## 4 Discussion
Therefore, to improve the image quality of sparse-sampling CT, an ideal deep learning model should reduce streak artifact associated with anatomical structures. RED-CNN used an image patch (size \(55\times 55\) pixels) for its training, therefore the algorithm had difficulty discerning between a streak artifact and anatomical structures. As a result, reducing the streak artifact related to anatomical structure may be limited in RED-CNN. In contrast, since deeper U-net used the entire CT image (size \(512\times 512\) pixels) for training, deeper U-net could be optimized to reduce streak artifact related to anatomical structures. This difference between the two deep learning models may lead to performance differences shown in the current study. Since score 5 was defined as image quality and visualization equivalent to original CT (Tables 1-3), the denoised CT images of deeper U-net was not the same image quality as original CT images from the viewpoint of score. However, the visual scores and detectability of deeper U-net were sufficiently high. Although patients with peritoneal dissemination are diagnosed as advanced stage, complete debulking surgery can be expected to improve the prognosis in epithelial malignant ovarian tumor [30]. In addition, there are some histological types with a favorable prognosis due to successful chemotherapy, such as yolk sac tumor [31]. Thus, the reduction of CT-radiation exposure is essential for patients with ovarian cancer. With our proposed method, theoretically, the CT radiation exposure can be reduced to one-tenth of that of the original CT. The reduction of radiation exposure may reduce the incidence of radiation-induced cancer. Furthermore, while we evaluated about only the detection of metastasis of malignant ovarian tumors in the current study, we speculate that the proposed method may be applied to other diseases. While our results show that deeper U-net proved useful in detecting cancer metastasis, there were several drawbacks in the model. First, fine anatomical structures were obscured due to excessive denoising. This effect might be minimized by blending images of FBP and deep learning-based reconstruction, such as hybrid IR and MBIR, by adjusting radiation exposure (rate of sparse sampling) and blending rate. Secondly, the strong streak artifacts around the prosthesis and the upper abdomen compromised diagnostic ability near these anatomical lesions. Furthermore, streak artifacts tended to be stronger on images at the upper abdomen level, especially where the lung and abdominal organs were visualized on the same image. This effect may have resulted from the relatively small number of training data images that included both lung and abdominal organs compared to images that included only abdominal organs. Since ovarian cancer primarily metastasizes to the peritoneal liver surface and the liver, improving the image quality in these areas is considered a future research area. Increasing the number of training data images with cross-sections displaying both the lung and abdominal organs may help improve image quality and reduce streak artifacts in deep learning models, including deeper U-net. Our study had several limitations. First, we used images from only one public database. The application of our deep learning model should be further evaluated in other databases. Second, sparse-sampling images cannot be obtained from real CT scanners at the current time. Our simulated subsampled images may differ from the images on real scanners. In future, we need to evaluate the performance of our deeper U-net using real CT acquisitions. Third, images obtained with the deep learning model of GAN tended to be more \"natural\" than those obtained with conventional deep learning model. However, the noise reduction of GAN is weaker than that of a conventional deep learning model [17]. There was a concern that the radiologist's ability to detect metastasis might decline if the noise reduction was insufficient. Therefore, GAN was not used in the current study.