A method for creating a magnetic resonance (MR) image with prospective motion correction with a recording of navigation signals and navigator reference signals for the determination of motion information is provided. During the determination of the motion information, the partial volumes of the navigator volume are not all treated equally. Different weightings are used.

A system and method are provided for determining a spatial distribution of susceptibility in a subject using a magnetic resonance imaging (MRI) system. The method includes directing the MRI system to acquire imaging data from an imaging volume within a subject, wherein the imaging volume is subject to both background fields (B.sub.B) originating outside the imaging volume and local fields (B.sub.L) originating from tissue within the imaging volume. The method also includes selecting a size and non-central compute point for an extended Poisson kernel to be applied to the imaging data, subtracting from a delta function to control the background fields (B.sub.B) but not the local fields (B.sub.L), and producing a susceptibility report attributable to the local fields (B.sub.L).

One example includes a method for fabricating a compound material. The method includes providing a first discrete material layer having a first thickness dimension. The first discrete material layer includes a first material having a first magnetic susceptibility. The method also includes depositing a second discrete material layer having a second thickness dimension over the first discrete material layer. The second discrete material layer can include a second material having a second magnetic susceptibility. The relative first and second thickness dimensions can be selected to provide a desired magnetic susceptibility of the compound material.

The estimation accuracy of a magnetic susceptibility value of tissue is improved by computing an edge image which represents the edge of the tissue on a magnetic susceptibility distribution and to reduce background noise without lowering the magnetic susceptibility value of the tissue. The present invention computes an absolute value image and a phase image from a complex image obtained by MRI, from the phase image, computes a low frequency region magnetic susceptibility image in which background noise is greater than a desired value, computes an edge information magnetic susceptibility image and computes a high frequency region magnetic susceptibility image, computes an edge mask from the edge information magnetic susceptibility image, smooths a magic angle region from the edge mask and the low frequency region magnetic susceptibility image and finally smooths a high frequency region using the high frequency region magnetic susceptibility image.

Systems and methods for high-resolution STAGE imaging can include acquisition of relatively low-resolution k-space datasets with two separate multi-echo GRE sequences. The multi-echo GRE sequences can correspond to separate and distinct flip angles. Various techniques for combining the low-resolution k-space datasets to generate a relatively high-resolution k-space are described. These techniques can involve combining low-resolution k-space datasets associated with various echo types. The STAGE imaging approaches described herein allow for rapid imaging, enhanced image resolution with relatively small or no increase in MR data acquisition time.

The invention relates to a method of MR imaging of an object (10) positioned in an examination volume of a MR device (1). It is an object of the invention to enable efficient and high-quality non-Cartesian MR imaging, even in situations of strong B.sub.0 inhomogeneity. In accordance with the invention, the method comprises: —subjecting the object to an imaging sequence comprising at least one RF excitation pulse and modulated magnetic field gradients, —acquiring MR signals along at least one non-Cartesian k-space trajectory, —reconstructing an MR image from the acquired MR signals, and —detecting one or more mal-sampling artefacts caused inhomogeneity induced insufficient k-space sampling in the MR image using a deep learning network. Moreover, the invention relates to a MR device (1) and to a computer program.

Exemplary methods for quantitative mapping of physical properties, systems and computer-accessible medium can be provided to generate images of tissue magnetic susceptibility, transport parameters and oxygen consumption from magnetic resonance imaging data using the Bayesian inference approach, which minimizes a data fidelity term under a constraint of a structure prior knowledge. The data fidelity term is constructed directly from the magnetic resonance imaging data. The structure prior knowledge can be characterized from known anatomic images using image feature extraction operation or artificial neural network. Thus, according to the exemplary embodiment, system, method and computer-accessible medium can be provided for determining physical properties associated with at least one structure.

Systems and methods for high-resolution STAGE imaging can include acquisition of relatively low-resolution k-space datasets with two separate multi-echo GRE sequences. The multi-echo GRE sequences can correspond to separate and distinct flip angles. Various techniques for combining the low-resolution k-space datasets to generate a relatively high-resolution k-space are described. These techniques can involve combining low-resolution k-space datasets associated with various echo types. The STAGE imaging approaches described herein allow for rapid imaging, enhanced image resolution with relatively small or no increase in MR data acquisition time.

Systems and methods for quantitative susceptibility mapping (“QSM”) using magnetic resonance imaging (“MRI”) are described. Localized magnetic field information is used when performing the inversion to compute quantitative susceptibility maps. The localized magnetic field information can include multi-resolution subvolumes obtained by segmenting, or dividing, a field shift map. In some instances, a trained machine learning algorithm, such as a trained neural network, can be implemented to convert the localized magnetic field information into quantitative susceptibility data. These local susceptibility maps can be combined to form a composite quantitative susceptibility map of the imaging volume.

Systems and methods provide accelerated MR thermometry utilizing prior knowledge about the images to be reconstructed from incomplete k-space data, thereby facilitating accurate reconstruction. In various embodiments, missing data is computationally estimated using a machine learning algorithm such as a neural network, and an image is generated based on iteratively updated estimated missing information.