University Clinic for Medical Radiation Physics, Medical Campus Pius Hospital, Carl von Ossietzky University, Oldenburg, Germany.
Department of Radiation Oncology, University of Florida, Gainesville, Florida, USA.
J Appl Clin Med Phys. 2021 Dec;22(12):64-71. doi: 10.1002/acm2.13447. Epub 2021 Oct 11.
The purpose of this work is to study the feasibility of photon beam profile deconvolution using a feedforward neural network (NN) in very small fields (down to 0.56 × 0.56 cm ). The method's independence of the delivery and scanning system is also investigated. Lateral beam profiles of photon fields between 0.56 × 0.56 cm and 4.03 × 4.03 cm were collected on a Siemens Artiste linear accelerator. Three scanning ionization chambers (SNC 125c, PTW 31021, and PTW 31022) of sensitive volumes ranging from 0.016 cm to 0.108 cm were used with a PTW MP3 water phantom. A reference dataset was also collected with a PTW 60019 microDiamond detector to train and test individual NNs for each ionization chamber. Further testing of the trained NNs was performed with additional test data collected on an Elekta Synergy linear accelerator using a Sun Nuclear 3D Scanner. The results were evaluated with a 1D gamma analysis (0.5 mm/0.5%). After the deconvolution, the gamma passing rates increased from 54.79% to 99.58% for the SNC 125c, from 57.09% to 99.83% for the PTW 31021, and from 91.03% to 96.36% for the PTW 31022. The delivery system, the scanning system, the scanning mode (continuous vs. step-by-step), and the electrometer had no significant influence on the results. This study successfully demonstrated the feasibility of using NN to correct the beam profiles of very small photon fields collected with ionization chambers of various sizes. Its independence of the delivery and scanning system was also shown.
这项工作的目的是研究使用前馈神经网络（NN）对非常小的射束（小至 0.56×0.56cm）进行射束轮廓反卷积的可行性。还研究了该方法对输送和扫描系统的独立性。在西门子 Artiste 直线加速器上收集了光子场的横向射束轮廓，大小从 0.56×0.56cm 到 4.03×4.03cm。使用具有 PTW MP3 水模的三种不同灵敏体积的扫描电离室（SNC 125c、PTW 31021 和 PTW 31022）进行测量。还使用 PTW 60019 微 Diamond 探测器收集了参考数据集，以训练和测试每个电离室的单独 NN。使用 Elekta Synergy 直线加速器和 Sun Nuclear 3D Scanner 收集额外的测试数据，对训练好的 NN 进行进一步测试。使用 1D 伽马分析（0.5mm/0.5%）进行评估。反卷积后，SNC 125c 的伽马通过率从 54.79%增加到 99.58%，PTW 31021 从 57.09%增加到 99.83%，PTW 31022 从 91.03%增加到 96.36%。输送系统、扫描系统、扫描模式（连续与分步）以及静电计对结果没有显著影响。这项研究成功地证明了使用 NN 校正各种尺寸电离室收集的非常小的光子场射束轮廓的可行性，同时也证明了该方法对输送和扫描系统的独立性。
