Reducing and correcting magnetic field gradient deviations
AssigneeSiemens Healthineers AG
InventorsManuel Stich, Philipp Puls
Life of this patent
Estimating the term…
Abstract
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.
Claims8
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.
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.
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.
Dependencies are inferred from the claim text.