One dimensional (1D) and two dimensional (2D) MR spectroscopy of the brain provides an objective test for pain and level of pain. There are two ways of evaluating the data. The first is conventional analysis of the 2D MRS. The second us the use of data mining methods such as testing for correlation between wavelet-based features and self-reported pain intensity, the MBDA method. Both found multiple spectral regions that were highly correlated with self-reported pain. Two of these spectral regions are consistent with changes to the recently assigned substrate -L Fucose and the Fuc-(1-2) glycans in the human brain. There are common features recorded compared with prior reports using the MBDA method to evaluate pain associated with spinal cord injury and low back pain. Accordingly, by detecting the levels of selected neurochemical markers in MR spectroscopy of an individual, one can determine the level of pain by comparing spectral data obtained with a reference database which correlates level of selected neurochemicals with levels of pain.

A method for automatically quantifying an analyte in a measurement sample includes providing a 1D-NMR spectrum and a 2D-NMR spectrum, providing at least one information item in relation to at least one analyte to be quantified, establishing a chemical shift of the NMR signal of the analyte to be quantified from the measured 2D-NMR spectrum using the at least one information item provided, establishing expected peak positions of the NMR signal of the analyte to be quantified, establishing measured peak positions from the measured 1D-NMR spectrum, and establishing disturbance signal peak positions using the expected peak positions and the actual peak positions. The method further includes modelling the 1D-NMR spectrum using the established disturbance signal peak positions using the established chemical shift and using the at least one information item provided, integrating the modelled 1D-NMR spectrum, and quantifying the analyte by internal or external referencing.

A method for processing two-dimensional (2D) nuclear magnetic resonance (NMR) Diffusion Ordered Spectroscopy (DOSY) based on deep learning comprises constructing a simulated dataset by generating simulated data using a mathematical model based on signal characteristics of the 2D NMR DOSY, generating labels for training a deep learning network model, wherein the labels comprise a first two-dimensional matrix, and two dimensions of the first two-dimensional matrix comprise chemical shift and diffusion coefficients, constructing the deep learning network model and setting training parameters of the deep learning network model, training the deep learning network model using the simulated dataset, and testing the deep learning network model.

An MRI system includes a gantry having a longitudinal axis (herein z-axis) and a magnet disposed about the gantry for generating a static magnetic field along the longitudinal axis. Additionally, the system comprises a first gradient magnet for generating a gradient magnetic field along the longitudinal axis; a second gradient magnet for generating a gradient magnetic field along a first transverse direction (herein x-axis) orthogonal the longitudinal axis; and a third gradient magnet for generating a gradient magnetic field along a second transverse direction (herein y-axis) orthogonal to the longitudinal axis and the first transverse direction. Magnetic sensors are positioned relative to the gantry to measure gradients of transverse components of magnetic field along one or more of the x, y and z axes. A controller receives measurement signals from the sensors and operates on those signals to determine gradients of the gradient magnetic field along the longitudinal axis.

The invention provides methods for processing nuclear magnetic resonance (NMR) spectroscopic data to assign resonance peaks in an NMR spectrum of a molecule to atomic nuclei in said molecule, based on graph-theoretical principles. In particular, the methods of the invention may be employed in the assignment of data obtained from methyl-TROSY spectroscopy of proteins.

A method of determining patient responsiveness to a drug by the use of spectroscopy to obtain patient metabolite data and relating that data to data obtained from a statistically significant group of patients taking the same drug in order to obtain a result indicating responsiveness of a particular patient to treatment with a particular drug.

A method and system is provided for mass spectrometry for identification of a specific elemental formula for an unknown compound which includes but is not limited to a metabolite. The method includes calculating a natural abundance probability (NAP) of a given isotopologue for isotopes of non-labelling elements of an unknown compound. Molecular fragments for a subset of isotopes identified using the NAP are created and sorted into a requisite cache data structure to be subsequently searched. Peaks from raw spectrum data from mass spectrometry for an unknown compound. Sample-specific peaks of the unknown compound from various spectral artifacts in ultra-high resolution Fourier transform mass spectra are separated. A set of possible isotope-resolved molecular formula (IMF) are created by iteratively searching the molecular fragment caches and combining with additional isotopes and then statistically filtering the results based on NAP and mass-to-charge (m/2) matching probabilities. An unknown compound is identified and its corresponding elemental molecular formula (EMF) from statistically-significant caches of isotopologues with compatible IMFs.

The present invention relates to a method and system for using neurochemical markers to enable whether a subject is experiencing acute stress, trauma, or PTSD and providing the capacity to monitor response to therapy on an individual basis. The markers can be an increase of NAA, glutamine or Fuc IV and lactate and a decrease of Fuc IV during the transition from acute stress to PTSD.

A 2-D MRS (magnetic resonance spectroscopy), or equivalently, NMR (nuclear magnetic resonance), pre-processing method produces clean MRS signals from raw data for possible use, among other applications, for diagnoses of neurological disorders such as PTSD and mTBI of the brain. The specific 2-D MRS data referred to in this invention are the 2-D MRS Correlation Spectroscopy or 2-D COSY data.

The present disclosure provides a method for ultrafastly obtaining a two-dimensional J-resolved NMR spectrum with a high-resolution against an inhomogeneous magnetic field. The method utilizes the selective excitation module and the reunion sampling module jointly, which breaks through the limitations of existing methods for obtaining the two-dimensional J-resolved NMR spectrum and effectively eliminates an influence of the inhomogeneous magnetic field along an encoding direction. At the same time, an inhomogeneous magnetic field along x and y directions is theoretically eliminated by a slow rotation of the sample. As a consequence, a two-dimensional J-resolved NMR spectrum with a high resolution is obtained by a single-scanning sampling under the inhomogeneous magnetic field, thus significantly shortening experimental duration and expanding application fields of the two-dimensional J-resolved NMR spectrum. The present disclosure further provides a method using multi-band sampling, which is used to obtain J-resolved NMR spectrum with improved signal-to-noise ratio.