Inspiratory Computed Tomography Scans Research Articles

Volume changes of diseased and normal areas in progressive fibrosing interstitial lung disease on inspiratory and expiratory computed tomography.

The diagnosis of progressive fibrosing interstitial lung disease (PF-ILD) using computed tomography (CT) is an important medical practice in respiratory care, and most imaging findings for this disease have been obtained with inspiratory CT. It is possible that some characteristic changes in respiration may be seen in normal and diseased lung in PF-ILD, which may lead to a new understanding of the pathogenesis of interstitial pneumonia, but it has never been examined. In this study, we collected and selected inspiratory and expiratory CT scans performed in pure PF-ILD cases, and evaluated the volumes of diseased and normal lung separately by manual detection and 3-dimensional volumetry to characterize the dynamic features of PF-ILD. Cases were collected retrospectively from a total of 753 inspiratory and expiratory CT scans performed at our hospital over a 3-year period. Sixteen cases of pure PF-ILD, excluding almost all other diseases, were included. We measured their diseased, normal, and the whole lung volumes manually and evaluated the correlation of their values and their relationship with respiratory function tests (FVC, FVC%-predicted, and DLCO%-predicted). The relative expansion rate of the diseased lung is no less than that of the normal lung. The "Expansion volume of total lung" divided by the "Expansion volume of normal lung" was found to be significantly associated with DLCO%-predicted abnormalities (p = 0.0073). The diseased lung in PF-ILD retained expansion capacity comparable to the normal lung, suggesting a negative impact on respiratory function.

Detection and staging of chronic obstructive pulmonary disease using a computed tomography-based weakly supervised deep learning approach.

Chronic obstructive pulmonary disease (COPD) is underdiagnosed globally. The present study aimed to develop weakly supervised deep learning (DL) models that utilize computed tomography (CT) image data for the automated detection and staging of spirometry-defined COPD. A large, highly heterogeneous dataset was established, consisting of 1393 participants retrospectively recruited from outpatient, inpatient, and physical examination center settings of four large public hospitals in China. All participants underwent both inspiratory chest CT scans and pulmonary function tests. CT images, spirometry data, demographic information, and clinical information of each participant were collected. An attention-based multi-instance learning (MIL) model for COPD detection was trained using CT scans from 837 participants. External validation of the COPD detection was performed with 620 low-dose CT (LDCT) scans acquired from the National Lung Screening Trial (NLST) cohort. A multi-channel 3D residual network was further developed to categorize GOLD stages among confirmed COPD patients. The attention-based MIL model used for COPD detection achieved an area under the receiver operating characteristic curve (AUC) of 0.934 (95% CI: 0.903, 0.961) on the internal test set and 0.866 (95% CI: 0.805, 0.928) on the LDCT subset acquired from the NLST. The multi-channel 3D residual network was able to correctly grade 76.4% of COPD patients in the test set (423/553) using the GOLD scale. The proposed chest CT-DL approach can automatically identify spirometry-defined COPD and categorize patients according to the GOLD scale. As such, this approach may be an effective case-finding tool for COPD diagnosis and staging. • Chronic obstructive pulmonary disease is underdiagnosed globally, particularly in developing countries. • The proposed chest computed tomography (CT)-based deep learning (DL) approaches could accurately identify spirometry-defined COPD and categorize patients according to the GOLD scale. • The chest CT-DL approach may be an alternative case-finding tool for COPD identification and evaluation.

Improved detection of air trapping on expiratory computed tomography using deep learning.

