Zarepisheh M, Uribe-Sanchez A, Li N, Jia X, Jiang S
University of California, San Diego, La Jolla, CA.
Southern Medical University, Guangzhou, China.
Med Phys. 2012 Jun;39(6Part24):3919. doi: 10.1118/1.4735997.
To establish a new mathematical framework for IMRT treatment optimization with voxel-dependent optimization parameters.
In IMRT inverse treatment planning, a physician seeks for a plan to deliver a prescribed dose to the target while sparing the nearby healthy tissues. The conflict between these objectives makes the multi-criteria optimization an appropriate tool. Traditionally, a clinically acceptable plan can be generated by fine-tuning organ-based parameters. We establish a new mathematical framework by using voxel-based parameters for optimization. We introduce three different Pareto surfaces, prove the relationship between those surfaces, and compare voxel-based and organ-based methods. We prove some new theorems providing conditions under which the Pareto optimality is guaranteed.
The new mathematical framework has shown that: 1) Using an increasing voxel penalty function with an increasing derivative, in particular the popular power function, it is possible to explore the entire Pareto surface by changing voxel-based weighting factors, which increases the chances of getting more desirable plan. 2) The Pareto optimality is always guaranteed by adjusting voxel-based weighting factors. 3) If the plan is initially produced by adjusting organ-based weighting factors, it is impossible to improve all the DVH curves at the same time by adjusting voxel-based weighting factors. 4) A larger Pareto surface is explored by changing voxel-based weighting factors than by changing organ-based weighting factors, possibly leading to a plan with better trade-offs. 5) The Pareto optimality is not necessarily guaranteed while we are adjusting the voxel reference doses, and hence, adjusting voxel-based weighting factors is preferred in terms of preserving the Pareto optimality.
We have developed a mathematical framework for IMRT optimization using voxel-based parameters. We can improve the plan quality by adjusting voxel-based weighting factors after organ-based parameter adjustment. This work is supported by Varian Medical Systems through a Master Research Agreement.
建立一个用于调强放射治疗（IMRT）治疗优化的新数学框架，该框架采用依赖体素的优化参数。
在IMRT逆向治疗计划中，医生寻求一种计划，以便在保护附近健康组织的同时向靶区给予规定剂量。这些目标之间的冲突使得多标准优化成为一种合适的工具。传统上，通过微调基于器官的参数可以生成临床可接受的计划。我们通过使用基于体素的参数进行优化来建立一个新的数学框架。我们引入三种不同的帕累托曲面，证明这些曲面之间的关系，并比较基于体素和基于器官的方法。我们证明了一些新定理，这些定理提供了保证帕累托最优性的条件。
新的数学框架表明：1）使用具有递增导数的递增体素惩罚函数，特别是流行的幂函数，通过改变基于体素的加权因子可以探索整个帕累托曲面，这增加了获得更理想计划的机会。2）通过调整基于体素的加权因子始终可以保证帕累托最优性。3）如果计划最初是通过调整基于器官的加权因子产生的，那么通过调整基于体素的加权因子不可能同时改善所有剂量体积直方图（DVH）曲线。4）通过改变基于体素的加权因子比通过改变基于器官的加权因子探索的帕累托曲面更大，这可能会产生具有更好权衡的计划。5）在调整体素参考剂量时不一定能保证帕累托最优性因此，就保持帕累托最优性而言，调整基于体素的加权因子更可取。
我们已经开发了一个使用基于体素的参数进行IMRT优化的数学框架。我们可以在基于器官的参数调整后通过调整基于体素的加权因子来提高计划质量。这项工作得到了瓦里安医疗系统公司通过一项主要研究协议的支持。
