BACKGROUND: An increase in airway caliber (airway distensibility) with lung inflation is attenuated in COPD. Furthermore, some subjects have a decrease in airway caliber with lung inflation. We aimed to test the hypothesis that airway caliber increases are lower in subjects with emphysema-predominant (EP) compared with airway-predominant (AP) CT scan subtypes. Additionally, we compared clinical and CT scan features of subjects with (airway constrictors) and without a decrease in airway caliber.
We present an image pipeline for airway phenotype extraction suitable for large-scale genetic and epidemiological studies including genome-wide association studies (GWAS) in Chronic Obstructive Pulmonary Disease (COPD). We use scale-space particles to densely sample intraparenchymal airway locations in a large cohort of high-resolution CT scans. The particle methodology is based on a constrained energy minimization problem that results in a set of candidate airway points situated in both physical space and scale. Those points are further clustered using connected components filtering to increase their specificity. Finally, we use the particle locations to perform airway wall detection using an edge detector based on the zero-crossing of the second order derivative. Given the airway wall locations, we compute three phenotypes for airway disease: wall thickening (Pi10,WA%) and luminal remodeling (P%). We validate the airway extraction technique and present results in 2,500 scans for the association of the extracted phenotypes with clinical outcomes that will be deployed as part of the COPDGene study GWAS analysis.
OBJECTIVES: The purposes of this study were to evaluate the reference range of quantitative computed tomography (QCT) measures of lung attenuation and airway parameter measurements in healthy nonsmoking adults and to identify sources of variation in those measures and possible means to adjust for them. MATERIALS AND METHODS: Within the COPDGene study, 92 healthy non-Hispanic white nonsmokers (29 men, 63 women; mean [SD] age, 62.7 [9.0] years; mean [SD] body mass index [BMI], 28.1 [5.1] kg/m(2)) underwent volumetric computed tomography (CT) at full inspiration and at the end of a normal expiration. On QCT analysis (Pulmonary Workstation 2, VIDA Diagnostics), inspiratory low-attenuation areas were defined as lung tissue with attenuation values -950 Hounsfield units or less on inspiratory CT (LAA(I-950)). Expiratory low-attenuation areas were defined as lung tissue -856 Hounsfield units or less on expiratory CT (LAA(E-856)). We used simple linear regression to determine the impact of age and sex on QCT parameters and multiple regression to assess the additional impact of total lung capacity and functional residual capacity measured by CT (TLC(CT) and FRC(CT)), scanner type, and mean tracheal air attenuation. Airways were evaluated using measures of airway wall thickness, inner luminal area, wall area percentage (WA%), and standardized thickness of an airway with inner perimeter of 10 mm (Pi10).
RATIONALE: A genome-wide association study (GWAS) for circulating chronic obstructive pulmonary disease (COPD) biomarkers could identify genetic determinants of biomarker levels and COPD susceptibility. OBJECTIVES: To identify genetic variants of circulating protein biomarkers and novel genetic determinants of COPD. METHODS: GWAS was performed for two pneumoproteins, Clara cell secretory protein (CC16) and surfactant protein D (SP-D), and five systemic inflammatory markers (C-reactive protein, fibrinogen, IL-6, IL-8, and tumor necrosis factor-α) in 1,951 subjects with COPD. For genome-wide significant single nucleotide polymorphisms (SNPs) (P 1 × 10(-8)), association with COPD susceptibility was tested in 2,939 cases with COPD and 1,380 smoking control subjects. The association of candidate SNPs with mRNA expression in induced sputum was also elucidated.
Diffusion tensor imaging (DTI) constitutes the most used paradigm among the diffusion-weighted magnetic resonance imaging (DW-MRI) techniques due to its simplicity and application potential. Recently, real-time estimation in DW-MRI has deserved special attention, with several proposals aiming at the estimation of meaningful diffusion parameters during the repetition time of the acquisition sequence. Specifically focusing on DTI, the underlying model of the noise present in the acquired data is not taken into account, leading to a suboptimal estimation of the diffusion tensor. In this paper, we propose an optimal real-time estimation framework for DTI reconstruction in single-coil acquisitions. By including an online estimation of the time-changing noise variance associated to the acquisition process, the proposed method achieves the sequential best linear unbiased estimator. Results on both synthetic and real data show that our method outperforms those so far proposed, reaching the best performance of the existing proposals by processing a substantially lower number of diffusion images.