BackgroundRadiologic evidence of air trapping (AT) on expiratory computed tomography (CT) scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for monitoring disease progression.ObjectiveTo investigate the effectiveness of a convolutional neural network (CNN) model for quantifying and monitoring AT, and to compare it with other quantitative AT measures obtained from threshold-based techniques.Materials and methodsPaired volumetric whole lung inspiratory and expiratory CT scans were obtained at four time points (0, 3, 12 and 24 months) on 36 subjects with mild CF lung disease. A densely connected CNN (DN) was trained using AT segmentation maps generated from a personalized threshold-based method (PTM). Quantitative AT (QAT) values, presented as the relative volume of AT over the lungs, from the DN approach were compared to QAT values from the PTM method. Radiographic assessment, spirometric measures, and clinical scores were correlated to the DN QAT values using a linear mixed effects model.ResultsQAT values from the DN were found to increase from 8.65% ± 1.38% to 21.38% ± 1.82%, respectively, over a two-year period. Comparison of CNN model results to intensity-based measures demonstrated a systematic drop in the Dice coefficient over time (decreased from 0.86 ± 0.03 to 0.45 ± 0.04). The trends observed in DN QAT values were consistent with clinical scores for AT, bronchiectasis, and mucus plugging. In addition, the DN approach was found to be less susceptible to variations in expiratory deflation levels than the threshold-based approach.ConclusionThe CNN model effectively delineated AT on expiratory CT scans, which provides an automated and objective approach for assessing and monitoring AT in CF patients.

Using individualized three-dimensional printed airway models to guide airway stent implantation

Airway stents are used to manage central airway obstructions by restoring airway patency. Current manufactured stents are limited in shape and size, which pose issues in stent fenestrations needed to be manually created to allow collateral ventilation to airway branches. The precise location to place these fenestrations can be difficult to predict based on 2-dimensional computed tomography images. Inspiratory computed tomography scans were obtained from 3 patients and analysed using 3D-Slicer™, Blender™ and AutoDesk® Meshmixer™ programmes to obtain working 3D-airway models, which were 3D printed. Stent customizations were made based on 3D-model dimensions, and fenestrations into the stent were cut. The modified stents were then inserted as per usual technique. Two patients reported improved airway performance; however, stents were later removed due to symptoms related to in-stent sputum retention. In a third patient, the stent was removed a few weeks later due to the persistence of fistula leakage. The use of a 3D-printed personalized airway model allowed for more precise stent customization, optimizing stent fit and allowing for cross-ventilation of branching airways. We determine that an airway model is a beneficial tool for stent optimization but does not prevent the development of some stent-related complications such as airway secretions.

Variable utility of mosaic attenuation todistinguish fibrotic hypersensitivity pneumonitis from idiopathic pulmonary fibrosis.

Mosaic attenuation on computed tomography (CT) has been identified in international guidelines as an important diagnostic feature of fibrotic hypersensitivity pneumonitis (FHP) as opposed to idiopathic pulmonary fibrosis (IPF). However, mosaic attenuation comprises several different radiological signs (low-density lobules, preserved lobules, air trapping and the so-called "headcheese sign") which may have differing diagnostic utility. Furthermore, the extent of mosaic attenuation required to distinguish these two diagnoses is uncertain and thresholds of mosaic attenuation from international guidelines have not been validated. Inspiratory and expiratory CT scans were evaluated by two readers in 102 patients (IPF n=57; FHP n=45) using a semiquantitative scoring system for mosaic attenuation. Findings were validated in an external cohort from a secondary referral institution (IPF n=34; FHP n=28). Low-density lobules and air trapping were a frequent finding in IPF, present in up to 51% of patients. A requirement for increasing extent of low-density lobules and air trapping based on guidelines (American Thoracic Society and Fleischner Society) was associated with increased specificity for the diagnosis of FHP (0.96 and 0.98, respectively) but reduced sensitivity (0.16 and 0.20, respectively). The headcheese sign was found to be highly specific (0.93) and moderately sensitive (0.49) for a high-confidence diagnosis of FHP. The high specificity of the headcheese sign was maintained in the validation cohort and when patients with other CT features of FHP were excluded. Mosaic attenuation is a frequent finding in IPF. However, the headcheese sign can be confidently considered as being inconsistent with a diagnosis of IPF and specific for FHP.

Changes in quantitative parameters of pulmonary nonsolid nodule induced by lung inflation according to paired inspiratory and expiratory computed tomography imaging.

