Three-Dimensional Amyloid-Beta PET Synthesis from Structural MRI with Conditional Generative Adversarial Networks
<|reference_start|>Three-Dimensional Amyloid-Beta PET Synthesis from Structural MRI with Conditional Generative Adversarial Networks: 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.<|reference_end|>
@article{vega2024three-dimensional,
    title={Three-Dimensional Amyloid-Beta PET Synthesis from Structural MRI with
  Conditional Generative Adversarial Networks},
    author={Fernando Vega, Abdoljalil Addeh and M. Ethan MacDonald},
    journal={arXiv preprint arXiv:2405.02109},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.02109},
    primaryClass={eess.IV cs.CV}
}