CT图像环形伪影去除方法研究现状及展望

Research status and prospect of CT image ring artifact removal methods

环形伪影是各类型计算机断层扫描(CT)图像中最常见的伪影之一,通常是由于探测器像素对X射线响应不一致导致的。有效去除环形伪影能极大提高CT图像质量,提升后期诊断和分析的精度,是CT图像重建中的必要步骤。因此,对环形伪影去除(又称“环形伪影校正”)方法进行了系统梳理。首先,介绍环形伪影的表现和成因,给出常用的数据集、算法库;其次,依次介绍基于探测器校正、基于解析和迭代求解(分为投影数据预处理、CT图像重建、CT图像后处理环节)、基于深度学习(分为卷积神经网络、生成对抗网络)的环形伪影去除方法,并分析每类方法的原理、发展过程及优缺点;最后,归纳现有环形伪影去除方法在鲁棒性、数据集多样化、模型构建等方面存在的技术瓶颈,并对解决方案进行展望。

Ring artifact is one of the most common artifacts in various types of CT(Computed Tomography)images,which is usually caused by the inconsistent response of detector pixels to X-rays.Effective removal of ring artifacts,which is a necessary step in CT image reconstruction,will greatly improve the quality of CT images and enhance the accuracy of later diagnosis and analysis.Therefore,the methods of ring artifact removal(also known as ring artifact correction)were systematically reviewed.Firstly,the performance and causes of ring artifacts were introduced,and commonly used datasets and algorithm libraries were given.Secondly,ring artifact removal methods were divided into three categories to introduce.The first category was based on detector calibration.The second category was based on analytical and iterative solution,including projection data preprocessing,CT image reconstruction and CT image post-processing.The last category was based on deep learning methods such as convolutional neural network and generative adversarial network.The principle,development process,advantages and limitations of each method were analyzed.Finally,the technical bottlenecks of existing ring artifact removal methods in terms of robustness,dataset diversity and model construction were summarized,and the solutions were prospected.