Generative adversarial networks based regularized image reconstruction for PET

Image reconstruction in positron emission tomography (PET), especially from low-count projection data, is challenging due to the ill-posed nature of the inverse problem. Prior information can substantially improve the quality of reconstructed PET images. Previously, a PET image reconstruction method using a convolutional neural network (CNN) representation was proposed. In this work, we replace the original network with a generative adversarial network (GAN) to improve the network performance under limited number of training data. We also introduce an additional likelihood function in the objective function, which acts as a soft constraint on the network input. Evaluation study using real patient data with artificially inserted lesions demonstrated noticeable improvements in terms of lesion contrast recovery versus background noise trade-off.