## 4 Results  
### Denoising Results for Synthetic Dataset  
We first compare the denoising performance of different methods on the synthetic dataset. The first column of Figure 2 demonstrates the noise-free images (ground-truth, GT), the second column illustrates the noisy images, and the rest of the columns show the denoised DW images obtained with different methods. By comparing the zoomed-in regions marked by the rectangle, we found that our method achieves the most similar result to the GT, with the smallest residual map. In contrast to the traditional methods (LPCA, NONLM, and BM3D) and some learning-based methods (SSECNN, DIP, Neighbor2 Neighbor (NB2 NB), and Patch2 Self (P2S)), our method generates less structural information in the residual map, confirming the superiority of these methods in edge preserving.  
To quantitatively compare the performance of the different methods, Table 1 shows the RMSE, PSNR, and SSIM between the noise-free image and denoised image of each method. We observe that even though LPCA can remove the noise visually and increase the SSIM, its RMSE is even higher than that of the noisy image, illustrating that LPCA can denoise but will change the image intensity distribution. DIP and NB2 NB have relatively small RMSE, but their denoised results are not visually better (Figure 1). However, the proposed method, Node2 Node, can achieve the best performance in terms of RMSE, PSNR, and AAE, which are improved by 47.5%, 19.3%, and 23.7%, respectively, when comparing them against the corresponding suboptimal methods. The SSIM of Node2 Node is the same as that of P2S, achieving the highest value of 0.97.  
From the denoised DW images, we also calculated the diffusion tensor (DT) images and the corresponding diffusion metrics, including FA, MD, and fiber orientations, as well as the residual maps for FA and MD, and the AEM, as shown in Figure 3. We notice that the SSECNN and the proposed method perform best in terms of FA (with the smallest residual), and P2S outperforms the others in terms of MD. As for the AEM, our method obtains the smallest angular error between the denoised fiber orientations and ground-truth orientations, with an average angular error of 2.48 (Table 1), decreased by 23.7% relative to the suboptimal method (P2S), demonstrating its stability for denoising the DW images along all the diffusion gradient directions. Although DIP and NB2 NB can retain the edge information in DW images, their denoising performance for FA and AEM maps is not satisfactory, with the biggest residual value in FA maps and biggest angular errors in fiber orientations, illustrating that they are not suitable for DTI denoising.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **RMSE** & **PSNR** & **SSIM** & **AAE** \\ \hline Noisy & 482.63 & 19.56 & 0.88 & 7.87 \\ LPCA & 499.82 & 19.25 & 0.93 & 3.42 \\ AONLM & 346.70 & 22.43 & 0.94 & 3.84 \\ BM3D & 360.52 & 22.09 & 0.92 & 3.76 \\ DIP & 273.21 & 24.50 & 0.93 & 7.12 \\ NB2 NB & 251.69 & 25.21 & 0.95 & 5.78 \\ P2S & 164.29 & 28.92 & **0.97** & 3.25 \\ SSECNN & 358.37 & 22.14 & 0.95 & 4.50 \\ Node2 Node & **86.29** & **34.51** & **0.97** & **2.48** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average quantitative evaluation metrics obtained with different methods for DW images in the test set of the synthetic dataset. The optimal results are highlighted in bold.  
Figure 2: Denoised DW images for the synthetic data and the corresponding residual maps obtained with different methods. The second and fourth rows show the zoomed-in regions to better visualize the details.