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    Item type:Publication, Access status: without files ,
    Dual-domain artifact disentanglement for metal artifact reduction in CT images
    (SPIE, 2026-04-03)
    Rudloff, Adrian
    ;
    Lugas, Dovydas
    ;
    Latus, Sarah
    ;
    Paulauskaitė-Tarasevičienė, Agnė
    ;
    Šutiene, Kristina
    Medical 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.
    Publicationtype: Conference Paper
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