## 5 Discussion and Conclusions  
In this work, we propose a novel self-supervised learning-based denoising model, Node2 Node, for cardiac DTI datasets. To avoid the influence of noise, it first searches the similar DW image pairs on a graph spectral domain based on the GFT coefficients at different bands, and then uses a ResNet-like architecture to denoise with the searched DW image pairs. Moreover, to avoid the problem of over-smoothing introduced by the denoising, a novel edge-aware loss is presented, which is implemented with the erosion and dilation through the pooling operations, allowing it to adaptively adjust the loss weights on both the edge and smooth regions, therefore promoting the detail-preserving ability of the model. To verify the superiority of Node2 Node, we compare it with several state-of-the-art denoising methods, including LPCA, AONLM, BM3D, DIP, Neighbor2 Neighbor, Patch2 Self, and SSECNN, on a synthetic dataset and two real datasets (ex vivo porcine cardiac DTI and in vivo human cardiac DTI acquired at three different phases). The experimental results demonstrate that the proposed Node2 Node can effectively remove the noise in both ex vivo and in vivo cardiac DW images, not influenced by the noise distributions and the number of diffusion directions, allowing it to explore both ex vivo and in vivo myocardial fiber structures with DW images of low SNR. AONLM, LPCA, and BM3D are the current commonly used methods in clinics to denoise medical images, and AONLM and BM3D fully utilize the self-similarity of image blocks to remove noise. Such of methods require the system to search similar image patches or blocks, which is very time-consuming. In addition, the performance of the patch- or block-similarity-based methods is sensitive to the image quality. For instance, in this work, AONLM performs better on the ex vivo porcine cardiac DTI, while it performs worse on the in vivo human cardiac DTI and the synthetic dataset. This because the noise level in the ex vivo porcine dataset is relatively lower than that in the synthetic dataset, so similar patches can be more accurately found. With the increase in the noise level, the similarity between the image patches is influenced, therefore affecting the denoising performance. In addition, comparing the ex vivo porcine dataset and the in vivo human cardiac dataset, in the former, there are 120 slices, while in the latter, only 1 slice was acquired. This limits the searching space for similar patches in the in vivo data, therefore decreasing the performance of AONLM. Regarding BM3D, it performs much better on the in vivo cardiac DTI than the ex vivo cardiac DTI. The major differences between the ex vivo and in vivo cardiac DTI are the number of diffusion directions and the noise levels. The ex vivo cardiac DTI data were acquired with 30 diffusion directions and a higher noise level (SNR = 0.59), while the in vivo cardiac DTI data were acquired with 12 diffusion directions and a lower noise level (SNR varies from 16 to 18). Since BM3D searches similar blocks and aggregates them along both diffusion directions and volumes, when the number of the diffusion directions is greater, the image blocks along different diffusion directions are the most similar. In this case, aggregating similar blocks along different directions in one DW image along a certain direction may cause problems. This is why BM3D performs worse on the ex vivo cardiac DTI. LPCA can fully exploit the multidirectional redundancy of DW images, and it uses the thresholded principal component decompositions of each 4D local block to remove the noise in DW images. When the number of diffusion directions is not enough, the degree of