| Taofeng Xie |
Inner Mongolia Medical University
Peoples Rep of China |
| Co-Author(s): Chentao Cao, Zhuoxu Cui, Yu Guo, Caiying Wu, Xuemei Wang, Qingneng Li, Zhanli Hu, Tao Sun, Ziru Sang, Yihang Zhuo, Yanjie Zhu, Dong Liang, Qiyu Jin, Hongwu Zeng, Guoqing Chen, Haifeng Wang |
|
| Abstract: |
| PET/MRI provides critical multimodal information for diagnosing diseases like Alzheimer`s. However, PET imaging faces challenges such as high costs, limited availability, and radiation risks from tracers. This report presents a novel framework that combines physical imaging models with data-driven diffusion models to improve PET/MRI accessibility and safety. We first propose an MRI-guided PET generation method based on Score-based Generative Models and Stochastic Differential Equations. This approach uses structural priors from MRI to synthesize high-quality PET images. Furthermore, we introduce a joint reconstruction method that integrates the Poisson noise characteristics of PET with the Gaussian noise of MRI. Experimental results across different magnetic field strengths demonstrate that our method significantly outperforms traditional GAN-based approaches in terms of signal-to-noise ratio and clinical accuracy. This research provides a robust technical path for low-dose, high-efficiency clinical imaging. |
|