Mitigating metal artifacts from cobalt-chromium alloy crowns in cone-beam CT images through deep learning techniques
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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