Engineering topics
Quiter, Brian J.
Publications and source records attributed to Quiter, Brian J..
Radiation image reconstruction and uncertainty quantification using a Gaussian process prior
We propose a complete framework for Bayesian image reconstruction and uncertainty quantification based on a Gaussian process prior (GPP) to overcome limitations of maximum likelihood expectation maximization (ML-EM) image reconstruction algorithm. The prior distribution is constructed with a zero-mean Gaussian process (GP) with a choice of a covariance function, and a link function is used to map the Gaussian process to an image. Unlike many other maximum a posteriori approaches, our method offers highly interpretable hyperparamters that are selected automatically with the empirical Bayes method. Furthermore, the GP covariance function can be modified to incorporate a priori structural priors, enabling multi-modality imaging or contextual data fusion. Lastly, we illustrate that our approach lends itself to Bayesian uncertainty quantification techniques, such as the preconditioned Crank–Nicolson method and the Laplace approximation. The proposed framework is general and can be employed in most radiation image reconstruction problems, and we demonstrate it with simulated free-moving single detector radiation source imaging scenarios. We compare the reconstruction results from GPP and ML-EM, and show that the proposed method can significantly improve the image quality over ML-EM, all the while providing greater understanding of the source distribution via the uncertainty quantification capability. Furthermore, significant improvement of the image quality by incorporating a structural prior is illustrated.
Tetris-inspired detector with neural network for radiation mapping
Abstract 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.
Threat Sources for Creating Synthetic Urban Search Data
Equivalent point source energy emission distributions were computed for various threat sources for use in simulating the detector responses for urban search scenarios. The sources include standard isotopic sources used in detector testing, medical and industrial sources occasionally encountered in urban searches, and several types of special nuclear materials. Most of the equivalent point source distributions represent small sources inside some amount of shielding, but the special nuclear material sources represent volumetrically distributed sources in spheres of metal. Text-based inputs for emission distributions are available for the Monte Carlo transport codes Monte Carlo N-Particle, SCALE/MAVRIC, and Omnibus/Shift, any of which can easily be converted to other formats. These sources were developed for use in the Radiological Anomaly Detection and Identification (RADAI) project and the follow-on project, the RADAI-Extended project, sponsored by the National Nuclear Security Administration Office of Defense Nuclear Nonproliferation Research and Development.
Surrogate Distributed Radiological Sources—Part II: Aerial Measurement Campaign
In this second part of a multipaper series, we present results from outdoor aerial measurements of surrogate distributed gamma-ray sources. Here we detail the design, manufacture, and testing of 300 individual ~7 mCi Cu-64 sealed sources at the Washington State University (WSU) research reactor and their deployment in various source patterns (each comprising up to 100 point sources) during the aerial measurement campaign. We show the results of two such measurements, in which approximate source shapes and qualitative source intensities can be seen from the count rate versus position plots, even without performing reconstructions. We also detail our efforts in ground-truthing the deployed sources and comparing measured gamma-ray data to model predictions. In particular, we compare measured versus expected count data using the Poisson deviance formalism of Part I to evaluate whether the fielded surrogate point-source arrays “look like” their truly continuous distributed source analogs. More generally, we find that the point-source array technique provides high source placement accuracy, relative ease of quantifying the true source configuration, scalability to source dimensions of ≲100 m, ease of reconfiguration and removal, and relatively low dose to personnel. Finally, we consider potential improvements and generalizations of the point-source array technique for future measurement campaigns.
Enclosure for light detection and ranging unit and related methods
This disclosure provides systems, methods, and apparatus related to light detection and ranging. In one aspect, a method comprises providing an apparatus. The apparatus comprises a LiDAR unit and a LiDAR enclosure. The LiDAR enclosure comprises a four-sided container having a square or rectangular cross-section with a first open end and a second open end comprising two opposite ends of the four-sided container. The LiDAR unit is attached to a mounting area inside the four-sided container. A reflective material is disposed on an interior of a second side and a third side of the four-sided container. Laser light is generated with the LiDAR unit. The laser light that has interacted with environment surrounding the LiDAR enclosure and that has been reflected back to the LiDAR unit is detected. The detected laser light is processed to construct an image of the environment surrounding the LiDAR enclosure.
