结合精确 DOI 和 TOF 的半单片元闪烁体仿真概念验证

IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-03-12 DOI:10.1109/TRPMS.2024.3368802
Georgios Konstantinou;Lei Zhang;Daniel Bonifacio;Riccardo Latella;Jose Maria Benlloch;Antonio J. Gonzalez;Paul Lecoq
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引用次数: 0

摘要

在这项研究中,我们提出并研究了一种独特的半片元闪烁体(SMMS)探测器设计,其中慢速闪烁体(BGO 或 LYSO)被分割成薄片,由 SiPM 阵列读取,从而提供交互深度(DOI)信息。这些闪烁体与薄的分段式快速闪烁体(塑料 EJ232 或 EJ232Q)交替使用,同样由单个 SiPM 读取,从而提供像素级的重合时间分辨率 (CTR)。该结构将尺寸为 0.3/times 25.5/times $ (15 或 24) mm3 的慢速闪烁体层与尺寸为 0.1/times 3.1/times $ (15 或 24) mm3 的快速闪烁体层结合在一起。我们使用蒙特卡洛门模拟来衡量这种新型半片探测器的性能。我们发现,SMMS 的时间分辨率可与采用相同材料的像素化偏闪器设计相媲美。例如,基于涟SO的 15 毫米深 SMMS 在应用时行校正前的 CTR 为 121 ps(校正后的 CTR 为 107 ps)。等效的基于 BGO 的 SMMS 的 CTR 为 241 ps,与之前工作中的偏闪烁像素实验结果相差 15%。我们还根据半片探测器的指导方针,将神经网络应用于 SiPM 阵列记录的光子分布和时间戳。这使得确定 DOI 的精度小于 3 毫米,在最佳情况下置信度为 0.85,而且在重建能量共享和相互作用能量方面的精度超过 2 个标准差。总之,神经网络预测能力优于标准能量计算方法或任何能量共享分析方法,这要归功于对光子分布理解的提高。
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Semi-Monolithic Meta-Scintillator Simulation Proof-of-Concept, Combining Accurate DOI and TOF
In this study, we propose and examine a unique semimonolithic metascintillator (SMMS) detector design, where slow scintillators (BGO or LYSO) are split into thin slabs and read by an array of SiPM, offering depth-of-interaction (DOI) information. These are alternated with thin segmented fast scintillators (plastic EJ232 or EJ232Q), also read by single SiPMs, which provides pixel-level coincidence time resolution (CTR). The structure combines layers of slow scintillators of size $0.3\times 25.5\times $ (15 or 24) mm3 with fast scintillators of size $0.1\times 3.1\times $ (15 or 24) mm3. We use a Monte Carlo Gate simulation to gauge this novel semimonolithic detector’s performance. We found that the time resolution of SMMS is comparable to pixelated metascintillator designs with the same materials. For example, a 15-mm deep LYSO-based SMMS yielded a CTR of 121 ps before applying timewalk correction (after correction, 107-ps CTR). The equivalent BGO-based SMMS presented a CTR of 241 ps, which is a 15% divergence from metascintillator pixel experimental findings from previous works. We also applied neural networks to the photon distributions and timestamps recorded at the SiPM array, following guidelines on semimonolithic detectors. This led to determining the DOI with less than 3-mm precision and a confidence level of 0.85 in the best case, plus more than 2 standard deviations accuracy in reconstructing energy sharing and interaction energy. In summary, neural network prediction capabilities outperform standard energy calculation methods or any analytical approach on energy sharing, thanks to the improved understanding of photon distribution.
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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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Affiliate Plan of the IEEE Nuclear and Plasma Sciences Society Table of Contents Introducing IEEE Collabratec IEEE Transactions on Radiation and Plasma Medical Sciences Publication Information Member Get-a-Member (MGM) Program
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