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Abstract #0870

Synthesis of amyloid PET images based on structural MRI data using specialized VQGAN

Zongpai Zhang1, Jingpu Wu1, Puyang Wang1, Keyi Chai1, Shanshan Jiang1, Chiadi Onyike2, and Jinyuan Zhou1
1Department of Radiology, Johns Hopkins School of Medicine, Baltimore, MD, United States, 2Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, United States

Synopsis

Keywords: Alzheimer's Disease, Alzheimer's Disease, PET/MR

Motivation: Alzheimer's disease (AD) is marked by amyloid-beta plaques, typically detected through amyloid-PET imaging, which is expensive and limited in availability. Developing a more accessible diagnostic tool is essential for early detection and monitoring.

Goal(s): This project aims to create a deep learning model that synthesizes amyloid-PET images from widely available MRI scans, offering a cost-effective, non-invasive alternative.

Approach: A specialized VQGAN is trained to map MRI features to amyloid-PET images, utilizing datasets from patients across various stages of AD.

Results: The model demonstrates accurate amyloid-PET synthesis, showing potential for early diagnosis and broad clinical application.

Impact: This MRI-based deep learning method provides a cost-effective, non-invasive alternative to amyloid-PET imaging, potentially expanding diagnostic tools in clinical settings, especially where PET imaging is unavailable.

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