Prediction of Cyclotron Ion Source Output using Deep Neural Networks

The consistency of cyclotron ion source output is crucial for ensuring high-quality radioactive isotope production, such as fluorine-18, which is widely used in positron emission tomography (PET) for cancer diagnosis. This study aims to develop a predictive model for cyclotron ion source output using deep neural networks (DNN). The model was trained on historical operational data from the Cyclotron at Dharmais Cancer Hospital, including variables such as ion source voltage, ion source current, bias voltage, bias current, gas flow, high vacuum, and magnetism. The DNN effectively captured the complex non-linear relationships among these parameters, overcoming the limitations of traditional physical models and linear methods. Evaluation results showed great performance, with a test MSE of 0.00178 and an MAE of 0.03064, demonstrating the model’s ability to provide accurate predictions. The findings highlight the potential of DNN for improving cyclotron efficiency and reliability, contributing to better outcomes in nuclear medicine services.