Tetris-inspired detector with neural network for radiation mapping.

Radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. Regarding materials and their configurations, radiation detectors have been developed to identify the position and strength of the radioactive sources. However, due to the complex mechanisms of radiation-matter interaction and data limitation, high-performance and low-cost radiation mapping is still challenging. Here, we present a radiation mapping framework using Tetris-inspired detector pixels. Applying inter-pixel padding for enhancing contrast between pixels and neural networks trained with Monte Carlo (MC) simulation data, a detector with as few as four pixels can achieve high-resolution directional prediction. A moving detector with Maximum a Posteriori (MAP) further achieved radiation position localization. Field testing with a simple detector has verified the capability of the MAP method for source localization. Our framework offers an avenue for high-quality radiation mapping with simple detector configurations and is anticipated to be deployed for real-world radiation detection.

Free-moving Quantitative Gamma-ray Imaging.

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.

Advances in Nuclear Radiation Sensing: Enabling 3-D Gamma-Ray Vision.

The enormous advances in sensing and data processing technologies in combination with recent developments in nuclear radiation detection and imaging enable unprecedented and "smarter" ways to detect, map, and visualize nuclear radiation. The recently developed concept of three-dimensional (3-D) Scene-data fusion allows us now to "see" nuclear radiation in three dimensions, in real time, and specific to radionuclides. It is based on a multi-sensor instrument that is able to map a local scene and to fuse the scene data with nuclear radiation data in 3-D while the instrument is freely moving through the scene. This new concept is agnostic of the deployment platform and the specific radiation detection or imaging modality. We have demonstrated this 3-D Scene-data fusion concept in a range of configurations in locations, such as the Fukushima Prefecture in Japan or Chernobyl in Ukraine on unmanned and manned aerial and ground-based platforms. It provides new means in the detection, mapping, and visualization of radiological and nuclear materials relevant for the safe and secure operation of nuclear and radiological facilities or in the response to accidental or intentional releases of radioactive materials where a timely, accurate, and effective assessment is critical. In addition, the ability to visualize nuclear radiation in 3-D and in real time provides new means in the communication with public and facilitates to overcome one of the major public concerns of not being able to "see" nuclear radiation.