# PadGAN: An End-to-End dMRI Data Augmentation Method for Macaque Brain
## 3 Results
### Xtract and DTI Estimation Results  
Xtract is a robust probabilistic tractography method integrated into the FSL6.0 software package. It utilizes dMRI data to estimate the trajectories and connectivity patterns of white matter tracts. To assess the effectiveness of the augmented macaque dMRI brain images through our proposed method, we employed Xtract to compute the structural connectivity of dMRI brain images. Eight subjects were selected from the UCDavis dataset, and the images generated by pix2 pix and PadGAN were respectively added to the corresponding dMRI data. Subsequently, we conducted Xtract tractography experiments on the dMRI images augmented by the pix2 pix and PadGAN methods, as well as the original reference dMRI images, resulting in a total of 42 fiber tracts.  
As shown in Figure 7, the fiber bundle visualization results demonstrate that, compared with pix2 pix, our method captures more fiber bundles visually, and the shapes are similar to the reference results. It is worth noting that our results display more and clearer fiber bundles within the white rectangular area.  
DTI is a magnetic resonance imaging technique used to study the diffusion properties of water molecules within tissues. DTI offers various diffusion parameters, with the most commonly used being fractional anisotropy (FA) and mean diffusivity (MD). FA represents the degree of directional diffusion of water molecules within the tissue, while MD represents the average strength of water molecule diffusion. To better evaluate the quality of the generated images, this study conducted DTI estimation on dMRI images augmented by the PadGAN and pix2 pix methods. Figure 8 displays the DTI estimation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **PSNR** & **SSIM** & **MI** \\ \hline
**PadGAN** & **32.3856** & **0.8825** & **1.3857** \\ Setting (1) & 27.2600 & 0.7600 & 1.3121 \\ Setting (2) & 28.1565 & 0.8176 & 1.3412 \\ Setting (3) & 30.5229 & 0.8440 & 1.3486 \\ \hline \hline \end{tabular} The bold font indicates the best result.  
\end{table}
Table 6: Quantitative comparison of ablation experiments.  
Figure 6: Visualization of 2 site datasets. These are 2 randomly selected data samples from the 2 datasets. The first column represents the source image, the last column represents the target image, and the middle column represents the comparative result. The red box highlights some details.  
results using FA and MD as examples. In the low b-value replacement experiment, our method demonstrates higher similarity to the original reference dMRI images compared with the pix2 pix method. In the experiment of augmenting the original reference dMRI, our method shows smoother results. The last column in the figure demonstrates that the absence of low b-value volumes in dMRI images significantly affects the DTI estimation results. Therefore, low b-value images are crucial for DTI computation.  
Figure 8: DTI estimation results. The first row displays FA, and the second row shows MD. The first and second columns respectively show the DTI estimation results after replacing the original low b-value volume with volumes generated using pix2 pix and our method. The third column shows the DTI estimation results after augmenting the original reference dMRI images using our method for data augmentation. The fourth and fifth columns respectively display the DTI estimation results for the reference images and dMRI without the low b-value volume.  
Figure 7: Fiber bundle visualization results. The left and middle columns respectively show the results after data enhancement with the pix2 pix and PadGAN methods, while the right column shows the results of the reference original dMRI image. The part inside the white rectangle is zoomed in for comparison.  
The experiments above indicate that Xtract and DTI estimation results can reflect the quality of different macaque image generation methods. Therefore, Xtract and DTI estimation are expected to become further validation methods for assessing the quality of generated macaque or medical images.