Suppr超能文献

基于笔形束扫描的单能和双能质子调强弧形治疗的射程优化

Range optimization for mono- and bi-energetic proton modulated arc therapy with pencil beam scanning.

作者信息

Sanchez-Parcerisa Daniel, Kirk Maura, Fager Marcus, Burgdorf Brendan, Stowe Malorie, Solberg Tim, Carabe Alejandro

机构信息

Department of Radiation Oncology, Hospital of the University of Pennsylvania, 3400 Civic Center Bvd, PA, USA. Departamento de Física Atómica, Molecular y Nuclear, Facultad de Ciencias Físicas, Universidad Complutense de Madrid, Av. Complutense s/n, 28040 Madrid, Spain.

出版信息

Phys Med Biol. 2016 Nov 7;61(21):N565-N574. doi: 10.1088/0031-9155/61/21/N565. Epub 2016 Oct 14.

Abstract

The development of rotational proton therapy plans based on a pencil-beam-scanning (PBS) system has been limited, among several other factors, by the energy-switching time between layers, a system-dependent parameter that ranges between a fraction of a second and several seconds. We are investigating mono- and bi-energetic rotational proton modulated arc therapy (PMAT) solutions that would not be affected by long energy switching times. In this context, a systematic selection of the optimal proton energy for each arc is vital. We present a treatment planning comparison of four different range selection methods, analyzing the dosimetric outcomes of the resulting treatment plans created with the ranges obtained. Given the patient geometry and arc definition (gantry and couch trajectories, snout elevation) our in-house treatment planning system (TPS) FoCa was used to find the maximum, medial and minimum water-equivalent thicknesses (WETs) of the target viewed from all possible field orientations. Optimal ranges were subsequently determined using four methods: (1) by dividing the max/min WET interval into equal steps, (2) by taking the average target midpoints from each field, (3) by taking the average WET of all voxels from all field orientations, and (4) by minimizing the fraction of the target which cannot be reached from any of the available angles. After the range (for mono-energetic plans) or ranges (for bi-energetic plans) were selected, the commercial clinical TPS in use in our institution (Varian Eclipse) was used to produce the PMAT plans using multifield optimization. Linear energy transfer (LET) distributions of all plans were also calculated using FoCa and compared among the different methods. Mono- and bi-energetic PMAT plans, composed of a single 180° arc, were created for two patient geometries: a C-shaped target located in the mediastinal area of a thoracic tissue-equivalent phantom and a small brain tumor located directly above the brainstem. All plans were optimized using the same procedure to (1) achieve target coverage, (2) reduce dose to OAR and (3) limit dose hot spots in the target. Final outcomes were compared in terms of the resulting dose and LET distributions. Data shows little significant differences among the four studied methods, with superior results obtained with mono-energetic plans. A streamlined systematic method has been implemented in an in-house TPS to find the optimal range to maximize target coverage with rotational mono- or bi-energetic PBS rotational plans by minimizing the fraction of the target that cannot be reached by any direction.

摘要

基于笔形束扫描(PBS)系统的旋转质子治疗计划的发展受到多种因素限制,其中包括层间能量切换时间,这是一个与系统相关的参数,范围在几分之一秒到几秒之间。我们正在研究单能和双能旋转质子调制弧形治疗(PMAT)解决方案,这些方案不会受到较长能量切换时间的影响。在这种情况下,为每个弧形系统地选择最佳质子能量至关重要。我们展示了四种不同射程选择方法的治疗计划比较,分析了使用所获得射程创建的治疗计划的剂量学结果。根据患者的几何形状和弧形定义(机架和治疗床轨迹、准直器仰角),我们使用内部治疗计划系统(TPS)FoCa来确定从所有可能的射野方向观察到的靶区的最大、中间和最小水等效厚度(WET)。随后使用四种方法确定最佳射程:(1)将最大/最小WET区间划分为相等的步长;(2)取每个射野的靶区中点平均值;(3)取所有射野方向上所有体素的平均WET;(4)通过最小化从任何可用角度都无法到达的靶区部分。在选择了射程(对于单能计划)或射程组(对于双能计划)之后,我们机构使用的商业临床TPS(Varian Eclipse)用于通过多野优化生成PMAT计划。还使用FoCa计算了所有计划的线能量转移(LET)分布,并在不同方法之间进行比较。针对两种患者几何形状创建了由单个180°弧形组成的单能和双能PMAT计划:一个位于胸部组织等效体模纵隔区域的C形靶区和一个位于脑干正上方的小脑肿瘤。所有计划均使用相同的程序进行优化,以(1)实现靶区覆盖,(2)降低对危及器官的剂量,以及(3)限制靶区内的剂量热点。根据所得剂量和LET分布对最终结果进行了比较。数据显示,在所研究的四种方法之间几乎没有显著差异,单能计划取得了更好的结果。一种简化的系统方法已在内部TPS中实施,通过最小化从任何方向都无法到达的靶区部分,找到最佳射程,以最大限度地提高旋转单能或双能PBS旋转计划的靶区覆盖。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验