由于计算机断层扫描（CT）成像技术对物体内部结构具有出色的可视化能力，其在临床医学诊断中获得广泛应用。但是X射线辐射会对人体造成伤害，通常采用降低扫描强度或者减少扫描角度数量的方式降低患者受到的X射线辐射剂量，然而在欠采样投影数据条件下重建的低剂量CT图像会有严重的条状伪影和噪声。近年来，深度学习技术快速发展，同时卷积神经网络在图像表示与特征提取等方面展现出巨大优势，应用在稀疏或者有限角度下的CT重建任务中可以实现快速和高质量的重建。因此面向稀疏或者有限角度条件下的CT重建技术，综述了深度学习技术在图像域后处理、正弦域预处理、双域数据联合处理、迭代重建算法和端到端映射重建5个方面的国内外最新研究进展，对当前基于深度学习的稀疏或者有限角度CT重建方法的技术特点及其局限性进行分析，并展望了未来可能的研究方向。

Computed tomography (CT) technology is widely used in clinical medical diagnosis thanks to the excellent visualization of the CT imaging technology for the internal cross-sectional structure of objects. Because X-ray radiation will be harmful to the human body, it is demanded to reduce the dose of X-ray radiation to patients by reducing the X-ray intensity of the scan or number of the view of the scan. However, low-dose CT images reconstructed from sub-sampling projection data will produce severe stripe artifacts and noise. In recent years, deep learning techniques have developed rapidly, and convolutional neural networks have shown great advantages in image representation and feature extraction, helping to achieve high speed and quality CT reconstruction from sparse-view or limited-angle projection data. This paper mainly focuses on the sparse-view or limited-angle CT reconstruction techniques, and reviews the latest research progresses of deep learning techniques in CT reconstruction in five directions, including image post-processing, sinogram domain pre-processing, joint processing of dual domain data, iterative reconstruction algorithms, and end-to-end mapping reconstruction. In the end, we analyze the technical characteristics, advantages, and limitations of existing sparse-view or limited-angle CT reconstruction methods based on deep learning, and discuss possible future research directions to address these challenges.