Inspiratory Chest Computed Tomography Research Articles

Deep Learning Estimation of Small Airways Disease from Inspiratory Chest CT is Associated with FEV1 Decline in COPD.

Quantifying functional small airways disease (fSAD) requires additional expiratory computed tomography (CT) scan, limiting clinical applicability. Artificial intelligence (AI) could enable fSAD quantification from chest CT scan at total lung capacity (TLC) alone (fSADTLC). To evaluate an AI model for estimating fSADTLC and study its clinical associations in chronic obstructive pulmonary disease (COPD). We analyzed 2513 participants from the SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS). Using a subset (n = 1055), we developed a generative model to produce virtual expiratory CTs for estimating fSADTLC in the remaining 1458 SPIROMICS participants. We compared fSADTLC with dual volume, parametric response mapping fSADPRM. We investigated univariate and multivariable associations of fSADTLC with FEV1, FEV1/FVC, six-minute walk distance (6MWD), St. George's Respiratory Questionnaire (SGRQ), and FEV1 decline. The results were validated in a subset (n = 458) from COPDGene study. Multivariable models were adjusted for age, race, sex, BMI, baseline FEV1, smoking pack years, smoking status, and percent emphysema. Inspiratory fSADTLC was highly correlated with fSADPRM in SPIROMICS (Pearson's R = 0.895) and COPDGene (R = 0.897) cohorts. In SPIROMICS, fSADTLC was associated with FEV1 (L) (adj.β = -0.034, P < 0.001), FEV1/FVC (adj.β = -0.008, P < 0.001), SGRQ (adj.β = 0.243, P < 0.001), and FEV1 decline (mL / year) (adj.β = -1.156, P < 0.001). fSADTLC was also associated with FEV1 (L) (adj.β = -0.032, P < 0.001), FEV1/FVC (adj.β = -0.007, P < 0.001), SGRQ (adj.β = 0.190, P = 0.02), and FEV1 decline (mL / year) (adj.β = -0.866, P = 0.001) in COPDGene. We found fSADTLC to be more repeatable than fSADPRM with intraclass correlation of 0.99 (95% CI: 0.98, 0.99) vs. 0.83 (95% CI: 0.76, 0.88). Inspiratory fSADTLC captures small airways disease as reliably as fSADPRM and is associated with FEV1 decline.

LungViT: Ensembling Cascade of Texture Sensitive Hierarchical Vision Transformers for Cross-Volume Chest CT Image-to-Image Translation.

Chest computed tomography (CT) at inspiration is often complemented by an expiratory CT to identify peripheral airways disease. Additionally, co-registered inspiratory-expiratory volumes can be used to derive various markers of lung function. Expiratory CT scans, however, may not be acquired due to dose or scan time considerations or may be inadequate due to motion or insufficient exhale; leading to a missed opportunity to evaluate underlying small airways disease. Here, we propose LungViT- a generative adversarial learning approach using hierarchical vision transformers for translating inspiratory CT intensities to corresponding expiratory CT intensities. LungViT addresses several limitations of the traditional generative models including slicewise discontinuities, limited size of generated volumes, and their inability to model texture transfer at volumetric level. We propose a shifted-window hierarchical vision transformer architecture with squeeze-and-excitation decoder blocks for modeling dependencies between features. We also propose a multiview texture similarity distance metric for texture and style transfer in 3D. To incorporate global information into the training process and refine the output of our model, we use ensemble cascading. LungViT is able to generate large 3D volumes of size 320×320×320 . We train and validate our model using a diverse cohort of 1500 subjects with varying disease severity. To assess model generalizability beyond the development set biases, we evaluate our model on an out-of-distribution external validation set of 200 subjects. Clinical validation on internal and external testing sets shows that synthetic volumes could be reliably adopted for deriving clinical endpoints of chronic obstructive pulmonary disease.

Capturing COPD heterogeneity: anomaly detection and parametric response mapping comparison for phenotyping on chest computed tomography.

