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

TL;DR

This paper introduces a new method using a latent diffusion model to reduce metal artifacts in dental CT scans, improving image quality and diagnostic accuracy.

Contribution

A novel metal artifact reduction approach combining latent diffusion models with an improved NMAR scheme for dental CBCT.

Findings

The proposed method outperformed classical NMAR and CNNMAR in terms of RMSE, PSNR, and SSIM.

The method successfully reduced metal artifacts while preserving tissue structures in clinical dental CBCT.

The improved NMAR prior estimated by the LDM significantly enhanced MAR performance.

Abstract

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.

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).

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.

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.

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.

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