4D 锥形束计算机断层扫描 (CBCT) 在肺癌适应性放射治疗中发挥着关键作用。然而，极其稀疏的采样投影数据会在 4D CBCT 图像中造成严重的条纹伪影。现有的深度学习（DL）方法严重依赖于大型标记训练数据集，而这些数据集在实际场景中很难获得。受这种困境的限制，深度学习模型常常难以同时保留动态运动、消除条纹退化和恢复精细细节。为了解决上述具有挑战性的问题，我们引入了深度先验图像约束运动补偿框架（DPI-MoCo），它将 4D CBCT 重建解耦为两个子任务，包括粗略图像恢复和结构细节微调。在第一阶段，所提出的 DPI-MoCo 结合了先前的图像引导、生成对抗网络和对比学习，以在保持呼吸运动的同时全局抑制伪影。之后，为了进一步增强局部解剖结构，采用运动估计和补偿技术。值得注意的是，我们的框架是在不需要配对数据集的情况下执行的，确保了临床案例的实用性。在蒙特卡罗模拟数据集中，与最先进的 (SOTA) 方法相比，DPI-MoCo 实现了有竞争力的定量性能。此外，我们在临床肺癌数据集中测试了 DPI-MoCo，实验验证了 DPI-MoCo 不仅可以恢复小的解剖结构和病变，还可以保留运动信息。

4D cone-beam computed tomography (CBCT) plays a critical role in adaptive radiation therapy for lung cancer. However, extremely sparse sampling projection data will cause severe streak artifacts in 4D CBCT images. Existing deep learning (DL) methods heavily rely on large labeled training datasets which are difficult to obtain in practical scenarios. Restricted by this dilemma, DL models often struggle with simultaneously retaining dynamic motions, removing streak degradations, and recovering fine details. To address the above challenging problem, we introduce a Deep Prior Image Constrained Motion Compensation framework (DPI-MoCo) that decouples the 4D CBCT reconstruction into two sub-tasks including coarse image restoration and structural detail fine-tuning. In the first stage, the proposed DPI-MoCo combines the prior image guidance, generative adversarial network, and contrastive learning to globally suppress the artifacts while maintaining the respiratory movements. After that, to further enhance the local anatomical structures, the motion estimation and compensation technique is adopted. Notably, our framework is performed without the need for paired datasets, ensuring practicality in clinical cases. In the Monte Carlo simulation dataset, the DPI-MoCo achieves competitive quantitative performance compared to the state-of-the-art (SOTA) methods. Furthermore, we test DPI-MoCo in clinical lung cancer datasets, and experiments validate that DPI-MoCo not only restores small anatomical structures and lesions but also preserves motion information.