A magnetic resonance imaging apparatus according to an embodiment includes processing circuitry. By using image data of a subject acquired by performing a first imaging process, the processing circuitry is configured to detect a first region including at least a part of a first site, the first site and a second site having a symmetrical relationship with each other in the subject. The processing circuitry is configured to derive a second region including at least a part of the second site on the basis of the first region. The processing circuitry is configured to set a Radio Frequency (RF) pulse application region used for performing a second imaging process, on the basis of the second region.

A method and system for image reconstruction are provided. A k-space including a first part and a second part may be set. The first part of the k-space may be filled with a matrix including data. The matrix may be filtered to produce a filtered data matrix. The second part of the k-space may be padded. Iterations of an objective function for a target array of data in image domain may be performed based on a constraint. The objective function may be based on a total variation of the target array of data and a function relating to the Fourier transform of the target array of data, the filtered data matrix in the first part, and the padded

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.

The invention relates to a method of MR imaging of an object (10) placed in an examination volume of a MR device (1). The method comprises the steps of: subjecting the object (10) to an imaging sequence for acquiring MR signal data, wherein the MR signal data are acquired as a function of k-space position and time by using an irregular k-space sampling pattern with sub-sampling of k-space; reconstructing MR image data from the MR signal data, which MR image data comprise spatial dimensions and a frequency dimension, sparsity of the MR image data in a transform domain being exploited for suppressing sub-sampling artefacts in the MR image data. Moreover, the invention relates to a MR device (1) and to a computer program.

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 and a system for simulating magnetic resonance echo-planar imaging artifacts. Firstly, for K-space artifacts, K-space data are restored through normal magnetic resonance images, and the K-space data are modified pertinently, and then images with artifacts are reconstructed; for susceptibility artifacts, a susceptibility model is constructed through normal magnetic resonance images, and the magnetic field distribution is reconstructed, and then the images with distortion artifacts are reconstructed. According to the present disclosure, a large number of artifact data sets with different artifact types and artifact degrees can be quickly created through a small number of normal images, thus laying a foundation for the research of identifying artifacts, eliminating or weakening artifacts. A simulation algorithm is designed according to the principle of generation of EPI sequence artifacts, and the obtained images such as stripe artifacts, Moer artifacts, Nyquist artifacts, susceptibility artifacts and the like have good scientificity, accuracy and interpretability.

A magnetic resonance tomography unit for localizing metallic objects and an operating method are provided. In one act of the method, an excitation pulse is used to excite nuclear spins in a region surrounding a compact metallic object. Magnetic resonance data is acquired with samplings along a plurality of trajectories, where the samplings take place using a bSSFP sequence, and the nuclear spins are dephased by a gradient. A position of a geometric focal point of the compact metallic object is ascertained based on a position of a visual focal point of acquired artifacts.

A system and method for denoising magnetic resonance images (MRI) in a transform domain are provided. In one aspect, the method incudes reconstructing a series of images of the target using the image data, transforming the series of images into a transform domain, and selecting patches in the image domain. The patches can be used to form matrices that can be decomposed to distinguish signal and noise components. Thresholding can be applied to remove the noise components in the transform domain. The denoised data can be inversely transformed back to the image domain to produce denoised images.

A magnetic resonance (MR) image may be created from MR data by receiving the MR data, applying a transform to the MR data, where a result of the applying is an image space representation of the MR data, determining a wrapped phase map of the image space representation of the MR data, obtaining an unwrapped phase map based on the wrapped phase map, scaling the unwrapped phase map into a B0 field map, reconstructing the MR image based on the MR data, correcting the MR image based on the B0 field map, and outputting the MR image. The scaling may be free of accounting for effects on the MR data by artifact sources secondary to B0 field inhomogeneities.

A method of image processing of magnetic resonance (MR) images for creating de-noised MR images, comprises the steps of providing image data sets including multiple complex MR images (S7), subjecting the MR images to a wavelet decomposition (S12) for creating coefficient data sets of wavelet coefficients (S.sub.n,m) representing the MR images in a wavelet frequency domain, calculating normalized coefficient data sets of wavelet coefficients Formula (I) (S17), wherein the coefficient data sets are normalized with a quantitative amount of variation, in particular standard deviation Formula (II), of noise contributions included in the coefficient data sets (S.sub.n,m), averaging the wavelet coefficients of each coefficient data set (S18) for providing averaged wavelet coefficients Formula (III) of the coefficient data sets, calculating phase difference maps (.sub.n,m) for all coefficient data sets (S20), wherein the phase difference maps provide phase differences between the phase of each wavelet coefficient and the phase of the averaged wavelet coefficients Formula (III), calculating scaled averaged coefficient data sets of wavelet coefficients by scaling the averaged wavelet coefficients Formula (III) with scaling factors (C.sub.n,m), which are obtained by comparing parts of the normalized wavelet coefficients of the normalized coefficient data sets Formula (I) that are in phase with the averaged wavelet coefficients Formula (III) (S22), calculating rescaled coefficient data sets of wavelet coefficients Formula (IV) (S24) by applying a transfer function Formula (V) on the coefficient data sets (S.sub.n,m) and on the scaled averaged coefficient data sets, wherein the transfer function includes combined amplitude and phase filters, each depending on the normalized coefficient data sets Formula (I) and me phase difference maps (.sub.n,m), resp., and subjecting the rescaled coefficient data sets to a wavelet reconstruction Formula (IV) (S25) for providing the denoised MR images.