Robust statistical reconstruction for charged particle tomography
Abstract
Systems and methods for charged particle detection including statistical reconstruction of object volume scattering density profiles from charged particle tomographic data to determine the probability distribution of charged particle scattering using a statistical multiple scattering model and determine a substantially maximum likelihood estimate of object volume scattering density using expectation maximization (ML/EM) algorithm to reconstruct the object volume scattering density. The presence of and/or type of object occupying the volume of interest can be identified from the reconstructed volume scattering density profile. The charged particle tomographic data can be cosmic ray muon tomographic data from a muon tracker for scanning packages, containers, vehicles or cargo. The method can be implemented using a computer program which is executable on a computer.
 Inventors:
 Issue Date:
 Research Org.:
 Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 1097256
 Patent Number(s):
 8552370
 Application Number:
 11/977,409
 Assignee:
 Los Alamos National Security, LLC (Los Alamos, NM)
 Patent Classifications (CPCs):

G  PHYSICS G01  MEASURING G01V  GEOPHYSICS
G  PHYSICS G01  MEASURING G01N  INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
 DOE Contract Number:
 AC5206NA25396
 Resource Type:
 Patent
 Country of Publication:
 United States
 Language:
 English
 Subject:
 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
Citation Formats
Schultz, Larry Joe, Klimenko, Alexei Vasilievich, Fraser, Andrew Mcleod, Morris, Christopher, Orum, John Christopher, Borozdin, Konstantin N, Sossong, Michael James, and Hengartner, Nicolas W. Robust statistical reconstruction for charged particle tomography. United States: N. p., 2013.
Web.
Schultz, Larry Joe, Klimenko, Alexei Vasilievich, Fraser, Andrew Mcleod, Morris, Christopher, Orum, John Christopher, Borozdin, Konstantin N, Sossong, Michael James, & Hengartner, Nicolas W. Robust statistical reconstruction for charged particle tomography. United States.
Schultz, Larry Joe, Klimenko, Alexei Vasilievich, Fraser, Andrew Mcleod, Morris, Christopher, Orum, John Christopher, Borozdin, Konstantin N, Sossong, Michael James, and Hengartner, Nicolas W. Tue .
"Robust statistical reconstruction for charged particle tomography". United States. https://www.osti.gov/servlets/purl/1097256.
@article{osti_1097256,
title = {Robust statistical reconstruction for charged particle tomography},
author = {Schultz, Larry Joe and Klimenko, Alexei Vasilievich and Fraser, Andrew Mcleod and Morris, Christopher and Orum, John Christopher and Borozdin, Konstantin N and Sossong, Michael James and Hengartner, Nicolas W},
abstractNote = {Systems and methods for charged particle detection including statistical reconstruction of object volume scattering density profiles from charged particle tomographic data to determine the probability distribution of charged particle scattering using a statistical multiple scattering model and determine a substantially maximum likelihood estimate of object volume scattering density using expectation maximization (ML/EM) algorithm to reconstruct the object volume scattering density. The presence of and/or type of object occupying the volume of interest can be identified from the reconstructed volume scattering density profile. The charged particle tomographic data can be cosmic ray muon tomographic data from a muon tracker for scanning packages, containers, vehicles or cargo. The method can be implemented using a computer program which is executable on a computer.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2013},
month = {10}
}