The present disclosure is related to systems and methods for determining a field map in magnetic resonance imaging (MRI). The method includes obtaining at least three images. Each may be acquired at one of at least three echo times by an MRI device via scanning a subject. The at least three echo times may define multiple pairs of adjacent echo times. Each of the multiple pairs of adjacent echo times may have a time difference between the adjacent echo times. At least two of the time differences may be different. The method includes determining a target function with an off-resonance frequency as an independent variable. The target function includes a phase deviation term and a sparsity constraint term.

Generation of a preview image using magnetic resonance signals is provided. A method for the generation of a preview image using magnetic resonance signals includes acquiring a first part and a second part of magnetic resonance signals. During the acquisition of the first part of the magnetic resonance signals, a first k-space is regularly sampled, while, during the acquisition of the second part of the magnetic resonance signals, a second k-space is sampled in a pseudorandomized manner. The first part of the magnetic resonance signals is used to generate a preview image. The second part or the second part and a subset of the first part of the magnetic resonance signals are stored for the generation of a second image.

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.

A method for magnetic resonance imaging reconstructs images that have reduced under-sampling artifacts from highly accelerated multi-spectral imaging acquisitions. The method includes performing by a magnetic resonance imaging (MRI) apparatus an accelerated multi-spectral imaging (MSI) acquisition within a field of view of the MRI apparatus, where the sampling trajectories of different spectral bins in the acquisition are different; and reconstructing bin images using neural network priors learned from training data as regularization to reduce under-sampling artifacts.

A method and system for suppressing metal artifacts in magnetic resonance (MR) images of slices of a patient containing a metallic implant. The method and system can use a Slice Encoding for Metal Artifact Correction (SEMAC) sequence. In the method and system, MR data of each slice is fully sampled in k-space in a reference region located in a center of k-space in a phase-encoding direction and a central section in a slice-selection direction. The MR-data of each slice outside the reference region can be undersampled in k-space. The fully sampled MR data from the reference regions of each slice can be combined to generate a reference data set for reconstructing an MR image of each slice.

In a method for generating an image data set and a reference image data set: a first raw data set is provided that is acquired with a MR system and that includes measurement signals at read-out points in k-space that lie on a first k-space trajectory; a second raw data set is provided that is acquired with the same MR system and at the same examination object at read-out points that lie on a second, different k-space trajectory that is different from the first k-space trajectory; image data sets are reconstructed from the first raw data set; a reference image data set is reconstructed from the second raw data set; the reference image data set is compared with each image dataset to generate respective similarity values; and an image data set is selected having a greatest similarity value.

Provided are a composition for removing noise from an MRI, including a compound having a hydrogen bond, and a pad using the same, wherein the composition is applied in a non-invasive way causing no side effects in the human body, the composition is not toxic and thus is safe, the composition is used in the manner of covering an area requiring diagnosis or being attached to an MRI apparatus, and thus has the advantages of improving images of a relatively wide range, and obtaining more accurate image information than restoration of a distorted image by a program, and the composition is expected to be easily commercialized by companies because the costs required to manufacture the composition are low.

Exemplary quantitative susceptibility mapping methods, systems and computer-accessible medium can be provided to generate images of tissue magnetism property from complex magnetic resonance imaging data using the Bayesian inference approach, which minimizes a cost function consisting of a data fidelity term and two regularization terms. The data fidelity term is constructed directly from the complex magnetic resonance imaging data. The first prior is constructed from matching structures or information content in known morphology. The second prior is constructed from a region having an approximately homogenous and known susceptibility value and a characteristic feature on anatomic images. The quantitative susceptibility map can be determined by minimizing the cost function. Thus, according to the exemplary embodiment, system, method and computer-accessible medium can be provided for determining magnetic susceptibility information associated with at least one structure.

A method is provided for magnetic resonance (MR) imaging near metal, including acquiring an image at a first magnetic field from a subject that includes a metal object, acquiring an image at a second magnetic field, and combining the images to provide a corrected image with reduced metal distortion. An MR imaging system for measuring near metal is also provided including a superconducting magnet to provide a magnetic field, a power supply for a current to ramp the magnetic field, a cryocooler in contact with the superconducting magnet, a magnetic field controller programmed to ramp the main magnetic field by adjusting the current generated by the power supply, a radio frequency system for transmitting and receiving signals, and a data acquisition and processing system to receive the MR signals, generate image data sets and combine the image data sets to provide a corrected image having a reduced metal distortion.

A magnetic resonance imaging system (200, 300, 400) includes a radio-frequency system (216, 214) with multiple coil elements (214) for acquiring magnetic resonance data (264) and a memory (250) for storing machine executable instructions (260) and pulse sequence commands (262). The pulse sequence commands are configured for controlling the magnetic resonance imaging system to acquire the magnetic resonance data according to a SENSE imaging protocol. Execution of the machine executable instructions causes a processor (244) to: control (500) the magnetic resonance imaging system to acquire the magnetic resonance data using the pulse sequence commands; reconstruct (502) a set of folded magnetic resonance images (266) from the magnetic resonance data; calculate (504) a voxel deformation map (270) from a magnetic field inhomogeneity map; and calculate (506) a set of unfolding matrices (274) using a least partially a coil sensitivity matrix (272) for the multiple coil elements, wherein the set of unfolding matrices includes at least one modified unfolding matrix which is calculated at least partially using the a coil sensitivity matrix and the voxel deformation map. Undistorted magnetic resonance image data (276) is calculated (508) using the set of folded magnetic resonance images and the set of unfolding matrices.