Los Alamos National Security, LLC

U.S. Department of Energy

Andrei Nikolaevich Matlashov

Michelle A Espy

Petr Lvovich Volegov

Robert Henry Kraus, Jr

Igor Mykhaylovich Savukov

Larry Joe Schultz

Vadim Sergeyevich Zotev

Algis Urbaitis

Don M Tucker

John Stevens George

Jacob Luther Yoder

Per Erik Magnelind

Stephen Surko

Sergei I Turovets

Robert H Kraus

Robert Henry Kraus

John C Mosher

Henrik Sandin

Methods for determining the identity of a substance are provided. A classification parameter set is defined to allow identification of substances that previously could not be identified or to allow identification of substances with a higher degree of confidence. The classification parameter set may include at least one of relative nuclear susceptibility (RNS) or an x-ray linear attenuation coefficient (LAC). RNS represents the density of hydrogen nuclei present in a substance relative to the density of hydrogen nuclei present in water. The extended classification parameter set may include T, T, and/or Tas well as at least one additional classification parameter comprising one of RNS or LAC. Values obtained for additional classification parameters as well as values obtained for T, T, and Tcan be compared to known classification parameter values to determine whether a particular substance is a known material.

Classification of materials using nuclear magnetic resonance dispersion and/or x-ray absorption

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, T, T, 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.

Hypothesis-driven classification of materials using nuclear magnetic resonance relaxometry

Preferred systems can include an electrical impedance tomography apparatus electrically connectable to an object; an ultra low field magnetic resonance imaging apparatus including a plurality of field directions and disposable about the object; a controller connected to the ultra low field magnetic resonance imaging apparatus and configured to implement a sequencing of one or more ultra low magnetic fields substantially along one or more of the plurality of field directions; and a display connected to the controller, and wherein the controller is further configured to reconstruct a displayable image of an electrical current density in the object. Preferred methods, apparatuses, and computer program products are also disclosed.

System and method for magnetic current density imaging at ultra low magnetic fields

A method and apparatus are provided for performing an in-situ magnetic resonance imaging of an object. The method includes the steps of providing an atomic magnetometer, coupling a magnetic field generated by magnetically resonating samples of the object through a flux transformer to the atomic magnetometer and measuring a magnetic resonance of the atomic magnetometer.

Method of performing MRI with an atomic magnetometer

Method comprising obtaining an NMR measurement from a sample wherein an ultra-low field NMR system probes the sample and produces the NMR measurement and wherein a sampling temperature, prepolarizing field, and measurement field are known; detecting the NMR measurement by means of inductive coils; analyzing the NMR measurement to obtain at least one measurement feature wherein the measurement feature comprises T1, T2, T1&#x3c1;, or the frequency dependence thereof; and, searching for the at least one measurement feature within a database comprising NMR reference data for at least one material to determine if the sample comprises a material of interest.

Ultra-low field nuclear magnetic resonance and magnetic resonance imaging to discriminate and identify materials

An ultra-low magnetic field NMR system can non-invasively examine containers. Database matching techniques can then identify hazardous materials within the containers. Ultra-low field NMR systems are ideal for this purpose because they do not require large powerful magnets and because they can examine materials enclosed in conductive shells such as lead shells. The NMR examination technique can be combined with ultra-low field NMR imaging, where an NMR image is obtained and analyzed to identify target volumes. Spatial sensitivity encoding can also be used to identify target volumes. After the target volumes are identified the NMR measurement technique can be used to identify their contents.

Using resonant interactions to directly and tomographically image neural activity in the human brain using magnetic resonance imaging (MRI) techniques at ultra-low field (ULF), the present inventors have established an approach that is sensitive to magnetic field distributions local to the spin population in cortex at the Larmor frequency of the measurement field. Because the Larmor frequency can be readily manipulated (through varying B), one can also envision using ULF-DNI to image the frequency distribution of the local fields in cortex. Such information, taken together with simultaneous acquisition of MEG and ULF-NMR signals, enables non-invasive exploration of the correlation between local fields induced by neural activity in cortex and more 'distant' measures of brain activity such as MEG and EEG.

Growing community of inventors

Los Alamos, NM, United States of America

Andrei Nikolaevich Matlashov