Northern Sydney Cancer Centre, Sydney, NSW, Australia.
ACRF Image X Institute, Sydney Medical School, University of Sydney, Sydney, NSW, Australia.
Med Phys. 2019 Apr;46(4):1814-1820. doi: 10.1002/mp.13425. Epub 2019 Mar 4.
Multileaf collimator (MLC) tracking is being clinically pioneered to continuously compensate for thoracic and pelvic motion during radiotherapy. The purpose of this work was to characterize the performance of two MLC leaf-fitting algorithms, direct optimization and piecewise optimization, for real-time motion compensation with different plan complexity and tumor trajectories.
To test the algorithms, both in silico and phantom experiments were performed. The phantom experiments were performed on a Trilogy Varian linac and a HexaMotion programmable motion platform. High and low modulation VMAT plans for lung and prostate cancer cases were used along with eight patient-measured organ-specific trajectories. For both MLC leaf-fitting algorithms, the plans were run with their corresponding patient trajectories. To compare algorithms, the average exposure errors, i.e., the difference in shape between ideal and fitted MLC leaves by the algorithm, plan complexity and system latency of each experiment were calculated.
Comparison of exposure errors for the in silico and phantom experiments showed minor differences between the two algorithms. The average exposure errors for in silico experiments with low/high plan complexity were 0.66/0.88 cm for direct optimization and 0.66/0.88 cm for piecewise optimization, respectively. The average exposure errors for the phantom experiments with low/high plan complexity were 0.73/1.02 cm for direct and 0.73/1.02 cm for piecewise optimization, respectively. The measured latency for the direct optimization was 226 ± 10 ms and for the piecewise algorithm was 228 ± 10 ms. In silico and phantom exposure errors quantified for each treatment plan demonstrated that the exposure errors from the high plan complexity (0.96 cm mean, 2.88 cm 95% percentile) were all significantly different from the low plan complexity (0.70 cm mean, 2.18 cm 95% percentile) (P < 0.001, two-tailed, Mann-Whitney statistical test).
The comparison between the two leaf-fitting algorithms demonstrated no significant differences in exposure errors, neither in silico nor with phantom experiments. This study revealed that plan complexity impacts the overall exposure errors significantly more than the difference between the algorithms.
多叶准直器（MLC）跟踪技术目前正处于临床探索阶段，旨在对放射治疗过程中的胸腹部和盆骨运动进行实时补偿。本研究旨在通过不同的计划复杂度和肿瘤轨迹来评估两种 MLC 叶片拟合算法（直接优化和分段优化）实时运动补偿的性能。
为了测试这些算法，我们进行了计算机模拟和体模实验。体模实验在一台 Trilogy 瓦里安直线加速器和一个 HexaMotion 可编程运动平台上进行。我们使用了肺癌和前列腺癌患者的高、低调制容积调制弧形治疗计划以及 8 个患者测量的器官特异性轨迹。对于这两种 MLC 叶片拟合算法，我们都使用相应的患者轨迹运行这些计划。为了比较算法，我们计算了每个实验的平均曝光误差（即算法拟合的理想 MLC 叶片和实际 MLC 叶片之间的形状差异）、计划复杂度和系统延迟。
计算机模拟和体模实验中暴露误差的比较表明，这两种算法之间存在较小差异。低复杂度/高复杂度计划的计算机模拟实验的平均曝光误差分别为 0.66/0.88cm 用于直接优化，0.66/0.88cm 用于分段优化。低复杂度/高复杂度计划的体模实验的平均曝光误差分别为 0.73/1.02cm 用于直接优化，0.73/1.02cm 用于分段优化。直接优化的实测延迟为 226±10ms，分段算法的实测延迟为 228±10ms。每个治疗计划的计算机模拟和体模实验量化的暴露误差表明，高复杂度计划（0.96cm 平均值，2.88cm 95%分位数）的暴露误差与低复杂度计划（0.70cm 平均值，2.18cm 95%分位数）相比均显著不同（P<0.001，双侧，曼-惠特尼统计检验）。
两种叶片拟合算法的比较表明，无论是在计算机模拟实验还是体模实验中，暴露误差均无显著差异。本研究表明，计划复杂度对总体曝光误差的影响明显大于算法之间的差异。
