Rationale: Emphysema occurs in distinct pathologic patterns, but little is known about the epidemiologic associations of these patterns. Standard quantitative measures of emphysema from computed tomography (CT) do not distinguish between distinct patterns of parenchymal destruction. Objectives: To study the epidemiologic associations of distinct emphysema patterns with measures of lung-related physiology, function, and health care use in smokers. Methods: Using a local histogram-based assessment of lung density, we quantified distinct patterns of low attenuation in 9,313 smokers in the COPDGene Study. To determine if such patterns provide novel insights into chronic obstructive pulmonary disease epidemiology, we tested for their association with measures of physiology, function, and health care use. Measurements and Main Results: Compared with percentage of low-attenuation area less than -950 Hounsfield units (%LAA-950), local histogram-based measures of distinct CT low-attenuation patterns are more predictive of measures of lung function, dyspnea, quality of life, and health care use. These patterns are strongly associated with a wide array of measures of respiratory physiology and function, and most of these associations remain highly significant (P < 0.005) after adjusting for %LAA-950. In smokers without evidence of chronic obstructive pulmonary disease, the mild centrilobular disease pattern is associated with lower FEV1 and worse functional status (P < 0.005). Conclusions: Measures of distinct CT emphysema patterns provide novel information about the relationship between emphysema and key measures of physiology, physical function, and health care use. Measures of mild emphysema in smokers with preserved lung function can be extracted from CT scans and are significantly associated with functional measures.

ABSTRACT 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. Since emphysema is linked to loss of airway tethering and may limit airway expansion, increases in WA% may be related to emphysema and not solely due to remodeling. We aimed to first determine if the stronger association of subsegmental vs. segmental WA% with FEV1% predicted is mitigated by emphysema; and second, to assess the relationships between emphysema, WA%, and total bronchial area (TBA). METHODS: We analyzed CT 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 to segmental, 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&lt;0.05 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&lt;0.05). 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 reduction in TBA and increase in WA%.

We present a probability model for lung airways in computed tomography (CT) images. Lung airways are tubular structures that display specific features, such as low intensity and proximity to vessels and bronchial walls. From these features, the posterior probability for the airway feature space was computed using a Bayesian model based on 20 CT images from subjects with different degrees of Chronic Obstructive Pulmonary Disease (COPD). The likelihood probability was modeled using both a Gaussian distribution and a nonparametric kernel density estimation method. After exhaustive feature selection, good specificity and sensitivity were achieved in a cross-validation study for both the Gaussian (0.83, 0.87) and the nonparametric method (0.79, 0.89). The model generalizes well when trained using images from a late stage COPD group. This probability model may facilitate airway extraction and quantitative assessment of lung diseases, which is useful in many clinical and research settings. View full abstract

BACKGROUND: Gas trapping quantified on chest CT scans has been proposed as a surrogate for small airway disease in COPD. We sought to determine if measurements using paired inspiratory and expiratory CT scans may be better able to separate gas trapping due to emphysema from gas trapping due to small airway disease. METHODS: Smokers with and without COPD from the COPDGene Study underwent inspiratory and expiratory chest CT scans. Emphysema was quantified by the percent of lung with attenuation < -950HU on inspiratory CT. Four gas trapping measures were defined: (1) Exp(-856), the percent of lung < -856HU on expiratory imaging; (2) E/I MLA, the ratio of expiratory to inspiratory mean lung attenuation; (3) RVC(856-950), the difference between expiratory and inspiratory lung volumes with attenuation between -856 and -950 HU; and (4) Residuals from the regression of Exp(-856) on percent emphysema. RESULTS: In 8517 subjects with complete data, Exp(-856) was highly correlated with emphysema. The measures based on paired inspiratory and expiratory CT scans were less strongly correlated with emphysema. Exp(-856), E/I MLA and RVC(856-950) were predictive of spirometry, exercise capacity and quality of life in all subjects and in subjects without emphysema. In subjects with severe emphysema, E/I MLA and RVC(856-950) showed the highest correlations with clinical variables. CONCLUSIONS: Quantitative measures based on paired inspiratory and expiratory chest CT scans can be used as markers of small airway disease in smokers with and without COPD, but this will require that future studies acquire both inspiratory and expiratory CT scans.

This paper investigates a diffeomorphic point-set registration based on non-stationary mixture models. The goal is to improve the non-linear registration of anatomical structures by representing each point as a general non-stationary kernel that provides information about the shape of that point. Our framework generalizes work done by others that use stationary models. We achieve this by integrating the shape at each point when calculating the point-set similarity and transforming it according to the calculated deformation. We also restrict the non-rigid transform to the space of symmetric diffeomorphisms. Our algorithm is validated in synthetic and human datasets in two different applications: fiber bundle and lung airways registration. Our results shows that non-stationary mixture models are superior to Gaussian mixture models and methods that do not take into account the shape of each point.

Motivated by the need to identify new and clinically relevant categories of lung disease, we propose a novel clustering with constraints method using a Dirichlet process mixture of Gaussian processes in a variational Bayesian nonparametric framework. We claim that individuals should be grouped according to biological and/or genetic similarity regardless of their level of disease severity; therefore, we introduce a new way of looking at subtyping/clustering by recasting it in terms of discovering associations of individuals to disease trajectories (i.e., grouping individuals based on their similarity in response to environmental and/or disease causing variables). The nonparametric nature of our algorithm allows for learning the unknown number of meaningful trajectories. Additionally, we acknowledge the usefulness of expert guidance by providing for their input using must-link and cannot- link constraints. These constraints are encoded with Markov random fields. We also provide an efficient variational approach for performing inference on our model.