## 4 Discussion  
In this study, the RDN improved the SNR in all measured areas in preclinical 7T MRI images. Moreover, the PSNR and SSIM also improved after reconstruction compared to the input images. The RDN also succeeded in reducing the shooting time. Algorithms are often used in image quality improvement studies in MRI using deep learning, such as DnCNN; however, more accurate algorithms are currently being investigated. We considered applying RDNs, which are highly accurate in the field of image quality improvement, to 7T MRI. In previous studies, there are many references in which the application of deep learning improved the image quality of MRI images, and some studies have evaluated it with the PSNR and SSIM [10]. The present study shows the same trend as previous studies. Therefore, we believe that RDN is effective in improving the image quality and reducing the imaging time in preclinical 7T MRI. In a study by [PERSON] et al. [11], an end-to-end deep neural network approach (AUTOMAP) was used in a low-field MRI to improve the image quality. In human brain MRI images obtained at 6.5 mT, the SNR (brain) was higher with AUTOMAP than with inverse fast Fourier transform. In this study, we investigated image quality improvement using RDNs in a 7T MRI network. The SNRs of the cortex, white matter, hippocampus, and thalamus were higher than those of the input images. However, the performances of AUTOMAP and RDN could not be compared because the previous study was performed on low-field MRI, and there was a large difference between the SNRs obtained in the two studies. In a study by [PERSON] et al. [3], deep learning-based reconstruction of brain MRI images (3T) of five people with different added noises was conducted, and the PSNR and SSIM were measured as quantitative evaluations. DnCNN and SCNN were used as network comparators, and both the PSNR and SSIM values were higher for deep learning-based reconstruction than for DnCNN and SCNN. In the present study, the use of RDN significantly increased the PSNR at NEX2 and the SSIM at NEX2 and NEX4, which is consistent \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **PSNR\_L\_NEX** & **PSNR\_RDN** & **SSIM\_L\_NEX** & **SSIM\_RDN** \\ \hline NEX2 & 35.41 \(\pm\) 1.95 & 37.64 \(\pm\) 2.87 & 0.78 \(\pm\) 0.05 & 0.91 \(\pm\) 0.05 \\ NEX4 & 37.89 \(\pm\) 1.94 & 38.71 \(\pm\) 2.88 & 0.88 \(\pm\) 0.04 & 0.93 \(\pm\) 0.03 \\ NEX8 & 41.21 \(\pm\) 2.59 & 41.70 \(\pm\) 1.24 & 0.95 \(\pm\) 0.03 & 0.96 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The table summarizes the mean and SD of the PSNR and SSIM, where L_NEX is the value calculated for the low NEX images (NEX2, 4, 8) and the teacher image (NEX12) and RDN is the value calculated for the reconstructed images (NEX2 to12, 4 to12, 8 to12) and the teacher image (NEX12). Figure 5: NEX2 input image (**A**) and its reconstructed image (**B**). The artifacts in the area surrounded by white lines are also reflected in the reconstructed image. NEX, number of excitations. with previous studies. We consider that RDN could be one of the methods used to improve the image quality in 7T MRI. Meanwhile, in this study, the presence of artifacts in the input image was reflected in the reconstructed image. This is thought to be due to the fact that the learning model performed noise reduction while retaining the artifacts. Therefore, in the network used in this study, the quality of the input image was guaranteed to some extent. This study has several limitations. First, the study used only a normal model; because MRI is a device for imaging various pathological conditions, it is necessary to examine how it can be reconstructed in a pathological model in the future. We intend to evaluate this by conducting a similar study on a brain tumor model. Second, no visual evaluation was conducted. As this study only conducted a quantitative image quality evaluation, we do not know whether the image quality is high enough to be used in clinical practice.