Chronic obstructive pulmonary disease (COPD) poses a substantial global health burden, demanding advanced diagnostic tools for early detection and accurate phenotyping. In this line, this study seeks to enhance COPD characterization on chest computed tomography (CT) by comparing the spatial and quantitative relationships between traditional parametric response mapping (PRM) and a novel self-supervised anomaly detection approach, and to unveil potential additional insights into the dynamic transitional stages of COPD. Non-contrast inspiratory and expiratory CT of 1,310 never-smoker and GOLD 0 individuals and COPD patients (GOLD 1-4) from the COPDGene dataset were retrospectively evaluated. A novel self-supervised anomaly detection approach was applied to quantify lung abnormalities associated with COPD, as regional deviations. These regional anomaly scores were qualitatively and quantitatively compared, per GOLD class, to PRM volumes (emphysema: PRMEmph, functional small-airway disease: PRMfSAD) and to a Principal Component Analysis (PCA) and Clustering, applied on the self-supervised latent space. Its relationships to pulmonary function tests (PFTs) were also evaluated. Initial t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization of the self-supervised latent space highlighted distinct spatial patterns, revealing clear separations between regions with and without emphysema and air trapping. Four stable clusters were identified among this latent space by the PCA and Cluster Analysis. As the GOLD stage increased, PRMEmph, PRMfSAD, anomaly score, and Cluster 3 volumes exhibited escalating trends, contrasting with a decline in Cluster 2. The patient-wise anomaly scores significantly differed across GOLD stages (p < 0.01), except for never-smokers and GOLD 0 patients. In contrast, PRMEmph, PRMfSAD, and cluster classes showed fewer significant differences. Pearson correlation coefficients revealed moderate anomaly score correlations to PFTs (0.41-0.68), except for the functional residual capacity and smoking duration. The anomaly score was correlated with PRMEmph (r = 0.66, p < 0.01) and PRMfSAD (r = 0.61, p < 0.01). Anomaly scores significantly improved fitting of PRM-adjusted multivariate models for predicting clinical parameters (p < 0.001). Bland-Altman plots revealed that volume agreement between PRM-derived volumes and clusters was not constant across the range of measurements. Our study highlights the synergistic utility of the anomaly detection approach and traditional PRM in capturing the nuanced heterogeneity of COPD. The observed disparities in spatial patterns, cluster dynamics, and correlations with PFTs underscore the distinct - yet complementary - strengths of these methods. Integrating anomaly detection and PRM offers a promising avenue for understanding of COPD pathophysiology, potentially informing more tailored diagnostic and intervention approaches to improve patient outcomes.

Children with severe asthma have substantial structural airway changes on computed tomography.

In adults with severe asthma (SA) bronchial wall thickening, bronchiectasis and low attenuation regions (LAR) have been described on chest computed tomography (CT) scans. The extent to which these structural abnormalities are present in children with SA is largely unknown. Our aim was to study the presence and extent of airway abnormalities on chest CT of children with SA. 161 inspiratory and expiratory CT scans, either spirometer-controlled or technician-controlled, obtained in 131 children with SA (mean±SD age 11.0±3.8 years) were collected retrospectively. Inspiratory scans were analysed manually using a semi-quantitative score and automatically using LungQ (v2.1.0.1; Thirona B.V., Nijmegen, the Netherlands). LungQ segments the bronchial tree, identifies the generation for each bronchus-artery (BA) pair and measures the following BA dimensions: outer bronchial wall diameter (Bout), adjacent artery diameter (A) and bronchial wall thickness (Bwt). Bronchiectasis was defined as Bout/A ≥1.1, bronchial wall thickening as Bwt/A ≥0.14. LAR, reflecting small airways disease (SAD), was measured automatically on inspiratory and expiratory scans and manually on expiratory scans. Functional SAD was defined as FEF25-75 and/or FEF75 z-scores <-1.645. Results are shown as median and interquartile range. Bronchiectasis was present on 95.8% and bronchial wall thickening on all CTs using the automated method. Bronchiectasis was present on 28% and bronchial wall thickening on 88.8% of the CTs using the manual semi-quantitative analysis. The percentage of BA pairs defined as bronchiectasis was 24.62% (12.7-39.3%) and bronchial wall thickening was 41.7% (24.0-79.8%) per CT using the automated method. LAR was observed on all CTs using the automatic analysis and on 82.9% using the manual semi-quantitative analysis. Patients with LAR or functional SAD had more thickened bronchi than patients without. Despite a large discrepancy between the automated and the manual semi-quantitative analysis, bronchiectasis and bronchial wall thickening are present on most CT scans of children with SA. SAD is related to bronchial wall thickening.

