Langer M, Brown R, Morrill S, Lane R, Lee O
Department of Radiation Therapy, University of Texas Medical Branch, Galveston 77555-0711, USA.
Med Phys. 1996 Jun;23(6):965-71. doi: 10.1118/1.597858.
A genetic algorithm for generating beam weights is described. The algorithm improves an objective measure of the dose distribution while respecting dose volume constraints placed on critical structures. The algorithm was used to select beam weights for treatment of abdominal tumors. Weights were selected for up to 36 beams. Dose volume limits were placed on normal organs and a dose inhomogeneity limit was placed on tumor. Volumes were represented as sets of several hundred discrete points. The algorithm searched for the beam weights that would make the minimum tumor dose as high as the constraints would allow. The results were checked using dose volume histograms with standard sized grids. Nineteen trials were created using six patient cases by changing the required field margin or allowed beam position in each case. The sampling of points was sufficiently dense to yield solutions that strictly satisfied the constraints when the prescribed dose was renormalized by a factor of less than 6%. The genetic algorithm supplied solutions in 49 min on average, and in a maximum time of 87 min. The randomized search does not guarantee optimality, but high tumor doses were obtained. An example is shown for which the solution of the genetic algorithm gave a minimum tumor dose 7 Gy higher than the solution given by a simulated annealing algorithm under the same set of constraints. The genetic algorithm can be generalized to admit nonlinear functions of the beam intensities in the objective or in the constraints. These can include tumor control and normal tissue complication probabilities. The genetic algorithm is an attractive procedure for assigning beam weights in multifield plans. It improves the dose distribution while respecting specified rules for tissue tolerance.
描述了一种用于生成射束权重的遗传算法。该算法在满足对关键结构施加的剂量体积约束的同时，改进了剂量分布的客观度量。该算法用于选择腹部肿瘤治疗的射束权重。最多为36个射束选择权重。对正常器官设置了剂量体积限制，对肿瘤设置了剂量不均匀性限制。体积表示为数百个离散点的集合。该算法搜索能使最小肿瘤剂量在约束允许的范围内尽可能高的射束权重。使用具有标准尺寸网格的剂量体积直方图来检查结果。通过在每个病例中改变所需的射野边缘或允许的射束位置，使用六个患者病例创建了19次试验。当规定剂量通过小于6%的因子进行重新归一化时，点的采样足够密集，以产生严格满足约束的解决方案。遗传算法平均在49分钟内提供解决方案，最长时间为87分钟。随机搜索不能保证最优性，但获得了高肿瘤剂量。给出了一个例子，在相同的一组约束下，遗传算法的解决方案给出的最小肿瘤剂量比模拟退火算法给出的解决方案高7 Gy。遗传算法可以推广到在目标函数或约束条件中允许射束强度的非线性函数。这些函数可以包括肿瘤控制和正常组织并发症概率。遗传算法是在多野计划中分配射束权重的一种有吸引力的方法。它在尊重组织耐受性指定规则的同时改进了剂量分布。
