Shearlet-based regularization in sparse dynamic tomography

Peer reviewe

Improved Passive Gamma Emission Tomography image quality in the central region of spent nuclear fuel

Reliable non-destructive methods for verifying spent nuclear fuel are essential to draw credible nuclear safeguards conclusions from spent fuel. In Finland, spent fuel items are verified prior to the soon starting disposal in a geological repository with Passive Gamma Emission Tomography (PGET), a uniquely accurate method capable of rod-level detection of missing active material. The PGET device consists of two highly collimated detector banks, collecting gamma emission data from a 360 degrees rotation around a fuel assembly. 2D cross-sectional activity and attenuation images are simultaneously computed. We present methods for improving reconstructed image quality in the central parts of the fuel. The results are based on data collected from 2017 to 2021 at the Finnish nuclear power plants with 10 fuel assembly types of varying characteristics, for example burnups from 5.7 to 55 GWd/tU and cooling times from 1.9 to 37 years. Data is acquired in different gamma energy windows, capturing the peaks of Cs-137 (at 662 keV) and Eu-154 (at 1274 keV), abundant isotopes in long-cooled spent nuclear fuel. Data from these gamma energy windows at well-chosen angles are used for higher-quality images, resulting in more accurate detection of empty rod positions. The method is shown to detect partial diversion of nuclear material also in the axial direction, demonstrated with a novel measurement series scanning over the edge of partial-length rods.Peer reviewe

Simultaneous reconstruction of emission and attenuation in passive gamma emission tomography of spent nuclear fuel

In the context of international nuclear safeguards, the International Atomic Energy Agency (IAEA) has recently approved passive gamma emission tomography (PGET) as a method for inspecting spent nuclear fuel assemblies (SFAs). The PGET instrument is essentially a single photon emission computed tomography (SPECT) system that allows the reconstruction of axial cross-sections of the emission map of an SFA. The fuel material heavily self-attenuates its gamma-ray emissions, so that correctly accounting for the attenuation is a critical factor in producing accurate images. Due to the nature of the inspections, it is desirable to use as little a priori information as possible about the fuel, including the attenuation map, in the reconstruction process. Current reconstruction methods either do not correct for attenuation, assume a uniform attenuation throughout the fuel assembly, or assume an attenuation map based on an initial filtered back-projection reconstruction. We propose a method to simultaneously reconstruct the emission and attenuation maps by formulating the reconstruction as a constrained minimization problem with a least squares data fidelity term and regularization terms. Using simulated data, we show that our approach produces clear reconstructions which allow for a highly reliable classification of spent, missing, and fresh fuel rods.Peer reviewe

Bilevel learning of regularization models and their discretization for image deblurring and super-resolution

Bilevel learning is a powerful optimization technique that has extensively been employed in recent years to bridge the world of model-driven variational approaches with data-driven methods. Upon suitable parametrization of the desired quantities of interest (e.g., regularization terms or discretization filters), such approach computes optimal parameter values by solving a nested optimization problem where the variational model acts as a constraint. In this work, we consider two different use cases of bilevel learning for the problem of image restoration. First, we focus on learning scalar weights and convolutional filters defining a Field of Experts regularizer to restore natural images degraded by blur and noise. For improving the practical performance, the lower-level problem is solved by means of a gradient descent scheme combined with a line-search strategy based on the Barzilai-Borwein rule. As a second application, the bilevel setup is employed for learning a discretization of the popular total variation regularizer for solving image restoration problems (in particular, deblurring and super-resolution). Numerical results show the effectiveness of the approach and their generalization to multiple tasks.Comment: Acknowledgments correcte

Learning the invisible : a hybrid deep learning-shearlet framework for limited angle computed tomography

The high complexity of various inverse problems poses a significant challenge to model-based reconstruction schemes, which in such situations often reach their limits. At the same time, we witness an exceptional success of data-based methodologies such as deep learning. However, in the context of inverse problems, deep neural networks mostly act as black box routines, used for instance for a somewhat unspecified removal of artifacts in classical image reconstructions. In this paper, we will focus on the severely ill-posed inverse problem of limited angle computed tomography, in which entire boundary sections are not captured in the measurements. We will develop a hybrid reconstruction framework that fuses model-based sparse regularization with data-driven deep learning. Our method is reliable in the sense that we only learn the part that can provably not be handled by model-based methods, while applying the theoretically controllable sparse regularization technique to the remaining parts. Such a decomposition into visible and invisible segments is achieved by means of the shearlet transform that allows to resolve wavefront sets in the phase space. Furthermore, this split enables us to assign the clear task of inferring unknown shearlet coefficients to the neural network and thereby offering an interpretation of its performance in the context of limited angle computed tomography. Our numerical experiments show that our algorithm significantly surpasses both pure model- and more data-based reconstruction methods.Peer reviewe

Gamma Ray Emission Imaging in the Medical and Nuclear Safeguards Fields

Gamma rays emitted from within an object can reveal information about that object in a non-destructive way, i.e. without physically opening the object and looking inside. This makes gamma ray emission imaging very useful in widely varying applications. In these notes, we highlight its application to the medical field, where we discuss molecular imaging in nuclear medicine and in vivo dose delivery verification in particle beam radiotherapy, and nuclear safeguards field, where imaging of spent nuclear fuel assemblies is part of monitoring the non-proliferation of nuclear weapons. The purpose and basic principles of gamma ray emission imaging are discussed as the foundation to look in more detail into the essential instrument design considerations and the iterative image reconstruction procedures. These notes are not intended to be a comprehensive review; their purpose is to introduce gamma ray emission imaging to those that are new to this technique. The examples of implementation that are presented were thus chosen in order to introduce the reader to a fairly wide range of applications and practical implementations

Gamma Ray Emission Imaging in the Medical and Nuclear Safeguards Fields

Gamma rays emitted from within an object can reveal information about that object in a non-destructive way, i.e. without physically opening the object and looking inside. This makes gamma ray emission imaging very useful in widely varying applications. In these notes, we highlight its application to the medical field, where we discuss molecular imaging in nuclear medicine and in vivo dose delivery verification in particle beam radiotherapy, and nuclear safeguards field, where imaging of spent nuclear fuel assemblies is part of monitoring the non-proliferation of nuclear weapons. The purpose and basic principles of gamma ray emission imaging are discussed as the foundation to look in more detail into the essential instrument design considerations and the iterative image reconstruction procedures. These notes are not intended to be a comprehensive review; their purpose is to introduce gamma ray emission imaging to those that are new to this technique. The examples of implementation that are presented were thus chosen in order to introduce the reader to a fairly wide range of applications and practical implementations