Beamlet-free optimization for Monte Carlo based treatment planning in proton therapy

TL;DR

This paper introduces a beamlet-free optimization algorithm for proton therapy treatment planning that significantly reduces computational time and memory usage while maintaining plan quality, enabling more efficient clinical workflows.

Contribution

The novel beamlet-free algorithm integrates dose calculation and optimization, eliminating the need for a dose influence matrix, and demonstrates substantial efficiency improvements.

Findings

70% reduction in computation time

95% reduction in peak memory usage

Maintains comparable plan quality to conventional methods

Abstract

Background: Dose calculation and optimization algorithms in proton therapy treatment planning often have high computational requirements regarding time and memory. This can hinder the implementation of efficient workflows in clinics and prevent the use of new, elaborate treatment techniques aiming to improve clinical outcomes like robust optimization, arc and adaptive proton therapy. Purpose: A new method, namely, the beamlet-free algorithm, is presented to address the aforementioned issue by combining Monte Carlo dose calculation and optimization into a single algorithm and omitting the calculation of the time-consuming and costly dose influence matrix. Methods: The beamlet-free algorithm simulates the dose in proton batches of randomly chosen spots and evaluates their relative impact on the objective function at each iteration. Based on the approximated gradient, the spot weights are then updated and used to generate a new spot probability distribution. The beamlet-free method is compared against a conventional, beamlet-based treatment planning algorithm on a brain case. Results: The beamlet-free algorithm maintained a comparable plan quality while reducing the computation time by 70% and the peak memory usage by 95%. Conclusion: The implementation of a beamlet-free treatment planning algorithm for proton therapy is feasible and capable of achieving a considerable reduction of time and memory requirements.