Elemental analysis of tumor in eye phantom using prompt gamma-rays counting in proton therapy

Abstract

The proton induced prompt gamma spectrum is applied for elemental analysis of irradiation tissues. The main purpose of the analysis in proton therapy is to track oxygen concentration in abnormal tissues. Online monitoring of oxygen concentration over a full course of treatment could provide a direct method for evaluating the response of these tissues to proton therapy and this information on the response of the tumor and healthy tissues to irradiation could then be used by the oncologist to adjust the patient’s treatment plan to ensure proper dose delivery to the tumor. In this study, the prompt gamma spectrum of a human eye phantom is simulated in an HPGe detector using the Geant4 toolkit, and the elemental analysis is accomplished using an artificial neural network (ANN). In the analysis, 33 eye phantoms are considered with different tumors, including different densities and elemental compositions. 21 samples were used as train data in ANN and 6 samples for testing and 6 samples for validation. The results show that there is a good correlation between outputs and targets. They show that the error percent of oxygen, carbon, and nitrogen in test samples are less than 5.8, 12.7, and 25%, respectively. Finally, the potential of quantitative elemental analysis of inhomogeneous targets is confirmed for providing a method to track change in the oxygen level of tumors.

Keywords

• elemental analysis
• prompt gamma
• proton therapy
• eye phantom
• neural network