Arm truncation artifact in PET/MR system was introduced by 1) smaller MR FOV to cover subject’s arm and 2) distorted gradient encoding at the boundary of FOV. PET imaging normally has larger FOV and non-distorted property. In this study, a cycle-GAN model trained with paired inputs of A (PET + pseudo-truncated-MR images) and B (pseudo-none-truncated-MR images) was proposed to synthesize none-truncated-MR images from PET and real-truncated-MR images then used for the generation of attenuation correction map. PET/MR whole body images from 10 patients were used for this feasibility evaluation. The cycle-GAN model was able to synthesize none-truncated-MR images for patients either with partial-arm covered or with distortion artifact in arm region.