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Precise Lung Density Quantification with a Physics-based CT Harmonizer
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DOI：10.1148/ryct.250124.png)
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Abstract
En 中文
Purpose: To develop a physics-based image harmonization method that transforms images into a reference quality index of noise, spatial resolution, and lung volume and to evaluate its performance for improving reproducibility of lung density measurement. Materials and Methods: This retrospective analysis of Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study data included participants who underwent chest CT with full-dose (200 mAs) and reduced-dose (40-80 mAs) protocols during the same visit between November 2014 and July 2017. A harmonization algorithm was developed to reduce variations in lung density by adjusting spatial resolution, noise, and lung volume in sequence, aligning scans to a reference condition based on full-dose, soft-kernel reconstruction. The percentage of lung voxels less than-950 HU (LAA-950) and the 15th percentile of the lung density histogram (Perc15) were calculated from scans. Improvements in bias, limits of agreement, and reproducibility coefficients were evaluated, and harmonizer performance was compared with that of two existing techniques: volume-adjusted lung density (VALD) and median filtering followed by VALD (MF-VALD). Results: A total of 1159 participants (mean age +/- SD, 65 years +/- 9; 586 male) were studied. Across all imaging conditions, the harmonization technique improved the Perc15 reproducibility coefficient 4.8-fold, from 35.6 HU +/- 0.7 to 7.4 HU +/- 0.2. The harmonizer outperformed VALD and MF-VALD across the full-dose and reduced-dose scans, with reproducibility coefficients improving from 32.4 HU +/- 0.7 before harmonization to 30.1 HU +/- 0.3 with VALD, 10.3 HU +/- 0.3 with MF-VALD, and 7.7 HU +/- 0.2 with the harmonizer. Conclusion: This physics-based technique harmonized CT images to a reference quality index, improving the reproducibility of lung density metrics.
Keywords:
COMPUTED-TOMOGRAPHY SCANS
EMPHYSEMA
ASSOCIATION
Journal
R
IF:
4.2
Papers:
584
Citations:
1.4K
