Estimation of the central-axis-reference percent depth dose in a water phantom using artificial intelligence

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 sourceto-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.