by newer optically based strategies. We suggest that these two imaging modalities, MDCT and optical imaging, provide complementary information about the normal airway, and the airway in various diseases. Methods for evaluating the human airway using MDCT images are presented facilitating automatic airway segmentation, branchpoint finding and airway dimension analysis. The airway wall color is objectively evaluated as an important surrogate for airway wall inflammation and cancer formation, and the integration of the color endoscopic information into the MDCT scan data set is currently ongoing. The amalgamation of these two digital imaging modalities appears increasingly useful for enabling biopsy techniques, and for relating structure and function of the airway. In addition, these developments may be progressively more useful in understanding the normal airway structure and function, for defining airway diseases patterns and for guiding biopsy and therapeutic procedures.

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.