BACKGROUND: A common promoter polymorphism (rs35705950) in MUC5B, the gene encoding mucin 5B, is associated with idiopathic pulmonary fibrosis. It is not known whether this polymorphism is associated with interstitial lung disease in the general population. METHODS: We performed a blinded assessment of interstitial lung abnormalities detected in 2633 participants in the Framingham Heart Study by means of volumetric chest computed tomography (CT). We evaluated the relationship between the abnormalities and the genotype at the rs35705950 locus. RESULTS: Of the 2633 chest CT scans that were evaluated, interstitial lung abnormalities were present in 177 (7%). Participants with such abnormalities were more likely to have shortness of breath and chronic cough and reduced measures of total lung and diffusion capacity, as compared with participants without such abnormalities. After adjustment for covariates, for each copy of the minor rs35705950 allele, the odds of interstitial lung abnormalities were 2.8 times greater (95% confidence interval [CI], 2.0 to 3.9; P<0.001), and the odds of definite CT evidence of pulmonary fibrosis were 6.3 times greater (95% CI, 3.1 to 12.7; P<0.001). Although the evidence of an association between the MUC5B genotype and interstitial lung abnormalities was greater among participants who were older than 50 years of age, a history of cigarette smoking did not appear to influence the association. CONCLUSIONS: The MUC5B promoter polymorphism was found to be associated with interstitial lung disease in the general population. Although this association was more apparent in older persons, it did not appear to be influenced by cigarette smoking. (Funded by the National Institutes of Health and others; ClinicalTrials.gov number, NCT00005121.).

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.

We present a novel airway labeling algorithm based on a Hidden Markov Tree Model (HMTM). We obtain a collection of discrete points along the segmented airway tree using particles sampling [1] and establish topology using Kruskal's minimum spanning tree algorithm. Following this, our HMTM algorithm probabilistically assigns labels to each point. While alternative methods label airway branches out to the segmental level, we describe a general method and demonstrate its performance out to the subsubsegmental level (two generations further than previously published approaches). We present results on a collection of 25 computed tomography (CT) datasets taken from a Chronic Obstructive Pulmonary Disease (COPD) study.

RATIONALE: Angiographic investigation suggests that pulmonary vascular remodeling in smokers is characterized by distal pruning of the blood vessels. OBJECTIVES: Using volumetric computed tomography scans of the chest we sought to quantitatively evaluate this process and assess its clinical associations. METHODS: Pulmonary vessels were automatically identified, segmented, and measured. Total blood vessel volume (TBV) and the aggregate vessel volume for vessels less than 5 mm(2) (BV5) were calculated for all lobes. The lobe-specific BV5 measures were normalized to the TBV of that lobe and the nonvascular tissue volume (BV5/T(issue)V) to calculate lobe-specific BV5/TBV and BV5/T(issue)V ratios. Densitometric measures of emphysema were obtained using a Hounsfield unit threshold of -950 (%LAA-950). Measures of chronic obstructive pulmonary disease severity included single breath measures of diffusing capacity of carbon monoxide, oxygen saturation, the 6-minute-walk distance, St George's Respiratory Questionnaire total score (SGRQ), and the body mass index, airflow obstruction, dyspnea, and exercise capacity (BODE) index. MEASUREMENTS AND MAIN RESULTS: The %LAA-950 was inversely related to all calculated vascular ratios. In multivariate models including age, sex, and %LAA-950, lobe-specific measurements of BV5/TBV were directly related to resting oxygen saturation and inversely associated with both the SGRQ and BODE scores. In similar multivariate adjustment lobe-specific BV5/T(issue)V ratios were inversely related to resting oxygen saturation, diffusing capacity of carbon monoxide, 6-minute-walk distance, and directly related to the SGRQ and BODE. CONCLUSIONS: Smoking-related chronic obstructive pulmonary disease is characterized by distal pruning of the small blood vessels (<5 mm(2)) and loss of tissue in excess of the vasculature. The magnitude of these changes predicts the clinical severity of disease.

Chronic obstructive pulmonary disease (COPD) is a progressive and irreversible lung condition typically related to emphysema. It hinders air from passing through airpaths and causes that alveolar sacs lose their elastic quality. Findings of COPD may be manifested in a variety of computed tomography (CT) studies. Nevertheless, visual assessment of CT images is time-consuming and depends on trained observers. Hence, a reliable computer-aided diagnosis system would be useful to reduce time and inter-evaluator variability. In this paper, we propose a new emphysema classification framework based on complex Gabor filters and local binary patterns. This approach simultaneously encodes global characteristics and local information to describe emphysema morphology in CT images. Kernel Fisher analysis was used to reduce dimensionality and to find the most discriminant nonlinear boundaries among classes. Finally, classification was performed using the k-nearest neighbor classifier. The results have shown the effectiveness of our approach for quantifying lesions due to emphysema and that the combination of descriptors yields to a better classification performance.

Emphysema has distinct and well-defined visually apparent CT patterns called centrilobular and panlobular emphysema. Existing studies concentrated on the classification of these patterns but they have not looked at the complete evolution of this disease as the destruction of lung parenchyma progresses from normal lung tissue to mild, moderate, and severe disease with complete effacement of the lung architecture. In this paper, we discretize this continuous process into five classes of increasing disease severity and construct a training set of 1161 CT patches. We exploit three solutions to this monotonic multi-class classification problem: a global rankSVM for ranking, hierarchical SVM for classification and a combination of these two, which we call a hierarchical rankSVM. Results showed that both hierarchical approaches were computationally efficient. The classification accuracies were slightly better for hierarchical SVM. However, in addition to classification, ranking approaches also provided a ranking of patterns, which can be utilized as a continuous disease progression score. In terms of the classification accuracy and ratio of pair-wise constraints satisfied, hierarchical rankSVM outperformed the global rankSVM.

