Shim Hyeong Seok, Cho Min Jeong, Lee Jae Sung
Interdisciplinary Program of Bioengineering, Seoul National University, Seoul, Korea.
Integrated Major in Innovative Medical Science, Seoul National University Graduate School, Seoul, Korea.
Biomed Eng Lett. 2025 Mar 4;15(3):489-496. doi: 10.1007/s13534-025-00464-w. eCollection 2025 May.
The performance and image quality of positron emission tomography (PET) systems can be enhanced by strategically employing multiple different scintillators, particularly those with different decay times. Two cutting-edge PET detector technologies employing different scintillators with different decay times are the phoswich detector and the emerging metascintillator. In PET imaging, accurate and precise energy measurement is important for effectively rejecting scattered gamma-rays and estimating scatter distribution. However, traditional measures of light output, such as amplitude or integration values of photosensor output pulses, cannot accurately indicate the deposit energy of gamma-rays across multiple scintillators. To address these issues, this study explores two methods for energy estimation in PET detectors that employ multiple scintillators. The first method uses pseudo-inverse matrix generated from the unique pulse profile of each crystal, while the second employs an artificial neural network (ANN) to estimate the energy deposited in each crystal. The effectiveness of the proposed methods was experimentally evaluated using three heavy and dense inorganic scintillation crystals (BGO, LGSO, and GAGG) and three fast plastic scintillators (EJ200, EJ224, and EJ232). The energy estimation method employing ANNs consistently demonstrated superior accuracy across all crystal combinations when compared to the approach utilizing the pseudo-inverse matrix. In the pseudo-inverse matrix approach, there is a negligible difference in accuracy when applying integral-based energy labels as opposed to amplitude-based energy labels. On the other hand, in ANN approach, employing integral-based energy labels consistently outperforms the use of amplitude-based energy labels. This study contributes to the advancement of PET detector technology by proposing and evaluating two methods for estimating the energy in the detector using multiple scintillators. The ANN approach appears to be a promising solution for improving the accuracy of energy estimation, addressing challenges posed by mixed scintillation pulses.
通过策略性地使用多种不同的闪烁体，特别是那些具有不同衰减时间的闪烁体，可以提高正电子发射断层扫描（PET）系统的性能和图像质量。两种采用不同衰减时间的不同闪烁体的前沿PET探测器技术是磷光体探测器和新兴的超闪烁体。在PET成像中，准确精确的能量测量对于有效排除散射伽马射线和估计散射分布很重要。然而，传统的光输出测量方法，如光电传感器输出脉冲的幅度或积分值，不能准确指示跨多个闪烁体的伽马射线沉积能量。为了解决这些问题，本研究探索了两种用于在采用多个闪烁体的PET探测器中进行能量估计的方法。第一种方法使用从每个晶体的独特脉冲轮廓生成的伪逆矩阵，而第二种方法采用人工神经网络（ANN）来估计沉积在每个晶体中的能量。使用三种重且致密的无机闪烁晶体（BGO、LGSO和GAGG）和三种快速塑料闪烁体（EJ200、EJ224和EJ232）对所提出方法的有效性进行了实验评估。与使用伪逆矩阵的方法相比，采用人工神经网络的能量估计方法在所有晶体组合中始终表现出更高的准确性。在伪逆矩阵方法中，应用基于积分的能量标签与基于幅度的能量标签时，准确性差异可忽略不计。另一方面，在人工神经网络方法中，采用基于积分的能量标签始终优于使用基于幅度的能量标签。本研究通过提出和评估两种使用多个闪烁体估计探测器中能量的方法，为PET探测器技术的发展做出了贡献。人工神经网络方法似乎是提高能量估计准确性、应对混合闪烁脉冲带来挑战的一个有前途的解决方案。
