具有垂直双硅光电倍增管读出的全向辐射探测器——方向灵敏度与机器学习源定位
Omnidirectional Radiation Detector with Perpendicular Dual Silicon Photomultiplier Readout - Directional Sensitivity and Machine Learning Source Positioning
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中文总结 AI 辅助
提出一种基于GAGG晶体和SiPMs的全向辐射探测器，利用康普顿散射实现方向灵敏，并通过XGBoost机器学习模型定位伽马射线源，为快速成像提供初始条件。
中文摘要 AI 辅助
在辐射探测中，入射伽马射线的方向信息对许多应用至关重要，从医学成像到核事故后的放射性污染测绘、退役期间核废料的表征以及核安保。在本工作中，我们提出了一种能够探测4π立体角内伽马射线光子的辐射探测器，利用钆铝镓石榴石（GAGG）闪烁晶体和硅光电倍增管（SiPMs）。GAGG晶体以立方体4×4×4矩阵组装，其光输出由两个相邻侧面的SiPMs读出。每个晶体的尺寸为3×3×3 mm³，而矩阵间距为3.2 mm，与4×4 SiPMs的尺寸和间距匹配，实现一对一耦合。垂直于SiPMs的层由光学反射器分隔，提供高效的光收集，同时降低晶体间泄漏的概率。该辐射探测器的设计通过康普顿散射提供高探测效率和伽马射线源的全视角成像。在Geant4中进行了蒙特卡洛模拟，以评估所提出设计因其几何非均匀性而导致的探测效率。使用XGBoost算法开发的机器学习模型在模拟数据上进行了训练和测试，以评估探测器定位511、662和1275 keV能量点源的能力。我们发现，所提出的设计不影响高能伽马射线光子的收集效率，并对入射伽马射线的方向具有灵敏度。训练后的模型能够根据测量确定Na-22和Cs-137点源的源到探测器距离，从而为更快的图像重建提供初始条件。
英文摘要
Directional information of incident gamma rays in radiation detection is essential to many applications, from medical imaging to mapping radioactive contamination after nuclear accidents, characterization of nuclear waste during decommissioning, and nuclear security. In this work, we present a radiation detector capable of detecting gamma-ray photons in 4$π$, utilizing Gadolinium Aluminum Gallium Garnet (GAGG) scintillating crystals and silicon photomultipliers (SiPMs). The GAGG crystals are assembled in a cubical 4$\times$4$\times$4 matrix, and their light output is read out by SiPMs from two neighboring sides. The size of each crystal is 3$\times$3$\times$3 mm$^3$, while the matrix pitch is 3.2 mm, matching the size and the pitch of the 4$\times$4 SiPMs for one-to-one coupling. The layers perpendicular to the SiPMs are separated by optical reflectors, providing efficient light collection while lowering the probability of inter-crystal leakage. The design of the radiation detector offers high detection efficiency and full-view imaging of gamma-ray sources through Compton scattering. Monte Carlo simulations were performed in Geant4 to evaluate the detection efficiency of the proposed design due to its geometrical non-uniformity. Machine learning models developed with the XGBoost algorithm were trained and tested on the simulated data to assess the capability of the detector to localize point sources of 511, 662, and 1275 keV energies. We find that the proposed design does not influence the collection efficiency of the high-energy gamma-ray photons and offers sensitivity to the direction of the incoming gamma rays. The trained models are capable of determining the source-to-detector distance of the Na-22 and Cs-137 point sources from measurements, thus providing initial conditions for faster image reconstruction.
发表机构
- Institute for Medical Research and Occupational Health（医学研究与职业健康研究所）
- Nucleometrix d.o.o.（Nucleometrix有限公司）
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