Dual-Ended Readout PET Detector Based on Multivoltage Threshold Sampling Combined With Convolutional Neural Network for Energy Calculation

IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Ran Cheng;Mingchen Sun;Fei Wang;Dengyun Mu;Yu Liu;Qingguo Xie;Bensheng Qiu;Xun Chen;Peng Xiao
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引用次数: 0

Abstract

To minimize parallax errors and achieve high spatial resolution positron emission tomography (PET) systems, developing depth-of-Interaction (DOI) encoding detectors has become a significant research topic. In this article, we investigated a dual-ended readout PET detector based on the multivoltage threshold (MVT) sampling method combined with a convolutional neural network (CNN) to calculate the pulse’s energy (MVT-CNN method). The MVT sampling method was used to acquire time-threshold samples and digitize scintillation pulses. The CNN model was employed to establish an accurate mapping between MVT sampling points and energy information. The dual-ended readout detector’s energy, DOI, and timing performance were evaluated with two irradiation configurations. The results demonstrated that the performance of the MVT-CNN method was close to that of the integration method based on oscilloscope sampling. Using the MVT-CNN method, the average energy resolution of the tested crystals over all depths was $14.5 \, \pm \, 1.2$ %, and the average DOI resolution was $2.81 \, \pm \, 0$ .70 mm. In the side irradiation configuration, the average coincidence timing resolution of the tested crystals at 2 mm depth was 435 ps. The performance of the dual-ended readout DOI-PET detector basedon the MVT-CNN method suggested that it could develop small animal and organ-dedicated PET systems with high sensitivity and uniform spatial resolutionxs.
基于多电压阈值采样的双端读出 PET 检测器与用于能量计算的卷积神经网络相结合
为了最大限度地减少视差误差并实现高空间分辨率的正电子发射断层扫描(PET)系统,开发交互深度(DOI)编码探测器已成为一个重要的研究课题。在本文中,我们研究了一种基于多电压阈值(MVT)采样法的双端读出 PET 检测器,该检测器与计算脉冲能量的卷积神经网络(CNN)相结合(MVT-CNN 法)。多电压阈值采样法用于获取时间阈值样本并将闪烁脉冲数字化。CNN 模型用于在 MVT 采样点和能量信息之间建立精确的映射关系。利用两种辐照配置对双端读出探测器的能量、DOI 和定时性能进行了评估。结果表明,MVT-CNN 方法的性能接近于基于示波器采样的集成方法。使用 MVT-CNN 方法,测试晶体在所有深度上的平均能量分辨率为 14.5 美元 (\pm \,1.2 美元 %),平均 DOI 分辨率为 2.81 美元 (\pm \,0 美元 .70 毫米)。在侧辐照配置中,测试晶体在 2 毫米深度的平均重合定时分辨率为 435 ps。基于 MVT-CNN 方法的双端读出 DOI-PET 探测器的性能表明,它可以开发具有高灵敏度和均匀空间分辨率的小动物和器官专用 PET 系统。
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来源期刊
IEEE Transactions on Radiation and Plasma Medical Sciences
IEEE Transactions on Radiation and Plasma Medical Sciences RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
8.00
自引率
18.20%
发文量
109
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