### 3-Dimensional Radiation Analysis  
As formerly mentioned, one active research area that has evolved alongside increasingly powerful miniaturized computing and post-processing is the 3D radiation mapping technique known as \"scene data fusion\" (SDF). The graphic representation of complex geospatial datasets by rendering radiometric data onto scenic, context-providing data was initially developed by researchers at Lawrence Berkeley National Laboratory (LBNL) and the University of California, Berkeley (UCB), for illicit source detection and emergency response situations [28]. The LBNL SDF approach uses a localization and mapping platform (LAMP) in combination with CsI-based gamma-ray spectrometers and a CdZnTe-based Polaris gamma-ray imaging system. Such sensing platforms employ sensor fusion methods that integrate Lidar, visual (optical) cameras, and radiation interaction data from an array of composite detectors to cumulatively develop 3-dimensional scenic maps of radiological environments that are intuitive to end-users and non-experts alike [28; 29]. Another approach, exploiting a system that utilizes a reduced number of detectors over LAMP, is termed Projective Linear Reconstruction (PLR), a technique presented by scientists from the University of Bristol. The PLR approach uses a computational method of radiological source localization from scanning survey measurements. The procedure uses an experimentally derived Detector Response Function (DRF) to perform a randomized Kaczmarz deconvolution on radiation field measurements acquired to date via a robotic deployment platform [30]. A simultaneous use of mathematical data processing tools for radiation mapping is also ongoing at the Institute of Environmental Geochemistry at the National Academy of Sciences of Ukraine [31]. Alongside these modern systems, which exploit enhanced back-end signal processing and numerical, graphic reconstruction/representation, other long-established methods of radioactive source localization and scene reconstruction also exist based on established detection technologies and data processing [32]. One such approach is the use of Compton Cameras, which exploit differential, consecutive scatter and absorption detectors to progressively reconstruct the \"cone of incidence\" or \"conic projection\" of the incident gamma-ray photons, thereby determining the position of the radioactive source [5; 33; 34]. A second class of established systems is the Gamma Camera, also termed a Scintillation Camera or Anger camera [5], which reconstructs the source position via a technique known as scintigraphy, a process originally developed for medical imaging applications where gamma-emitting radioactive tracers are utilized to explore and assess bodily functions. Unlike source reconstruction using a Compton Camera, a Gamma Camera uses only a single scintillator rather than a pair. To define the location of the source that would otherwise yield an uninformative (non-directional) detection event within the photon-counting, pulse-shaping, electronic-augmented scintillator monolith, either an array of collimators or a rotating and intricately patterned \"coded-mas\" is placed in front of the detection face between the source and camera [35; 36]. While being vastly less computationally intensive as well as faster at reconstructing a scene containing a radioactive source than a Compton Camera system, the Gamma Camera requires an appreciable level of post-processing to accurately derive the position (in both 2D and 3D, with depth) of one or more radiation sources. Despite both approaches representing established means of radioactive source localization, advancements continue to improve on acquisition rates, positional accuracy, multiple-source delineation, dosimetric calibration, and size/mass reduction, with single pixel compressive gamma-ray imaging using randomly encoded masks based on the principles of compressed sensing possessing the potential to exploit the sparsity typically found in Gamma Camera imagery.