4D digital tomosynthesis (DTS) techniques for image-guided radiation therapy (IGRT) are able to reduce radiation dose, scan and reconstruction time compared to 4D cone-beam computed tomography (CBCT). In spite of these benefits, the 4D DTS techniques cause the degradation of image quality due to an intrinsic imaging strategy and consequently reduce treatment accuracy. In this study, a deep learning-based convolutional neural network (CNN) framework was proposed for 4D DTS imaging. The proposed CNN framework consisted of the data restoration network based on a U-Net and the denoising network combined with a 2D wavelet transform, and the network training was implemented with clinical images. The quality of the 4D DTS images obtained from the proposed model was evaluated in terms of quantitative accuracy, spatial resolution and noise property. The results showed that the proposed CNN framework improved the quantitative accuracy of 4D DTS images by 3–19%, and the spatial resolution and noise for the proposed CNN framework were reduced by 2.24–7.33% and 8.92–40.07%, respectively, in comparison to other imaging models. These results represented that the degradation of the 4D DTS image quality can be recovered using the proposed CNN framework, and the proposed model is suitable for maintaining spatial resolution as well as suppressing noise and artifacts. In conclusion, the proposed CNN framework can be potentially used to improve the quality of 4D DTS images for the IGRT.

与4D锥形束计算机断层扫描（CBCT）相比，用于图像引导放射治疗（IGRT）的4D数字断层合成（DTS）技术能够减少辐射剂量，扫描和重建时间。尽管有这些好处，但4D DTS技术由于固有的成像策略而导致图像质量下降，并因此降低了治疗精度。在这项研究中，提出了基于深度学习的卷积神经网络（CNN）框架用于4D DTS成像。所提出的CNN框架由基于U-Net的数据恢复网络和结合2D小波变换的去噪网络组成，并使用临床图像进行网络训练。从定量模型，空间分辨率和噪声特性方面评估了从提出的模型获得的4D DTS图像的质量。结果表明，相比之下，拟议的CNN框架将4D DTS图像的定量精度提高了3–19％，而拟议的CNN框架的空间分辨率和噪声分别降低了2.24–7.33％和8.92–40.07％到其他成像模型。这些结果表明，使用提出的CNN框架可以恢复4D DTS图像质量的下降，并且提出的模型适用于保持空间分辨率以及抑制噪声和伪影。总之，提出的CNN框架可潜在地用于提高IGRT的4D DTS图像质量。与其他成像模型相比。这些结果表明，使用提出的CNN框架可以恢复4D DTS图像质量的下降，并且提出的模型适用于保持空间分辨率以及抑制噪声和伪影。总之，提出的CNN框架可潜在地用于提高IGRT的4D DTS图像质量。与其他成像模型相比。这些结果表明，使用提出的CNN框架可以恢复4D DTS图像质量的下降，并且提出的模型适用于保持空间分辨率以及抑制噪声和伪影。总之，提出的CNN框架可潜在地用于提高IGRT的4D DTS图像质量。