Bayesian Activity Estimation and Uncertainty Quantification of Spent Nuclear Fuel Using Passive Gamma Emission Tomography.

In this paper, we address the problem of activity estimation in passive gamma emission tomography (PGET) of spent nuclear fuel. Two different noise models are considered and compared, namely, the isotropic Gaussian and the Poisson noise models. The problem is formulated within a Bayesian framework as a linear inverse problem and prior distributions are assigned to the unknown model parameters. In particular, a Bernoulli-truncated Gaussian prior model is considered to promote sparse pin configurations. A Markov chain Monte Carlo (MCMC) method, based on a split and augmented Gibbs sampler, is then used to sample the posterior distribution of the unknown parameters. The proposed algorithm is first validated by simulations conducted using synthetic data, generated using the nominal models. We then consider more realistic data simulated using a bespoke simulator, whose forward model is non-linear and not available analytically. In that case, the linear models used are mis-specified and we analyse their robustness for activity estimation. The results demonstrate superior performance of the proposed approach in estimating the pin activities in different assembly patterns, in addition to being able to quantify their uncertainty measures, in comparison with existing methods.

Quantitative imaging and automated fuel pin identification for passive gamma emission tomography.

Compliance of member States to the Treaty on the Non-Proliferation of Nuclear Weapons is monitored through nuclear safeguards. The Passive Gamma Emission Tomography (PGET) system is a novel instrument developed within the framework of the International Atomic Energy Agency (IAEA) project JNT 1510, which included the European Commission, Finland, Hungary and Sweden. The PGET is used for the verification of spent nuclear fuel stored in water pools. Advanced image reconstruction techniques are crucial for obtaining high-quality cross-sectional images of the spent-fuel bundle to allow inspectors of the IAEA to monitor nuclear material and promptly identify its diversion. In this work, we have developed a software suite to accurately reconstruct the spent-fuel cross sectional image, automatically identify present fuel rods, and estimate their activity. Unique image reconstruction challenges are posed by the measurement of spent fuel, due to its high activity and the self-attenuation. While the former is mitigated by detector physical collimation, we implemented a linear forward model to model the detector responses to the fuel rods inside the PGET, to account for the latter. The image reconstruction is performed by solving a regularized linear inverse problem using the fast-iterative shrinkage-thresholding algorithm. We have also implemented the traditional filtered back projection (FBP) method based on the inverse Radon transform for comparison and applied both methods to reconstruct images of simulated mockup fuel assemblies. Higher image resolution and fewer reconstruction artifacts were obtained with the inverse-problem approach, with the mean-square-error reduced by 50%, and the structural-similarity improved by 200%. We then used a convolutional neural network (CNN) to automatically identify the bundle type and extract the pin locations from the images; the estimated activity levels finally being compared with the ground truth. The proposed computational methods accurately estimated the activity levels of the present pins, with an associated uncertainty of approximately 5%.

Radiation Mapping for an Unmanned Aerial Vehicle: Development and Simulated Testing of Algorithms for Source Mapping and Navigation Path Generation.

ABSTRACT: Image reconstruction algorithms were developed for radiation source mapping and used for generating the search path of a moving radiation detector, such as one onboard an unmanned aerial vehicle. Simulations consisted of first assuming radioactive sources of varying complexity and estimating the radiation fields that would then be produced by that source distribution. Next, the "measurements" that would result from a pair of adjacent spatial locations were computed. A crude estimate of the source distribution likely to have produced such "measurements" was reconstructed based upon the limited measurements. Location of the next "measurement" was then determined as halfway between the location of the estimated source and the current "measurement." With each additional sample, improved source distribution reconstructions were made and used to inform the immediate direction of detector motion. Source reconstruction or mapping was formulated as an inverse problem solved with either maximum a posteriori or least squares (LS) regression deconvolution methods. Different amounts of noise were added to the simulated "measurements," allowing evaluation of the methods' performances as functions of signal-to-noise ratio of the measured map. As expected, methods that promote sparsity were better suited in reconstructing point sources. Reliable prior information of the source distribution also improved the reconstruction results, especially with distributed sources. With a non-negative least square algorithm and the suggested paths it generated, location of sources was successfully estimated to an accuracy of 0.014 m within nine iterations in a single-source scenario and 12 iterations in a two-source scenario, given a 10% error on the integrated counts and a Poisson distribution of the noise associated with the measured counts.

Expectation-propagation for weak radionuclide identification at radiation portal monitors.

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 NORM-bearing materials found in shipped cargoes, such as ceramics and fertilizers, or radionuclides in recently treated nuclear medicine patients.

ORGANIC SCINTILLATOR FOR REAL-TIME NEUTRON DOSIMETRY.

We developed a radiation detector based on an organic scintillator for spectrometry and dosimetry of out-of-field secondary neutrons from clinical proton beams. The detector consists of an EJ-299-34 crystalline organic scintillator, coupled by fiber optic cable to a silicon photomultiplier (SiPM). Proof of concept measurements were taken with 137Cs and 252Cf, and corresponding simulations were performed in MCNPX-PoliMi. Despite its small size, the detector is able to discriminate between neutron and gamma-rays via pulse shape discrimination. We simulated the response function of the detector to monoenergetic neutrons in the 100 keV-0 MeV range using MCNPX-PoliMi. The measured unfolded 252Cf neutron spectrum is in good agreement with the theoretical Watt fission spectrum. We determined the ambient dose equivalent by folding the spectrum with the fluence-to-ambient dose conversion coefficient, with a 1.4% deviation from theory. Some preliminary proton beam experiments were preformed at the Bronowice Cyclotron Center patient treatment facility using a clinically relevant proton pencil beam for brain tumor and craino-spinal treatment directed at a child phantom.

Characterization of a low-cost PIN photodiode for dosimetry in diagnostic radiology.

A commercial silicon PIN-photodiode was tested and characterized as ionizing radiation detector for radiological applications. A current-to-voltage amplification stage was designed and realized in order to acquire the photodiode signal in current mode. The system was tested with clinical beams routinely used for radiography and mammography. A Monte Carlo simulation of the detector was performed with the MCNPX code in order to model and fully understand, in particular, the impact of the sensor casing on the low energy response of the device. A reproducible output linearity was found over the dose range 0.03-4.5 mGy of great clinical relevance. The system sensitivity was found to be stable at 0.2 V s Gy(-1) for effective X-ray energies between 17 and 40 keV. The batch-to-batch reproducibility of the diodes was also experimentally investigated for two different batches of 14 diodes each. An inter-comparison with dosimeters routinely used in medical physics (i.e. Barracuda MPD RTI) showed a linear correlation between PIN-photodiode readout and absorbed dose measured with Barracuda, in the range of doses received by mammography and radiology patients.