To evaluate quantitative parameters of nonsolid nodules on paired inspiratory and expiratory computed tomography (CT) and to examine whether these parameters are sensitive to lung inflation reflected by lung volume. Thirty-three patients with 41 nonsolid nodules were included in this prospective study. Paired inspiratory and low-dose respiratory plain chest CT were performed. The volume and density of nonsolid nodule(s), both lungs, the right and left lung, and five lobes, were analyzed in inspiratory and expiratory CT scans. The ratio of expiratory to inspiratory parameters was calculated and labeled as parameter(E-I)/I. To standardize the changes in nonsolid nodule quantitative parameters, the ratio of nonsolid nodule parameter to lung parameter was also calculated. Quantitative parameters were compared between inspiratory and expiratory CT. Nonsolid nodule volumes on expiratory CT were reduced by 19.8% ± 12.9%, while the density was increased by 11.4% ± 8.8%. The volume of nonsolid nodules was significantly greater on inspiratory compared with expiratory CT (p < 0.001). The density of nonsolid nodules was significantly greater on expiratory than inspiratory CT (p < 0.001). The volume(E-I)/I was significantly greater than density(E-I)/I both in nonsolid nodules and lung. The volume(E-I)/I and density(E-I)/I of nonsolid nodules were independent of size. The density(E-I)/I of nonsolid nodule was greater in the lower lobe than that in the upper lobe (p = 0.002). Volume changes in nonsolid nodules were more sensitive than density changes in expiratory phase. The density of lower lobe nodules was more susceptible to respiration. Expiratory scanning is not recommended for quantification of nonsolid nodules and/or follow-up. • The nonsolid nodule volume on expiratory CT was reduced by 19.8% ± 12.9%. • The nonsolid nodule density on expiratory CT was increased by 11.4% ± 8.8%. • The volume (E-I)/I and density (E-I)/I of nonsolid nodules were independent of size.

Fractal analysis of low attenuation clusters on computed tomography in chronic obstructive pulmonary disease

BackgroundThe fractal dimension characterizing the cumulative size distribution of low attenuation area (LAA) clusters, identified with a fixed threshold such as − 950 Hounsfield Units (HU), on computed tomography (CT) sensitively detects parenchymal destruction in chronic obstructive pulmonary disease (COPD) even when the percent LAA (LAA%), a standard emphysema index, is unchanged. This study examines whether the cumulative size distribution of LAA clusters, defined with thresholds of the 15th, 25th, and 35th percentiles of a CT density histogram instead of the fixed-threshold of − 950 HU, exhibits a fractal property and whether its fractal dimension (D’15, D’25, and D’35, respectively) provides additional structural information in emphysematous lungs that is difficult to detect with the conventional − 950-HU-based fractal dimension (D950).MethodsChest inspiratory CT scans and pulmonary functions were cross-sectionally examined in 170 COPD subjects. A proxy for the inspiration level at CT scan was obtained by dividing CT-measured total lung volume (CT-TLV) by physiologically measured total lung capacity. Moreover, long-term (> 5 years) changes in D950 and the new fractal dimensions were longitudinally evaluated in 17 current and 42 former smokers with COPD.ResultsD950, but not D’15, D’25, or D’35 was weakly correlated with the proxy for the inspiration. D950, D’25, and D’35 but not D’15 correlated with LAA% and diffusion capacity. In the long-term longitudinal study, LAA% was increased and D950 and D’35 were decreased in both current and former smokers, while D’25 was decreased only in current smokers and D’15 was not changed in either group. The longitudinal changes in D’25 but not those in LAA%, D950, D’15, and D’35 were greater in current smokers than in former smokers. This greater change in D’25 in current smokers was confirmed after adjusting the change in CT-TLV and the baseline D’25.ConclusionsD’25 reflects diffusion capacity in emphysematous lungs and is robust against inspiration levels during CT scans. This new fractal dimension might provide additional structural information that is difficult to detect with the conventional D950 and LAA% and allow for more sensitive evaluation of emphysema progression over time.

3D-measurement of tracheobronchial angles on inspiratory and expiratory chest CT in COPD: respiratory changes and correlation with airflow limitation.

