## 5 Discussion and Conclusions
Since the regression methods are heavily dependent on the amount of data, if the number of diffusion gradient directions or the number of slices is not enough, the bias caused by the linear regression will be significant. That is why Patch2 Self performs better on the ex vivo porcine cardiac DTI dataset (30 diffusion directions, 120 slices) but worse on the synthetic (6 directions, 55 slices) and in vivo (12 directions, 1 slice) datasets. As for the proposed Node2 Node model, it achieves almost the best performance on all the datasets, demonstrating its stability and generalization ability. This means it is not sensitive to the noise distribution (it performs well on both the synthetic and real datasets), the number of diffusion directions, or noise levels (no significant difference in performance on the ex vivo and in vivo datasets). In Node2 Node, similar DW image pairs are searched based on the GFT coefficients, which allows it to compare the different image pairs on several sub-bands in the spectral domain. Considering that sub-band coefficients of the random noise are relatively slower than those of the signal, accordingly, their contributions on the similarity calculation can be overlooked. Therefore, using GFT coefficients to find the similar image pairs is more robust and stable. This can also be found in the ablation results (Figure 8); the performance of GraphSim used in Node2 Node is much better than the others. In addition, as indicated by the red arrows in Figure 9, with the help of edge-aware loss, the denoising performance at the edge regions is clearly improved, demonstrating the effectiveness of the proposed edge-aware loss. However, there are still some limitations in this work. For example, the denoising performance for the regions with fiber crossings (Figure 9) is not good. This may be caused by the few diffusion directions. Additionally, for real data, the model was trained with the ex vivo porcine dataset and directly tested on the in vivo human cardiac dataset without using any transfer learning strategies. Even though the performance on the current in vivo dataset is satisfactory, it will be better to test it on other datasets to further verify the generality of the proposed method. **Author Contributions:** Conceptualization, L.W. ; Methodology, H.D. ; Validation, N.Y.; Formal analysis, L.W. ; Investigation, H.D. ; Data curation, N.Y.; Writing--original draft, H.D. ; Writing--review & editing, L.W. ; Visualization, H.D. ; Supervision, L.W. ; Funding acquisition, L.W. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was partially funded by the National Nature Science Foundations of China (Grant Nos.62161004, 61661010,) and the Guizhou Provincial Scientific Research Project (ZK[2021]key 002). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Publicly available datasets were analyzed in this study. These data can be found here: [[http://www.emmanuelcaruyer.com/phantomas.php](http://www.emmanuelcaruyer.com/phantomas.php)]([http://www.emmanuelcaruyer.com/phantomas.php](http://www.emmanuelcaruyer.com/phantomas.php)), [[https://med.stanford.edu/cmrgroup/data/ex_vivo_dt_mri.html](https://med.stanford.edu/cmrgroup/data/ex_vivo_dt_mri.html)]([https://med.stanford.edu/cmrgroup/data/ex_vivo_dt_mri.html](https://med.stanford.edu/cmrgroup/data/ex_vivo_dt_mri.html)), [[https://med.stanford.edu/cmrgroup/data/myofiber_data.html](https://med.stanford.edu/cmrgroup/data/myofiber_data.html)]([https://med.stanford.edu/cmrgroup/data/myofiber_data.html](https://med.stanford.edu/cmrgroup/data/myofiber_data.html)) (accessed on 5 September 2023). **Conflicts of Interest:** The authors declare no conflict of interest.