## 3 Results  
The results of the input image, teacher image, and image reconstructed using the RDN are shown in Figure 2. The input images were the NEX2, 4, and 8 patterns, the teacher image was NEX12, and the reconstructed images were NEX2 to 12, 4 to 12, and 8 to 12. The SNR of the cortex was \(10.4\pm 0.82\) for NEX2 and \(32.1\pm 6.1\) for NEX2 to 12, showing a significant increase (\(p<0.0001\)) with reconstruction (Figure 3A). The SNRs were also 15.0 \(\pm\) 1.6 for NEX4, 45.0 \(\pm\) 14.9 for NEX4 to 12 (\(p<0.0001\)), 19.7 \(\pm\) 4.7 for NEX8, and 46.7 \(\pm\) 20.0 for NEX8 to 12 (\(p=0.0005\)) (Figure 3A). The SNR for NEX12, the teacher image, was 19.6 \(\pm\) 7.9 (Figure 3A). Similarly, SNRs for white matter were 9.1 \(\pm\) 2.8 for NEX2, 24.9 \(\pm\) 11.8 for NEX2 to 12 (\(p=0.003\)), 10.7 \(\pm\) 3.2 for NEX4, 23.9 \(\pm\) 13.9 for NEX4 to 12 (\(p=0.008\)), 12.3 \(\pm\) 3.8 for NEX8, and 16.8 \(\pm\) 7.6 for NEX8 to 12 (\(p=0.009\)). Therefore, the SNR was significantly increased by reconstruction at each NEX (Figure 3B). The SNR for NEX12 was 15.5 \(\pm\) 5.6 (Figure 3B). The hippocampal SNR was 11.4 \(\pm\) 2.5 for NEX2, 41.5 \(\pm\) 19.2 for NEX2 to 12 (\(p=0.0005\)), 13.1 \(\pm\) 2.5 for NEX4, 59.1 \(\pm\) 51.2 for NEX4 to 12 (\(p=0.02\)), 9.8 \(\pm\) 5.9 for NEX8, and 53.7 \(\pm\) 25.9 for NEX8 to 12 (\(p=0.0007\)), with a significant increase in the SNR at each NEX (Figure 3C). The SNR of NEX12 was 22.7 \(\pm\) 7.1 (Figure 3C). Figure 1: This is a diagram of the network structure of the residual dense network. The network is a combination of a convolution layer and residual dense block (RDB). Input images (NEX2, 4, 8) were given to the model that trained the teacher images (NEX12), and reconstructed images for each NEX were obtained. [PERSON], [PERSON], et al. IEEE transactions on pattern analysis and machine intelligence 43.7 (2020): 2480–2495. The SNR for the thalamus was 9.0 \(\pm\) 0.64 for NEX2, 18.0 \(\pm\) 2.4 for NEX2 to 12 (\(p\) < 0.0001), 11.8 \(\pm\) 0.96 for NEX4, 22.5 \(\pm\) 7.7 for NEX4 to 12 (\(p\) = 0.001), 15.3 \(\pm\) 1.3  
Figure 3: SNRs for the cortex (**A**), white matter (**B**), hippocampus (**C**), and thalamus (**D**). The vertical axis is the SNR, and N on the horizontal axis represents the NEX. (N2 is NEX2, and N2 to12 represents NEX2 reconstructed to NEX12). The SNR was significantly increased at each measurement site (*: \(p\) < 0.05, **: \(p\) < 0.01, ***: \(p\) < 0.001). SNR, signal-to-noise ratio; NEX, number of excitations. Figure 2: Input images with low NEX ((**A**): NEX2, (**B**): NEX4, (**C**): NEX8), teacher image ((**D**): NEX12), and reconstructed images (**E–G**), where (**E**) is the image obtained by reconstructing NEX2, (**F**) is NEX4, and (**G**) is NEX8 with RDN. NEX, number of excitations. for NEX8, and 24.6 \(\pm\) 4.9 for NEX8 to 12 (\(p\)! 0.0001), with a significant increase in the SNR at each NEX (Figure 3D). The SNR for NEX12 was 17.0 \(\pm\) 1.5 (Figure 3D). Reconstruction with the RDNs resulted in a significant improvement in the SNR at all measured sites. The PSNR in NEX2 was significantly increased to 35.4 \(\pm\) 2.0 compared to the input image and 37.6 \(\pm\) 2.9 compared to the reconstructed image (\(p\) = 0.05) (Figure 4A). On the other hand, for NEX4, it was 37.9 \(\pm\) 1.9 compared to the input image and 38.7 \(\pm\) 2.9 compared to the reconstructed image (\(p\) = 0.30). For NEX8, it was 41.2 \(\pm\) 2.6 compared to the input image and 41.7 \(\pm\) 1.2 compared to the reconstructed image (\(p\) = 0.48), and no significant difference was observed (Figure 4A). The SSIM in NEX2 was 0.78 \(\pm\) 0.05 compared to the input image and 0.91 \(\pm\) 0.05 compared to the reconstructed image (\(p\) = 0.0003). In NEX4, it was 0.9 \(\pm\) 0.04 compared to the input image and 0.9 \(\pm\) 0.03 compared to the reconstructed image (\(p\) = 0.005), indicating that NEX2 and 4 showed significant increases in the SSIM (Figure 4B).