## 4 Experiments and Results  
We firstly evaluated the resolution preservation ability enhancement brought by the proposed method over the original unified method previously proposed by our group. As two unified models, they should be able to convert any MR image within a certain range into an image of a reference  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Train** & **Test** \\ \hline Instrument & SIEMENS MAGNETOM & GE Discovery MR750 \\  & Verio 3.0T MRI scanner & 3.0T MRI scanner \\ Patient with HGG & 34 & 17 \\ Patient with LGG & 32 & 11 \\ Slice thickness (mm) & 6 & 4 \\ Pulse repetition time/Echo time/ & 8000/102/2370 & 8525/141.9/2100 \\ Inverse time (ms/ms/ms) & 123.1678 & 127.7706 \\ Imaging frequency (MHz) & 287 & 195.312 \\ Transplant coil name & Body & 8 HRBRAIN \\ Pixel spacing (mm) & 0.4492 & 0.4688 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The details of patient imaging acquisition protocols for the images used for glioma grade classification.  
group. Therefore, we first use a larger number of images with the same imaging parameters regarded as the reference image group as the generator input, comparing the resolution loss of the output by measuring consistency of the input and the output. We then used the paired volunteer dataset to measure resolution parameters of the standardized images in the presence of intensity distribution variation between the target and reference datasets.  
Then, we compared the proposed method in general properties with the original unified method and also the representative methods of the two major types of intensity standardization methods, the histogram matching method proposed by [PERSON] et al. and the joint histogram registration method previously proposed by our group. To meet the dataset requirements of all above methods, the volunteer dataset was applied.  
To conduct a numerical comparison between these methods, the resolution-oriented metrics including the peak signal-to-noise ratio (PSNR), the structural similarity index (SSIM), the visual information fidelity (VIF), the universal quality index (UQI), and the image fidelity criterion (IFC) were utilized. The widely-used general properties containing the gradient magnitude similarity deviation (GMSD) and the mean square error (MSE) were applied. The histogram correlation (HC) and the average disparity (AD) we proposed before were also employed. All numerical results were obtained by comparing the transformed (generated) three-dimensional (3D) image and the reference 3D image and then averaging between different participants of the corresponding experiment. The HC is defined as  
\[HC=1-\sqrt{1-\frac{\sum\sqrt{Cnt_{standard}\cdot Cnt_{ref}}}{\sqrt{\sum Cnt_{ standard}\times\sum Cnt_{ref}}}}, \tag{7}\]  
where the \(Cnt_{standard}\) records the values of histogram bins in the standardized image and \(Cnt_{ref}\) records the values of histogram bins in the reference image. The AD is defined as  
\[Average\;Disparity=100\times mean\left(\frac{\left|I_{standard}-I_{ref}\right| }{I_{ref}}\right), \tag{8}\]  
where \(I_{standard}\) and \(I_{ref}\) are the standardized and reference image, respectively. Better standardization result is denoted with larger PSNR, SSIM, IFC, UQI, VIF, and HC as well as smaller GMSD, MSE, and AD.