Improved correlation between CT emphysema quantification and pulmonary function test by density correction of volumetric CT data based on air and aortic density

ROBUST QUANTIFICATION OF PERCENT EMPHYSEMA ON CT VIA DOMAIN ATTENTION: THE MULTI-ETHNIC STUDY OF ATHEROSCLEROSIS (MESA) LUNG STUDY

Robust quantification of pulmonary emphysema on computed tomography (CT) remains challenging for large-scale research studies that involve scans from different scanner types and for translation to clinical scans. Although the domain shifts in different CT scanners are subtle compared to shifts existing in other modalities (e.g., MRI) or cross-modality, emphysema is highly sensitive to it. Such subtle difference limits the application of general domain adaptation methods, such as image translation-based methods, as the contrast difference is too subtle to be distinguished. Existing studies have explored several directions to tackle this challenge, including density correction, noise filtering, regression, hidden Markov measure field (HMMF) model-based segmentation, and volume-adjusted lung density. Despite some promising results, previous studies either required a tedious workflow or eliminated opportunities for downstream emphysema subtyping, limiting efficient adaptation on a large-scale study. To alleviate this dilemma, we developed an end-to-end deep learning framework based on an existing HMMF segmentation framework. We first demonstrate that a regular UNet cannot replicate the existing HMMF results because of the lack of scanner priors. We then design a novel domain attention block, a simple yet efficient cross-modal block to fuse image visual features with quantitative scanner priors (a sequence), which significantly improves the results.

Attenuation of fibroblast activation and fibrosis by adropin in systemic sclerosis

Fibrotic diseases impose a major socioeconomic challenge on modern societies and have limited treatment options. Adropin, a peptide hormone encoded by the energy homeostasis-associated (ENHO) gene, is implicated in metabolism and vascular homeostasis, but its role in the pathogenesis of fibrosis remains enigmatic. Here, we used machine learning approaches in combination with functional in vitro and in vivo experiments to characterize adropin as a potential regulator involved in fibroblast activation and tissue fibrosis in systemic sclerosis (SSc). We demonstrated consistent down-regulation of adropin/ENHO in skin across multiple cohorts of patients with SSc. The prototypical profibrotic cytokine TGFβ reduced adropin/ENHO expression in a JNK-dependent manner. Restoration of adropin signaling by therapeutic application of bioactive adropin34-76 peptides in turn inhibited TGFβ-induced fibroblast activation and fibrotic tissue remodeling in primary human dermal fibroblasts, three-dimensional full-thickness skin equivalents, mouse models of bleomycin-induced pulmonary fibrosis and sclerodermatous chronic graft-versus-host-disease (sclGvHD), and precision-cut human skin slices. Knockdown of GPR19, an adropin receptor, abrogated the antifibrotic effects of adropin in fibroblasts. RNA-seq demonstrated that the antifibrotic effects of adropin34-76 were functionally linked to deactivation of GLI1-dependent profibrotic transcriptional networks, which was experimentally confirmed in vitro, in vivo, and ex vivo using cultured human dermal fibroblasts, a sclGvHD mouse model, and precision-cut human skin slices. ChIP-seq confirmed adropin34-76-induced changes in TGFβ/GLI1 signaling. Our study characterizes the TGFβ-induced down-regulation of adropin/ENHO expression as a potential pathomechanism of SSc as a prototypical systemic fibrotic disease that unleashes uncontrolled activation of profibrotic GLI1 signaling.

Quantification of Emphysema Progression at CT Using Simultaneous Volume, Noise, and Bias Lung Density Correction

