Recently, deep learning-based denoising methods have been gradually used for PET images denoising and have shown great achievements. Among these methods, one interesting framework is conditional deep image prior (CDIP) which is an unsupervised method that does not need prior training or a large number of training pairs. In this work, we combined CDIP with Logan parametric image estimation to generate high-quality parametric images. In our method, the kinetic model is the Logan reference tissue model that can avoid arterial sampling. The neural network was utilized to represent the images of Logan slope and intercept. The patient’s computed tomography (CT) image or magnetic resonance (MR) image was used as the network input to provide anatomical information. The optimization function was constructed and solved by the alternating direction method of multipliers (ADMM) algorithm. Both simulation and clinical patient datasets demonstrated that the proposed method could generate parametric images with more detailed structures. Quantification results showed that the proposed method results had higher contrast-to-noise (CNR) improvement ratios (PET/CT datasets: $62.25\%\pm 29.93\%$; striatum of brain PET datasets : $129.51\%\pm 32.13\%$, thalamus of brain PET datasets: $128.24\%\pm 31.18\%$) than Gaussian filtered results (PET/CT datasets: $23.33\%\pm 18.63\%$; striatum of brain PET datasets: $74.71\%\pm 8.71\%$, thalamus of brain PET datasets: $73.02\%\pm 9.34\%$) and nonlocal mean (NLM) denoised results (PET/CT datasets: $37.55\%\pm 26.56\%$; striatum of brain PET datasets: $100.89\%\pm 16.13\%$, thalamus of brain PET datasets: $103.59\%\pm 16.37\%$).

近年来，基于深度学习的去噪方法逐渐应用于PET图像去噪，并取得了巨大的成就。在这些方法中，一个有趣的框架是条件深度图像先验（CDIP），它是一种不需要先验训练或大量训练对的无监督方法。在这项工作中，我们将 CDIP 与 Logan 参数图像估计相结合，以生成高质量的参数图像。在我们的方法中，动力学模型是可以避免动脉采样的洛根参考组织模型。神经网络用于表示洛根斜率和截距的图像。患者的计算机断层扫描 (CT) 图像或磁共振 (MR) 图像用作网络输入以提供解剖信息。通过乘法器交替方向法（ADMM）算法构造和求解优化函数。模拟和临床患者数据集都表明，所提出的方法可以生成具有更详细结构的参数图像。量化结果表明，所提出的方法结果具有更高的对比噪声 (CNR) 改进率（PET/CT 数据集：$62.25\%\pm 29.93\%$; 脑 PET 数据集的纹状体：$129.51\%\pm 32.13\%$，大脑 PET 数据集的丘脑：$128.24\%\pm 31.18\%$) 比高斯滤波结果（PET/CT 数据集：$23.33\%\pm 18.63\%$; 脑 PET 数据集的纹状体：$74.71\%\pm 8.71\%$，大脑 PET 数据集的丘脑：$73.02\%\pm 9.34\%$) 和非局部均值 (NLM) 去噪结果（PET/CT 数据集：$37.55\%\pm 26.56\%$; 脑 PET 数据集的纹状体：$100.89\%\pm 16.13\%$，大脑 PET 数据集的丘脑：$103.59\%\pm 16.37\%$）。