Production of Yttrium-86 radioisotope using genetic algorithm and neural network

• Published in: Biomedical signal processing and control Vol. 66; p. 102449
• Main Authors: Rabiei, Mobina, Khorshidi, Abdollah, Soltani-Nabipour, Jamshid
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2021
• Subjects: Artificial neural network; Cost function; Genetic algorithm training; Iteration numbers; Modified weights; Nuclear cross section; Pollutant; Radioisotope production; TALYS code; Yttrium-86; Iteration numbers; Pollutant; Nuclear cross section; TALYS code; Radioisotope production; Cost function; Yttrium-86; Genetic algorithm training; Artificial neural network; Modified weights
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2021.102449

•TALYS, Artificial Neural Network and Genetic Algorithm were used in radioisotope production.•86Sr(p,n)86Y reaction was assessed at 12–16 MeV energy range of incident proton.•At 14 MeV, ANN enhanced the generated 86Y while reducing contamination.•Maximum 86Y simultaneously with minimum pollutant production reduced the purification error. Recently, there has been a great deal of attention for applying radioisotopes in medical applications such as photon or positron emission tomography. In this study, TALYS code was utilized for prediction of nuclear reactions and led to a cross section calculation along with excitation function assessment of different nuclear reactions for production of target radioisotopes in medical areas. Subsequently, some parameters related to this code were changed in different reactions to achieve optimal outputs. Here, the range of optimal proton energy from 12–16 MeV, 86Sr target thickness and the 86Y production gain were calculated. The obtained data were optimized by using Artificial Neural Network (ANN) and Genetic Algorithm (GA). At 14 MeV, ANN revealed the greater generated 86Y while reducing contamination against trained bad neural network in wrong conditions. Also through GA optimization method and using ANN outputs, the average error achieved a 15 % improvement in ANN performance versus wrong training outputs. The rate of produced pollutant decreased significantly and the errors diminished due to the proper training of GA performance. This procedure may affect the control of radioactive contamination in medical radioisotope production to decrease the imposed absorbed dose on the patient.