Statistical reconstruction for cosmic ray muon tomography

Momentum informed muon scattering tomography for monitoring spent nuclear fuels in dry storage cask

Development of an effective monitoring method for spent nuclear fuel (SNF) in a dry storage cask (DSC) is important to meet the increasing demand for dry storage investigations. The DSC investigation should provide information about the quantity of stored SNF, and quality assurance of materials should be possible without opening the cask. However, traditional nondestructive examination (NDE) methods such as x-rays are difficult to deploy for DSC investigation because a typical DSC is intentionally designed to shield against radiation. To address this challenge, cosmic ray muons (CRMs) are used as an alternative NDE radiation probe because they can easily penetrate an entire DSC system; however, a wide application of muons is often hindered due to the naturally low CRM flux (~104 muons/m2/min). This paper introduces a newly proposed imaging algorithm, momentum-informed muon scattering tomography (MMST), and presents how a limitation of the current muon scattering tomography technique has been addressed by measuring muon momentum. To demonstrate its functionality, a commercial DSC with 24 pressurized light water reactor fuel assemblies (FAs) and the MMST system were designed in GEANT4. Three noticeable improvements were observed for MMST system as a DSC investigation tool: (1) a signal stabilization, (2) an enhanced capability to differentiate various materials, and (3) statistically increased precision to identify and locate missing FAs. The results show that MMST improves the investigation accuracy from 79 to 98% when one FA is missing and 51% to 88% when one-half FA is missing. The advancement of the NDE technique using CRM for DSC verification is expected to resolve long-standing problems in increasing demand for DSC inspections and nuclear security.

Research on rapid imaging with cosmic ray muon scattering tomography

Cosmic ray muons tomography is a non-destructive imaging technique that uses the natural radiation of cosmic ray muons to create tomographic images of objects. This article presents a novel imaging algorithm that effectively utilizes experimental measurement data to achieve rapid and clear imaging of cosmic ray muons. A clear image can be obtained with only 20 min of measurement time and approximately 200 effective muons. However, the current detection flux is only about 0.044 cm[Formula: see text] min[Formula: see text], which is significantly lower than the natural cosmic ray flux of about 1 cm[Formula: see text] min[Formula: see text].

Muon scattering tomography: review

Cosmic-ray muon scattering tomography has gathered attention in the security and nuclear industries in the last 10 years. Muon scattering tomography is capable of identifying atomic numbers of objects, is highly sensitivity to high-atomic-number materials such as uranium, and is very useful for detecting them in a background of low-atomic-number material. The principle, detectors, and applications of muon tomography are presented, as well as its future aspect.

Muography of different structures using muon scattering and absorption algorithms

In recent decades, muon imaging has found a plethora of applications in many fields. This technique succeeds to infer the density distribution of big inaccessible structures where conventional techniques cannot be used. The requirements of different applications demand specific implementations of image reconstruction algorithms for either multiple scattering or absorption-transmission data analysis, as well as noise-suppression filters and muon momentum estimators. This paper presents successful results of image reconstruction techniques applied to simulated data of some representative applications. In addition to well-known reconstruction methods, a novel approach, the so-called μCT, is proposed for the inspection of spent nuclear fuel canisters. Results obtained based on both μCT and the maximum-likelihood expectation maximization reconstruction algorithms are presented.This article is part of the Theo Murphy meeting issue 'Cosmic-ray muography'.

Non-destructive testing of industrial equipment using muon radiography

A new application of muon radiography (MR) is presented in the context of non-destructive testing of industrial equipment. The long-term operation of industrial facilities frequently involves the deterioration of critical components such as pipes and cauldrons due to corrosion and other processes. The precise determination of the inner state of this equipment is needed to ensure the integrity of the facility. MR can be used to infer the thickness of these components through the comparison and further classification of muon observables with respect to well-known templates. A simulation example is presented where the thickness of a pipe made of steel is studied using the Point of Closest Approach method and simple Kolmogorov-Smirnov statistical tests. A precision of about 2-4 mm is obtained using a simple detector with a spatial resolution of 4 mm and exposure times of about 2 h.This article is part of the Theo Murphy meeting issue 'Cosmic-ray muography'.

INFN muon tomography demonstrator: past and recent results with an eye to near-future activities

A short description of the muon tomography demonstrator at the INFN Laboratori Nazionali di Legnaro near Padua, Italy, is given and the principal achievements owing to the data collected at that experimental facility are presented. In particular, the feasibility studies for several applications based on the muon-tomographic technology, within national and European projects, are discussed. The experimental problems and the procedures used to improve the performance are underlined. In addition, new activities and the related detector optimization are illustrated.This article is part of the Theo Murphy meeting issue 'Cosmic-ray muography'.

Bayesian image reconstruction for improving detection performance of muon tomography

Muon tomography is a novel technology that is being developed for detecting high-Z materials in vehicles or cargo containers. Maximum likelihood methods have been developed for reconstructing the scattering density image from muon measurements. However, the instability of maximum likelihood estimation often results in noisy images and low detectability of high-Z targets. In this paper, we propose using regularization to improve the image quality of muon tomography. We formulate the muon reconstruction problem in a Bayesian framework by introducing a prior distribution on scattering density images. An iterative shrinkage algorithm is derived to maximize the log posterior distribution. At each iteration, the algorithm obtains the maximum a posteriori update by shrinking an unregularized maximum likelihood update. Inverse quadratic shrinkage functions are derived for generalized Laplacian priors and inverse cubic shrinkage functions are derived for generalized Gaussian priors. Receiver operating characteristic studies using simulated data demonstrate that the Bayesian reconstruction can greatly improve the detection performance of muon tomography.