Quantitative High-resolution Computed Tomography Research Articles

Lung ultrasound and high-resolution computed tomography quantitative variations during nintedanib treatment for systemic sclerosis-associated interstitial lung disease.

Lung ultrasound (LUS) and high-resolution computed tomography (HRCT) are commonly used for the evaluation of interstitial lung disease (ILD). Nintedanib (NIN) is an antifibrotic therapy approved for systemic sclerosis-associated ILD (SSc-ILD). We assessed LUS and quantitative HRCT changes in SSc-ILD patients treated with NIN during a one-year follow-up, evaluating relationships between imaging variations and functional or quality-of-life outcomes. SSc-ILD patients who started NIN were enrolled and followed for twelve months. Pulmonary function tests and patient-reported outcome measures (PROMs) were assessed half-yearly and quarterly, respectively. LUS was performed quarterly evaluating the presence of B-lines (BL) and pleural line irregularities (PLI). HRCT was repeated after one year and quantitatively analysed with CALIPER software. Ten patients (70% female, mean age 62 years) were enrolled. The mean total number of both BL and PLI was constantly decreased during NIN treatment, being significantly reduced after twelve months (from 175.1 ± 66.7-120.8 ± 70.3 for BL, p= 0.005 and from 50.6 ± 32.5-37.2 ± 22.4 for PLI, p= 0.05). Male gender, smoking habit and baseline forced vital capacity <70% predicted were associated with worse LUS outcomes. A greater reduction of both BL and PLI was observed in those who improved in PROMs, especially modified Medical Research Council dyspnoea scale (p= 0.016 and p= 0.04, respectively) and Saint George's Respiratory Questionnaire (p= 0.006 and p= 0.026, respectively). No significant changes in the CALIPER percentages of normal parenchyma or ILD elements were observed after twelve months of NIN, thus paralleling the stabilization obtained at pulmonary function tests. We present preliminary results on NIN effects on SSc-ILD as assessed by LUS, a useful method for frequently repeated monitoring, and CALIPER, a valid implementation whenever a HRCT is performed.

Weak to no correlation between quantitative high-resolution computed tomography metrics and lung function change in fibrotic diseases.

Identifying systemic sclerosis (SSc) and idiopathic pulmonary fibrosis (IPF) patients at risk of more rapid forced vital capacity (FVC) decline could improve trial design. The purpose of the present study was to explore the prognostic value of quantitative high-resolution computed tomography (HRCT) metrics derived by Imbio lung texture analysis (LTA) tool in predicting FVC slope. This retrospective study used data from patients who were not treated with investigational drugs with and without background antifibrotic therapies in tocilizumab phase 3 SSc, lebrikizumab phase 2 IPF, and zinpentraxin alfa phase 2 IPF studies conducted from 2015 to 2021. Controlled HRCT axial volumetric multidetector computed tomography scans were evaluated using the Imbio LTA tool. Associations between HRCT metrics and FVC slope were assessed through the Spearman correlation coefficient and adjusted R2 in a linear regression model adjusted by demographics and baseline clinical characteristics. A total of 271 SSc and IPF patients were analysed. Correlation coefficients of highest magnitude were observed in the SSc study between the extent of ground glass, normal volume, quantification of interstitial lung disease, reticular pattern, and FVC slope (-0.25, 0.28, -0.28, and -0.33, respectively), while the correlation coefficients observed in IPF studies were in general <0.2. The incremental prognostic value of the baseline HRCT metrics was marginal after adjusting baseline characteristics and was inconsistent across study arms. Data from the SSc and IPF studies suggested weak to no and inconsistent correlation between quantitative HRCT metrics derived by the Imbio LTA tool and FVC slope in the studied SSc and IPF population.

Quantitative image analysis in COVID-19 acute respiratory distress syndrome: a cohort observational study.

