Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