PurposeTo assess tracheobronchial angles and their changes on combined inspiratory and expiratory thoracic computed tomography (CT) scans and to determine correlations between tracheobronchial angles and several indices of chronic obstructive pulmonary disease (COPD).Materials and methodsA total of 80 smokers underwent combined inspiratory and expiratory CT scans. Of these, 65 subjects also performed spirometry and 55 patients were diagnosed with COPD. On CT scans, 3-dimensinal tracheobronchial angles (trachea–right main bronchus [RMB], trachea–left main bronchus [LMB], and RMB–LMB) were automatically measured by software. Lung volumes at inspiration and expiration were also automatically calculated. Changes in tracheobronchial angles between inspiration and expiration were assessed by the Mann–Whitney test. Correlations of the angles with lung volume, airflow limitation, and CT-based emphysema index were evaluated by Spearman rank correlation.ResultsThe trachea–LMB angle was significantly smaller and the RMB–LMB angle was significantly larger at expiration than inspiration (P<0.0001). The trachea–LMB and RMB–LMB angles were significantly correlated with lung volume, particularly at expiration. The RMB–LMB angle was significantly correlated with airflow limitation and CT emphysema index (P<0.001–0.05) at inspiration and expiration, suggesting that narrowed RMB–LMB angle indicates more severe airflow limitation and larger extent of emphysema.ConclusionTracheobronchial angles change during respiration and are correlated with severity of COPD and emphysema.

Quantitative computed tomography determined regional lung mechanics in normal nonsmokers, normal smokers and metastatic sarcoma subjects.

ObjectivesExtra-thoracic tumors send out pilot cells that attach to the pulmonary endothelium. We hypothesized that this could alter regional lung mechanics (tissue stiffening or accumulation of fluid and inflammatory cells) through interactions with host cells. We explored this with serial inspiratory computed tomography (CT) and image matching to assess regional changes in lung expansion.Materials and methodsWe retrospectively assessed 44 pairs of two serial CT scans on 21 sarcoma patients: 12 without lung metastases and 9 with lung metastases. For each subject, two or more serial inspiratory clinically-derived CT scans were retrospectively collected. Two research-derived control groups were included: 7 normal nonsmokers and 12 asymptomatic smokers with two inspiratory scans taken the same day or one year apart respectively. We performed image registration for local-to-local matching scans to baseline, and derived local expansion and density changes at an acinar scale. Welch two sample t test was used for comparison between groups. Statistical significance was determined with a p value < 0.05.ResultsLung regions of metastatic sarcoma patients (but not the normal control group) demonstrated an increased proportion of normalized lung expansion between the first and second CT. These hyper-expanded regions were associated with, but not limited to, visible metastatic lung lesions. Compared with the normal control group, the percent of increased normalized hyper-expanded lung in sarcoma subjects was significantly increased (p < 0.05). There was also evidence of increased lung “tissue” volume (non-air components) in the hyper-expanded regions of the cancer subjects relative to non-hyper-expanded regions. “Tissue” volume increase was present in the hyper-expanded regions of metastatic and non-metastatic sarcoma subjects. This putatively could represent regional inflammation related to the presence of tumor pilot cell-host related interactions.ConclusionsThis new quantitative CT (QCT) method for linking serial acquired inspiratory CT images may provide a diagnostic and prognostic means to objectively characterize regional responses in the lung following oncological treatment and monitoring for lung metastases.

Glottis Closure Influences Tracheal Size Changes in Inspiratory and Expiratory CT in Patients with COPD

The opened or closed status of the glottis might influence tracheal size changes in inspiratory and expiratory computed tomography (CT) scans. We investigated if the glottis status makes the tracheal collapse differently correlate with lung volume difference between inspiratory and expiratory CT scans. Forty patients with chronic obstructive pulmonary disease whose glottis was included in the acquired scanned volume for lung CT were divided into two groups: 16 patients with the glottis closed in both inspiratory and expiratory CT, and 24 patients with the glottis open in at least one CT acquisition. Lung inspiratory (Vinsp) and expiratory (Vexp) volumes were automatically computed and lung ΔV was calculated using the following formula: (Vinsp - Vexp)/Vinsp × 100. Two radiologists manually measured the anteroposterior diameter and cross-sectional area of the trachea 1 cm above the aortic arch and 1 cm above the carina. Tracheal collapse was then calculated and correlated with lung ΔV. In the 40 patients, the correlations between tracheal Δanteroposterior diameter and Δcross-sectional area at each level and lung ΔV ranged between 0.68 and 0.74 (ρ) at Spearman rank correlation test. However, in the closed glottis group, the correlations were higher for all measures at the two levels (ρ range: 0.84-0.90), whereas in the open glottis group, correlations were low and not statistically significant (ρ range: 0.29-0.34) at the upper level, and moderate at the lower level (ρ range: 0.51-0.55). A closed or open glottis influences the tracheal size change in inspiratory and expiratory CT scans. With closed glottis, the tracheal collapse shows a stronger correlation with the lung volume difference between inspiratory and expiratory CT scans.

