The ability to localize and map the distribution of gamma-ray emitting radionuclides in 3D has applications ranging from medical imaging to nuclear security. In the case of radiological source search and nuclear contamination remediation, the deployment of freely moving detection systems such as handheld instruments or ground/aerial-based vehicles are critical in overcoming the inverse square law and complex shielding scenarios. Using contextual sensors, these systems can simultaneously generate 3D maps of the surrounding environment and track the position and orientation of the gamma-ray sensitive detectors in that environment. The fusion of contextual scene data and gamma-ray detector data to facilitate real-time 3D gamma-ray image reconstruction has been demonstrated with mobile HPGe and CdZnTe-based Compton cameras for gamma-ray energies ranging from a few hundred keV to several MeV. Here we apply this approach for lower energy (50..400 keV) gamma-rays, using a hand-held CdZnTe-based omnidirectional imaging system and an active coded mask imaging modality.We present our approach to real-time reconstruction using a scene data constrained GPUaccelerated list-mode MLEM algorithm and show results from several measurements in the lab and in the field.

The ability to map and estimate the activity of radiological source distributions in unknown three-dimensional environments has applications in the prevention and response to radiological accidents or threats as well as the enforcement and verification of international nuclear non-proliferation agreements. Such a capability requires well-characterized detector response functions, accurate time-dependent detector position and orientation data, a digitized representation of the surrounding 3D environment, and appropriate image reconstruction and uncertainty quantification methods. We have previously demonstrated 3D mapping of gamma-ray emitters with free-moving detector systems on a relative intensity scale using a technique called Scene Data Fusion (SDF). Here we characterize the detector response of a multi-element gamma-ray imaging system using experimentally benchmarked Monte Carlo simulations and perform 3D mapping on an absolute intensity scale. We present experimental reconstruction results from hand-carried and airborne measurements with point-like and distributed sources in known configurations, demonstratingquantitativeSDF in complex 3D environments.

Gamma-ray imaging facilitates the efficient detection, characterization, and localization of compact radioactive sources in cluttered environments. Fieldable detector systems employing active planar coded apertures have demonstrated broad energy sensitivity via both coded aperture and Compton imaging modalities. However, planar configurations suffer from a limited field of view, especially in the coded aperture mode. To improve upon this limitation, we introduce a novel design by rearranging the detectors into an active coded spherical configuration, resulting in a 4π isotropic field of view for both coded aperture and Compton imaging. This paper focuses on the low-energy coded aperture modality and the optimization techniques used to determine the optimal number and configuration of 1-cm³ CdZnTe coplanar grid detectors on a 14-cm diameter sphere with 192 available detector locations.

Artificial neural networks (ANNs) for performing spectroscopic gamma-ray source identification have been previously introduced, primarily for applications in controlled laboratory settings. To understand the utility of these methods in scenarios and environments more relevant to nuclear safety and security, this work examines the use of ANNs for mobile detection, which involves highly variable gamma-ray background, low signal-to-noise ratio measurements, and low false alarm rates. Simulated data from a 2” × 4” × 16” NaI(Tl) detector are used in this work for demonstrating these concepts, and the minimum detectable activity (MDA) is used as a performance metric in assessing model performance.In addition to examining simultaneous detection and identification, binary spectral anomaly detection using autoencoders is introduced in this work, and benchmarked using detection methods based on Non-negative Matrix Factorization (NMF) and Principal Component Analysis (PCA). On average, the autoencoder provides a 12% and 23% improvement over NMF- and PCA-based detection methods, respectively. Additionally, source identification using ANNs is extended to leverage temporal dynamics by means of recurrent neural networks, and these time-dependent models outperform their time-independent counterparts by 17% for the analysis examined here. The paper concludes with a discussion on tradeoffs between the ANN-based approaches and the benchmark methods examined here.

Networked detector systems can be deployed in urban environments to aid in the detection and localization of radiological and/or nuclear material. However, effectively responding to and interpreting a radiological alarm using spectroscopic data alone may be hampered by a lack of situational awareness, particularly in complex environments. This study investigates the use of LiDAR and streaming video to enable real-time object detection and tracking, and the fusion of this tracking information with radiological data for the purposes of enhanced situational awareness and increased detection sensitivity. This work presents an object detection, tracking, and novel source-object attribution analysis that is capable of operating in real-time. By implementing this analysis pipeline on a custom developed system that comprises a static 2 × 4 × 16 inch NaI(Tl) detector co-located with a 64-beam LiDAR and 4 monocular cameras, we demonstrate the ability to accurately correlate trajectories from tracked objects to spectroscopic gamma-ray data in real time, and use physics-based models to reliably discriminate between source-carrying and non-source-carrying objects. In this work, we describe our approach in detail and present a quantitative performance assessment that characterizes the source-object attribution capabilities of both video and Li-DAR. Additionally, we demonstrate the ability to simultaneously track pedestrians and vehicles in a mock urban environment, and use this tracking information to improve both detection sensitivity and situational awareness using our contextual-radiological data fusion methodology.

We present an experimental demonstration of Additive Point Source Localization (APSL), a sparse parametric imaging algorithm that reconstructs the 3D positions and activities of multiple gamma-ray point sources. Using a handheld gamma-ray detector array and up to four 8 μCi 137Cs gamma-ray sources, we performed both source-search and source-separation experiments in an indoor laboratory environment. In the majority of the source-search measurements, APSL reconstructed the correct number of sources with position accuracies of ~20 cm and activity accuracies (unsigned) of ~20%, given measurement times of two to three minutes and distances of closest approach (to any source) of ~20 cm. In source-separation measurements where the detector could be moved freely about the environment, APSL was able to resolve two sources separated by 75 cm or more given only ~60 s of measurement time. In these source-separation measurements, APSL produced larger total activity errors of ~40%, but obtained source separation distances accurate to within 15 cm. We also compare our APSL results against traditional Maximum Likelihood-Expectation Maximization (ML-EM) reconstructions, and demonstrate improved image accuracy and interpretability using APSL over ML-EM. These results indicate that APSL is capable of accurately reconstructing gamma-ray source positions and activities using measurements from existing detector hardware.