BACKGROUND: Cor pulmonale (right ventricular dilation) and cor pulmonale parvus (right ventricular shrinkage) are both described in chronic obstructive pulmonary disease (COPD). The identification of emphysema as a shared risk factor suggests that additional disease characterization is needed to understand these widely divergent cardiac processes. Here, we explored the relationship between CT measures of emphysema and distal pulmonary arterial morphology with RV volume, as well as their association with exercise capacity and mortality in ever-smokers with COPD enrolled in the COPDGene Study. METHODS: Epicardial (myocardium and chamber) RV volume (RVEV), distal pulmonary arterial blood vessel volume (arterial BV5: vessels <5mm2 in cross section) as well as objective measures of emphysema were extracted from 3,506 COPDGene CT scans. Multivariable linear and Cox regression models as well as the log rank test were used to explore the association between emphysema, arterial BV5 and RVEV with exercise capacity (6-MWD) and all-cause mortality. RESULTS: The RVEV was approximately 10% smaller in GOLD 4 vs GOLD 1 COPD (P<0.0001). In multivariable modeling, a 10mL decrease in arterial BV5 (pruning) was associated with a 1mL increase in RVEV. For a given amount of emphysema, relative preservation of the arterial BV5 was associated with a smaller RVEV. An increased RVEV was associated with reduced 6-MWD and in those with arterial pruning an increased mortality. CONCLUSIONS: Pulmonary arterial pruning is associated with clinically significant increases in right ventricular volume in smokers with COPD and is related to exercise capacity and mortality in COPD.
Acute exposure to cold dry air is a trigger of bronchoconstriction, but little is known about how daily outdoor temperature influences lung function.We investigated associations of temperature from a model using satellite remote sensing data with repeated measures of lung function among 5896 participants of the Framingham Heart Study Offspring and Third Generation cohorts residing in the Northeastern US. We further tested if temperature modified previously reported associations between pollution and lung function. We constructed linear mixed-effects models, and assessed departures from linearity using penalised splines.In fully adjusted linear models, 1-, 2- and 7-day average temperatures were all associated with lower lung function: each 5°C higher previous-week temperature was associated with a 20 mL lower (95% CI -34---6) forced expiratory volume in 1 s. There was significant effect modification by season: negative associations of temperature and lung function were present in winter and spring only. Negative associations between previous-day fine particulate matter and lung function were present during unseasonably warm but not unseasonably cool days, with a similar pattern for other pollutants.We speculate that temperature-related differences in lung function may be explained by behavioural changes on relatively warm days, which may increase outdoor exposures.
Lung vessel segmentation has been widely explored by the biomedical image processing community; however, the differentiation of arterial from venous irrigation is still a challenge. Pulmonary artery-vein (AV) segmentation using computed tomography (CT) is growing in importance owing to its undeniable utility in multiple cardiopulmonary pathological states, especially those implying vascular remodelling, allowing the study of both flow systems separately. We present a new framework to approach the separation of tree-like structures using local information and a specifically designed graph-cut methodology that ensures connectivity as well as the spatial and directional consistency of the derived subtrees. This framework has been applied to the pulmonary AV classification using a random forest (RF) pre-classifier to exploit the local anatomical differences of arteries and veins. The evaluation of the system was performed using 192 bronchopulmonary segment phantoms, 48 anthropomorphic pulmonary CT phantoms, and 26 lungs from noncontrast CT images with precise voxel-based reference standards obtained by manually labelling the vessel trees. The experiments reveal a relevant improvement in the accuracy ( ∼ 20%) of the vessel particle classification with the proposed framework with respect to using only the pre-classification based on local information applied to the whole area of the lung under study. The results demonstrated the accurate differentiation between arteries and veins in both clinical and synthetic cases, specifically when the image quality can guarantee a good airway segmentation, which opens a huge range of possibilities in the clinical study of cardiopulmonary diseases.
The Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study, which began in 2007, is an ongoing multicenter observational cohort study of more than 10,000 current and former smokers. The study is aimed at understanding the etiology, progression, and heterogeneity of chronic obstructive pulmonary disease (COPD). In addition to genetic analysis, the participants have been extensively characterized by clinical questionnaires, spirometry, volumetric inspiratory and expiratory computed tomography, and longitudinal follow-up, including follow-up computed tomography at 5 years after enrollment. The purpose of this state-of-the-art review is to summarize the major advances in our understanding of COPD resulting from the imaging findings in the COPDGene study. Imaging features that are associated with adverse clinical outcomes include early interstitial lung abnormalities, visual presence and pattern of emphysema, the ratio of pulmonary artery to ascending aortic diameter, quantitative evaluation of emphysema, airway wall thickness, and expiratory gas trapping. COPD is characterized by the early involvement of the small conducting airways, and the addition of expiratory scans has enabled measurement of small airway disease. Computational advances have enabled indirect measurement of nonemphysematous gas trapping. These metrics have provided insights into the pathogenesis and prognosis of COPD and have aided early identification of disease. Important quantifiable extrapulmonary findings include coronary artery calcification, cardiac morphology, intrathoracic and extrathoracic fat, and osteoporosis. Current active research includes identification of novel quantitative measures for emphysema and airway disease, evaluation of dose reduction techniques, and use of deep learning for phenotyping COPD.
PURPOSE: To develop and validate a CT harmonization technique by combining noise-stabilization and autocalibration methodologies to provide reliable densitometry measurements in heterogeneous acquisition protocols.
METHODS: We propose to reduce the effects of spatially-variant noise such as non-uniform patterns of noise and biases. The method combines the statistical characterization of the signal-to-noise relationship in the CT image intensities, which allows us to estimate both the signal and spatially-variant variance of noise, with an autocalibration technique that reduces the non-uniform biases caused by noise and reconstruction techniques. The method is firstly validated with anthropomorphic synthetic images that simulate CT acquisitions with variable scanning parameters: different dosage, non-homogeneous variance of noise, and various reconstruction methods. We finally evaluate these effects and the ability of our method to provide consistent densitometric measurements in a cohort of clinical chest CT scans from two vendors (Siemens, n=54 subjects; and GE, n=50 subjects) acquired with several reconstruction algorithms (filtered back-projection and iterative reconstructions) with high-dose and low-dose protocols.
RESULTS: The harmonization reduces the effect of non-homogeneous noise without compromising the resolution of the images (25% RMSE reduction in both clinical datasets). An analysis through hierarchical linear models showed that the average biases induced by differences in dosage and reconstruction methods are also reduced up to 74:20%, enabling comparable results between highdose and low-dose reconstructions. We also assessed the statistical similarity between acquisitions obtaining increases of up to 30 percentage points and showing that the low-dose vs. high-dose comparisons of harmonized data obtain similar and even higher similarity than the observed for high-dose vs. high-dose comparisons of non-harmonized data.
CONCLUSION: The proposed harmonization technique allows to compare measures of low-dose with high-dose acquisitions without using a specific reconstruction as a reference. Since the harmonization does not require a precalibration with a phantom, it can be applied to retrospective studies. This approach might be suitable for multicenter trials for which a reference reconstruction is not feasible or hard to define due to differences in vendors, models and reconstruction techniques. This article is protected by copyright. All rights reserved.
RATIONALE: Cigarette smoke exposure is a risk factor for many lung diseases, and histologic studies suggest tobacco-related vasoconstriction and vessel loss plays a role in the development of emphysema. However, it remains unclear how tobacco affects the pulmonary vasculature in general populations with a typical range of tobacco exposure, and whether these changes are detectable by radiographic methods.
OBJECTIVE: To determine whether tobacco exposure in a generally healthy population manifests as lower pulmonary blood vessel volumes and vascular pruning on imaging.
