The invention relates to a method of MR imaging of a body (10) of a patient. It is an object of the invention to provide a method that enables efficient compensation of image artefacts in combination with PROPELLER imaging. The invention proposes to combine k-space blades in image space, and not in k-space like in conventional PROPELLER imaging. Local image artefacts are detected and corrected in single-blade MR images. The artefact detection and correction in the image domain prior to combining the single-blade MR images into a final MR image results in an improved image quality by better suppression of local artefacts and, thus, an increased signal-to-noise. Moreover, the invention relates to a MR device (1) and to a computer program for a MR device (1).

In a method and system, a reference dataset is recorded using a reference scan based on a GRE or RA RT sequence. A correction dataset is also recorded using a phase correction scan based on a non-phase-encoding EPI sequence. A measurement dataset is recorded using an SMS sequence. Slice-specific GRAPPA kernels are determined from the reference dataset and magnetic resonance images are created by a slice GRAPPA method. Data of the measurement dataset belonging to different slices is separated from one another using the slice-specific GRAPPA kernels and N/2 ghost artifacts are corrected using the correction dataset.

A magnetic resonance imaging (MRI) apparatus and an MRI method are provided. The MRI apparatus includes a data acquisition unit configured to acquire a first image by performing an MRI scan on an object. The MRI apparatus further includes an image acquisition unit configured to acquire a second image by performing de-noising filtering on the first image in a spatial domain, acquire a third image based on the first image and the second image, acquire a fourth image by performing low-pass filtering on the third image in a frequency domain, and acquire a final image based on the second image and the fourth image.

Magnetic resonance imaging uses regularized SENSE reconstruction for a reduced field of view, but minimizes folding artifacts. A reference scan is oversampled relative to the reduced field of view. The oversampling provides coil sensitivity information for a region greater than the reduced field of view. The reconstruction of the object for the reduced field of view using the coil sensitivities for the larger region may have fewer folding artifacts.

In one embodiment, an MRI apparatus includes processing circuitry configured to: calculate phase correction data, which includes information on phase rotation amount due to non-uniformity of a static magnetic field, from MR signals; generate an image by using the phase correction data and the MR signals such that a signal from at least one of substances having different magnetic resonance frequencies in an imaging region of an object is suppressed in the image; generate first phase correction data composed of phase data that correspond to phase rotation amount selected from choices of phase rotation amount; calculate discontinuity of the first phase correction data; and generate second phase correction data by substituting at least part of the first phase correction data with non-selected phase data, which corresponds to phase rotation amount being not selected among the choices of phase rotation amount, depending on the discontinuity.

A magnetic resonance imaging (MRI) apparatus for obtaining a magnetic resonance (MR) image, based on a multi-echo sequence, and a method of the MRI apparatus are provided. The MRI apparatus includes a data obtainer configured to obtain first echo data, based on an echo that is generated at a first echo time, and obtain second echo data, based on an echo that is generated at a second echo time later than the first echo time, the first echo data including a part overlapping a part included in the second echo data in a k-space. The MRI apparatus further includes an image processor configured to reconstruct the MR image, based on the first echo data and the second echo data.

Provided is a deep learning based accelerated MRI image quality restoring method. The deep learning based accelerated MRI image quality restoring method includes extracting test information from an input accelerated MRI image, selecting at least one deep learning model corresponding to the test information, among a plurality of previously trained deep learning models, and outputting an MRI image with a restored image quality for the input accelerated MRI image with the input accelerated MRI image as an input of at least one selected deep learning model.

A synergized pulsing-imaging network is described. A method of optimizing a magnetic resonance imaging (MRI) system includes optimizing, by a synergized pulsing-imaging network (SPIN) circuitry a pulse sequence based, at least in part, on a loss function associated with a reconstruction network. The method further includes optimizing, by the SPIN circuitry, the reconstruction network based, at least in part, on intermediate raw MRI data and based, at least in part, on a ground truth MRI image data. The intermediate raw MRI data is determined based, at least in part on the pulse sequence.

Techniques are disclosed for positioning a patient within a patient receiving area for a magnetic resonance examination. The techniques may include positioning the patient on a mobile patient table of a patient support facility and moving the patient table in a first horizontal direction until a region of the patient to be examined is arranged in a field of view of a patient receiving area of a magnetic resonance system. Moreover, the patient positioning techniques may include moving the patient table in a second horizontal direction and/or in a vertical direction within the patient receiving area, and acquiring medical magnetic resonance data of the region of the patient to be examined.

A system and method are provided for determining a spatial distribution of susceptibility in a subject using a magnetic resonance imaging (MRI) system. The method includes directing the MRI system to acquire imaging data from an imaging volume within a subject, wherein the imaging volume is subject to both background fields (B.sub.B) originating outside the imaging volume and local fields (B.sub.L) originating from tissue within the imaging volume. The method also includes selecting a size and non-central compute point for an extended Poisson kernel to be applied to the imaging data, subtracting from a delta function to control the background fields (B.sub.B) but not the local fields (B.sub.L), and producing a susceptibility report attributable to the local fields (B.sub.L).