文献类型： 会议论文
第一作者： Marion Savanier
作者： Marion Savanier 1 ; Claude Comtat 1 ; Florent Sureau 1 ;
作者机构： 1.BioMaps, Université Paris-Saclay, CEA, CNRS, Inserm, SHFJ, Orsay, France
关键词： Deep learning;Training;Image quality;Reconstruction algorithms;Convolutional neural networks;Image reconstruction;Convergence
会议名称： IEEE International Symposium on Biomedical Imaging
主办单位：
页码： 1-5
摘要： 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.
分类号： tp391.4
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