Improved radiological/nuclear source localization in variable NORM background: An MLEM approach with segmentation data

Penny, R.; Crowley, Tanya M.; Gardner, B.; Mandell, M. J.; Guo, Yanlin; Haas, Eric B.; Knize, Duane J.; Kuharski, Robert A.; Ranta, Dale; Shyffer, Ryan; Labov, S. E.; Nelson, K.; Seilhan, B.; Valentine, J.D.

doi:10.1016/j.nima.2015.01.025

Cited by 9 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bayesian approaches to detect, classify, and estimate smuggled nuclear and radiological materials are not a new consideration 6,12 , and were extensively studied for the development of the Statistical Radiation Detection System at Lawrence Livermore National Laboratory. This group has used Bayesian model-based sequential statistical processing techniques to overcome the low signal-to-background ratio that complicates traditional gamma spectroscopy techniques with high-resolution HPGe and inorganic scintillation detectors 13,14 . Bayesian approaches have also been applied to radionuclide identification for NaI(Tl) detectors using a wavelet-based peak identification algorithm with Bayesian classifiers 15 , for LaBr 3 (Ce) using a sequential approach 16 , and to HPGe detectors using non-parametric Bayesian deconvolution to resolve overlapping peaks 17 .…”

Section: Algorithms For Rpm Signal Unmixingmentioning

confidence: 99%

Expectation-propagation for weak radionuclide identification at radiation portal monitors

Altmann

Fulvio

Paff

et al. 2020

Sci Rep

View full text Add to dashboard Cite

We propose a sparsity-promoting Bayesian algorithm capable of identifying radionuclide signatures from weak sources in the presence of a high radiation background. the proposed method is relevant to radiation identification for security applications. In such scenarios, the background typically consists of terrestrial, cosmic, and cosmogenic radiation that may cause false positive responses. We evaluate the new Bayesian approach using gamma-ray data and are able to identify weapons-grade plutonium, masked by naturally-occurring radioactive material (noRM), in a measurement time of a few seconds. We demonstrate this identification capability using organic scintillators (stilbene crystals and EJ-309 liquid scintillators), which do not provide direct, high-resolution, source spectroscopic information. Compared to the EJ-309 detector, the stilbene-based detector exhibits a lower identification error, on average, owing to its better energy resolution. organic scintillators are used within radiation portal monitors to detect gamma rays emitted from conveyances crossing ports of entry. the described method is therefore applicable to radiation portal monitors deployed in the field and could improve their threat discrimination capability by minimizing "nuisance" alarms produced either by noRMbearing materials found in shipped cargoes, such as ceramics and fertilizers, or radionuclides in recently treated nuclear medicine patients.The growing terrorism threat based on the use of special nuclear materials (SNMs), i.e., highly enriched uranium (HEU), weapons-grade plutonium (WGPu), or high-activity radiological sources has reinforced the need for improved population protection mechanisms. Nuclear security aims to deter and detect the smuggling of these materials across state borders. One major defense mechanism involves the installation of radiation portal monitors (RPMs) at border crossings. These RPMs typically consist of 3 He proportional counters embedded in polyethylene for neutron detection, and slabs of polyvinyl-toluene (PVT) scintillators for gamma-ray detection. Only a tiny fraction of the millions of vehicles and cargo containers entering a country like the United States are likely to be carrying radiological contraband. The International Atomic Energy Agency's Incident and Trafficking Database (ITDB) merely counts a few dozen reported successful interdictions of nuclear and radiological materials globally per year 1,2 . The ITDB provides only a partial picture of the number of smuggling attempts. The reported figures should be considered a lower bound of the number of successful interdictions, because they include only successful interdictions, voluntarily reported by the member states.Complicating matters, the radiological contraband might be well shielded. In 2017, the United Nations Conference on Trade and Development estimated the global container port throughput at over 750 million 20-foot equivalent units 3 . As a consequence, RPMs are limited in measurement time to minimize unnecessary impediments to the fl...

