Dong Xue, Yang Xiaofeng, Rosenfield Jonathan, Elder Eric, Dhabaan Anees
Emory University, Winship Cancer Institute, Atlanta, GA.
Proc SPIE Int Soc Opt Eng. 2017 Feb;10132. doi: 10.1117/12.2255083. Epub 2017 Mar 9.
X-ray computed tomography (CT) is widely used in radiation therapy treatment planning in recent years. However, metal implants such as dental fillings and hip prostheses can cause severe bright and dark streaking artifacts in reconstructed CT images. These artifacts decrease image contrast and degrade HU accuracy, leading to inaccuracies in target delineation and dose calculation. In this work, a metal artifact reduction method is proposed based on the intrinsic anatomical similarity between neighboring CT slices. Neighboring CT slices from the same patient exhibit similar anatomical features. Exploiting this anatomical similarity, a gamma map is calculated as a weighted summation of relative HU error and distance error for each pixel in an artifact-corrupted CT image relative to a neighboring, artifact-free image. The minimum value in the gamma map for each pixel is used to identify an appropriate pixel from the artifact-free CT slice to replace the corresponding artifact-corrupted pixel. With the proposed method, the mean CT HU error was reduced from 360 HU and 460 HU to 24 HU and 34 HU on head and pelvis CT images, respectively. Dose calculation accuracy also improved, as the dose difference was reduced from greater than 20% to less than 4%. Using 3%/3mm criteria, the gamma analysis failure rate was reduced from 23.25% to 0.02%. An image-based metal artifact reduction method is proposed that replaces corrupted image pixels with pixels from neighboring CT slices free of metal artifacts. This method is shown to be capable of suppressing streaking artifacts, thereby improving HU and dose calculation accuracy.
近年来，X射线计算机断层扫描（CT）在放射治疗治疗计划中得到了广泛应用。然而，诸如牙科填充物和髋关节假体等金属植入物会在重建的CT图像中导致严重的亮暗条纹伪影。这些伪影会降低图像对比度并降低HU准确性，从而导致靶区勾画和剂量计算不准确。在这项工作中，基于相邻CT切片之间的内在解剖相似性，提出了一种金属伪影减少方法。来自同一患者的相邻CT切片呈现出相似的解剖特征。利用这种解剖相似性，计算一个伽马图，作为伪影损坏的CT图像中每个像素相对于相邻的无伪影图像的相对HU误差和距离误差的加权总和。伽马图中每个像素的最小值用于从无伪影的CT切片中识别合适的像素，以替换相应的伪影损坏像素。使用所提出的方法，头部和骨盆CT图像上的平均CT HU误差分别从360 HU和460 HU降低到24 HU和34 HU。剂量计算准确性也得到了提高，因为剂量差异从大于20%降低到了小于4%。使用3%/3mm标准，伽马分析失败率从23.25%降低到了0.02%。提出了一种基于图像的金属伪影减少方法，该方法用来自相邻无金属伪影的CT切片中的像素替换损坏的图像像素。该方法被证明能够抑制条纹伪影，从而提高HU和剂量计算准确性。