Background Acute respiratory distress syndrome (ARDS) is a severe form of acute lung injury commonly associated with pneumonia, including coronavirus disease-19 (COVID-19). The resultant effect can be persistent lung damage, but its extent is not known. We used quantitative high resolution computed tomography (QHR-CT) lung scans to radiographically characterize the lung damage in COVID-19 ARDS (CARDS) survivors. Methods Patients with CARDS (N=20) underwent QHR-CT lung scans 60 to 90 days after initial diagnosis, while hospitalized at a long-term acute care hospital (LTACH). QHR-CT assessed for mixed disease (QMD), ground glass opacities (QGGO), consolidation (QCON) and normal lung tissue (QNL). QMD was correlated with respiratory support on admission, tracheostomy decannulation and supplementary oxygen need on discharge. Results Sixteen patients arrived with tracheostomy requiring invasive mechanical ventilation. Four patients arrived on nasal oxygen support. Of the patients included in this study 10 had the tracheostomy cannula removed, four remained on invasive ventilation, and two died. QHR-CT showed 45% QMD, 28.1% QGGO, 3.0% QCON and QNL=23.9%. Patients with mandatory mechanical ventilation had the highest proportion of QMD when compared to no mechanical ventilation. There was no correlation between QMD and tracheostomy decannulation or need for supplementary oxygen at discharge. Conclusions Our data shows severe ongoing lung injury in patients with CARDS, beyond what is usually expected in ARDS. In this severely ill population, the extent of mixed disease correlates with mechanical ventilation, signaling formation of interstitial lung disease. QHR-CT analysis can be useful in the post-acute setting to evaluate for interstitial changes in ARDS.

Subtyping preserved ratio impaired spirometry (PRISm) by using quantitative HRCT imaging characteristics

BackgroundPreserved Ratio Impaired Spirometry (PRISm) is defined as FEV1/FVC ≥ 70% and FEV1 < 80%pred by pulmonary function test (PFT). It has highly prevalence and is associated with increased respiratory symptoms, systemic inflammation, and mortality. However, there are few radiological studies related to PRISm. The purpose of this study was to investigate the quantitative high-resolution computed tomography (HRCT) characteristics of PRISm and to evaluate the correlation between quantitative HRCT parameters and pulmonary function parameters, with the goal of establishing a nomogram model for predicting PRISm based on quantitative HRCT.MethodsA prospective and continuous study was performed in 488 respiratory outpatients from February 2020 to February 2021. All patients underwent both deep inspiratory and expiratory CT examinations, and received pulmonary function test (PFT) within 1 month. According to the exclusion criteria and Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification standard, 94 cases of normal pulmonary function, 51 cases of PRISm and 48 cases of mild to moderate chronic obstructive lung disease (COPD) were included in the study. The lung parenchyma, parametric response mapping (PRM), airway and vessel parameters were measured by automatic segmentation software (Aview). One-way analysis of variance (ANOVA) was used to compare the differences in clinical features, pulmonary function parameters and quantitative CT parameters. Spearman rank correlation analysis was used to evaluate the correlation between CT quantitative index and pulmonary function parameters. The predictors were obtained by binary logistics regression analysis respectively in normal and PRISm as well as PRISm and mild to moderate COPD, and the nomogram model was established.ResultsThere were significant differences in pulmonary function parameters among the three groups (P < 0.001). The differences in pulmonary parenchyma parameters such as emphysema index (EI), pixel indices-1 (PI-1) and PI-15 were mainly between mild to moderate COPD and the other two groups. The differences of airway parameters and pulmonary vascular parameters were mainly between normal and the other two groups, but were not found between PRISm and mild to moderate COPD. Especially there were significant differences in mean lung density (MLD) and the percent of normal in PRM (PRMNormal) among the three groups. Most of the pulmonary quantitative CT parameters had mild to moderate correlation with pulmonary function parameters. The predictors of the nomogram model using binary logistics regression analysis to distinguish normal from PRISm were smoking, MLD, the percent of functional small airways disease (fSAD) in PRM (PRMfSAD) and Lumen area. It had a good goodness of fit (χ2 = 0.31, P < 0.001) with the area under curve (AUC) value of 0.786. The predictor of distinguishing PRISm from mild to moderate COPD were PRMEmph (P < 0.001, AUC = 0.852).ConclusionsPRISm was significantly different from subjects with normal pulmonary function in small airway and vessel lesions, which was more inclined to mild to moderate COPD, but there was no increase in pulmonary parenchymal attenuation. The nomogram based on quantitative HRCT parameters has good predictive value and provide more objective evidence for the early screening of PRISm.

