Multi-criteria proton treatment planning incorporating spot boosting for prompt gammain-vivorange verification.

Abstract

Objective.Prompt gamma (PG) detection enables beam range verification and margin reduction in proton therapy, but its effectiveness is limited by counting statistics. Spot boosting has been proposed to enhance the signal-to-noise ratio by increasing the number of protons in selected spots of a pencil beam scanning treatment plan. This work integrates PG monitoring into an multi-criteria optimization (MCO) framework to investigate trade-offs between proton counts in boosted spots and dosimetric metrics.Approach.Intensity modulated proton therapy plans for a prostate-tumor and a brain-tumor patient were optimized in OpenTPS. Dose, LET weighted dose (LETxdose) and PG distributions were simulated for each pencil beam using the fast Monte Carlo (MC) engine MCSquare. For each field, ten candidate spots were selected based on three metrics quantifying the quality of PG monitoring and an even spatial distribution across the clinical target volume. Initial pencil beam weights and corresponding dose and LETxdose distributions were imported into an MCO framework for re-optimization, simultaneously considering both dosimetric goals and boosting objectives.Main results.Boosting up toprotons/spot maintained the plan quality without violating constraints in the prostate case. In the brain case, spot boosting required moderate deviations from the initial target dose constraints. For both cases, only minor boosting gains were obtained by relaxing dose metrics for organs at risk.Significance.This study is the first to visualize spot boosting trade-offs via Pareto fronts to support an informed decision, integrating verification quality aspects into the treatment planning stage. The approach enables selection of plans that preserve dose and LETxdose objectives while enhancing PG signal in targeted spots for reliablein-vivorange verification. The framework is broadly applicable and can provide a strategy for embedding verification-driven objectives into treatment optimization beyond PG monitoring.