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.

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.

A method for measuring a gradient field of a nuclear magnetic resonance (NMR) system based on a diffusion effect uses a non-uniform field magnet, an NMR spectrometer, a radio frequency (RF) power amplifier, an RF coil, and a standard quantitative phantom with known apparent diffusion coefficient (ADC) and time constant for decay of transverse magnetization after RF-pulse (T2). A plurality of sets of signals are acquired by an NMR sequence with different diffusion-sensitive gradient durations or different echo spacings and the magnitude of the gradient field is calculated by fitting based on the plurality of sets of signals. The method does not require an additional dedicated magnetic field detection device, has a short measurement time, is easy to use with the NMR system, and is convenient to complete gradient field measurement at the installation site, thereby improving the installation and service efficiency of the NMR system.

Systems and methods are provided for using machine learning (ML) models to evaluate data generated during MRI procedures, such as magnetic resonance spectroscopy (MRS) data, to evaluate patient disc pain. An ML-based disc assessment system can evaluate MRS spectrum data ML models trained to classify the MRS spectrum based on diagnostic and/or outcome-based ground truth data.

An NMR relaxation time inversion method based on an unsupervised neural network includes simulating inversion kernel matrix, simulating continuous NMR relaxation time spectrum, simulating noise, calculating NMR relaxation signals as samples, various samples forming a sample set, constructing an unsupervised neural network model, and defining a loss function of the unsupervised neural network model; and taking the samples in the training sample set as an input of the unsupervised neural network model, to obtain an optimal mapping relationship between the NMR relaxation signals and the NMR relaxation time spectrum with a minimum loss function. The present invention provides the possibility of using experimental data as the sample for training since the trading sample does not need to be labeled, can automatically learn the optimal regularization parameters without depending on the initial value and manual experience, and predicts fast.

Multi-dimensional spectra associated with a specimen are reconstructed using lower dimensional spectra as constraints. For example, a two-dimensional spectrum associated with diffusivity and spin-lattice relaxation time is obtained using one-dimensional spectra associated with diffusivity and spin-lattice relaxation time, respectively, as constraints. Data for a full two dimensional spectrum are not acquired, leading to significantly reduced data acquisition times.

Embodiments of a compact portable nuclear magnetic resonance (NMR) device are described which generally include a housing that provides a magnetic shield; an axisymmetric permanent magnet assembly in the housing and having a bore, a plurality of magnetic elements that together provide a well confined axisymmetric magnetization for generating a near-homogenous magnetic dipole field B.sub.0 directed along a longitudinal axis and providing a sample cavity for receiving a sample, and high magnetic permeability soft steel poles to improve field uniformity: a shimming assembly with coils disposed at the longitudinal axis for spatially correcting the near homogenous magnetic field B.sub.0; and a spectrometer having a control unit for measuring a metabolite in the sample by applying magnetic stimulus pulses to the sample, measuring free induction delay signals generated by an ensemble of hydrogen protons within the sample; and suppressing a water signal by using a dephasing gradient with frequency selective suppression.

A method comprising collecting magnetic resonance imaging (MRI) scanner data corresponding to a region of interest, establishing a spectral peak profile associated with at least one metabolite in the region of interest, wherein the spectral peak profile comprises a term in the FID vector signal included in the collected MRI scanner data, selecting at least three counter indices and corresponding points on the spectral peak profile to compute a linear fractional transformation (LFT), computing an N-dimensional vector outlining a spectral circle in a complex plane by applying the LFT to each counter index included in a set of equally-spaced counter indices associated with a three-dimensional spectrum representation of the collected MRI scanner data, shifting the spectral circle to eliminate a baseline offset for a magnitude spectrum associated with the complex plane, rotating the shifted spectral circle to produce a rotated spectral circle.

Magnetic resonance imaging (“MRI”) data are corrected from corruptions due to physiological changes using a self-navigated phase correction technique. Unlike motion correction techniques, the effects of physiological changes (e.g., breathing and respiration) are corrected by making the MRI data self-consistent relative to an absolute uncorrupted phase reference. This phase correction information can be extracted from the acquisition itself, thereby eliminating the need for a separate navigator scan, and establishing an accelerated acquisition. This absolute reference can be computed in a data segmented space, and the subsequent data can be corrected relative to this absolute reference with low-resolution phases.

Multi-dimensional spectra associated with a specimen are reconstructed using lower dimensional spectra as constraints. For example, a two-dimensional spectrum associated with diffusivity and spin-lattice relaxation time is obtained using one-dimensional spectra associated with diffusivity and spin-lattice relaxation time, respectively, as constraints. Data for a full two dimensional spectrum are not acquired, leading to significantly reduced data acquisition times.