Predicted versus CT-derived total lung volume in a general population: The ImaLife study.

Predicted lung volumes based on the Global Lung Function Initiative (GLI) model are used in pulmonary disease detection and monitoring. It is unknown how well the predicted lung volume corresponds with computed tomography (CT) derived total lung volume (TLV). The aim of this study was to compare the GLI-2021 model predictions of total lung capacity (TLC) with CT-derived TLV. 151 female and 139 male healthy participants (age 45-65 years) were consecutively selected from a Dutch general population cohort, the Imaging in Lifelines (ImaLife) cohort. In ImaLife, all participants underwent low-dose, inspiratory chest CT. TLV was measured by an automated analysis, and compared to predicted TLC based on the GLI-2021 model. Bland-Altman analysis was performed for analysis of systematic bias and range between limits of agreement. To further mimic the GLI-cohort all analyses were repeated in a subset of never-smokers (51% of the cohort). Mean±SD of TLV was 4.7±0.9 L in women and 6.2±1.2 L in men. TLC overestimated TLV, with systematic bias of 1.0 L in women and 1.6 L in men. Range between limits of agreement was 3.2 L for women and 4.2 L for men, indicating high variability. Performing the analysis with never-smokers yielded similar results. In conclusion, in a healthy cohort, predicted TLC substantially overestimates CT-derived TLV, with low precision and accuracy. In a clinical context where an accurate or precise lung volume is required, measurement of lung volume should be considered.

Inspiratory and Expiratory Computed Tomography Imaging Clusters Reflect Functional Characteristics in Chronic Obstructive Pulmonary Disease.

Disease probability measure (DPM) is a useful voxel-wise imaging assessment of gas-trapping and emphysematous lesions in patients with chronic obstructive pulmonary disease (COPD). To elucidate the progression of COPD, we performed a cluster analysis using the following DPM parameters: normal (DPMNormal), gas-trapping (DPMGasTrap), and emphysematous lesions (DPMEmph). Our findings revealed the characteristics of each cluster and the 3-year disease progression using imaging parameters. Inspiratory and expiratory chest computed tomography (CT) images of 131 patients with COPD were examined, of which 84 were followed up for 3 years. The percentage of low attenuation volume (LAV%) and the square root of the wall area of a hypothetical airway with an internal perimeter of 10 mm (√Aaw at Pi10) were quantitatively measured using inspiratory chest CT. A hierarchical cluster analysis was performed using the DPM parameters at baseline. Five clusters were named according to the dominant DPM parameters: normal (NL), normal-GasTrap (NL-GT), GasTrap (GT), GasTrap-Emphysema (GT-EM), and Emphysema (EM). Women were predominantly diagnosed with GT. Forced expiratory volume in 1 s gradually decreased in the following order: NL, NL-GT, GT, GT-EM, and EM. DPMEmph correlated well with LAV%. Four clusters other than NL showed significantly higher values of √Aaw at Pi10 than NL; however, no significant differences were observed among them. In all clusters, DPMEmph increased after 3 years. DPMNormal only increased in the GT cluster. Clusters using DPM parameters may reflect the characteristics of COPD and help understand the pathophysiology of the disease.

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.

