Investigate gantry position error effect on dynamic spot-scanning proton arc (SPArc) therapy

<i>Objective.</i>Spot-scanning proton arc therapy (SPArc) enables dynamic dose delivery through continuous gantry rotation, offering potential improvements in dose conformity for complex targets. However, dynamic delivery introduces susceptibility to gantry position errors (GPEs), which may degrade the treatment accuracy, particularly in highly precise and conformal techniques. This study aims to quantitatively assess the dosimetric impact of systematic and random GPEs during the dynamic SPArc delivery.<i>Method.</i>Twolve clinical cases, including brain stereotactic radiosurgery (SRS), lung, liver, and prostate cancer, were evaluated under simulated systematic and random GPEs with ±0.5°, ±.1°, ±1.5°, ±2° per 180° arc scenarios. SPArc plans were generated using a published algorithm implemented in RayStation through scripting and simulated the treatment via a validated IBA DynamicARC® system model. Virtual machine logfiles reconstructed control points and spot positions, incorporating GPEs through modified gantry velocities. Target coverage (D98) and gamma passing ratios (GPR) under 3%/3 mm and 2%/2 mm criteria were analyzed.<i>Results.</i>Under the 3%/3 mm criterion, GPR exceeded 98% for all cases; liver, lung, and prostate plans remained >99% across GPE magnitudes, with lung, liver, and prostate plans exceeding 99.4% even at a 2° systematic error. Brain SRS cases were most sensitive; under a 2%/2 mm criterion, the GPR dropped to 89.52 ± 4.74% for a 2° systematic error. In contrast, lung, liver, and prostate plans maintained GPRs above 93% under the same conditions. The impact of random GPEs was minimal, with all plans achieving GPRs greater than 96% even for the strictest 2%/2 mm criterion.<i>Significance.</i>This study provides the first comprehensive quantitative evaluation of dose perturbation caused by GPE in the dynamic SPArc treatment delivery. Systematic GPEs introduce more significant dose deviations compared to random errors, with the impact varying by cases, disease sites and error magnitude. These findings could guide machine quality assurance protocols and site-specific error tolerance thresholds for future clinical implementation.