在单晶PET探测器中进行无监督的相互作用深度估计,采用瓦瑟斯坦基于距离的嵌入式
IEEE transactions on medical imaging
|January 21, 2026
概括
一种新的方法,Wasserstein基于距离的局部线性嵌入 (W-LLE),仅使用光分布来估计PET探测器中的相互作用深度 (DOI). 这种数据驱动的方法提高了DOI估计的准确性,这对于先进的探测器设计至关重要.
科学领域:
- 医学物理 医学物理
- 核仪器仪表 核仪器仪表 核仪器仪表
- 机器学习 机器学习
背景情况:
- 单晶正子发射断层扫描 (PET) 探测器使用相互作用深度 (DOI) 编码来提高性能.
- 神经网络 (NN) 对于从光分布 (LD) 中估计DOI是有效的,但需要配对的LD-DOI数据集.
- 获得LD-DOI配对数据很难,只有垂直照射.
研究的目的:
- 引入一种新的数据驱动方法,Wasserstein基于距离的局部线性嵌入 (W-LLE),用于从LD估计DOI.
- 为了实现DOI估计而不需要配对的LD-DOI数据集,解决一个关键的实验挑战.
- 为了评估W-LLE在PET探测器中的行列总和信号读出方面的有效性.
主要方法:
- 开发并应用了瓦瑟斯坦基于距离的局部线性嵌入 (W-LLE) 方法.
- 利用模拟和实验数据进行W-LLE评估.
- 将W-LLE性能与基于模型的方法和原始局部线性嵌入 (LLE) 进行比较.
- 间接使用经过W-LLE和基于模型的方法的DOI估计训练的NN来评估DOI分辨率.
主要成果:
- 在模拟中,W-LLE与基于模型的方法相比,表现优越.
- 在单个读取设置中,W-LLE 实现了与 LLE 相比的 DOI 精度.
- 用W-LLE衍生的DOI估计进行培训的NN优于用基于模型的估计进行培训的NN.
- 在实验配置中实现了高达1.8mm全宽半最大 (FWHM) 的DOI分辨率.
结论:
- W-LLE是一种有效的数据驱动方法,用于估计单晶PET探测器中的光分布的DOI.
- 该方法克服了获得配对LD-DOI数据集的挑战.
- W-LLE显示了提高DOI分辨率和PET检测器整体性能的巨大潜力.
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