DuDoTransGAN: Dual-Domain transformer based generative adversarial unsupervised network for metal artifact reduction in paired and unpaired CT
In brief
DuDoTransGAN gets radiologists' top pick in 45% of knee CT comparisons
In a retrospective evaluation, this unsupervised method for reducing metal artifacts had the highest radiologist selection rate on clinical knee CT scans: 45%, versus 2% to 21% for other unsupervised methods. It also scored highest on four of five image-quality measures, but the study did not test whether improved images change diagnosis or patient care.
- Journal
- European journal of radiology (Q1)
- Published
- 8 October 2026
- Study design
- Cohort / observational study
- Evidence level
- Level 3, Low (CEBM 3b)
- Authors
- Yan Wang, Jia Deng, Zhaojie Peng, Yule Zeng, Jikang Jiang, Langtao Zhou, et al.
- PMID
- 42863450
- DOI
- 10.1016/j.ejrad.2026.113289
Why clinicians should know about it
- Picked for Medical Physics (paper of the day, 14 October 2026): Dual‑domain GAN for metal artifact reduction in CT
Abstract
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.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.