Wisotzky Eric, O'Brien Ricky, Keall Paul J
Radiation Physics Laboratory, Sydney Medical School, University of Sydney, Sydney, NSW 2006, Australia.
Med Phys. 2016 Jan;43(1):132. doi: 10.1118/1.4937781.
Multileaf collimator (MLC) tracking radiotherapy is complex as the beam pattern needs to be modified due to the planned intensity modulation as well as the real-time target motion. The target motion cannot be planned; therefore, the modified beam pattern differs from the original plan and the MLC sequence needs to be recomputed online. Current MLC tracking algorithms use a greedy heuristic in that they optimize for a given time, but ignore past errors. To overcome this problem, the authors have developed and improved an algorithm that minimizes large underdose and overdose regions. Additionally, previous underdose and overdose events are taken into account to avoid regions with high quantity of dose events.
The authors improved the existing MLC motion control algorithm by introducing a cumulative underdose/overdose map. This map represents the actual projection of the planned tumor shape and logs occurring dose events at each specific regions. These events have an impact on the dose cost calculation and reduce recurrence of dose events at each region. The authors studied the improvement of the new temporal optimization algorithm in terms of the L1-norm minimization of the sum of overdose and underdose compared to not accounting for previous dose events. For evaluation, the authors simulated the delivery of 5 conformal and 14 intensity-modulated radiotherapy (IMRT)-plans with 7 3D patient measured tumor motion traces.
Simulations with conformal shapes showed an improvement of L1-norm up to 8.5% after 100 MLC modification steps. Experiments showed comparable improvements with the same type of treatment plans.
A novel leaf sequencing optimization algorithm which considers previous dose events for MLC tracking radiotherapy has been developed and investigated. Reductions in underdose/overdose are observed for conformal and IMRT delivery.
多叶准直器（MLC）跟踪放射治疗很复杂，因为由于计划的强度调制以及实时靶区运动，射野形状需要修改。靶区运动无法提前规划；因此，修改后的射野形状与原始计划不同，MLC序列需要在线重新计算。当前的MLC跟踪算法使用贪婪启发式方法，即它们针对给定时间进行优化，但忽略了过去的误差。为了克服这个问题，作者开发并改进了一种算法，该算法可将大的剂量不足和剂量过量区域最小化。此外，还考虑了先前的剂量不足和剂量过量事件，以避免出现高剂量事件区域。
作者通过引入累积剂量不足/剂量过量图改进了现有的MLC运动控制算法。该图表示计划肿瘤形状的实际投影，并记录每个特定区域发生的剂量事件。这些事件会影响剂量成本计算，并减少每个区域剂量事件的重复发生。与不考虑先前剂量事件相比，作者研究了新的时间优化算法在剂量过量和剂量不足总和的L1范数最小化方面的改进。为了进行评估，作者模拟了5个适形放疗计划和14个调强放疗（IMRT）计划的交付，这些计划具有7条三维患者测量的肿瘤运动轨迹。
适形形状的模拟显示，在100个MLC修改步骤后，L1范数提高了8.5%。实验表明，相同类型的治疗计划也有类似的改进。
已开发并研究了一种用于MLC跟踪放射治疗的新型叶片排序优化算法，该算法考虑了先前的剂量事件。在适形放疗和IMRT放疗中观察到剂量不足/剂量过量有所减少。
