Deep learning-based ring artifact removal algorithm with inter-slice complementarity

Removing ring artifacts presents a significant challenge in x-ray computed tomography (CT) systems, particularly in those utilizing photon-counting detectors. To solve this problem, this study proposes the Inter-slice Complementarity Enhanced Ring Artifact Removal (ICE-RAR) algorithm, which is based on a learning-based approach. The variability and complexity of detector responses make it challenging to acquire enough paired data for training neural networks in real-world scenarios. To address this, the research first introduces a data simulation strategy that incorporates the characteristics of specific systems in accordance with the principles of ring artifact formation. Following this, a dual-branch neural network is designed, consisting of a global artifact removal branch and a central region enhancement branch, aimed at improving artifact removal, especially in the central region of interest where artifacts are more difficult to eliminate. Additionally, considering the independence of different detector element responses, the study proposes leveraging inter-slice complementarity to improve image restoration. The effectiveness of the central region reinforcement and inter-slice complementarity was confirmed through ablation experiments on simulated data. Both simulated and real-world results demonstrated that the ICE-RAR method effectively reduces ring artifacts while preserving image details. More importantly, by incorporating specific system characteristics into the data simulation process, models trained on simulated data can be directly applied to unseen real data, presenting significant potential for addressing ring artifact removal (RAR) issue in practical CT systems.