刚才翻译不够正式请重新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

The article proposes a hybrid domain metal artifact correction method, which first obtains the segmented metal areas in the corrected CT image based on linear interpolation and the iterative reconstruction image. Then, two sets of virtual forward projection images are obtained as prior information by forward projecting these two parts of the image. Subsequently, linear correction is performed to obtain the corrected projection image, and the linearly corrected CT image and the linearly interpolated CT image are respectively corrected and fused in two U-Net networks to achieve metal artifact correction. While this article has some innovation in the application of different methods for fusion, according to the quantitative analysis results presented in the article, the improvement in image quality brought about by the new method is limited. In other words, compared with other methods that use deep convolutional neural networks, there is no significant improvement. Therefore, the authors are recommended to further explore the innovation points and clinical significance. The main concerns are as follows:

1. The quantitative analysis results are relatively limited compared to other deep learning methods. To demonstrate the clinical significance of the method, can evaluations from dental clinical doctors be included?

2. This method involves multiple iterations of reconstruction and deep learning training, which requires 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, the clinical application value may be limited.

3. The flowchart in Fig. 3 is difficult to understand. It is recommended to reorganize the structure. Additionally, the repeated appearance of LI and Linear correction in the article may also be confusing.

4. In the results presented in Fig. 9 and Fig. 10, the image quality of the second row in the tooth area is similar. Can different tissue area ROIs be selected for a more comprehensive comparison in the quantitative analysis and list