Generative AI unlocks PET insights: brain amyloid dynamics and quantification.

Area of Science:

• Neuroimaging
• Artificial Intelligence
• Medical Diagnostics

Background:

• Alzheimer's disease (AD) is characterized by amyloid beta (Aβ) accumulation in the brain.
• Positron Emission Tomography (PET) imaging is vital for visualizing and quantifying Aβ load.
• Understanding the spatiotemporal patterns of Aβ is crucial for AD research and treatment evaluation.

Purpose of the Study:

• To explore the potential of Generative Adversarial Networks (GANs) in modeling brain amyloid dynamics.
• To develop a low-dimensional representation space for brain amyloid load and its temporal changes.
• To demonstrate the utility of generative AI in Alzheimer's disease neuroimaging.

Main Methods:

• Utilized a cohort of 1,259 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with AV45 PET images.
• Developed a 3D GAN model to project PET images into a latent representation space and generate synthetic images.
• Constructed a progression model using non-parametric ordinary differential equations on the latent space to study Aβ evolution.

Main Results:

• Global Standardized Uptake Value Ratio (SUVR) was accurately predicted from the latent space (RMSE = 0.08 ± 0.01).
• Generated synthetic PET imaging trajectories to simulate Aβ progression over four years.
• Demonstrated the ability to predict and illustrate Aβ changes compared to actual patient progression.

Conclusions:

• Generative AI, specifically GANs, offers powerful tools for statistical prediction and progression modeling in brain amyloid imaging.
• Synthetic patient data and simulated disease trajectories can be generated for research and clinical trial design.
• This study highlights the significant potential of generative AI to advance Alzheimer's disease understanding, diagnosis, and therapeutic development.