Background CT attenuation is affected by lung volume, dosage, and scanner bias, leading to inaccurate emphysema progression measurements in multicenter studies. Purpose To develop and validate a method that simultaneously corrects volume, noise, and interscanner bias for lung density change estimation in emphysema progression at CT in a longitudinal multicenter study. Materials and Methods In this secondary analysis of the prospective Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study, lung function data were obtained from participants who completed baseline and 5-year follow-up visits from January 2008 to August 2017. CT emphysema progression was measured with volume-adjusted lung density (VALD) and compared with the joint volume-noise-bias-adjusted lung density (VNB-ALD). Reproducibility was studied under change of dosage protocol and scanner model with repeated acquisitions. Emphysema progression was visually scored in 102 randomly selected participants. A stratified analysis of clinical characteristics was performed that considered groups based on their combined lung density change measured by VALD and VNB-ALD. Results A total of 4954 COPDGene participants (mean age, 60 years ± 9 [SD]; 2511 male, 2443 female) were analyzed (1329 with repeated reduced-dose acquisition in the follow-up visit). Mean repeatability coefficients were 30 g/L ± 0.46 for VALD and 14 g/L ± 0.34 for VNB-ALD. VALD measurements showed no evidence of differences between nonprogressors and progressors (mean, -5.5 g/L ± 9.5 vs -8.6 g/L ± 9.6; P = .11), while VNB-ALD agreed with visual readings and showed a difference (mean, -0.67 g/L ± 4.8 vs -4.2 g/L ± 5.5; P < .001). Analysis of progression showed that VNB-ALD progressors had a greater decline in forced expiratory volume in 1 second (-42 mL per year vs -32 mL per year; Tukey-adjusted P = .002). Conclusion Simultaneously correcting volume, noise, and interscanner bias for lung density change estimation in emphysema progression at CT improved repeatability analyses and agreed with visual readings. It distinguished between progressors and nonprogressors and was associated with a greater decline in lung function metrics. Clinical trial registration no. NCT00608764 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Goo in this issue.

Contrastive learning and subtyping of post-COVID-19 lung computed tomography images

Patients who recovered from the novel coronavirus disease 2019 (COVID-19) may experience a range of long-term symptoms. Since the lung is the most common site of the infection, pulmonary sequelae may present persistently in COVID-19 survivors. To better understand the symptoms associated with impaired lung function in patients with post-COVID-19, we aimed to build a deep learning model which conducts two tasks: to differentiate post-COVID-19 from healthy subjects and to identify post-COVID-19 subtypes, based on the latent representations of lung computed tomography (CT) scans. CT scans of 140 post-COVID-19 subjects and 105 healthy controls were analyzed. A novel contrastive learning model was developed by introducing a lung volume transform to learn latent features of disease phenotypes from CT scans at inspiration and expiration of the same subjects. The model achieved 90% accuracy for the differentiation of the post-COVID-19 subjects from the healthy controls. Two clusters (C1 and C2) with distinct characteristics were identified among the post-COVID-19 subjects. C1 exhibited increased air-trapping caused by small airways disease (4.10%, p = 0.008) and diffusing capacity for carbon monoxide %predicted (DLCO %predicted, 101.95%, p < 0.001), while C2 had decreased lung volume (4.40L, p < 0.001) and increased ground glass opacity (GGO%, 15.85%, p < 0.001). The contrastive learning model is able to capture the latent features of two post-COVID-19 subtypes characterized by air-trapping due to small airways disease and airway-associated interstitial fibrotic-like patterns, respectively. The discovery of post-COVID-19 subtypes suggests the need for different managements and treatments of long-term sequelae of patients with post-COVID-19.

Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease and the traditional variables extracted from computed tomography (CT) images may not be sufficient to describe all the topological features of lung tissues in COPD patients. We employed an unsupervised three-dimensional (3D) convolutional autoencoder (CAE)-feature constructor (FC) deep learning network to learn from CT data and derive tissue pattern-clusters jointly. We then applied exploratory factor analysis (EFA) to discover the unobserved latent traits (factors) among pattern-clusters. CT images at total lung capacity (TLC) and residual volume (RV) of 541 former smokers and 59 healthy non-smokers from the cohort of the SubPopulations and Intermediate Outcome Measures in the COPD Study (SPIROMICS) were analyzed. TLC and RV images were registered to calculate the Jacobian (determinant) values for all the voxels in TLC images. 3D Regions of interest (ROIs) with two data channels of CT intensity and Jacobian value were randomly extracted from training images and were fed to the 3D CAE-FC model. 80 pattern-clusters and 7 factors were identified. Factor scores computed for individual subjects were able to predict spirometry-measured pulmonary functions. Two factors which correlated with various emphysema subtypes, parametric response mapping (PRM) metrics, airway variants, and airway tree to lung volume ratio were discriminants of patients across all severity stages. Our findings suggest the potential of developing factor-based surrogate markers for new COPD phenotypes.