QUANTITATIVE AND VISUAL CT ASSESSMENTS IN THE DIAGNOSIS OF EMPHYSEMA: A COPDGENE SINGLE-CENTER ANALYSIS

SESSION TITLE: Wednesday Electronic Posters 2 SESSION TYPE: Original Inv Poster Discussion PRESENTED ON: 10/23/2019 09:45 AM - 10:45 AM PURPOSE: Early and accurate diagnosis of emphysema is vital to reducing the morbidity and mortality associated with chronic obstructive pulmonary disease (COPD). Visual assessments of chest computed tomography (CT) scans are routinely employed in the radiologic diagnosis of emphysema. While the advent of quantitative CT (QCT) has complemented visual CT, a phenomenon of discordant visual and QCT findings for the presence or absence of emphysema has been described. The purposes of this study are: to examine the association between visual and QCT assessments within a large cohort of subjects; and to identify variables that may explain discordant visual and QCT findings. METHODS: We included volumetric inspiratory CT scans of 1221 subjects enrolled in Phase 1 of the COPDGene study conducted at the University of Iowa study center. Subjects comprised of normal non-smokers, smokers with Preserved Ratio Impaired Spirometry, and smokers with all stages of COPD. CT scans were quantitatively scored and visually interpreted by the COPDGene Imaging Center and the study center’s radiologists. Individual-level visual assessments were compared with QCT measurements. Interobserver agreement was calculated using the kappa statistic. We assessed variables associated with discordant results using multivariable logistic regression analysis. RESULTS: There was a fair agreement for the presence or absence of emphysema between the study center’s radiologists and QCT [61% concordance, kappa 0.22 (0.17-0.28)]. Similar comparisons showed a slight agreement between the COPDGene Imaging Center and QCT [56% concordance, kappa 0.16 (0.11-0.21)], and a moderate agreement between both sets of visual assessments [80% concordance, kappa 0.60 (0.54-0.65)]. Smoking pack-years, airway wall area percent in subsegmental bronchi, and female sex were significantly associated with QCT-detectable but visually-negative emphysema. CONCLUSIONS: The slight-to-fair agreement between visual and quantitative CT assessment of emphysema highlights the need to utilize both modalities for a comprehensive radiologic evaluation. Discordant results may be attributable to one or more factors that warrant further exploration in larger studies. CLINICAL IMPLICATIONS: Visual and quantitative CT analyses are complementary tools in the radiologic identification of emphysema. Awareness of the factors potentially affecting discordant results is necessary for making a clinical judgment about the presence or absence of emphysema. DISCLOSURES: No relevant relationships by Iliya Amaza, source=Web Response Consultant relationship with VIDA Diagnostics and GlaxoSmithKline Please note: $1001 - $5000 Added 03/14/2019 by Alejandro Comellas, source=Web Response, value=Consulting fee Removed 03/14/2019 by Alejandro Comellas, source=Web Response Consultant relationship with VIDA Diagnostics Please note: $1-$1000 Added 03/14/2019 by Alejandro Comellas, source=Web Response, value=software support Consultant relationship with GlaxoSmithKline Please note: $1001 - $5000 Added 03/14/2019 by Alejandro Comellas, source=Web Response, value=Consulting fee No relevant relationships by Spyridon Fortis, source=Web Response No relevant relationships by Amy O'Shea, source=Web Response

Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net.

We propose a novel airway segmentation method in volumetric chest computed tomography (CT) and evaluate its performance on multiple datasets. The segmentation is performed voxel-by-voxel by a 2.5D convolutional neural net (2.5D CNN) trained in a supervised manner. To enhance the accuracy of the segmented airway tree, we simultaneously took three adjacent slices in each of the orthogonal directions including axial, sagittal, and coronal and fine-tuned the parameters that influence the tree length and the number of leakage. The gold standard of airway segmentation was generated by a semi-automated method using AVIEW™. The 2.5D CNN was trained and evaluated on a subset of inspiratory thoracic CT scans taken from the Korean obstructive lung disease study, which includes normal subjects and chronic obstructive pulmonary disease patients. The reliability and further practicality of our proposed method was demonstrated in multiple datasets. In eight test datasets collected by the same imaging protocol, the percentage detected tree length, false positive rate, and Dice similarity coefficient of our method were 92.16%, 7.74%, and 0.8997 ± 0.0892, respectively. In 20 test datasets of the EXACT'09 challenge, the percentage detected tree length was 60.1% and the false positive rate was 4.56%. Our fully automated (end-to-end) segmentation method could be applied in radiologic practice.

