3D imaging using combined neutron-photon fan-beam tomography: A Monte Carlo study

Unmanned Aerial System Integrated Sensor for Remote Gamma and Neutron Monitoring

Tools for remote radiation sensing are essential for environmental safety and nuclear power applications. The use of unmanned aerial systems (UASs) equipped with sensors allows for substantially reducing the radiation exposure of personnel. An ambient temperature Cs₂LiYCl₆:Ce³⁺ (CLYC) elpasolite scintillation sensor for simultaneous gamma and neutron measurements was designed as a user-friendly "plug and fly" module integrated into an octocopter robotic platform. Robot Operating System (ROS) was used to analyze the sensor's data. The measured CLYC's energy resolution was <5% at 662 keV gamma rays; neutron flux was measured using ⁶Li(n,α)t reaction. Time and GPS data were combined with radiation data in the ROS, supporting real time monitoring and assessment tasks, as well as radiation source search missions. Because UASs can be irradiated, radiation damage of the sensor and robot's electronics was estimated using FLUKA code.

Total variation-based neutron computed tomography

We perform the neutron computed tomography reconstruction problem via an inverse problem formulation with a total variation penalty. In the case of highly under-resolved angular measurements, the total variation penalty suppresses high-frequency artifacts which appear in filtered back projections. In order to efficiently compute solutions for this problem, we implement a variation of the split Bregman algorithm; due to the error-forgetting nature of the algorithm, the computational cost of updating can be significantly reduced via very inexact approximate linear solvers. We present the effectiveness of the algorithm in the significantly low-angular sampling case using synthetic test problems as well as data obtained from a high flux neutron source. The algorithm removes artifacts and can even roughly capture small features when an extremely low number of angles are used.

Sparse-view neutron-photon computed tomography: Object reconstruction and material discrimination

Taking into account the advantages of both neutron- and photon-based systems, we propose combined neutron-photon computed tomography (CT) under a sparse-view setting and demonstrate its performance for 3D object visualization and material discrimination. We use a high-performance regularization method for CT reconstruction by combining regularization based on total variation (TV) and curvelet transform in cone beam geometry. It is coupled with proposed 2D material signatures which is pairs of photon to neutron transmission ratios and neutron transmission values per object space voxels. Classification of materials is performed by association of a voxel signature with library signatures; and per object - by majority of voxels in the object. Representation of object-material pairs, for the model in our experiment, a complex scene with group of high-Z and low-Z materials, attains the reconstruction accuracy of 92.1% and the overall high-Z discrimination accuracy of object representation is 85%, and by about 7.5% higher discrimination accuracy than that with 1D signatures which are ratios of photon to neutron transmissions. With a relative noise level of 10%, the method yields the reconstruction accuracies of 87.2%. The analyses are performed in cone beam configuration, with Monte Carlo modeling of neutron-photon transport for the model of object geometry and material contents.