A method of characterizing chemical and/or morphological features of a material, comprising acquiring energy relaxation data from 1H low field nuclear magnetic resonance (.sup.1H LF-NMR) measurements of said material, converting the relaxation signals into a multidimensional distribution of longitudinal and transverse relaxation times by solving an inverse problem under both L.sub.1 and L.sub.2 regularizations and further imposing a non-negativity constraint, and identifying one or more characteristics of said material with the aid of said multidimensional T1-T2 distribution. The method is useful, inter alia, in monitoring chemical processes, screening of additives and quality control.

The present invention provides a method for detecting synthetic indole and indazole cannabinoids in a sample known or suspected to contain a synthetic indole or indazole cannabinoid. A deuterated solvent is added to the solid sample, creating a suspension. The suspension is mixed to release the cannabinoid from the solid sample. The suspension is subject to a NMR spectroscopy process to produce a sample NMR spectrum. The synthetic cannabinoid is detected in the suspension by analysis of the sample NMR spectrum. When one-dimensional proton NMR is used, detection of a first peak between 8.00 and 8.50 ppm and a second peak between 4.00 and 4.40 ppm, indicates the presence of a synthetic indole or indazole cannabinoid. When two-dimensional Correlation Spectroscopy (COSY) NMR is used, detection of a first spot between 6.50 and 9.00 ppm and a second spot between 1.50 and 4.50 ppm indicates the presence of a synthetic indole or indazole cannabinoid. The method is performed in the absence of chromatography and optionally, may be used to quantify the amount of synthetic cannabinoid.

Systems and methods are provided for processing a set of multiple serially acquired magnetic resonance spectroscopy (MRS) free induction decay (FID) frames from a multi-frame MRS acquisition series from a region of interest (ROI) in a subject, and for providing a post-processed MRS spectrum. Processing parameters are dynamically varied while measuring results to determine the optimal post-processed results. Spectral regions opposite water from chemical regions of interest are evaluated and used in at least one processing operation. Frequency shift error is estimated via spectral correlation between free induction decay (FID) frames and a reference spectrum. Multiple groups of FID frames within the acquired set are identified to different phases corresponding with a phase step cycle of the acquisition. Baseline correction is also performed via rank order filter (ROF) estimate and a polynomial fit. Sections of the ROF may be excluded from the polynomial fit, such as for example sections determined to be associated with relevant spectral peaks.

Systems and methods provide a parallelized deep learning approach to spectral fitting for magnetic resonance spectroscopy data enabling accurate and rapid spectral fitting and determination of metabolite measurements using a conventional computer. The method may include processing multi-spectra magnetic resonance (MR) spectroscopy data of a region of interest through a series of neural networks. The method may include determining baseline components of each spectrum using a first neural network of the series, generating baseline-corrected components for each spectrum using the baseline components; and determining one or more peak components of each spectrum using a second neural network of the series and the baseline-corrected components. The method may further include determining one or more metabolite measurements of the one or more metabolites in the region of interest using the one or more peak components.

Described herein are methods for the determination of patient mortality from alcoholic hepatitis in biosamples by NMR spectroscopy and more specifically for the determination of a Z index score based on lipoprotein constituent LP-Z in blood plasma and serum.

Various methods and systems are provided for correcting transmit attenuation of an amplifier of a transmit radio frequency (RF) coil for use in a magnetic resonance imaging (MRI) system. In one example, a method includes setting a reference value of transmit attenuation for an amplifier of a transmit radio frequency (RF) coil, acquiring a two-dimensional B.sub.1 field map with the transmit attenuation set at the reference value, determining a mean flip angle from the B.sub.1 field map, determining a transmit attenuation correction value based on a prescribed flip angle and the mean flip angle, correcting the reference value of transmit attenuation with the transmit attenuation correction value to obtain a final value of transmit attenuation, and performing an MRI scan with the transmit attenuation set at the value.

The present disclosure relates to a method for analysis of an average deuterium substitution rate of a deuterium-substituted sample using information of a .sup.1H-NMR spectrum of the deuterium-substituted sample.

The present invention relates to a method based on proton NMR technique to differentiate genuine and spurious Hand-rub formulations. This method identifies and estimates all four components present in WHO-recommended Hand-rub formulations. Further, this method also identifies the presence of non-recommended/additional components present in WHO-recommended Hand-rub formulations. The method described in this invention utilizes experimental parameters and derived equations to quantify all four components in just fifteen minutes without using any organic solvents.

A new method for reconstructing a full spectrum from under-sampled magnetic resonance spectrum data by using a deep learning network. First, the exponential function is used to generate a time-domain signal of the magnetic resonance spectrum, and a zero-filling time-domain signal is obtained after the under-sampled operation is completed in the time domain. The zero-filling time-domain signal and the full spectrum corresponding to the full sampling are combined to form a training data set. Then, a data verification convolutional neural network model is established for magnetic resonance spectrum reconstruction, where the training data set is used to train neural network parameters to form a trained neural network. Finally, the under-sampled magnetic resonance time-domain signal is input to the trained data verification convolutional neural network, and the full magnetic resonance spectrum is reconstructed.

A system and method include an analysis control unit configured to receive nuclear magnetic resonance (NMR) data of a sample. A reference database is in communication with the analysis control unit. The reference database stores standard data including information regarding a plurality of standards for chemical compounds, and may also store information regarding quantum mechanical spectral analysis (QMSA) model data and test method data. The analysis control unit is further configured to compare the NMR data with the standard data to determine if the NMR data is associated with one of the plurality of standards, and identify one or more differences between the NMR data and the one of the plurality of standards.