In this work, we investigate hybrid PET reconstruction algorithms based on coupling a model-based variational reconstruction and the application of a separately learnt Deep Neural Network operator (DNN) in an ADMM Plug and Play framework. Following recent results in optimization, fixed point convergence of the scheme can be achieved by enforcing an additional constraint on network parameters during learning. We propose such an ADMM algorithm and show in a realistic [18F]-FDG synthetic brain exam that the proposed scheme indeed lead experimentally to convergence to a meaningful fixed point. When the proposed constraint is not enforced during learning of the DNN, the proposed ADMM algorithm was observed experimentally not to converge.

翻译：本文研究基于模型驱动变分重建与单独学习的深度神经网络算子（DNN）相结合的混合PET重建算法，该算法采用ADMM即插即用框架。根据近期优化领域的研究成果，通过在训练过程中对网络参数施加额外约束，可以实现该方案的固定点收敛。我们提出此类ADMM算法，并在真实的[18F]-FDG合成脑部扫描实验中证明，所提方案确实能实验性地收敛到有意义的固定点。若在DNN训练过程中未施加该约束，实验观察到所提ADMM算法无法收敛。