Quantitative imaging analysis detects subtle airway abnormalities in symptomatic military deployers

BackgroundExposure to inhalational hazards during post-9/11 deployment to Southwest Asia and Afghanistan puts military personnel at risk for respiratory symptoms and disease. Pulmonary function and qualitative chest high resolution computed tomography (HRCT) are often normal in “deployers” with persistent respiratory symptoms. We explored the utility of quantitative HRCT imaging markers of large and small airways abnormalities, including airway wall thickness, emphysema, and air trapping, in symptomatic deployers with clinically-confirmed lung disease compared to controls.MethodsChest HRCT images from 45 healthy controls and 82 symptomatic deployers with asthma, distal lung disease or both were analyzed using Thirona Lung quantification software to calculate airway wall thickness (by Pi10), emphysema (by percentage of lung volume with attenuation < -950 Hounsfield units [LAA%-950]), and three parameters of air trapping (expiratory/inspiratory total lung volume and mean lung density ratios, and LAA%-856). SAS v.9.4 was used to compare demographic and clinical characteristics between deployers and controls using Chi-Square, Fisher Exact or t-tests. Linear regression was used to assess relationships between pulmonary function and quantitative imaging findings.ResultsGender and smoking status were not statistically significantly different between groups, but deployers were significantly younger than controls (42 vs 58 years, p < 0.0001), had higher body mass index (31 vs 28 kg/m2, p = 0.01), and had fewer total smoking pack-years (8 vs. 26, p = 0.007). Spirometric measures were not statistically significantly different between groups. Pi10 and LAA%-950 were significantly elevated in deployers compared to controls in unadjusted analyses, with the emphysema measure remaining significantly higher in deployers after adjustment for age, sex, smoking, BMI, and expiratory total lung volume. Air trapping parameters were more common in control images, likely due to differences in age and smoking between groups. Among deployers, LAA%-950 and Pi10 were significantly correlated with spirometric markers of obstruction based on ratio of forced expiratory volume in one second (FEV1)/forced vital capacity (FVC) and/or percent predicted FEV1.ConclusionsQuantitative chest HRCT imaging analysis identifies emphysema in deployers with asthma and distal lung disease, and may be useful in detecting and monitoring deployment-related lung disease in a population where spirometry is typically normal.

Quantitative Progression analysis of Post-Acute Sequelae of COVID-19, Pulmonary Fibrosis (PASC-PF) and Artificial Intelligence driven CT scoring of Lung involvement in Covid-19 infection using HRCT-Chest images

Covid-19 is a contagious respiratory disease caused by SARS-COV-2 coronavirus, continue to spread across the world since Jan'2020. Covid-19 can be diagnosed by Nucleic Acid Testing, Antigens Test and Serology Tests or by Digitized Medical Imaging tools like X-ray and High-Resolution Computed Tomography (HRCT)-Chest images. Advanced computational technologies like Artificial Intelligence (AI) - Deep Learning models can assist the radiologist in quantitative, accurate, infection severity and consistent diagnosis, by training the computational system to learn the features of Covid-19 by feeding practitioner's annotated CT images. Quantitative HRCT assessment of severity of COVID-19 infection was widely appreciated by medical fraternity, various government administrations across the world during the early phase of infection. During digitized HRCT-Chest images analysis, the typical features of COVID-19 like bilateral peripheral Ground Glass Opacities (GGO) and/or consolidation, predominantly involving the lower lobes of lung are analyzed. Bilateral peripheral rounded patchy ground glass opacities have been analyzed. HRCT images analysis helps to identify pre-dominant patterns of lung abnormalities like unilateral, multifocal and peripherally GGOs. Most patients make a complete recovery, few of them continue to experience sequalae long after they recover from the acute infection. The most common sequalae symptoms reported were lost of taste or smell, fatigue, and shortness of breath. This constellation of sequalae symptoms, is called Post-Acute Sequelae of COVID-19 (PASC). A subset of patients recovering from infection continue to have persistent respiratory symptoms and chest imaging abnormalities. Our AI-Deep Learning Model is calculating the extent of lung involvement (% of involvement) which is common in the acute phase of infection, was recognized early in the pandemic. While the GGOs and consolidations slowly improved after Covid-19 recovery, fibrosis was seen considerable percentage of patients on follow-up scans after discharge. Results arriving out from our AI model is very much helpful to focus on the post-acute lung disease in COVID-19. Our Deep Learning model analytics on quantitative HRCT metrics of fibrotic lung disease quickly facilitate a role in the evaluation of post covid symptoms, lung fibrotic changes or pulmonary fibrosis.

Quantitative image analysis in COVID-19 acute respiratory distress syndrome: a cohort observational study.

