Investigation of the optimum location of external markers for patient setup accuracy enhancement at external beam radiotherapy.
Investigation of the optimum location of external markers for patient setup accuracy enhancement at external beam radiotherapy.
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DOI：
10.1120/jacmp.v17i6.6265
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发表时间：
2016-11-08
影响因子：
2.1
通讯作者：
Nankali S
中科院分区：
文献类型：
--
作者：
Miandoab PS;Torshabi AE;Nankali S
在体外放射治疗中，最常见和最可靠的方法之一是使用外部标记物来确定患者的几何形状和/或预测肿瘤的位置。在这项研究中，主要具有挑战性的问题是通过调查外部标记物的位置来增加患者设置的准确性。由于每个外部标记物的位置可能产生不同的患者设置精度，因此使用适当的选择性算法评估外部标记物的不同位置是重要的。为了做到这一点，提出了两种商业上可用的算法，即a)典型相关分析(CCA)和b)主成分分析(PCA)作为输入选择算法。它们的工作基础是给定数据集之间的最大相关系数和最小方差。所提出的输入选择算法与自适应神经模糊推理系统(ANFIS)相结合作为关联模型来给出患者的位置信息作为输出。我们提出的算法准确地提供了ANFIS关联模型的输入文件。本研究所需的数据集是通过基于NURBS的4DXCAT拟人模型来准备的，该模型可以模拟人体复杂器官的形状和结构以及动态器官的运动信息。此外，在这项研究中使用了四个接受肺癌放射治疗的真实患者的数据库来验证所提出的策略。最后的分析结果表明，输入选择算法可以从ANFIS模型的均方根误差(RMSE)在给定区域具有最小值的胸部区域中合理地选择特定的外部标记。研究还发现，选定的标志点位置紧靠在表面点运动幅度大、相关性强的区域。PAC数量(S)：87.55公里，87.55.N
In external beam radiotherapy, one of the most common and reliable methods for patient geometrical setup and/or predicting the tumor location is use of external markers. In this study, the main challenging issue is increasing the accuracy of patient setup by investigating external markers location. Since the location of each external marker may yield different patient setup accuracy, it is important to assess different locations of external markers using appropriate selective algorithms. To do this, two commercially available algorithms entitled a) canonical correlation analysis (CCA) and b) principal component analysis (PCA) were proposed as input selection algorithms. They work on the basis of maximum correlation coefficient and minimum variance between given datasets. The proposed input selection algorithms work in combination with an adaptive neuro‐fuzzy inference system (ANFIS) as a correlation model to give patient positioning information as output. Our proposed algorithms provide input file of ANFIS correlation model accurately. The required dataset for this study was prepared by means of a NURBS‐based 4D XCAT anthropomorphic phantom that can model the shape and structure of complex organs in human body along with motion information of dynamic organs. Moreover, a database of four real patients undergoing radiation therapy for lung cancers was utilized in this study for validation of proposed strategy. Final analyzed results demonstrate that input selection algorithms can reasonably select specific external markers from those areas of the thorax region where root mean square error (RMSE) of ANFIS model has minimum values at that given area. 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. PACS number(s): 87.55.km, 87.55.N
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