show abstract

Section: Algorithms For Rpm Signal Unmixingmentioning

confidence: 99%

Expectation-propagation for weak radionuclide identification at radiation portal monitors

Altmann

Fulvio

Paff

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…While a constant background assumption was made here and may be appropriate for the small search space and short measurement duration, this may not be appropriate in widearea urban search scenarios [33], [34], [35]. Therefore a treatment for variable background rates must be incorporated into the algorithm for larger search spaces and longer measurements.…”

Section: Future Workmentioning

confidence: 99%

Gamma-Ray Point-Source Localization and Sparse Image Reconstruction Using Poisson Likelihood

Hellfeld

Joshi

Bandstra

et al. 2019

IEEE Trans. Nucl. Sci.

View full text Add to dashboard Cite

Gamma-ray imaging attempts to reconstruct the spatial and intensity distribution of gamma-emitting radionuclides from a set of measurements. Generally, this problem is solved by discretizing the spatial dimensions and employing the maximum likelihood expectation maximization (ML-EM) algorithm, with or without some form of regularization. While the generality of this formulation enables use in a wide variety of scenarios, it is susceptible to overfitting, limited by the discretization of spatial coordinates, and can be computationally expensive. We present a novel approach to 3D gamma-ray image reconstruction for scenarios where sparsity may be assumed, for example radiological source search. In this work we first formulate a point-source localization (PSL) approach as an optimization problem where both position and source intensity are continuous variables. We then extend and generalize this formulation to an iterative algorithm called additive point-source localization (APSL) for sparse parametric image reconstruction. A set of simulated source search scenarios using a single non-directional detector are considered, finding improved image accuracy and computational efficiency with APSL over traditional grid-based approaches.

show abstract

“…An approach to spatial deconvolution of airborne spectrometric data which relies on an analytical model for the response function, and allows underdetermined problems, has been published previously [9]. A related method for spatial inversion to the approach presented here, but using an iterative inversion and neglecting uncertainties, was published recently [10]. Other groups are taking a similar approach to that advocated here, but applied to the inversion of spectra rather than spatial maps [11,12].…”

Section: Introductionmentioning

confidence: 99%

Spatial deconvolution of aerial radiometric survey and its application to the fallout from a radiological dispersal device

Sinclair

Fortin

2019

Journal of Environmental Radioactivity

View full text Add to dashboard Cite

Mapping radioactive contamination using aerial survey measurements is an area under active investigation today. The radiometric aerial survey technique has been extensively applied following reactor accidents and also would provide a key tool for response to a malicious radiological or nuclear incident. Methods exist to calibrate the aerial survey system for quantification of the concentration of natural radionuclides, which can provide guidance. However, these methods have anticipated a spatial distribution of the source which is large in comparison to the survey altitude. In rapid emergency-response aerial surveys of areas of safety concern, deposits of relatively small spatial extent may be expected. The activity of such spatially restricted hot spots is underestimated using the traditional methods. We present here a spatial deconvolution method which can recover some of the variation smoothed out by the averaging due to survey at altitude. We show that the method can recover the true spatial distribution of concentration of a synthetic source. We then apply the method to real aerial survey data collected following detonation of a radiological dispersal device. The findings and implications of the deconvolution are then discussed by reference to a groundbased truckborne survey over the same contamination.

show abstract

Improved radiological/nuclear source localization in variable NORM background: An MLEM approach with segmentation data

Cited by 9 publications

References 2 publications

Expectation-propagation for weak radionuclide identification at radiation portal monitors

Expectation-propagation for weak radionuclide identification at radiation portal monitors

Gamma-Ray Point-Source Localization and Sparse Image Reconstruction Using Poisson Likelihood

Spatial deconvolution of aerial radiometric survey and its application to the fallout from a radiological dispersal device

Contact Info

Product

Resources

About