Convergent ADMM Plug and Play PET Image Reconstruction
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Abstract (arXiv)
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.
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References · 7
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2016Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applicationscited by 244
- 2019On instabilities of deep learning in image reconstruction - Does AI come at a cost?cited by 211
- 2019Plug-and-Play Methods Provably Converge with Properly Trained Denoiserscited by 197
- 2017Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problemscited by 107
- 2020Learning Maximally Monotone Operators for Image Recoverycited by 62
- 2017Iterative PET Image Reconstruction Using Convolutional Neural Network Representationcited by 21
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