## 2 Materials and Methods
### Medical Imaging  
While MRI images are anatomical imaging with high spatial resolutions but a limited physiological information, PET images provide metabolic details on the target but with poor spatial resolution leading to low-valued anatomical information. As a consequence, it would be advantageous to integrate useful data from those two images into a single one with complementary anatomical and functional information yielding more accurate disease information that will significantly aid in the early detection of tumors and enhance the efficiency of diagnosis. Compared with PET/CT, hybrid PET/MRI is capable of providing superior anatomical detail while reducing the cost of significant radiation exposure. The adoption of hybrid PET/MRI, however, is still limited. Consequently, while PET/CT is an image acquired with a single device in almost all hospital centers, PET/MRI performed in a single scanning session is not yet a widely used technology. For this reason, PET/MRI image co-registration, which is a process of overlaying images from different modalities taken at different time points of the same organ, plays an increasingly important role in the part of medical imaging analysis. Therefore, seventeen glioblastoma patients undergoing [11C]-MET PET (Biograph Horizon, Siemens Healthcare, Erlangen, Germany) and MRI (T1-weighted and FLAIR sequences, Achieva, Philips Healthcare, Best, The Netherlands) examinations were considered. An interval of no more than 15 days between PET and MRI examinations was considered. Specifically, the mean interval was of 6.6 days (range 2-15 days). PET images were reconstructed using the Ordered Subset Expectation Maximization (OSEM) with 4 iterations with a 512 \(\times\) 512 image matrix and a voxel size of 0.4821 mm \(\times\) 0.4821 mm \(\times\) 3 mm. T1-weighted sequences had a matrix resolution of 288 \(\times\) 288 with a voxel size of 0.8888 mm \(\times\) 0.8888 mm \(\times\) 2 mm, while FLAIR sequences had a matrix resolution 240 \(\times\) 240 image matrix with a voxel size of 1 mm \(\times\) 1 mm \(\times\) 2 mm. PET/MRI co-registrations were obtained using the automatic registration MIM software (MIM v.7.0.5 software; Cleveland, OH, USA). Rigid Assisted Alignment is MIM's default  
Figure 1: An overview of the study workflow. method for aligning images by maximization of mutual information. An optimization routine adjusts the translation and rotation between the two series in order to maximize a mutual information metric. The mutual information metric is based on the intensities of the overlapping voxels between the two images that are being aligned and is partially based on joint entropy calculations between the volumes. The theory is that the ratio of intensity levels should vary little in regions of similar structures contained in the series. Therefore, the variability of this ratio should be minimized, and mutual information maximized, when the images are aligned correctly. The advantage of this technique is that it is general in nature and can be used to align series of the same modality or different modalities. In our study, this function produces a link between PET and MRI series that allows you to localize and scroll on both series simultaneously, transfer contours, and more. The following steps were performed for all patient studies such that there were no differences in the co-registration algorithm while avoiding other sources of bias:  
1. In an open session, click the \"Create Fusion\" tool;
2. Select the main series;
3. Select the secondary series;
4. The co-registered image is created and appears on the current page. The primary series is the series that remains unaltered when co-registration is performed (MRI in our study). The secondary series (PET) is rotated and translated to be alignment with the main series which aligns images by maximization of mutual information. An optimization routine adjusts translation and rotation between the two series in order to maximize a mutual information metric.