A novel approach for proton therapy pencil beam scanning patient specific quality assurance using an integrated detector system and 3D dose reconstruction

一种利用集成探测器系统和三维剂量重建技术进行质子治疗笔形束扫描,以实现患者个体化质量保证的新方法

阅读:1

Abstract

PURPOSE: Current proton beam therapy patient-specific quality assurance (PSQA) methods rely on time-intensive phantom measurements or machine-reported parameters without independent verification. This work presents an integrated detector system for phantom-less Pencil Beam Scanning (PBS) PSQA, providing independent spot-by-spot measurements of all critical beam parameters and 3D dose reconstruction. METHODS: The integrated detector combines three separate systems: a scintillator range telescope for range and energy measurement; a CMOS pixel sensor for spot position and size verification; and a Transmission Calorimeter (TC) for beam intensity measurements. Measured parameters feed Monte Carlo simulations to reconstruct 3D dose distributions for comparison with treatment planning predictions. Validation was performed at UCLH using single spot position spread out Bragg Peak (SOBP) and 5×5×10 spot box field configurations. RESULTS: Energy values obtained from range measurements showed strong correlation with DICOM values (R (2) > 0.998) with an accuracy of between 2.17 mm and 1.23 mm for different beam deliveries. CMOS pixel sensor measurements succeeded for single spot fields but experienced saturation at higher intensities and incomplete coverage for the larger box field. The TC demonstrated excellent dose linearity (R (2) = 1.000). Monte Carlo reconstructions agreed well with reference simulations for longitudinal profiles, though lateral reconstructions proved challenging with 77% gamma pass rates (2%/2mm) for the box field. DISCUSSION: This proof-of-concept demonstrates feasibility of independent beam parameter verification for PBS PSQA while maintaining patient geometry. The approach offers advantages over current methods but requires resolution of energy calibration offsets and detector limitations before clinical implementation. Future work will address these challenges and expand validation to clinical treatment plans.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。