Semi-quantitative CT imaging in improving visualization of faint ground glass opacities seen in early/mild coronavirus (covid-19) cases

Background

Chest CT is an essential and simple diagnostic method for early detection of pulmonary changes in COVID-19 patients. Semi-quantitative technique depending on both visual and color coded images helps to improve the early detection of COVID-19 chest affection and thus help to control spread of infection.

Results

From first of May to July 15, 2020, 30 patients in Cairo, Egypt who have positive RT-PCR tests and positive pulmonary manifestation were included in our study, 26 patients (86.6%) with faint ground glass opacities were detected by both visual and color coded images, while in 4 patients (13.3%) were only visualized by color coded images and confirmed by CT density assessment.

Conclusion

The combined use of visual and color coded images enhance and improve the early detection of faint ground glass opacities seen in early COVID-19 affection.

Emphysema - The review of radiological presentation and its clinical impact in the LDCT screening era

Emphysema is one of three main lung pathologies in Chronic Obstructive Pulmonary Disease, along with chronic bronchitis and small airway obstruction. The diagnosis is based on detection of low attenuation areas in lung tissue on chest Computed Tomography, either visual by a radiologist, or automatic by the applied Computed Tomography software. Results of the studies on the association between emphysema and lung cancer incidence are mixed. Many studies have demonstrated, that chronic lung diseases, like Chronic Obstructive Pulmonary Disease, are associated with lung cancer morbidity. There is also evidence, that emphysema can be related with worse prognosis in patients with detected lung cancer. In this review article we aim to summarize current knowledge about emphysema detection and evaluation on Computed Tomography, both quantitative and qualitative. We also summarize current data on correlation between emphysema and lung cancer, as well as its potential use in selecting patients, who would most benefit from lung cancer screening.

Exploring technical issues in personalized medicine: NSCLC survival prediction by quantitative image analysis-usefulness of density correction of volumetric CT data

The aim of this study was to apply density correction method to the quantitative image analysis of non-small cell lung cancer (NSCLC) computed tomography (CT) images, determining its influence on overall survival (OS) prediction of surgically treated patients. Clinicopathological (CP) data and preoperative CT scans, pre- and post-contrast medium (CM) administration, of 57 surgically treated NSCLC patients, were retrospectively collected. After CT volumetric density measurement of primary gross tumour volume (GTV), aorta and tracheal air, density correction was conducted on GTV (reference values: aortic blood and tracheal air). For each resulting data set (combining CM administration and normalization), first-order statistical and textural features were extracted. CP and imaging data were correlated with patients 1-, 3- and 5-year OS, alone and combined (uni-/multivariate logistic regression and Akaike information criterion). Predictive performance was evaluated using the ROC curves and AUC values and compared among non-normalized/normalized data sets (DeLong test). The best predictive values were obtained when combining CP and imaging parameters (AUC values: 1 year 0.72; 3 years 0.82; 5 years 0.78). After normalization resulted an improvement in predicting 1-year OS for some of the grey level size zonebased features (large zone low grey level emphasis) and for the combined CP-imaging model, a worse performance for grey level co-occurrence matrix (cluster prominence and shade) and first-order statistical (range) parameters for 1- and 5-year OS, respectively. The negative performance of cluster prominence in predicting 1-year OS was the only statistically significant result (p value 0.05). Density corrections of volumetric CT data showed an opposite influence on the performance of imaging quantitative features in predicting OS of surgically treated NSCLC patients, even if no statistically significant for almost all predictors.

Harmonization of chest CT scans for different doses and reconstruction methods

PURPOSE

To develop and validate a computed tomography (CT) harmonization technique by combining noise-stabilization and autocalibration methodologies to provide reliable densitometry measurements in heterogeneous acquisition protocols.

METHODS

We propose to reduce the effects of spatially variant noise such as nonuniform patterns of noise and biases. The method combines the statistical characterization of the signal-to-noise relationship in the CT image intensities, which allows us to estimate both the signal and spatially variant variance of noise, with an autocalibration technique that reduces the nonuniform biases caused by noise and reconstruction techniques. The method is firstly validated with anthropomorphic synthetic images that simulate CT acquisitions with variable scanning parameters: different dosage, nonhomogeneous variance of noise, and various reconstruction methods. We finally evaluate these effects and the ability of our method to provide consistent densitometric measurements in a cohort of clinical chest CT scans from two vendors (Siemens, n = 54 subjects; and GE, n = 50 subjects) acquired with several reconstruction algorithms (filtered back-projection and iterative reconstructions) with high-dose and low-dose protocols.

