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Muon Tracing and Image Reconstruction Algorithms for Cosmic Ray Muon Computed Tomography

Journal Article · · IEEE Transactions on Image Processing
 [1];  [2];  [2];  [3];  [3];  [1]
  1. Univ. of Tennessee, Knoxville, TN (United States). Dept. of Nuclear Engineering
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  3. Oregon State Univ., Corvallis, OR (United States). Dept. of Nuclear Engineering and Radiation Health Physics
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Cosmic ray muon-computed tomography (μCT) is a new imaging modality with unique characteristics that could be particularly important for diverse applications including nuclear nonproliferation, spent nuclear fuel monitoring, cargo scanning, and volcano imaging. The strong scattering dependence of muons on atomic number Z in combination with high penetration range could offer a significant advantage over existing techniques when dense, shielded containers must be imaged. However, μCT reconstruction using conventional filtered back-projection is limited due to the overly simple assumptions that do not consider the curved path caused by multiple Coulomb scattering prompting the need for more sophisticated approaches to be developed. Here, we argue that the use of improved muon tracing and scattering angle projection algorithms as well as an algebraic reconstruction technique should produce muon tomographic images with improved quality–or require fewer muons to produce the same image quality–compared to the case where conventional methods are used. We report on the development and assessment of three novel muon tracing methods and two scattering angle projection methods for μCT. Simulated dry storage casks with single and partial missing fuel assemblies were used as numerical examples to assess and compare the proposed methods. The reconstructed images showed an expected improvement in image quality when compared with conventional techniques, even without muon momentum information, which should lead to improved detection capability, even for partial defects.
Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Nuclear Energy (NE)
Grant/Contract Number:
AC05-00OR22725; NE0008292
OSTI ID:
1474448
Journal Information:
IEEE Transactions on Image Processing, Journal Name: IEEE Transactions on Image Processing Journal Issue: 1 Vol. 28; ISSN 1057-7149
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
97 MATHEMATICS AND COMPUTING
Dry storage cask
algebraic reconstruction technique
fuels
image reconstruction
mesons
muon computed tomography
scattering
x-rays