Dynamic proton arc therapy sequencing optimization algorithm for brain stereotactic radiosurgery.

Abstract

Proton arc (PAT) therapy combines the dosimetric advantages of protons with the efficiency of arc delivery, but current planning algorithms are based on a static delivery sequence. The discrepancy between the static plan and the actual delivery can result in dosimetric deviations during stereotactic radiosurgery (SRS), where precision is critical. Approach: A dynamic arc delivery sequencing optimization framework was developed, consisting of three steps: (1) static irradiation and dynamic arc delivery time calculation, (2) incorporation of timing information into static control points, and (3) spot-weighting fine-tuning. Eight multi-metastatic brain cases were retrospectively selected to validate the framework. Plan quality, delivery accuracy, and efficiency were evaluated by reconstructing the delivered dose from virtual logfiles and comparing dosimetric parameters and treatment times. Main results: Sequencing optimization maintained nominal plan quality, with no significant differences in target coverage (D98) or normal brain sparing (V12, V8) compared with static-control-point PAT. Delivery accuracy improved substantially. For the total gross tumor volume, mean absolute D98 deviation decreased from 62.9 ± 70.1 cGyE (3.4% ± 3.8%) with static-control-point PAT to 11.0 ± 7.4 cGyE (0.6% ± 0.6%) with sequencing optimization. For the worst metastasis, deviations were reduced from 116.4 ± 89.1 cGyE (6.3% ± 5.3%) to 79.4 ± 69.2 cGyE (3.6% ± 3.0%). Delivery efficiency was preserved, with minimal changes in spot number, energy layers, and total treatment time. Significance: Dynamic sequencing optimization significantly improves the dosimetric fidelity of PAT therapy for brain SRS while maintaining efficiency. By addressing machine-specific timing and mechanical constraints, this framework bridges the gap between nominal planning and clinical delivery, representing an essential step toward routine PAT implementation.&#xD.