Correlation and prediction uncertainties in the CyberKnife Synchrony respiratory tracking system
Correlation and prediction uncertainties in the CyberKnife Synchrony respiratory tracking system
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DOI：
10.1118/1.3596527
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发表时间：
2011-07-01
期刊：
影响因子：
3.8
通讯作者：
Lord, Bryce
中科院分区：
文献类型：
--
作者：
Pepin, Eric W.;Wu, Huanmei;Lord, Bryce
目的：CyberKnife使用在线预测模型来改善治疗肺部肿瘤时的辐射输送。本研究评估了CyberKnife放射治疗系统在治疗边缘的肿瘤体积（GTV）方面所使用的预测模型：从CyberKnife同步模型产生的数据日志文件中，可以计算辐射输送的不确定性。模型点指示肿瘤的跟踪位置，预测点预测未来约115 ms的位置。针对来自23名去识别肺部患者的100个治疗模型数据集，分析了预测器点与其对应的建模器点之间的差异。在每个解剖方向上确定治疗边界，以覆盖从Modeler点导出的GTV的任意体积，当辐射靶向预测点时。每个治疗模型有大约30分钟的运动数据，其中大约10分钟构成治疗时间;仅这10分钟用于分析。分析了边缘大小的频率，并将截断高斯正态函数拟合到每个方向的分布。然后，每个高斯分布的标准偏差用于描述每个有符号维度中必要的边缘扩展，以实现所需的覆盖。在这项研究中，95%的建模点覆盖率与99%的建模覆盖率进行了比较。研究了另外两个误差源：相关误差和瞄准误差。这些都被添加到预测误差，给一个总的误差为射波刀在治疗过程中的肺tumors.Results：考虑到从高斯的平均值在每个有符号的dimensions.2西格玛的大小，95%建模点覆盖率所需的边缘扩展是1.2毫米的横向（LAT）方向和1.7毫米的前后（AP）方向。对于上-下（SI）方向，拟合较差;但根据经验，扩张为3.5 mm。对于99%建模者点覆盖，AP边缘为3.6 mm，外侧边缘为2.9 mm。99%建模者点覆盖的SI边缘高度可变。在95%时，SI方向的总误差为6.9 mm，AP方向为4.6 mm，横向为3.5 mm。结论：预测点紧密跟随建模点。在每个临床方向上都发现了边缘，这将为本研究中审查的95%的模型提供95%的建模点覆盖率。在95%的模型中，对于99%的建模点覆盖率，在两个临床方向上发现了相似的边缘。这些结果可以为CyberKnife治疗CTV边缘的选择提供指导。(C)2011年美国医学物理学家协会。[DOI 10.1118/1.3596527]
Purpose: The CyberKnife uses an online prediction model to improve radiation delivery when treating lung tumors. This study evaluates the prediction model used by the CyberKnife radiation therapy system in terms of treatment margins about the gross tumor volume (GTV).Methods: From the data log files produced by the CyberKnife synchrony model, the uncertainty in radiation delivery can be calculated. Modeler points indicate the tracked position of the tumor and Predictor points predict the position about 115 ms in the future. The discrepancy between Predictor points and their corresponding Modeler points was analyzed for 100 treatment model data sets from 23 de-identified lung patients. The treatment margins were determined in each anatomic direction to cover an arbitrary volume of the GTV, derived from the Modeler points, when the radiation is targeted at the Predictor points. Each treatment model had about 30 min of motion data, of which about 10 min constituted treatment time; only these 10 min were used in the analysis. The frequencies of margin sizes were analyzed and truncated Gaussian normal functions were fit to each direction's distribution. The standard deviation of each Gaussian distribution was then used to describe the necessary margin expansions in each signed dimension in order to achieve the desired coverage. In this study, 95% modeler point coverage was compared to 99% modeler coverage. Two other error sources were investigated: the correlation error and the targeting error. These were added to the prediction error to give an aggregate error for the CyberKnife during treatment of lung tumors.Results: Considering the magnitude of 2 sigma from the mean of the Gaussian in each signed dimension, the margin expansions needed for 95% modeler point coverage were 1.2 mm in the lateral (LAT) direction and 1.7 mm in the anterior-posterior (AP) direction. For the superior-inferior (SI) direction, the fit was poor; but empirically, the expansions were 3.5 mm. For 99% modeler point coverage, the AP margin was 3.6 mm and the lateral margin was 2.9 mm. The SI margins for 99% modeler point coverage were highly variable. The aggregate error at 95% was 6.9 mm in the SI direction, 4.6 mm in the AP direction, and 3.5 in the lateral direction.Conclusions: The Predictor points follow the Modeler points closely. Margins were found in each clinical direction that would provide 95% modeler point coverage for 95% of the models reviewed in this study. Similar margins were found in two clinical directions for 99% modeler point coverage in 95% of models. These results can offer guidance in the selection of CTV margins for treatment with the CyberKnife. (C) 2011 American Association of Physicists in Medicine. [DOI:10.1118/1.3596527]