RESULTS

The harmonization reduces the effect of nonhomogeneous noise without compromising the resolution of the images (25% RMSE reduction in both clinical datasets). An analysis through hierarchical linear models showed that the average biases induced by differences in dosage and reconstruction methods are also reduced up to 74.20%, enabling comparable results between high-dose and low-dose reconstructions. We also assessed the statistical similarity between acquisitions obtaining increases of up to 30% points and showing that the low-dose vs high-dose comparisons of harmonized data obtain similar and even higher similarity than the observed for high-dose vs high-dose comparisons of nonharmonized data.

CONCLUSION

The proposed harmonization technique allows to compare measures of low-dose with high-dose acquisitions without using a specific reconstruction as a reference. Since the harmonization does not require a precalibration with a phantom, it can be applied to retrospective studies. This approach might be suitable for multicenter trials for which a reference reconstruction is not feasible or hard to define due to differences in vendors, models, and reconstruction techniques.

A CT Scan Harmonization Technique to Detect Emphysema and Small Airway Diseases

Recent studies have suggested the central role of small airway destruction in the pathogenesis of COPD leading to further parenchymal destruction. This evidence has sparked the interest in in-vivo assessment of small airway disease overall at the early onset of the disease. The parametric response mapping (PRM) technique has been proposed to distinguish gas trapping due to small airway disease from low attenuation areas due to emphysema. Despite its success, the PRM technique shows some limitations that are precluding the interpretation of its results. The density value used to assess gas trapping highly depends on acquisition parameters, such as dose and reconstruction kernel, and changes in body size, that introduce inhomogeneous photon absorption patterns. In particular, many studies using PRM employ inspiratory and expiratory images that are obtained at different dose levels. Emphysema impact in early disease may be confounded with the gas trapping due to the noise introduced by differences in the acquisition during the PRM. In this work, we propose a CT harmonization technique to remove the nuisance factors to distinguish between small airway disease and emphysema. Our results show that the measurements based on CT harmonization provide an increase in the detection of both emphysema and airway disease, resulting in a statistically significant impact of both components and a better association with lung function measures.

Diagnostic Value of Quantitative Chest CT Scan in a Case of Spontaneous Pneumothorax

An 18-year-old woman with no previous medical history presented to an outside hospital facility with acute chest pain. She had mild shortness of breath, particularly with exertion, for the prior 2 months.

A method for normalizing pathology images to improve feature extraction for quantitative pathology

PURPOSE

With the advent of digital slide scanning technologies and the potential proliferation of large repositories of digital pathology images, many research studies can leverage these data for biomedical discovery and to develop clinical applications. However, quantitative analysis of digital pathology images is impeded by batch effects generated by varied staining protocols and staining conditions of pathological slides.

METHODS

To overcome this problem, this paper proposes a novel, fully automated stain normalization method to reduce batch effects and thus aid research in digital pathology applications. Their method, intensity centering and histogram equalization (ICHE), normalizes a diverse set of pathology images by first scaling the centroids of the intensity histograms to a common point and then applying a modified version of contrast-limited adaptive histogram equalization. Normalization was performed on two datasets of digitized hematoxylin and eosin (H&E) slides of different tissue slices from the same lung tumor, and one immunohistochemistry dataset of digitized slides created by restaining one of the H&E datasets.

RESULTS

The ICHE method was evaluated based on image intensity values, quantitative features, and the effect on downstream applications, such as a computer aided diagnosis. For comparison, three methods from the literature were reimplemented and evaluated using the same criteria. The authors found that ICHE not only improved performance compared with un-normalized images, but in most cases showed improvement compared with previous methods for correcting batch effects in the literature.

CONCLUSIONS

ICHE may be a useful preprocessing step a digital pathology image processing pipeline.

Emphysema Quantification on Cardiac CT Scans Using Hidden Markov Measure Field Model: The MESA Lung Study

Cardiac computed tomography (CT) scans include approximately 2/3 of the lung and can be obtained with low radiation exposure. Large cohorts of population-based research studies reported high correlations of emphysema quantification between full-lung (FL) and cardiac CT scans, using thresholding-based measurements. This work extends a hidden Markov measure field (HMMF) model-based segmentation method for automated emphysema quantification on cardiac CT scans. We show that the HMMF-based method, when compared with several types of thresholding, provides more reproducible emphysema segmentation on repeated cardiac scans, and more consistent measurements between longitudinal cardiac and FL scans from a diverse pool of scanner types and thousands of subjects with ten thousands of scans.

