A computer-implemented magnetic resonance imaging method for subject with a metal implant, including steps of: S1: building an ultra-low field MR system with a field strength ranged from 10 to 100 mT; S2: setting sequences with optimized parameters of the ultra-low field MR system for minimizing the sensitivity to field distortion and improving SNR; S3: magnetic resonance imaging with the ultra-low field MR system using the sequences for the subject with a metal implant to obtain MR images; and S4: improving SNR of obtained MR images by deep learning based image reconstruction.

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.

Systems and methods for taking into account susceptibility deviations in magnetic-resonance-based therapy planning by a magnetic resonance tomography unit. A B0 field map is determined by the magnetic resonance tomography unit. A location blur distribution is determined from the B0 field map and from the location blur distribution in turn, a parameter of an image acquisition as a function of the location blur distribution, in such a way that an image acquisition brings about a reduced location blur with the determined parameter.

A computer-implemented magnetic resonance imaging method for subject with a metal implant, including steps of: S1: building an ultra-low field MR system with a field strength ranged from 10 to 100 mT; S2: setting sequences with optimized parameters of the ultra-low field MR system for minimizing the sensitivity to field distortion and improving SNR; S3: magnetic resonance imaging with the ultra-low field MR system using the sequences for the subject with a metal implant to obtain MR images; and S4: improving SNR of obtained MR images by deep learning based image reconstruction.

A method and apparatus for susceptibility-weighted imaging, and a magnetic resonance imaging system. The method includes, in planar echo imaging of a plurality of excitations, performing flow compensation in directions of layered encoding, phase encoding, and frequency encoding for echoes of each excitation; after determination of excitation each time, when a linear reordering mode is adopted, for excitation each time, collecting each echo towards space k in a positive direction or a negative direction from the central echo of the plurality of echoes, and collecting echoes of the current excitation in a direction opposite to a direction of collecting echoes of the previous excitation; and subjecting the collected echoes to susceptibility-weighted imaging. An aspect of the present disclosure allows a reduction of flow artifacts in an image created by susceptibility-weighted imaging based on a planar echo sequence.

Improved magnetic resonance imaging systems, methods and software are described including a low field strength main magnet, a gradient coil assembly, an RF coil system, and a control system configured for the acquisition and processing of magnetic resonance imaging data from a patient while utilizing a sparse sampling imaging technique.

A method and apparatus for susceptibility-weighted imaging, and a magnetic resonance imaging system. The method includes, in planar echo imaging of a plurality of excitations, performing flow compensation in directions of layered encoding, phase encoding, and frequency encoding for echoes of each excitation; after determination of excitation each time, when a linear reordering mode is adopted, for excitation each time, collecting each echo towards space k in a positive direction or a negative direction from the central echo of the plurality of echoes, and collecting echoes of the current excitation in a direction opposite to a direction of collecting echoes of the previous excitation; and subjecting the collected echoes to susceptibility-weighted imaging. An aspect of the present disclosure allows a reduction of flow artifacts in an image created by susceptibility-weighted imaging based on a planar echo sequence.

Artifacts caused by metallic needles used in MRI-guided procedures such as tumor biopsies significantly decrease the visibility of therapy targets and diminish the ability of the physician to accurately monitor and perform the procedure. As described in the present application, a needle including active shimming can self-compensate for these artifacts and significantly improve the visualization and monitoring of targeted tissue. The accuracy and overall outcomes of MRI-guided treatments can be significantly improved with the use of the needle.

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.

Quantitative susceptibility mapping methods, systems and computer-accessible medium generate images of tissue magnetism property from complex magnetic resonance imaging data using the Bayesian inference approach, which minimizes a cost function comprising of a data fidelity term and regularization terms. The data fidelity term is constructed directly from the multiecho complex magnetic resonance imaging data. The regularization terms include a prior constructed from matching structures or information content in known morphology, and a prior constructed from regions of low susceptibility contrasts characterized on image features. The quantitative susceptibility map can be determined by minimizing the cost function that involves nonlinear functions in modeling the obtained signals, and the corresponding inverse problem is solved using nonconvex optimization using a scaling approach or deep neural network. The nonconvex optimization is also developed for solving other inverse problems of nonlinear signal models in fat-water separation, tissue transport and oxygen extraction fraction.