First direct machine‐specific parameters incorporated in Spot‐scanning Proton Arc (SPArc) optimization algorithm

Abstract Background Spot‐scanning Proton Arc (SPArc) has been of significant interest in recent years because of its superior plan quality. Currently, the primary focus of research and development is on deliverability and treatment efficiency. Purpose To address the challenges in generating a deliverable and efficient SPArc plan for a proton therapy system with a massive gantry, we developed a novel SPArc optimization algorithm (SPArc DMPO ) by directly incorporating the machine‐specific parameters such as gantry mechanical constraints and proton delivery sequence. Methods SPArc delivery sequence model (DSM arc ) was built based on the machine‐specific parameters of the prototype arc delivery system, IBA ProteusONE®, including mechanical constraint (maximum gantry speed, acceleration, and deceleration) and proton delivery sequence (energy and spot delivery sequence, and irradiation time). SPArc DMPO resamples and adjusts each control point's delivery speed based on the DSM arc calculation through the iterative approach. In SPArc DMPO, users could set a reasonable arc delivery time during the plan optimization, which aims to minimize the gantry momentum changes and improve the delivery efficiency. Ten cases were selected to test SPArc DMPO . Two kinds of SPArc plans were generated using the same planning objective functions: (1) original SPArc plan (SPArc original ); (2) SPArc DMPO plan with a user‐pre‐defined delivery time. Additionally, arc delivery sequence was simulated based on the DSM arc and was compared. Treatment delivery time was compared between SPArc original and SPArc DMPO . Dynamic arc delivery time, the static irradiation time, and its corresponding time differential (time differential = dynamic arc delivery time—static irradiation time) were analyzed, respectively. The total gantry velocity change was accumulated throughout the treatment delivery. Results With a similar plan quality, objective value, number of energy layers, and spots, both SPArc original and SPArc DMPO plans could be delivered continuously within the ± 1 degree tolerance window. However, compared to the SPArc original , the strategy of SPArc DMPO is able to reduce the time differential from 30.55 ± 11.42%(90 ± 32 s) to 14.67 ± 6.97%(42 ± 20 s), p < 0.01. Furthermore, the corresponding total variations of gantry velocity during dynamic arc delivery are mitigated (SPArc original vs. SPArc DMPO ) from 14.73 ± 9.14 degree/s to 4.28 ± 2.42 degree/s, p < 0.01. Consequently, the SPArc DMPO plans could minimize the gantry momentum change based on the clinical user's input compared to the SPArc original plans , which could help relieve the mechanical challenge of accelerating or decelerating the massive proton gantry. Conclusions For the first time, clinical users not only could generate a SPArc plan meeting the mechanical constraint of their proton system but also directly control the arc treatment speed and momentum changes of the gantry during the plan optimization process. This work paved the way for the routine clinical implementation of proton arc therapy in the treatment planning system.