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

Abstract

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%.

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

“…Indeed, the presence map can be used to build a linear mapping from x and y whereby attenuation involving neighbouring pins is accounted for (the attenuation is mostly due to the presence of pins, not their activity). Hence, in this work, the function G is approximated by a linear operator A ∈ R M×N , which is built using the simulator recently considered in [22]. Consequently, the forward model considered in this work can be expressed as:…”

“…In contrast to Section 5 where we used a linear operator A constructed using physical model of collimator-detector system response and simulated data using the linear model in Equation ( 1), here the data are generated using the MCNP 6.2 software that simulates all photon interactions in the PGET system [38]. More details on the simulation set up can be found in [22]. The simulated detection unit is a high-fidelity model of the PGET and consists of two collimated CdTe detector arrays on the top and bottom sides of the fuel assembly.…”

“…The resulting matrix is of size 65, 520 × 5329 and it coincides with the IDEAL matrix of Case 1. The last strategy considered here to build A is referred to as EMPIRICAL and consists of identifying, as a pre-processing step, the likely pin support using a fast inversion algorithm (filtered backprojection (FBP), as in [22]). FBP provides a coarse activity map which is then thresholded to identify potential pin locations.…”

“…As outlined in Section 6.2, the proposed approach, and existing methods, find it difficult to provide reliable estimates for cases "4" and "5", which is probably because of the mismatch between the linear operator and the observations. Hence, the last strategy considered here is to build A by identifying as a pre-processing step, the likely pin support using a fast inversion algorithm (filtered backprojection (FBP), as in [22]). FBP provides a coarse activity map which is then thresholded to identify potential pin locations.…”

“…However, the forward model proposed in [7] considered only monoenergetic gamma rays and did not account for the scattering effects of gamma rays in the PGET system that make non-neglible contributions to the total counts, which added additional uncertainty to the activity estimation results. Similarly, in [22], the authors formulated the problem of pin activity estimation as an optimization problem where a sparse regularization function is used. Although the advantages of these methods in providing fast estimates, they can provide only point estimates; hence, they can neither quantify the uncertainty of the estimates nor provide probability of presence of the pins.…”