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Generative adversarial network with radiomic feature reproducibility analysis for computed tomography denoisingopen access

Authors
Lee, J.Jeon, J.Hong, Y.Jeong, D.Jang, Y.Jeon, B.Baek, H.J.Cho, E.Shim, H.Chang, H.-J.
Issue Date
Jun-2023
Publisher
Elsevier Ltd
Keywords
Generative adversarial networks; Medical image denoising; Parameter tuning; Radiomics
Citation
Computers in Biology and Medicine, v.159
Indexed
SCIE
SCOPUS
Journal Title
Computers in Biology and Medicine
Volume
159
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/68708
DOI
10.1016/j.compbiomed.2023.106931
ISSN
0010-4825
1879-0534
Abstract
Background: Most computed tomography (CT) denoising algorithms have been evaluated using image quality analysis (IQA) methods developed for natural image, which do not adequately capture the texture details in medical imaging. Radiomics is an emerging image analysis technique that extracts texture information to provide a more objective basis for medical imaging diagnostics, overcoming the subjective nature of traditional methods. By utilizing the difficulty of reproducing radiomics features under different imaging protocols, we can more accurately evaluate the performance of CT denoising algorithms. Method: We introduced radiomic feature reproducibility analysis as an evaluation metric for a denoising algorithm. Also, we proposed a low-dose CT denoising method based on a generative adversarial network (GAN), which outperformed well-known CT denoising methods. Results: Although the proposed model produced excellent results visually, the traditional image assessment metrics such as peak signal-to-noise ratio and structural similarity failed to show distinctive performance differences between the proposed method and the conventional ones. However, radiomic feature reproducibility analysis provided a distinctive assessment of the CT denoising performance. Furthermore, radiomic feature reproducibility analysis allowed fine-tuning of the hyper-parameters of the GAN. Conclusion: We demonstrated that the well-tuned GAN architecture outperforms the well-known CT denoising methods. Our study is the first to introduce radiomics reproducibility analysis as an evaluation metric for CT denoising. We look forward that the study may bridge the gap between traditional objective and subjective evaluations in the clinical medical imaging field. © 2023 The Author(s)
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