基于改进的剂量不确定度势累积模型的三维剂量验证中有限探测器元件的预测伽马通过率。
Predictive gamma passing rate for three-dimensional dose verification with finite detector elements via improved dose uncertainty potential accumulation model.
机构信息
Department of Radiation Oncology, Hospital of the University of Occupational and Environmental Health, Fukuoka, 807-8556, Japan.
Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan.
出版信息
Med Phys. 2020 Mar;47(3):1349-1356. doi: 10.1002/mp.13985. Epub 2020 Jan 6.
PURPOSE
We aim to develop a method to predict the gamma passing rate (GPR) of a three-dimensional (3D) dose distribution measured by the Delta4 detector system using the dose uncertainty potential (DUP) accumulation model.
METHODS
Sixty head-and-neck intensity-modulated radiation therapy (IMRT) treatment plans were created in the XiO treatment planning system. All plans were created using nine step-and-shoot beams of the ONCOR linear accelerator. Verification plans were created and measured by the Delta4 system. The planar DUP (pDUP) manifesting on a field edge was generated from the segmental aperture shape with a Gaussian folding on the beam's-eye view. The DUP at each voxel ( ) was calculated by projecting the pDUP on the Delta4 phantom with its attenuation considered. The learning model (LM), an average GPR as a function of the DUP, was approximated by an exponential function to compensate for the low statistics of the learning data due to a finite number of the detectors. The coefficient was optimized to ensure that the difference between the measured and predicted GPRs ( ) was minimized. The standard deviation (SD) of the was evaluated for the optimized LM.
RESULTS
It was confirmed that the coefficient was larger for tighter tolerance. This result corresponds to the expectation that the attenuation of the will be large for tighter tolerance. The and were observed to be proportional for all tolerances investigated. The SD of was 2.3, 4.1, and 6.7% for tolerances of 3%/3 mm, 3%/2 mm, 2%/2 mm, respectively.
CONCLUSION
The DUP-based predicting method of the GPR was extended to 3D by introducing DUP attenuation and an optimized analytical LM to compensate for the low statistics of the learning data due to a finite number of detector elements. The precision of the predicted GPR is expected to be improved by improving the LM and by involving other metrics.
目的
我们旨在开发一种使用剂量不确定度潜力（DUP）累积模型来预测 Delta4 探测器系统测量的三维（3D）剂量分布的伽马通过率（GPR）的方法。
方法
在 XiO 治疗计划系统中创建了 60 个头颈部强度调制放射治疗（IMRT）治疗计划。所有计划均使用 ONCOR 直线加速器的九个步进射击束创建。验证计划由 Delta4 系统创建和测量。在射束视线图上，通过对分段孔径形状进行高斯折叠生成边缘出现的平面 DUP（pDUP）。通过考虑 Delta4 体模的衰减将 pDUP 投影到每个体素（ ）上，计算出 DUP 。学习模型（LM）是 DUP 的平均 GPR 函数，通过指数函数 来逼近，以补偿由于有限数量的探测器导致学习数据的统计数据较低。优化系数 以确保测量和预测 GPR 之间的差异（ ）最小化。优化 LM 的 标准偏差（SD）进行了评估。
结果
确认系数 对于更严格的容差较大。这一结果对应于这样的期望，即对于更严格的容差，衰减将较大。对于所研究的所有容差，均观察到 和 成比例。对于容差分别为 3%/3mm、3%/2mm、2%/2mm， 的 SD 分别为 2.3%、4.1%和 6.7%。
结论
通过引入 DUP 衰减和优化的分析 LM 来补偿由于有限数量的探测器元件导致学习数据的统计数据较低，将基于 DUP 的 GPR 预测方法扩展到 3D。通过改进 LM 和纳入其他指标，预计预测 GPR 的精度将得到提高。
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