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Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
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Related Experiment Video

Updated: Mar 6, 2026

Automated 90Sr Separation and Preconcentration in a Lab-on-Valve System at Ppq Level
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Feasibility study of fast nuclide identification for beta-emitting sources using learning-based models.

Min Ji Kim1, Hee Reyoung Kim1

  • 1Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea.

Applied Radiation and Isotopes : Including Data, Instrumentation and Methods for Use in Agriculture, Industry and Medicine
|March 4, 2026
PubMed
Summary

This study demonstrates a fast, AI-powered method for identifying beta-emitting radionuclides using beta spectrum data. This approach enhances radiological safety by enabling rapid nuclide identification in emergencies and nuclear facilities.

Keywords:
Artificial intelligenceBeta-emitting radionuclidesFast identificationSupport vector machineTime-series classification with transformer

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Area of Science:

  • Nuclear Physics
  • Radiological Science
  • Artificial Intelligence

Background:

  • Rapid identification of beta-emitting radionuclides is crucial for radiological safety during emergencies and in nuclear facilities.
  • Current methods for beta nuclide identification are often slow, requiring chemical preprocessing or sophisticated equipment due to continuous beta spectra.
  • Fast identification is needed for environmental monitoring (e.g., radiostrontium) and immediate response to airborne contamination alarms.

Purpose of the Study:

  • To investigate the feasibility of rapidly identifying beta-emitting radionuclides using machine learning models and beta spectrum data measured in air.
  • To develop and validate an artificial intelligence-based approach for fast beta nuclide identification.
  • To enhance the speed and efficiency of beta nuclide identification for improved radiation protection strategies.

Main Methods:

  • An experimental system was established using beta-disk sources and a suitable detection system.
  • Two learning-based models, Support Vector Machine (SVM) and Time-Series Classification with Transformer (TSCT), were employed.
  • The models were trained to classify 15 nuclide combinations from 60Co, 90Sr/90Y, 137Cs, and 152Eu.

Main Results:

  • The SVM and TSCT models achieved high classification accuracies of 100% and 98.0%, respectively.
  • Both trained models could identify nuclide combinations within seconds under controlled laboratory conditions.
  • The proposed AI-based method significantly enhances the speed of beta nuclide identification.

Conclusions:

  • The study confirms the feasibility of using learning-based models for rapid beta nuclide identification from air-measured beta spectrum data.
  • The developed AI approach offers a significant improvement in the speed of nuclide identification compared to traditional methods.
  • Further validation in variable field conditions is recommended to apply this method to real-world radiation protection scenarios.