论文标题

使用人工智能的清除原型检测模块和事件重建的详细模拟

Detailed simulation for the ClearMind prototype detection module and event reconstruction using artificial intelligence

论文作者

Sung, Chi-Hsun, Cappellugola, Laurie, Follin, Megane, Curtoni, Sébastien, Dupont, Mathieu, Morel, Christian, Galindo-Tellez, Aline, Chyzh, Roman, Breton, Dominique, Maalmi, Jihane, Yvon, Dominique, Sharyy, Viatcheslav

论文摘要

ClearMind项目旨在开发针对时间分辨率,空间分辨率和检测效率优化的TOF-PET位置敏感检测模块。为此,ClearMind项目使用一个大型(59 $ \ times $ 59毫米$^2 $)单片PBWO $ _4 $(PWO)闪烁的晶体,带有Bialkali光电层直接存放在晶体上。闪烁和Cherenkov光子从511 keV伽马射线相互作用到PWO晶体中产生。封装PWO晶体放大的微通道板光电倍增管(MCP-PMT)可在光电阴极下生成的光电子子弹,并通过在两端读取的传输线收集相应的阳极信号,并通过sampic模块数字化。在这项工作中,我们介绍了清晰原型检测器的现实geant4模拟,包括晶体中可见光子的传播,光电阴道和PMT的逼真响应的建模以及在传输线上的电信号的传播。检测器量中伽马转换的重建是根据在传输线两端注册的信号进行的。我们将统计算法的重建精度与使用TMVA软件包开发的机器学习算法进行了比较。我们希望达到几毫米$^3 $(FWHM)的空间分辨率。最后,我们将讨论Clearmind探测器的前景。

The ClearMind project aims to develop the TOF-PET position sensitive detection module optimized for the time resolution, spatial resolution, and detection efficiency. For this, the ClearMind project uses a large (59 $\times$ 59 mm$^2$) monolithic PbWO$_4$ (PWO) scintillating crystal with a bialkali photo-electric layer deposited directly on the crystal. Scintillation and Cherenkov photons result together from the 511 keV gamma-ray interation into the PWO crystal. A micro-channel plate photomultiplier tube (MCP-PMT) encapsulating the PWO crystal amplifies photoelectrons generated at the photocathode, and the corresponding anode signals are collected through the transmission lines read out at both ends and digitized by a SAMPIC module. In this work, we present a realistic Geant4 simulation of the ClearMind prototype detector, including the propagation of the visible photons in the crystal, the modelling of a realistic response of the photocathode and of the PMT, and the propagation of the electrical signals over the transmission lines. The reconstruction of the gamma conversion in the detector volume is performed from the signals registered at both ends of the transmission lines. We compare the reconstruction precision of a statistical algorithm against machine learning algorithms developed using the TMVA package. We expect to reach a spatial resolution down to a few mm$^3$ (FWHM). Finally, we will discuss prospects for the ClearMind detector.

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