基于决策树的 prism-PET 脱多元化
Yixin Li1,2, Xinjie Zeng1,2, Amir H Goldan2
1Department of Electrical and Computer Engineering, College of Engineering and Applied Sciences, Stony Brook University, Stony Brook, NY 11794, US.
概括
本研究引入了一个决策树模型,以准确地解复合正电子发射断层扫描 (PET) 探测器中的信号. 这种方法在多重检测器模块中保持了高的空间和相互作用深度 (DOI) 分辨率.
科学领域:
- 医疗成像医学成像
- 核物理 核物理 核物理
- 仪器化 仪器化 仪器化
背景情况:
- 在正子发射断层扫描 (PET) 中,信号复杂化对于减少读出通道,最大限度地降低成本和降低功耗至关重要.
- 像加权平均能量这样的传统方法对于局部化多重事件是不够的,需要先进的脱多重化技术.
研究的目的:
- 提出和评估一个非参数决策树模型,用于在体PET (Prism-PET) 探测器模块中准确的信号解复.
- 评估拟议的脱多元化方法对空间和互动深度 (DOI) 分辨率性能的影响.
主要方法:
- 开发了一个决策树模型,从Prism-PET探测器模块中去复数信号,其中包括一个16x16的lutium yttrium oxyorthosilicate (LYSO) 水晶阵列和8x8的光倍增器 (SiPM) 像素,具有64:16的复数读数.
- 训练了64个个别的回归树,用于解复杂化每个SiPM像素的编码读数.
- 采用重心 (CoG) 和重心截断 (TCoG) 方法用于使用解复数据进行晶体识别.
主要成果:
- 决策树模型展示了准确的信号解复合能力.
- 包括洪水直方图,能量分辨率和DOI分辨率在内的性能指标被评估,比较多重复和非多重复读数.
- 提出的方法成功地保持了Prism-PET探测器模块的高空间和DOI分辨率.
结论:
- 非参数决策树模型为多重复合PET探测器模块中的信号解复提供了有效的解决方案.
- 这种方法可以实现信号复杂化的好处,而不会影响探测器的关键性能特征,如空间和DOI分辨率.
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