Related Experiment Video
Updated: Jul 27, 2025
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
Published on: April 12, 2024
Inspiratory and Expiratory Computed Tomography Imaging Clusters Reflect Functional Characteristics in Chronic
Yumiko Matsuo1,2, Emiko Ogawa1,2, Yoko Tsunoda1
1Division of Respiratory Medicine, Department of Internal Medicine, Shiga University of Medical Science, Otsu, Shiga, Japan.
Purpose:
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.
Patients And Methods:
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).
Results:
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.
Conclusion:
Clusters using DPM parameters may reflect the characteristics of COPD and help understand the pathophysiology of the disease.
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