# US 10,307,106 B2 · Systems and methods for estimating and removing magnetic resonance imaging gradient field-induced voltages from electrophysiology signals

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- Number: US 10,307,106 B2
- Document ID: US10307106B2
- Status: Granted
- Kind: B2
- Application number: 15/325,323
- Filed: 2015-07-10
- Published: 2017-06-29
- Granted: 2019-06-04
- Earliest claimed: 2014-07-11
- Assignees: The Brigham and Women's Hosptial, Inc.; University of Georgia Research Foundation, Inc.
- Applicants: Brigham and Women's Hospital, Inc.; University of Georgia Research Foundation, Inc.
- Inventors: Wei Wang; HuaLei Zhang; Ehud J. Schmidt; Tsz Ho Tse
- Web page: https://patentgrep.com/patent/US10307106B2

## CPC

- A61B 5/7217: {of noise originating from a therapeutic or surgical apparatus, e.g. from a pacemaker}
- A61B 5/055: involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
- A61B 5/33: specially adapted for cooperation with other devices
- G01R 33/5673: {Gating or triggering based on a physiological signal other than an MR signal, e.g. ECG gating or motion monitoring using optical systems for monitoring the motion of a fiducial marker}
- A61B 5/725: {using specific filters therefor, e.g. Kalman or adaptive filters (specific diagnostics methods using using bioelectric or biomagnetic signals A61B5/316)}
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12)

## Abstract

Systems and methods for estimating time-dependent voltages that are induced in electrophysiological monitoring systems by magnetic field gradients generated during a magnetic resonance imaging (“MRI”) scan are provided. The gradient-induced voltages are subsequently removed from signals acquired with the electrophysiological monitoring system during an MRI scan. As an example, the electrophysiological monitoring system can include an electrocardiography (“ECG”) system, an electroencephalography (“EEG”) system, an electromyography (“EMG”) system, a voltage device tracking (“VDT”) system, and so on. The gradient-induced voltages are estimated using a two-step procedure in which a learning algorithm is used to determine fitting parameters to be used in a model of the gradient-induced voltages. The fitting parameters are then used together with the model to extract the gradient-induced voltages from signals acquired during an MRI scan. The gradient-induced voltages can then be removed from the acquired signals.

## Claims

### Claim 1

A method for correcting electrophysiology signals by removing voltages induced by magnetic field gradients generated by a magnetic resonance imaging (MRI) system, the steps of the method comprising:
(a) providing gradient waveforms;
(b) computing derivatives of the provided gradient waveforms;
(c) acquiring electrophysiology signals from a subject positioned in the MRI system while the MRI system is generating magnetic field gradients based on the provided gradient waveforms;
(d) estimating fitting parameters for a physical model of gradient-induced voltages;
(e) estimating voltages induced by the generated magnetic field gradients by fitting the provided gradient waveforms, the computed derivatives of the gradient waveforms, and the estimated fitting parameters to the physical model of gradient-induced voltages;
(f) removing the estimated gradient-induced voltages from the acquired electrophysiology signals to produce corrected electrophysiology signals; and
(g) displaying the corrected electrophysiological signals on a display.

### Claim 2 (depends on 1)

The method as recited in claim 1, wherein step (d) includes estimating the fitting parameters by adaptively filtering the acquired electrophysiology signals.

### Claim 3 (depends on 1)

The method as recited in claim 1, wherein step (d) includes estimating the fitting parameters by:
(i) providing an estimate of gradient-induced voltages associated with the provided gradient waveforms; and
(ii) fitting the provided gradient waveforms, the computed derivatives of the gradient waveforms, and the estimate of the gradient-induced voltages to the physical model of the gradient-induced voltages.

### Claim 4 (depends on 3)

The method as recited in claim 3, wherein providing the estimate of gradient-induced voltages includes:
(i) providing training electrophysiology signal data acquired from the subject while the MRI system is generating magnetic field gradients based on the provided gradient waveforms;
(ii) providing template electrophysiology signal data acquired from the subject while the MRI system is not generating magnetic field gradients; and
(iii) computing the estimate of gradient-induced voltages based on the provided training electrophysiology signal data and the provided template electrophysiology signal data.

### Claim 5 (depends on 4)

The method as recited in claim 4, wherein computing the estimate of gradient-induced voltages includes subtracting the training electrophysiology signal data and the template electrophysiology signal data.

### Claim 6 (depends on 4)

The method as recited in claim 4, wherein the provided template electrophysiology signal data is the electrophysiology signal data acquired in step (c).

### Claim 7 (depends on 1)

The method as recited in claim 1, wherein the electrophysiology signals include at least one of electrocardiography (ECG) signals, intracardiac electrocardiogram (EGM) signals, electroencephalography (EEG) signals, electromyography (EMG) signals, voltage device tracking (VDT) signals, or a combination thereof.

### Claim 8

A system for correcting electrophysiology signals affected by magnetic field gradients generated by a magnetic resonance imaging (MRI) system, the system comprising:
an input configured to receive electrophysiology signals acquired from a subject positioned in an MRI system;
at least one processor configured to:
i) to receive gradient waveforms from the input;
ii) compute derivatives of the received gradient waveforms;
iii) estimate fitting parameters for a physical model of gradient-induced voltages;
iv) estimate voltages induced by the generated magnetic field gradients by fitting the gradient waveforms, the computed derivatives of the gradient waveforms, and the estimated fitting parameters of the physical model of gradient-induced voltages;
v) remove the estimated gradient-induced voltages from the acquired electrophysiology signals to produce corrected electrophysiology signals; and
vi) generate a report using the corrected electrophysiology signals.

### Claim 9 (depends on 8)

The system as recited in claim 8, wherein the at least one processor is further configured to estimate the fitting parameters by adaptively filtering the acquired electrophysiology signals.

### Claim 10 (depends on 9)

The system as recited in claim 9, wherein the at least one processor is further configured to estimate the fitting parameters by:
a) providing an estimate of gradient-induced voltages associated with the provided gradient waveforms; and
b) fitting the provided gradient waveforms, the computed derivatives of the gradient waveforms, and the estimate of the gradient-induced voltages to the physical model of the gradient-induced voltages.

### Claim 11 (depends on 10)

The system as recited in claim 10, wherein the at least one processor is configured to provide the estimate of gradient-induced voltages by:
a) providing training electrophysiology signal data acquired from the subject while the MRI system is generating magnetic field gradients based on the provided gradient waveforms;
b) providing template electrophysiology signal data acquired from the subject while the MRI system is not generating magnetic field gradients; and
c) computing the estimate of gradient-induced voltages based on the provided training electrophysiology signal data and the provided template electrophysiology signal data.

### Claim 12 (depends on 11)

The system as recited in claim 11, wherein computing the estimate of gradient-induced voltages including subtracting the training electrophysiology signal data and the template electrophysiology signal data.

### Claim 13 (depends on 11)

The system as recited in claim 11, wherein the at least one processor is further configured to direct acquisition of the electrophysiology signals from the subject to generate the training electrophysiology signal data, the template electrophysiology signal data, or both.

### Claim 14 (depends on 8)

The system as recited in claim 8, wherein the electrophysiology signals include at least one of electrocardiography (ECG) signals, intracardiac electrocardiogram (EGM) signals, electroencephalography (EEG) signals, electromyography (EMG) signals, voltage device tracking (VDT) signals, or a combination thereof.