ArticleEuropean radiology2020
Emphysema quantification using low-dose computed tomography with deep learning-based kernel conversion comparison.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- Chest CT in the evaluation of chronic obstructive pulmonary disease: recommendations of Asian Society of Thoracic Radiology.The British journal of radiology · 2026Guideline
- The Use of Computed Tomography Densitometry for the Assessment of Emphysema in Clinical Trials: A Position Paper from the Fleischner Society.American journal of respiratory and critical care medicine · 2025Pooled it
- Segmentation-based deep learning emphysema quantification using chest CT: improved accuracy and robustness vs LAA-950.European radiology · 2026Article
- Harmonizing the National Lung Screening Trial: Kernel resolution and field of view compensation.PloS one · 2026Article
- Multipath cycleGAN for harmonization of paired and unpaired low-dose lung computed tomography reconstruction kernels.Medical physics · 2025Article
- Impact of Deep Learning-Based Image Conversion on Fully Automated Coronary Artery Calcium Scoring Using Thin-Slice, Sharp-Kernel, Non-Gated, Low-Dose Chest CT Scans: A Multi-Center Study.Korean journal of radiology · 2025Article
- Coronary calcium score and emphysema extent on different CT radiation dose protocols in lung cancer screening.European radiology · 2025Article
- Kernel Conversion Improves the Correlation between the Extent of Emphysema and Clinical Parameters in Chronic Obstructive Pulmonary Disease: A Multicenter Cohort Study.Tuberculosis and respiratory diseases · 2025Article
- Investigating the impact of kernel harmonization and deformable registration on inspiratory and expiratory chest CT images for people with COPD.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Age-related lung structure changes by quantitative assessment: a cross-sectional study in a Chinese male cohort.Frontiers in aging · 2025Article
- Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks.Medical physics · 2024Article
- Inter-vendor harmonization of CT reconstruction kernels using unpaired image translation.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Deep learning-based harmonization of CT reconstruction kernels towards improved clinical task performance.European radiology · 2023Article
- New Method for Combined Quantitative Assessment of Air-Trapping and Emphysema on Chest Computed Tomography in Chronic Obstructive Pulmonary Disease: Comparison with Parametric Response Mapping.Korean journal of radiology · 2021Article
- [Pulmonary Emphysema: Visual Interpretation and Quantitative Analysis].Taehan Yongsang Uihakhoe chi · 2021Review
- Kernel Conversion for Robust Quantitative Measurements of Archived Chest Computed Tomography Using Deep Learning-Based Image-to-Image Translation.Frontiers in artificial intelligence · 2021Article
- Analysis of Tracheobronchial Diverticula Based on Semantic Segmentation of CT ImagesFrontiers in public health · 2021Article
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Authors and funding
7 authors at 5 institutions in 3 countries.
Funding
Abstract
objectiveThis study determined the effect of dose reduction and kernel selection on quantifying emphysema using low-dose computed tomography (LDCT) and evaluated the efficiency of a deep learning-based kernel conversion technique in normalizing kernels for emphysema quantification.
methodsA sample of 131 participants underwent LDCT and standard-dose computed tomography (SDCT) at 1- to 2-year intervals. LDCT images were reconstructed with B31f and B50f kernels, and SDCT images were reconstructed with B30f kernels. A deep learning model was used to convert the LDCT image from a B50f kernel to a B31f kernel. Emphysema indices (EIs), lung attenuation at 15th percentile (perc15), and mean lung density (MLD) were calculated. Comparisons among the different kernel types for both LDCT and SDCT were performed using Friedman's test and Bland-Altman plots.
resultsAll values of LDCT B50f were significantly different compared with the values of LDCT B31f and SDCT B30f (p < 0.05). Although there was a statistical difference, the variation of the values of LDCT B50f significantly decreased after kernel normalization. The 95% limits of agreement between the SDCT and LDCT kernels (B31f and converted B50f) ranged from - 2.9 to 4.3% and from - 3.2 to 4.4%, respectively. However, there were no significant differences in EIs and perc15 between SDCT and LDCT converted B50f in the non-chronic obstructive pulmonary disease (COPD) participants (p > 0.05).
conclusionThe deep learning-based CT kernel conversion of sharp kernel in LDCT significantly reduced variation in emphysema quantification, and could be used for emphysema quantification. KEY POINTS: • Low-dose computed tomography with smooth kernel showed adequate performance in quantifying emphysema compared with standard-dose CT. • Emphysema quantification is affected by kernel selection and the application of a sharp kernel resulted in a significant overestimation of emphysema. • Deep learning-based kernel normalization of sharp kernel significantly reduced variation in emphysema quantification.
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