Defective and inconsistent responses in CT detectors can cause ring and streak artifacts in the reconstructed images, making them unusable for clinical purposes. In recent years, several ring artifact reduction solutions have been proposed in the image domain or in the sinogram domain using supervised deep learning methods. However, these methods require dedicated datasets for training, leading to a high data collection cost. Furthermore, existing approaches focus exclusively on either image-space or sinogram-space correction, neglecting the intrinsic correlations from the forward operation of the CT geometry. Based on the theoretical analysis of non-ideal CT detector responses, the RAR problem is reformulated as an inverse problem by using an unrolled network, which considers non-ideal response together with linear forward-projection with CT geometry. Additionally, the intrinsic correlations of ring artifacts between the sinogram and image domains are leveraged through synthetic data derived from natural images, enabling the trained model to correct artifacts without requiring real-world clinical data. Extensive evaluations on diverse scanning geometries and anatomical regions demonstrate that the model trained on synthetic data consistently outperforms existing state-of-the-art methods.

翻译：CT探测器的缺陷与不一致响应会导致重建图像中出现环形与条纹伪影，使其无法用于临床诊断。近年来，已有若干基于监督深度学习的环形伪影抑制方法被提出，这些方法在图像域或正弦图域进行处理。然而，此类方法需要专用数据集进行训练，导致数据采集成本高昂。此外，现有方法仅专注于图像空间或正弦图空间的校正，忽略了CT几何前向操作所蕴含的内在关联性。基于对非理想CT探测器响应的理论分析，本研究通过采用展开网络将环形伪影抑制问题重构为逆问题求解框架，该框架同时考虑了非理想响应与CT几何的线性前向投影。进一步地，通过从自然图像生成的合成数据，我们利用了正弦图域与图像域之间环形伪影的内在相关性，使得训练后的模型能够在无需真实临床数据的情况下实现伪影校正。在不同扫描几何与解剖区域上的大量实验表明，基于合成数据训练的模型在性能上持续优于现有的先进方法。