Correlation between heart size and emphysema in patients with chronic obstructive pulmonary disease: CT-based analysis using inspiratory and expiratory scans.

The objective of this study was to investigate the relationship between the extent of emphysema and heart size in patients with chronic obstructive pulmonary disease (COPD) using inspiratory and expiratory chest computed tomography (CT). This retrospective study was approved by the institutional review board and informed consent was waived. We measured lung volume (LV), low attenuation area percent (%LAA; less than or equal to −950 HU), maximum cardiac area, and maximum transverse cardiac diameter on inspiratory/expiratory chest CT in 60 patients with COPD. Spearman rank correlation analysis was used to determine the correlations between the heart and lung CT measurements, and the correlations between these measurements and spirometric values. On inspiratory CT, the maximum transverse cardiac diameter was negatively correlated with LV (ρ = −0.42; p < 0.01) and %LAA (ρ = −0.43; p < 0.001). Furthermore, on expiratory CT, the maximum cardiac area was negatively correlated with LV (ρ = −0.35; p < 0.01) and %LAA (ρ = −0.37; p < 0.01), and there was a negative correlation between transverse cardiac diameter and %LAA (ρ = −0.34; p < 0.01). Although inspiratory cardiac size was not correlated with any of the spirometric values, the maximum cardiac area and transverse diameter on expiratory scans were significantly correlated with the reduced airflow values on spirometry (p < 0.01). In patients with COPD, the transverse cardiac diameter decreased as the emphysema progressed. A smaller cardiac area on expiratory CT suggested the presence of large LVs, emphysema, and airflow limitation in COPD.

Idiopathic Pulmonary Fibrosis: Data-driven Textural Analysis of Extent of Fibrosis at Baseline and 15-Month Follow-up.

Purpose To evaluate associations between pulmonary function and both quantitative analysis and visual assessment of thin-section computed tomography (CT) images at baseline and at 15-month follow-up in subjects with idiopathic pulmonary fibrosis (IPF). Materials and Methods This retrospective analysis of preexisting anonymized data, collected prospectively between 2007 and 2013 in a HIPAA-compliant study, was exempt from additional institutional review board approval. The extent of lung fibrosis at baseline inspiratory chest CT in 280 subjects enrolled in the IPF Network was evaluated. Visual analysis was performed by using a semiquantitative scoring system. Computer-based quantitative analysis included CT histogram-based measurements and a data-driven textural analysis (DTA). Follow-up CT images in 72 of these subjects were also analyzed. Univariate comparisons were performed by using Spearman rank correlation. Multivariate and longitudinal analyses were performed by using a linear mixed model approach, in which models were compared by using asymptotic χ2 tests. Results At baseline, all CT-derived measures showed moderate significant correlation (P < .001) with pulmonary function. At follow-up CT, changes in DTA scores showed significant correlation with changes in both forced vital capacity percentage predicted (ρ = -0.41, P < .001) and diffusing capacity for carbon monoxide percentage predicted (ρ = -0.40, P < .001). Asymptotic χ2 tests showed that inclusion of DTA score significantly improved fit of both baseline and longitudinal linear mixed models in the prediction of pulmonary function (P < .001 for both). Conclusion When compared with semiquantitative visual assessment and CT histogram-based measurements, DTA score provides additional information that can be used to predict diminished function. Automatic quantification of lung fibrosis at CT yields an index of severity that correlates with visual assessment and functional change in subjects with IPF. © RSNA, 2017.

CT Quantification of Lungs and Airways in Normal Korean Subjects.

