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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