Positron emission tomography (PET) is an ill-posed inverse problem and suffers high noise due to limited number of detected events. Prior information can be used to improve the quality of reconstructed PET images. Deep neural networks have also been applied to regularized image reconstruction. One method is to use a pretrained denoising neural network to represent the PET image and to perform a constrained maximum likelihood estimation. In this work, we propose to use a generative adversarial network (GAN) to further improve the network performance. We also modify the objective function to include a data-matching term on the network input. Experimental studies using computer-based Monte Carlo simulations and real patient datasets demonstrate that the proposed method leads to noticeable improvements over the kernel-based and U-net-based regularization methods in terms of lesion contrast recovery versus background noise trade-offs.

中文翻译：

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基于生成对抗网络的正则化PET图像重建

正电子发射断层扫描（PET）是一个不适定的逆问题，由于检测到的事件数量有限，因此遭受高噪声干扰。可以使用先验信息来改善重建的PET图像的质量。深度神经网络也已应用于正则化图像重建。一种方法是使用预训练的降噪神经网络来表示PET图像并执行约束最大似然估计。在这项工作中，我们建议使用生成对抗网络（GAN）来进一步提高网络性能。我们还修改了目标函数，以在网络输入中包含一个数据匹配项。