Deep Learning-Based Classification of Gamma Photon Interaction in Room-Temperature Semiconductor Radiation Detectors

Abstract

Photon counting radiation detectors have become an integral part of medical imaging modalities such as Positron Emission Tomography or Computed Tomography. One of the most promising detectors is the wide bandgap room temperature semiconductor detectors, which depends on the interaction gamma/x-ray photons with the detector material involves Compton scattering which leads to multiple interaction photon events (MIPEs) of a single photon. For semiconductor detectors like CdZnTeSe (CZTS), which have a high overlap of detected energies between Compton and photoelectric events, it is nearly impossible to distinguish between Compton scattered events from photoelectric events using conventional readout electronics or signal processing algorithms. Herein, we report a deep learning classifier CoPhNet that distinguishes between Compton scattering and photoelectric interactions of gamma/x-ray photons with CdZnTeSe (CZTS) semiconductor detectors. Our CoPhNet model was trained using simulated 662 keV samples to resemble actual CZTS detector pulses and validated using both simulated and experimental data. The model remarkably exhibited a 100% accuracy in predicting the type of interaction. These results demonstrated that our CoPhNet model can achieve high classification accuracy over the simulated test set. It also holds its performance robustness under operating parameter shifts such as Signal-Noise-Ratio (SNR) and incident energy. Our work thus show a positive direction for developing next-generation high energy gamma-rays detectors for better biomedical imaging.

Keywords

• CdZnTeSe
• deep learning
• gamma-photon detection
• medical imaging
• multiple interaction photon events
• photon counting detection