Deep learning based timing calibration for PET

Neural network has been found an increasingly wide utilization in all fields. Owing to the fact that the traditional optimized algorithm, Iterative Shrinkage-Thresholding Algorithm (ISTA) or Alternating Direction Method of Multi-pliers (ADMM), could be presented by a form of network, and it could overcome some shortcomings of traditional algorithms, which inspired us to introduce the structured deep network into PET timing calibration. In this paper, by reformulating an ADMM algorithm to a deep network, we introduce a ADMM-Net framework for calibration, which combines the advantage of compatibility of consistency condition method. To verify the performance, several experiments of Monte Carlo simulation in GATE are performed.