Optimal surface marker locations for tumor motion estimation in lung cancer radiotherapy
Optimal surface marker locations for tumor motion estimation in lung cancer radiotherapy
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DOI：
10.1088/0031-9155/57/24/8201
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发表时间：
2012-12-21
影响因子：
3.5
通讯作者：
Jiang, Steve B.
中科院分区：
文献类型：
--
作者：
Dong, Bin;Graves, Yan Jiang;Jiang, Steve B.
在肺癌放射治疗中，使用患者体表上的基准标记来预测肿瘤的位置是一种广泛使用的方法。这项工作的目的是提出一种算法，自动识别患者表面上具有最佳预测能力的肿瘤运动的稀疏位置集。在我们的算法中，假设表面标记物的运动与肿瘤的运动之间存在线性关系。外表面标志物的稀疏选择以及标志物运动与肿瘤内部运动之间的线性关系由预测矩阵表示。这样的矩阵是通过求解优化问题来确定的，其中目标函数包含一个稀疏项，该稀疏项惩罚了在患者表面上选择的标志物的数量。采用Bregman迭代法求解所提出的优化问题。我们的算法已经在四个肺癌患者的实际临床数据上进行了测试。研究中使用了10个时相的胸部4DCT扫描。在参考相位上，点的网格被投射到患者的表面(除了患者的背部)，并通过相应CT图像的可变形图像配准传播到其他相位。每个阶段的肿瘤位置也是手动勾画的。我们使用4DCT图像十分之九的相位来识别与肿瘤运动最相关的一小组表面标志，并同时找到预测矩阵。然后使用第十阶段来测试预测的准确性。研究发现，平均需要6到7个表面标记来预测肿瘤位置，3D误差约为1 mm。研究还发现，所选择的标记位置紧密地位于表面点运动幅度较大且与肿瘤运动高度相关的区域。我们的方法可以自动选择患者外表面上的稀疏位置，并基于4DCT估计相关矩阵，以便选择的表面位置可以用于放置基准标记，以最佳地预测内部肿瘤的运动。
Using fiducial markers on the patient's body surface to predict the tumor location is a widely used approach in lung cancer radiotherapy. The purpose of this work is to propose an algorithm that automatically identifies a sparse set of locations on the patient's surface with the optimal prediction power for the tumor motion. In our algorithm, it is assumed that there is a linear relationship between the surface marker motion and the tumor motion. The sparse selection of markers on the external surface and the linear relationship between the marker motion and the internal tumor motion are represented by a prediction matrix. Such a matrix is determined by solving an optimization problem, where the objective function contains a sparsity term that penalizes the number of markers chosen on the patient's surface. Bregman iteration is used to solve the proposed optimization problem. The performance of our algorithm has been tested on realistic clinical data of four lung cancer patients. Thoracic 4DCT scans with ten phases are used for the study. On a reference phase, a grid of points are casted on the patient's surfaces (except for the patient's back) and propagated to other phases via deformable image registration of the corresponding CT images. Tumor locations at each phase are also manually delineated. We use nine out of ten phases of the 4DCT images to identify a small group of surface markers that are mostly correlated with the motion of the tumor and find the prediction matrix at the same time. The tenth phase is then used to test the accuracy of the prediction. It is found that on average six to seven surface markers are necessary to predict tumor locations with a 3D error of about 1 mm. It is also found that the selected marker locations lie closely in those areas where surface point motion has a large amplitude and a high correlation with the tumor motion. Our method can automatically select sparse locations on the patient's external surface and estimate a correlation matrix based on 4DCT, so that the selected surface locations can be used to place fiducial markers to optimally predict internal tumor motions.