European journal of radiology · 2026
DuDoTransGAN: Dual-Domain transformer based generative adversarial unsupervised network for metal artifact reduction in paired and unpaired CT.
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Wang Yan, Deng Jia, Peng Zhaojie, Zeng Yule, Jiang Jikang, Zhou Langtao, Zeng Zisan, Zhou Hong
Аннотация
PURPOSE: Metal artifacts from implants degrade CT image quality, while supervised metal artifact reduction (MAR) methods require paired clean data that are rarely available clinically.
METHODS: In this retrospective study, we developed DuDoTransGAN, an unsupervised dual-domain generative adversarial network for paired and unpaired training. It was evaluated on a publicly available paired synthetic dataset (SYN; 90,000 training and 2,000 test slices) and a clinical unpaired knee CT dataset (KNE; 19,032 slices from 66 patients; mean age, 55.8 ± 16.1 years; 36 males), including 6,168 slices with metal artifacts. On KNE, two radiologists independently assessed five image-quality aspects using Likert scales, with ratings reviewed by a senior radiologist.
RESULTS: In the paired SYN experiment, DuDoTransGAN achieved a mean PSNR of 38.19 dB, the highest mean SSIM (0.975), and the lowest mean RMSE (5.65) among unsupervised methods. Performance remained stable with increasing metal-mask size, demonstrating robustness under larger-metal-mask conditions. In the unpaired KNE experiment, inter-rater agreement was excellent (ICC, 0.941). DuDoTransGAN achieved the highest mean scores among unsupervised methods for artifact removal (3.87 ± 0.48), diagnostic value (3.75 ± 0.50), visual effect (3.90 ± 0.36), and detail retention (3.96 ± 0.40), together with a comparable sharpness score (3.87 ± 0.58), and the highest radiologist selection rate (45 % vs 2 %-21 %).
CONCLUSION: DuDoTransGAN is an adaptable unsupervised dual-domain MAR framework that performs effectively on paired synthetic and unpaired clinical data, producing high-quality, clinically interpretable CT images in the presence of metallic implants.
METHODS: In this retrospective study, we developed DuDoTransGAN, an unsupervised dual-domain generative adversarial network for paired and unpaired training. It was evaluated on a publicly available paired synthetic dataset (SYN; 90,000 training and 2,000 test slices) and a clinical unpaired knee CT dataset (KNE; 19,032 slices from 66 patients; mean age, 55.8 ± 16.1 years; 36 males), including 6,168 slices with metal artifacts. On KNE, two radiologists independently assessed five image-quality aspects using Likert scales, with ratings reviewed by a senior radiologist.
RESULTS: In the paired SYN experiment, DuDoTransGAN achieved a mean PSNR of 38.19 dB, the highest mean SSIM (0.975), and the lowest mean RMSE (5.65) among unsupervised methods. Performance remained stable with increasing metal-mask size, demonstrating robustness under larger-metal-mask conditions. In the unpaired KNE experiment, inter-rater agreement was excellent (ICC, 0.941). DuDoTransGAN achieved the highest mean scores among unsupervised methods for artifact removal (3.87 ± 0.48), diagnostic value (3.75 ± 0.50), visual effect (3.90 ± 0.36), and detail retention (3.96 ± 0.40), together with a comparable sharpness score (3.87 ± 0.58), and the highest radiologist selection rate (45 % vs 2 %-21 %).
CONCLUSION: DuDoTransGAN is an adaptable unsupervised dual-domain MAR framework that performs effectively on paired synthetic and unpaired clinical data, producing high-quality, clinically interpretable CT images in the presence of metallic implants.