Background Acute respiratory distress syndrome (ARDS) is a severe form of acute lung injury commonly associated with pneumonia, including coronavirus disease-19 (COVID-19). The resultant effect can be persistent lung damage, but its extent is not known. We used quantitative high resolution computed tomography (QHR-CT) lung scans to radiographically characterize the lung damage in COVID-19 ARDS (CARDS) survivors. Methods Patients with CARDS (N=20) underwent QHR-CT lung scans 60 to 90 days after initial diagnosis, while hospitalized at a long-term acute care hospital (LTACH). QHR-CT assessed for mixed disease (QMD), ground glass opacities (QGGO), consolidation (QCON) and normal lung tissue (QNL). QMD was correlated with respiratory support on admission, tracheostomy decannulation and supplementary oxygen need on discharge. Results Sixteen patients arrived with tracheostomy requiring invasive mechanical ventilation. Four patients arrived on nasal oxygen support. Of the patients included in this study 10 had the tracheostomy cannula removed, four remained on invasive ventilation, and two died. QHR-CT showed 45% QMD, 28.1% QGGO, 3.0% QCON and QNL=23.9%. Patients with mandatory mechanical ventilation had the highest proportion of QMD when compared to no mechanical ventilation. There was no correlation between QMD and tracheostomy decannulation or need for supplementary oxygen at discharge. Conclusions Our data shows severe ongoing lung injury in patients with CARDS, beyond what is usually expected in ARDS. In this severely ill population, the extent of mixed disease correlates with mechanical ventilation, signaling formation of interstitial lung disease. QHR-CT analysis can be useful in the post-acute setting to evaluate for interstitial changes in ARDS.

Quantitative assessment of pulmonary function in lymphangioleiomyomatosis patients using high-resolution computed tomography and pulmonary function tests.

BackgroundTo explore the feasibility of using quantitative high-resolution computed tomography (HRCT) to evaluate pulmonary function in patients with pulmonary lymphangioleiomyomatosis (PLAM).MethodsPulmonary function tests (PFTs) were performed in 30 patients with pathologically confirmed PLAM with the use of HRCT. These results were correlated with quantitative HRCT in 21 patients.ResultsThere were significant correlations between the HRCT parameters for lung function and PFT parameters. Among these parameters, emphysema volume (EV), pulmonary volume with a pixel index less than the trigger threshold (−950 HU) to account for a proportion of total lung volume [PI-950 (%)] and forced expiratory volume in 1 second/forced vital capacity [FEV1/FVC (%)] had the strongest correlations, reaching values between −0.71 and −0.68. HRCT lung function might therefore also be helpful for predicting changes in lung function before and after treatment.ConclusionsHRCT is helpful for the assessment of pulmonary function in PLAM patients and can assist in the clinical evaluation of lung function and treatment response in patients with this disease.

Detection, screening, and classification of interstitial lung disease in patients with systemic sclerosis.

Interstitial lung disease (ILD) associates with disease burden and reduced life expectancy in systemic sclerosis (SSc). As ILD afflicts around 50% of SSc patients and is often present from early on, there is rationale for early and universal ILD screening. With the approval of the first SSc-ILD therapy last year, there is an increasing need for ILD classifications to assign the right treatment to the right patient. Here, we discuss recent advances on screening, detection, and classification of SSc-ILD. Although prospective datasets from a nationwide population-based SSc cohort confirm insufficient sensitivity of pulmonary function tests (PFTs) for ILD screening, they provide strong support for lung high resolution computed tomography (HRCT) as the primary tool to detect ILD. Lung ultrasound shows promise as an additional screening tool. Interpreting statements from a new European consensus on SSc-ILD management, we propose an urgent need for integrated SSc classification, grading ILD severity at time of diagnosis and evaluate risk for ILD progression. We discuss advances on potential parameters for such classification, including PFTs, quantitative HRCT analyses, patient-reported outcome measures, functional exercise capacity tests, and soluble biomarkers. Early screening to diagnose ILD is feasible. With new therapies at hand, there is a need for integrated ILD classification including severity grading and risk for progression.

