Convolutional neural network-based reconstruction for positronium annihilation localization

A novel hermetic detector composed of 200 bismuth germanium oxide crystal scintillators and 393 channel silicon photomultipliers has been developed for positronium (Ps) annihilation studies. This compact 4 pi detector is capable of simultaneously detecting gamma-ray decay in all directions, enabling not only the study of visible and invisible exotic decay processes but also tumor localization in positron emission tomography for small animals. In this study, we investigate the use of a convolutional neural network (CNN) for the localization of Ps annihilation synonymous with tumor localization. Two-gamma decay systems of the Ps annihilation from Na-22 and F-18 radioactive sources are simulated using a GEANT4 simulation. The simulated datasets are preprocessed by applying energy cutoffs. The spatial error in the XY plane from the CNN is compared to that from the classical weighted k-means algorithm centroiding, and the feasibility of CNN-based Ps annihilation reconstruction with tumor localization is discussed.

Automated summary

A novel hermetic detector composed of bismuth germanium oxide crystal scintillators and silicon photomultipliers has been developed for positronium annihilation studies. This detector is capable of detecting gamma-ray decay in all directions, allowing for the study of exotic decay processes and tumor localization. A convolutional neural network (CNN) is employed for Ps annihilation reconstruction and tumor localization.