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European Radiology
Published in: European Radiology 12/2020

01-12-2020 | Computed Tomography | Chest

Emphysema quantification using low-dose computed tomography with deep learning–based kernel conversion comparison

Authors: So Hyeon Bak, Jong Hyo Kim, Hyeongmin Jin, Sung Ok Kwon, Bom Kim, Yoon Ki Cha, Woo Jin Kim

Published in: European Radiology | Issue 12/2020

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Abstract

Objective

This 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.

Methods

A 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.

Results

All 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).

Conclusion

The 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.
Appendix
Available only for authorised users
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Metadata
Title
Emphysema quantification using low-dose computed tomography with deep learning–based kernel conversion comparison
Authors
So Hyeon Bak
Jong Hyo Kim
Hyeongmin Jin
Sung Ok Kwon
Bom Kim
Yoon Ki Cha
Woo Jin Kim
Publication date
01-12-2020
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 12/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-020-07020-3

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