Technical note: Beamlet‐free optimization for Monte‐Carlo‐based treatment planning in proton therapy

Pross, Danah [UCL] Wuyckens, Sophie [UCL] Deffet, Sylvain Sterpin, Edmond [UCL] Lee, John Aldo [UCL] Souris, Kevin

Background:Dose calculation and optimization algorithms in proton therapytreatment planning often have high computational requirements regarding timeand memory.This can hinder the implementation of efficient workflows in clinicsand prevent the use of new, elaborate treatment techniques aiming to improveclinical outcomes like robust optimization, arc, and adaptive proton therapy.Purpose:A new method, namely, the beamlet-free algorithm, is presented toaddress the aforementioned issue by combining Monte Carlo dose calculationand 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 ofrandomly chosen spots and evaluates their relative impact on the objective func-tion at each iteration. Based on the approximated gradient, the spot weightsare then updated and used to generate a new spot probability distribution.The beamlet-free method is compared against a conventional, beamlet-basedtreatment planning algorithm on a brain case and a prostate case.Results:The beamlet-free algorithm maintained a comparable plan qualitywhile largely reducing the dependence of computation time and memory usageon the number of spots.Conclusion:The implementation of a beamlet-free treatment planning algo-rithm for proton therapy is feasible and capable of achieving treatment plansof comparable quality to conventional methods. Its efficient usage of compu-tational resources and low spot dependence makes it a promising method forlarge plans, robust optimization, and arc proton therapy.