Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA.
Med Phys. 2011 Feb;38(2):701-11. doi: 10.1118/1.3533711.
The streak artifacts caused by metal implants have long been recognized as a problem that limits various applications of CT imaging. In this work, the authors propose an iterative metal artifact reduction algorithm based on constrained optimization.
After the shape and location of metal objects in the image domain is determined automatically by the binary metal identification algorithm and the segmentation of "metal shadows" in projection domain is done, constrained optimization is used for image reconstruction. It minimizes a predefined function that reflects a priori knowledge of the image, subject to the constraint that the estimated projection data are within a specified tolerance of the available metal-shadow-excluded projection data, with image non-negativity enforced. The minimization problem is solved through the alternation of projection-onto-convex-sets and the steepest gradient descent of the objective function. The constrained optimization algorithm is evaluated with a penalized smoothness objective.
The study shows that the proposed method is capable of significantly reducing metal artifacts, suppressing noise, and improving soft-tissue visibility. It outperforms the FBP-type methods and ART and EM methods and yields artifacts-free images.
Constrained optimization is an effective way to deal with CT reconstruction with embedded metal objects. Although the method is presented in the context of metal artifacts, it is applicable to general "missing data" image reconstruction problems.
金属植入物引起的条纹伪影一直以来都是限制 CT 成像各种应用的一个问题。在这项工作中，作者提出了一种基于约束优化的迭代金属伪影减少算法。
通过二进制金属识别算法自动确定图像域中金属物体的形状和位置，并在投影域中对“金属阴影”进行分割后，使用约束优化进行图像重建。它最小化一个预定义的函数，该函数反映了图像的先验知识，同时受限于估计的投影数据与可用的无金属阴影投影数据在指定容差内的约束，并且强制执行图像非负性。通过交替使用投影到凸集和目标函数的最陡下降来解决最小化问题。约束优化算法通过惩罚平滑目标进行评估。
研究表明，所提出的方法能够显著减少金属伪影、抑制噪声并提高软组织的可见度。它优于 FBP 类方法和 ART 和 EM 方法，并产生无伪影的图像。
约束优化是处理嵌入式金属物体的 CT 重建的有效方法。尽管该方法是在金属伪影的背景下提出的，但它适用于一般的“缺失数据”图像重建问题。
