Optimization is a time-consuming part of radiation treatment planning. We
propose to reduce the optimization problem by only using a representative
subset of informative voxels. This way, we improve planning efficiency while
maintaining or enhancing the plan quality. To reduce the computational
complexity of the optimization problem, we propose to subsample the set of
voxels via importance sampling. We derive a sampling distribution based on an
importance score that we obtain from pre-solving an easy optimization problem
involving a simplified probing objective. By solving a reduced version of the
original optimization problem using this subset, we effectively reduce the
problem's size and computational demands while accounting for regions in which
satisfactory dose deliveries are challenging. In contrast to other stochastic
(sub-)sampling methods, our technique only requires a single sampling step to
define a reduced optimization problem. This problem can be efficiently solved
using established solvers. Empirical experiments on open benchmark data
highlight substantially reduced optimization times, up to 50 times faster than
the original ones, for intensity-modulated radiation therapy (IMRT), all while
upholding plan quality comparable to traditional methods. Our approach has the
potential to significantly accelerate radiation treatment planning by
addressing its inherent computational challenges. We reduce the treatment
planning time by reducing the size of the optimization problem rather than
improving the optimization method. Our efforts are thus complementary to much
of the previous developments.

通过使用代表性的信息体素子集，我们减少了辐射治疗计划中耗时的优化问题，提高了规划效率同时保持或增加了计划质量。为了降低优化问题的计算复杂性，我们提出了通过重要性抽样对体素集合进行子采样的方法。我们根据从一个包含简化探测目标的简单优化问题的预解答中获得的重要性分数推导了一个抽样分布。通过使用这个子集解决原始优化问题的简化版本，我们有效地减小了问题的大小和计算需求，同时考虑了区域内对满足剂量投递要求具有挑战性的情况。与其他随机子采样方法相比，我们的技术只需一次抽样步骤来定义一个简化的优化问题。这个问题可以使用成熟的求解器高效地解决。对开放基准数据的实证实验凸显了缩短优化时间的显著效果，强度调控辐射治疗 (IMRT) 的优化速度提高了 50 倍，同时保持了传统方法的计划质量。我们的方法能够通过解决计算方面的挑战显著加速辐射治疗规划，我们通过减小优化问题的规模而非改进优化方法来缩短治疗规划时间。因此，我们的努力是对之前发展的一个有益补充。

基于体素重要性的高效放射治疗计划

Efficient Radiation Treatment Planning based on Voxel Importance

We propose a simple approach which, given distributed computing resources,
can nearly achieve the accuracy of $k$-NN prediction, while matching (or
improving) the faster prediction time of $1$-NN. The approach consists of
aggregating denoised $1$-NN predictors over a small number of distributed
subsamples. We show, both theoretically and experimentally, that small
subsample sizes suffice to attain similar performance as $k$-NN, without
sacrificing the computational efficiency of $1$-NN.

该论文提出了一种简单的方法，利用分布式计算资源，通过聚合去噪的 $1$-NN 预测器，以少量分布式子样本实现接近于 $k$-NN 预测的准确性，同时匹配（或优化）$1$-NN 更快的预测时间，理论和实验结果表明，小的子样本大小足以达到与 $k$-NN 相似的性能，而不牺牲 $1$-NN 的计算效率。