Automatic Quantitative Computed Tomography Evaluation of the Lungs in Patients With Systemic Sclerosis Treated With Autologous Stem Cell Transplantation

Interstitial lung disease stands among the leading causes of death in systemic sclerosis (SSc) patients. Autologous hematopoietic stem cell transplantation (AHSCT) has been proven superior to conventional immunosuppressive therapy in severe and progressive SSc. Here, pulmonary quantitative measurements were obtained in high-resolution computed tomography (HRCT) scans of patients with SSc before and after AHSCT. The medical records of thirthy-three patients who underwent AHSCT between 2011 and 2017 were evaluated for clinical and tomographic features at baseline (pre-AHCST) and 18 months after the procedure. Quantitative analysis of HRCT images by a fully automated program calculated lung volumes, densities, attenuation percentiles, and vascular volume. Patients were divided into 2 groups, according to changes in forced vital capacity (FVC). The "best response" group included patients that had an increased FVC of 10% or greater, and the "stable response" group included those who had a decreased or an increased FVC of less than 10%. In the best response group (15 patients), there was reduction (p < 0.05) of mean lung density and density percentile values after AHSCT. In the stable response group (18 patients), there were no significant changes in lung volumes and pulmonary densities after AHSCT. Pulmonary HRCT densities showed moderate/strong correlation with function. Quantitative HRCT analysis identified significant reduction in pulmonary densities in patients with improved pulmonary function after AHSCT. Lung density, as evaluated by the quantitative HRCT analysis tool, has potential to become a biomarker in the evaluation of interstitial lung disease treatment in patients with SSc.

Correlation of quantitative and qualitative parameters of high-resolution computed tomography with pulmonary function test for diagnosing and assessing the severity of obstructive pulmonary disease.

PurposeThe aim of the study was to evaluate the role of high-resolution computed tomography (HRCT) in the prediction of presence and severity of chronic obstructive pulmonary disease (COPD), based on the pulmonary function test (PFT), to correlate the various quantitative and qualitative indices of COPD in HRCT with PFT values, and to derive at the threshold values for various quantitative HRCT indices of COPD.Material and methodsA one-year retrospective and prospective evaluation of the HRCTs of 90 cases and 38 controls was performed. Quantitative and qualitative parameters of COPD were assessed in HRCT and were correlated with PFT.ResultsAmong the qualitative parameters, significant correlation with spirometry was found with the qualitative scoring for emphysema and presence and inhomogeneous attenuation. Among the quantitative indices, anterior junction line length, thoracic cage ratio at both aortic arch and inferior pulmonary vein level, thoracic cross-sectional area/[height]² at the aortic arch were found to have good correlation with spirometry. There was significant strong correlation of anterior junction line length and tracheal index with residual volume/total lung capacity (RV/TLC). The threshold values of chest X-ray and HRCT quantitative parameters were calculated and were found to be lower than those of the western population.ConclusionHRCT has a definite role in the diagnosis of COPD and can be used to predict the severity of emphysema.

Obstructive pulmonary disease in patients with previous tuberculosis: Pathophysiology of a community-based cohort.

An association between chronic airflow limitation (CAL) and a history of pulmonary tuberculosis (PTB) has been confirmed in epidemiological studies, but the mechanisms responsible for this association are unclear. It is debated whether CAL in this context should be viewed as chronic obstructive pulmonary disease (COPD) or a separate phenotype. To compare lung physiology and high-resolution computed tomography (HRCT) findings in subjects with CAL and evidence of previous (healed) PTB with those in subjects with smoking-related COPD without evidence of previous PTB. Subjects with CAL identified during a Burden of Obstructive Lung Disease (BOLD) study performed in South Africa were studied. Investigations included questionnaires, lung physiology (spirometry, body plethysmography and diffusing capacity) and quantitative HRCT scans to assess bronchial anatomy and the presence of emphysema (&lt;-950 HU), gas trapping (&lt;-860 HU) and fibrosis (&gt;-200 HU). Findings in subjects with a past history and/or HRCT evidence of PTB were compared with those in subjects without these features. One hundred and seven of 196 eligible subjects (54.6%) were enrolled, 104 performed physiology tests and 94 had an HRCT scan. Based on history and HRCT findings, subjects were categorised as no previous PTB (NPTB, n=31), probable previous PTB (n=33) or definite previous PTB (DPTB, n=39). Subjects with DPTB had a lower diffusing capacity (Δ=-17.7%; p=0.001) and inspiratory capacity (Δ=-21.5%; p=0.001) than NPTB subjects, and higher gas-trapping and fibrosis but not emphysema scores (Δ=+6.2% (p=0.021), +0.36% (p=0.017) and +3.5% (p=0.098), respectively). The mechanisms of CAL associated with previous PTB appear to differ from those in the more common smoking-related COPD and warrant further study.