ObjectiveTo measure and compare the quantitative parameters of the lungs and airways in Korean never-smokers and current or former smokers (“ever-smokers”).Materials and MethodsNever-smokers (n = 119) and ever-smokers (n = 45) who had normal spirometry and visually normal chest computed tomography (CT) results were retrospectively enrolled in this study. For quantitative CT analyses, the low attenuation area (LAA) of LAAI-950, LAAE-856, CT attenuation value at the 15th percentile, mean lung attenuation (MLA), bronchial wall thickness of inner perimeter of a 10 mm diameter airway (Pi10), total lung capacity (TLCCT), and functional residual capacity (FRCCT) were calculated based on inspiratory and expiratory CT images. To compare the results between groups according to age, sex, and smoking history, independent t test, one way ANOVA, correlation test, and simple and multiple regression analyses were performed.ResultsThe values of attenuation parameters and volume on inspiratory and expiratory quantitative computed tomography (QCT) were significantly different between males and females (p < 0.001). The MLA and the 15th percentile value on inspiratory QCT were significantly lower in the ever-smoker group than in the never-smoker group (p < 0.05). On expiratory QCT, all lung attenuation parameters were significantly different according to the age range (p < 0.05). Pi10 in ever-smokers was significantly correlated with forced expiratory volume in 1 second/forced vital capacity (r = −0.455, p = 0.003). In simple and multivariate regression analyses, TLCCT, FRCCT, and age showed significant associations with lung attenuation (p < 0.05), and only TLCCT was significantly associated with inspiratory Pi10.ConclusionIn Korean subjects with normal spirometry and visually normal chest CT, there may be significant differences in QCT parameters according to sex, age, and smoking history.

Quantitative Computed Tomography Measurement of Tracheal Cross-Sectional Areas in Relapsing Polychondritis: Correlations with Spirometric Values

Background: Although tracheal stenosis occurs in relapsing polychondritis (RP), no studies exist that have clarified correlations between quantitative airway measurement and spirometry in RP patients. Objectives: The aim of this study was to investigate correlations between the cross-sectional area (CSA) of the trachea and spirometric values in patients with RP. Methods: The institutional review board approved this retrospective study, and written informed consent was waived. Twenty-six patients with RP underwent spirometry and chest computed tomography (CT) at full inspiration and end-expiration. On inspiratory and expiratory chest CT images, CSA at the intrathoracic trachea was measured for all CT slices, and the mean and minimum tracheal CSA were obtained. Correlations between the tracheal CSA and spirometric values were assessed by Spearman's rank correlation analysis. Results: Tracheal CSA measurements for inspiratory and expiratory scans were significantly correlated with FEV₁, FEV_25-75%, and peak flow values (ρ = 0.51-0.86, p < 0.01). During each inspiratory or expiratory phase, the minimum tracheal CSA achieved a higher correlation coefficient with spirometric values than the mean CSA. Conclusion: Tracheal dimensions for both inspiratory and expiratory CT are significant predictors of pulmonary function in patients with RP. The narrowest tracheal dimension likely determines the severity of airflow limitation in RP.

Changes in Cross-Sectional Area and Transverse Diameter of the Heart on Inspiratory and Expiratory Chest CT: Correlation with Changes in Lung Size and Influence on Cardiothoracic Ratio Measurement.

ObjectiveThe aim of this study was to investigate physiological changes in cardiac area and diameters between inspiratory and expiratory chest computed tomography (CT), and to assess their correlation with lung size change and influence on cardiothoracic ratio (CTR) measurements.Materials and MethodsThe institutional review board of our institution approved this study, and informed consent was waived. Forty-three subjects underwent inspiratory and expiratory chest CT as part of routine clinical care. On both inspiratory and expiratory scans, lung volumes and maximum lung diameters (transverse and vertical directions) were measured. The maximum cardiac cross-sectional area (CSA) and the maximum transverse cardiac diameter were measured on both scans, and the CT-based CTR was calculated. Changes in the lung and cardiac measurements were expressed as the expiratory/inspiratory (E/I) ratios. Comparisons between inspiratory and expiratory measurements were made by the Wilcoxon signed-rank test. Correlations between the E/I ratios of lung and heart measurements were evaluated by Spearman’s rank correlation analysis.ResultsCardiac CSA and transverse cardiac diameter was significantly larger on expiratory than on inspiratory CT (p < 0.0001). Significant negative correlations were found between the E/I ratios of these cardiac measurements and the E/I ratios of lung volume and vertical lung diameter (p < 0.01). CT-based CTR was significantly larger on expiration than on inspiration (p < 0.0001).ConclusionsHeart size on chest CT depends on the phase of ventilation, and is correlated with changes in lung volume and craniocaudal lung diameter. The CTR is also significantly influenced by ventilation.

