Machine Model‐Specific Delivery Sequence Optimization for Spot‐scanning Proton Arc Therapy Using a Compact Superconducting Synchrocyclotron

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Abstract Background Spot scanning proton arc therapy (SPArc) combines the dosimetric advantages of proton therapy with the beam‐angle freedom of arc delivery. However, current planning algorithms rely on static delivery assumptions that do not account for the temporal characteristics of pulsed‐beam synchrocyclotron systems during continuous gantry rotation. This mismatch between nominal plans and actual treatment delivery may lead to clinically meaningful dose deviations. Purpose To develop and evaluate a dynamic arc delivery sequencing optimization framework that incorporates machine‐specific delivery characteristics to minimize deviations between planned and delivered dose in SPArc. Methods A five‐step dynamic arc delivery sequencing optimization framework was developed. The framework includes: (1) static and dynamic delivery time calculation, (2) spot and energy‐layer disassembling, (3) incorporation of dynamic delivery timing into control points, (4) spot‐weight fine‐tuning, and (5) reconstruction of energy‐layer sequences. Five multi‐metastatic brain stereotactic radiosurgery cases were retrospectively evaluated. Delivery accuracy, efficiency and plan quality were assessed using virtual machine logfiles. Results The sequencing optimization framework substantially improved delivery accuracy while preserving plan quality and efficiency. For the total gross tumor volume, the mean absolute D98 deviation between planned and virtual logfile reconstructed doses decreased from 77.4 ± 81.0 cGyE (4.2 ± 4.5%) with static SPArc plans to 9.6 ± 4.0 cGyE (0.5 ± 0.2%) after sequencing optimization. For the worst metastasis in each case, D98 deviation decreased from 184.4 ± 145.2 cGyE (9.8 ± 7.9%) to 19.0 ± 16.4 cGyE (1.0 ± 0.8%), and D2 deviation decreased from 148.4 ± 114.4 cGyE (6.8 ± 5.6%) to 13.2 ± 8.6 cGyE (0.6 ± 0.4%). Target coverage and normal brain sparing remained statistically unchanged (p > 0.05), and total delivery times differed by < 1 s. Conclusions The proposed sequencing optimization framework addresses the temporal mismatch between static SPArc planning and dynamic delivery in synchrocyclotron‐based systems. By improving delivery accuracy without compromising plan quality or delivery efficiency, the framework demonstrates the feasibility of incorporating machine‐specific delivery timing into dynamic proton arc therapy.