School of Biomedical Engineering, Capital Medical University, Beijing, China.
J Xray Sci Technol. 2018;26(4):593-602. doi: 10.3233/XST-17325.
Metal artifacts severely degrade CT image quality in clinical diagnosis, which are difficult to removed, especially for the beam hardening artifacts. The metal artifact reduction (MAR) based on prior images are the most frequently-used methods. However, there exists a lot misclassification in most prior images caused by absence of prior information such as the spectrum distribution of X-ray beam source, especially many or big metal included. The purpose of this work is to find a more accurate prior image to improve image quality.
The proposed method comprise of following four steps. First, the metal image is segmented by thresholding an initial image, where the metal traces are identified in the initial projection data using the forward projection of the metal image. Second, the accurate absorbent model of certain metal image is calculated according to the spectrum distribution of certain X-ray beam source and energy-dependent attenuation coefficients of metal. Then, a new metal image is reconstructed by the general analytical reconstruction algorithm such as filtered back projection (FPB). The prior image is obtained by segmenting the difference image between the initial image and the new metal image into air, tissue and bone. Finally, the initial projection data are normalized by dividing the projection data of prior image pixel to pixel, the corrected image is obtained by interpolation, denormalization and reconstruction.
Some clinical images with dental fillings and knee prostheses are used to evaluate the proposed algorithm and normalized metal artifact reduction (NMAR) and linear interpolation (LI) method. The results demonstrate the artifacts can be reduced efficiently by the proposed method.
The proposed method could obtain an exact prior image using the prior information about X-ray beam source and energy-dependent attenuation coefficients of metal. As a result, the better performance of reducing beam hardening artifacts can be improved, even though there were many or big implants. Moreover, the process of the proposed method is rather simple and little extra calculation burden is necessary. It has superiorities over other algorithms when include big or many implants.
金属伪影严重降低了 CT 图像在临床诊断中的质量，这些伪影很难去除，尤其是对于束硬化伪影。基于先验图像的金属伪影减少（MAR）是最常用的方法。然而，由于缺乏 X 射线束源的光谱分布等先验信息，大多数先验图像中存在大量分类错误，尤其是包含许多或大金属的情况。本工作旨在找到更准确的先验图像以提高图像质量。
所提出的方法包括以下四个步骤。首先，通过对初始图像进行阈值处理来分割金属图像，其中使用金属图像的正向投影在初始投影数据中识别金属痕迹。其次，根据特定 X 射线束源的光谱分布和金属的能量依赖性衰减系数计算特定金属图像的精确吸收体模型。然后，通过滤波反投影（FPB）等一般解析重建算法重建新的金属图像。通过将初始图像与新的金属图像之间的差分图像分割成空气、组织和骨骼来获得先验图像。最后，通过逐像素划分先验图像像素的投影数据来对初始投影数据进行归一化，通过插值、去归一化和重建获得校正图像。
使用具有牙填充物和膝关节假体的一些临床图像来评估所提出的算法和归一化金属伪影减少（NMAR）和线性插值（LI）方法。结果表明，该方法可以有效地减少伪影。
所提出的方法可以利用 X 射线束源和金属的能量依赖性衰减系数的先验信息获得准确的先验图像。因此，可以改善减少束硬化伪影的性能，即使存在许多或大的植入物。此外，该方法的过程相当简单，不需要额外的计算负担。当包含大或多植入物时，它优于其他算法。
