Radiograph and passive data analysis using mixed variable optimization

Temple, Brian A.; Armstrong, Jerawan C.; Buescher, Kevin L.; Favorite, Jeffrey A.

Title: Radiograph and passive data analysis using mixed variable optimization

Abstract

Disclosed herein are representative embodiments of methods, apparatus, and systems for performing radiography analysis. For example, certain embodiments perform radiographic analysis using mixed variable computation techniques. One exemplary system comprises a radiation source, a two-dimensional detector for detecting radiation transmitted through a object between the radiation source and detector, and a computer. In this embodiment, the computer is configured to input the radiographic image data from the two-dimensional detector and to determine one or more materials that form the object by using an iterative analysis technique that selects the one or more materials from hierarchically arranged solution spaces of discrete material possibilities and selects the layer interfaces from the optimization of the continuous interface data.

Inventors:: Temple, Brian A.; Armstrong, Jerawan C.; Buescher, Kevin L.; Favorite, Jeffrey A.

Issue Date:: 2015-06-02

Research Org.:: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1183344

Patent Number(s):: 9047397

Application Number:: 13/631,607

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

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G - PHYSICS G01 - MEASURING G01V - GEOPHYSICS
G01V5/0075 - {Passive interrogation

G - PHYSICS G01 - MEASURING G01N - INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
G01N2223/402 - mapping distribution of elements
G01N23/04 - and forming images of the material
G01N2223/401 - image processing

G - PHYSICS G16 - INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS G16H - HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
G16H50/20 - for computer-aided diagnosis, e.g. based on medical expert systems

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DOE Contract Number:: AC52-06NA25396

Resource Type:: Patent

Country of Publication:: United States

Language:: English

Subject:: 46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; 97 MATHEMATICS AND COMPUTING

Citation Formats


                    Temple, Brian A., Armstrong, Jerawan C., Buescher, Kevin L., and Favorite, Jeffrey A. Radiograph and passive data analysis using mixed variable optimization.  United States: N. p., 2015. 
        Web.

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                    Temple, Brian A., Armstrong, Jerawan C., Buescher, Kevin L., & Favorite, Jeffrey A. Radiograph and passive data analysis using mixed variable optimization.  United States.

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                    Temple, Brian A., Armstrong, Jerawan C., Buescher, Kevin L., and Favorite, Jeffrey A. Tue .  
        "Radiograph and passive data analysis using mixed variable optimization".  United States.  https://www.osti.gov/servlets/purl/1183344.

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@article{osti_1183344,

  title        = {Radiograph and passive data analysis using mixed variable optimization},

  author       = {Temple, Brian A. and Armstrong, Jerawan C. and Buescher, Kevin L. and Favorite, Jeffrey A.},

  abstractNote = {Disclosed herein are representative embodiments of methods, apparatus, and systems for performing radiography analysis. For example, certain embodiments perform radiographic analysis using mixed variable computation techniques. One exemplary system comprises a radiation source, a two-dimensional detector for detecting radiation transmitted through a object between the radiation source and detector, and a computer. In this embodiment, the computer is configured to input the radiographic image data from the two-dimensional detector and to determine one or more materials that form the object by using an iterative analysis technique that selects the one or more materials from hierarchically arranged solution spaces of discrete material possibilities and selects the layer interfaces from the optimization of the continuous interface data.},

  doi          = {},

  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2015},

  month        = {6}

}

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Works referenced in this record:

Quantitative Object Reconstruction Using Abel Transform X-Ray Tomography and Mixed Variable Optimization
journal, January 2008

Abramson, Mark A.; Asaki, Thomas J.; Dennis,, J. E.
SIAM Journal on Imaging Sciences, Vol. 1, Issue 3
https://doi.org/10.1137/08071380X

Quantitative Abel tomography robust to noisy, corrupted and missing data
journal, November 2009

Asaki, Thomas J.
Optimization and Engineering, Vol. 11, Issue 3
https://doi.org/10.1007/s11081-009-9097-z

Algorithm 909: NOMAD: Nonlinear Optimization with the MADS Algorithm
journal, February 2011

Le Digabel, Sébastien
ACM Transactions on Mathematical Software, Vol. 37, Issue 4
https://doi.org/10.1145/1916461.1916468

Quantitative Object Reconstruction Using Abel Transform X-Ray Tomography and Mixed Variable Optimization
journal, January 2008

Abramson, Mark A.; Asaki, Thomas J.; Dennis,, J. E.
SIAM Journal on Imaging Sciences, Vol. 1, Issue 3
https://doi.org/10.1137/08071380X

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Title: Radiograph and passive data analysis using mixed variable optimization

Abstract

Citation Formats

Quantitative Object Reconstruction Using Abel Transform X-Ray Tomography and Mixed Variable Optimization journal, January 2008

Quantitative Abel tomography robust to noisy, corrupted and missing data journal, November 2009

Algorithm 909: NOMAD: Nonlinear Optimization with the MADS Algorithm journal, February 2011

Quantitative Object Reconstruction Using Abel Transform X-Ray Tomography and Mixed Variable Optimization journal, January 2008

Quantitative Object Reconstruction Using Abel Transform X-Ray Tomography and Mixed Variable Optimization
journal, January 2008

Quantitative Abel tomography robust to noisy, corrupted and missing data
journal, November 2009

Algorithm 909: NOMAD: Nonlinear Optimization with the MADS Algorithm
journal, February 2011

Quantitative Object Reconstruction Using Abel Transform X-Ray Tomography and Mixed Variable Optimization
journal, January 2008