## 4 Discussion  
In the current study, we compared the quantitative and qualitative image quality of sparse-sampling CT denoised with deeper U-net and RED-CNN. RED-CNN was compared with our deeper U-net because of its similar network structure [7]. For quantitative analysis, mean scores of PSNR and SSIM of CT image quality with deeper U-net were better than those with RED-CNN. For all of the visual evaluation items, the scores of CT image quality with deeper U-net were significantly better than those of RED-CNN. In addition, the detectability of ovarian cancer metastasis was more than 95% in deeper U-net. A few studies on deep learning-based reconstruction have shown that it improved image quality and reduced noise and artifacts better than hybrid IR and MBIR [8, 16, 28]. [PERSON] et al. reported that deep learning reconstruction could reduce noise and artifacts more than hybrid IR could and that it may improve the detection of low-contrast lesions when evaluating hypovascular hepatic metastases [16]. While their study did not evaluate low-dose CT, the deep learning model is also considered an effective method with the potential to use lower-dose CT techniques such as sparse sampling with clinically acceptable results [5]. Our results showed that denoising with deeper U-net could be used to detect ovarian cancer metastasis. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Deeper U-net** & **RED-CNN** & \(p\) \\ \hline \multicolumn{5}{c}{Entire CT image quality} \\ (A) & Overall image quality & Reader 1 & median, 3; IQR, 3 to 4 & median, 1; IQR, 1 to 1 & \textless{}0.0001 \\ (B) & \multirow{2}{*}{Noise and artifacts} & Reader 2 & median, 4; IQR, 3 to 4 & median, 1; IQR, 1 to 1 & \textless{}0.0001 \\ (B) & \multirow{2}{*}{Noise and artifacts} & Reader 1 & median, 3; IQR, 3 to 4 & median, 1; IQR, 1 to 1 & \textless{}0.0001 \\  & & Reader 2 & median, 4; IQR, 4 to 4 & median, 1; IQR, 1 to 1 & \textless{}0.0001 \\ Normal local lesions & \multirow{2}{*}{Normal local lesions} & Reader 1 & median, 5; IQR, 4 to 5 & median, 3; IQR, 2 to 3 & \textless{}0.0001 \\  & & Reader 2 & median, 4; IQR, 4 to 4 & median, 2; IQR, 2 to 2 & \textless{}0.0001 \\ Abnormal lesions & \multirow{2}{*}{Bracular 2} & Reader 1 & median, 4; IQR, 3 to 5 & median, 3; IQR, 2 to 3 & \textless{}0.0001 \\  & & Reader 2 & median, 4; IQR, 3 to 4 & median, 2; IQR, 2 to 3 & \textless{}0.0001 \\ \hline \multicolumn{5}{c}{Abbreviation: IQR, interquartile range; RED-CNN, Residual Encoder-Decoder Convolutional Neural Network.} \\ \end{tabular}
\end{table} 
Table 7: Results of visual evaluations by the two readers using different deep learning algorithms. Figure 6: A representative undiagnosable lesion on denoised CT image with deeper U-net. Note: The circle shows non-measurable peritoneal dissemination. With deeper U-net, the score was 1 point for reader 1 and 2 points for reader 2. To the best of our knowledge, this was the first study that evaluated the detectability of cancer metastasis, including peritoneal dissemination, liver metastasis, and lymph node metastasis on deep learning-based reconstructed CT images. The usefulness of sparse-sampling CT with deep learning has been previously reported [6; 7; 9; 10], but image evaluation was limited to quantitative measures in most of these studies. While [PERSON] et al. reported on quantitative and qualitative assessments of the efficacy of deep learning on chest CT images [6], our study evaluated the usefulness of sparse-sampling CT denoised with deep learning techniques from the clinical viewpoint. We have proven that deeper U-net has an excellent ability to improve image quality and detectability of metastasis, and it could prove effective in clinical practice. The performance difference between deeper U-net and RED-CNN was significant when assessing sparse-sampling CT images. A strong streak artifact around bony structures affected the image quality of sparse-sampling CT [29].