  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.
