一种基于梯度信息的直接子野优化算法

A gradient-based direct aperture optimization

针对传统直接子野优化算法(DAO)收敛速度慢、易停滞、全局搜索能力低的缺点,本文提出一种基于梯度信息的直接子野优化方法(GDAO)。在GDAO中分别采用不同的优化方法对子野形状和子野权重进行迭代优化。首先为提高子野形状优化时每次搜索的有效性,对传统模拟退火算法(SA)进行了改进,将梯度信息融合在SA算法中。采用基于梯度的SA法确定子野形状,并在优化同时充分考虑多叶准直器(MLC)叶片间的约束条件,保证优化后的子野形状满足临床放射治疗的要求。之后再利用计算量少、迭代代价低、收敛快且稳定的梯度类具有求解大规模约束优化问题能力的带约束最小存储拟牛顿算法(L-BFGS-B)优化子野权重。实验结果表明,与传统SA算法相比,新算法计算时间减少了15.90%,同时得到的治疗方案靶区最低剂量提高了0.29%,最高剂量降低了0.45%;危及器官膀胱最高剂量降低了0.25%;危及器官直肠最高剂量降低了0.09%,说明在调强放射治疗(IMRT)中采用GDAO方法直接优化子野,可在短时间内得到满足临床要求并可直接实施照射的治疗方案,具有较好的临床实用价值。

Aiming at the disadvantages of traditional direct aperture optimization（DAO） method, such as slow convergence rate, prone to stagnation and weak global searching ability, a gradient-based direct aperture optimization（GDAO） is proposed. In this work, two different optimization methods are used to optimize the shapes and the weights of the apertures. Firstly, in order to improve the validity of the aperture shapes optimization of each search, the traditional simulated annealing（SA） algorithm is improved, the gradient is introduced to the algorithm. The shapes of the apertures are optimized by the gradient based SA method. At the same time, the constraints between the leaves of multileaf collimator（MLC） have been fully considered, the optimized aperture shapes are meeting the requirements of clinical radiation therapy. After that, the weights of the apertures are optimized by the limited-memory BFGS for boundconstrained（L-BFGS-B） algorithm, which is simple in calculation, fast in convergence rate, and suitable for solving large scale constrained optimization. Compared with the traditional SA algorithm, the time cost of this program decreased by15.90%; the minimum dose for the planning target volume was improved by 0.29%, the highest dose for the planning target volume was reduced by 0.45%; the highest dose for the bladder and rectum, which are the organs at risk, decreased by 0.25% and 0.09%, respectively. The results of experiment show that the new algorithm can produce highly efficient treatment planning a short time and can be used in clinical practice.