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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords