Compton PET: A Simulation Study for a PET Module with Novel Geometry and Machine Learning for Position Decoding

康普顿PET:一种采用新型几何结构和机器学习进行位置解码的PET模块的仿真研究

阅读:1

Abstract

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.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。