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Image Translation for Estimating Two-Dimensional Axial Amyloid-Beta PET From Structural MRI.
Fernando Vega1,2,3,4, Abdoljalil Addeh1,2,3,4, Aravind Ganesh3,4,5
1Department of Biomedical, University of Calgary, Calgary, Alberta, Canada.
Journal of Magnetic Resonance Imaging : JMRI
|November 3, 2023
Summary
This study developed a model to create Amyloid-beta PET images from MRI scans, showing high similarity to real PET images. This offers a cheaper, more accessible alternative for Alzheimer's Disease research.
Area of Science:
- Neuroimaging
- Artificial Intelligence
Background:
- Alzheimer's Disease is characterized by amyloid-beta plaques and brain atrophy.
- Positron emission tomography (PET) and magnetic resonance imaging (MRI) are used to detect these hallmarks.
- MRI is more accessible and less invasive than PET, and its data can potentially infer PET images due to known correlations.
Purpose of the Study:
- To develop an image translation model using a Conditional Generative Adversarial Network (cGAN).
- The model aims to synthesize Amyloid-beta PET images from structural MRI data.
Main Methods:
- A retrospective study involving 882 adults with varying cognitive decline.
- A cGAN model was trained on 552 subjects and tested on 331 subjects using T1-weighted MRI.
- Model performance was evaluated using Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR), alongside blinded observer assessments.
Main Results:
- The synthesized PET images demonstrated a high degree of similarity to actual PET images (mean SSIM = 0.905, PSNR = 2.685).
- Pixel-wise Pearson correlation showed R² > 0.96.
- Blinded observers could not significantly differentiate between real and synthetic images (P = 0.68).
Conclusions:
- It is feasible to synthesize Amyloid-beta PET images from structural MRI.
- The developed model achieves a high degree of similarity, suggesting potential for wider application in Alzheimer's research.
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