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

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 and a prostate case.

RESULTS: The beamlet-free algorithm maintained a comparable plan quality while largely reducing the dependence of computation time and memory usage on the number of spots.

CONCLUSION: The implementation of a beamlet-free treatment planning algorithm for proton therapy is feasible and capable of achieving treatment plans of comparable quality to conventional methods. Its efficient usage of computational resources and low spot dependence makes it a promising method for large plans, robust optimization, and arc proton therapy.