Objective To develop and evaluate metal artifact removal systems(MARS)based on deep learning to assess their effectiveness in removing artifacts caused by different thicknesses of metals in cone-beam CT(CBCT)images.Methods A full-mouth standard model(60 mmx75 mmx110 mm)was three-dimensional(3D)printed using photosensitive resin.The model included a removable and replaceable target tooth position where cobalt-chromium alloy crowns with varying thicknesses were inserted to generate matched CBCT images.The artifacts resulting from cobalt-chromium alloys with different thicknesses were evaluated using the structural similarity index measure(SSIM)and peak signal-to-noise ratio(PSNR).CNN-MARS and U-net-MARS were developed using a convolutional neural network and U-net architecture,respectively.The effectiveness of both MARSs were assessed through visualization and by measuring SSIM and PSNR values.The SSIM and PSNR values were statistically analyzed using one-way analysis of variance(α=0.05).Results Significant differences were observed in the range of artifacts produced by different thicknesses of cobalt-chromium alloys(all P＜0.05),with 1 mm resulting in the least artifacts.The SSIM values for specimens with thicknesses of 1.0,1.5,and 2.0 mm were 0.916± 0.019,0.873±0.010,and 0.833±0.010,respectively(F=447.89,P＜0.001).The corresponding PSNR values were 20.834±1.176,17.002±0.427,and 14.673±0.429,respectively(F=796.51,P＜0.001).After applying CNN-MARS and U-net-MARS to artifact removal,the SSIM and PSNR values significantly increased for images with the same thickness of metal(both P＜0.05).When using the CNN-MARS for artifact removal,the SSIM values for 1.0,1.5 and 2.0 mm were 0.938±0.023,0.930± 0.029,and 0.928±0.020(F=2.22,P=0.112),while the PSNR values were 30.938±1.495,30.578±2.154 and 30.553±2.355(F=0.54,P=0.585).When using the U-net-MARS for artifact removal,the SSIM values for 1.0,1.5 and 2.0 mm were 0.930±0.024,0.932±0.017 and 0.930±0.012(F=0.24,P=0.788),and the PSNR values were 30.291±0.934,30.351±1.002 and 30.271±1.143(F=0.07,P=0.929).No significant differences were found in SSIM and PSNR values after artifact removal using CNN-MARS and U-net-MARS for different thicknesses of cobalt-chromium alloys(all P＞0.05).Visualization demonstrated a high degree of similarity between the images before and after artifact removal using both MARS.However,CNN-MARS displayed clearer metal edges and preserved more tissue details when compared with U-net-MARS.Conclusions Both the CNN-MARS and U-net-MARS models developed in this study effectively remove the metal artifacts and enhance the image quality.CNN-MARS exhibited an advantage in restoring tissue structure information around the artifacts compared to U-net-MARS.
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