Method for correcting the magnetic field gradient waveform in a magnetic resonance measurement including extracting an impulse response from the measured step response of a magnetic resonance system, determining the slew rate of the system during the step response measurement, modifying the desired output waveform such that the desired output waveform is constrained to within the slew rate and the bandwidth of the system, and determining the required pre-equalized input waveform.

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.

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.

In some aspects, a magnetic system for use in a low-field MRI system. The magnetic system comprises at least one electromagnet configured to, when operated, generate a magnetic field to contribute to a B.sub.0 field for the low-field MRI system, and at least one permanent magnet to produce a magnetic field to contribute to the B.sub.0 field.

In some aspects, a magnetic system for use in a low-field MRI system. The magnetic system comprises at least one electromagnet configured to, when operated, generate a magnetic field to contribute to a B.sub.0 field for the low-field MRI system, and at least one permanent magnet to produce a magnetic field to contribute to the B.sub.0 field.

A method to calibrate a nuclear magnetic resonance tool is disclosed having steps of starting a nuclear magnetic resonance sequence from the nuclear magnetic resonance tool, disabling an active damping circuit in the nuclear magnetic resonance tool, collecting auxiliary calibration data for the nuclear magnetic resonance tool, estimating a natural Q value for the nuclear magnetic resonance tool, determining an optimal active damping setting for the tool, deploying the optimal active damping setting for the tool, collecting nuclear magnetic resonance response data generated from the nuclear magnetic resonance sequence and calibrating the nuclear magnetic resonance data.

A method to calibrate a nuclear magnetic resonance tool is disclosed having steps of starting a nuclear magnetic resonance sequence from the nuclear magnetic resonance tool, disabling an active damping circuit in the nuclear magnetic resonance tool, collecting auxiliary calibration data for the nuclear magnetic resonance tool, estimating a natural Q value for the nuclear magnetic resonance tool, determining an optimal active damping setting for the tool, deploying the optimal active damping setting for the tool, collecting nuclear magnetic resonance response data generated from the nuclear magnetic resonance sequence and calibrating the nuclear magnetic resonance data.

In a medical imaging auto-calibrated reconstruction method, an imaging scan is performed using a data acquisition scanner to generate image data, calibration data having a uniform sampling is determined, a point-spread function is determined based on the calibration data, and an image is reconstructed from the image data based on the point-spread function. A central region of k-space may have uniform sampling. The calibration data may be determined by extracting a uniformly-sampled central region of k-space from the image data. An outer region of k-space may have non-uniform sampling. A calibration scan may be performed to generate the calibration data.

In a medical imaging auto-calibrated reconstruction method, an imaging scan is performed using a data acquisition scanner to generate image data, calibration data having a uniform sampling is determined, a point-spread function is determined based on the calibration data, and an image is reconstructed from the image data based on the point-spread function. A central region of k-space may have uniform sampling. The calibration data may be determined by extracting a uniformly-sampled central region of k-space from the image data. An outer region of k-space may have non-uniform sampling. A calibration scan may be performed to generate the calibration data.

In a medical imaging auto-calibrated reconstruction method, an imaging scan is performed using a data acquisition scanner to generate image data, calibration data having a uniform sampling is determined, a point-spread function is determined based on the calibration data, and an image is reconstructed from the image data based on the point-spread function. A central region of k-space may have uniform sampling. The calibration data may be determined by extracting a uniformly-sampled central region of k-space from the image data. An outer region of k-space may have non-uniform sampling. A calibration scan may be performed to generate the calibration data.