Ring artifacts in computed tomography images, arising from the undesirable
responses of detector units, significantly degrade image quality and diagnostic
reliability. To address this challenge, we propose a dual-domain regularization
model to effectively remove ring artifacts, while maintaining the integrity of
the original CT image. The proposed model corrects the vertical stripe
artifacts on the sinogram by innovatively updating the response inconsistency
compensation coefficients of detector units, which is achieved by employing the
group sparse constraint and the projection-view direction sparse constraint on
the stripe artifacts. Simultaneously, we apply the sparse constraint on the
reconstructed image to further rectified ring artifacts in the image domain.
The key advantage of the proposed method lies in considering the relationship
between the response inconsistency compensation coefficients of the detector
units and the projection views, which enables a more accurate correction of the
response of the detector units. An alternating minimization method is designed
to solve the model. Comparative experiments on real photon counting detector
data demonstrate that the proposed method not only surpasses existing methods
in removing ring artifacts but also excels in preserving structural details and
image fidelity.

提出了一种双域正则化模型来有效地消除计算机断层扫描图像中的环形伪影，并保持原始 CT 图像的完整性。该方法通过创新地更新探测器单元的响应不一致性补偿系数，以解决正弦图中的垂直条纹伪影，通过对条纹伪影应用组稀疏约束和投影视图方向稀疏约束。同时，对重建图像施加稀疏约束，进一步纠正图像域中的环形伪影。实验证明，该方法不仅在消除环形伪影方面超越了现有方法，而且在保持结构细节和图像保真度方面也表现出色。

X 光 CT 环形伪影去除的双域正则化方法

A Dual-domain Regularization Method for Ring Artifact Removal of X-ray  CT

Ring artifacts in X-ray micro-CT images are one of the primary causes of
concern in their accurate visual interpretation and quantitative analysis. The
geometry of X-ray micro-CT scanners is similar to the medical CT machines,
except the sample is rotated with a stationary source and detector. The ring
artifacts are caused by a defect or non-linear responses in detector pixels
during the MicroCT data acquisition. Artifacts in MicroCT images can often be
so severe that the images are no longer useful for further analysis. Therefore,
it is essential to comprehend the causes of artifacts and potential solutions
to maximize image quality. This article presents a convolution neural network
(CNN)-based Deep Learning (DL) model inspired by UNet with a series of encoder
and decoder units with skip connections for removal of ring artifacts. The
proposed architecture has been evaluated using the Structural Similarity Index
Measure (SSIM) and Mean Squared Error (MSE). Additionally, the results are
compared with conventional filter-based non-ML techniques and are found to be
better than the latter.

通过使用基于卷积神经网络（CNN）的深度学习模型，本文介绍了一种受到 UNet 启发的架构，其中包括一系列的编码器、解码器单元以及跳跃连接，用于去除 X 射线微 CT 图像中的环形伪影。结果表明，与传统的基于滤波器的非机器学习技术相比，所提出的架构在消除环形伪影方面取得了更好的效果。