ABSTRACT

The water phantom is used as a standard device for the calibration of measuring instruments used in radiation therapy. To carry out this calibration, it is essential to characterize the distribution of the percent depth dose (PDD) along the central reference axis, since this is where the instruments to be calibrated are located. The PDD depends on some magnitudes, such as the size of the field in the phantom, the depth of the central reference axis, the source-to-surface distance (SDD) and the radiation energy used [23]. A phantom is a fundamental element for the training of cancer specialists and medical physicists, and can be used to propose more effective procedures for the clinical radiation treatment of patients. We report on some models and simulation of the PDD data provided by the International Atomic Energy Agency (IAEA) and the British Journal of Radiology [1] by using artificial intelligence. PDD predictions by using artificial neural networks (ANN) and genetic programming (GP) are hereby given. It is shown how our approach has superior performance compared to the current state of the art.

摘要

水体模被用作校准用于放射治疗的测量仪器的标准设备。要执行此校准，必须表征沿中心参考轴的深度剂量百分比（PDD）的分布，因为这是要校准的仪器所在的位置。PDD取决于某些大小，例如体模中的场大小，中心参考轴的深度，源到表面的距离（SDD）和所使用的辐射能量[23]。幻影是培训癌症专家和医学物理学家的基本要素，可用于为患者的临床放射治疗提出更有效的程序。我们使用人工智能报告了国际原子能机构（IAEA）和《英国放射学杂志》 [1]提供的PDD数据的一些模型和仿真。特此给出通过使用人工神经网络（ANN）和遗传编程（GP）进行的PDD预测。展示了我们的方法与现有技术相比如何具有卓越的性能。