Full 3D beam profiling and quality assurance (QA) of therapeutic megavoltage linear accelerator (LINAC) x-ray photon beams is not routinely performed due to the slow point-by-point measurement nature of conventional scanning ionization chamber systems. In this study we explore a novel optical-based dose imaging approach using a standard commercial camera, water tank, and fluorescent dye, which when excited by the Čerenkov emission induced by the radiation beam, allows 2D projection imaging in a fast timeframe, potentially leading towards 3D tomographic beam profiling. Detailed analysis was done to optimize the imaging parameters in the experimental setup. The results demonstrate that the captured images are linear with delivered dose, independent of dose rate, and comparison of experimentally captured images to a reference dose distribution for a 4×4 cm 6 MV x-ray photon beam yielded results with improved accuracy over a previous study which used direct imaging and Monte Carlo calibration of the Čerenkov emission itself. The agreement with the reference dose distribution was within 1-2% in the lateral direction, and ± 3 % in the depth direction. The study was restricted to single 2D image projection, with the eventual goal of creating full 3D profiles after tomographic reconstruction from multiple projections. Given the increasingly complex advances in radiation therapy, and the increased emphasis on patient-specific treatment plans, further refinement of the technique could prove to be an important tool for fast and robust QA of x-ray photon LINAC beams.
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机译:由于常规扫描电离室系统的缓慢逐点测量特性,通常无法执行治疗性兆电压线性加速器(LINAC)X射线光子束的完整3D光束轮廓分析和质量保证(QA)。在这项研究中,我们探索了一种使用标准商用相机,水箱和荧光染料的基于光学的剂量成像方法,当受到辐射束引起的Čerenkov发射激发时,该方法可以在较短的时间内进行2D投影成像,这可能走向3D断层扫描轮廓。进行了详细的分析,以优化实验设置中的成像参数。结果表明,所捕获的图像与所输送的剂量呈线性关系,与剂量率无关,并且对4×4 cm 6 MV x射线光子束的实验所捕获的图像与参考剂量分布进行比较得出的结果具有比以前更高的精度这项研究使用了直接成像和Čerenkov辐射本身的蒙特卡罗校准。与参考剂量分布的一致性在横向上为1-2%以内,在深度方向上为±3%。这项研究仅限于单个2D图像投影,最终目标是从多个投影的断层图像重建后创建完整的3D轮廓。鉴于放射疗法的进展日益复杂,并且对患者特定的治疗计划的重视程度日益提高,该技术的进一步完善可能被证明是对X射线光子LINAC光束进行快速,强大QA的重要工具。
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