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:
- Issue Date:
- 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
- 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.
Espy, Michelle A., Matlashov, Andrei N., Schultz, Larry J., & Volegov, Petr L. Hypothesis-driven classification of materials using nuclear magnetic resonance relaxometry. United States.
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.
@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}
}
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
SQUID-Based Simultaneous Detection of NMR and Biomagnetic Signals at Ultra-Low Magnetic Fields
journal, June 2005
- Espy, M. A.; Matlachov, A. N.; Volegov, P. L.
- IEEE Transactions on Appiled Superconductivity, Vol. 15, Issue 2
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
Liquid-State NMR and Scalar Couplings in Microtesla Magnetic Fields
journal, March 2002
- McDermott, R.
- Science, Vol. 295, Issue 5563
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