Correlations Between Panoramic Imagery and Gamma-Ray Background in an Urban Area
When searching for radiological sources in an urban area, a vehicle-borne detector system will often measure complex, varying backgrounds primarily from natural gamma-ray sources. Much work has been focused on developing spectral algorithms that retain sensitivity and minimize the false-positive rate even in the presence of such spectral and temporal variability. However, information about the environment surrounding the detector system might also provide useful clues about the expected background, which if incorporated into an algorithm, could improve performance. Recent work has focused on extensive measuring and modeling of urban areas with the goal of understanding how these complex backgrounds arise. This work presents an analysis of panoramic video images and gamma-ray background data collected in Oakland, California, by the radiological multisensor analysis platform (RadMAP) vehicle. Features were extracted from the panoramic images by semantically labeling the images and then convolving the labeled regions with the detector response. A linear model was used to relate the image-derived features to gamma-ray spectral features obtained using nonnegative matrix factorization (NMF) under different regularizations. Here we find some gamma-ray background features correlate strongly with image-derived features that measure the response-adjusted solid angle subtended by sky and buildings, and we discuss the implications for the development of future, contextually aware detection algorithms.
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.
Monte Carlo Simulation of Background and Source Measurements with CSG and CAD Geometries
Detecting radioactive sources in an urban environment is difficult due to the large magnitude and variability of the background radiation. To support the search mission of the National Nuclear Security Administration, a project was undertaken to determine if first-principles modeling and simulation could be used to accurately predict the response of radiation detectors in urban environments. This study demonstrates that the simulated detector responses to photon radiation from both an isotopic source and the background compare well to benchmark-quality measurements in a large urban-like test environment. Simulation results using a traditional combinatorial solid geometry and a CAD model generated from LiDAR measurements gave very similar detector responses. With this validation, further simulations could be used to predict detector responses for various situations in real urban settings.
Radiation Detection Data Competition Report
In FY2018 through FY2020, NA-22, the Defense Nuclear Nonproliferation Research and Development Program, funded a Data Science project to develop and implement statistical methodology to effectively host data competitions with the goal of leveraging the opportunity provided by crowdsourcing. By accessing and engaging expertise from a broader research community, there is an opportunity to attract innovative solutions from a variety of different research disciplines to advance the ability to solve important non-proliferation problems. This report summarizes the key results of this project after hosting two data competitions focused on urban radiation detection. The first competition was focused on attracting participants from the U.S. national laboratories, while the second, hosted by TopCoder, was open to the broader international community and awarded prize money to the top 10 competitors. At the start of the project, there was strong interest from NA-22 to explore and develop the capability to host data competitions as a means of leveraging the broader community to solve important nuclear nonproliferation problems. Having a standard data set on which to compare different approaches based on clearly defined criteria was desirable to be able to evaluate the state of solutions for important problems. Initially, it was not clear that it would even be possible logistically and bureaucratically to host a competition with an international field of competitors and to award the prize money needed to attract solutions from top competitors. Happily, a path to host the competitions was ultimately found that allowed this powerful accelerator of improvements to be leveraged.
Deployment of a double scatter system for directional detection of background neutron radiation
The detection of special nuclear materials (SNM) requires the understanding of nuclear signatures that allow the discrimination against background. In particular, understanding neutron background characteristics such as count rates and energies and their correlations with environmental conditions and surroundings of measurement locations is important in enhancing SNM detection capabilities. The Mobile Imager of Neutrons for Emergency Responders (MINER) was deployed for 8 weeks in downtown San Francisco (CA) to study such neutron background characteristics in an urban environment. Of specific interest was the investigation of the impact of surrounding buildings on the neutron background count rates and to answer the question whether buildings act as absorber of neutrons or as sources via the so-called ship effect. MINER consists of 16 liquid scintillator detector elements and can be operated as a neutron spectrometer, as a neutron imager, or simply as a counter of fast neutrons. As expected, the neutron background rate was found to be inversely proportional to the atmospheric pressure. In the energy range where MINER is most sensitive, approximately 1–10 MeV, it was found that the shape of the detected background spectrum is similar to that of a detected fission spectrum, indicating the limited discrimination power of the neutron energy. The similarities between the detected background neutron spectrum and fission sources makes it difficult to discriminate SNM from background based solely on the energies observed. The images produced using maximum likelihood expectation maximization revealed that neutrons preferentially are coming from areas in the environment that have open sky, indicating that the surrounding buildings act as absorbers of neutrons rather than sources as expected by the ship effect. Here, the inherent properties of a neutron scatter camera limit the achievable image quality and the effective deployment to systematically map neutron background signatures due to the low count rate.
