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Abstract #2424

Zero-dose FDG PET Brain Imaging

Jiahong Ouyang1, kevin Chen2, and Greg Zaharchuk2
1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Department of Radiology, Stanford University, Stanford, CA, United States

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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