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By combining radiation detection technologies with robotics sensing, the ability to continuously conduct gamma-ray imaging using freely-moving systems was demonstrated in 2015.1 This new method, which was named free-moving 3D Scene Data Fusion (SDF), was then applied to mapping radioactive contamination and to contextualizing the extent of contamination and the efficacy of radiological clean-up efforts.2,3 Since then, further studies into the types of radiation detection systems to which SDF could be applied resulted in the discovery and demonstration that neutron activity could be mapped using neutron-sensitive CLLBC scintillators, arrays of pix-elated CZT detectors could be used to create multi-modal imagers, and more rudimentary detector systems such as arrays of four CsI modules could still achieve good-quality mapping by inferring source positioning through the encoded modulation of source-to-detector distance. This paper provides an overview of the SDF technology, highlights recent measurements leveraging SDF-equipped systems, discusses the continued development of quantitative algorithms4,5 and their ramifications for developing autonomous SDF-capabilities, and summarizes future directions of research and application development for free moving radiation detection systems.

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, demonstrating quantitative SDF in complex 3D environments.

We present an experimental demonstration of additive point source localization (APSL), a sparse parametric imaging algorithm that reconstructs the 3-D positions and activities of multiple gamma-ray point sources. Using a handheld gamma-ray detector array and up to four 8 mu Ci 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 2 to 3 min 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.

G4NRF (Jordan and Warren, 2007; Vavrek et al., 2018a,b) is a simulation module for modeling nuclear resonance fluorescence (NRF) interactions in the Geant4 framework (Allison et al., 2016). In this work, we validate G4NRF against both absolute and relative measurements of three NRF interactions near 2.2 MeV in 238U and 27Al using the transmission NRF data from the experiments described in Jayson et al. (2018). Agreement between the absolute NRF count rates observed in the data and predicted by extensive Geant4+G4NRF modeling validate the combined Geant4+G4NRF to approximately 15–20% in the 238U NRF transitions and 10% in 27Al, for an average 14% discrepancy across the entire study. The difference between simulation and experiment in relative NRF rates, as expressed as ratios of count rates in various NRF lines, is found at the level of ≲3%, and is statistically identical to zero. Inverting the analysis, approximate values of the absolute level widths and branching ratios for 238U and 27Al are also obtained.

Nuclear resonance fluorescence (NRF) is a photonuclear interaction that enables highly isotope-specific measurements in both pure and applied physics scenarios. High-accuracy design and analysis of NRF measurements in complex geometries is aided by Monte Carlo simulations of photon physics and transport, motivating Jordan and Warren (2007) to develop the G4NRF codebase for NRF simulation in Geant4. In this work, we enhance the physics accuracy of the G4NRF code and perform improved benchmarking simulations. The NRF cross section calculation in G4NRF, previously a Gaussian approximation, has been replaced with a full numerical integration for improved accuracy in thick-target scenarios. A high-accuracy semi-analytical model of expected NRF count rates in a typical NRF measurement is then constructed and compared against G4NRF simulations for both simple homogeneous and more complex heterogeneous geometries. Agreement between rates predicted by the semi-analytical model and G4NRF simulation is found at a level of ∼1% in simple test cases and ∼3% in more realistic scenarios, improving upon the ∼20% level of the initial benchmarking study and establishing a highly-accurate NRF framework for Geant4.