## 4 Experiments and Results
### The Resolution Preservation Ability Enhancement Brought by the Optimizations on Methodology  
To prove the proposed method's improvement over the resolution preservation of the standardized images, the resolution-related metrics of the methods was firstly explored. For the sake of achieving stable test results on a large test set, the first experiment was conducted by obtaining 320 images with the same image acquisition conditions as Group 1, which is the reference group, in the training set. Then, the standardized images were obtained with both the original unified method and the proposed method. In theory, the standardized images should be the same as the original images, thus we used the original images as the gold standard to calculate the resolution-related features of the two sets of standardized images. PSNR, SSIM, VIF, UQI and IFC were applied. Table 4 illustrates such a comparison. In this experiment, the proposed method was superior to the original unified method in most resolution-related metrics while the fidelity-related indicators were significantly improved. These demonstrate the improved effectiveness of the proposed method in maintaining MR image resolution. To better reflect the resolution preservation ability enhancement of the proposed method in the presence of intensity distribution variation between input MR images and output ones, we conducted another experiment by comparing the standardization result of volunteer MR images obtained from GE scanner with the paired MR images obtained from SIEMENS scanner. Table 5 illustrates the comparison of PSNR and SSIM. The proposed method not only successfully eliminated the huge difference between the original target image and the reference image, but also had a significant performance improvement compared with the original unified method, due to the absence of resolution-oriented optimizations. Figure 3 shows the local details of some of the MR images within the experiment above. Since the proposed method uses a network structure that is more focused on improving image resolution, a loss function with normalized mutual information, an advanced weak-pair data augmentation method, and a multi-GPU training strategy, the output images of the proposed method have a higher local accuracy and resolution. This allows a series of image texture features to be extracted more accurately on the standardized image generated by the proposed method. Moreover, the performances of differential diagnosis with the original dataset and the datasets standardized with different methods were evaluated to reflect the advant \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation Criteria** & **PSNR** & **SSIM** & **IFC** & **UQI** & **VIF** \\ \hline The proposed method & 37.31 & 0.9663 & 4.164 & 0.6169 & 0.1588 \\ Original universal method & 37.21 & 0.9690 & 3.212 & 0.6138 & 0.1233 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The resolution-related metrics comparison between the proposed method and the original universal method over the images with the same conditions as the reference group. Figure 3: The local detail of the standardization result of two example MR images with the original and proposed standardization method. The aim is to transform target images into the reference group: (**a1,b1**) the reference MR images, the red boxes are zoomed in (**a2,b2**), respectively; (**a3,b3**) the details of the target MR images; (**a4,b4**) the details of the results produced by the original many-to-one method; and (**a5,b5**) the details of the results produced by the proposed method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation Criteria** & **PSNR** & **SSIM** & **IFC** & **UQI** & **VIF** \\ \hline The proposed method & 65.51 & 0.9992 & 4.318 & 0.7568 & 0.2413 \\ Original universal method & 65.13 & 0.9989 & 4.294 & 0.7538 & 0.2431 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The resolution-related metrics comparison between the proposed method and the original universal method over the volunteer images.