• Title/Summary/Keyword: Coded-aperture imaging

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Improvement of Tomographic Imaging in Coded Aperture System based on Simulated annealing

  • Noritoshi Kitabatake;Chen, Yen-Wei;Zensyo Nakao
    • Proceedings of the IEEK Conference
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    • 2000.07a
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    • pp.425-428
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    • 2000
  • In this paper, we propose a new method based on SA(simulated annealing) with a fast algorithm for 3D image reconstructrion from the coded apereture images. The reconstructed images can be significantly improved by SA and to large computation cost of SA can be significantly reduced by the fast algorithm.

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Comparative study of the pulse shape discrimination (PSD) performance of pixelated stilbene and plastic scintillator (EJ-276) arrays for a coded-aperture-based hand-held dual-particle imager

  • Jihwan Boo ;Manhee Jeong
    • Nuclear Engineering and Technology
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    • v.55 no.5
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    • pp.1677-1686
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    • 2023
  • As the demand for the detection of special nuclear materials (SNMs) increases, the use of imaging instruments that can sensitively image both gamma-ray and neutron signatures has become necessary. This study compared the pulse shape discrimination (PSD) performance of gamma/neutron events when employing either a pixelated stilbene or a plastic (EJ-276) scintillator array coupled to a silicon photomultiplier (SiPM) array in a dual-particle imager. The stilbene array allowed a lower energy threshold above which neutron and gamma-ray events can be clearly distinguished. A greater number of events can, therefore, be used when forming both gamma-ray and neutron images, which shortens the time required to acquire the images by nearly seven times.

Application of a deep learning algorithm to Compton imaging of radioactive point sources with a single planar CdTe pixelated detector

  • Daniel, G.;Gutierrez, Y.;Limousin, O.
    • Nuclear Engineering and Technology
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    • v.54 no.5
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    • pp.1747-1753
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    • 2022
  • Compton imaging is the main method for locating radioactive hot spots emitting high-energy gamma-ray photons. In particular, this imaging method is crucial when the photon energy is too high for coded-mask aperture imaging methods to be effective or when a large field of view is required. Reconstruction of the photon source requires advanced Compton event processing algorithms to determine the exact position of the source. In this study, we introduce a novel method based on a Deep Learning algorithm with a Convolutional Neural Network (CNN) to perform Compton imaging. This algorithm is trained on simulated data and tested on real data acquired with Caliste, a single planar CdTe pixelated detector. We show that performance in terms of source location accuracy is equivalent to state-of-the-art algorithms, while computation time is significantly reduced and sensitivity is improved by a factor of ~5 in the Caliste configuration.