Department of Physics, Stanford University, Stanford, California 94305, USA.
Med Phys. 2011 May;38(5):2424-9. doi: 10.1118/1.3577601.
Four-dimensional (4D) computed tomography (CT) has been widely used as a tool to characterize respiratory motion in radiotherapy. The two most commonly used 4D CT algorithms sort images by the associated respiratory phase or displacement into a predefined number of bins, and are prone to image artifacts at transitions between bed positions. The purpose of this work is to demonstrate a method of reducing motion artifacts in 4D CT by incorporating anatomic similarity into phase or displacement based sorting protocols.
Ten patient datasets were retrospectively sorted using both the displacement and phase based sorting algorithms. Conventional sorting methods allow selection of only the nearest-neighbor image in time or displacement within each bin. In our method, for each bed position either the displacement or the phase defines the center of a bin range about which several candidate images are selected. The two dimensional correlation coefficients between slices bordering the interface between adjacent couch positions are then calculated for all candidate pairings. Two slices have a high correlation if they are anatomically similar. Candidates from each bin are then selected to maximize the slice correlation over the entire data set using the Dijkstra's shortest path algorithm. To assess the reduction of artifacts, two thoracic radiation oncologists independently compared the resorted 4D datasets pairwise with conventionally sorted datasets, blinded to the sorting method, to choose which had the least motion artifacts. Agreement between reviewers was evaluated using the weighted kappa score.
Anatomically based image selection resulted in 4D CT datasets with significantly reduced motion artifacts with both displacement (P = 0.0063) and phase sorting (P = 0.00022). There was good agreement between the two reviewers, with complete agreement 34 times and complete disagreement 6 times.
Optimized sorting using anatomic similarity significantly reduces 4D CT motion artifacts compared to conventional phase or displacement based sorting. This improved sorting algorithm is a straightforward extension of the two most common 4D CT sorting algorithms.
四维（4D）计算机断层扫描（CT）已广泛应用于放射治疗中的呼吸运动特征描述。两种最常用的 4D CT 算法通过与相关呼吸相位或位移相关的图像对来对图像进行分类，将其分为预定数量的箱，并容易在床位之间转换时出现图像伪影。本研究旨在通过将解剖相似性纳入基于相位或位移的分类方案来减少 4D CT 中的运动伪影。
回顾性地对 10 个患者数据集使用基于位移和相位的分类算法进行分类。传统的分类方法仅允许在每个箱中选择时间或位移的最近邻图像。在我们的方法中，对于每个床位，要么位移，要么相位定义了关于其选择几个候选图像的箱范围的中心。然后计算相邻床位之间界面处边界切片之间的二维相关系数。如果切片在解剖上相似，则它们具有较高的相关性。然后，使用迪杰斯特拉最短路径算法，针对整个数据集，对每个箱中的候选物进行选择，以最大化切片相关性。为了评估伪影的减少程度，两位胸部放射肿瘤学家使用加权 Kappa 评分，在不知道分类方法的情况下，独立地将重新分类的 4D 数据集与传统分类数据集进行比较，以选择具有最少运动伪影的数据集。
基于解剖的图像选择导致位移（P = 0.0063）和相位分类（P = 0.00022）的 4D CT 数据集的运动伪影显著减少。两位审阅者之间的一致性良好，完全一致 34 次，完全不一致 6 次。
与传统的基于相位或位移的分类相比，使用解剖相似性进行优化分类可显著减少 4D CT 运动伪影。这种改进的分类算法是最常用的两种 4D CT 分类算法的直接扩展。
