A system and method for magnetic resonance imaging is provided. The method may include: determining a scanning parameter, wherein the scanning parameter includes one or more predetermined values; obtaining one or more MR signal sets based on the one or more predetermined values; generating one or more original images based on the one or more MR signal sets, wherein an original image corresponds to an MR signal set; determining one or more virtual values associated with the scanning parameter; and generating one or more virtual images based on the one or more virtual values and the one or more original images, wherein a virtual image corresponds to a virtual value.

A method for magnetic resonance imaging corrects non-stationary off-resonance image artifacts. A magnetic resonance imaging (MRI) apparatus performs an imaging acquisition using non-Cartesian trajectories and processes the imaging acquisitions to produce a final image. The processing includes reconstructing a complex-valued image and using a convolutional neural network (CNN) to correct for non-stationary off-resonance artifacts in the image. The CNN is preferably a residual network with multiple residual layers.

Systems and methods for estimating the actual k-space trajectory implemented when acquiring data with a magnetic resonance imaging (MRI) system while jointly reconstructing an image from that acquired data are described. An objective function that accounts for deviations between the actual k-space trajectory and a designed k-space trajectory while also accounting for the target image is optimized. To reduce the computational burden of the optimization, a reduced model for the parameters associated with the k-space trajectory deviation and the target image can be implemented.

A method of parallel MR imaging includes subjecting the portion of the body (10) to an imaging sequence of at least one RF pulse and a plurality of switched magnetic field gradients. The MR signals are acquired in parallel via a plurality of RF coils (11, 12, 13) having different spatial sensitivity profiles within the examination volume. The method further includes deriving an estimated ghost level map from the acquired MR signals and from spatial sensitivity maps of the RF coils (11, 12, 13), and reconstructing a MR image from the acquired MR signals, the spatial sensitivity maps, and the estimated ghost level map.

An MRI image processing and analysis system may identify instances of structure in MRI flow data, e.g., coherency, derive contours and/or clinical markers based on the identified structures. The system may be remotely located from one or more MRI acquisition systems, and perform: perform error detection and/or correction on MRI data sets (e.g., phase error correction, phase aliasing, signal unwrapping, and/or on other artifacts); segmentation; visualization of flow (e.g., velocity, arterial versus venous flow, shunts) superimposed on anatomical structure, quantification; verification; and/or generation of patient specific 4-D flow protocols. An asynchronous command and imaging pipeline allows remote image processing and analysis in a timely and secure manner even with complicated or large 4-D flow MRI data sets.

A method for magnetic resonance imaging (MRI) scans a field of view and acquires sub-sampled multi-channel k-space data U. An imaging model A is estimated. Sub-sampled multi-channel k-space data U is divided into sub-sampled k-space patches, each of which is processed using a deep convolutional neural network (ConvNet) to produce corresponding fully-sampled k-space patches, which are assembled to form fully-sampled k-space data V, which is transformed to image space using the imaging model adjoint A.sub.adj to produce an image domain MRI image. The processing of each k-space patch u.sub.i preferably includes applying the k-space patch u.sub.i as input to the ConvNet to infer an image space bandpass-filtered image y.sub.i, where the ConvNet comprises repeated de-noising blocks and data-consistency blocks; and estimating the fully-sampled k-space patch v.sub.i from the image space bandpass-filtered image y.sub.i using the imaging model A and a mask matrix.

The present disclosure is related to systems and methods for image processing. The method may include obtaining a first set of image data. The method may also include generating a second set of image data by processing, based on a trained machine learning model, the first set of image data. The second set of image data may have a relatively high resolution and/or a relatively low level of artifacts with respect to the first set of image data. The method may further include generating a target image by performing a weighted fusion on the first set of image data and the second set of image data.

A magnetic resonance imaging system includes an array radiofrequency coil and processing circuitry operatively linked to the array radiofrequency coil and configured to receive output signals from the array radiofrequency coil commensurate with a simultaneous multi-slice magnetic imaging characterized by simultaneous multi-slice parameters, estimate distorted regions of the image volume using either data obtained via a pre-scan or a pre-computed model, minimize overlap of the distorted regions with image voxels representing tissue to obtain optimized values of the simultaneous multi-slice parameters, configuring and executing the simultaneous multi-slice imaging sequence based on the optimized values of the simultaneous multi-slice parameters, and reconstruct simultaneous multi-slice images with minimized artifacts.

Provided is an MRI image generation method including: acquiring first phase encoding lines obtained by undersampling along a first direction using an MRI device; acquiring second phase encoding lines obtained by undersampling in a second direction different from the first direction using the MRI device; generating a first MRI image based on the first phase encoding lines and the second phase encoding lines; and generating a second MRI image different from the first MRI image based on the first phase encoding lines and the second phase encoding lines.

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.