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一种基于卷积神经网络的四层DOI编码探测器,使用LYSO和BGO闪烁体用于小动物PET成像。

A CNN-based four-layer DOI encoding detector using LYSO and BGO scintillators for small animal PET imaging.

作者信息

He Wen, Zhao Yangyang, Zhao Xin, Huang Wenjie, Zhang Lei, Prout David L, Chatziioannou Arion F, Ren Qiushi, Gu Zheng

机构信息

Peking University Shenzhen Graduate School, Shenzhen, People's Republic of China.

Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, People's Republic of China.

出版信息

Phys Med Biol. 2023 Apr 27;68(9). doi: 10.1088/1361-6560/accc07.

Abstract

. We propose a novel four-layer depth-of-interaction (DOI) encoding phoswich detector using lutetium-yttrium oxyothosilicate (LYSO) and bismuth germanate (BGO) scintillator crystal arrays for high sensitivity and high spatial resolution small animal PET imaging.. The detector was comprised of a stack of four alternating LYSO and BGO scintillator crystal arrays coupled to an 8 × 8 multi-pixel photon counter (MPPC) array and read out by a PETsys TOFPET2 application specific integrated circuit. The four layers from the top (gamma ray entrance) to the bottom (facing the MPPC) consisted of a 24 × 24 array of 0.99 × 0.99 × 6 mmLYSO crystals, a 24 × 24 array of 0.99 × 0.99 × 6 mmBGO crystals, a 16 × 16 array of 1.53 × 1.53 × 6 mmLYSO crystals and a 16 × 16 array of 1.53 × 1.53 × 6 mmBGO crystals.. Events that occurred in the LYSO and BGO layers were first separated by measuring the pulse energy (integrated charge) and duration (time over threshold (ToT)) from the scintillation pulses. Convolutional neural networks (CNNs) were then used to distinguish between the top and lower LYSO layers and between the upper and bottom BGO layers. Measurements with the prototype detector showed that our proposed method successfully identified events from all four layers. The CNN models achieved a classification accuracy of 91% for distinguishing the two LYSO layers and 81% for distinguishing the two BGO layers. The measured average energy resolution was 13.1% ± 1.7% for the top LYSO layer, 34.0% ± 6.3% for the upper BGO layer, 12.3% ± 1.3% for the lower LYSO layer, and 33.9% ± 6.9% for the bottom BGO layer. The timing resolution between each individual layer (from the top to the bottom) and a single crystal reference detector was 350 ps, 2.8 ns, 328 ps, and 2.1 ns respectively.. In conclusion, the proposed four-layer DOI encoding detector achieved high performance and is an attractive choice for next-generation high sensitivity and high spatial resolution small animal positron emission tomography systems.

摘要

我们提出了一种新型的四层相互作用深度(DOI)编码磷光体探测器,该探测器使用硅酸钇镥(LYSO)和锗酸铋(BGO)闪烁体晶体阵列,用于高灵敏度和高空间分辨率的小动物正电子发射断层扫描(PET)成像。该探测器由四层交替的LYSO和BGO闪烁体晶体阵列堆叠组成,这些晶体阵列与一个8×8多像素光子计数器(MPPC)阵列耦合,并由PETsys TOFPET2专用集成电路读出。从顶部(γ射线入射处)到底部(面向MPPC)的四层分别由一个24×24阵列的0.99×0.99×6 mm LYSO晶体、一个24×24阵列的0.99×0.99×6 mm BGO晶体、一个16×16阵列的1.53×1.53×6 mm LYSO晶体和一个16×16阵列的1.53×1.53×6 mm BGO晶体组成。首先通过测量闪烁脉冲的脉冲能量(积分电荷)和持续时间(阈值时间(ToT))来分离在LYSO和BGO层中发生的事件。然后使用卷积神经网络(CNN)来区分顶部和下部的LYSO层以及上部和底部的BGO层。使用原型探测器进行的测量表明,我们提出的方法成功地识别了来自所有四层的事件。CNN模型区分两个LYSO层的分类准确率为91%,区分两个BGO层的分类准确率为81%。测量得到的顶部LYSO层的平均能量分辨率为13.1%±1.7%,上部BGO层为34.0%±6.3%,下部LYSO层为12.3%±1.3%,底部BGO层为33.9%±

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