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Open Access Publications from the University of California

Scholarly Works (4 results)

  • Thesis
  • Peer Reviewed
UC Berkeley Electronic Theses and Dissertations (2016)

An approach to gamma-ray imaging has been developed that enables near

real-time volumetric (3D) imaging of unknown environments thus improving the

utility of gamma-ray imaging for source-search and radiation mapping

applications.

The approach, herein dubbed scene data fusion (SDF), is based on integrating

mobile radiation imagers with real-time tracking and scene reconstruction

algorithms to enable a mobile mode of operation and 3D localization of

gamma-ray sources.

The real-time tracking allows the imager to be moved throughout the environment

or around a particular object of interest, obtaining the multiple perspectives

necessary for standoff 3D imaging.

A 3D model of the scene, provided in real-time by a simultaneous localization

and mapping (SLAM) algorithm, can be incorporated into the image reconstruction

reducing the reconstruction time and improving imaging performance.

The SDF concept is demonstrated in this work with a Microsoft Kinect RGB-D

sensor, a real-time SLAM solver, and two different mobile gamma-ray imaging

platforms.

The first is a cart-based imaging platform known as the Volumetric Compton

Imager (VCI), comprising two 3D position-sensitive high purity germanium (HPGe)

detectors, exhibiting excellent gamma-ray imaging characteristics, but with

limited mobility due to the size and weight of the cart.

The second system is the High Efficiency Multimodal Imager (HEMI) a

hand-portable gamma-ray imager comprising 96 individual cm$^{3}$ CdZnTe

crystals arranged in a two-plane, active-mask configuration.

The HEMI instrument has poorer energy and angular resolution than the VCI, but

is truly hand-portable, allowing the SDF concept to be tested in multiple

environments and for more challenging imaging scenarios.

An iterative algorithm based on Compton kinematics is used to reconstruct

the gamma-ray source distribution in all three spatial dimensions.

Each of the two mobile imaging systems are used to demonstrate SDF for a

variety of scenarios, including general search and mapping scenarios with

several point gamma-ray sources over the range of energies relevant for

Compton imaging.

More specific imaging scenarios are also addressed, including directed

search and object interogation scenarios.

Finally, the volumetric image quality is quantitatively investigated with

respect to the number of Compton events acquired during a measurement, the

list-mode uncertainty of the Compton cone data, and the uncertainty in the

pose estimate from the real-time tracking algorithm.

SDF advances the real-world applicability of gamma-ray imaging for many

search, mapping, and verification scenarios by improving the tractiblity

of the gamma-ray image reconstruction and providing context for the 3D

localization of gamma-ray sources within the environment in real-time.

    Cover page: Development and Evaluation of Real-Time Volumetric Compton Gamma-Ray Imaging
    • Article
    • Peer Reviewed
    UC Berkeley Previously Published Works (2018)
    We describe a project-based introduction to reproducible and collaborative neuroimaging analysis. Traditional teaching on neuroimaging usually consists of a series of lectures that emphasize the big picture rather than the foundations on which the techniques are based. The lectures are often paired with practical workshops in which students run imaging analyses using the graphical interface of specific neuroimaging software packages. Our experience suggests that this combination leaves the student with a superficial understanding of the underlying ideas, and an informal, inefficient, and inaccurate approach to analysis. To address these problems, we based our course around a substantial open-ended group project. This allowed us to teach: (a) computational tools to ensure computationally reproducible work, such as the Unix command line, structured code, version control, automated testing, and code review and (b) a clear understanding of the statistical techniques used for a basic analysis of a single run in an MR scanner. The emphasis we put on the group project showed the importance of standard computational tools for accuracy, efficiency, and collaboration. The projects were broadly successful in engaging students in working reproducibly on real scientific questions. We propose that a course on this model should be the foundation for future programs in neuroimaging. We believe it will also serve as a model for teaching efficient and reproducible research in other fields of computational science.
      Cover page: Teaching Computational Reproducibility for Neuroimaging.
      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2019)
      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.
        Cover page: Advances in Nuclear Radiation Sensing: Enabling 3-D Gamma-Ray Vision
        • Article
        • Peer Reviewed
        LBL Publications (2024)
        The analysis and interpretation of coincidence events in a Compton camera requires the comparison of the expected rates of observed events from sources with various emission rates, energy spectra and spatial distributions. Radioactive source distributions are often represented by the activity distributed among numerous voxels; each voxel having uniform internal activity and spectra within a cube. In this paper a mathematical model is constructed that predicts the expected rate of coincident Compton events from the rate of emissions from a single voxel source. This detailed model incorporates (1) the finite voxel size, (2) the blurring of the “Compton cone” by the limitations of energy resolution in the detectors and (3) the uncertainty in the Compton cone-axis due to the limited spatial resolution and ‘lever-arm’ separation between the coincident interactions. The resultant rates can be used to generate the system response matrix for source reconstruction and, therefore, are directly applicable in list-mode MLEM source reconstruction algorithms.
        • 1 supplemental ZIP
        Cover page: Modeling the Compton Camera Response for Extended Voxel SourcesCreative Commons 'BY-NC-ND' version 4.0 license
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