JOURNAL ARTICLE

Three-dimensional localization and radioactivity quantification of radiation sources through inverse estimation based on Compton camera measurements.

  • Published In: Radiation Protection Dosimetry, 2025, v. 201, n. 7. P. 490 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Sato, Yuki 3 of 3

Abstract

This article focuses on a method to accurately locate multiple radioactive sources in three dimensions and quantify their radioactivity levels using an inverse estimation technique applied to images captured by a Compton camera. The method involves dividing the target region into multiple regions of interest (ROIs), acquiring 3D images of known radiation sources in each ROI, and reconstructing images of unknown sources by summing these known-source images scaled by coefficients determined through inverse estimation. Tested with multiple cesium-137 (^137Cs) sources of varying radioactivity, the approach demonstrated the ability to identify source locations and estimate radioactivity, particularly for stronger sources, while weaker sources posed challenges due to overlapping signals and measurement fluctuations. The study highlights the method's potential application in decommissioning environments like the Fukushima Daiichi Nuclear Power Station and discusses factors affecting accuracy, such as shielding and gamma-ray attenuation, suggesting further research to refine threshold settings and improve quantification of weak sources.

Additional Information

  • Source:Radiation Protection Dosimetry. 2025/05, Vol. 201, Issue 7, p490
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2025
  • ISSN:01448420
  • DOI:10.1093/rpd/ncaf046
  • Accession Number:186054226
  • Copyright Statement:Copyright of Radiation Protection Dosimetry is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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