Ling T, Lewellen T K, Miyaoka R S
Department of Physics, University of Washington, Seattle, WA 98107, USA.
Phys Med Biol. 2007 Apr 21;52(8):2213-28. doi: 10.1088/0031-9155/52/8/012. Epub 2007 Mar 29.
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 look-up 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 approximately 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.
我们提出了一种聚类方法，用于从我们的连续微型晶体元件（cMiCE）小动物PET探测器的8毫米厚晶体版本中提取相互作用深度（DOI）信息。这种基于最大似然（ML）方法的聚类方法可以有效地为不同的DOI区域构建查找表（LUT）。结合我们基于统计的定位（SBP）方法，该方法使用基于ML方法的LUT搜索算法以及每个光电倍增管通道相对于不同伽马射线相互作用位置的光响应的二维均值-方差LUT，可以同时估计相互作用的位置和DOI。使用DETECT2000模拟的数据来帮助验证我们的方法。设计了一个使用我们的cMiCE探测器的实验来评估性能。将两个和四个DOI区域聚类应用于模拟数据。实验数据使用两个DOI区域。对于模拟数据，两个DOI区域的误分类率约为3.5%，四个DOI区域的误分类率为10.2%。对于实验数据，估计该比率约为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落区域。这些结果表明，我们的ML聚类方法是在连续晶体探测器中表征DOI的一种一致且可靠的方法，无需对晶体或探测器前端电子设备进行任何修改。从测量数据中表征深度依赖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一个重大进步。
