PURPOSE: Quantitative analysis of PET images in brain PET/CT relies on MRI-derived regions of interest (ROIs). However, the pairs of PET/CT and MR images are not always available, and their alignment is challenging if their acquisition times differ considerably. To address these problems, this study proposes a deep learning framework for translating CT of PET/CT to synthetic MR images (MR METHODS: In this retrospective study, 139 subjects who underwent brain [ RESULTS: Compared to MR CONCLUSION: We demonstrated a deep learning framework for automated regional brain analysis in PET/CT with MR