ArticleJournal of applied physiology (Bethesda, Md. : 1985)2014
Improved CT-based estimate of pulmonary gas trapping accounting for scanner and lung-volume variations in a multicenter asthmatic study.
Article in Journal of applied physiology (Bethesda, Md. : 1985), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
31 citing papers in PubMed, 44 citations in OpenAlex.
- Assessment of ventilation heterogeneity and particle deposition in asthmatics using combined SPECT/CT imaging and computational modeling approaches.European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences · 2025Article
- Electrical impedance tomography-based temporal signals correlate with quantitative computed tomography-based spatial variables in asthma subjects: a pilot study.Frontiers in physiology · 2025Article
- Analysis of upper airway CT-based radiomics in adult obstructive sleep apnea.Frontiers in medicine · 2025Article
- The effects of airway disease on the deposition of inhaled drugs.Expert opinion on drug delivery · 2024Review
- Quantification of Emphysema Progression at CT Using Simultaneous Volume, Noise, and Bias Lung Density Correction.Radiology · 2024Article
- Computed tomography-based imaging biomarker identifies coal workers' pneumoconiosis.Frontiers in physiology · 2023Article
- Phenotypic clusters on computed tomography reflects asthma heterogeneity and severity.The World Allergy Organization journal · 2022Article
- Contrastive learning and subtyping of post-COVID-19 lung computed tomography images.Frontiers in physiology · 2022Article
- Exercise-induced airflow changes in horses with asthma measured by electrical impedance tomography.Journal of veterinary internal medicine · 2021Article
- Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images.Scientific reports · 2021Article
- Quantitative CT-based structural alterations of segmental airways in cement dust-exposed subjects.Respiratory research · 2020Article
- Imaging-based clusters in former smokers of the COPD cohort associate with clinical characteristics: the SubPopulations and intermediate outcome measures in COPD study (SPIROMICS).Respiratory research · 2019Article
- Structural and Functional Features on Quantitative Chest Computed Tomography in the Korean Asian versus the White American Healthy Non-Smokers.Korean journal of radiology · 2019Article
- Harmonization of chest CT scans for different doses and reconstruction methods.Medical physics · 2019Article
- 1D network simulations for evaluating regional flow and pressure distributions in healthy and asthmatic human lungs.Journal of applied physiology (Bethesda, Md. : 1985) · 2019Article
- Imaging-based clusters in current smokers of the COPD cohort associate with clinical characteristics: the SubPopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS).Respiratory research · 2018Article
- Transfer Learning for Multicenter Classification of Chronic Obstructive Pulmonary Disease.IEEE journal of biomedical and health informatics · 2018Article
- A CT Scan Harmonization Technique to Detect Emphysema and Small Airway Diseases.Image analysis for moving organ, breast, and thoracic images : third International Workshop, RAMBO 2018, fourth International Workshop, BIA 2018, and first International Workshop, TIA 2018, held in conjunction with MICCAI 2018, Granada,... · 2018Article
- Recent Advances in Computed Tomography Imaging in Chronic Obstructive Pulmonary Disease.Annals of the American Thoracic Society · 2018Review
- Contributions of Kinetic Energy and Viscous Dissipation to Airway Resistance in Pulmonary Inspiratory and Expiratory Airflows in Successive Symmetric Airway Models With Various Bifurcation Angles.Journal of biomechanical engineering · 2018Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.