Rudloff, AdrianAdrianRudloffLugas, DovydasDovydasLugasLatus, SarahSarahLatusPaulauskaitė-Tarasevičienė, AgnėAgnėPaulauskaitė-TarasevičienėŠutiene, KristinaKristinaŠutieneSchlaefer, AlexanderAlexanderSchlaefer2026-05-222026-05-222026-04-03SPIE Medical Imaging 2026https://hdl.handle.net/11420/63193Medical diagnosis from Computed Tomography (CT) often suffers from artifacts caused by high-density materials like metal implants. Metal Artifact Reduction (MAR) remains a challenge which has been addressed using deep learning. Supervised learning methods for MAR traditionally require paired scans with and without metal - a requirement rarely met in practice. Unsupervised approaches such as the Artifact Disentanglement Network (ADN) learn from unpaired data, while Variational Autoencoders (VAE) reconstruct artifact-free images via latent representations. Dual-domain methods have been proposed, combining image and sinogram data to exploit the localization of metal artifacts in Radon domain for improved reduction. This study evaluates (1) using VAE as a baseline, (2) performing disentanglement of ADN solely in Radon domain, and (3) integrating Cartesian and Radon features into ADN. Experiments on synthetic and clinical data show ADN outperforms VAE, and our dual-domain approach RadonADN further improves results, achieving an SSIM of 0.92 versus 0.91 without Radon domain information, demonstrating the benefit of incorporating the sinogram representation.enMetal Artifact ReductionComputer TomographyUnsupervised LearningDual-DomainTechnology::620: EngineeringConference Paper10.1117/12.3085638