Evaluation of a Convolutional Neural Network-Based Artifact Reduction Algorithm for Zirconia Single Crown Artifacts in Cone-Beam Computed Tomography

OBJECTIVES: Zirconia-induced artifacts frequently degrade the image quality in dental cone-beam computed tomography (CBCT), and complicate clinical diagnosis. This study aimed to develop and evaluate a novel image-domain artifact reduction algorithm based on convolutional neural networks, named CARNet, specifically for mitigating artifacts caused by zirconia single crowns. METHODS: The CARNet model was architectured on a UNet backbone, enhanced with a dedicated ResASPP module designed for multi-scale feature extraction and fusion via residual and short-cut connections. A dataset was constructed from the public Zenodo clinical repository by simulating zirconia artifacts. Algorithm performance was assessed using both objective metrics-Normalized Mean Absolute Error (NMAE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR)-and subjective evaluation via a Likert scale. CARNet's efficacy was compared against established image-domain algorithms, including ADN and CycleGAN, as well as traditional Linear Interpolation (LI). RESULTS: CARNet processing yielded significantly superior results for zirconia artifacts (axial/occlusal surfaces) than other methods, with NMAE: 0.013 ± 0.001/0.013 ± 0.001; SSIM : 0.959 ± 0.007/0.956 ± 0.008; PSNR: 40.39 ± 0.77/41.41 ± 0.80 dB. Subjective assessments confirmed that CARNet effectively suppressed artifacts while better preserving crown details and marginal integrity. CONCLUSIONS: The CARNet model proves to be an effective and reliable method for reducing zirconia restoration artifacts in CBCT, demonstrating satisfactory performance in restoring images affected by zirconia single crowns.