PET is a widely used imaging technique but it requires exposing subjects to radiation and is not offered in the majority of medical centers in the world. Here, we proposed to synthesize FDG-PET images from multi-contrast MR images by a U-Net based network with symmetry-aware spatial-wise attention, channel-wise attention, split-input modules, and random dropout training strategy. The experiments on a brain tumor dataset of 70 patients demonstrated that the proposed method was able to generate high-quality PET from MR images without the need for radiotracer injection. We also demonstrate methods to handle potential missing or corrupted sequences.
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