Daniel H. Chivers

Standoff 3D Gamma-Ray Imaging

We present a new standoff imaging technique able to provide 3-dimensional (3D) images of gamma-ray sources distributed in the environment. Unlike standard 3D tomographic methods, this technique does not require the radioactive sources to be bounded within a predefined physical space. In the present implementation, the gamma-ray imaging system is based on two large planar HPGe double sided segmented detectors, which are used in a Compton camera configuration. A LIDAR system is used in conjunction with the gamma-ray imaging system to confine the gamma-ray image space to the interior of physical objects situated within the detection range of the gamma-ray imager. This approach results in superior image contrast and efficient image reconstruction. Results demonstrating the operating principle are reported.

Routine Surveys for Gamma-Ray Background Characterization

In gamma-ray spectroscopy and imaging, the natural gamma-ray background can significantly reduce detection sensitivity, especially when the source is weak and the background varies substantially. This project aims to systematically measure and characterize the spatial and temporal variations of the background in order to assess their impact on detection sensitivity and specificity for homeland security applications. An extensive survey of typical backgrounds found in the San Francisco bay area was performed, and initial measurement results are presented here.

Effects of Background on Gamma-Ray Detection for Mobile Spectroscopy and Imaging Systems

The presence of gamma-ray background significantly reduces detection sensitivity when searching for radioactive sources in the field, particularly in mobile systems which must contend with a variable background that is not known a priori . An extensive survey of the background was performed in the San Francisco Bay Area using both sodium iodide and high-purity germanium detectors, covering a wide variety of environments that might be encountered in an operational scenario. This data was used as a basis for source injection in a moving detector scenario in order to assess the effects of the background on different detection approaches. Both imaging and spectroscopic algorithms were implemented for the sodium iodide array, and their performances are compared for a variety of source energies and stand-off distances in the presence of the measured background.

Experimental Benchmark of Electron Trajectory Reconstruction Algorithm for Advanced Compton Imaging

Electron-tracking-based Compton imaging of gamma rays reduces the background level of the backprojected Compton image through the additional measurement of the initial momentum vector of the Compton electron. This reduction in image background has the potential for the detection of weaker sources in a complex background radiation field. Electron-tracking-based Compton imaging was demonstrated recently in solid-state detectors through the use of scientific Si charge-coupled devices (CCDs) with excellent position and energy resolution characteristics. In addition, the sensitivity of the electron track reconstruction algorithm has been evaluated extensively on the modeled detector response to Monte-Carlo electron tracks. We have now benchmarked the modeled algorithm sensitivity with our experimentally observed algorithm sensitivity, by measuring CCD electron tracks from a collimated 662 keV gamma-ray source in coincidence with a position-sensitive HPGe detector. For all coincident events the electron momentum vector deduced by the reconstruction algorithm is compared to the electron momentum vector calculated from the measured positions. This measured distribution of angular error of the algorithm agrees well with the angular error distribution calculated from our electron transport and detector models.

Measurements of Fukushima fallout by the Berkeley Radiological Air and Water Monitoring project

The massive earthquake and tsunami off the coast of Japan on March 11, 2011 caused extensive damage at the Fukushima Daiichi nuclear power plant. During subsequent venting and explosions at the reactor site, there were releases of fission products such as 131I, 134Cs, 136Cs, 137Cs, and 132Te. Trace amounts of these isotopes were detectable in California around March 17. In the days after the disaster, the Berkeley Radiological Air and Water Monitoring (BRAWM) Project was started to measure the amounts of radioisotopes in the local environment around Berkeley. BRAWM has detected radioactive isotopes from Fukushima Daiichi in the air, rainwater, creek runoff, milk, soil, berries, and leafy vegetables. The team continues to monitor fallout levels in order to understand the nature of the radioactive releases from Fukushima as well as quantify the dilution or accumulation of the radioisotopes as they make their way through the environment and food chain.

Proximity localization with the Mobile Imaging and Spectroscopic Threat Identification (MISTI) system

The Mobile Imaging and Spectroscopic Threat Identification (MISTI) system was developed by the Naval Research Lab to perform stand-off gamma ray detection of potential threat sources. A new method for localizing sources at standoff using proximity techniques has been developed and demonstrated using the MISTI system.

The Machine Vision Radiation Detection System

The emerging threats created by a global expansion of nuclear technologies and terrorism demand improved nuclear materials detection systems to aid in nuclear security and nonproliferation. This project develops the idea of using machine vision combined with a large-area gamma-ray imager to improve sensitivity to threats and their rapid localization in a crowded environment (e.g., subway stations, airports, and bridges). We have achieved our first coded-mask images with a 1 m2 array of 100 NaI(Tl) detectors. In addition, two video cameras have been used in stereo to create a three-dimensional map of points in front of the array, and image segmentation is being implemented to distinguish and track individual objects in the field of view. We are currently gearing up to simultaneously perform real-time gamma-ray imaging and object tracking so that we can eventually merge the two data streams and achieve the expected increase in sensitivity of this method.

The Gamma-Ray Imaging Framework

The Gamma-Ray Imaging Framework (GRIF) is an open source (LGPL) software framework for creating real-time gamma-ray imaging applications. GRIF is written in C++ using Qt, and uses ROOT and the Boost Graph Library. GRIF provides automatic multi-threading and data management to make it easy to quickly develop power gamma-ray imagining applications. The model for application developers is built around the separation of data acquisition (DAQ) and analysis units. Users are expected to use the APIs for the DAQ and analysis units to build their applications. Memory and data are managed by GRIF, so the user does not need to worry about allocating and de-allocating memory for data or thread management and locking schemes. The user only needs to post data to and read data from the GRIF memory manager. GRIF uses XML configuration files for determining data dependences between DAQ and analysis units in the system. We will give an overview of the first release of GRIF, as well as show example applications that have been built using the framework.

Web-based visual data exploration for improved radiological source detection

Radiation detection can provide a reliable means of detecting radiological material. Such capabilities can help to prevent nuclear and/or radiological attacks, but reliable detection in uncontrolled surroundings requires algorithms that account for environmental background radiation. The Berkeley Data Cloud (BDC) facilitates the development of such methods by providing a framework to capture, store, analyze, and share data sets. In the era of big data, both the size and variety of data make it difficult to explore and find data sets of interest and manage the data. Thus, in the context of big data, visualization is critical for checking data consistency and validity, identifying gaps in data coverage, searching for data relevant to an analysts use cases, and choosing input parameters for analysis. Downloading the data and exploring it on an analysts desktop using traditional tools are no longer feasible due to the size of the data. This paper describes the design and implementation of a visualization system that addresses the problems associated with data exploration within the context of the BDC. The visualization system is based on a JavaScript front end communicating via REST with a back end web server.

Relations between system matrices and the complete data space in MLEM using the Kullback-Leibler distance

We present a quantitative method for relating system matrices to the complete-data space in maximum likelihood expectation maximization (MLEM) using the Kullback-Leibler distance. We show that a more accurate system matrix has a smaller Kullback-Leibler (KL) distance. System matrices of a coded aperture imaging system were used for comparison. The calculation of the KL distance is based on the Monte Carlo integral. We note that system matrices for the KL distance evaluation should be generated by underlying physics processes.