Data for training and testing radiation detection algorithms in an urban environment
The detection, identification, and localization of illicit nuclear materials in urban environments is of utmost importance for national security. Most often, the process of performing these operations consists of a team of trained individuals equipped with radiation detection devices that have built-in algorithms to alert the user to the presence nuclear material and, if possible, to identify the type of nuclear material present. To encourage the development of new detection, radioisotope identification, and source localization algorithms, a dataset consisting of realistic Monte Carlo-simulated radiation detection data from a 2 in. × 4 in. × 16 in. NaI(Tl) scintillation detector moving through a simulated urban environment based on Knoxville, Tennessee, was developed and made public in the form of a Topcoder competition. The methodology used to create this dataset has been verified using experimental data collected at the Fort Indiantown Gap National Guard facility. Realistic signals from special nuclear material and industrial and medical sources are included in the data for developing and testing algorithms in a dynamic real-world background.
Gamma-Ray Point-Source Localization and Sparse Image Reconstruction using Poisson Likelihood
Gamma-ray imaging trys to reconstruct the spatial and intensity distribution of gamma-emitting radionuclides from a set of measurements. Wholly, this problem is solved by discretizing the spatial dimensions and employing the maximum likelihood expectation maximization (ML-EM) algorithm, with or without some form of regularization. While the generality of this formulation enables use in a wide variety of scenarios, it is susceptible to overfitting, limited by the discretization of spatial coordinates, and can be computationally expensive. We present a novel approach to 3D gamma-ray image reconstruction for scenarios where sparsity may be assumed, for example radiological source search. In this work we first formulate a point-source localization (PSL) approach as an optimization problem where both position and source intensity are continuous variables. We then extend and generalize this formulation to an iterative algorithm called additive point-source localization (APSL) for sparse parametric image reconstruction. A set of simulated source search scenarios using a single non-directional detector are considered, finding improved image accuracy and computational efficiency with APSL over traditional grid-based methods.
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.
Gamma-Ray imaging for nuclear security and safety: Towards 3-D gamma-ray vision
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Modeling Urban Scenarios & Experiments: Fort Indiantown Gap Data Collections Summary and Analysis
This report summarizes experimental radiation detector, contextual sensor, weather, and global positioning system (GPS) data collected to inform and validate a comprehensive, operational radiation transport modeling framework to evaluate radiation detector system and algorithm performance. This framework will be used to study the influence of systematic effects (such as geometry, background activity, background variability, environmental shielding, etc.) on detector responses and algorithm performance using synthetic time series data. This work consists of performing data collection campaigns at a canonical, controlled environment for complete radiological characterization to help construct and benchmark a high-fidelity model with quantified system geometries, detector response functions, and source terms for background and threat objects. This data also provides an archival, benchmark dataset that can be used by the radiation detection community. The data reported here spans four data collection campaigns conducted between May 2015 and September 2016.
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 analyst's use cases, and choosing input parameters for analysis. Downloading the data and exploring it on an analyst's desktop using traditional tools are no longer feasible due to the size of the data. This study describes the design and implementation of a visualization system that addresses the problems associated with data exploration within the context of the BDC. Finally, the visualization system is based on a JavaScript front end communicating via REST with a back end web server.
RadMAP: The Radiological Multi-sensor Analysis Platform
The variability of gamma-ray and neutron background during the operation of a mobile detector system greatly limits the ability of the system to detect weak radiological and nuclear threats. The natural radiation background measured by a mobile detector system is the result of many factors, including the radioactivity of nearby materials, the geometric configuration of those materials and the system, the presence of absorbing materials, and atmospheric conditions. Background variations tend to be highly non-Poissonian, making it difficult to set robust detection thresholds using knowledge of the mean background rate alone. The Radiological Multi-sensor Analysis Platform (RadMAP) system is designed to allow the systematic study of natural radiological background variations and to serve as a development platform for emerging concepts in mobile radiation detection and imaging. To do this, RadMAP has been used to acquire extensive, systematic background measurements and correlated contextual data that can be used to test algorithms and detector modalities at low false alarm rates. By combining gamma-ray and neutron detector systems with data from contextual sensors, the system enables the fusion of data from multiple sensors into novel data products. The data are curated in a common format that allows for rapid querying across all sensors, creating detailed multi-sensor datasets that are used to study correlations between radiological and contextual data, and develop and test novel techniques in mobile detection and imaging. Here we will describe the instruments that comprise the RadMAP system, the effort to curate and provide access to multi-sensor data, and some initial results on the fusion of contextual and radiological data.