Improving the performance of direct Monte Carlo optimization for large tumor vol

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Improving the performance of direct Monte Carlo optimization for large tumor vol

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Direct Monte Carlo optimization is a powerful approach used in radiation therapy planning, particularly for treating complex large tumor volumes. This method relies on simulating numerous particle trajectories to determine the optimal radiation dose distribution, but its computational demands can be significant when dealing with extensive tumor volumes.

To improve performance, several strategies can be employed. First, variance reduction techniques can minimize the number of required simulations while maintaining accuracy, such as importance sampling or stratified sampling. Second, parallel computing frameworks (like GPU acceleration) can drastically speed up the simulation process by distributing computations across multiple processors. Third, adaptive mesh refinement helps by dynamically adjusting the resolution of the simulation grid, focusing computational effort on critical regions near the tumor while coarsening less critical areas.

Additionally, machine learning-based surrogate models can predict dose distributions faster than full Monte Carlo simulations once trained on historical data. Hybrid approaches that combine deterministic algorithms with Monte Carlo sampling for specific subproblems can also reduce runtime without sacrificing precision.

By optimizing these aspects, direct Monte Carlo methods become more practical for clinical use, enabling precise, personalized radiation therapy even for large and irregularly shaped tumors.