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Derivative based sensitivity analysis of gamma index

机译:基于导数的伽玛指数敏感性分析

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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.
机译:最初开发为在高级治疗提供方法中针对患者特定质量保证的工具,用于在测量的剂量分布和计算的剂量分布之间进行比较,后来扩展了伽玛指数(γ)概念以在任意两种剂量分布之间进行比较。在比较中,它会同时测量剂量差(DD)和协议距离(DTA)。与其他方法不同,它的优势在于能够为分析提供定量值。对于参考曲线上的每个点,如果评估曲线中至少有一个点满足通过标准(例如,δDD= 1%,δDTA= 1 mm),则该点在定量得分中包括“通过”。 ”伽玛分析不考虑评估曲线的斜率-它仅查看最小伽玛值,如果它小于1,则无论评估曲线的斜率是多少,点都会通过。在这项工作中,已尝试提出一种基于导数的方法来识别剂量梯度。从误差函数生成了代表6 MV 10 cm×10 cm视野的半影区的数学推导参考轮廓(RP)。通过在每个点引入1 mm的距离误差和1%的剂量误差,从该RP中创建了通用的测试曲线(GTP)。这被认为是两条评估曲线中的第一条。就其性质而言,该曲线是平滑曲线,并且将满足其中所有点的通过条件。生成的第二个评估轮廓为锯齿测试轮廓(STTP),该轮廓将再次满足RP上每个点的通过标准。但是,由于是锯齿形曲线,它不是平滑曲线,与平滑曲线相比显然很差。将平滑GTP视为通过RP的伽玛通过标准(1%DD和1 mm DTA)时可以接受的轮廓,得出这两条曲线之间DD的一阶和二阶导数(δD',δD“)并用作评估针对RP的STTP的边界值。即使STTP通过了简单的伽玛通过标准,但当将导数用作边界值时,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,并且可以证明是用于改善伽玛指数灵敏度的有用工具。

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