Depth of interaction decoding of a continuous crystal detector module
Depth of interaction decoding of a continuous crystal detector module
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DOI：
10.1088/0031-9155/52/8/012
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发表时间：
2007-04-21
影响因子：
3.5
通讯作者：
Miyaoka, R. S.
中科院分区：
文献类型：
--
作者：
Ling, T.;Lewellen, T. K.;Miyaoka, R. S.
我们提出了一种聚类方法，以提取相互作用的深度（DOI）的信息，从我们的连续微型晶体元件（cMiCE）小动物PET探测器的8毫米厚的晶体版本。该聚类方法基于最大似然（ML）方法，可以有效地为不同的DOI区域建立查找表（LUT）。结合我们的基于几何学的定位（SBP）方法，它使用的LUT搜索算法的基础上ML方法和二维平均方差LUT的光响应从每个光电倍增管通道相对于不同的伽马射线相互作用的位置，相互作用的位置和DOI可以同时估计。使用DETECT2000模拟的数据被用来帮助验证我们的方法。设计了使用我们的cMiCE检测器的实验来评估性能。两个和四个DOI区域聚类应用于模拟数据。两个DOI区域用于实验数据。模拟数据的误分类率约为3.5%的两个DOI区域和10.2%的四个DOI区域。对于实验数据，估计该速率类似于25%。通过使用多DOI LUT，我们还观察到探测器空间分辨率的提高，特别是对于晶体的角区域。这些结果表明，我们的ML聚类方法是一种一致和可靠的方式来表征DOI在连续晶体检测器，而不需要任何修改的晶体或检测器前端电子。根据测量数据表征深度相关光响应函数的能力是开发具有DOI定位能力的实用探测器的重要一步。
We present a clustering method to extract the depth of interaction (DOI) information from an 8 mm thick crystal version of our continuous miniature crystal element (cMiCE) small animal PET detector. This clustering method, based on the maximum-likelihood (ML) method, can effectively build lookup tables (LUT) for different DOI regions. Combined with our statistics-based positioning (SBP) method, which uses a LUT searching algorithm based on the ML method and two-dimensional mean-variance LUTs of light responses from each photomultiplier channel with respect to different gamma ray interaction positions, the position of interaction and DOI can be estimated simultaneously. Data simulated using DETECT2000 were used to help validate our approach. An experiment using our cMiCE detector was designed to evaluate the performance. Two and four DOI region clustering were applied to the simulated data. Two DOI regions were used for the experimental data. The misclassification rate for simulated data is about 3.5% for two DOI regions and 10.2% for four DOI regions. For the experimental data, the rate is estimated to be similar to 25%. By using multi-DOI LUTs, we also observed improvement of the detector spatial resolution, especially for the corner region of the crystal. These results show that our ML clustering method is a consistent and reliable way to characterize DOI in a continuous crystal detector without requiring any modifications to the crystal or detector front end electronics. The ability to characterize the depth-dependent light response function from measured data is a major step forward in developing practical detectors with DOI positioning capability.