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Unsupervised Metal Artifact Reduction in Dental CBCT Using Fine-Tuned Cycle-Consistent Adversarial Networks

Metal artifacts generated by dental implants significantly degrade cone-beam computed tomography (CBCT) volumes, obscuring critical anatomical structures and compromising diagnostic precision. To address this, an unsupervised deep learning framework has been proposed for Metal Artifact Reduction (MA...

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Principais autores: Thamindu Chamika, Sithum N. A. Dhanapala, Sasindu Nimalaweera, Maheshi B. Dissanayake, Ruwan D. Jayasinghe
פורמט: Artigo
שפה:Inglês
יצא לאור: MDPI AG 2026-04-01
סדרה:Digital
נושאים:
cone-beam computed tomography
metal artifact reduction
CycleGAN
unsupervised learning
dental imaging
deep learning
גישה מקוונת:https://www.mdpi.com/2673-6470/6/2/31
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