A computer-implemented method is provided for generating correction information for correcting mismatches in magnetic resonance measurements. Magnetic resonance data is received, wherein a generation of the magnetic resonance data includes several partial measurements by a magnetic resonance device. During each partial measurement, a k-space region is sampled at least partially, wherein the k-space regions of different partial measurements differ at least partially in their extent in the readout direction, and wherein the extent in the readout direction depends on prephasing gradients and readout gradients generated by the magnetic resonance device during the partial measurements. A trained function of a machine learning algorithm is applied to the received magnetic resonance data, wherein correction information for correcting a mismatch of the prephasing gradients and readout gradients is generated and output.

In a method for measuring a gradient field in a magnetic resonance tomography (MRT) system, a first slice is excited by a first radio frequency (RF) pulse being emitted and by a first slice selection gradient being switched at least partly at the same time as the first RF pulse. A second slice is excited by a second RF pulse being emitted and by a second slice selection gradient being switched at least partly at the same time as the second RF pulse. The second slice intersects with the first slice in an intersection region. After the excitation of the second slice, a readout gradient is switched, and an MR signal emitted from the intersection region is acquired. Depending on the MR signal, a disruption variable is computed, which determines a deviation of a temporal course of an amplitude of the readout gradient from a predetermined required course.

A method for operating a magnetic resonance facility in which a measurement gradient pulse is used to record magnetic resonance signals for sampling k-space along a trajectory section. The recorded magnetic resonance signals are assigned to k-space points using a shape function describing the time profile of the measurement gradient pulse. To correct deviations of the real time profile of the measurement gradient pulse from an assumed target profile, a first correction measurement is performed to ascertain first magnetic resonance signals of the trajectory section. A second correction measurement is then performed using a reference sampling pattern or a reference gradient pulse with fewer deviations from an assigned reference target profile. If a deviation criterion is met, a correction function for the shape function is ascertained by aligning the first and second magnetic resonance signals to one another, providing correction information to be used in an imaging measurement.

A MRI device including a main field unit for establishing a main magnetic field (MF) in an imaging region, a gradient coil assembly for generating a gradient field in the imaging region, a RF arrangement for sending excitation signals to and receiving MR signals from the imaging region, a field camera for determining MF information in the imaging region, the field camera comprising multiple MF sensors arranged at measurement positions enclosing the imaging region, and a controller. The controller is configured to receive sensor data for each measurement positions, from the sensor data, calculate the MF information for the imaging region, and implement a calibration and/or correction measure depending on the MF information. The field camera may be a vector-field camera acquiring vector-valued sensor data describing the MF at each measurement positions three-dimensionally. The controller may determine the MF information to three dimensionally describe the MF in the imaging region.

In a method for reducing and/or correcting deviations from a target gradient of a magnetic field gradient created by an MR system input data is provided for a trained function trained by a machine-learning algorithm, wherein the input data comprises information about the target gradient of the MR system. The trained function further creates output data with the aid of the input data. The deviations from the target gradient of the magnetic field gradient created by the MR system are reduced and/or corrected with the aid of the output data created.

A deviation from a target gradient of a magnetic field gradient created by an MR system is reduced or corrected. The MR system includes an amplifier, which amplifies an amplifier input signal and outputs an amplifier output signal, and a gradient coil, which creates the magnetic field gradient with the aid of the amplifier output signal. Input data is provided for a trained function trained by a machine-learning algorithm. The input data includes information about the target gradient of the MR system. Output data is created by the trained function with the aid of the input data. A gradient characterization function of the gradient coil is determined. The deviation from the target gradient of the magnetic field gradient created by the MR system is reduced and/or corrected. A deviation caused by the amplifier is reduced and/or corrected with the aid of the output data created, and a deviation caused by the gradient coil is reduced and/or corrected with the aid of the gradient characterization function.

A method, a magnetic resonance apparatus, and a computer program product are disclosed. In particular, a method is provided for determining an RF transmission pulse for a magnetic resonance scan by a magnetic resonance apparatus including a gradient coil unit. The method includes a provision of a deviation information item, wherein the deviation information item characterizes a position-dependent deviation from a target state, caused by the gradient coil unit, in an imaging region of the magnetic resonance apparatus. The RF transmission pulse is determined taking account of the deviation information item.

For radial sampling in magnetic resonance imaging (MRI), a rescaling factor is determined from k-space data for each coil. The rescale factor is inversely proportional to the streak energy in the k-space data. The k-space data from the coils is rescaled for reconstruction, such as weighting the k-space data by the rescale factor in a data consistency term of iterative reconstruction. The rescale factor is additionally or alternatively used to determine a correction field for correction of intensity bias applied to intensities in the image-object space after reconstruction. These approaches may result in a diagnostically useful bias-corrected image with reduced streak artifact while benefiting from the efficient computation (i.e., computer operates to reconstruct more quickly).

A MRI device including a main field unit for establishing a main magnetic field (MF) in an imaging region, a gradient coil assembly for generating a gradient field in the imaging region, a RF arrangement for sending excitation signals to and receiving MR signals from the imaging region, a field camera for determining MF information in the imaging region, the field camera comprising multiple MF sensors arranged at measurement positions enclosing the imaging region, and a controller. The controller is configured to receive sensor data for each measurement positions, from the sensor data, calculate the MF information for the imaging region, and implement a calibration and/or correction measure depending on the MF information. The field camera may be a vector-field camera acquiring vector-valued sensor data describing the MF at each measurement positions three-dimensionally. The controller may determine the MF information to three dimensionally describe the MF in the imaging region.

The present disclosure provides a system for MRI. The system may obtain MRI scan data of a subject by directing an MRI scanner to perform an MRI scan on the subject according to a first gradient waveform. The system may also determine a second gradient waveform based on the first gradient waveform and a gradient waveform determination model. The gradient waveform determination model may have been trained according to a machine learning algorithm. The system may further generate a target reconstruction image of the subject based on the second gradient waveform and the MRI scan data.