ACRF Image X Institute, Faculty of Medicine and Health, University of Sydney, NSW, Australia.
School of Biomedical Engineering, University of Technology Sydney, NSW, Australia.
Phys Med Biol. 2021 Mar 15;66(6):065027. doi: 10.1088/1361-6560/abe836.
Motion in the patient anatomy causes a reduction in dose delivered to the target, while increasing dose to healthy tissue. Multi-leaf collimator (MLC) tracking has been clinically implemented to adapt dose delivery to account for intrafraction motion. Current methods shift the planned MLC aperture in the direction of motion, then optimise the new aperture based on the difference in fluence. The drawback of these methods is that 3D dose, a function of patient anatomy and MLC aperture sequence, is not properly accounted for. To overcome the drawback of current fluence-based methods, we have developed and investigated real-time adaptive MLC tracking based on dose optimisation. A novel MLC tracking algorithm, dose optimisation, has been developed which accounts for the moving patient anatomy by optimising the MLC based on the dose delivered during treatment, simulated using a simplified dose calculation algorithm. The MLC tracking with dose optimisation method was applied in silico to a prostate cancer VMAT treatment dataset with observed intrafraction motion. Its performance was compared to MLC tracking with fluence optimisation and, as a baseline, without MLC tracking. To quantitatively assess performance, we computed the dose error and 3D γ failure rate (2 mm/2%) for each fraction and method. Dose optimisation achieved a γ failure rate of (4.7 ± 1.2)% (mean and standard deviation) over all fractions, which was significantly lower than fluence optimisation (7.5 ± 2.9)% (Wilcoxon sign-rank test p < 0.01). Without MLC tracking, a γ failure rate of (15.3 ± 12.9)% was achieved. By considering the accumulation of dose in the moving anatomy during treatment, dose optimisation is able to optimise the aperture to actively target regions of underdose while avoiding overdose.
患者解剖结构的运动会导致靶区剂量减少，同时增加健康组织的剂量。多叶准直器（MLC）跟踪已在临床上实施，以适应剂量输送，以弥补分次内运动。当前的方法是在运动方向上移动计划的 MLC 孔径，然后根据通量的差异优化新的孔径。这些方法的缺点是，3D 剂量（患者解剖结构和 MLC 孔径序列的函数）没有得到适当的考虑。为了克服基于通量的当前方法的缺点，我们已经开发并研究了基于剂量优化的实时自适应 MLC 跟踪。已经开发了一种新的 MLC 跟踪算法，剂量优化，通过基于治疗期间递送的剂量来优化 MLC，从而考虑到移动的患者解剖结构，这是使用简化的剂量计算算法来模拟的。基于剂量优化的 MLC 跟踪方法已应用于具有观察到的分次内运动的前列腺癌 VMAT 治疗数据集。与通量优化进行了比较，并与没有 MLC 跟踪的基线进行了比较。为了定量评估性能，我们为每个分数和方法计算了剂量误差和 3Dγ失败率（2mm/2%）。剂量优化在所有分数中实现了（4.7±1.2）%的γ失败率（平均值和标准差），明显低于通量优化（7.5±2.9）%（Wilcoxon 符号秩检验 p<0.01）。没有 MLC 跟踪，γ失败率为（15.3±12.9）%。通过考虑在治疗过程中移动解剖结构中的剂量积累，剂量优化能够优化孔径，主动瞄准剂量不足的区域，同时避免剂量过高。
