Spectral unmixing applied to fast identification of γ-emitting radionuclides using NaI(Tl) detectors

Paradis, H.; Bobin, C.; Bobin, J.; Bouchard, Jacques; Lourenço, Valérie; Thiam, C.; André, Rémi; Ferreux, L.; Ott, Anne de Vismes; Thévenin, Mathieu

doi:10.1016/j.apradiso.2020.109068

Cited by 6 publications

(11 citation statements)

References 9 publications

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“…The multiplicative update algorithm, called hereafter non-negative Poisson unmixing (NNPU), allows a full-spectrum analysis without the use of region-of-interest related to each γ-emitting radionuclides to be identified. The pseudo-code of the NNPU algorithm is given in [5].…”

Section: Application Of a Multiplicative Update Algorithm For Spectra...mentioning

confidence: 99%

“…The problem of decision-making was first investigated in a previous work [5] in the case of anomaly detection of γ-emitting radionuclides in natural Bkg radiation when using a 3 × 3 NaI(Tl) detector. At low statistics, the problem of overfitting has a direct impact on false positive alarms and thus on the robustness of decision-making.…”

Section: Application Of Sparsity To Decision-makingmentioning

confidence: 99%

“…At low statistics, the problem of overfitting has a direct impact on false positive alarms and thus on the robustness of decision-making. This issue was addressed by computing DT when only natural Bkg is present by using MC calculations [5] for several γ-emitting radionuclides generally used for RPM testing ( 57 Co, 60 Co, 133 Ba, 137 Cs, 241 Am) [7] according to a false positive rate equal to 0.1% (probability of type I error) [6]. This first study figures out that this solution was not optimal to account for the significant variation of DT values when γ-emitting radionuclides are mixed with natural Bkg.…”

Section: Application Of Sparsity To Decision-makingmentioning

confidence: 99%

“…First results of the spectral unmixing of γ-spectra obtained with a 3 × 3 NaI(Tl) detector were described in a previous paper [5]. Inspired from environmental radioactivity measurements [6], decision thresholds (DT) corresponding to a false positive rate equal to 0.1% (probability of type I error) when only natural Bkg is present, were determined by Monte Carlo (MC) simulations with a dictionary comprising several γ-emitters generally used for RPM testing ( 57 Co, 60 Co, 133 Ba, 137 Cs, 241 Am) [7].…”

Section: Introductionmentioning

confidence: 99%

“…Inspired from environmental radioactivity measurements [6], decision thresholds (DT) corresponding to a false positive rate equal to 0.1% (probability of type I error) when only natural Bkg is present, were determined by Monte Carlo (MC) simulations with a dictionary comprising several γ-emitters generally used for RPM testing ( 57 Co, 60 Co, 133 Ba, 137 Cs, 241 Am) [7]. As pointed out in [5], the robustness of this approach for automatic decision-making is limited by its inability to take properly into account the sensitivity of the DTs in the case of γ-emitting radionuclides mixed with natural Bkg as well as the variability of their related counting. In order to overcome this problem, which is also sensitive to overlapping between spectral signatures of γ-emitting radionuclides, a sparsity constraint applied on identified radionuclides was implemented for decision-making along with counting estimation with spectral unmixing.…”

Section: Introductionmentioning

confidence: 99%

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Metrological approach of γ-emitting radionuclides identification at low statistics: application of sparse spectral unmixing to scintillation detectors

André¹,

Bobin²,

Bobin³

et al. 2021

Metrologia

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This paper presents a metrological approach of spectral unmixing for automatic identification and quantitative analysis of γ-emitting radionuclides in natural background radiation at low statistics. Based on full-spectrum analysis, the proposed method relies on the maximum likelihood estimation based on Poisson statistics that accounts for the spectral signatures of the γ-emitters to be identified and natural background. In order to obtain robust decision-making at low statistics, a sparsity constraint is implemented along with counting estimation given by spectral unmixing. In contrast with the standard approach, this technique relies on a single decision threshold applied for a likelihood ratio test. Standard deviations on estimated counting are determined using the Fisher information matrix. The robustness of decision-making and counting estimation was investigated by means of Monte Carlo calculations based on experimental spectral signatures of two types of scintillation detectors [NaI(Tl), plastic]. This study demonstrates that sparse spectral unmixing is a reliable method for γ-spectra analysis based on low-level measurements. The sparsity constraint acts as an efficient technique for decision-making in the case of complex mixtures of γ-emitters with significant contribution of natural background. This method also yields unbiased counting estimation related to the identified radionuclides. Reliable assessment of standard deviations are obtained and the Gaussian approximation of the coverage intervals is validated. The proposed method can be applied either by non-expert users for automatic analysis of γ-spectra or to help experts in decision-making in the case of complex mixtures of γ-emitters at low statistics.

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Section: Application Of a Multiplicative Update Algorithm For Spectra...mentioning

confidence: 99%

Section: Application Of Sparsity To Decision-makingmentioning

confidence: 99%