What did we learn from two decades of chest computed tomography in cystic fibrosis?

Despite our current treatment, many cystic fibrosis (CF) patients still show progressive bronchiectasis and small airways disease. Adequate detection and monitoring of progression of these structural abnormalities is needed to personalize treatment to the severity of CF lung disease of the patient. Chest computed tomography (CT) is the gold standard to diagnose and monitor bronchiectasis. Many studies have been done to validate the role of chest CT in CF and to improve the protocols. From these studies it became clear that for correct interpretation of the severity of bronchiectasis and small airways disease standardization of lung volume for the inspiratory and expiratory CT scan acquisition is needed. The risk related to the radiation exposure of a chest CT scan every second year is considered low. Automated and quantitative image analysis systems are developed to improve the reliability and sensitivity of assessments of structural lung changes in CF, particularly in early life. In this paper an overview is given of the lessons learned from two decades of monitoring CF lung disease using chest CT.

Improved air trapping evaluation in chest computed tomography in children with cystic fibrosis using real-time spirometric monitoring and biofeedback

The quality of chest Computed Tomography (CT) images in children is dependent upon a sufficient breath hold during CT scanning. This study evaluates the influence of spirometric breath hold monitoring with biofeedback software on inspiratory and expiratory chest CT lung density measures, and on trapped air (TA) scoring in children with cystic fibrosis (CF). This is important because TA is an important component of early and progressive CF lung disease. A cross sectional comparison study was completed for chest CT imaging in two cohorts of CF children with comparable disease severity, using spirometric breath hold monitoring and biofeedback software (Copenhagen (COP)) or unmonitored breath hold manoeuvres (Gothenburg (GOT)). Inspiratory-expiratory lung density differences were calculated, and TA was scored to assess the difference between the two cohorts. Eighty-four chest CTs were evaluated. Mean (95%CI) change in inspiratory-expiratory lung density differences was 436 Hounsfield Units (HU) (408 to 464) in the COP cohort with spirometric breath hold monitoring versus 229 HU (188 to 269) in the GOT cohort with unmonitored breath hold manoeuvres (p<0.0001). The Mean TA (95%CI) score was 6.93 (6.05 to 7.82) in COP patients and 3.81 (2.89 to 4.73) in GOT (p<0.0001) patients. In children with comparable CF lung disease, spirometric breath hold monitoring during examination yielded a large difference in lung volume between inhalation and exhalation, and allowed for a significantly greater measured change in lung density and TA score, compared to unmonitored breath hold maneuvers. This has implications to the clinical use of chest CT, especially in children with early CF lung disease.

Air Trapping Detected on End-Expiratory High-Resolution Computed Tomography in Symptomatic World Trade Center Rescue and Recovery Workers

We utilized end-expiratory chest computed tomography (CT) to investigate air trapping (AT) in symptomatic former World Trade Center (WTC) workers, and correlated the findings with clinical, physiological, and exposure-related characteristics. Twenty-nine WTC workers with lower respiratory symptoms were evaluated. Clinical data included symptom inventories, quantitative respiratory symptom scores, WTC dust exposure duration, pulmonary function tests, and inspiratory and end-expiratory high-resolution chest CT scans. The latter were scored quantitatively for AT (by two methods) and interstitial changes, and those scores were correlated with the clinical data. The two AT scoring methods yielded highly correlated results. AT was demonstrated in 25 of 29 patients, with scores ranging from 0 to 24 (mean, 10.6). There was a statistically significant correlation between AT and the duration of dust exposure. AT scores were significantly higher in patients with restrictive lung function data, and in lifetime nonsmokers. Our data suggest that AT from small airways disease may account for some of the reported clinical and pulmonary functional abnormalities in WTC dust-exposed workers, and support the use of high-resolution CT scans in the investigation and characterization of the pulmonary ailments of selected workers.