BACKGROUND: Exacerbations of chronic obstructive pulmonary disease (COPD) are associated with accelerated loss of lung function and death. Identification of patients at risk for these events, particularly those requiring hospitalization, is of major importance. Severe pulmonary hypertension is an important complication of advanced COPD and predicts acute exacerbations, though pulmonary vascular abnormalities also occur early in the course of the disease. We hypothesized that a computed tomographic (CT) metric of pulmonary vascular disease (pulmonary artery enlargement, as determined by a ratio of the diameter of the pulmonary artery to the diameter of the aorta [PA:A ratio] of >1) would be associated with severe COPD exacerbations. METHODS: We conducted a multicenter, observational trial that enrolled current and former smokers with COPD. We determined the association between a PA:A ratio of more than 1 and a history at enrollment of severe exacerbations requiring hospitalization and then examined the usefulness of the ratio as a predictor of these events in a longitudinal follow-up of this cohort, as well as in an external validation cohort. We used logistic-regression and zero-inflated negative binomial regression analyses and adjusted for known risk factors for exacerbation. RESULTS: Multivariate logistic-regression analysis showed a significant association between a PA:A ratio of more than 1 and a history of severe exacerbations at the time of enrollment in the trial (odds ratio, 4.78; 95% confidence interval [CI], 3.43 to 6.65; P
BACKGROUND: Exercise tolerance in COPD is only moderately well predicted by airflow obstruction assessed by FEV(1). We determined whether other phenotypic characteristics, including CT scan measures, are independent predictors of 6-min walk distance (6MWD) in the COPDGene cohort. METHODS: COPDGene recruits non-Hispanic Caucasian and African American current and ex-smokers. Phenotyping measures include postbronchodilator FEV(1) % predicted and inspiratory and expiratory CT lung scans. We defined % emphysema as the percentage of lung voxels -950 Hounsfield units on the inspiratory scan and % gas trapping as the percentage of lung voxels -856 Hounsfield units on the expiratory scan. RESULTS: Data of the first 2,500 participants of the COPDGene cohort were analyzed. Participant age was 61 ± 9 years; 51% were men; 76% were non-Hispanic Caucasians, and 24% were African Americans. Fifty-six percent had spirometrically defined COPD, with 9.3%, 23.4%, 15.0%, and 8.3% in GOLD (Global Initiative for Chronic Obstructive Lung Disease) stages I to IV, respectively. Higher % emphysema and % gas trapping predicted lower 6MWD (P .001). However, in a given spirometric group, after adjustment for age, sex, race, and BMI, neither % emphysema nor % gas trapping, or their interactions with FEV(1) % predicted, remained a significant 6MWD predictor. In a given spirometric group, only 16% to 27% of the variance in 6MWD could be explained by age, male sex, Caucasian race, and lower BMI as significant predictors of higher 6MWD. CONCLUSIONS: In this large cohort of smokers in a given spirometric stage, phenotypic characteristics were only modestly predictive of 6MWD. CT scan measures of emphysema and gas trapping were not predictive of 6MWD after adjustment for other phenotypic characteristics.
Automatic aorta segmentation in thoracic computed tomography (CT) scans is important for aortic calcification quantification and to guide the segmentation of other central vessels. We propose an aorta segmentation algorithm consisting of an initial boundary detection step followed by 3D level set segmentation for refinement. Our algorithm exploits aortic cross-sectional circularity: we first detect aorta boundaries with a circular Hough transform on axial slices to detect ascending and descending aorta regions, and we apply the Hough transform on oblique slices to detect the aortic arch. The centers and radii of circles detected by Hough transform are fitted to smooth cubic spline functions using least-squares fitting. From these center and radius spline functions, we reconstruct an initial aorta surface using the Frenet frame. This reconstructed tubular surface is further refined with 3D level set evolutions. The level set framework we employ optimizes a functional that depends on both edge strength and smoothness terms and evolves the surface to the position of nearby edge location corresponding to the aorta wall. After aorta segmentation, we first detect the aortic calcifications with thresholding applied to the segmented aorta region. We then filter out the false positive regions due to nearby high intensity structures. We tested the algorithm on 45 CT scans and obtained a closest point mean error of 0.52 ± 0.10 mm between the manually and automatically segmented surfaces. The true positive detection rate of calcification algorithm was 0.96 over all CT scans.
BACKGROUND: Schizophrenia has been associated with disturbances in brain connectivity; however the exact nature of these disturbances is not fully understood. Measuring temporal correlations between the functional MRI time courses of spatially disparate brain regions obtained during rest has recently emerged as a popular paradigm for estimating brain connectivity. Previous resting state studies in schizophrenia explored connections related to particular clinical or cognitive symptoms (connectivity within a-priori selected networks), or connections restricted to functional networks obtained from resting state analysis. Relatively little has been done to understand global brain connectivity in schizophrenia. METHODS: Eighteen patients with chronic schizophrenia and 18 healthy volunteers underwent a resting state fMRI scan on a 3T magnet. Whole brain temporal correlations have been estimated using resting-state fMRI data and free surfer cortical parcellations. A multivariate classification method was then used to indentify brain connections that distinguish schizophrenia patients from healthy controls. RESULTS: The classification procedure achieved a prediction accuracy of 75% in differentiating between groups on the basis of their functional connectivity. Relative to controls, schizophrenia patients exhibited co-existing patterns of increased connectivity between parietal and frontal regions, and decreased connectivity between parietal and temporal regions, and between the temporal cortices bilaterally. The decreased parieto-temporal connectivity was associated with the severity of patients’ positive symptoms, while increased fronto-parietal connectivity was associated with patients’ negative and general symptoms. DISCUSSION: Our analysis revealed two co-existing patterns of functional connectivity abnormalities in schizophrenia, each related to different clinical profiles. Such results provide further evidence that abnormalities in brain connectivity, characteristic of schizophrenia, are directly related to the clinical features of the disorder.
