Dento maxillo facial radiology · 2026
Evaluation of a convolutional neural network-based artifact reduction algorithm for zirconia single crown artifacts in cone-beam CT.
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Ma Xieer, Zhao Shouwei, Hu Shuai, Tao Jianxiang
Аннотация
OBJECTIVES: Zirconia-induced artifacts frequently degrade the image quality in dental cone-beam CT (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 Crown Artifact Reduction Net (CARNet), specifically for mitigating artifacts caused by zirconia single crowns.
METHODS: The CARNet model was architectured on a UNet backbone, enhanced with a dedicated Residual Atrous Spatial Pyramid Pooling (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 Artifact Disentanglement Network (ADN) and Cycle-Consistent Adversarial Network (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.
METHODS: The CARNet model was architectured on a UNet backbone, enhanced with a dedicated Residual Atrous Spatial Pyramid Pooling (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 Artifact Disentanglement Network (ADN) and Cycle-Consistent Adversarial Network (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.