2023/11/01 by Sandeep K. Chaudhuri, Chaudhuri, Sandeep K., Qinyang Li +5
Engineering · Physics and Astronomy · #Advanced Semiconductor Detectors and Materials #Advanced X-ray and CT Imaging #FOS: Computer and information sciences #FOS: Physical sciences #Instrumentation and Detectors (physics.ins-det) #Machine Learning (cs.LG) #Radiation Detection and Scintillator Technologies
paper · pdf · doi:10.48550/arxiv.2311.00682
openalex publication_date 2023/11/01 · openalex created_date 2023/11/03 · openalex updated_date 2026/08/01
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.