Deep Learning-Based CT Reconstruction Kernel Conversion in the Quantification of Interstitial Lung Disease: Effect on Reproducibility

Yura Ahn; Sang Min Lee; Yujin Nam; Hyunna Lee; Jooae Choe; Kyung-Hyun Do; Joon Beom Seo

doi:10.1016/j.acra.2023.06.008

Deep Learning-Based CT Reconstruction Kernel Conversion in the Quantification of Interstitial Lung Disease: Effect on Reproducibility

Acad Radiol. 2024 Feb;31(2):693-705. doi: 10.1016/j.acra.2023.06.008. Epub 2023 Jul 27.

Authors

Yura Ahn¹, Sang Min Lee², Yujin Nam³, Hyunna Lee⁴, Jooae Choe¹, Kyung-Hyun Do¹, Joon Beom Seo¹

Affiliations

¹ Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 138-736, Republic of Korea (Y.A., S.M.L., J.C., K.-H.D., J.B.S.).
² Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 138-736, Republic of Korea (Y.A., S.M.L., J.C., K.-H.D., J.B.S.). Electronic address: sangmin.lee.md@gmail.com.
³ Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, College of Medicine, University of Ulsan, Seoul, Republic of Korea (Y.N.).
⁴ Bigdata Research Center, Asan Institute for Life Science, Asan Medical Center, Seoul, Republic of Korea (H.L.).

PMID: 37516583
DOI: 10.1016/j.acra.2023.06.008

Abstract

Rationale and objectives: The effect of different computed tomography (CT) reconstruction kernels on the quantification of interstitial lung disease (ILD) has not been clearly demonstrated. The study aimed to investigate the effect of reconstruction kernels on the quantification of ILD on CT and determine whether deep learning-based kernel conversion can reduce the variability of automated quantification results between different CT kernels.

Materials and methods: Patients with ILD or interstitial lung abnormality who underwent noncontrast high-resolution CT between June 2022 and September 2022 were retrospectively included. Images were reconstructed with three different kernels: B30f, B50f, and B60f. B60f was regarded as the reference standard for quantification, and B30f and B50f images were converted to B60f images using a deep learning-based algorithm. Each disease pattern of ILD and the fibrotic score were quantified using commercial software. The effect of kernel conversion on measurement variability was estimated using intraclass correlation coefficient (ICC) and Bland-Altman method.

Results: A total of 194 patients were included in the study. Application of different kernels induced differences in the quantified extent of each pattern. Reticular opacity and honeycombing were underestimated on B30f images and overestimated on B50f images. After kernel conversion, measurement variability was reduced (mean difference, from -2.0 to 3.9 to -0.3 to 0.4%, and 95% limits of agreement [LOA], from [-5.0, 12.7] to [-2.7, 2.1]). The fibrotic score for converted B60f from B50f images was almost equivalent to the original B60f (ICC, 1.000; mean difference, 0.0; and 95% LOA [-0.4, 0.4]).

Conclusion: Quantitative CT analysis of ILD was affected by the application of different kernels, but deep learning-based kernel conversion effectively reduced measurement variability, improving the reproducibility of quantification.

Keywords: Deep learning; Interstitial; Lung disease.

MeSH terms

Deep Learning*
Humans
Lung / diagnostic imaging
Lung Diseases, Interstitial* / diagnostic imaging
Reproducibility of Results
Retrospective Studies
Tomography, X-Ray Computed / methods