南方医科大学学报

南方医科大学学报 ›› 2022, Vol. 42 ›› Issue (6): 832-839.doi: 10.12122/j.issn.1673-4254.2022.06.06

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投影插值联合物理校正的自适应CT金属伪影消除算法

朱其森，王永波，朱曼曼，陶 熙，边兆英，马建华   

  1. 南方医科大学生物医学工程学院，广东 广州 510515；琶洲实验室，广东 广州 510330
  • 出版日期:2022-06-20 发布日期:2022-06-28

An adaptive CT metal artifact reduction algorithm that combines projection interpolation and physical correction

ZHU Qisen, WANG Yongbo, ZHU Manman, TAO Xi, BIAN Zhaoying, MA Jianhua   

  1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Pazhou Lab, Guangzhou 510330, China
  • Online:2022-06-20 Published:2022-06-28

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摘要： 目的 为去除CT图像中的金属伪影,提升CT图像质量，本文提出一种结合投影插值和物理校正的自适应加权CT金属伪影消除算法。方法 采用归一化金属投影插值算法得到初始校正投影数据，在此基础上引入金属物理校正模型得到物理校正投影数据，通过自适应权重加权融合两者投影得到最终的校正投影，最后经过滤波反投影重建获得最终校正图像。为验证本文算法的有效性，采用仿真数据和临床数据进行实验。对于仿真数据本文采用PSNR和SSIM定量指标进行评估，而临床数据采用影像专家对结果图像评分方式来比较不同方法的伪影消除性能。结果 在仿真数据实验中，与对比方法结果相比，本文方法结果的PSNR定量指标至少提升了0.2 dB且获得最高SSIM定量指标。影像专家评分结果显示本文方法在临床数据中获得最高的评分结果3.616±0.338（5分制），与对比方法之间的伪影消除性能差异具有统计学意义（P<0.001）。结论 本文提出的金属伪影消除算法，可有效去除金属伪影，同时保持更多的组织结构信息，减少新伪影的产生。

关键词: CT金属伪影；物理校正；投影插值

Abstract: Objective To propose an adaptive weighted CT metal artifact reduce algorithm that combines projection interpolation and physical correction. Methods A normalized metal projection interpolation algorithm was used to obtain the initial corrected projection data. A metal physical correction model was then introduced to obtain the physically corrected projection data. To verify the effectiveness of the method, we conducted experiments using simulation data and clinical data. For the simulation data, the quantitative indicators PSNR and SSIM were used for evaluation, while for the clinical data, the resultant images were evaluated by imaging experts to compare the artifact-reducing performance of different methods. Results For the simulation data, the proposed method improved the PSNR value by at least 0.2 dB and resulted in the highest SSIM value among the methods for comparison. The experiment with the clinical data showed that the imaging experts gave the highest scores of 3.616±0.338 (in a 5-point scale) to the images processed using the proposed method, which had significant better artifact-reducing performance than the other methods (P<0.001). Conclusion The metal artifact reduction algorithm proposed herein can effectively reduce metal artifacts while preserving the tissue structure information and reducing the generation of new artifacts.

Key words: CT metal artifacts; physical correction; projection interpolation