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基于像素电离室的旋转椭圆束轮廓测量的多几何参数高斯拟合算法。

Gaussian fitting algorithm with multi-geometric parameters for rotated elliptical beam profiling using pixel ion chamber.

机构信息

State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Hubei, China.

出版信息

Med Phys. 2021 Sep;48(9):4799-4811. doi: 10.1002/mp.15140. Epub 2021 Aug 11.

DOI:10.1002/mp.15140
PMID:34314534
Abstract

PURPOSE

A high-precision rotated elliptical beam profiling method based on pixel ion chamber is proposed in this paper. This method aims to improve the accuracy by modeling the transverse profile of rotated beam as an ellipse with additional correlation coefficient and eliminating the fitting error due to the volume averaging effect of pixel ion chamber.

METHODS

In pencil beam scanning (PBS) proton therapy systems, the transverse beam profile model is generally represented as a standard Gaussian distribution. Considering the elliptical spots, two-dimensional (2D) joint Gaussian distribution characterized with the correlation coefficient ρ is adopted in this study. Gaussian-type particle distribution with white noise was generated and processed in MATLAB to simulate the secondary particle collection in the pixel ion chamber. The simulated pixel ion chamber is a commercially available ion chamber which consists of 12 × 12 small square pixels (3.75 × 3.75 mm ) with a 0.05 mm interval. The simulated signals were preprocessed by filtering with the noise threshold and extracting the maximum simply connected domain (MSCD) of the signal. Then, five geometric parameters that identify the transverse beam profiles were fitted under different signal-to-noise ratio (SNR) conditions: the center of the beam (x , y ), the spot size (σ , σ ), and the rotation angle θ formed between the major axes of elliptical spot and the x axes of the ion chamber. First, the simulated signals were preprocessed by filtering with the noise threshold and extracting the MSCD of the signal. Second, a rectification curve of systematic error in fitted spot size versus the prescribed spot size was used to predict the systematic error due to the volume averaging effect. Finally, the effects of fitting errors on therapeutic dose were evaluated in terms of gamma index and relative dose difference.

RESULTS

When the SNR is not less than 20 dB, the relative fitting error of spot size and the absolute fitting error of angle θ are less than 1% and 6.1°, respectively. The fitting error of beam center increases with spot size and will not exceed 0.22 mm when spot size reaches up to 12 mm. At a SNR equal to 20 dB, neither cold nor hot spots were presented in dose distribution calculated with the fitted spot parameters.

CONCLUSION

The improved Gaussian fitting algorithm performs well when SNR is not less than 20 dB. This method can effectively distinguish the nominal beam and rotated elliptical beam. An ideal systematic error curve can be predicted and used to correct the fitted spot size, thus eliminating the systematic error due to the volume averaging effect of the pixel ion chamber. The fitting error of spot size cannot be fully corrected, but it is negligible and shows little effect on the overall therapeutic dose.

摘要

目的

本文提出了一种基于像素电离室的高精度旋转椭圆束轮廓测量方法。该方法通过对旋转束的横向轮廓建模为具有附加相关系数的椭圆,以及消除像素电离室体积平均效应引起的拟合误差,旨在提高精度。

方法

在铅笔束扫描(PBS)质子治疗系统中,横向束轮廓模型通常表示为标准高斯分布。考虑到椭圆光斑,本研究采用二维(2D)联合高斯分布,其特征是相关系数 ρ。采用高斯型粒子分布和白噪声在 MATLAB 中进行处理,以模拟像素电离室中的次级粒子收集。模拟像素电离室是一种商用电离室,由 12×12 个小正方形像素(3.75×3.75mm)组成,间隔为 0.05mm。模拟信号通过噪声阈值滤波和提取信号的最大简单连通域(MSCD)进行预处理。然后,在不同信噪比(SNR)条件下拟合五个识别横向束轮廓的几何参数:束中心(x, y )、光斑大小(σ, σ )和椭圆光斑的主轴与电离室 x 轴之间形成的旋转角度θ。首先,通过噪声阈值滤波和提取信号的 MSCD 对模拟信号进行预处理。其次,使用拟合光斑大小与规定光斑大小之间的系统误差校正曲线来预测由于体积平均效应引起的系统误差。最后,通过伽玛指数和相对剂量差异评估拟合误差对治疗剂量的影响。

结果

当 SNR 不小于 20dB 时,光斑大小的相对拟合误差和角度θ的绝对拟合误差均小于 1%和 6.1°。当光斑尺寸达到 12mm 时,束中心的拟合误差会增加,但不会超过 0.22mm。在 SNR 等于 20dB 时,计算得到的剂量分布中既没有冷点也没有热点。

结论

在 SNR 不小于 20dB 时,改进的高斯拟合算法性能良好。该方法可以有效地区分标称束和旋转椭圆束。可以预测理想的系统误差曲线,并用于校正拟合光斑尺寸,从而消除像素电离室体积平均效应引起的系统误差。光斑尺寸的拟合误差不能完全修正,但可以忽略不计,对整体治疗剂量影响很小。

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