翻译如下审稿意见为英文：本文提出了一种混合域金属伪影修正方法首先获得基于线性插值方法的修正后CT图像和迭代重建图像中的金属区域分割再对这两部分图像进行正向投影获得两组虚拟正投图像作为先验信息接着进行线性修正并获得修正后的投影图线性修正的CT图像和线性插值的CT图像在两个U-Net网络中分别进行修正和融合实现金属伪影修正。在不同方法的融合应用方面本文有一定创新但根据文中提出的定量分析结果新方法带来的

Translation:

This article proposes a mixed-domain metal artifact correction method, which first obtains metal region segmentation in the corrected CT image based on linear interpolation and the iteratively reconstructed image. Then, two sets of virtual forward projection images are obtained from the two parts of the image as prior information. Linear correction is then performed to obtain the corrected projection image. The CT images corrected by linear interpolation and linear correction are respectively corrected and fused in two U-Net networks to achieve metal artifact correction. This article has some innovation in the application of different methods for fusion, but according to the quantitative analysis results presented in the article, the improvement in image quality brought by the new method is relatively limited, that is, there is no significant improvement compared to other methods that use deep convolutional neural networks. It is recommended that the authors further explore the innovation and clinical significance. The main questions include:

(1) The quantitative analysis results are relatively limited compared to other deep learning methods. In order to explain the clinical significance of the method, can the evaluation of dental clinical doctors be added?

(2) This method uses multiple iterative reconstructions and deep learning training, which both require a long time. Is there an evaluation of the method's execution efficiency? If the cost of improving image quality is several times the calculation time, then the clinical application value is limited.

(3) The flowchart in Fig. 3 is difficult to understand, and it is recommended to reorganize the structure. In addition, the repeated appearance of LI and Linear correction in the text can easily confuse readers.

(4) In the results presented in Fig. 9 and Fig. 10, the image quality in the tooth area in the second row is similar. Can different tissue areas be selected as ROI for a more comprehensive comparison in quantitative analysis and listing