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. View full abstract

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. METHODS: Based on GOLD (Global Initiative for Chronic Obstructive Lung Disease) stages and CT scan subtypes, we created a control group (n = 46) and the following matched COPD groups (n = 23 each): GOLD-2-AP, GOLD-2-EP, GOLD-4-AP, and GOLD-4-EP. From the CT scans of all 138 subjects, we measured emphysema, lung volumes, and caliber changes in the third and fourth airway generations of two bronchi. We expressed airway distensibility (ratio of airway lumen diameter change to lung volume change from end tidal breathing to full inspiration) as a global or lobar measure based on normalization by whole-lung or lobar volume changes. RESULTS: Global distensibility in the third and fourth airway generations was significantly lower in the GOLD-2-EP and GOLD-4-EP groups than in control subjects. In GOLD-2 subjects, lobar distensibility of the right-upper-lobe fourth airway generation was significantly lower in those with EP than in those with AP. In multivariate analysis, emphysema was an independent determinant of global and lobar airway distensibility. Compared with nonconstrictors, airway constrictors experienced more dyspnea, were more hyperinflated, and had a higher percentage of emphysema. CONCLUSIONS: Distensibility of large- to medium-sized airways is reduced in subjects with an EP CT scan subtype. Emphysema seems to alter airway-parenchyma interdependence. Trial registry: ClinicalTrials.gov; No.: NCT00608764; URL: www.clinicaltrials.gov.

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.

We present a fully automatic computational vascular morphometry (CVM) approach for the clinical assessment of pulmonary vascular disease (PVD). The approach is based on the automatic extraction of the lung intraparenchymal vasculature using scale-space particles. Based on the detected features, we developed a set of image-based biomarkers for the assessment of the disease using the vessel radii estimation provided by the particle's scale. The biomarkers are based on the interrelation between vessel cross-section area and blood volume. We validate our vascular extraction method using simulated data with different complexity and we present results in 2,500 CT scans with different degrees of chronic obstructive pulmonary disease (COPD) severity. Results indicate that our CVM pipeline may track vascular remodeling present in COPD and it can be used in further clinical studies to assess the involvement of PVD in patient populations.

This article investigates the suitability of local intensity distributions to analyze six emphysema classes in 342 CT scans obtained from 16 sites hosting scanners by 3 vendors and a total of 9 specific models in subjects with Chronic Obstructive Pulmonary Disease (COPD). We propose using kernel density estimation to deal with the inherent sparsity of local intensity histograms obtained from scarcely populated regions of interest. We validate our approach by leave-one-subject-out classification experiments and full-lung analyses. We compare our results with recently published LBP texture-based methodology. We demonstrate the efficacy of using intensity information alone in multi-scanner cohorts, which is a simpler, more intuitive approach.

BACKGROUND:In COPD patients, hyperinflation impairs cardiac function. We examined whether lung deflation improves oxygen pulse, a surrogate marker of stroke volume.METHODS:In 129 NETT patients with cardiopulmonary exercise testing (CPET) and arterial blood gases (ABG substudy), hyperinflation was assessed with residual volume to total lung capacity ratio (RV/TLC), and cardiac function with oxygen pulse (O(2) pulse=VO(2)/HR) at baseline and 6 months. Medical and surgical patients were divided into "deflators" and "non-deflators" based on change in RV/TLC from baseline (∆RV/TLC). We defined deflation as the ∆RV/TLC experienced by 75% of surgical patients. We examined changes in O(2) pulse at peak and similar (iso-work) exercise. Findings were validated in 718 patients who underwent CPET without ABGs.RESULTS:In the ABG substudy, surgical and medical deflators improved their RV/TLC and peak O(2) pulse (median ∆RV/TLC -18.0% vs. -9.3%, p=0.0003; median ∆O(2) pulse 13.6% vs. 1.8%, p=0.12). Surgical deflators also improved iso-work O(2) pulse (0.53 mL/beat, p=0.04 at 20 W). In the validation cohort, surgical deflators experienced a greater improvement in peak O(2) pulse than medical deflators (mean 18.9% vs. 1.1%). In surgical deflators improvements in O(2) pulse at rest and during unloaded pedaling (0.32 mL/beat, p<0.0001 and 0.47 mL/beat, p<0.0001, respectively) corresponded with significant reductions in HR and improvements in VO(2). On multivariate analysis, deflators were 88% more likely than non-deflators to have an improvement in O(2) pulse (OR 1.88, 95% CI 1.30-2.72, p=0.0008).CONCLUSION:In COPD, decreased hyperinflation through lung volume reduction is associated with improved O(2) pulse.

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.