## 3 Data and Preprocessing  
### Dataset  
This study was approved by the Ethics Committee of all four participating hospitals while informed consent was obtained from every patient and volunteer. In total, 8192 MR image slices were obtained from 489 patients and divided into nine groups according to the different acquisition parameter used. These image slices were used to train the proposed model. The details of the patient images are shown in Table 1. Image Groups 1-5 were from Department of Neurosurgery, Huashan Hospital, Fudan University, the first three groups of which were acquired with a Siemens Magnetom Verio 3.0T MRI scanner and the other two groups were acquired with a GE Discovery MR750 3.0T MRI scanner. Image Groups 6 and 7 were from Department of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center using a Siemens Magnetom Trio Tim 3.0T MRI scanner. Image Group 8 was obtained from Department of Neurosurgery, Huadong Hospital, Fudan University with a Siemens Magnetom Verio 3.0T MRI scanner. Image Group 9 was obtained from Department of Neurosurgery, Shandong Provincial Hospital using a Siemens Magnetom Skyra 3.0T MRI scanner. Meanwhile, two groups of T2-FLAIR MR images were acquired successively with the Siemens Magnetom Verio 3.0T MRI scanner (Group 3) and GE Discovery MR750 3.0T MRI scanner (Group 6) from ten volunteers. MR images of another two modalities were acquired at the same time to meet the requirements of the joint histogram registration method (Groups 1 and 2 with the Siemens scanner and Groups 4 and 5 with the GE scanner). Therefore, 60 image sets from the ten volunteers were treated as the paired gold standard to perform all paired comparisons. The details of the volunteer images are shown in Table 2.  
Moreover, to prove that the proposed method as a pre-processing method not only improves the visual perception of the resolution and related indicators, but also is conducive to the performance of various resolution-sensitive post-processing algorithms, the effectiveness of the proposed intensity standardization method for aiding the computer-aided diagnostic algorithms was evaluated. Two sets of brain glioma MR images from two MR scanners were used to build a radiomics model for differentiating high grade glioma (HGG) from lower grade glioma (LGG). The clinical image data and radiomics model are briefly described as follows. The MR images of glioma patients from the Department of Neurosurgery, Huashan Hospital affiliated to Fudan University were used, which were collected from a SIEMENS MAGNETOR(r) Verio 3.0T MRI scanner and a GE DiscoveryTM MR750 3.0T MRI scanner, respectively. Among them, the images obtained from the SIEMENS scanner were treated as a subset of the reference images in the proposed standardization method, and the images obtained from the GE scanner comprised a subset of one target image group. HGG was considered as positive sample, and LGG was considered as negative sample. Therefore, we used the MR images obtained from the SIEMENS scanner (34 HGG and 32 LGG) to train the model and tested the model with the original images from the GE scanner and the standardized GE images (17 HGG and 11 LGG). The detail acquisition protocols are shown in Table 3.