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Abstract：
The purpose of this study is to develop an electronic portal imaging device-based multi-leaf collimator calibration procedure using log files. Picket fence fields with 2-14 mm nominal strip widths were performed and normalized by open field. Normalized pixel intensity profiles along the direction of leaf motion for each leaf pair were taken. Three independent algorithms and an integration method derived from them were developed according to the valley value, valley area, full-width half-maximum (FWHM) of the profile, and the abutment width of the leaf pairs obtained from the log files. Three data processing schemes (Scheme A, Scheme B, and Scheme C) were performed based on different data processing methods. To test the usefulness and robustness of the algorithm, the known leaf position errors along the direction of perpendicular leaf motion via the treatment planning system were introduced in the picket fence field with nominal 5, 8, and 11 mm. Algorithm tests were performed every 2 weeks over 4 months. According to the log files, about 17.628% and 1.060% of the leaves had position errors beyond ± 0.1 and ± 0.2 mm, respectively. The absolute position errors of the algorithm tests for different data schemes were 0.062 ± 0.067 (Scheme A), 0.041 ± 0.045 (Scheme B), and 0.037 ± 0.043 (Scheme C). The absolute position errors of the algorithms developed by Scheme C were 0.054 ± 0.063 (valley depth method), 0.040 ± 0.038 (valley area method), 0.031 ± 0.031 (FWHM method), and 0.021 ± 0.024 (integrated method). For the efficiency and robustness test of the algorithm, the absolute position errors of the integration method of Scheme C were 0.020 ± 0.024 (5 mm), 0.024 ± 0.026 (8 mm), and 0.018 ± 0.024 (11 mm). Different data processing schemes could affect the accuracy of the developed algorithms. The integration method could integrate the benefits of each algorithm, which improved the level of robustness and accuracy of the algorithm. The integration method can perform multi-leaf collimator (MLC) quality assurance with an accuracy of 0.1 mm. This method is simple, effective, robust, quantitative, and can detect a wide range of MLC leaf position errors.
摘要：
本研究的目的是使用日志文件开发基于电子射野成像设备的多叶准直器校准程序。执行标称条带宽度为2-14mm的栅栏场，并通过开放场进行归一化。获取每个叶片对的沿着叶片运动方向的归一化像素强度分布。根据谷值，开发了三种独立的算法及其积分方法，山谷地区,轮廓的半峰全宽(FWHM)，和从日志文件中获得的叶片对的基台宽度。三种数据处理方案(方案A，方案B,和方案C)是基于不同的数据处理方法进行的。测试了算法的有用性和鲁棒性，通过治疗计划系统沿垂直叶片运动方向的已知叶片位置误差被引入到标称5、8和11毫米的栅栏中。算法测试在4个月内每2周进行一次。根据日志文件,约有17.628％和1.060％的叶子的位置误差超过±0.1和±0.2mm，分别。不同数据方案的算法测试的绝对位置误差为0.062±0.067(方案A),0.041±0.045(方案B),和0.037±0.043(方案C)。方案C开发的算法的绝对位置误差为0.054±0.063（谷深度法），0.040±0.038(谷面积法),0.031±0.031(FWHM法),0.021±0.024(综合法)。为了测试算法的效率和鲁棒性,方案C积分法的绝对位置误差为0.020±0.024(5mm),0.024±0.026(8mm),0.018±0.024(11mm)。不同的数据处理方案可能会影响所开发算法的准确性。集成方法可以集成每个算法的好处，提高了算法的鲁棒性和准确性。集成方法可以以0.1mm的精度执行多叶准直器（MLC）质量保证。此方法简单,有效,健壮,定量,并且可以检测大范围的MLC叶片位置误差。
