Metal artifact reduction in CT using tissue-class modeling and adaptive prefiltering
Metal artifact reduction in CT using tissue-class modeling and adaptive prefiltering
复制标题
DOI：
10.1118/1.2218062
复制
发表时间：
2006-08-01
期刊：
影响因子：
3.8
通讯作者：
Spies, Lothar
中科院分区：
文献类型：
--
作者：
Bal, Matthieu;Spies, Lothar
金属假体、手术夹或牙科填充物等高密度物体会在计算机断层扫描图像中产生条纹状伪影。我们提出了一种通过将丢失的信息修复到损坏的正弦图中来减少金属伪影的新方法。该信息由从失真图像中提取的组织类模型提供。为此，首先对图像进行自适应滤波，以减少噪声内容并平滑条纹伪影。接下来，使用聚类算法将图像分割为不同的材料类别。原始正弦图中损坏和丢失的信息是使用来自组织类模型的正向投影信息来完成的。校正方法的性能在幻像图像上进行评估。研究了具有广泛金属伪影的临床图像。模型和临床研究表明，金属伪影（例如条纹）显着减少，图像中的阴影也被消除。此外，这种新方法提高了器官轮廓的可检测性。例如，在放射治疗计划中，这可能具有很大的相关性，其中受金属伪影影响的图像可能会导致治疗计划不理想。 (C) 2006 年美国医学物理学家协会。
High-density objects such as metal prostheses, surgical clips, or dental fillings generate streak-like artifacts in computed tomography images. We present a novel method for metal artifact reduction by in-painting missing information into the corrupted sinogram. The information is provided by a tissue-class model extracted from the distorted image. To this end the image is first adaptively filtered to reduce the noise content and to smooth out streak artifacts. Consecutively, the image is segmented into different material classes using a clustering algorithm. The corrupted and missing information in the original sinogram is completed using the forward projected information from the tissue-class model. The performance of the correction method is assessed on phantom images. Clinical images featuring a broad spectrum of metal artifacts are studied. Phantom and clinical studies show that metal artifacts, such as streaks, are significantly reduced and shadows in the image are eliminated. Furthermore, the novel approach improves detectability of organ contours. This can be of great relevance, for instance, in radiation therapy planning, where images affected by metal artifacts may lead to suboptimal treatment plans. (C) 2006 American Association of Physicists in Medicine.