Reducing and correcting magnetic field gradient deviations
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.
1 . A method for reducing, correcting, or reducing and correcting deviations from a target gradient of a magnetic field gradient created by a magnetic resonance (MR) system, the method comprising:
providing input data for a function trained by a machine-learning algorithm, wherein the input data comprises information about the target gradient of the MR system;
determining an estimation for the magnetic field gradient by the trained function when input the input data;
determining a gradient characterization function is determined using the estimation for the magnetic field gradients;
creating a corrected amplifier input signal or a correction signal for correction of an amplifier input signal using the gradient characterization function; and
applying the corrected amplifier input signal to an amplifier of the MR system, wherein the deviations from the target gradient of the magnetic field gradient created by the MR system are reduced, corrected, or reduced and corrected by applying the corrected amplifier input signal.
2 . The method of claim 1 , wherein the MR system comprises the amplifier that is configured to amplify the amplifier input signal and output an amplifier output signal, and wherein the MR system further comprises a gradient coil that is configured to create the magnetic field gradient using at least the amplifier output signal; wherein the trained function determines the corrected amplifier input signal or a correction signal for correction of the amplifier input signal; and wherein the amplifier output signal is created as a function of the corrected amplifier input signal or of the correction signal.
3 . The method of claim 1 , wherein the input data for the trained function further comprises at least one of:
the amplifier input signal of the amplifier of the MR system, wherein the amplifier amplifies the amplifier input signal and outputs it as an amplifier output signal to a gradient coil of the MR system, which creates the magnetic field gradient using the amplifier output signal,
the measured amplifier output signal,
at least one measured temperature of the amplifier, of the gradient coils, or of the amplifier and the of the gradient coils,
a measured magnetic field gradient created by the MR system,
a diagnostically relevant imaging region, or information regarding an object being examined by the MR system.
4 . A computer-implemented method for creation of a trained function, the method comprising:
providing training data, the training data comprising at least one item of information about a target gradient of a magnetic resonance (MR) system with associated magnetic field gradient created by the MR system;
training a function using a machine-learning algorithm, based on the training data, wherein the function is trained to output output data that is able to be used for reducing, correcting, or reducing and correcting deviations from the target gradient of magnetic field gradients created by the MR system;
determining an estimation for a magnetic field gradient created by the MR system using the trained function;
determining a gradient characterization function using the estimation for the magnetic field gradient created by the MR system; and
creating, using the gradient characterization function, a corrected amplifier input signal or a correction signal for correction of an amplifier input signal or creating MR image data using raw data that is created by the MR system by the magnetic field gradient and is created using the gradient characterization function.
5 . The method of claim 4 , wherein the training data comprises information about at least one of the target gradient of the MR system and associated magnetic field gradients created by the MR system for a plurality of different temperatures of at least one component of the MR system, different frequencies, different pulse widths, different amplitudes, or different slew rates.
6 . The method of claim 4 , wherein the training data for a pair consists of the target gradient and an associated magnetic field gradient created by the MR system, wherein the training data further comprises at least one of
an amplifier input signal of an amplifier of the MR system, wherein the amplifier amplifies the amplifier input signal and outputs an amplifier output signal to gradient coils of the MR system that create the magnetic field gradient using the amplifier output signal,
the amplifier output signal,
at least one measured temperature of the amplifier, of the gradient coils, or of the amplifier and the gradient coils,
a measured magnetic field gradient created by the MR system,
an image region, or
information related to an object being examined by the MR system.
7 . An apparatus for reducing, correcting, or reducing and correcting deviations from a target gradient of a magnetic field gradient created by a magnetic resonance (MR) system, the apparatus comprising:
a computing facility configured to provide input data to a trained function trained by a machine-learning algorithm, wherein the input data comprises information about the target gradient of the MR system, and to create output data by the trained function when input the input data, wherein the trained function is configured to determine an estimation for the magnetic field gradient created by the MR system, wherein the computing facility is further configured to determine a gradient characterization function using the estimation for the magnetic field gradients created by the MR system; and
a reduction/correction facility configured using the output data of the trained function to create MR image data using raw data created by the MR system and the gradient characterization function to reduce, correct, or reduce and correct deviations from the target gradient of the magnetic field gradient created by the MR system.
8 . The apparatus of claim 7 , wherein the MR system comprises:
an amplifier configured to amplify an amplifier input signal and to output an amplifier output signal; and
a gradient coil configured to create the magnetic field gradient using the amplifier output signal.