[2405.02109] Three-Dimensional Amyloid-Beta PET Synthesis from Structural MRI with Conditional Generative Adversarial Networks
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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2405.02109 (eess)
[Submitted on 3 May 2024]

Title:Three-Dimensional Amyloid-Beta PET Synthesis from Structural MRI with Conditional Generative Adversarial Networks

Authors:Fernando Vega, Abdoljalil Addeh, M. Ethan MacDonald
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Abstract:Motivation: Alzheimer's Disease hallmarks include amyloid-beta deposits and brain atrophy, detectable via PET and MRI scans, respectively. PET is expensive, invasive and exposes patients to ionizing radiation. MRI is cheaper, non-invasive, and free from ionizing radiation but limited to measuring brain atrophy.
Goal: To develop an 3D image translation model that synthesizes amyloid-beta PET images from T1-weighted MRI, exploiting the known relationship between amyloid-beta and brain atrophy.
Approach: The model was trained on 616 PET/MRI pairs and validated with 264 pairs.
Results: The model synthesized amyloid-beta PET images from T1-weighted MRI with high-degree of similarity showing high SSIM and PSNR metrics (SSIM>0.95&PSNR=28).
Impact: Our model proves the feasibility of synthesizing amyloid-beta PET images from structural MRI ones, significantly enhancing accessibility for large-cohort studies and early dementia detection, while also reducing cost, invasiveness, and radiation exposure.
Comments: Abstract Submitted and Presented at the 2024 International Society of Magnetic Resonance in Medicine. Singapore, Singapore, May 4-9. Abstract Number 2239
Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2405.02109 [eess.IV]
  (or arXiv:2405.02109v1 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2405.02109

Submission history

From: Fernando Vega Lara [view email]
[v1] Fri, 3 May 2024 14:10:29 UTC (2,096 KB)
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