The use of a multiobjective evolutionary algorithm to increase flexibility in the search for better IMRT plans
The use of a multiobjective evolutionary algorithm to increase flexibility in the search for better IMRT plans
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DOI：
10.1118/1.3697535
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发表时间：
2012-04-01
期刊：
影响因子：
3.8
通讯作者：
Phillips, Mark
中科院分区：
文献类型：
--
作者：
Holdsworth, Clay;Kim, Minsun;Phillips, Mark
目的：评估更灵活和彻底的可行IMRT计划的多目标搜索如何影响IMRT优化的性能。方法：使用多目标进化算法（MOEA）作为工具，研究扩展搜索空间以包含更广泛的惩罚函数如何影响生成的IMRT计划集的质量。MOEA通过确定性最小化重组惩罚函数（多个组织特异性目标函数的加权和），使用一组IMRT计划生成新的IMRT计划。生成的计划的质量由一组独立的非凸、临床相关的决策标准来判断，所有占主导地位的计划被淘汰。随着这个过程的重复，更好的计划被产生，使得IMRT计划的人口将接近帕累托前线。我们使用了三种不同的方法来探索扩展搜索空间的效果。首先，该进化算法利用遗传优化原理，同时优化罚函数中的权值和组织特异性剂量参数进行搜索；其次，针对MOEA中所有危险器官（OARs）的每个体素单独优化惩罚函数参数。最后，开发了一种可用于任何IMRT计划的启发式体素特定改进（VSI）算法，该算法可逐步改进所有结构（桨和目标）的体素特定惩罚函数参数。将优势比较的概念应用于多目标优化得到的规划集，对不同方法进行了比较。结果：同时搜索重要性权重和剂量参数的MOEA优化产生的IMRT计划集优于固定任何一种参数时产生的计划集，用于四个示例前列腺计划。随着桨和目标之间的更多重叠，改进的数量增加。允许MOEA搜索体素特定的惩罚函数可以改善具有三个结构的简单情况的结果，但对于具有七个结构的更复杂情况却没有改善结果。对于这种改进，改进量增加，桨和目标之间的重叠减少。体素特异性改进算法改善了所有病例的结果，并且在复杂的前列腺和非常复杂的头颈部病例中证明了其临床相关性。结论：使用进化算法作为工具，发现在搜索空间中允许更大的灵活性可以提高性能。(a)改变目标函数中的权重和参考剂量，(b)消除结构中所有体素的同等惩罚约束，这两种策略都生成了一组计划，这些计划在传统的、更有限的搜索空间中被认为是“帕累托最优”的计划集。当考虑体素特定目标时，非常大的搜索空间可能导致MOEA在复杂情况下的收敛问题，但这对VSI算法来说不是问题。(C) 2012年美国医学物理学家协会。[http://dx.doi.org/10.1118/1.3697535]
Purpose: To evaluate how a more flexible and thorough multiobjective search of feasible IMRT plans affects performance in IMRT optimization.Methods: A multiobjective evolutionary algorithm (MOEA) was used as a tool to investigate how expanding the search space to include a wider range of penalty functions affects the quality of the set of IMRT plans produced. The MOEA uses a population of IMRT plans to generate new IMRT plans through deterministic minimization of recombined penalty functions that are weighted sums of multiple, tissue-specific objective functions. The quality of the generated plans are judged by an independent set of nonconvex, clinically relevant decision criteria, and all dominated plans are eliminated. As this process repeats itself, better plans are produced so that the population of IMRT plans will approach the Pareto front. Three different approaches were used to explore the effects of expanding the search space. First, the evolutionary algorithm used genetic optimization principles to search by simultaneously optimizing both the weights and tissue-specific dose parameters in penalty functions. Second, penalty function parameters were individually optimized for each voxel in all organs at risk (OARs) in the MOEA. Finally, a heuristic voxel-specific improvement (VSI) algorithm that can be used on any IMRT plan was developed that incrementally improves voxel-specific penalty function parameters for all structures (OARs and targets). Different approaches were compared using the concept of domination comparison applied to the sets of plans obtained by multiobjective optimization.Results: MOEA optimizations that simultaneously searched both importance weights and dose parameters generated sets of IMRT plans that were superior to sets of plans produced when either type of parameter was fixed for four example prostate plans. The amount of improvement increased with greater overlap between OARs and targets. Allowing the MOEA to search for voxel-specific penalty functions improved results for simple cases with three structures but did not improve results for a more complex case with seven structures. For this modification, the amount of improvement increased with less overlap between OARs and targets. The voxel-specific improvement algorithm improved results for all cases, and its clinical relevance was demonstrated in a complex prostate and a very complex head and neck case.Conclusions: Using an evolutionary algorithm as a tool, it was found that allowing more flexibility in the search space enhanced performance. The two strategies of (a) varying the weights and reference doses in the objective function and (b) removing the constraint of equal penalties for all voxels in a structure both generated sets of plans that dominated sets of plans considered to be "Pareto optimal" within the conventional, more limited search space. When considering voxel-specific objectives, the very large search space can lead to convergence problems in the MOEA for complex cases, but this is not an issue for the VSI algorithm. (C) 2012 American Association of Physicists in Medicine. [http://dx.doi.org/10.1118/1.3697535]