Iterative regularization in intensity-modulated radiation therapy optimization
Iterative regularization in intensity-modulated radiation therapy optimization
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DOI：
10.1118/1.2148918
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发表时间：
2006-01-01
期刊：
影响因子：
3.8
通讯作者：
Forsgren, A
中科院分区：
文献类型：
--
作者：
Carlsson, F;Forsgren, A
解决调强放射治疗(IMRT)优化问题的一种常见方法是使用基于射束的方法。这种方法通常分三步进行：首先求解小波束权重优化问题，然后将通量分布转换为步进式分段，最后对分段权重进行后优化。基于小波束的方法的一个缺点是，小波束权重优化问题是病态的，必须正则化，以便产生适合于转换的平滑的通量分布。本文的目的有两个：一是说明对角初始Hessian估计的BFGS拟牛顿序列二次规划方法求解基于Beamlet的IMRT问题的适宜性；二是经验表明，在使用这种优化方法时，Beamlet-Weight优化问题应该用相对较少的迭代来求解。这种适应性的解释是基于将优化方法视为迭代正则化方法。在迭代正则化中，通过迭代足够长的时间以获得接近最优解的解，但在出现太多噪声之前终止，从而近似地解决优化问题。迭代正则化需要一种最优化方法，这种优化方法最初是沿着平滑的方向进行的，并且在初始阶段进展迅速。通过对10个剂量-体积目标和射束权值为界的射束调强放射治疗问题的求解，我们发现所考虑的优化方法满足迭代正则化的要求。在分段加权优化后，在目标值和目标均匀度方面，使用35次波束加权迭代得到的处理都优于使用100次波束加权迭代获得的处理。我们的结论是，迭代过长实际上可能会降低可交付计划的质量。(C)2006年美国医学物理学家协会。
A common way to solve intensity-modulated radiation therapy (IMRT) optimization problems is to use a beamlet-based approach. The approach is usually employed in a three-step manner: first a beamlet-weight optimization problem is solved, then the fluence profiles are converted into stepand-shoot segments, and finally postoptimization of the segment weights is performed. A drawback of beamlet-based approaches is that beamlet-weight optimization problems are ill-conditioned and have to be regularized in order to produce smooth fluence profiles that are suitable for conversion. The purpose of this paper is twofold: first, to explain the suitability of solving beamlet-based IMRT problems by a BFGS quasi-Newton sequential quadratic programming method with diagonal initial Hessian estimate, and second, to empirically show that beamlet-weight optimization problems should be solved in relatively few iterations when using this optimization method. The explanation of the suitability is based on viewing the optimization method as an iterative regularization method. In iterative regularization, the optimization problem is solved approximately by iterating long enough to obtain a solution close to the optimal one, but terminating before too much noise occurs. Iterative regularization requires an optimization method that initially proceeds in smooth directions and makes rapid initial progress. Solving ten beamlet-based IMRT problems with dose-volume objectives and bounds on the beamlet-weights, we find that the considered optimization method fulfills the requirements for performing iterative regularization. After segment-weight optimization, the treatments obtained using 35 beamlet-weight iterations outperform the treatments obtained using 100 beamlet-weight iterations, both in terms of objective value and of target uniformity. We conclude that iterating too long may in fact deteriorate the quality of the deliverable plan. (c) 2006 American Association of Physicists in Medicine.