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This article introduces a new method, MCR-VQGAN, for synthesizing Tau PET images, aiming to improve scalability and cost-effectiveness in Alzheimer's disease imaging. The focus is on a specific application (Tau PET) within the broader field of medical imaging and AI. The use of 'scalable' and 'cost-effective' suggests a practical focus on improving existing workflows.
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