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.

患者解剖结构中的运动导致递送至目标的剂量减少，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，以适应剂量输送以解释分次内运动。当前方法将计划的 MLC 孔径沿运动方向移动，然后根据通量的差异优化新孔径。这些方法的缺点是没有适当考虑 3D 剂量，即患者解剖结构和 MLC 孔径序列的函数。为了克服当前基于注量的方法的缺点，我们开发并研究了基于剂量优化的实时自适应 MLC 跟踪。一种新的 MLC 跟踪算法，剂量优化，已经开发出来，它通过基于治疗期间提供的剂量优化 MLC 来解释移动的患者解剖结构，并使用简化的剂量计算算法进行模拟。应用了剂量优化方法的 MLC 跟踪计算机模拟到具有观察到的分次内运动的前列腺癌 VMAT 治疗数据集。将其性能与具有通量优化的 MLC 跟踪进行比较，作为基线，没有 MLC 跟踪。为了定量评估性能，我们计算了每个分数和方法的剂量误差和 3D γ故障率 (2 mm/2%)。剂量优化在所有分数上实现了(4.7 ± 1.2)%（平均值和标准差）的γ故障率，显着低于能量优化 (7.5 ± 2.9)%（Wilcoxon 符号秩检验p < 0.01）。在没有 MLC 跟踪的情况下，一个γ达到(15.3 12.9)%的故障率。通过考虑治疗期间移动解剖结构中的剂量累积，剂量优化能够优化孔径以主动瞄准剂量不足区域，同时避免过量。