IJ Radiology
Chronic obstructive pulmonary disease (COPD) is a devastating disease.While there is no cure for COPD and the lung damage associated with this disease cannot be reversed, it is still very important to diagnose it as early as possible.
In this paper, we propose a novel method based on the measurement of air trapping in the lungs from CT images to detect COPD and to evaluate its severity.
Twenty-five patients and twelve normal adults were included in this study. The proposed method found volumetric changes of the lungs from inspiration to expiration. To this end, trachea CT images at full inspiration and expiration were compared and changes in the areas and volumes of the lungs between inspiration and expiration were used to define quantitative measures (features). Using these features, the subjects were classified into two groups of normal and COPD patients using a Bayesian classifier. In addition, t-tests were applied to evaluate discrimination powers of the features for this classification.
For the cases studied, the proposed method estimated air trapping in the lungs from CT images without human intervention. Based on the results, a mathematical model was developed to relate variations of lung volumes to the severity of the disease.
As a computer aided diagnosis (CAD) system, the proposed method may assist radiologists in the detection of COPD. It quantifies air trapping in the lungs and thus may assist them with the scoring of the disease by quantifying the severity of the disease.
© 2012, Author(s). This open-access article is available under the Creative Commons Attribution 4.0 (CC BY 4.0) International License (https://creativecommons.org/licenses/by/4.0/), which allows for unrestricted use, distribution, and reproduction in any medium, provided that the original work is properly cited.
Fang J, Li H, Liang M, Deng D, Zhou Q. Pulmonary Dual Hemodynamic Changes in Severe COPD Patients: A Quantitative Study Using Low-Dose CT Lung Perfusion Scan. I J Radiol. 2019;16(1):e68920. doi: https://doi.org/10.5812/iranjradiol.68920
Jiang H, Shi W, Ma Y, Ma M, Ma Y, et al. Diagnostic and Prognostic Value of Dynamic CT Time-Density Curve Parameters in Non-small Cell Lung Cancer: A Retrospective Cohort Study. I J Radiol. 2025;22(4):e167129. doi: https://doi.org/10.5812/iranjradiol-167129
Tahery N, Zarea K, Cheraghi M, Hatamzadeh N, Farhadi M, et al. Chronic Obstructive Pulmonary Disease (COPD) and Air Pollution: A Review. Jundishapur J Chronic Dis Care. 2021;10(1):e110273. doi: https://doi.org/10.5812/jjcdc.110273
Chen L, Huang Y. Application of Spectral Computed Tomography (CT) Combined with Conventional CT Features in Differentiating Malignant Pulmonary Mass-Like Lesions. I J Radiol. 2023;20(2):e131348. doi: https://doi.org/10.5812/iranjradiol-131348
Song H, Lee IJ. Differences of Low-Dose Chest Computed Tomography Findings Between Smokers and Nonsmokers Based on Smoking Cessation Program. I J Radiol. 2018;15(4):e64340. doi: https://doi.org/10.5812/iranjradiol.64340
Checking
Last Update: 2 weeks ago
Last Update: 2 weeks ago
Last Update: 2 weeks ago
Last Update: 4 days ago
Ordering Reprints
Articles are published under the Creative Commons license stated on each article. No permission or royalty fee is required for uses permitted by that license. CCC handles optional bulk and customized reprint orders. Any quotation covers production and delivery services only, not copyright permission. > Request Reprints from CCC
Author(s):
