Neural network-based position estimators for PET detectors using monolithic LSO blocks
Neural network-based position estimators for PET detectors using monolithic LSO blocks
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DOI：
10.1109/tns.2004.835782
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发表时间：
2004-10-01
影响因子：
1.8
通讯作者：
Krieguer, M
中科院分区：
文献类型：
--
作者：
Bruyndonckx, P;Léonard, S;Krieguer, M
利用雪崩光电二极管（APD）阵列等像素化光电探测器测量的光分布，可以得到511kev光子在连续闪烁体上的撞击位置。这些信息是使用针对特定入射角事件训练的神经网络提取的。我们在S8550 Hamamatsu APD矩阵上安装了一块20 x 10 x 10 mm的氧化硅酸镥块，获得了垂直入射光子时1.9 mm全宽半最大（FWHM）和40度入射角时2.6 mm全宽半最大（FWHM）的固有分辨率。提出了一种可能的层析成像实现方法。
The impinging position of a 511 keV photon onto a continuous scintillator can be obtained from the light distribution measured by a pixelated photodetector such as avalanche photodiode (APD) arrays. This information is extracted using neural networks trained for events with a particular incidence angle. Using a 20 x 10 x 10 mm block of lutetium oxyorthosilicate mounted onto a S8550 Hamamatsu APD matrix we achieved an intrinsic resolution of 1.9 mm full-width at half-maximum (FWHM) for perpendicular incident photons and 2.6 nun FWHM at a 40degrees incidence angle. A possible implementation for tomographic imaging is presented.