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http://dx.doi.org/10.1016/j.net.2021.10.031

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

Daniel, G. (Universite Paris-Saclay, CEA)
Gutierrez, Y. (AIM, CEA, CNRS, Universite Paris-Saclay)
Limousin, O. (AIM, CEA, CNRS, Universite Paris-Saclay)
Publication Information
Nuclear Engineering and Technology / v.54, no.5, 2022 , pp. 1747-1753 More about this Journal
Abstract
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.
Keywords
CdTe; Compton imaging; Deep learning; Machine learning; Convolutional neural networks;
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Times Cited By KSCI : 2  (Citation Analysis)
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