METHODS: 2,410 Framingham Heart Study participants with demographic data and smoking history underwent volumetric whole-lung computed tomography (CT) from 2008-2011. Automated algorithms calculated the total volume of all intrapulmonary vessels (TBV), smaller peripheral vessels (BV5), and the relative fraction of small vessels (BV5/TBV). Tobacco exposure was assessed as smoking status, cumulative pack-years, and second-hand exposure. We constructed multivariable linear regression models to evaluate associations of cigarette exposure and pulmonary blood vessel volume measures, adjusting for demographic covariates including age, sex, height, weight, education, occupation, and median neighborhood income.
RESULTS: All metrics of tobacco exposure (including smoking status, pack-years, and second-hand exposure) were consistently associated with higher absolute pulmonary blood vessel volume, higher small vessel volume, and/or higher small vessel fraction. For example, ever-smokers had a 4.6mL higher TBV (95% CI: 2.9-6.3, p<0.0001), 2.1mL higher BV5 (95% CI: 1.3-2.9, p<0.0001), and 0.28 percentage-point higher BV5/TBV (95% CI: 0.03-0.52, p=0.03) compared to never-smokers. These associations remained significant after adjustment for percent-predicted FEV, cardiovascular comorbidities, and did not differ based on presence or absence of airflow obstruction.
CONCLUSIONS: Using CT imaging, we found that cigarette exposure was associated with higher pulmonary blood vessel volumes, especially in the smaller peripheral vessels. While histologically, tobacco-related vasculopathy is characterized by vessel narrowing and loss, our results suggest that radiographic vascular pruning may not be a surrogate of these pathologic changes.
Rationale Interstitial lung abnormalities (ILA) are radiologic abnormalities on chest CT scans that have been associated with an early or mild form of pulmonary fibrosis. While ILA have been associated with radiologic progression, it is not known if specific imaging patterns are associated with progression or risk of mortality. Objectives To determine the role of imaging patterns on the risk of death and ILA progression. Methods ILA (and imaging pattern) were assessed in 5320 participants from the AGES-Reykjavik Study, ILA progression was assessed in 3167 participants. Multivariable logistic regression was used to assess factors associated with ILA progression, Cox-proportional hazards models were used to assess time to mortality. Measurements and Main Results Over five years, 327 (10%) had ILA on at least one CT, 1435 (45%) did not have ILA on either CT. Of those with ILA, 238 (73%) had imaging progression, while 89 (27%) had stable to improved imaging; increasing age and copies of MUC5B genotype were associated with imaging progression. The Definite Fibrosis pattern was associated with the highest risk of progression (OR=8.4, 95% CI 2.7-25, P=0.0003). Specific imaging patterns were also associated with an increased risk of death. After adjustment, both a probable UIP and UIP pattern were associated with an increased risk of death when compared to those indeterminate for UIP, (HR=1.7, 95% CI 1.2-2.4, P=0.001) (HR=3.9, 95% CI 2.3-6.8, P<0.0001) respectively. Conclusions In those with ILA, imaging patterns can be used to help predict who is at the greatest risk of progression and early death.
RATIONALE: There is increasing evidence that aberrant processes occurring in the airways may precede the development of idiopathic pulmonary fibrosis (IPF); however, there has been no prior confirmatory data derived from imaging studies.
OBJECTIVES: To assess quantitative measures of airway wall thickness (AWT) in populations characterized for interstitial lung abnormalities (ILA) and for IPF.
METHODS: Computed tomographic imaging of the chest and measures of AWT were available for 6,073, 615, 1,167, and 38 participants from COPDGene (Genetic Epidemiology of COPD study), ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints study), and the Framingham Heart Study (FHS) and in patients with IPF from the Brigham and Women's Hospital Herlihy Registry, respectively. To evaluate these associations, we used multivariable linear regression to compare a standardized measure of AWT (the square root of AWT for airways with an internal perimeter of 10 mm [Pi10]) and characterizations of ILA and IPF by computed tomographic imaging of the chest.