Tracheal collapse diagnosed by multidetector computed tomography: evaluation of different image analysis methods

ABSTRACT Background: The gold standard for diagnosing excessive tracheal collapse is still evaluation during bronchoscopy. Today, multidetector computed tomography (MDCT) is used to confirm a suspicion of abnormal tracheal collapse. There is no gold standard for computed tomography (CT) image analysis of tracheal collapse. Purpose: To evaluate four different methods for the diagnosis of tracheal collapse using the images obtained through MDCT to help clinicians evaluate the images in daily practice. Objectives: 374 consecutive high-resolution CT scans with full inspiratory and end-expiratory CT scans were retrospectively analyzed. Methods: The images were analyzed in four different ways. The degree of collapse was based on cross-sectional areas of individual locations or volumes of entire regions: (1) 1 cm above the carina, (2) the level of maximal collapse of the trachea, (3) the entire region from the carina to the thoracic inlet, and (4) the trachea and bronchial region as defined by the software. Results: We compared three existing and one new method for image analysis of tracheal collapse by MDCT. The prevalence of tracheal collapse varied from 10.7% to 19.5% in this cohort of patients suffering from mixed lung diseases when using an expiratory collapse of ≥50% as a threshold. The four methods were comparable with highly significant Pearsons correlation coefficients (0.764–0.856). However, the four methods identified different patients with collapse of ≥50%. There was no correlation between symptoms and the degree of collapse. Conclusion: The different methods identify tracheal collapse in different patients. Hence, the diagnosis of excessive tracheal collapse can not rely solely on MDCT images. Generally, there is a poor correlation between symptoms and the degree of collapse in the different methods. However, when using the maximal collapse, there is some correlation with symptoms. When in doubt regarding the diagnosis, further investigations, such as bronchoscopy, should be carried out.

Improving airway segmentation in computed tomography using leak detection with convolutional networks.

We propose a novel method to improve airway segmentation in thoracic computed tomography (CT) by detecting and removing leaks. Leak detection is formulated as a classification problem, in which a convolutional network (ConvNet) is trained in a supervised fashion to perform the classification task. In order to increase the segmented airway tree length, we take advantage of the fact that multiple segmentations can be extracted from a given airway segmentation algorithm by varying the parameters that influence the tree length and the amount of leaks. We propose a strategy in which the combination of these segmentations after removing leaks can increase the airway tree length while limiting the amount of leaks. This strategy therefore largely circumvents the need for parameter fine-tuning of a given airway segmentation algorithm. The ConvNet was trained and evaluated using a subset of inspiratory thoracic CT scans taken from the COPDGene study. Our method was validated on a separate independent set of the EXACT'09 challenge. We show that our method significantly improves the quality of a given leaky airway segmentation, achieving a higher sensitivity at a low false-positive rate compared to all the state-of-the-art methods that entered in EXACT09, and approaching the performance of the combination of all of them.

