The invention provides for a magnetic resonance imaging system (100, 300, 100) for acquiring magnetic resonance data (110, 1104) from a subject (118) within an imaging zone (108). The magnetic resonance imaging system comprises a memory (136) for storing machine executable instructions (160, 162, 164, 166, 316) and pulse sequence data (140, 1102). The pulse sequence data comprises instructions for controlling the magnetic resonance imaging system to acquire magnetic resonance data according to a magnetic resonance imaging method. The magnetic resonance imaging system further comprises a processor (130) for controlling the magnetic resonance imaging system. Execution of the machine executable instructions causes the processor to: acquire (1200) the magnetic resonance data by controlling the magnetic resonance imaging system with the pulse sequence data; calculate (1202) a B0 inhomogeneity map (148) by analyzing the magnetic resonance data according to the magnetic resonance imaging method, calculate (1204) a B1 phase map (150) and/or a B1 amplitude map (1106) by analyzing the magnetic resonance data according to the magnetic resonance imaging method; and calculate (1206) a second derivative (1110) of the B1 phase map and/or a second derivative of the B1 magnitude map 1 and/or a second derivative of the B0 in homogeneity map in at least one predetermined direction. The second derivative is calculated using a corrected voxel size in the at least one predetermined direction, wherein the corrected voxel size is calculated using a correction factor calculated from the derivative of the B0 inhomogeneity map.

Various methods and systems are provided for correcting transmit attenuation of an amplifier of a transmit radio frequency (RF) coil for use in a magnetic resonance imaging (MRI) system. In one example, a method includes setting a reference value of transmit attenuation for an amplifier of a transmit radio frequency (RF) coil, acquiring a three-dimensional B.sub.1 field map with the transmit attenuation set at the reference value, determining a plurality of mean flip angles for a plurality of slice locations in a pre-scan imaging volume from the B.sub.1 field map, determining a transmit attenuation correction value for each of the slice locations based on a prescribed flip angle and the mean flip angle determined for the respective slice location, correcting the reference value of transmit attenuation with the transmit attenuation correction value at each of the slice locations to obtain a final value of transmit attenuation for each of the slice locations, and performing an MRI scan with the transmit attenuation set at the value.

An information processing apparatus according to an embodiment includes a processing circuit. The processing circuit acquires a measurement field corresponding to a spatial distribution of a predetermined physical quantity in a subject of measurement. The processing circuit calculates an unknown quantity in the subject of measurement based on a first equation between the measurement field and the unknown quantity having spatial dependence, and on the acquired measurement field. The first equation is one that is acquired based on a second equation expressing a dual field divergence of which can be expressed using the measurement field, by using the measurement field and the unknown quantity, and on the Helmholtz decomposition of the dual field.

An apparatus includes a plurality of particles, wherein each particle contains a plurality of magnetizable (for example, ferromagnetic) and ferroelectric materials in fixed physical relationship (for example, physical contact) with one another. A method and apparatus measure magnetic fields arising from or within the plurality of particles.

Disclosed is a quantitative susceptibility mapping image processing method using an unsupervised learning-based neural network and an apparatus therefor. The quantitative susceptibility mapping image processing method includes receiving a phase image and a magnitude image for reconstructing the quantitative susceptibility mapping image, and reconstructing the quantitative susceptibility mapping image corresponding to the received phase image and the received magnitude image using an unsupervised learning-based neural network, and the neural network may be generated based on an optimal transport theory.

A B.sub.0-mapping method determines the spatial distribution of a static magnetic field in a pre-selected imaging zone comprising computation of the spatial distribution of a static magnetic field from a spatial distribution of spin-phase accruals between magnetic resonance echo signals from the imaging zone and an estimate of the proton density distribution in the imaging zone. The invention provides the field estimate also in cavities and outside tissue. Also the field estimate of the invention suffers less from so-called phase-wraps.

Object specific in-homogeneities in an MRI system are corrected. Prescan information available at the MR imaging system is determined. The prescan information includes at least object specific information of an object located in the MR imaging system from which an MR image is to be generated. The prescan information does not include a B1 map of the MRI system with the object being present in the MR imaging system. The prescan information is applied to a trained machine learning module provided at the MRI system. The trained machine learning module determines and generates shimming information as output. The shimming information is applied to a shimming module of the MR imaging system, wherein the shimming module uses the shimming information to generate a corrected magnetic field B0.

In a method and apparatus for determining parameter values in voxels of an examination object using magnetic resonance fingerprinting (MRF), a first signal comparison is made of signal characteristics of established voxel time series with first comparison signal characteristics. Further synthetic comparison signal characteristics are generated from the first comparison signal characteristics and values determined in the first signal comparison. The generated further comparison signal characteristics are used to perform a further signal comparison, with which values of at least a first and a second further parameter are determined. From the further comparison signal characteristics, a value of at least one further parameter is determined that could not necessarily already be determined in the first signal comparison.

A method for determining a B.sub.0 map for, for example, performing an imaging magnetic resonance measurement using a magnetic resonance apparatus, includes measuring an original magnetic field distribution in a measurement volume of the magnetic resonance apparatus, and computing a final B.sub.0 map that describes a magnetic field distribution produced in the measurement volume of the magnetic resonance apparatus by setting a shim state. The magnetic field distribution produced in the measurement volume of the magnetic resonance apparatus by setting the shim state differs from the original magnetic field distribution.

The exemplary embodiments provides an apparatus and a method for reconstructing an electrical conductivity map which reconstruct an electrical conductivity map for high resolution clinical data with a low SNR by generating virtual magnetic resonance imaging data and ground-truth electrical conductivity map data based on simulated data and adding a noise to the virtual magnetic resonance imaging data to train a previously designed deep learning network.