ArticleJournal of applied clinical medical physics2025
Metal artifact reduction algorithm with conditional latent diffusion model for dental cone-beam CT.
Article in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current evidence of generative artificial intelligence specifically developed for dental and maxillofacial radiology: a systematic review.Dento maxillo facial radiology · 2026Pooled it
- Metal artifact reduction algorithm with conditional latent diffusion model for dental cone-beam CT.Journal of applied clinical medical physics · 2025Article
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4 authors.
Funding
Abstract
backgroundMetal artifacts in computed tomography pose great challenges to diagnosis and treatment planning. Various metal artifact reduction techniques have been developed tackling the artifacts in the sinogram, projection, and image domains. PURPOSE: We aimed to reduce the metal artifacts via latent diffusion network and improve normalized metal artifact reduction (NMAR) scheme with metal segmentation network and secondary artifact correction network in dental cone-beam computed tomography (CBCT).
methodsWe first produced a metal-artifact-reduced image through a latent diffusion model (LDM) with the metal-artifact-corrupted image as the condition. A combination of mean squared error (MSE) and learned perceptual image patch similarity (LPIPS) loss were used as the objective function to train the network. To resolve the concerns of an image from the generative model such as hallucination, we used the image as a prior for a modified normalized metal artifact reduction (NMAR) process which is a well-known analytic scheme. The modified NMAR in this work includes an automatic metal segmentation network and a secondary artifact correction network to enhance the MAR performance.
resultsThe proposed method showed significant outperformance over the models such as classical NMAR and a state-of-the-art convolutional-neural-network-based MAR (CNNMAR) approach for CBCT. The major improvements to metal artifact reduction are attributed to the improved NMAR prior estimated by the LDM. The proposed method improved the image quality compared to CNNMAR in terms of RMSE, PSNR, and SSIM from 34.78 × 10
conclusionsThe proposed method has shown its practical utility in dental CBCT for reducing metal artifacts and is believed to contribute to dental diagnosis and treatment.
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