Metal artifact reduction in x-ray computed tomography (CT) by constrained optimization
Metal artifact reduction in x-ray computed tomography (CT) by constrained optimization
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DOI：
10.1118/1.3533711
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发表时间：
2011-02-01
期刊：
影响因子：
3.8
通讯作者：
Xing, Lei
中科院分区：
文献类型：
--
作者：
Zhang, Xiaomeng;Wang, Jing;Xing, Lei
目的：金属植入物引起的条纹伪影一直被认为是限制CT成像各种应用的问题。本文提出了一种基于约束优化的迭代金属伪影消除算法，方法：利用二值金属识别算法自动确定图像域中金属物体的形状和位置，在投影域中分割出“金属阴影”，然后利用约束优化算法进行图像重建。它最小化反映图像的先验知识的预定义函数，受到估计的投影数据在可用的排除金属阴影的投影数据的指定容差内的约束，并强制执行图像非负性。最小化问题是通过交替的投影到凸集和最速梯度下降的目标函数。约束优化算法进行评估与惩罚的平滑objective.Results：研究表明，该方法能够显着减少金属伪影，抑制噪声，提高软组织的可见性。它优于FBP-type方法和ART和EM方法，并产生无伪影images.Conclusions：约束优化是一种有效的方法来处理嵌入金属物体的CT重建。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“丢失数据”图像重建问题。(c)2011年美国医学物理学家协会。[DOI：10.1118/1.3533711]
Purpose: 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.Methods: 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.Results: 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.Conclusions: 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. (c) 2011 American Association of Physicists in Medicine. [DOI: 10.1118/1.3533711]