Meeting Banner
Abstract #3229

MRI-Driven Diffusion Model of 18F-FP-CIT PET Image Synthesis for Parkinson's Disease

Chunfa Liu1, Yuxi Jin1, Haizhou Liu1, Jidong Han1, Fan Fu2, Biao Li2, Na Zhang1, Hairong Zheng1, Dong Liang1, and Zhanli Hu1
1Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2Department of Nuclear Medicine, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China

Synopsis

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.

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.

Click here for more information on becoming a member.

Keywords