## 4 Results
### Denoising Results for Real Data
#### 4.2.2 Denoising Results for In Vivo Human Cardiac DTI  
In vivo cardiac DTI is more sensitive to noise than other DTI. To further verify the effectiveness of the proposed method, we compare the performance of different methods on removing the noise of in vivo cardiac DTI at different phases. Figure 5 demonstrates the denoising results for cardiac DTI acquired at early systole. We notice that, different from the ex vivo porcine cardiac DTI, the performance of AONLM, Patch2 Self, and SSECNN is not good for early-systole cardiac DTI, with the higher residual values and the structural information being contained in the residual maps. In addition, the performance of DIP and NB2 NB on in vivo cardiac DTI is much worse, while the performance of BM3D is improved. In terms of FA and MD maps, DIP, NB2 NB, P2S, and SSECNN generate more singular FA and MD values; for example, DIP and NB2 NB result in FA values close to 1, while P2S leads to FA values close to 0. Regarding the fiber orientations in HA and TA maps, we notice that, after denoising, DIP and P2S can not restore the helical structure of cardiac fibers. Although the other methods can restore the helical structures, AONLM still has some noise remaining. The HA maps obtained using the other methods are comparable, but the TA map obtained using the proposed method is better than the others, with a relatively smooth  
Figure 3: Denoised diffusion metrics for synthetic data obtained with different methods. FA_res and MD_res represent the residual maps of FA and MD, respectively, and AEM indicates the angular error map. transition in the transverse angle. The performance of LPCA and BM3D is similar to that of the proposed method, Node2 Node, but its TA values at the interaction between the left ventricle and right ventricle are not smooth like those of Node2 Node. Figure 4: Denoised results for the ex vivo porcine cardiac DTI dataset obtained with different methods. The 2 nd and 7 th rows demonstrate the zoomed-in red rectangle regions for DW images and fiber orientations, respectively. Figure 5: Denoised results for in vivo human cardiac DTI acquired at early systole phase with different methods. The 2 nd and 7 th rows demonstrate the zoomed-in red rectangle regions for DW images and fiber orientations, respectively. DIP and P2S achieve the worst performance. Figure 6 illustrates the denoised results for the in vivo cardiac DTI at the end systole phase. We see that the performance of DIP on end systole data is better than that on early systole data. (In the fifth column of Figure 5, the denoised DW image is almost black), but its denoised DW image is still too blurry to tell the myocardium from the background. Like the early systole results, DIP, NB2 NB, and SSECNN generate large residual values with obvious structure information remaining (the third row). Although the values in the residual map of the DW images obtained using P2S are not so large, P2S cannot restore the helical fiber architecture (the seventh row). As for AONLM, its denoised result is very similar to the original noisy image; the values of the residual map are very small but with the myocardium structure retained. LPCA, BM3D, and the proposed Node2 Node result in lower and random residual values; consequently, their FA, MD, HA, and TA maps are much better than those of the others. In contrast to LPCA and BM3D, the transverse angle derived from the proposed model is smoother than that of LPCA and BM3D. The denoised results for in the vivo cardiac DTI at the end diastole phase are shown in Figure 7. Different from the results for the systole datasets, the performance of DIP, NB2 NB, and SECNN are significantly improved with the residual values of the DW images decreased, but there is still myocardium structure information in their residual maps. Although there are still singular values in their FA and MD maps, DIP can reconstruct the helical myocardium architecture from its denoised DW images. As for P2S, it achieves the worst performance, with extremely low FA values and disarranged fiber orientations.