Wu Pengwei, Sun Xiaonan, Hu Hongjie, Mao Tingyu, Zhao Wei, Sheng Ke, Cheung Alice A, Niu Tianye
Sir Run Run Shaw Hospital, Zhejiang University School of Medicine; Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, 310016, People's Republic of China.
Phys Med Biol. 2015 Nov 7;60(21):8437-55. doi: 10.1088/0031-9155/60/21/8437. Epub 2015 Oct 14.
Shading artifacts in CT images are caused by scatter contamination, beam-hardening effect and other non-ideal imaging conditions. The purpose of this study is to propose a novel and general correction framework to eliminate low-frequency shading artifacts in CT images (e.g. cone-beam CT, low-kVp CT) without relying on prior information. The method is based on the general knowledge of the relatively uniform CT number distribution in one tissue component. The CT image is first segmented to construct a template image where each structure is filled with the same CT number of a specific tissue type. Then, by subtracting the ideal template from the CT image, the residual image from various error sources are generated. Since forward projection is an integration process, non-continuous shading artifacts in the image become continuous signals in a line integral. Thus, the residual image is forward projected and its line integral is low-pass filtered in order to estimate the error that causes shading artifacts. A compensation map is reconstructed from the filtered line integral error using a standard FDK algorithm and added back to the original image for shading correction. As the segmented image does not accurately depict a shaded CT image, the proposed scheme is iterated until the variation of the residual image is minimized. The proposed method is evaluated using cone-beam CT images of a Catphan©600 phantom and a pelvis patient, and low-kVp CT angiography images for carotid artery assessment. Compared with the CT image without correction, the proposed method reduces the overall CT number error from over 200 HU to be less than 30 HU and increases the spatial uniformity by a factor of 1.5. Low-contrast object is faithfully retained after the proposed correction. An effective iterative algorithm for shading correction in CT imaging is proposed that is only assisted by general anatomical information without relying on prior knowledge. The proposed method is thus practical and attractive as a general solution to CT shading correction.
CT图像中的阴影伪影是由散射污染、束硬化效应和其他非理想成像条件引起的。本研究的目的是提出一种新颖且通用的校正框架，以消除CT图像（如锥束CT、低千伏CT）中的低频阴影伪影，而无需依赖先验信息。该方法基于一个组织成分中CT值分布相对均匀的一般知识。首先对CT图像进行分割以构建模板图像，其中每个结构都填充有特定组织类型的相同CT值。然后，通过从CT图像中减去理想模板，生成来自各种误差源的残差图像。由于正向投影是一个积分过程，图像中不连续的阴影伪影在线积分中变成连续信号。因此，对残差图像进行正向投影，并对其线积分进行低通滤波，以估计导致阴影伪影的误差。使用标准的FDK算法从滤波后的线积分误差重建补偿图，并将其加回到原始图像进行阴影校正。由于分割后的图像不能准确描绘有阴影的CT图像，因此迭代所提出的方案，直到残差图像的变化最小化。使用Catphan©600体模和骨盆患者的锥束CT图像以及用于颈动脉评估的低千伏CT血管造影图像对所提出的方法进行评估。与未校正的CT图像相比，所提出的方法将整体CT值误差从超过200 HU降低到小于30 HU，并将空间均匀性提高了1.5倍。在所提出的校正之后，低对比度物体被如实地保留。提出了一种有效的CT成像阴影校正迭代算法，该算法仅由一般解剖信息辅助，而不依赖先验知识。因此，所提出的方法作为CT阴影校正的通用解决方案是实用且有吸引力的。
