Keywords: AI Diffusion Models, PET/MR, image synthesis
Motivation: In diagnosing Parkinson's disease (PD), MRI provides clear structural information and is radiation-free, but not capture functional data like PET.
Goal(s): Develop a method to convert MRI images to PET using a conditional diffusion model, aiming to improve PD diagnosis.
Approach: We developed a conditional diffusion-based method that uses MRI images and basic patient information as inputs to generate corresponding PET images.
Results: Our model demonstrated high accuracy and robustness compared to conventional synthetic image methods.
Impact: This development is valuable for the diagnosis of PD in patients, offering a potential tool to enhance diagnostic capabilities.
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