A method for producing images of a subject with a MRI system is provided. A radio frequency (RF) excitation field in combination with a slice-select magnetic gradient field along a slice-select direction is provided. At least one readout magnetic field gradient is established along a frequency-encoding direction and at least one phase encoding magnetic field gradient along a phase-encoding direction. The RF field or magnetic field gradient is manipulated along a slice-select direction in order to impart a sequence of phase shifts to the formed echo signals such that image data corresponding to an at least one adjacent slice location is incoherently aliased across a field-of-view (FOV) of a current slice location. Image data is acquired indicative of the formed echo signals. A plurality of images of the subject is reconstructed.

In one embodiment a magnetic resonance imaging method includes the steps of comparing a first image and a second image to determine whether there is a distorted region present in the first image or the second image, each of the first image and second image having a total field of view that is the distance of the image along an axis, assigning an affected field of view to a width of the distorted region, determining an acceleration factor by dividing the total field of view of one or both of the first image and the second image by the affected field of view, acquiring sampled image data according to the acceleration factor of one or both of the first image and the second image and applying a mask to a third image in the affected field of view.

Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.

The present disclosure relates to a method comprising: receiving (201) acquired k-space data of an object, reconstructing (203) an image from the acquired k-space data, generating (205) reconstructed k-space data from the reconstructed image, determining (207) delta k-space data as a difference between the acquired k-space data and the reconstructed k-space data, splitting (209) the k-space data into one or more data chunks, wherein each data chunk of the data chunks comprises a set of one or more samples having a set of k-space coordinates, for each set of k-space coordinates of the one or more sets of coordinates, selecting (211), from the delta k-space data, a residual data set having the set of k-space coordinates, inputting (213) at least part of the data chunks and corresponding residual data sets to a trained machine learning model, thereby obtaining from the trained machine learning model probabilities of motion corruption for each of the data chunks of the acquired k-space.

Disclosed herein is a medical system (100, 300) comprising a memory (110) storing machine executable instructions (120) and an image generating neural network (122). The image generating neural network is configured for outputting synthetic magnetic resonance image data (128) in response to receiving reference magnetic resonance image data (126) as input. The synthetic magnetic resonance image data is a simulation of magnetic resonance image data acquired according to a first configuration of a magnetic resonance imaging system when the reference magnetic resonance image data is acquired according to a second configuration of the magnetic resonance imaging system. Execution of the machine executable instructions causes a computational system (106) to: receive (200) measured k-space data (124) acquired according to the first configuration of the magnetic resonance imaging system; receive (202) the reference magnetic resonance image data; receive (204) the synthetic magnetic resonance image data by inputting the reference magnetic resonance image data into the image generating neural network; and reconstruct (206) corrected magnetic resonance image data (132) from the measured k-space data and the synthetic magnetic resonance image data.

Techniques are provided for determining a magnetic resonance imaging (MRI) image using multiple measurement data sets that form a propeller pattern. Partial MRI images are reconstructed for each measurement data set. The partial MRI images are then combined.

A fat suppressed diffusion image determination apparatus, a corresponding method and a corresponding computer program determine a diffusion weighted magnetic resonance image (DWI) of an object. The fat suppressed diffusion image determination apparatus includes a diffusion reference image providing unit for providing a diffusion reference MR image of the object, a fat image determination unit for determining a fat image from the diffusion reference MR image, a diffusion weighted image providing unit for providing a diffusion weighted MR image of the object, a fat suppressed image determination unit for determining a fat suppressed diffusion weighted MR image using a combination of the diffusion weighted MR image and the fat image.

The present disclosure in some embodiments provides a method and an apparatus for processing MRI images wherein a plurality of slices of an object is applied with a spatial encoding gradient and a corrected gradient for applying a radial sampling, and radially sampled magnetic resonance signals of the slices are received, and MRI images are generated with the radial sampling applied over multi-bands.

Described here are systems and methods for reconstructing images of a subject using a magnetic resonance imaging (“MRI”) system. As part of the reconstruction, synthesized data are estimated at arbitrarily specified k-space locations from measured data at known k-space locations. In general, the synthesized data is estimated using a convolution operation that is based on measured or estimated covariances in the acquired data. The systems and methods described here can thus be referred to as Convolution Operations for Data Estimation from Covariance (“CODEC”).

The invention relates to a method for generating MR images (10, 20) of an object in its environment within a region of interest, said object executing motion comprising a plurality of moving phases within a period of time. According to several aspects of the invention, the method comprises the steps of: —providing a first dataset pertaining to one of the moving phases of the object (Si); —generating a first image (10) of a region of interest from the first dataset (S2); —identifying a dynamic region (12) and a static region (14) inside the first image (10), wherein the regions (12, 14) are predominantly dynamic or static respectively within the periodeperiod of time (S3); —editing the first image (10) by masking out the dynamic region (14) (S4); —performing an inverse Fourier transformation of the edited first image (16) showing the remaining static region (14) (S5); —providing a second dataset pertaining to one of the moving phases of the object (S6); —subtraction of the inverse Fourier transformation of the edited first image (16) with the remaining static region (14) from the second dataset (S7); —performing a Fourier transformation on the subtracted second dataset (18) (S8); and —generating a second image (20) of a reduced region of interest with respect to the region of interest of the first image (10), which reduced region of interest includes the dynamic region (12) (S9). The invention further relates to a corresponding MRI system for generating MR images of an object in its environment within a region of interest.