Quantitative computed tomography: emphysema and airway wall thickness by sex, age and smoking

We investigated how quantitative high-resolution computed tomography (HRCT) measures of emphysema and airway wall thickness (AWT) vary with sex, age and smoking history. We included 463 chronic obstructive pulmonary disease (COPD) cases and 431 controls. All included subjects were current or ex-smokers aged > or = 40 yrs, and all underwent spirometry and HRCT examination. The HRCT images were quantitatively assessed, providing indices on lung density and airway dimensions. The median (25-75th percentile) %LAA950 (% low-attenuation area < -950 HU) was 8.9 (3-19) and 4.7 (1-16) in male and female COPD cases, respectively, and 0.71 (0.3-1.6) and 0.32 (0.1-0.8) in male and female controls, respectively. %LAA950 was higher in ex-smokers and increased with increasing age and with increasing number of pack-years. The mean+/-SD standardised AWT was 0.504+/-0.030 and 0.474+/-0.031 in male and female COPD cases, respectively, and 0.488+/-0.028 and 0.463+/-0.025 in male and female controls, respectively. AWT decreased with increasing age in cases, and increased with the degree of current smoking in all subjects. We found significant differences in quantitative HRCT measures of emphysema and AWT between varying sex, age and smoking groups of both control and COPD subjects.

Correlation between high-resolution computed tomography features and patients' characteristics in chronic obstructive pulmonary disease.

BACKGROUND:During the last few decades, high-resolution computed tomography (HRCT) has come up as a new diagnostic modality to diagnose emphysematous and chronic bronchitis components of chronic obstructive pulmonary disease (COPD). The present study was undertaken to evaluate for various quantitative and qualitative HRCT features in patients with COPD, and to detect patients' characteristics that correlate with these HRCT features.MATERIALS AND METHODS:Forty male patients with COPD attending the COPD clinic at a tertiary referral hospital and postgraduate medical institute were included in the study. Various HRCT features, including tracheal index, thoracic cage ratio, sterno-aortic distance, thoracic cross-sectional area, vascular attenuation, vascular distortion, mosaic attenuation pattern, and directly visible small airways, were evaluated and correlated with patients' characteristics, including age, duration of illness, quantum of smoking, dyspnea score, quality-of-life index, and various spirometric indices.RESULTS:We found significant correlations of various quantitative and qualitative HRCT features with age, duration of illness, quantum of smoking, quality-of-life index, and the spirometric indices showing the extent of airways obstruction.CONCLUSIONS:Various quantitative and qualitative HRCT features were found to correlate with patients' characteristics, spirometric indices, and health-related quality-of-life score, suggesting that HRCT is useful not only in detecting emphysema and its various subtypes but also in predicting the extent and severity of COPD.

Quantitative computed tomography detects peripheral airway disease in asthmatic children

The aim of this study was to compare air-trapping as quantified by high-resolution computed tomography (HRCT) of the chest with measures of lung function and airway inflammation in children with mild to moderate asthma. Plethysmography indices, respiratory resistance, and reactance before and after bronchodilator with impulse oscillation (IOS), exhaled nitric oxide (eNO), total eosinophil count (TEC), and serum eosinophil cationic protein (ECP) levels were measured in 21 subjects. A single-cut HRCT image at end-expiration was obtained. Air-trapping was quantified and expressed in terms of the pixel index (PI) by determining the percentage of pixels in lung fields below -856 and -910 Hounsfeld units (HU). Pairwise linear correlations between PI and other parameters were evaluated. Subjects had only mild airflow limitation based on prebronchodilator forced expiratory volume in 1 sec (FEV(1)), but were hyperinflated and had air-trapping based on elevated total lung capacity (TLC) and residual volume (RV)/TLC ratio, respectively. The PI at -856 HU was positively correlated with % predicted TLC, total gas volume (TGV), and ECP level, and was inversely correlated with FEV(1)/forced vital capacity (FVC) and % predicted forced expiratory flow between 25-75% FVC (FEF(25-75)). The PI at -910 HU correlated similarly with these variables, and also correlated positively with IOS bronchodilator reversibility. This data suggest that quantitative HRCT may be a useful tool in the evaluation of peripheral airflow obstruction in children with asthma.