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首次将机器特定参数纳入 Spot-scanning Proton Arc(SPArc)优化算法中。

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

机构信息

Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Hubei Key Laboratory of Precision Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

出版信息

Med Phys. 2024 Aug;51(8):5682-5692. doi: 10.1002/mp.16985. Epub 2024 Feb 10.

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) by directly incorporating the machine-specific parameters such as gantry mechanical constraints and proton delivery sequence.

METHODS

SPArc delivery sequence model (DSM) 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 resamples and adjusts each control point's delivery speed based on the DSM calculation through the iterative approach. In SPArc 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. Two kinds of SPArc plans were generated using the same planning objective functions: (1) original SPArc plan (SPArc); (2) SPArc plan with a user-pre-defined delivery time. Additionally, arc delivery sequence was simulated based on the DSM and was compared. Treatment delivery time was compared between SPArc and SPArc. 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 and SPArc plans could be delivered continuously within the ± 1 degree tolerance window. However, compared to the SPArc, the strategy of SPArc 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 vs. SPArc) from 14.73 ± 9.14 degree/s to 4.28 ± 2.42 degree/s, p < 0.01. Consequently, the SPArc plans could minimize the gantry momentum change based on the clinical user's input compared to the SPArc 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.

摘要

背景

由于其优越的计划质量,点扫描质子弧形(SPArc)近年来引起了极大的关注。目前,研究和开发的主要重点是交付能力和治疗效率。

目的

为了解决为具有大型龙门架的质子治疗系统生成可交付和高效的 SPArc 计划所面临的挑战,我们开发了一种新的 SPArc 优化算法(SPArc),该算法通过直接结合机器特定参数(例如龙门机械约束和质子输送顺序)来实现。

方法

基于原型弧形输送系统(IBA ProteusONE®)的机器特定参数,建立了 SPArc 输送顺序模型(DSM),包括机械约束(最大龙门速度,加速度和减速度)和质子输送顺序(能量和点输送顺序以及辐照时间)。SPArc 通过迭代方法根据 DSM 计算结果对每个控制点的输送速度进行重采样和调整。在计划优化过程中,SPArc 用户可以设置合理的弧形输送时间,旨在最大程度地减少龙门的动量变化并提高输送效率。选择了十个案例来测试 SPArc。使用相同的计划目标函数生成了两种 SPArc 计划:(1)原始 SPArc 计划(SPArc);(2)具有用户预定义输送时间的 SPArc 计划。此外,基于 DSM 模拟了弧形输送序列并进行了比较。比较了 SPArc 和 SPArc 之间的治疗输送时间。分析了动态弧形输送时间,静态辐照时间及其相应的时间差(时间差=动态弧形输送时间-静态辐照时间)。在整个治疗输送过程中,累计了总龙门速度变化。

结果

在具有相似计划质量,目标值,能量层数量和点数量的情况下,SPArc 和 SPArc 计划都可以在±1 度容差窗口内连续输送。然而,与 SPArc 相比,SPArc 策略能够将时间差从 30.55±11.42%(90±32 s)降低到 14.67±6.97%(42±20 s),p<0.01。此外,动态弧形输送过程中龙门速度的总变化也得到缓解(SPArc 与 SPArc 相比),从 14.73±9.14 度/秒降低至 4.28±2.42 度/秒,p<0.01。因此,与 SPArc 计划相比,SPArc 计划可以根据临床用户的输入最小化龙门动量变化,从而有助于减轻加速或减速大型质子龙门架的机械挑战。

结论

这是首次,临床用户不仅可以生成符合其质子系统机械约束的 SPArc 计划,还可以在计划优化过程中直接控制弧形治疗速度和龙门的动量变化。这项工作为质子弧形治疗在治疗计划系统中的常规临床实施铺平了道路。

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