A novel planning framework for efficient spot-scanning proton arc therapy via particle swarm optimization (SPArc-particle swarm)

Objective. Delivery efficiency is the bottleneck of spot-scanning proton arc therapy (SPArc) because of the numerous energy layers (ELs) ascending switches. This study aims to develop a new algorithm to mitigate the need for EL ascending via water equivalent thickness (WET) sector selection followed by particle swarm optimization (SPArc-(particle swarm)). Approach. SPArc-(particle swarm) divided the full arc trajectory into the optimal sectors based on K-means clustering analysis of the relative mean WET. Within the sector, particle swarm optimization was used to minimize the total energy switch time, optimizing the energy selection integrated with the EL delivery sequence and relationship. This novel planning framework was implemented on the open-source platform matRad (Department of Medical Physics in Radiation Oncology, German Cancer Research Center-DKFZ). Three representative cases (brain, liver, and prostate cancer) were selected for testing purposes. Two kinds of plans were generated: SPArc_seq and SPArc-(particle swarm). The plan quality and delivery efficiency were evaluated. Main results. With a similar plan quality, the delivery efficiency was significantly improved using SPArc-(particle swarm) compared to SPArc_seq. More specifically, it reduces the number of ELs ascending switching compared to the SPArc_seq (from 21 to 7 in the brain, from 21 to 5 in the prostate, from 21 to 6 in the liver), leading to a 16%-26% reduction of the beam delivery time (BDT) in the SPArc treatment. Significance. A novel planning framework, SPArc-(particle swarm), could significantly improve the delivery efficiency, which paves the roadmap towards routine clinical implementation.