In an MRI image, signals from plural metabolites are accurately separated. A signal separation unit generates, as a dictionary, plural signal patterns in which values of variables of substances are changed based on prior information, and performs signal separation of the substances by matching the dictionary with a measured signal. At this time, an order of signal intensities of the substances is used as the prior information, and selection of a most matched dictionary for each of the substances is repeated while changing a dictionary used for matching according to the order.

In a method for the optimized acquisition of diffusion-weighted measurement data of an object under examination using a magnetic resonance (MR) system, a first set of diffusion-weighted measurement data is captured by excitation, and, in an acquisition phase, acquisition of at least one position-encoded echo signal, where prior to the acquisition phase, diffusion gradients are switched for diffusion encoding of the diffusion-weighted measurement data in a diffusion preparation phase, and at least one further set of diffusion-weighted measurement data is captured by excitation, and, in an acquisition phase, acquisition of at least one further position-encoded echo signal, where prior to the acquisition phase, diffusion gradients are switched for diffusion encoding of the diffusion-weighted measurement data in the associated diffusion preparation phase. The diffusion gradients switched in consecutive diffusion preparation phases may have an inverted polarity.

The invention provides a method of medical imaging. The method comprises: receiving, for a current active time window (204A-N) and during a brain activity analysis session (200, 500), fMRI data of a region of interest (309) of a subject (318) in an active state. A transverse relaxation, T2*, map may be generated from the fMRI data using a predefined model of fMRI data variations. The generated T2* map may be compared with a reference T2* map. A blood-oxygen-level dependent (BOLD) response of the region of interest (309) during the current active time window (204 A-N) may be estimated using the results of the comparison.

In a method for quantitative detection of parameters in MRI, first and second spoiled gradient echo images and at least one multi-echo steady-state first/second magnetization image are acquired; based on the extended phase graph theory, signal equations are obtained corresponding to the first and second spoiled gradient echo images, the at least one multi-echo steady-state first magnetization image and the at least one multi-echo steady-state second magnetization image; based on the signal equations of the first and second spoiled gradient echo images, the signal equation of the at least one multi-echo steady-state first magnetization image and the signal equation of the at least one multi-echo steady-state second magnetization image, a spoiled gradient echo proton density map, a multi-echo steady-state proton density map, a longitudinal relaxation time map and a transverse relaxation time map of the target tissue are obtained.

A method for NMR measurements on borehole materials, e.g., sidewall cores, is based on performing a standard measurement in substantially homogeneous magnetic fields with a sensitivity volume covering an entire sample and a measurement on a fragment of the sample (local measurement), the fragment having a predetermined volume independent of the irregularities of the sample shape (e.g., irregular shaped edges). The fragment of the sample is selected using a switchable static magnetic field gradient or a localized radio-frequency magnetic field. The homogeneous and the local measurement data are processed jointly to obtain volume normalized NMR relaxation data (in porosity units), the processing also using a calibration sample data. A measurement apparatus with an automated sample transfer can be used to implement the method in order to perform high-throughput NMR relaxation measurements that do not require independent measurement of the sample volume.

Accelerated GRASE with controlled T2 blurring improves a point spread function (PSF) and temporal signal-to-noise ratio (tSNR) with many slices. The approach seeks to minimize a trade-off between SNR and blurring for functional sensitivity, and uses a new GRASE-optimized random encoding, which takes into accounts for the complex signal decays of T2 and T2* weightings, by achieving incoherent aliasing for constrained reconstruction. Numerical and experimental studies validate the effectiveness of the new method over regular and VFA GRASE (R- and V-GRASE).

Simultaneous multi-orientation (“SMO”) magnetic resonance imaging (“MRI”), in which arbitrarily-oriented slices are simultaneously imaged, is described. The SMO techniques can include any number of pulse sequences that are adapted to acquire data from two or more arbitrarily oriented slices. In general, an SMO acquisition includes sequentially exciting two or more arbitrarily rotated slices that share a common spatial encoding axis (e.g., a common frequency encoding direction) and simultaneously acquiring data from the excited slices.

In the field of obesity related disease, identification of patients with nonalcoholic steatohepatitis (NASH) would be useful to counsel them more intensively on diet and lifestyle changes and propose new pharmacological treatments. As a consequence, the inventors worked on a method for post-processing images of a region of interest of the liver for reconstructing a map of the internal magnetic susceptibility by using a Bayesian regularization approach to inverse the internal magnetic field. Such method can be implemented on computer and provides better results than other known methods for obesity related disease. This method may be applied for predicting that a subject is at risk of suffering from such disease, diagnosing a disease, identifying a therapeutic or a biomarker and screening compounds useful as a medicine.

It is an object of present invention to provide for a faster method of multi-component analysis. This object is achieved by a method for multi-component analysis on MRI measurement data, wherein a component is defined by one or more tissue component parameters among which preferably one is a T2 or T1 value. The method comprising steps of receiving the MRI measurement data, wherein the MRI measurement data comprises multiple signals corresponding to multiple voxels in an MRI image and wherein the MRI measurement data is acquired by means of a sequence encoding the one or more tissue component parameters; identifying components in the multiple voxels by minimizing a difference between the multiple signals and a linear combination of weighted simulated temporal signal evolutions, wherein different simulated temporal signal evolutions represent different components and are based on different values of the one or more tissue component parameters, and wherein the identification of the components is performed under the assumption that the possible components are the same for all of the multiple voxels and wherein a higher total number of components is penalized over a lower total number of components, and wherein the simulated temporal signal evolutions are weighted by a weight factor that is non-negative.

A method of analysing the magnitude of Magnetic Resonance Imaging (MRI) data is described. The method comprising: using the magnitude only of the multi-echo MRI data of images from the subject, where images are acquired at arbitrarily timed echoes including at least one echo time where water and fat are not substantially in-phase; fitting the magnitude of said multi-echo MRI data to a single signal model to produce a plurality of potential solutions for the relative signal contributions for each of the at least two species from the model, by using a plurality of different starting conditions to generate a particular cost function value for each of the plurality of starting conditions, where said cost function values are independent of a field map term for the MRI data; analysing said cost function values to calculate relative signal separation contribution for each species at each voxel of the images.