Reconstructing the Position and Intensity of Multiple Gamma-Ray Point Sources with a Sparse Parametric Algorithm
Abstract
We present an experimental demonstration of Additive Point Source Localization (APSL), a sparse parametric imaging algorithm that reconstructs the 3D positions and activities of multiple gamma-ray point sources. Using a handheld gamma-ray detector array and up to four Ci Cs gamma-ray sources, we performed both source-search and source-separation experiments in an indoor laboratory environment. In the majority of the source-search measurements, APSL reconstructed the correct number of sources with position accuracies of cm and activity accuracies (unsigned) of , given measurement times of two to three minutes and distances of closest approach (to any source) of cm. In source-separation measurements where the detector could be moved freely about the environment, APSL was able to resolve two sources separated by cm or more given only s of measurement time. In these source-separation measurements, APSL produced larger total activity errors of , but obtained source separation distances accurate to within cm. We also compare our APSL results against traditional Maximum Likelihood-Expectation Maximization (ML-EM) reconstructions, and demonstrate improved image accuracy and interpretability using APSL over ML-EM. These results indicate that APSL is capable of accurately reconstructing gamma-ray source positions and activities using measurements from existing detector hardware.
Cite
@article{arxiv.2009.07303,
title = {Reconstructing the Position and Intensity of Multiple Gamma-Ray Point Sources with a Sparse Parametric Algorithm},
author = {Jayson R. Vavrek and Daniel Hellfeld and Mark S. Bandstra and Victor Negut and Kathryn Meehan and William J. Vanderlip and Joshua W. Cates and Ryan Pavlovsky and Brian J. Quiter and Reynold J. Cooper and Tenzing H. Y. Joshi},
journal= {arXiv preprint arXiv:2009.07303},
year = {2020}
}
Comments
10 pages, 9 figures. Accepted for publication at IEEE Transactions on Nuclear Science