JSR-Net：一种用于不完整数据联合空间-氡域CT重建的深度网络
JSR-Net: A Deep Network for Joint Spatial-Radon Domain CT Reconstruction from incomplete data
摘要：
稀疏视图和有限角度重建等不完整数据的CT图像重建是医学成像领域的一个重要而具有挑战性的问题。这项工作提出了一种新的深度卷积神经网络（CNN），称为JSR-Net，它联合重建CT图像及其相关的Radon域投影。JSR-Net将传统的基于模型的方法与深度学习的深度架构设计相结合。采用混合损失函数来提高JSR-Net的性能，使其更有效地保护重要的图像结构。数值实验表明，JSR-Net优于一些最新的基于模型的重建方法，以及最近提出的深度模型。
英文摘要：
CT image reconstruction from incomplete data, such as sparse views and limited angle reconstruction, is an important and challenging problem in medical imaging. This work proposes a new deep convolutional neural network (CNN), called JSR-Net, that jointly reconstructs CT images and their associated Radon domain projections. JSR-Net combines the traditional model-based approach with deep architecture design of deep learning. A hybrid loss function is adapted to improve the performance of the JSR-Net making it more effective in protecting important image structures. Numerical experiments demonstrate that JSR-Net outperforms some latest model-based reconstruction methods, as well as a recently proposed deep model.
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