Diagnostic assessment of acute respiratory distress syndrome with lung ultrasound – comparison with Computed Tomography- preliminary data

Objectives: Lung ultrasound (LUS) is increasingly used in intensive care medicine to monitor invasive ventilation, however little data exists on the comparison of common lung ultrasound (LUS) findings in Acute Respiratory Distress Syndrome (“b-lines”, consolidations) with the imaging gold standard Computed Tomography (CT). Therefore the aim of our study was to examine these findings under controlled conditions at different Positive End Expiratory Pressure (PEEP) levels in healthy and diseased piglets and compare them with dynamic CT scans. Methods: After approval of the ethics comittee, 8 piglets were studied during pressure controlled mechanical ventilation before and after surfactant depletion injury. Inspiratory, expiratory and dynamic CT scans and ultrasound examinations were performed at defined PEEP levels (0, 5, 15) by one radiologist. (curved transducer 3.5 MHz, in oblique/transverse orientation at approx. 5th/6th intercostal space ventral and dorsal in the anterior axillary line on both sides and and additional transhepatic scans on the right side in 6 pigs). Offline evaluation of the CT images and ultrasound images was performed in separate sessions, blinded to the results. Results: Transhepatic ultrasound evaluation of lung consolidations as compared with CT showed an excellent correlation. Higher B-line counts were present in the diseased lung (compared with healthy lung), dorsally (compared with ventrally), and at lower PEEP levels (compared with higher PEEP levels). No CT correlate for the B-lines could be identified. Conclusion: Transhepatic assessment of consolidations for dynamic modification of respiratory management seems feasible with excellent CT correlation. B lines seemed to be influenced by respiratory parameters and position, however, no CT correlate could be found.

Computed Tomography Image Matching in Chronic Obstructive Pulmonary Disease.

Chronic obstructive pulmonary disease (COPD), characterized by progressive airflow obstruction due to the combined effects of emphysema and small airways disease, is associated with high morbidity and mortality. The complex link between emphysema and airways disease is associated with significant heterogeneity in clinical presentation. Spirometry is the current gold standard for diagnosis and stratification of the severity of airflow obstruction in COPD. Although spirometry is simple to use, it does not enable the separation of emphysema from airways disease. Computed tomography (CT), on the other hand, provides the anatomic localization of disease and has been increasingly used to phenotype COPD. The majority of current CT measures are extracted from a single-volume CT scan and although useful to characterize emphysema and airways disease, they do not link structural and functional abnormalities. Alternatively, CT image matching combines information from both inspiratory and expiratory CT scans, thus enabling determination of functional changes such as regional ventilation and mechanical properties of the lung. In this review, we discuss recent applications of CT image matching that provide clinically meaningful information beyond spirometry and single-volume CT scan measures.

Parametric Response Mapping Monitors Temporal Changes on Lung CT Scans in the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS)

The longitudinal relationship between regional air trapping and emphysema remains unexplored. We have sought to demonstrate the utility of parametric response mapping (PRM), a computed tomography (CT)-based biomarker, for monitoring regional disease progression in chronic obstructive pulmonary disease (COPD) patients, linking expiratory- and inspiratory-based CT metrics over time. Inspiratory and expiratory lung CT scans were acquired from 89 COPD subjects with varying Global Initiative for Chronic Obstructive Lung Disease (GOLD) status at 30days (n=13) or 1 year (n=76) from baseline as part of the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) clinical trial. PRMs of CT data were used to quantify the relative volumes of normal parenchyma (PRM(Normal)), emphysema (PRM(Emph)), and functional small airways disease (PRM(fSAD)). PRM measurement variability was assessed using the 30-day interval data. Changes in PRM metrics over a 1-year period were correlated to pulmonary function (forced expiratory volume at 1 second [FEV1]). A theoretical model that simulates PRM changes from COPD was compared to experimental findings. PRM metrics varied by ∼6.5% of total lung volume for PRM(Normal) and PRM(fSAD) and 1% for PRM(Emph) when testing 30-day repeatability. Over a 1-year interval, only PRM(Emph) in severe COPD subjects produced significant change (19%-21%). However, 11 of 76 subjects showed changes in PRM(fSAD) greater than variations observed from analysis of 30-day data. Mathematical model simulations agreed with experimental PRM results, suggesting fSAD is a transitional phase from normal parenchyma to emphysema. PRM of lung CT scans in COPD patients provides an opportunity to more precisely characterize underlying disease phenotypes, with the potential to monitor disease status and therapy response.

