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Comparison of 3D and 2D gamma passing rate criteria for detection sensitivity to IMRT delivery errors

机译:比较3D和2D伽玛通过率标准对IMRT传递错误的检测灵敏度

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This study compared three‐dimensional (3D) and two‐dimensional (2D) percentage gamma passing rates (%GPs) for detection sensitivity to IMRT delivery errors and investigated the correlation between two kinds of %GP. Eleven prostate IMRT cases were selected, and errors in multileaf collimator (MLC) bank sag, MLC leaf traveling, and machine output were simulated by recalculating the dose distributions in patients. 2D doses were extracted from the 3D doses at the isocenter position. The 3D and 2D %GPs with different gamma criteria were then obtained by comparing the recalculated and original doses in specific regions of interest (ROI), such as the whole body, the planning target volume (PTV), the bladder, and the rectum. The sensitivities to simulated errors of the two types of %GP were compared, and the correlation between the 2D and 3D %GPs for different ROIs were analyzed. For the whole‐body evaluation, both the 2D and 3D %GPs with the 3%/3 mm criterion were above 90% for all tested MLC errors and for MU deviations up to 4%, and the 3D %GP was higher than the 2D %GP. In organ‐specific evaluations, the PTV‐specific 2D and 3D %GP gradients were ?4.70% and ?5.14% per millimeter of the MLC traveling error, and ?17.79% and ?20.50% per percentage of MU error, respectively. However, a stricter criterion (2%/1 mm) was needed to detect the tested MLC sag error. The Pearson correlation analysis showed a significant strong correlation ( r > 0.8 and P.
机译:这项研究比较了三维(3D)和二维(2D)伽玛通过率(%GPs)对IMRT传递错误的检测灵敏度,并研究了两种%GP之间的相关性。选择了11例前列腺IMRT病例,并通过重新计算患者的剂量分布来模拟多叶准直器(MLC)库下垂,MLC叶片行进和机器输出中的误差。从等中心位置的3D剂量中提取2D剂量。然后,通过比较特定目标区域(ROI)(例如整个身体,计划目标体积(PTV),膀胱和直肠)中的重新计算剂量和原始剂量,获得具有不同伽马标准的3D和2D%GP。比较了两种类型的%GP对模拟误差的敏感性,并分析了不同ROI的2D和3D%GP之间的相关性。对于全身评估,对于所有测试的MLC误差和MU偏差高达4%,具有3%/ 3 mm准则的2D和3D%GP均高于90%,并且3D%GP高于2D %GP。在器官特定评估中,PTV特定的2D和3D%GP梯度分别为MLC行进误差的每毫米?4.70%和?5.14%,每MU误差的百分比分别为?17.79%和?20.50%。但是,需要更严格的标准(2%/ 1 mm)来检测测试的MLC垂度误差。皮尔逊相关分析显示出显着的强相关性(r> 0.8和P.

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