Learning with Fixed Point Condition for Convergent PNP PET Reconstruction

This work explores plug-and-play algorithms for PET reconstruction, combining deep learning with model-based variational methods. We aim to integrate classical convolutional architectures into algorithms such as ADMM and Forward-Backward (FB) while ensuring convergence and maintaining fixed-point control. We focus on the scenario where only high- and low-count reconstructed PET images are available for training our networks. Experimental results demonstrate that the proposed methods consistently reach fixed points with high likelihood and low mean squared error, thus showcasing the potential of convergent plug-and-play techniques for PET reconstruction.