Production of Yttrium-86 radioisotope using genetic algorithm and neural network. (April 2021)

• Record Type: Journal Article
• Authors: Rabiei, Mobina; Khorshidi, Abdollah; Soltani-Nabipour, Jamshid
• Is Part Of: Biomedical signal processing and control. Volume 66(2021)
• Publication Date: 2021-04
• Subjects: Radioisotope production -- Yttrium-86 -- Pollutant -- TALYS code -- Nuclear cross section -- Artificial neural network -- Modified weights -- Genetic algorithm training -- Cost function -- Iteration numbers; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102449
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: TALYS, Artificial Neural Network and Genetic Algorithm were used in radioisotope production. 86 Sr(p, n) 86 Y reaction was assessed at 12–16 MeV energy range of incident proton. At 14 MeV, ANN enhanced the generated 86 Y while reducing contamination. Maximum 86 Y simultaneously with minimum pollutant production reduced the purification error. Abstract: 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, 86 Sr target thickness and the 86 Y 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 86 Y 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.