Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA.
Med Phys. 2010 Nov;37(11):5867-75. doi: 10.1118/1.3505294.
To develop a binary image reconstruction method for the autolocalization of metallic object(s) in CT with sparse projections.
The authors divide the system into two types of contents: Metal(s) and nonmetal(s). The boundaries of metallic objects are obtained by using a penalized weighted least-squares algorithm with the adequate intensity gradient-controlled. A novel mechanism of "amplifying" the difference between metal(s) and nonmetallic substances is introduced by preprocessing the sinogram data, which is shown to be necessary in dealing with a case with sparse projection data. A series of experimental studies are performed to evaluate the proposed approach.
A novel binary CT image reconstruction formalism is established for the autodetermination of the shape and location of metallic objects in the presence of limited number of projections. Experimental studies reveal that the presented algorithm works well even when the embedded metal object(s) has different shape(s). It is also shown that when the projection data are sparse, a differential manipulation of projection data can greatly facilitate the binary reconstruction process and allow the authors to obtain accurate binary CT images that would otherwise be unattainable.
Binary CT reconstruction provides a viable method for determining the geometric distribution information of the implanted metal objects in CT imaging.
开发一种用于 CT 稀疏投影下金属物体自动定位的二值图像重建方法。
作者将系统分为金属和非金属两种类型。通过使用带有适当强度梯度控制的惩罚加权最小二乘算法，获得金属物体的边界。通过对正弦图数据进行预处理，引入了一种“放大”金属与非金属物质之间差异的新机制，这在处理稀疏投影数据的情况下是必要的。进行了一系列实验研究来评估所提出的方法。
建立了一种新颖的二进制 CT 图像重建形式，用于在有限数量的投影下自动确定金属物体的形状和位置。实验研究表明，即使嵌入的金属物体具有不同的形状，所提出的算法也能很好地工作。还表明，当投影数据稀疏时，对投影数据进行微分处理可以极大地促进二进制重建过程，并允许作者获得否则无法获得的准确二进制 CT 图像。
二进制 CT 重建为确定 CT 成像中植入金属物体的几何分布信息提供了一种可行的方法。