RESULTS: In COPDGene, ECLIPSE, and FHS, research participants with ILA had increased measures of Pi10 compared with those without ILA. Patients with IPF had mean measures of Pi10 that were even greater than those noted in research participants with ILA. After adjustment for important covariates (e.g., age, sex, race, body mass index, smoking behavior, and chronic obstructive pulmonary disease severity when appropriate), research participants with ILA had increased measures of Pi10 compared with those without ILA (0.03 mm in COPDGene, 95% confidence interval [CI], 0.02-0.03; P < 0.001; 0.02 mm in ECLIPSE, 95% CI, 0.005-0.04; P = 0.01; 0.07 mm in FHS, 95% CI, 0.01-0.1; P = 0.01). Compared with COPDGene participants without ILA older than 60 years of age, patients with IPF were also noted to have increased measures of Pi10 (2.0 mm, 95% CI, 2.0-2.1; P < 0.001). Among research participants with ILA, increases in Pi10 were correlated with reductions in lung volumes in some but not all populations.
CONCLUSIONS: These results demonstrate that measurable increases in AWT are consistently noted in research participants with ILA and in patients with IPF. These findings suggest that abnormalities of the airways may play a role in, or be correlated with, early pathogenesis of pulmonary fibrosis.
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is characterized by airway remodeling. Characterization of airway changes on computed tomography has been challenging due to the complexity of the recurring branching patterns, and this can be better measured using fractal dimensions.
METHODS: We analyzed segmented airway trees of 8,135 participants enrolled in the COPDGene cohort. The fractal complexity of the segmented airway tree was measured by the Airway Fractal Dimension (AFD) using the Minkowski-Bougliand box-counting dimension. We examined associations between AFD and lung function and respiratory morbidity using multivariable regression analyses. We further estimated the extent of peribronchial emphysema (%) within 5 mm of the airway tree, as this is likely to affect AFD. We classified participants into 4 groups based on median AFD, percentage of peribronchial emphysema, and estimated survival.
RESULTS: AFD was significantly associated with forced expiratory volume in one second (FEV1; P < 0.001) and FEV1/forced vital capacity (FEV1/FVC; P < 0.001) after adjusting for age, race, sex, smoking status, pack-years of smoking, BMI, CT emphysema, air trapping, airway thickness, and CT scanner type. On multivariable analysis, AFD was also associated with respiratory quality of life and 6-minute walk distance, as well as exacerbations, lung function decline, and mortality on longitudinal follow-up. We identified a subset of participants with AFD below the median and peribronchial emphysema above the median who had worse survival compared with participants with high AFD and low peribronchial emphysema (adjusted hazards ratio [HR]: 2.72; 95% CI: 2.20-3.35; P < 0.001), a substantial number of whom were not identified by traditional spirometry severity grades.
CONCLUSION: Airway fractal dimension as a measure of airway branching complexity and remodeling in smokers is associated with respiratory morbidity and lung function change, offers prognostic information additional to traditional CT measures of airway wall thickness, and can be used to estimate mortality risk.
TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00608764.
FUNDING: This study was supported by NIH K23 HL133438 (SPB) and the COPDGene study (NIH Grant Numbers R01 HL089897 and R01 HL089856). The COPDGene project is also supported by the COPD Foundation through contributions made to an Industry Advisory Board comprised of AstraZeneca, Boehringer Ingelheim, Novartis, Pfizer, Siemens, Sunovion and GlaxoSmithKline.
A single-nucleotide polymorphism (rs35705950) in the mucin 5B () gene promoter is associated with pulmonary fibrosis and interstitial features on chest CT but may also have beneficial effects. In non-Hispanic whites in the COPDGene cohort with interstitial features (n=454), the promoter polymorphism was associated with a 61% lower odds of a prospectively reported acute respiratory disease event (P=0.001), a longer time-to-first event (HR=0.57; P=0.006) and 40% fewer events (P=0.016). The promoter polymorphism may have a beneficial effect on the risk of acute respiratory disease events in smokers with interstitial CT features.
