Inter-slice complementarity enhanced ring artifact removal using central region reinforced neural network

In computed tomography (CT), non-uniform detector responses often lead to ring artifacts in reconstructed images. For conventional energy-integrating detectors, such artifacts can be effectively addressed through dead-pixel correction and flat-dark field calibration. However, the response characteristics of photon-counting detectors (PCDs) are more complex, and standard calibration procedures can only partially mitigate ring artifacts. Consequently, developing high-performance ring artifact removal (RAR) algorithms is essential for PCD-based CT systems. To this end, we propose the inter-slice complementarity enhanced RAR (ICE-RAR) algorithm. Since artifact removal in the central region is particularly challenging, ICE-RAR utilizes a dual-branch neural network that could simultaneously perform global artifact removal and enhance the central region restoration. Moreover, recognizing that the detector response is also non-uniform in the vertical direction, ICE-RAR suggests extracting and utilizing inter-slice complementarity to enhance its performance in artifact elimination and image restoration. Experiments on simulated data and two real datasets acquired from PCD-based CT systems demonstrate the effectiveness of ICE-RAR in reducing ring artifacts while preserving structural details. More importantly, since the system-specific characteristics are incorporated into the data simulation process, models trained on the simulated data can be directly applied to unseen real data from the target PCD-based CT system, demonstrating ICE-RAR’s potential to address the RAR problem in practical CT systems. The implementation is publicly available at https://github.com/DarkBreakerZero/ICE-RAR.