Dual-Ended Readout PET Detector Based on Multivoltage Threshold Sampling Combined With Convolutional Neural Network for Energy Calculation

To minimize parallax errors and achieve high spatial resolution positron emission tomography (PET) systems, developing depth-of-Interaction (DOI) encoding detectors has become a significant research topic. In this article, we investigated a dual-ended readout PET detector based on the multivoltage threshold (MVT) sampling method combined with a convolutional neural network (CNN) to calculate the pulse’s energy (MVT-CNN method). The MVT sampling method was used to acquire time-threshold samples and digitize scintillation pulses. The CNN model was employed to establish an accurate mapping between MVT sampling points and energy information. The dual-ended readout detector’s energy, DOI, and timing performance were evaluated with two irradiation configurations. The results demonstrated that the performance of the MVT-CNN method was close to that of the integration method based on oscilloscope sampling. Using the MVT-CNN method, the average energy resolution of the tested crystals over all depths was $14.5 \, \pm \, 1.2$ %, and the average DOI resolution was $2.81 \, \pm \, 0$ .70 mm. In the side irradiation configuration, the average coincidence timing resolution of the tested crystals at 2 mm depth was 435 ps. The performance of the dual-ended readout DOI-PET detector basedon the MVT-CNN method suggested that it could develop small animal and organ-dedicated PET systems with high sensitivity and uniform spatial resolutionxs.