Quantification of Pathologic Air Trapping in Lung Transplant Patients Using CT Density Mapping: Comparison with Other CT Air Trapping Measures

To determine whether density mapping (DM) is more accurate for detection and quantification of pathologic air trapping (pAT) in patients after lung transplantation compared to other CT air trapping measures. One-hundred forty-seven lung and heart-lung transplant recipients underwent CT-examinations at functional residual capacity (FRC) and total lung capacity (TLC) and PFT six months after lung transplantation. Quantification of air trapping was performed with the threshold-based method in expiration (EXP), density mapping (DM) and the expiratory to inspiratory ratio of the mean lung density (E/I-ratio MLD). A non-rigid registration of inspiration-expiration CT-data with a following voxel-to-voxel mapping was carried out for DM. Systematic variation of attenuation ranges was performed for EXP and DM and correlated with the ratio of residual volume to total lung capacity (RV/TLC) by Spearman rank correlation test. AT was considered pathologic if RV/TLC was above the 95th percentile of the predicted upper limit of normal values. Receiver operating characteristic (ROC) analysis was performed. The optimal attenuation range for the EXP method was from -790 HU to -950 HU (EXP(-790 to -950HU)) (r = 0.524, p<0.001) to detect air trapping. Within the segmented lung parenchyma, AT was best defined as voxel difference less than 80 HU between expiration and registered inspiration using the DM method. DM correlated best with RV/TLC (r = 0.663, p<0.001). DM and E/I-ratio MLD showed a larger AUC (0.78; 95% CI 0.69-0.86; 0.76, 95% CI 0.67-0.85) than EXP(-790 HU to -950 HU) (0.71, 95% CI 0.63-0.78). DM and E/I-ratio MLD showed better correlation with RV/TLC and are more suited quantitative CT-methods to detect pAT in lung transplant patients than the EXP(-790HU to -950HU).

The Impact of Sources of Variability on Parametric Response Mapping of Lung CT Scans

Parametric response mapping (PRM) of inspiration and expiration computed tomography (CT) images improves the radiological phenotyping of chronic obstructive pulmonary disease (COPD). PRM classifies individual voxels of lung parenchyma as normal, emphysematous, or nonemphysematous air trapping. In this study, bias and noise characteristics of the PRM methodology to CT and clinical procedures were evaluated to determine best practices for this quantitative technique. Twenty patients of varying COPD status with paired volumetric inspiration and expiration CT scans of the lungs were identified from the baseline COPDGene cohort. The impact of CT scanner manufacturer and reconstruction kernels were evaluated as potential sources of variability in PRM measurements along with simulations to quantify the impact of inspiration/expiration lung volume levels, misregistration, and image spacing on PRM measurements. Negligible variation in PRM metrics was observed when CT scanner type and reconstruction were consistent and inspiration/expiration lung volume levels were near target volumes. CT scanner Hounsfield unit drift occurred but remained difficult to ameliorate. Increasing levels of image misregistration and CT slice spacing were found to have a minor effect on PRM measurements. PRM-derived values were found to be most sensitive to lung volume levels and mismatched reconstruction kernels. As with other quantitative imaging techniques, reliable PRM measurements are attainable when consistent clinical and CT protocols are implemented.

Comparison of a New Integral-Based Half-Band Method for CT Measurement of Peripheral Airways in COPD With a Conventional Full-Width Half-Maximum Method Using Both Phantom and Clinical CT Images

OBJECTIVES

To compare a new integral-based half-band method (IBHB) and a conventional full-width half-maximum (FWHM) method in measuring peripheral airway dimensions at airway phantoms and thin-section computed tomography of chronic obstructive pulmonary disease (COPD).

METHODS

The IBHB was validated and compared using airway phantoms and 50 patients with COPD. Airway parameters (wall area percentage [WA%], mean lumen radius, and mean wall thickness) were measured at fourth to sixth generations of the right apical bronchus. Matched results from 2 methods were compared and correlated with forced expiratory volume (FEV) in 1 second (FEV1), FEV1 / forced vital capacity (FVC), and global initiative for chronic obstructive lung disease (GOLD) stage. Linear regression analysis was performed using airway dimensions and emphysema index.

