Sparse-view and limited-angle Computed Tomography (CT) are very challenging problems in real applications. Due to the high ill-posedness, both analytical and iterative reconstruction methods may present distortions and artifacts for such incomplete data problems. In this work, we propose a novel reconstruction model to jointly reconstruct a high-quality image and its corresponding high-resolution projection data. The model is built up by deploying regularization on both CT image and projection data, as well as by introducing a novel full-sampling condition to fuse information from both domains. Inspired by the success of deep learning methods in imaging, we utilize the convolutional neural networks to embed and learn both the interrelationship between raw data and reconstructed images and prior information such as regularization, which is implemented in an end-to-end training process. Numerical results demonstrate that the proposed approach outperforms both variational and popular learning-based reconstruction methods for the sparse-view and limited-angle CT problems.

中文翻译：

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学会了从不完整数据中进行全采样重建


稀疏视图和有限角度计算机断层扫描（CT）在实际应用中是非常具有挑战性的问题。由于高度不适定性，分析和迭代重建方法都可能会出现此类不完整数据问题的扭曲和伪影。在这项工作中，我们提出了一种新颖的重建模型来联合重建高质量图像及其相应的高分辨率投影数据。该模型是通过在 CT 图像和投影数据上部署正则化以及引入一种新颖的全采样条件来融合来自两个域的信息而建立的。受到成像深度学习方法成功的启发，我们利用卷积神经网络来嵌入和学习原始数据和重建图像之间的相互关系以及正则化等先验信息，这是在端到端训练过程中实现的。数值结果表明，对于稀疏视图和有限角度 CT 问题，所提出的方法优于变分法和流行的基于学习的重建方法。