Positron emission tomography (PET) is a promising medical imaging technology that provides non-invasive and quantitative measurement of biochemical process in the human bodies. PET image reconstruction is challenging due to the ill-poseness of the inverse problem. With lower statistics caused by the limited detected photons, low-dose PET imaging leads to noisy reconstructed images with much quality degradation. Recently, deep neural networks (DNN) have been widely used in computer vision tasks and attracted growing interests in medical imaging. In this paper, we proposed a maximum a posteriori (MAP) reconstruction algorithm incorporating a convolutional neural network (CNN) representation in the formation of the prior. Rather than using the CNN in post-processing, we embedded the neural network in the reconstruction framework for image representation. Using the simulated data, we first quantitatively evaluated our proposed method in terms of the noise-bias tradeoff, and compared with the filtered maximum likelihood (ML), the conventional MAP, and the CNN post-processing methods. In addition to the simulation experiments, the proposed method was further quantitatively validated on the acquired patient brain and body data with the tradeoff between noise and contrast. The results demonstrated that the proposed CNN-MAP method improved noise-bias tradeoff compared with the filtered ML, the conventional MAP, and the CNN post-processing methods in the simulation study. For the patient study, the CNN-MAP method achieved better noise-contrast tradeoff over the other three methods. The quantitative enhancements indicate the potential value of the proposed CNN-MAP method in low-dose PET imaging.

正电子发射断层扫描 (PET) 是一种很有前途的医学成像技术，它提供了对人体生化过程的非侵入性和定量测量。由于逆问题的不良姿势，PET 图像重建具有挑战性。由于检测到的光子有限导致统计数据较低，低剂量 PET 成像会导致重建图像噪声很大，质量下降很多。最近，深度神经网络 (DNN) 已广泛应用于计算机视觉任务，并引起了人们对医学成像的兴趣。在本文中，我们提出了一个最大后验(MAP) 重建算法在先验的形成中结合了卷积神经网络 (CNN) 表示。我们没有在后处理中使用 CNN，而是将神经网络嵌入到图像表示的重建框架中。使用模拟数据，我们首先在噪声偏差权衡方面对我们提出的方法进行了定量评估，并与滤波最大似然 (ML)、传统 MAP 和 CNN 后处理方法进行了比较。除了模拟实验之外，所提出的方法在获得的患者大脑和身体数据上进一步量化验证，并在噪声和对比度之间进行权衡。结果表明，与过滤后的 ML、传统 MAP、以及模拟研究中的CNN后处理方法。对于患者研究，CNN-MAP 方法比其他三种方法实现了更好的噪声对比度权衡。定量增强表明所提出的 CNN-MAP 方法在低剂量 PET 成像中的潜在价值。