RESULTS

The IBHB generated more accurate measurements at phantom study. Measured airway parameters by both methods at thin-section computed tomography study were significantly different (all P < 0.05, paired t test). The IBHB method-measured WA% and wall thickness were significantly smaller. Mean WA% with IBHB also showed better correlation than that with FWHM (FEV1, r = -0.52 vs -0.28; FEV1 / FVC, r = -0.41 vs r = -0.20; GOLD, 0.52 vs 0.33, respectively). Linear regression analysis revealed fifth-generation WA% measured by IBHB was an independent variable, and addition to emphysema index increased predictability (FEV1, r = 0.63; FEV1 / FVC, r = 0.61; GOLD, r = 0.70).

CONCLUSIONS

The new IBHB measured peripheral airway dimensions differently than FWHM and showed better correlations with functional parameters in COPD.

Feasibility of Computed Tomography in a Multicenter COPD Trial: A Study of the Effect of AZD9668 on Structural Airway Changes

INTRODUCTION

The aim of this study was to establish the feasibility of using computed tomography (CT) in a multicenter setting to assess structural airway changes.

METHODS

This was a 12-week, randomized, double-blind, placebo-controlled, Phase IIb trial using CT to investigate the effect of a novel, oral, reversible neutrophil elastase inhibitor, AZD9668 60 mg twice daily (BID), on structural airway changes in patients aged 50-80 years with chronic obstructive pulmonary disease (COPD) (ex-smokers).

PRIMARY OUTCOME VARIABLE

airway wall thickness at an extrapolated interior perimeter of 10 mm (AWT-Pi10). Secondary outcome variables: fifth-generation wall area %; air trapping index; pre- and post-bronchodilator forced expiratory volume in 1 s (FEV1); morning and evening peak expiratory flow and FEV1; body plethysmography; EXAcerbations of Chronic pulmonary disease Tool (EXACT); Breathlessness, Cough, and Sputum Scale (BCSS); St George's Respiratory Questionnaire for COPD; and proportion of reliever-medication-free trial days. Safety variables were also assessed.

RESULTS

There was no difference between placebo (n = 19) and AZD9668 (n = 17) for AWT-Pi10 at treatment end. This was consistent with results for most secondary variables. However, patients randomized to AZD9668 experienced an improvement versus placebo for morning and evening FEV1, and EXACT and BCSS cough and sputum scores. AZD9668 60 mg BID was well tolerated and no new safety concerns were identified.

CONCLUSIONS

This study confirmed the feasibility of using CT to assess structural airway changes in COPD. However, there was no evidence of improvements in CT structural measures following 12 weeks' treatment with AZD9668 60 mg BID.

FUNDING

AstraZeneca.

Improved CT-based estimate of pulmonary gas trapping accounting for scanner and lung-volume variations in a multicenter asthmatic study

Lung air trapping is estimated via quantitative computed tomography (CT) using density threshold-based measures on an expiration scan. However, the effects of scanner differences and imaging protocol adherence on quantitative assessment are known to be problematic. This study investigates the effects of protocol differences, such as using different CT scanners and breath-hold coaches in a multicenter asthmatic study, and proposes new methods that can adjust intersite and intersubject variations. CT images of 50 healthy subjects and 42 nonsevere and 52 severe asthmatics at total lung capacity (TLC) and functional residual capacity (FRC) were acquired using three different scanners and two different coaching methods at three institutions. A fraction threshold-based approach based on the corrected Hounsfield unit of air with tracheal density was applied to quantify air trapping at FRC. The new air-trapping method was enhanced by adding a lung-shaped metric at TLC and the lobar ratio of air-volume change between TLC and FRC. The fraction-based air-trapping method is able to collapse air-trapping data of respective populations into distinct regression lines. Relative to a constant value-based clustering scheme, the slope-based clustering scheme shows the improved performance and reduced misclassification rate of healthy subjects. Furthermore, both lung shape and air-volume change are found to be discriminant variables for differentiating among three populations of healthy subjects and nonsevere and severe asthmatics. In conjunction with the lung shape and air-volume change, the fraction-based measure of air trapping enables differentiation of severe asthmatics from nonsevere asthmatics and nonsevere asthmatics from healthy subjects, critical for the development and evaluation of new therapeutic interventions.