# Synthesizing beta-amyloid PET images from T1-weighted Structural MRI: A Preliminary Study

arXiv: https://arxiv.org/abs/2409.18282 · HTML: https://mlsanity.com/paper/2409.18282/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Builds on (most cited first, 7 of 7)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis](https://mlsanity.com/paper/2008.03483/) (2020)
- [MRI to PET Cross-Modality Translation using Globally and Locally Aware GAN (GLA-GAN) for Multi-Modal Diagnosis of Alzheimer's Disease](https://mlsanity.com/paper/2108.02160/) (2021)
- [GANBERT: Generative Adversarial Networks with Bidirectional Encoder Representations from Transformers for MRI to PET synthesis](https://mlsanity.com/paper/2008.04393/) (2020)
- [Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model](https://mlsanity.com/paper/2305.03901/) (2023)
- [MRI to FDG-PET: Cross-Modal Synthesis Using 3D U-Net For Multi-Modal Alzheimer's Classification](https://mlsanity.com/paper/1807.10111/) (2018)
- [TauPETGen: Text-Conditional Tau PET Image Synthesis Based on Latent Diffusion Models](https://mlsanity.com/paper/2306.11984/) (2023)

## Abstract (arXiv)

Beta-amyloid positron emission tomography (Aβ-PET) imaging has become a critical tool in Alzheimer's disease (AD) research and diagnosis, providing insights into the pathological accumulation of amyloid plaques, one of the hallmarks of AD. However, the high cost, limited availability, and exposure to radioactivity restrict the widespread use of Aβ-PET imaging, leading to a scarcity of comprehensive datasets. Previous studies have suggested that structural magnetic resonance imaging (MRI), which is more readily available, may serve as a viable alternative for synthesizing Aβ-PET images. In this study, we propose an approach to utilize 3D diffusion models to synthesize Aβ-PET images from T1-weighted MRI scans, aiming to overcome the limitations associated with direct PET imaging. Our method generates high-quality Aβ-PET images for cognitive normal cases, although it is less effective for mild cognitive impairment (MCI) patients due to the variability in Aβ deposition patterns among subjects. Our preliminary results suggest that incorporating additional data, such as a larger sample of MCI cases and multi-modality information including clinical and demographic details, cognitive and functional assessments, and longitudinal data, may be necessary to improve Aβ-PET image synthesis for MCI patients.