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Updated: Apr 30, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
Published on: April 12, 2024
Emphysema quantification by combining percentage and size distribution of low-attenuation lung regions
Mizuho Nishio1, Sumiaki Matsumoto, Hisanobu Koyama
11 Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chūō-ku, Kobe, Hyogo 650-0017, Japan.
Objective:
The purpose of this study was to investigate efficacy of two types of emphysema quantification: percentage of low-attenuation lung regions (%LA); and size distribution of these regions. On a log-log plot, cumulative frequency-size distribution of low-attenuation lung regions can be fitted by a straight line whose slope (D) has been reported to reflect diffusing capacity. In this study, %LA and D were compared with pulmonary function test (PFT) parameters, especially with ratio of diffusing capacity of carbon monoxide to effective alveolar ventilation (i.e., DLCO/VA).
Materials And Methods:
Thin-section unenhanced CT images were acquired from 30 patients (25 men, five women; mean [SD] age, 70.1 ± 12.1 years), of whom 25 had received diagnosis of COPD, and %LA and D were calculated at 20 thresholds, ranging from -995 to -900 HU. To determine utility of %LA and D, we used Pearson's correlation for emphysema quantification and PFT. Significance of the coefficients was determined with Bonferroni correction (p < 0.0025). Finally, the relationships between emphysema quantification and DLCO/VA were examined by linear models and Akaike information criterion (AIC).
Results:
The correlation coefficients for %LA and DLCO/VA were statistically significant at all the thresholds (optimal coefficient, -0.761). The correlation coefficients for D and DLCO/VA were statistically significant at the thresholds from -945 to -900 HU (optimal coefficient, -0.646). AIC values showed that the most accurate prediction of DLCO/VA was obtained by the model incorporating both %LA and D.
Conclusion:
Both %LA and D showed significant correlation with DLCO/VA. Combining %LA and D resulted in more accurate evaluation of DLCO/VA than did using %LA or D alone.
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