MRI-Driven Diffusion Model of 18F-FP-CIT PET Image Synthesis for Parkinson's Disease
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.
Authors
- Hairong Zheng (ORCID: https://orcid.org/0000-0002-8558-5102)
- Yuxi Jin (ORCID: https://orcid.org/0000-0002-9334-5205)
- Fan Fu (ORCID: https://orcid.org/0000-0001-5313-8925)
- Zhanli Hu (ORCID: https://orcid.org/0000-0003-0618-6240)
- Haizhou Liu (ORCID: https://orcid.org/0000-0003-2200-0063)
- Jidong Han (ORCID: https://orcid.org/0000-0002-4945-2150)
- Biao Li (ORCID: https://orcid.org/0000-0003-4206-8487)
- Na Zhang (ORCID: https://orcid.org/0000-0001-9510-4520)
- Chunfa Liu (ORCID: https://orcid.org/0000-0003-3343-123X)
- Dong Liang (ORCID: https://orcid.org/0000-0001-6257-0875)
Publication Details
- Journal
- Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
- Published
- 2025-09-16
- DOI
- https://doi.org/10.58530/2025/3229
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.00