Proton minibeam (pMBRT) radiation therapy: experimental validation of Monte Carlo dose calculation in the RayStation TPS.

IF 3.4 3区 医学 Q2 ENGINEERING, BIOMEDICAL Physics in medicine and biology Pub Date : 2025-02-13 DOI:10.1088/1361-6560/adae4f
Yuting Lin, Erik Traneus, Aoxiang Wang, Wangyao Li, Hao Gao
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Abstract

Background.Proton minibeam radiation therapy (pMBRT) is a spatially fractionated radiation therapy modality that uses a multi-slit collimator (MSC) to create submillimeter slit openings for spatial dose modulation. The pMBRT dose profile is characterized by highly heterogeneous dose in the plane perpendicular to the beam and rapidly changing depth dose profiles. Dose measurements are typically benchmarked against in-house Monte Carlo (MC) simulation tools. For preclinical and clinical translation, a treatment planning system (TPS) capable of accurately predicting pMBRT doses in tissue and accessible on a commercial platform is essential. This study focuses on the beam modeling and verification of pMBRT using the RayStation TPS, a critical step in advancing its clinical implementation.Methods.The pMBRT system was implemented in RayStation for the IBA Proteus®ONE single-room compact proton machine. The RayStation pMBRT model is an extension of the clinical beam model, allowing pMBRT dose calculations through the MSC using the existing clinical beam model. Adjustable MSC parameters include air gap, slit thickness, slit pitch, number of slits, slits direction and slit thickness. The pMBRT TPS was validated experimentally against measurements using six different collimators with various slit widths (0.4-1.4 mm) and center-to-center slit distances (2.8-4.0 mm). Each collimator comprised five non-divergent slits. Validation involved MatriXX measurements for average dose, Gafchromic film placed at varying depths to measure lateral dose profiles, and film placed along the beam axis to measure depth-dose curves in solid water phantoms. A single 150 MeV energy layer with a 0.5 cm spot spacing was used to create a uniform radiation map across the MSC field.Results.The comparison of average depth dose measurements with RayStation MC calculations showed a gamma passing rate better than 95% using 3 mm/3% criteria, except for the 0.4 mm slit width. After adjusting the slit width by 40-60μm to account for machining uncertainties, the gamma passing rate exceeded 95% under the same criteria. For the peaks and valleys of the percentage depth doses, agreement between RayStation and film measurements was above 90% using 2 mm/5% criteria, except in the high linear energy transfer region. Lateral profile comparisons at depths of 2, 6, and 10 cm demonstrated over 90% agreement for all curves using 0.2 mm/5% criteria.Conclusions.The pMBRT beam model for the Proteus®ONE-based system has been successfully implemented in RayStation TPS, with its initial accuracy validated experimentally. Further measurements, including additional energies and Spread Out Bragg Peaks, are required to complete the clinical commissioning process.

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质子微束(pMBRT)放疗:RayStation TPS中蒙特卡罗剂量计算的实验验证。
背景:质子微束放射治疗(pMBRT)的剂量谱具有垂直于光束方向的高度不均匀剂量和快速变化的深度剂量谱的特点。通常,剂量测量是根据内部蒙特卡罗模拟工具进行基准测试的。必须有一个治疗计划系统(TPS),能够准确预测pMBRT在组织中的剂量,并通过商业平台提供临床前和临床使用。方法:在IBA Proteus®ONE单室紧凑型质子机的RayStation中实现pMBRT光束模型。RayStation pMBRT光束模型是临床使用的光束模型的附加组件。可调参数包括气隙、狭缝厚度、狭缝间距、狭缝数、狭缝方向和狭缝厚度。pMBRT TPS通过实验验证了测量结果。使用六种不同的准直器,它们具有不同的狭缝宽度和中心到中心的狭缝距离。狭缝宽度在0.4 mm ~ 1.4 mm之间,中心距离(c-t-c)在2.8 mm ~ 4.0 mm之间。狭缝不发散,共5条。结果:当将平均深度剂量测量值与RayStation剂量MC计算值进行比较时,除0.4 mm狭缝宽度外,一致性优于使用3mm/3%标准的95%伽马通过率。然而,在我们将狭缝宽度调整40 - 60 μm以考虑加工不确定性之后,使用3mm/3%标准,一致性再次超过95%的伽马通过率。当比较RayStation和薄膜测量之间的峰谷PDDs时,使用2mm/5%的标准,一致性超过90%。当比较不同深度的后期剖面时,使用0.2mm/5%的曲线的一致性都在90%以上。结论:使用RayStation TPS,我们已经成功地为基于Proteus®one的pMBRT系统建立了pMBRT光束建模,并通过实验验证了其准确性。
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来源期刊
Physics in medicine and biology
Physics in medicine and biology 医学-工程:生物医学
CiteScore
6.50
自引率
14.30%
发文量
409
审稿时长
2 months
期刊介绍: The development and application of theoretical, computational and experimental physics to medicine, physiology and biology. Topics covered are: therapy physics (including ionizing and non-ionizing radiation); biomedical imaging (e.g. x-ray, magnetic resonance, ultrasound, optical and nuclear imaging); image-guided interventions; image reconstruction and analysis (including kinetic modelling); artificial intelligence in biomedical physics and analysis; nanoparticles in imaging and therapy; radiobiology; radiation protection and patient dose monitoring; radiation dosimetry
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