In one embodiment, an MRI apparatus includes a scanner and processing circuitry. The scanner includes at least two gradient coils. The processing circuitry is configured to cause the scanner to acquire k-space data for correction in a band-shaped two-dimensional k-space along a readout direction, or in a columnar three-dimensional k-space along a readout direction, while changing rotation angles, wherein each of the rotation angles corresponds to the readout direction, generate correction data for correcting an error due to a gradient magnetic field generated by the gradient coils, by using the acquired k-space data for correction, cause the scanner to acquire k-space data for reconstruction, based on a radial acquisition method, while correcting the gradient magnetic field by using the correction data, and generate an image by reconstructing the acquired k-space data for reconstruction.

In an image acquired by a plurality of receiver coils with the use of MRI, separated images are obtained by separating spatially overlapping signals according to PI method, and noise in the separated images is eliminated with a high degree of precision. A complex image spatially overlapping is measured from nuclear magnetic resonance signals received by a plurality of receiver coils, and spatially overlapping signals are separated and a plurality of separated images are calculated, by using sensitivity information of the plurality of receiver coils. Then, noise is eliminated based on a correlation of noise mixed between the separated images.

In a method for operating an MRI device, image data is acquired using a spin echo sequence with an additional readout per pulse train for acquiring correction data. By comparing subsequent correction data of later pulse trains to reference data acquired during a first pulse train of the sequence a difference indicating a parameter shift is determined. A corresponding compensation is then automatically determined in dependence on the difference and is applied to a set of predetermined parameters for at least a respective next pulse train and/or to the image data acquired in at least a respective next pulse train of the sequence.

A method for correcting image inhomogeneity includes acquiring a non-normalized image and a reference image using receiver coils. A high-signal mask and a low-signal mask are created. Each pixel in the high-signal mask is set to a predetermined integer value if the reference image pixel at the same specific location has a value above a threshold value. Each pixel in the low-signal mask is set to the predetermined integer value if the reference image pixel at the same specific location has a value below or equal to the threshold value. A coil normalization map is created by smoothing the reference image with filters. Then, an iterative procedure is performed to update the coil normalization map using the high-signal mask and the low-signal mask. Following the iterative procedure, the non-normalized image is divided by the current coil normalization map to yield a normalized image.

Systems and methods for performing diffusion-weighted multi-spectral imaging (MS!) with a magnetic resonance imaging (MRI) system are provided, Diffusion-weighted images can thus be acquired from a subject in which a metallic object, such as an implant or other device, is present. In general, a two-dimensional or three-dimensional diffusion-weighted PROPELLER acquisition is performed to acquire data from multiple different spectral bins. Images from the spectral bins are reconstructed and combined to form diffusion-weighted composite images. Non-CPMG phase-cycling and split-blade PROPELLER techniques are combined with PROPELLER MSI metal artifact mitigation principles to this end.

Processing techniques of volumetric anatomic and vector field data from volumetric phase-contrast MRI on a magnetic resonance imaging (MRI) system are provided to evaluate the physiology of the heart and vessels. This method includes the steps of: (1) correcting for phase-error in the source data, (2) visualizing the vector field superimposed on the anatomic data, (3) using this visualization to select and view planes in the volume, and (4) using these planes to delineate the boundaries of the heart and vessels so that measurements of the heart and vessels can be accurately obtained.

An exemplary system, method and computer-accessible medium for removing noise and Gibbs ringing from a magnetic resonance (MR) image(s), can be provided, which can include, for example, receiving information related to the MR image(s), receiving information related to the MR image(s), and removing the Gibbs ringing from the information by extrapolating data in a k-space from the MR image(s) beyond an edge(s) of a measured portion of the k-space. The data can be extrapolated by formatting the data as a regularized minimization problem(s). A first weighted term of the regularized minimization problem(s) can preserve a fidelity of the extrapolated data, and a second weighted term of the regularized minimization problem(s) can be a penalty term that can be based a norm(s) of the MR image(s), which can be presumed to be sparse.

A method for magnetic resonance imaging may include acquiring first k-space data that is generated by entering acquired magnetic resonance (MR) data into a plurality of first k-space locations. The method may further include synthesizing second k-space data for a plurality of second k-space locations that are not filled with the acquired MR data. The method may further include reconstructing an image from the first k-space data and the second k-space data by applying a reconstruction algorithm. The reconstruction algorithm is based at least in part on a neural network technique.

Methods and devices for reconstructing magnetic resonance images for contrasts are provided. In an example, the method includes: for each channel for each contrast, collecting k-space data of a subject in the channel by scanning the subject in an undersampling manner, collecting central k-space data by scanning a k-space central region of the subject in k-a fullsampling manner, training a convolution kernel of respective phase encoding lines in the channel based on the central k-space data of the contrasts, and obtaining entire k-space data in the channel based on the convolution kernel of respective phase encoding lines in the channel and collected k-space data in the channels, and obtaining a respective magnetic resonance image for each of the contrasts by performing image reconstruction on the entire k-space data in each channel for the contrast.

A general framework is for signal encoding in MRF that enables simultaneous transmit and receive encoding to accelerate the acquisition process, or improve the fidelity of the final image/parameter-map per unit scan time. The proposed method and systems capitalize on the distinct spatial variations in the sensitivity profile of each transmit-coil to reduce the acquisition time, and/or improve the fidelity of the final parameter-map per unit time.