A new Monte Carlo-based treatment plan optimization approach for intensity modulated radiation therapy
A new Monte Carlo-based treatment plan optimization approach for intensity modulated radiation therapy
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一种新的基于蒙特卡罗的调强放射治疗治疗计划优化方法
DOI：
10.1088/0031-9155/60/7/2903
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发表时间：
2015-04-07
影响因子：
3.5
通讯作者：
Jia, Xun
中科院分区：
文献类型：
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作者：
Li, Yongbao;Tian, Zhen;Jia, Xun
调强放射治疗（IMRT）计划的优化需要考虑小射束剂量分布。通常使用笔形波束或叠加/卷积型算法，因为它们的计算速度高。然而，不准确的小射束剂量分布可能误导优化过程，并妨碍所得到的计划质量。为了解决这个问题，蒙特卡罗（MC）模拟方法已被用来计算所有的子束剂量之前的优化步骤。传统的方法从每个子束中采样相同数量的粒子。然而，这不是MC在这个问题中的最佳使用。事实上，在解决计划优化问题之后，存在具有非常小的强度的细光束。对于那些子束，可以在剂量计算中使用更少的粒子以提高效率。基于这个想法，我们已经开发了一个新的基于MC的调强放射治疗计划优化框架，迭代执行MC剂量计算和计划优化。在每个剂量计算步骤中，基于通过在最后一个迭代步骤中求解计划优化问题获得的子束强度来调整子束的粒子数。我们修改了一个基于GPU的MC剂量引擎，允许同时计算大量的细光束剂量。为了测试我们修改后的剂量引擎的准确性，我们比较了来自宽波束的剂量和在该波束中的非均匀体模中的小波束剂量的总和。观察到最大差异的一致性在1%以内，平均差异的一致性在0.55%以内。然后，我们在单肺调强放射治疗的情况下，验证了所提出的MC为基础的优化方案。发现常规方案需要来自每个子束的106个粒子来实现最优化结果，该最优化结果是注量图中的3%差异和剂量与地面真实值的1%差异。相比之下，所提出的方案达到了相同的精度水平，平均每个子束1.2 × 105个粒子。相应地，仅使用一个GPU卡，包括MC剂量计算和计划优化的计算时间减少了4.4倍，从494秒减少到113秒。
Intensity-modulated radiation treatment (IMRT) plan optimization needs beamlet dose distributions. Pencil-beam or superposition/convolution type algorithms are typically used because of their high computational speed. However, inaccurate beamlet dose distributions may mislead the optimization process and hinder the resulting plan quality. To solve this problem, the Monte Carlo (MC) simulation method has been used to compute all beamlet doses prior to the optimization step. The conventional approach samples the same number of particles from each beamlet. Yet this is not the optimal use of MC in this problem. In fact, there are beamlets that have very small intensities after solving the plan optimization problem. For those beamlets, it may be possible to use fewer particles in dose calculations to increase efficiency. Based on this idea, we have developed a new MC-based IMRT plan optimization framework that iteratively performs MC dose calculation and plan optimization. At each dose calculation step, the particle numbers for beamlets were adjusted based on the beamlet intensities obtained through solving the plan optimization problem in the last iteration step. We modified a GPU-based MC dose engine to allow simultaneous computations of a large number of beamlet doses. To test the accuracy of our modified dose engine, we compared the dose from a broad beam and the summed beamlet doses in this beam in an inhomogeneous phantom. Agreement within 1% for the maximum difference and 0.55% for the average difference was observed. We then validated the proposed MC-based optimization schemes in one lung IMRT case. It was found that the conventional scheme required 106 particles from each beamlet to achieve an optimization result that was 3% difference in fluence map and 1% difference in dose from the ground truth. In contrast, the proposed scheme achieved the same level of accuracy with on average 1.2 × 105 particles per beamlet. Correspondingly, the computation time including both MC dose calculations and plan optimizations was reduced by a factor of 4.4, from 494 to 113 s, using only one GPU card.