基于光扩散概率模型的PET图像光
Kuang Gong1,2,3, Keith Johnson4, Georges El Fakhri4
1J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, 32611, FL, USA. kgong@bme.ufl.edu.
European journal of nuclear medicine and molecular imaging
|October 3, 2023
概括
本研究介绍了无声扩散概率模型 (DDPM) 以提高正子发射断层扫描 (PET) 图像质量. 基于DDPM的方法优于现有技术,特别是当包含先前的成像信息以获得更清晰的结果时.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- pozitron发射断层扫描 (PET) 图像质量往往受到物理退化和低光子计数的影响.
- 现有的无色化方法难以完全恢复PET图像的真实性.
研究的目的:
- 提出和评估基于无噪声扩散概率模型 (DDPM) 的方法来提高PET图像质量.
- 调查将预先成像信息纳入DDPM框架中的影响.
主要方法:
- 使用[18F]FDG和[18F]MK-6240脑数据集开发并测试了PET图像无色化DDPM框架.
- 探索了包括直接PET图像输入和使用先前图像 (例如MRI) 作为网络输入或约束的策略.
主要成果:
- 基于DDPM的方法显著优于非本地平均值,UNET和生成对抗网络 (GAN) 拒绝技术.
- 整合磁共振 (MR) 预先信息提高了性能,降低了不确定性.
- 最佳的方法是使用MR先前作为网络输入,并将PET数据作为推理过程中的一致性约束.
结论:
- DDPM为PET图像消噪提供了一个灵活和有效的框架.
- 基于DDPM的方法超过了传统和基于GAN的方法,特别是在利用先前的成像数据时.
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