A computer-implemented method for ascertaining flip angles of a magnetic resonance sequence with variable flip angles, a magnetic resonance apparatus, and a computer program product are disclosed. In this case, the magnetic resonance sequence includes at least one echo train with an excitation pulse and a plurality of refocusing pulses. In each case, an adapted flip angle is ascertained for at least one part of the plurality of refocusing pulses.

A method for actuating a magnetic resonance device according to an MR control sequence, wherein the MR control sequence includes a bipolar gradient pulse between an excitation pulse and a first refocusing pulse, and the bipolar gradient pulse induces a defined Maxwell phase and generates a dephasing gradient moment for a readout gradient.

Magnetic resonance imaging (MRI) using a spin- and gradient-echo (SAGE) pulse sequence with blip up-down acquisition (BUDA) encoding enables distortion-free, high-resolution diffusion-weighted imaging and/or quantitative parameter mapping. Phase-encoding polarities are alternated across shots during a multi-shot acquisition. In each shot, multi-contrast data are acquired at echo times associated with a gradient echo, a mixed gradient-and-spin echo, and a spin echo. High in-plane resolution and distortion-free quantitative parameter maps can be generated, such as T2 maps. T2* maps, paramagnetic susceptibility maps, and diamagnetic susceptibility maps. Diffusion-weighted data can be acquired using diffusion encoding gradients and BUDA encoding, where multi-contrast data are acquired in the b=0 acquisition. Diffusion parameter maps can be generated from the b=0 and diffusion-weighted data.

A method for accelerating magnetic resonance imaging is proposed. In 3D MRI, the method utilizes two sub-echo-trains in each repetition time for the simultaneous acquisition of two contrasts. The first sub-echo-train is a turbo spin echo train and the second sub-echo-train is a gradient echo train. The method acquires two different contrasts simultaneously in a single acquisition, for example one water image plus one fat image, or one turbo spin echo image plus one susceptibility weighted image.

In a method and magnetic resonance apparatus for generating at least one combination image dataset, a first image dataset is acquired with a turbo spin echo sequence, wherein the echo signals are timed so that the spins of two spin species in the region to be examined are in-phase. A second image dataset is acquired with a turbo spin echo sequence, wherein the echo signals are timed so that the spins of two spin species in the region to be examined have opposed phase. The first image dataset and the second image dataset are combined to form a combination image.

In a method and magnetic resonance tomography apparatus for diffusion imaging, coherences are determined in a processor, which would occur during the diffusion imaging measurement, and an implicit spoil moment M.sub.A resulting from a diffusion gradient pulse is determined in the processor. A spoiler moment M.sub.S is established in the processor as a function of a comparison value and threshold value formed from the implicit spoil moment M.sub.A and the suppression moment M. Depending on whether this comparison value lies below or above the threshold value, different calculation techniques are applied for the spoiler moment M.sub.S. Diffusion gradient pulses and spoiler gradient pulses in accordance with the moments M.sub.A and M.sub.S in a pulse sequence for operating the magnetic resonance tomography apparatus.

Systems and methods for accelerating magnetic resonance fingerprinting (MRF} acquisitions are described. Acquisition parameters can be optimized to reduce the number of acquisitions necessary while maximizing the discrimination between the physical parameters to be estimated. The systems and methods may also include implementing pulse sequences that rapidly acquire large volumes of k-space data, including echo-planar imaging (EPI} and segmented EPI sequences.

A method for post-processing images of a region of interest in a subject, the images being acquired with a magnetic resonance imaging technique, the method for post-processing comprising at least the step of: unwrapping the phase of each image, extracting a real signal over echo time for at least one pixel of the unwrapped images, and calculating fat characterization parameters by using a fitting technique applied on a model, the model being a function which associates to a plurality of parameters each extracted real signal, the plurality of parameters comprising at least two fat characterization parameters and at least one parameter obtained by a measurement, the fitting technique being a non-linear least-square fitting technique using pseudo-random initial conditions.

A system and method for creating magnetic resonance images includes performing a first pulse sequence that saturates a selected labile spin species of the subject by applying a radiofrequency (RF) irradiation at a reference frequency and performing a second pulse sequence that saturates a selected labile spin species of the subject by applying an RF irradiation at a labeling frequency. A plurality of echoes having information pertaining to at least one of metabolites and metabolite byproducts is acquired to form a chemical exchange saturation transfer (CEST) medical imaging data set and the CEST medical imaging data set is reconstructed to form a CEST image of the subject including information about the at least one of metabolites and metabolite byproducts within the subject.

A method and system for simultaneous quantitative multiparametric MRI are provided in the disclosure. The method includes: designing a magnetic resonance imaging (MRI) sequence which includes a signal excitation part, a shift gradient part, and a data acquisition part; generating training samples for a deep neural network according to the MRI sequence; training the deep neural network by using the training samples, to obtain a trained deep neural network; and reconstructing multiple magnetic-resonance parametric maps by using the trained deep neural network and k-space data acquired by the MRI sequence. The disclosure can implement quantitative multiparametric MRI, and can correct image distortions caused by magnetic field inhomogeneity.