Deep learning estimations of the production cross sections of Br medical radionuclide.

Journal: Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
Published Date: Jun 13, 2025

Abstract

Bromine-77 has a half-life of 56 h and decays nearly exclusively (99.3 %) by electron capture, with prominent gamma rays at 239.0 and 520.7 keV. Once considered primarily for SPECT imaging, this nuclide is increasingly being evaluated for its potential in Auger electron therapy. In this study, deep learning algorithms with Python programming language are improved to predict the production cross sections of bromine-77 radionuclide. Experimental cross sections data used in artificial neural network were taken from the EXFOR nuclear reactions database. The deep learning results obtained for the Se(p,n)Br, Se(p,2n)Br, Se(p,4n)Br and As(α,2n)Br reactions were compared with the calculation results obtained from the TALYS code. It was observed that the results obtained with deep learning obey the experimental values much better.

Authors

  • Abdullah Aydin
    Kırıkkale University, Faculty of Engineering and Natural Sciences, Department of Physics, Kırıkkale, Türkiye. Electronic address: a.aydin63@gmail.com.
  • R Gökhan Türeci
    Kırıkkale University, Kırıkkale Vocational School, Kırıkkale, Türkiye.
  • Ismail Hakki Sarpün
    Akdeniz University, Physics Department, Antalya, Türkiye; Akdeniz University, Faculty of Medicine, Radiation Oncology, Antalya, Türkiye; Akdeniz University, Nuclear Research and Application Centre, Antalya, Türkiye.
  • Hasan Özdoğan
    Antalya Bilim University, Vocational School of Health Services, Department of Medical Imaging Techniques, 07190, Antalya, Turkey. Electronic address: hasan.ozdogan@antalya.edu.tr.

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