  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.
