Hypothesis-driven classification of materials using nuclear magnetic resonance relaxometry

Espy, Michelle A.; Matlashov, Andrei N.; Schultz, Larry J.; Volegov, Petr L.

Title: Hypothesis-driven classification of materials using nuclear magnetic resonance relaxometry

Abstract

Technologies related to identification of a substance in an optimized manner are provided. A reference group of known materials is identified. Each known material has known values for several classification parameters. The classification parameters comprise at least one of T.sub.1, T.sub.2, T.sub.1.rho., a relative nuclear susceptibility (RNS) of the substance, and an x-ray linear attenuation coefficient (LAC) of the substance. A measurement sequence is optimized based on at least one of a measurement cost of each of the classification parameters and an initial probability of each of the known materials in the reference group.

Inventors:: Espy, Michelle A.; Matlashov, Andrei N.; Schultz, Larry J.; Volegov, Petr L.

Issue Date:: 2016-08-09

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

Sponsoring Org.:: USDOE

OSTI Identifier:: 1288655

Patent Number(s):: 9411031

Application Number:: 13/869,718

Assignee:: Los Alamos National Security, LLC (Los Alamos, NM)

Patent Classifications (CPCs):: G - PHYSICS G01 - MEASURING G01N - INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES

G - PHYSICS G01 - MEASURING G01R - MEASURING ELECTRIC VARIABLES

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G - PHYSICS G01 - MEASURING G01N - INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
G01N24/084 - {Detection of potentially hazardous samples, e.g. toxic samples, explosives, drugs, firearms, weapons}

G - PHYSICS G01 - MEASURING G01R - MEASURING ELECTRIC VARIABLES
G01R33/448 - {Relaxometry, i.e. quantification of relaxation times or spin density
G01R33/44 - using nuclear magnetic resonance [NMR]

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DOE Contract Number:: AC52-06NA25396

Resource Type:: Patent

Resource Relation:: Patent File Date: 2013 Apr 24

Country of Publication:: United States

Language:: English

Subject:: 46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY

Citation Formats


                    Espy, Michelle A., Matlashov, Andrei N., Schultz, Larry J., and Volegov, Petr L. Hypothesis-driven classification of materials using nuclear magnetic resonance relaxometry.  United States: N. p., 2016. 
        Web.

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                    Espy, Michelle A., Matlashov, Andrei N., Schultz, Larry J., & Volegov, Petr L. Hypothesis-driven classification of materials using nuclear magnetic resonance relaxometry.  United States.

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                    Espy, Michelle A., Matlashov, Andrei N., Schultz, Larry J., and Volegov, Petr L. Tue .  
        "Hypothesis-driven classification of materials using nuclear magnetic resonance relaxometry".  United States.  https://www.osti.gov/servlets/purl/1288655.

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@article{osti_1288655,

  title        = {Hypothesis-driven classification of materials using nuclear magnetic resonance relaxometry},

  author       = {Espy, Michelle A. and Matlashov, Andrei N. and Schultz, Larry J. and Volegov, Petr L.},

  abstractNote = {Technologies related to identification of a substance in an optimized manner are provided. A reference group of known materials is identified. Each known material has known values for several classification parameters. The classification parameters comprise at least one of T.sub.1, T.sub.2, T.sub.1.rho., a relative nuclear susceptibility (RNS) of the substance, and an x-ray linear attenuation coefficient (LAC) of the substance. A measurement sequence is optimized based on at least one of a measurement cost of each of the classification parameters and an initial probability of each of the known materials in the reference group.},

  doi          = {},

  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2016},

  month        = {8}

}

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Works referenced in this record:

Toward direct mapping of neuronal activity: MRI detection of ultraweak, transient magnetic field changes
journal, June 2002

Bodurka, Jerzy; Bandettini, Peter A.
Magnetic Resonance in Medicine, Vol. 47, Issue 6, p. 1052-1058
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SQUID-Based Simultaneous Detection of NMR and Biomagnetic Signals at Ultra-Low Magnetic Fields
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Espy, M. A.; Matlachov, A. N.; Volegov, P. L.
IEEE Transactions on Appiled Superconductivity, Vol. 15, Issue 2
https://doi.org/10.1109/TASC.2005.849978

SQUID detected NMR in microtesla magnetic fields
journal, September 2004

Matlachov, Andrei N.; Volegov, Petr L.; Espy, Michelle A.
Journal of Magnetic Resonance, Vol. 170, Issue 1, p. 1-7
https://doi.org/10.1016/j.jmr.2004.05.015

Liquid-State NMR and Scalar Couplings in Microtesla Magnetic Fields
journal, March 2002

McDermott, R.
Science, Vol. 295, Issue 5563
https://doi.org/10.1126/science.1069280

Directly mapping magnetic field effects of neuronal activity by magnetic resonance imaging
journal, August 2003

Xiong, Jinhu; Fox, Peter T.; Gao, Jia-Hong
Human Brain Mapping, Vol. 20, Issue 1, p. 41-49
https://doi.org/10.1002/hbm.10124

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Title: Hypothesis-driven classification of materials using nuclear magnetic resonance relaxometry

Abstract

Citation Formats

Toward direct mapping of neuronal activity: MRI detection of ultraweak, transient magnetic field changes journal, June 2002

SQUID-Based Simultaneous Detection of NMR and Biomagnetic Signals at Ultra-Low Magnetic Fields journal, June 2005

SQUID detected NMR in microtesla magnetic fields journal, September 2004

Liquid-State NMR and Scalar Couplings in Microtesla Magnetic Fields journal, March 2002

Directly mapping magnetic field effects of neuronal activity by magnetic resonance imaging journal, August 2003

Toward direct mapping of neuronal activity: MRI detection of ultraweak, transient magnetic field changes
journal, June 2002

SQUID-Based Simultaneous Detection of NMR and Biomagnetic Signals at Ultra-Low Magnetic Fields
journal, June 2005

SQUID detected NMR in microtesla magnetic fields
journal, September 2004

Liquid-State NMR and Scalar Couplings in Microtesla Magnetic Fields
journal, March 2002

Directly mapping magnetic field effects of neuronal activity by magnetic resonance imaging
journal, August 2003