Airflow Limitation in Chronic Obstructive Pulmonary Disease: Ratio and Difference of Percentage of Low-attenuation Lung Regions in Paired Inspiratory/Expiratory Computed Tomography

The purpose of this study was to analyze the relationship between airflow limitation and two types of computed tomography (CT) measurements: expiratory/inspiratory (E/I) ratio and E/I difference of percentage of low-attenuation lung regions (LAA%). Thirty patients who underwent inspiratory and expiratory CT scans were included in this study. The CT data were used to calculate the LAA% E/I ratio and E/I difference. Other types of CT measurements were also obtained, including the E/I ratio and E/I difference of lung volume, mean lung density, standard deviation, skewness, and kurtosis. LAA% was calculated at 20 thresholds (-990 to -800 HU). Pearson's correlation between the measurements and forced expiratory flow in 1 second was used to determine the efficacy of LAA% E/I ratio and E/I difference. P values of <5.88 × 10⁻⁵ were considered statistically significant with Bonferroni correction. The LAA% E/I ratio and E/I difference significantly correlated with forced expiratory flow in 1 second. The best correlation coefficient for the LAA% E/I ratio was -0.699 (P = 1.75 × 10⁻⁵) and for the LAA% E/I difference was -0.723 (P = 6.53 × 10⁻⁶). The best correlation coefficient for the LAA% E/I difference was stronger than that for the other types of CT measurements. The LAA% E/I ratio and E/I difference significantly correlated with airflow limitation in chronic obstructive pulmonary disease.

Quantitative computed tomography in chronic obstructive pulmonary disease.

Quantitative computed tomography is being increasingly used to quantify the features of chronic obstructive pulmonary disease, specifically emphysema, air trapping, and airway abnormality. For quantification of emphysema, the density mask technique is most widely used, with threshold on the order of-950 HU, but percentile cutoff may be less sensitive to volume changes. Sources of variation include depth of inspiration, scanner make and model, technical parameters, and cigarette smoking. On expiratory computed tomography (CT), air trapping may be quantified by evaluating the percentage of lung volume less than a given threshold (eg, -856 HU) by comparing lung volumes and attenuation on expiration and inspiration or, as done more recently, by coregistering inspiratory and expiratory CT scans. All of these indices correlate well with the severity of physiological airway obstruction. By constructing a 3-dimensional model of the airway from volumetric CT, it is possible to measure dimensions (external and internal diameters and airway wall thickness) of segmental and subsegmental airways orthogonal to their long axes. Measurement of airway parameters correlates with the severity of airflow obstruction and with the history of chronic obstructive pulmonary disease exacerbation.

Identification of Chronic Obstructive Pulmonary Disease in Lung Cancer Screening Computed Tomographic Scans

Smoking is a major risk factor for both cancer and chronic obstructive pulmonary disease (COPD). Computed tomography (CT)-based lung cancer screening may provide an opportunity to detect additional individuals with COPD at an early stage. To determine whether low-dose lung cancer screening CT scans can be used to identify participants with COPD. Single-center prospective cross-sectional study within an ongoing lung cancer screening trial. Prebronchodilator pulmonary function testing with inspiratory and expiratory CT on the same day was obtained from 1140 male participants between July 2007 and September 2008. Computed tomographic emphysema was defined as percentage of voxels less than -950 Hounsfield units (HU), and CT air trapping was defined as the expiratory:inspiratory ratio of mean lung density. Chronic obstructive pulmonary disease was defined as the ratio of forced expiratory volume in the first second to forced vital capacity (FEV(1)/FVC) of less than 70%. Logistic regression was used to develop a diagnostic prediction model for airflow limitation. Diagnostic accuracy of COPD diagnosis using pulmonary function tests as the reference standard. Four hundred thirty-seven participants (38%) had COPD according to lung function testing. A diagnostic model with CT emphysema, CT air trapping, body mass index, pack-years, and smoking status corrected for overoptimism (internal validation) yielded an area under the receiver operating characteristic curve of 0.83 (95% CI, 0.81-0.86). Using the point of optimal accuracy, the model identified 274 participants with COPD with 85 false-positives, a sensitivity of 63% (95% CI, 58%-67%), specificity of 88% (95% CI, 85%-90%), positive predictive value of 76% (95% CI, 72%-81%); and negative predictive value of 79% (95% CI, 76%-82%). The diagnostic model showed an area under the receiver operating characteristic curve of 0.87 (95% CI, 0.86-0.88) for participants with symptoms and 0.78 (95% CI, 0.76-0.80) for those without symptoms. Among men who are current and former heavy smokers, low-dose inspiratory and expiratory CT scans obtained for lung cancer screening can identify participants with COPD, with a sensitivity of 63% and a specificity of 88%.

