Peng Peng, Judenhofer Martin S, Jones Adam Q, Cherry Simon R
Department of Biomedical Engineering, University of California-Davis, One Shields Avenue, Davis, CA 95616, USA.
Department of Electrical and Computer Engineering, University of California-Davis, One Shields Avenue, Davis, CA 95616, USA.
Biomed Phys Eng Express. 2019 Jan;5(1). doi: 10.1088/2057-1976/aaef03. Epub 2018 Nov 30.
This paper describes a simulation study of a positron emission tomography (PET) detector module that can reconstruct the kinematics of Compton scattering within the scintillator. We used a layer structure, with which we could recover the positions and energies for the multiple interactions of a gamma ray in the different layers. Using the Compton scattering formalism, the sequence of interactions can be estimated. The true first interaction position extracted in the Compton scattering will help minimize the degradation of the reconstructed image resolution caused by intercrystal scatter events. Because of the layer structure, this module also has readily available user-defined resolution for the depth of interaction. The semi-monolithic crystals enable high light collection efficiency and an energy resolution of ~10% has been achieved in the simulation. We used machine learning to decode the gamma ray interaction locations, achieving an average spatial resolution of 0.40 mm. Our proposed detector design provides a pathway to increase the sensitivity of a system without affecting other key performance features.
本文描述了一种正电子发射断层扫描（PET）探测器模块的模拟研究，该模块能够重建闪烁体内康普顿散射的运动学。我们采用了一种层结构，利用它可以恢复伽马射线在不同层中的多次相互作用的位置和能量。使用康普顿散射形式理论，可以估计相互作用的顺序。在康普顿散射中提取的真实首次相互作用位置将有助于最小化由晶体间散射事件导致的重建图像分辨率的下降。由于层结构，该模块对于相互作用深度也具有易于获得的用户定义分辨率。半单片晶体实现了高光收集效率，并且在模拟中已实现约10%的能量分辨率。我们使用机器学习来解码伽马射线相互作用位置，实现了0.40毫米的平均空间分辨率。我们提出的探测器设计提供了一条在不影响其他关键性能特征的情况下提高系统灵敏度的途径。