RATIONALE AND OBJECTIVES: Computed tomography (CT) of the chest can be used to assess pectoralis muscle area (PMA) and subcutaneous adipose tissue (SAT) area. Adipose tissue content is associated with inflammatory mediators in chronic obstructive pulmonary disease (COPD) subjects. Based on gender differences in body composition, we aimed to assess the hypothesis that in subjects with COPD, the relationships between PMA, SAT, and blood biomarkers of inflammation differ by gender. MATERIALS AND METHODS: We compared chest CT measures of PMA and SAT on a single slice at aortic arch and supraesternal notch levels from 73 subjects (28 women) with COPD between genders. The relationships of PMA and SAT area to biomarkers were assessed using within-gender regression models. RESULTS: Women had a lesser PMA and a greater SAT area than men (difference range for PMA, 13.3-22.8 cm²; for SAT, 11.8-12.4 cm²; P < .05 for all comparisons) at both anatomic levels. These differences in PMA and SAT remained significant after adjustment for age and body mass index. Within-gender regression models adjusted for age showed that SAT was directly associated with C-reactive protein (for aortic arch level, P = .04) and fibrinogen (for both anatomic locations, P = .003) only in women, whereas PMA was not associated with any biomarkers in either gender. CONCLUSIONS: It appears that in subjects with COPD, there are gender-based differences in the relationships between subcutaneous adipose tissue and inflammatory biomarkers.

The main contribution of this work is a framework to register anatomical structures characterized as a point set where each point has an associated symmetric matrix. These matrices can represent problem-dependent characteristics of the registered structure. For example, in airways, matrices can represent the orientation and thickness of the structure. Our framework relies on a dense tensor field representation which we implement sparsely as a kernel mixture of tensor fields. We equip the space of tensor fields with a norm that serves as a similarity measure. To calculate the optimal transformation between two structures we minimize this measure using an analytical gradient for the similarity measure and the deformation field, which we restrict to be a diffeomorphism. We illustrate the value of our tensor field model by comparing our results with scalar and vector field based models. Finally, we evaluate our registration algorithm on synthetic data sets and validate our approach on manually annotated airway trees.

RATIONALE AND OBJECTIVES: There are limited data on, and controversies regarding gender differences in the airway dimensions of smokers. Multi-detector CT (MDCT) images were analyzed to examine whether gender could explain differences in airway dimensions of anatomically matched airways in smokers. MATERIALS AND METHODS: We used VIDA imaging software to analyze MDCT scans from 2047 smokers (M:F, 1021:1026) from the COPDGene® cohort. The airway dimensions were analyzed from segmental to subsubsegmental bronchi. We compared the differences of luminal area, inner diameter, wall thickness, wall area percentage (WA%) for each airway between men and women, and multiple linear regression including covariates (age, gender, body sizes, and other relevant confounding factors) was used to determine the predictors of each airway dimensions. RESULTS: Lumen area, internal diameter and wall thickness were smaller for women than men in all measured airway (18.4 vs 22.5 mm(2) for segmental bronchial lumen area, 10.4 vs 12.5 mm(2) for subsegmental bronchi, 6.5 vs 7.7 mm(2) for subsubsegmental bronchi, respectively p < 0.001). However, women had greater WA% in subsegmental and subsubsegmental bronchi. In multivariate regression, gender remained one of the most significant predictors of WA%, lumen area, inner diameter and wall thickness. CONCLUSION: Women smokers have higher WA%, but lower luminal area, internal diameter and airway thickness in anatomically matched airways as measured by CT scan than do male smokers. This difference may explain, in part, gender differences in the prevalence of COPD and airflow limitation.

RATIONALE: Emphysema is a heritable trait that occurs in smokers with and without chronic obstructive pulmonary disease. Emphysema occurs in distinct pathologic patterns, but the genetic determinants of these patterns are unknown. OBJECTIVES: To identify genetic loci associated with distinct patterns of emphysema in smokers and investigate the regulatory function of these loci. METHODS: Quantitative measures of distinct emphysema patterns were generated from computed tomography scans from smokers in the COPDGene Study using the local histogram emphysema quantification method. Genome-wide association studies (GWAS) were performed in 9,614 subjects for five emphysema patterns, and the results were referenced against enhancer and DNase I hypersensitive regions from ENCODE and Roadmap Epigenomics cell lines. MEASUREMENTS AND MAIN RESULTS: Genome-wide significant associations were identified for seven loci. Two are novel associations (top single-nucleotide polymorphism rs379123 in MYO1D and rs9590614 in VMA8) located within genes that function in cell-cell signaling and cell migration, and five are in loci previously associated with chronic obstructive pulmonary disease susceptibility (HHIP, IREB2/CHRNA3, CYP2A6/ADCK, TGFB2, and MMP12). Five of these seven loci lay within enhancer or DNase I hypersensitivity regions in lung fibroblasts or small airway epithelial cells, respectively. Enhancer enrichment analysis for top GWAS associations (single-nucleotide polymorphisms associated at P < 5 × 10(-6)) identified multiple cell lines with significant enhancer enrichment among top GWAS loci, including lung fibroblasts. CONCLUSIONS: This study demonstrates for the first time genetic associations with distinct patterns of pulmonary emphysema quantified by computed tomography scan. Enhancer regions are significantly enriched among these GWAS results, with pulmonary fibroblasts among the cell types showing the strongest enrichment.