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First direct machine-specific parameters incorporated in Spot-scanning Proton Arc (SPArc) optimization algorithm
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DOI：10.1002/mp.16985.png)
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Abstract
En 中文
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 (DSMarc) was built based on the machine-specific parameters of the prototype arc delivery system, IBA ProteusONE (R), 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 DSMarc 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 DSMarc 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.
Keywords:
machine parameter
optimization
proton arc
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