Deep Learning-Based Classification of Gamma Photon Interactions in
  Room-Temperature Semiconductor Radiation Detectors
Sandeep K. Chaudhuri Qinyang Li Krishna C. Mandal Jianjun Hu
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
data to resemble actual CZTS detector pulses and validated using both simulated
and experimental data. 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 laid solid
foundation for developing next-generation high energy gamma-rays detectors for
better biomedical imaging.