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Proton arc therapy has emerged as a promising approach for faster treatmentdelivery in comparison to conventional proton treatments. However, unexpectedprolonged treatment times in several proton arc planning studies have raisedconcerns. In this study, we aim to address the challenge of minimizing deliverytime through dosimetry-guided energy layer filtering, comparing its performanceto a baseline approach without filtering. We developed three methods of energylayer (EL) filtering: unrestricted, switch-up (SU), and switch-up gap (SU gap)filtering. The unrestricted method filters the lowest weighted EL while the SUgap filtering removes in priority the EL around a new SU to minimize the gantryrotation braking. The SU filtering removes the lowest weighted group of EL thatincludes a SU. These post-processing filters were used in conjunction with theRayStation dynamic proton arc optimization framework (ELSA). Four bilateraloropharyngeal cancer patients' data were used for evaluation. Objectivefunction values, target coverage robustness, organ-at-risk doses and NTCPevaluations, as well as comparisons to IMPT plans, were used to assess planquality. The SU gap filtering algorithm performed best in three out of the fourcases, maintaining plan quality within tolerance while significantly reducingbeam delivery time. It achieved up to approximately 22% reduction in deliverytime. The unrestricted filtering algorithm followed closely. In contrast, theSU filtering showed limited improvement, suppressing hardly a few SU withoutsubstantial delivery time shortening. Robust target coverage was kept within 1%of variation compared to the ELSA baseline plan while OAR doses slighltydecreased in all four patients. Both arc plans present large reductions in NTCPvalues for dysphagia and xerostomia compared to the reference IMPT plan.

Accelerating proton arc therapy delivery through dosimetry-guided energy layer filtering