Multiobjective inverse planning or intensity modulated radiotherapy with constraint-free gradient-based optimization algorithms
Multiobjective inverse planning or intensity modulated radiotherapy with constraint-free gradient-based optimization algorithms
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DOI：
10.1088/0031-9155/48/17/308
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发表时间：
2003-09-07
影响因子：
3.5
通讯作者：
Baltas, D
中科院分区：
文献类型：
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作者：
Lahanas, M;Schreibmann, E;Baltas, D
我们认为有限记忆L-BFGS算法的行为作为一个代表性的约束自由基于梯度的算法，用于调强放射治疗（IMRT）的多目标（MO）剂量优化。使用参数变换，负束流通量的正性约束问题完全消除：一个功能，迄今尚未完全理解的所有调查。我们分析的全局收敛性L-BFGS搜索的存在性和可能的局部极小值的影响。我们用快速模拟退火（FSA）算法来检验L-BFGS解是否是全局Pareto最优的。在我们的分析中使用的三个例子是脑肿瘤，前列腺肿瘤和C形PTV的测试情况。在1%的优化中，全局收敛性被破坏。一个简单的机制，几乎消除了这种故障的影响，得到的解决方案是全局最优的。一个单目标剂量优化需要不到4秒的5400个参数和40 000个采样点。消除负束流的问题和高的计算速度允许约束自由的基于梯度的优化算法用于MO剂量优化。在这种情况下，获得可能的解决方案的代表性谱，其包含诸如目标和剂量值范围之间的权衡的信息。使用简单的决策工具，可以选择所有可能的解决方案中最好的。我们对三个例子进行MO剂量优化，并比较解决方案的光谱，首先使用推荐的危险器官的临界剂量值，其次将这些剂量值设置为零。
We consider the behaviour of the limited memory L-BFGS algorithm as a representative constraint-free gradient-based algorithm which is used for multiobjective (MO) dose optimization for intensity modulated radiotherapy (IMRT). Using a parameter transformation, the positivity constraint problem of negative beam fluences is entirely eliminated: a feature which to date has not been fully understood by all investigators. We analyse the global convergence properties of L-BFGS by searching for the existence and the influence of possible local minima. With a fast simulated annealing (FSA) algorithm we examine whether the L-BFGS solutions are globally Pareto optimal. The three examples used in our analysis are a brain tumour, a prostate tumour and a test case with a C-shaped PTV. In 1% of the optimizations global convergence is violated. A simple mechanism practically eliminates the influence of this failure and the obtained solutions are globally optimal. A single-objective dose optimization requires less than 4 s for 5400 parameters and 40 000 sampling points. The elimination of the problem of negative beam fluences and the high computational speed permit constraint-free gradient-based optimization algorithms to be used for MO dose optimization. In this situation, a representative spectrum of possible solutions is obtained which contains information such as the trade-off between the objectives and range of dose values. Using simple decision making tools the best of all the possible solutions can be chosen. We perform an MO dose optimization for the three examples and compare the spectra of solutions, firstly using recommended critical dose values for the organs at risk and secondly, setting these dose values to zero.