列表模式TOF-PET3D图像重建使用随机初级-双元网络
Kun Tian1, Rui Hu1, Yiming Wan1
1State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, People's Republic of China.
Medical physics
|February 15, 2026
概括
一种新的深度学习方法LM-SPD-Net直接从列表模式数据中重建正电子发射断层扫描 (PET) 图像. 这种方法提高了图像质量,并克服了飞行时间 (TOF) PET重建中的计算挑战.
科学领域:
- 核医学成像技术 核医学成像技术
- 医疗图像重建 医疗图像重建
- 深度学习应用程序
背景情况:
- 定子发射断层扫描 (PET) 为可视化生物活动提供了高灵敏度.
- 传统的PET成像在空间分辨率和信号噪声比 (SNR) 上面临限制.
- 飞行时间 (TOF) 数据集成提高了PET图像质量,但增加了计算需求.
研究的目的:
- 引入一个新的深度学习框架,用于直接列表模式PET图像重建.
- 解决与TOF-PET数据相关的计算和内存挑战.
- 为了提高重建的PET图像的质量.
主要方法:
- LM-SPD-Net,一个列表模式的TOF-PET重建框架,使用一个随机原始双重网络架构.
- 一个用于图像域处理的原始模块 (CNN) 和一个用于数据域特征的双模块 (FCNN).
- 整合基于物理的投影模型和子集分区,以实现高效的3D重建.
主要成果:
- 与LM-OSEM,LM-SPDHG和Fast-PET相比,LM-SPD-Net显示出更高的性能.
- 在峰值信号与噪声比率 (PSNR) 和结构相似性指数 (SSIM) 度量方面实现了5%-20%的改进.
- 在模拟和半真实临床数据中明显提高图像质量.
结论:
- 拟议的LM-SPD-Net方法有效地重建了高保真度的整体主体结构.
- 在瘤和乳头等临床相关区域保持出色的表现.
- 展示了强的表现,特别是在低计数条件下.
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