Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305-5847, USA.
J Xray Sci Technol. 2010;18(4):403-14. doi: 10.3233/XST-2010-0271.
The presence of metals in patients causes streaking artifacts in X-ray CT and has been recognized as a problem that limits various applications of CT imaging. Accurate localization of metals in CT images is a critical step for metal artifacts reduction in CT imaging and many practical applications of CT images. The purpose of this work is to develop a method of auto-determination of the shape and location of metallic object(s) in the image space. The proposed method is based on the fact that when a metal object is present in a patient, a CT image can be divided into two prominent components: high density metal and low density normal tissues. This prior knowledge is incorporated into an objective function as the regularization term whose role is to encourage the solution to take a form of two intensity levels. A computer simulation study and four experimental studies are performed to evaluate the proposed approach. Both simulation and experimental studies show that the presented algorithm works well even in the presence of complicated shaped metal objects. For a hexagonally shaped metal embedded in a water phantom, for example, it is found that the accuracy of metal reconstruction is within sub-millimeter.
患者体内金属的存在会导致 X 射线 CT 中的条纹伪影，这已被认为是限制 CT 成像各种应用的一个问题。在 CT 图像中准确定位金属是减少 CT 图像金属伪影和许多 CT 图像实际应用的关键步骤。这项工作的目的是开发一种自动确定图像空间中金属物体形状和位置的方法。所提出的方法基于这样一个事实，即当金属物体存在于患者体内时，CT 图像可以分为两个突出的成分：高密度金属和低密度正常组织。该先验知识被纳入目标函数作为正则化项，其作用是鼓励解采用两个强度级别的形式。进行了计算机模拟研究和四项实验研究来评估所提出的方法。模拟和实验研究都表明，即使存在形状复杂的金属物体，所提出的算法也能很好地工作。例如，在一个水模中嵌入一个六边形的金属，发现金属重建的精度在亚毫米范围内。
