Work

Metal artifact reduction algorithm with conditional latent diffusion model for dental cone‐beam CT

Year: 2025

Type: article

Abstract: Background Metal 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). Methods We 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. Results The 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 −4 to 19.30 × 10 −4 , 49.3–54.3, and 91.2–97.2, respectively. Its clinical dental implementation was also explored and showcased success in reducing metal artifacts while preserving other tissue structures. Conclusions The proposed method has shown its practical utility in dental CBCT for reducing metal artifacts and is believed to contribute to dental diagnosis and treatment.

Source: Journal of Applied Clinical Medical Physics

DOI: 10.1002/acm2.70317

Authors: Da‐in Choi, Sungho Yun, Subong Hyun, Seungryong Cho

Institution: Korea Advanced Institute of Science and Technology

Language: English

Topic: Advanced X-ray and CT Imaging

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $900