# US 12,601,806 B2 · Imaging device of eliminating electromagnetic interference of magnetic resonance and imaging method thereof

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- Number: US 12,601,806 B2
- Document ID: US12601806B2
- Status: Granted
- Kind: B2
- Application number: 18/655,366
- Filed: 2024-05-06
- Published: 2024-11-07
- Granted: 2026-04-14
- Earliest claimed: 2023-05-06
- Assignees: Hangzhou Weiying Medical Technology Co., Ltd.
- Applicants: Hangzhou Weiying Medical Technology Co., Ltd.
- Inventors: Linfang Xiao; Ruixing Zhu; Yilong Liu
- Web page: https://patentgrep.com/patent/US12601806B2

## CPC

- G01R 33/56572: {caused by a distortion of a gradient magnetic field, e.g. non-linearity of a gradient magnetic field (G01R33/56509, G01R33/56518, G01R33/56536 take precedence)}
- G01R 33/28: Details of apparatus provided for in groups G01R33/44 - G01R33/64
- G01R 33/5608: {Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels (image data processing or generation, in general G06T)}
- G01R 33/565: Correction of image distortions, e.g. due to magnetic field inhomogeneities
- G01R 33/5659: {caused by a distortion of the RF magnetic field, e.g. spatial inhomogeneities of the RF magnetic field (G01R33/56509, G01R33/56518, G01R33/56536 take precedence)}
- G01R 33/583: {Calibration of signal excitation or detection systems, e.g. for optimal RF excitation power or frequency (G01R33/246 takes precedence)}
- G06T 12/10: Image preprocessing, e.g. calibration, positioning of sources or scatter correction
- G06T 2210/41: Medical
- G06T 2211/441: AI-based methods, deep learning or artificial neural networks
- Y02A 90/30: Assessment of water resources
- G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- Y02A: TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE

## Abstract

An imaging device and method of eliminating electromagnetic interference of magnetic resonance are provided. The imaging device includes: means for acquiring magnetic resonance imaging signals, acquiring a first electromagnetic interference signal in an electromagnetic interference affected environment, and acquiring second electromagnetic interference signals in the electromagnetic interference affected environment; means for superposing the magnetic resonance imaging signals with the first electromagnetic interference signal to obtain interfered imaging signals, respectively, taking the interfered imaging signals and corresponding second electromagnetic interference signals as input, taking corresponding magnetic resonance imaging signals as output, and training and obtaining an electromagnetic interference eliminating model; means for inputting a real-time magnetic resonance imaging signal and a real-time electromagnetic interference signal into the electromagnetic interference eliminating model, to obtain a predicted magnetic resonance imaging signal that eliminates electromagnetic interference; means for performing image reconstruction on the predicted magnetic resonance imaging signal to obtain a magnetic resonance image.

## Claims

### Claim 1

An imaging method of eliminating electromagnetic interference of magnetic resonance, applied to an imaging device of eliminating electromagnetic interference of magnetic resonance, comprising:
Step 1, the imaging device acquiring a plurality of magnetic resonance imaging signals, acquiring a first electromagnetic interference signal, by a receiving coil, obtained by measurement in an electromagnetic interference affected environment, and acquiring second electromagnetic interference signals, by a plurality of sensing coils, obtained by measurement in the electromagnetic interference affected environment;
Step 2, the imaging device superposing each of the plurality of magnetic resonance imaging signals with the first electromagnetic interference signal to obtain interfered imaging signals, respectively, taking the interfered imaging signals and corresponding second electromagnetic interference signals as input, and taking the plurality of magnetic resonance imaging signals as output, to train an electromagnetic interference eliminating model;
Step 3, the imaging device inputting a real-time magnetic resonance imaging signal that is acquired by the receiving coil and a real-time electromagnetic interference signal that is acquired by the plurality of sensing coils into the electromagnetic interference eliminating model in a magnetic resonance imaging process, to obtain a predicted magnetic resonance imaging signal that eliminates electromagnetic interference; and
Step 4, the imaging device performing image reconstruction on the predicted magnetic resonance imaging signal to obtain a magnetic resonance image;
a model structure of the electromagnetic interference eliminating model comprises a first convolution layer, a first residual network layer, a first strided convolution layer, a second residual network layer, a second strided convolutional layer, a third residual network layer, a third strided convolution layer, a fourth residual network layer, a first deconvolution layer, a fifth residual network layer, a second deconvolution layer, a sixth residual network layer, a third deconvolution layer, a seventh residual network layer, and a second convolution layer,
an input of the first convolution layer is used as an input of the electromagnetic interference eliminating model;
an input of the first residual network layer is connected to an output of the first convolution layer;
an input of the first strided convolution layer is connected to an output of the first residual network layer;
an input of the second residual network layer is connected to an output of the first strided convolution layer;
an input of the second strided convolution layer is connected to an output of the second residual network layer;
an input of the third residual network layer is connected to an output of the second strided convolution layer;
an input of the third strided convolution layer is connected to an output of the third residual network layer;
an input of the fourth residual network layer is connected to an output of the third strided convolution layer;
an input of the first deconvolution layer is connected to an output of the fourth residual network layer;
an input of the fifth residual network layer is connected to an output of the first deconvolution layer;
an input of the second deconvolution layer is connected to an output of the fifth residual network layer;
an input of the sixth residual network layer is connected to an output of the second deconvolution layer;
an input of the third deconvolution layer is connected to an output of the sixth residual network layer;
an input of the seventh residual network layer is connected to an output of the third deconvolution layer;
an input of the second convolution layer is connected to an output of the seventh residual network, and an output of the second convolution layer is used as an output of the electromagnetic interference eliminating model; and
the first strided convolution layer is in skip connection with the third strided convolution layer, the second strided convolution layer is in skip connection with the second strided convolution layer, and the third strided convolution layer is in skip connection with the first strided convolution layer.

### Claim 2 (depends on 1)

The imaging method of claim 1, wherein at Step 1, acquiring the first electromagnetic interference signal further comprises:
the receiving coil obtaining the first electromagnetic interference signal by measurement in a period of a calibration window, or a period of waiting for a longitudinal magnetization vector to be restored to an original state; and
acquiring the second electromagnetic interference signals further comprises:
the plurality of sensing coils obtaining the second electromagnetic interference signals by measurement in the period of the calibration window, or the period of waiting for the longitudinal magnetization vector to be restored to the original state.

### Claim 3 (depends on 1)

The imaging method of claim 1, wherein acquiring the plurality of magnetic resonance imaging signals further comprises:
acquiring, in a non-electromagnetic interference affected environment, magnetic resonance signals acquired by the receiving coil as the magnetic resonance imaging signals;
acquiring, in an electromagnetic interference affected environment, magnetic resonance signals acquired by the receiving coil as the magnetic resonance imaging signals;
emulating and generating the magnetic resonance imaging signals; or
randomly generating the magnetic resonance imaging signals.

### Claim 4 (depends on 1)

The imaging method of claim 1, wherein the imaging device further comprises a built-in radio frequency system, acquiring the first electromagnetic interference signal further comprises:
acquiring, in an environment in which electromagnetic interference is caused and the radio frequency system disables transmitting radio frequency, an electromagnetic signal measured by the receiving coil as the first electromagnetic interference signal.

### Claim 5 (depends on 1)

The imaging method of claim 1, wherein acquiring the first electromagnetic interference signal further comprises:
converting the first electromagnetic interference signal to obtain corresponding frequency domain spatial data, and taking high frequency data of the frequency domain spatial data as a new first electromagnetic interference signal, wherein in the high frequency data, an amplitude of the magnetic resonance imaging signals is lower than that of the first electromagnetic interference.

### Claim 6 (depends on 1)

The imaging method of claim 1, wherein taking the interfered imaging signals and corresponding second electromagnetic interference signals as input, taking the plurality of magnetic resonance imaging signals as output, to training the electromagnetic interference eliminating model further comprises:
converting the interfered imaging signals into corresponding first frequency domain data, converting the corresponding second electromagnetic interference signals into corresponding second frequency domain data, converting the plurality of magnetic resonance imaging signals into corresponding third frequency domain data, taking the first frequency domain data and the second frequency domain data as input, taking the corresponding third frequency domain data as output, to train the electromagnetic interference eliminating model.

### Claim 7 (depends on 1)

The imaging method of claim 1, wherein taking the interfered imaging signals and corresponding second electromagnetic interference signals as input, taking the plurality of magnetic resonance imaging signals as output, to train the electromagnetic interference eliminating model further comprises:
truncating first segments from the interfered imaging signals, truncating corresponding second segments from the corresponding second electromagnetic interference signals, truncating corresponding third segments from the plurality of magnetic resonance imaging signals, taking the first segments and the corresponding second segments as input, taking the corresponding third segments as output, to train the electromagnetic interference eliminating model.