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一种用于预测 CyberKnife M6 机器上 PinPoint 腔读数的 3D 校正方法。

A 3D correction method for predicting the readings of a PinPoint chamber on the CyberKnife M6 machine.

机构信息

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出版信息

Phys Med Biol. 2018 Feb 13;63(4):045010. doi: 10.1088/1361-6560/aaa90d.

Abstract

The use of small fields in radiation therapy techniques has increased substantially in particular in stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT). However, as field size reduces further still, the response of the detector changes more rapidly with field size, and the effects of measurement uncertainties become increasingly significant due to the lack of lateral charged particle equilibrium, spectral changes as a function of field size, detector choice, and subsequent perturbations of the charged particle fluence. This work presents a novel 3D dose volume-to-point correction method to predict the readings of a 0.015 cc PinPoint chamber (PTW 31014) for both small static-fields and composite-field dosimetry formed by fixed cones on the CyberKnife M6 machine. A 3D correction matrix is introduced to link the 3D dose distribution to the response of the PinPoint chamber in water. The parameters of the correction matrix are determined by modeling its 3D dose response in circular fields created using the 12 fixed cones (5 mm-60 mm) on a CyberKnife M6 machine. A penalized least-square optimization problem is defined by fitting the calculated detector reading to the experimental measurement data to generate the optimal correction matrix; the simulated annealing algorithm is used to solve the inverse optimization problem. All the experimental measurements are acquired for every 2 mm chamber shift in the horizontal planes for each field size. The 3D dose distributions for the measurements are calculated using the Monte Carlo calculation with the MultiPlan treatment planning system (Accuray Inc., Sunnyvale, CA, USA). The performance evaluation of the 3D conversion matrix is carried out by comparing the predictions of the output factors (OFs), off-axis ratios (OARs) and percentage depth dose (PDD) data to the experimental measurement data. The discrepancy of the measurement and the prediction data for composite fields is also performed for clinical SRS plans. The optimization algorithm used for generating the optimal correction factors is stable, and the resulting correction factors were smooth in the spatial domain. The measurement and prediction of OFs agree closely with percentage differences of less than 1.9% for all the 12 cones. The discrepancies between the prediction and the measurement PDD readings at 50 mm and 80 mm depth are 1.7% and 1.9%, respectively. The percentage differences of OARs between measurement and prediction data are less than 2% in the low dose gradient region, and 2%/1 mm discrepancies are observed within the high dose gradient regions. The differences between the measurement and prediction data for all the CyberKnife based SRS plans are less than 1%. These results demonstrate the existence and efficiency of the novel 3D correction method for small field dosimetry. The 3D correction matrix links the 3D dose distribution and the reading of the PinPoint chamber. The comparison between the predicted reading and the measurement data for static small fields (OFs, OARs and PDDs) yield discrepancies within 2% for low dose gradient regions and 2%/1 mm for high dose gradient regions; the discrepancies between the predicted and the measurement data are less than 1% for all the SRS plans. The 3D correction method provides an access to evaluate the clinical measurement data and can be applied to non-standard composite fields intensity modulated radiation therapy point dose verification.

摘要

在立体定向放射外科(SRS)和立体定向体部放射治疗(SBRT)中,放射治疗技术中小野的使用大幅增加。然而,随着野的进一步缩小,探测器的响应随野的变化更快,由于缺乏侧向带电粒子平衡、随野变化的谱变化、探测器的选择以及随后对带电粒子通量的后续干扰,测量不确定度的影响变得越来越显著。本工作提出了一种新的 3D 剂量体积到点校正方法,用于预测 CyberKnife M6 机器上固定锥形形成的小静态场和复合场剂量测定中 0.015 cc PinPoint 腔(PTW 31014)的读数。引入了一个 3D 校正矩阵,将 3D 剂量分布与水中 PinPoint 腔的响应联系起来。校正矩阵的参数通过在 CyberKnife M6 机器上的 12 个固定锥形(5mm-60mm)创建的圆形场中对其 3D 剂量响应进行建模来确定。通过将计算出的探测器读数拟合到实验测量数据,定义了一个惩罚最小二乘优化问题,以生成最佳校正矩阵;使用模拟退火算法求解逆优化问题。对于每个场尺寸,在水平平面上,每个腔的每次 2mm 移位都会进行所有的实验测量。使用多计划治疗计划系统(Accuray Inc.,加利福尼亚州森尼韦尔)的蒙特卡罗计算来计算测量的 3D 剂量分布。通过将输出因子(OFs)、离轴比(OARs)和百分深度剂量(PDD)数据的预测与实验测量数据进行比较,对 3D 转换矩阵的性能进行评估。还对临床 SRS 计划的复合场的测量和预测数据之间的差异进行了评估。用于生成最佳校正因子的优化算法是稳定的,并且得到的校正因子在空间域中是平滑的。对于所有 12 个锥形,OFs 的测量和预测之间的一致性非常好,差异百分比小于 1.9%。在 50mm 和 80mm 深度处,PDD 读数的预测与测量之间的差异分别为 1.7%和 1.9%。OARs 的测量与预测数据之间的差异百分比在低剂量梯度区域小于 2%,在高剂量梯度区域为 2%/1mm。所有基于 CyberKnife 的 SRS 计划的测量和预测数据之间的差异小于 1%。这些结果表明,对于小野剂量测定,存在并有效利用了新的 3D 校正方法。3D 校正矩阵将 3D 剂量分布和 PinPoint 腔的读数联系起来。静态小场(OFs、OARs 和 PDDs)的预测读数与测量数据之间的比较,在低剂量梯度区域的差异在 2%以内,在高剂量梯度区域的差异在 2%/1mm 以内;所有 SRS 计划的预测和测量数据之间的差异小于 1%。3D 校正方法提供了一种评估临床测量数据的方法,并可应用于非标准复合场强度调制放射治疗点剂量验证。

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