Although sparse-view computed tomography (CT) has significantly reduced
radiation dose, it also introduces severe artifacts which degrade the image
quality. In recent years, deep learning-based methods for inverse problems have
made remarkable progress and have become increasingly popular in CT
reconstruction. However, most of these methods suffer several limitations:
dependence on high-quality training data, weak interpretability, etc. In this
study, we propose a fully unsupervised framework called Deep Radon Prior (DRP),
inspired by Deep Image Prior (DIP), to address the aforementioned limitations.
DRP introduces a neural network as an implicit prior into the iterative method,
thereby realizing cross-domain gradient feedback. During the reconstruction
process, the neural network is progressively optimized in multiple stages to
narrow the solution space in radon domain for the under-constrained imaging
protocol, and the convergence of the proposed method has been discussed in this
work. Compared with the popular pre-trained method, the proposed framework
requires no dataset and exhibits superior interpretability and generalization
ability. The experimental results demonstrate that the proposed method can
generate detailed images while effectively suppressing image
artifacts.Meanwhile, DRP achieves comparable or better performance than the
supervised methods.

我们提出了一个名为 Deep Radon Prior (DRP) 的全无监督框架，通过将神经网络作为一个隐性先验引入迭代方法中，解决了现有方法中的一些限制。该方法在逐步优化神经网络的多个阶段中，在 Radon 域中缩小了欠约束成像协议的解空间，并讨论了所提方法的收敛性。与常用的预训练方法相比，该提出的框架不需要数据集，同时具有更好的可解释性和泛化能力。实验结果表明，该方法能够生成细节图像并有效抑制图像伪影，同时与有监督方法相比，DRP 的性能相当或更好。