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Robust optimization in IMPT IMPT using quadratic objective functions to account for the minimum MU MU constraint

机译:IMPT中的强大优化使用二次目标函数来解释最小MU MU约束

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Purpose Currently, in clinical practice of intensity‐modulated proton therapy ( IMPT ), the influence of the minimum monitor unit ( MU ) constraint is taken into account through postprocessing after the optimization is completed. This may degrade the plan quality and plan robustness. This study aims to mitigate the impact of the minimum MU constraint directly during the plan robust optimization. Methods and materials Cao et?al. have demonstrated a two‐stage method to account for the minimum MU constraint using linear programming without the impact of uncertainties considered. In this study, we took the minimum MU constraint into consideration using quadratic optimization and simultaneously had the impact of uncertainties considered using robust optimization. We evaluated our method using seven cancer patients with different machine settings. Result The new method achieved better plan quality than the conventional method. The D 95% of the clinical target volume ( CTV ) normalized to the prescription dose was (mean [min–max]): (99.4% [99.2%–99.6%]) vs. (99.2% [98.6%–99.6%]). Plan robustness derived from these two methods was comparable. For all seven patients, the CTV dose–volume histogram band gap (narrower band gap means more robust plans) at D 95% normalized to the prescription dose was (mean [min–max]): (1.5% [0.5%–4.3%]) vs. (1.2% [0.6%–3.8%]). Conclusion Our new method of incorporating the minimum MU constraint directly into the plan robust optimization can produce machine‐deliverable plans with better tumor coverage while maintaining high‐plan robustness.
机译:目的目的,在强度调制质子治疗(IMPT)的临床实践中,通过在优化完成后通过后处理考虑最小监测单元(MU)约束的影响。这可能会降低计划质量和计划鲁棒性。本研究旨在在计划稳健优化期间减轻最小MU约束的影响。方法和材料CaO等。已经证明了一种使用线性编程的最小MU限制来展示两阶段方法,而不会考虑不确定性的影响。在这项研究中,我们使用二次优化考虑了最小的MU限制,并同时对使用稳健优化考虑的不确定性的影响。我们评估了我们使用不同机器设置的七种癌症患者的方法。结果新方法实现了比传统方法更好的计划质量。向处方剂量归一化的临床目标体积(CTV)的D 95%(平均值[min-max]):(99.4%[99.2%-99.6%])与(99.2%[98.6%-99.6%]] )。从这两种方法衍生的计划稳健性是可比的。对于所有7名患者,CTV剂量 - 体积直方图带隙(较窄的带隙意味着在D 95%上归一化到处方剂量的D 95%(平均值[min-max]):(1.5%[0.5%-4.3% ])与(1.2%[0.6%-3.8%])。结论我们将最小MU限制直接进入计划稳健优化的新方法可以生产具有更好的肿瘤覆盖率的机器可交付计划,同时保持高计划鲁棒性。

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