基于导数的伽马指数灵敏度分析

B. Sarkar, A. Pradhan, T. Ganesh
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引用次数: 5

摘要

gamma指数(γ)概念最初是作为一种工具,用于在高级治疗递送方法中对测量和计算剂量分布进行比较,以保证患者特定的质量,后来扩展到比较任何两个剂量分布。它在比较中同时考虑剂量差(DD)和距离一致(DTA)测量。与其他方法不同,它的优势在于它能够为分析提供定量值。对于参考曲线上的每个点,如果在评估曲线中至少有一个点满足通过标准(例如,δDD = 1%, δDTA = 1 mm),则该点作为“通过”计入定量评分。Gamma分析不考虑所评估曲线的梯度——它只考虑最小的Gamma值,如果它<1,那么无论所评估曲线的梯度是什么,该点都通过。在这项工作中,尝试提出了一种基于导数的剂量梯度识别方法。利用误差函数生成了代表6 MV 10 cm × 10 cm视场半影区的数学推导参考轮廓(RP)。通过在每个点引入1mm距离误差和1%剂量误差,从该RP创建了一般测试剖面(GTP)。这被认为是两条评估曲线中的第一条。就其性质而言,这条曲线是一条光滑曲线,并且满足其中所有点的通过标准。第二个评估的轮廓被生成为锯齿形测试轮廓(STTP),它将再次满足RP上每个点的通过标准。但由于是锯齿形曲线,所以它不是光滑的曲线,与光滑的曲线相比,效果会明显差一些。考虑到光滑的GTP是一个可接受的剖面,当它通过伽马通过标准(1% DD和1 mm DTA)相对于RP时,推导了这两条曲线之间的DD (δD', δD')的一阶和二阶导数,并将其用作评估相对于RP的STTP的边界值。尽管STTP通过了简单的伽玛通过标准,但当导数用作边界值时,发现它在许多位置都失败了。所提出的基于导数的方法可以识别噪声曲线,是提高伽马指数灵敏度的有效工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Derivative based sensitivity analysis of gamma index
Originally developed as a tool for patient-specific quality assurance in advanced treatment delivery methods to compare between measured and calculated dose distributions, the gamma index (γ) concept was later extended to compare between any two dose distributions. It takes into effect both the dose difference (DD) and distance-to-agreement (DTA) measurements in the comparison. Its strength lies in its capability to give a quantitative value for the analysis, unlike other methods. For every point on the reference curve, if there is at least one point in the evaluated curve that satisfies the pass criteria (e.g., δDD = 1%, δDTA = 1 mm), the point is included in the quantitative score as "pass." Gamma analysis does not account for the gradient of the evaluated curve - it looks at only the minimum gamma value, and if it is <1, then the point passes, no matter what the gradient of evaluated curve is. In this work, an attempt has been made to present a derivative-based method for the identification of dose gradient. A mathematically derived reference profile (RP) representing the penumbral region of 6 MV 10 cm × 10 cm field was generated from an error function. A general test profile (GTP) was created from this RP by introducing 1 mm distance error and 1% dose error at each point. This was considered as the first of the two evaluated curves. By its nature, this curve is a smooth curve and would satisfy the pass criteria for all points in it. The second evaluated profile was generated as a sawtooth test profile (STTP) which again would satisfy the pass criteria for every point on the RP. However, being a sawtooth curve, it is not a smooth one and would be obviously poor when compared with the smooth profile. Considering the smooth GTP as an acceptable profile when it passed the gamma pass criteria (1% DD and 1 mm DTA) against the RP, the first and second order derivatives of the DDs (δD', δD") between these two curves were derived and used as the boundary values for evaluating the STTP against the RP. Even though the STTP passed the simple gamma pass criteria, it was found failing at many locations when the derivatives were used as the boundary values. The proposed derivative-based method can identify a noisy curve and can prove to be a useful tool for improving the sensitivity of the gamma index.
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