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.

本研究通过将基于模型的变分重建与独立学习的深度神经网络操作器（DNN）应用到 ADMM 插拔框架中，研究了混合 PET 重建算法。在优化方面的最新结果基础上，通过对网络参数进行额外约束来实现方案的不动点收敛。我们提出了这样的 ADMM 算法，并在一个真实的 [18F]-FDG 合成脑检查中展示了该方案确实实验性地收敛到一个有意义的不动点。当没有在 DNN 的学习过程中强制执行所提出的约束时，实验观察到所提出的 ADMM 算法没有收敛。