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PET image reconstruction is challenging due to the ill-poseness of theinverse problem and limited number of detected photons. Recently deep neuralnetworks have been widely and successfully used in computer vision tasks andattracted growing interests in medical imaging. In this work, we trained a deepresidual convolutional neural network to improve PET image quality by using theexisting inter-patient information. An innovative feature of the proposedmethod is that we embed the neural network in the iterative reconstructionframework for image representation, rather than using it as a post-processingtool. We formulate the objective function as a constraint optimization problemand solve it using the alternating direction method of multipliers (ADMM)algorithm. Both simulation data and hybrid real data are used to evaluate theproposed method. Quantification results show that our proposed iterative neuralnetwork method can outperform the neural network denoising and conventionalpenalized maximum likelihood methods.

Iterative PET Image Reconstruction Using Convolutional Neural Network Representation