Li Na, Yin Yingchun, Zhao Junxiong, Xia Jun
Department of Biomedical Engineering, Guangdong Medical University, Dongguan, China.
Department of Pathology, Zibo Central Hospital, Zibo, China.
Quant Imaging Med Surg. 2025 Apr 1;15(4):2889-2904. doi: 10.21037/qims-24-1102. Epub 2025 Mar 28.
Ring artifacts are a common and persistent issue in computed tomography (CT) imaging, arising from detector calibration errors, gain variations, and other hardware inconsistencies, and their presence can significantly compromise the diagnostic accuracy and clinical utility of CT scans. This study aimed to propose a comprehensive approach for removing the ring artifacts in CT images, with a particular focus on the challenges posed by the cutting-edge photon counting CT (PCCT) technology.
This method skillfully combines super-pixel segmentation with an adaptive form of Relative Total Variation, resulting in a robust, two-stage process for artifact correction. In the first stage, high-intensity ring artifacts are efficiently addressed using super-pixel segmentation. The second stage involves the application of adaptive relative total variance, fine-tuned by the mesh adaptive direct search algorithm, to effectively correct low-intensity artifacts. This dual-stage strategy is key to preserving crucial image details while successfully eliminating artifacts. The effectiveness of the proposed method is thoroughly validated through various experiments, including digital simulations and studies involving phantoms and patients.
These tests reveal a significant reduction in both high and low-intensity ring artifacts, while maintaining the structural integrity and grayscale balance of the images. Additionally, the versatility and robustness of this method are highlighted by its suitability for a wide range of imaging scenarios and equipment.
This study not only tackles a significant challenge in medical imaging but also paves the way for new applications of PCCT in precision medicine. The code is publicly available for reproducibility, Github: https://github.com/XiaoNa-gdmu/Ring.
环形伪影是计算机断层扫描（CT）成像中常见且持续存在的问题，由探测器校准误差、增益变化和其他硬件不一致性引起，其存在会显著损害CT扫描的诊断准确性和临床效用。本研究旨在提出一种去除CT图像中环形伪影的综合方法，特别关注前沿光子计数CT（PCCT）技术带来的挑战。
该方法巧妙地将超像素分割与自适应形式的相对全变差相结合，形成了一个强大的两阶段伪影校正过程。在第一阶段，使用超像素分割有效地处理高强度环形伪影。第二阶段涉及应用由网格自适应直接搜索算法微调的自适应相对全变差，以有效校正低强度伪影。这种双阶段策略是在成功消除伪影的同时保留关键图像细节的关键。通过各种实验，包括数字模拟以及涉及体模和患者的研究，对所提出方法的有效性进行了全面验证。
这些测试表明，高强度和低强度环形伪影均显著减少，同时保持了图像的结构完整性和灰度平衡。此外，该方法适用于广泛的成像场景和设备，突出了其通用性和鲁棒性。
本研究不仅解决了医学成像中的一个重大挑战，还为PCCT在精准医学中的新应用铺平了道路。代码可公开获取以实现可重复性，Github：https://github.com/XiaoNa-gdmu/Ring 。
