A magnetic resonance imaging (MRI) device according to one embodiment is provided. The MRI device includes: a display configured to display a first image of an object; a controller configured to determine a signal region of the object based on the first image of the object; and a user input device configured to receive a user input of setting a region of interest (ROI) on the first image. When the set ROI is part of the signal region of the object, the controller is further configured to control the display to display guide information for preventing aliasing artifacts from occurring in a second image to be generated based on the set ROI.

The present disclosure relates to operating an MR system in which MR signals of an object under examination are acquired in an examining region using a multi echo imaging sequence, in which an RF excitation pulse and a plurality of RF refocusing pulses are applied. The techniques include determining a first accumulated phase of a magnetization in the object under examination. Then, a second accumulated phase of the magnetization in the object under examination is determined due to concomitant magnetic fields occurring between a second pair of consecutive RF pulses. Finally, it is determined whether a deviation from the predefined relationship is larger than a threshold and, if this is the case, a measure is applied in view of the fact that the deviation is larger than the threshold.

Magnetic resonance imaging (MRI) systems and methods to determine and/or correct slice leakage and/or residual aliasing in the image domain in accelerated MRI imaging. Some implementations process one slice of MRI image domain data by input to a sensitivity encoding (SENSE) un-aliasing matrix built from predetermined RF signal reception sensitivity maps, thereby producing SENSE-decoded MRI image domain data for one pass-through image slice and at least one extra slice, and determine inter-slice leakage and/or in-plane residual aliasing based on content of the at least one extra output slice from the SENSE-decoded MRI image domain data. Some implementations correct slice leakage in reconstructed images by generating a fractional leakage matrix of inter-slice leakage measurements, and by multiplying the inverted fractional leakage matrix with uncorrected reconstructed images.

A system and method for magnetic resonance imaging reconstruction using novel k-space sampling sequences is provided. The method includes dividing k-space into a plurality of regions along a dividing direction; scanning an object using a plurality of sampling sequences; acquiring a plurality of groups of data lines; filling the plurality of groups of data lines into the plurality of regions of the k-space; and reconstructing an image based on the filled k-space.

In a magnetic resonance (MR) method and apparatus, first and second MR data are acquired from respective echo trains with gradient moments of one echo train being in a sequence that is an inversion of at least a portion of the sequence of gradient moments in the second echo train. The MR signals are acquired from at least two substances in a volume of a subject, so that the relaxation of the respective nuclear spins influences the manner by which the first and second data are entered into k-space, so that when an image is reconstructed, the filter effect induced by such relaxation is compensated for.

Method for eliminating aliasing artifacts in a magnetic resonance image, comprising the steps of obtaining a first and a second starting image (100a,100b) obtained by a determined acquisition sequence and using, respectively a phase encoding for columns, and a phase encoding for rows. Both the first and the second starting image (100a,100b) are organized in according to a matrix structure (m.Math.n) comprising a plurality of portions (101a,101b) arranged according to m rows and n columns, each of which is associated to a respective numerical value corresponding to the light intensity of the portion. The method provides a translation step for translating at least one between the first and the second starting image (100a,100b) with respect to a respective reference system, in such a way to minimize the differences among the numerical values of the homologous portions of the first and of the second starting image due to the fact that the first and the second starting image are obtained by a different encoding phase.

Magnetic resonance (MR) data are acquired by applying magnetic fields to an examination region concurrent with stimulated echo signals, such that trajectories, which are not straight lines, are generated in k-space. For this purpose, sequence of RF pulses is applied to generate the stimulated echo signals in the examination object, undersampled MR measurement data are detected during reception of the stimulated echo signals in the at least two receiving coils, along the curved k-space trajectories, and fully sampled MR measurement are generated from the undersampled MR measurement data using sensitivity information of the at least two receiving coils. Alternatively, the MR measurement data are fully sampled in a central region of k-space, and a region outside the central region is not fully sampled, and a phase correction with a Partial Fourier technique is executed on the MR measurement data using fully sampled MR measurement data from the central region of k-space.

A method for magnetic resonance fingerprinting with out-of-view artifact suppression includes acquiring MRF data from a region of interest in a subject. The MRF data is acquired using a non-Cartesian, variable density sampling trajectory. The MRF data includes data from within a desired field-of-view and data from outside the desired field-of-view. The method also includes generating a set of coil images based on the MRF data with a field-of-view larger than the desired field-of-view, determining a noise covariance based on the MRF data from outside the desired field-of-view, generating a coil combined image using an adaptive coil combination determined based on the noise covariance, applying the adaptive coil combination to the MRF data to grid each frame of the MRF data and generate MRF data with out-of-view artifact suppression. The method also includes identifying at least one property of the MRF data and generating a report.

Images are reconstructed from undersampled k-space data using a residual machine learning algorithm (e.g., a ResNet architecture) to estimate missing k-space lines from acquired k-space data with improved noise resilience. Using a residual machine learning algorithm provides for combining the advantages of both linear and nonlinear k-space reconstructions. The linear residual connection can implement a convolution that estimates most of the energy in k-space, and the multi-layer machine learning algorithm can be implemented with nonlinear activation functions to estimate imperfections, such as noise amplification due to coil geometry, that arise from the linear component.

The disclosure includes a method for generating quantitative magnetic resonance (MR) images of an object under investigation. A first MR data set of the object under investigation is captured in an undersampled raw data space, wherein the object under investigation is captured in a plurality of 2D slices, in which the resolution in a slice plane of the slices is in each case higher than perpendicular to the slice plane, wherein the plurality of 2D slices are in each case shifted relative to one another by a distance which is smaller than the resolution perpendicular to the slice plane. Further MR raw data points of the first MR data set are reconstructed with the assistance of a model using a cost function which is minimized. The cost function takes account of the shift of the plurality of 2D slices perpendicular to the slice plane.