Computed tomography (CT) is a widely used imaging modality for screening and diagnosis. However, the deleterious effects of radiation exposure inherent in CT imaging require the development of image reconstruction methods which can reduce exposure levels. The development of iterative reconstruction techniques is now enabling the acquisition of low-dose CT images whose quality is comparable to that of CT images acquired with much higher radiation dosages. However, the characterization and calibration of the CT signal due to changes in dosage and reconstruction approaches is crucial to provide clinically relevant data. Although CT scanners are calibrated as part of the imaging workflow, the calibration is limited to select global reference values and does not consider other inherent factors of the acquisition that depend on the subject scanned (e.g. photon starvation, partial volume effect, beam hardening) and result in a non-stationary noise response. In this work, we analyze the effect of reconstruction biases caused by non-stationary noise and propose an autocalibration methodology to compensate it. Our contributions are: 1) the derivation of a functional relationship between observed bias and non-stationary noise, 2) a robust and accurate method to estimate the local variance, 3) an autocalibration methodology that does not necessarily rely on a calibration phantom, attenuates the bias caused by noise and removes the systematic bias observed in devices from different vendors. The validation of the proposed methodology was performed with a physical phantom and clinical CT scans acquired with different configurations (kernels, doses, algorithms including iterative reconstruction). The results confirmed the suitability of the proposed methods for removing the intra-device and inter-device reconstruction biases.
Introduction: The Agatston score is a well-established metric of cardiovascular disease related to clinical outcomes. It is computed from CT scans by a) measuring the volume and intensity of the atherosclerotic plaques and b) aggregating such information in an index.
Objective: To generate a convolutional neural network that inputs a non-contrast chest CT scan and outputs the Agatston score associated with it directly, without a prior segmentation of Coronary Artery Calcifications (CAC).
Materials and methods: We use a database of 5973 non-contrast non-ECG gated chest CT scans where the Agatston score has been manually computed. The heart of each scan is cropped automatically using an object detector. The database is split in 4973 cases for training and 1000 for testing. We train a 3D deep convolutional neural network to regress the Agatston score directly from the extracted hearts.
Results: The proposed method yields a Pearson correlation coefficient of = 0.93; ≤ 0.0001 against manual reference standard in the 1000 test cases. It further stratifies correctly 72.6% of the cases with respect to standard risk groups. This compares to more complex state-of-the-art methods based on prior segmentations of the CACs, which achieve = 0.94 in ECG-gated pulmonary CT.
Conclusions: A convolutional neural network can regress the Agatston score from the image of the heart directly, without a prior segmentation of the CACs. This is a new and simpler paradigm in the Agatston score computation that yields similar results to the state-of-the-art literature.
A number of chronic diseases have benefited from both imaging and personalised medicine, but unfortunately, for patients with chronic obstructive pulmonary disease (COPD), there has been little clinical uptake or recognition of the key advances in thoracic imaging that might help detect disease early, or, perhaps more importantly, might help develop and phenotype patients for novel or personalised therapies that may halt disease progression. We outline our vision for how computed tomography and magnetic resonance imaging may be used to better inform COPD patient care, and, perhaps more importantly, how these may be used to help develop new therapies directed at early disease. We think that imaging and precision medicine should be considered and used together as "precision imaging" at specific stages of COPD when the major pathologies may be more responsive to therapy. While "precision medicine" is the tailoring of medical treatment to individual patients, we define "precision imaging" as the tailoring of specific therapies and interventions to individual patients with a detailed quantitative understanding of their specific imaging phenotypes and measurements. Finally, we stress the importance of "seeing" the pathology, because without this understanding, you can neither treat nor cure patients with COPD.