Kurtosis and Skewness of Density Histograms on Inspiratory and Expiratory CT Scans in Smokers

The aim of this study is to evaluate the relationship between lung function and kurtosis or skewness of lung density histograms on computed tomography (CT) in smokers. Forty-six smokers (age range 46−81 years), enrolled in the Lung Tissue Research Consortium, underwent pulmonary function tests (PFT) and chest CT at full inspiration and full expiration. On both inspiratory and expiratory scans, kurtosis and skewness of the density histograms were automatically measured by open-source software. Correlations between CT measurements and lung function were evaluated by the linear regression analysis. Although no significant correlations were found between inspiratory kurtosis or skewness and PFT results, expiratory kurtosis significantly correlated with the following: the percentage of predicted value of forced expiratory volume in the first second (FEV1), the ratio of FEV1 to forced vital capacity (FVC), and the ratio of residual volume (RV) to total lung capacity (TLC) (FEV1%predicted, R = −0.581, p < 0.001; FEV1/FVC, R = −0.612, p < 0.001; RV/TLC, R = 0.613, p < 0.001, respectively). Similarly, expiratory skewness showed significant correlations with PFT results (FEV1%predicted, R = −0.584, p < 0.001; FEV1/FVC, R = −0.619, p < 0.001; RV/TLC, R = 0.585, p < 0.001, respectively). Also, the expiratory/inspiratory (E/I) ratios of kurtosis and skewness significantly correlated with FEV1%predicted (p < 0.001), FEV1/FVC (p < 0.001), RV/TLC (p < 0.001), and the percentage of predicted value of diffusing capacity for carbon monoxide (kurtosis E/I ratio, p = 0.001; skewness E/I ratio, p = 0.03, respectively). We conclude therefore that expiratory values and the E/I ratios of kurtosis and skewness of CT densitometry reflect airflow limitation and air-trapping. Higher kurtosis or skewness on expiratory CT scan indicates more severe conditions in smokers.

Intrathoracic Tracheal Volume and Collapsibility on Inspiratory and End-expiratory CT Scans: Correlations with Lung Volume and Pulmonary Function in 85 Smokers

To evaluate the correlations of tracheal volume and collapsibility on inspiratory and end-expiratory computed tomography (CT) with lung volume and with lung function in smokers. The institutional review board approved this study at each institution. 85 smokers (mean age 68, range 45-87 years; 40 females and 45 males) underwent pulmonary function tests and chest CT at full inspiration and end-expiration. On both scans, intrathoracic tracheal volume and lung volume were measured. Collapsibility of the trachea and the lung was expressed as expiratory/inspiratory (E/I) ratios of these volumes. Correlations of the tracheal measurements with the lung measurements and with lung function were evaluated by the linear regression analysis. Tracheal volume showed moderate or strong, positive correlations with lung volume on both inspiratory (r = 0.661, P < .0001) and end-expiratory (r = 0.749, P < .0001) scans. The E/I ratio of tracheal volume showed a strong, positive correlation with the E/I ratio of lung volume (r = 0.711, P < .0001). A weak, negative correlation was found between the E/I ratio of tracheal volume and the ratio of forced expiratory volume in the first second to forced vital capacity (r = -0.436, P < .0001). Also, a weak, positive correlation was observed between the E/I ratio of tracheal volume and the ratio of residual volume to total lung capacity (r = 0.253, P = .02). Tracheal volume and collapsibility, measured by inspiratory and end-expiratory CT scans, is related to lung volume and collapsibility. The highly collapsed trachea on end-expiratory CT does not indicate more severe airflow limitation or air-trapping in smokers.