# US 11,307,280 B2 · Methods for correcting motion-encoding gradient nonlinearities in magnetic resonance elastography

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- Number: US 11,307,280 B2
- Document ID: US11307280B2
- Status: Granted
- Kind: B2
- Application number: 17/059,931
- Filed: 2019-06-03
- Published: 2021-06-03
- Granted: 2022-04-19
- Earliest claimed: 2018-06-01
- Assignees: Mayo Foundation for Medical Education and Research
- Applicants: Mayo Foundation For Medical Education and Research
- Inventors: Joshua D. Trzasko; Philip A. Araoz; Matthew A. Bernstein; Richard L. Ehman; Arvin Forghanian-Arani; John Huston, III; Yunhong Shu
- Web page: https://patentgrep.com/patent/US11307280B2

## 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 33/56358: {Elastography}
- G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12)

## Abstract

Described here are systems and methods for correcting motion-encoding gradient nonlinearities in magnetic resonance elastography (“MRE”). In general, the systems and methods described in the present disclosure compute gradient nonlinearity corrected displacement data based on information about the motion-encoding gradients used when acquiring magnetic resonance data.

## Claims

### Claim 1

A method for generating an image that depicts mechanical properties of a subject using a magnetic resonance imaging (MRI) system, the method comprising:
(a) acquiring motion-encoded magnetic resonance data from a subject using an MRI system operating a pulse sequence that applies at least one motion-encoding gradient having gradient nonlinearities;
(b) providing to a computer system:
gradient waveform data comprising gradient waveforms of the at least one motion-encoding gradient; and
spatial response function data comprising spatial response functions of gradient coils used to apply the at least one motion-encoding data;
(c) computing gradient nonlinearity corrected displacement data from the motion-encoded magnetic resonance data using the gradient waveform data and the spatial response function data; and
(d) generating an image that depicts mechanical properties of the subject from the gradient nonlinearity corrected displacement data.

### Claim 2 (depends on 1)

The method as recited in claim 1, wherein step (c) includes forming a displacement vector from phase information contained in the motion-encoded magnetic resonance data and computing the gradient nonlinearity corrected displacement data using the gradient waveform data and the spatial response function data.

### Claim 3 (depends on 2)

The method as recited in claim 2, wherein the displacement vector is formed from phase information contained in a subset of the motion-encoded magnetic resonance data.

### Claim 4 (depends on 1)

The method as recited in claim 1, wherein step (c) includes computing a Fourier transform of the gradient waveform evaluated at a spatial frequency of the motion-encoding gradient and computing the gradient nonlinearity corrected displacement data from the motion-encoded magnetic resonance data using the Fourier transform of the gradient waveform data and the spatial response function data.

### Claim 5 (depends on 1)

The method as recited in claim 1, wherein the gradient nonlinearity corrected displacement data comprise gradient nonlinearity corrected harmonic displacement data.

### Claim 6 (depends on 1)

The method as recited in claim 1, wherein the gradient nonlinearity corrected displacement data are computed from an ensemble of the motion-encoded magnetic resonance data using the gradient waveform data and the spatial response function data.

### Claim 7 (depends on 1)

The method as recited in claim 1, wherein the gradient nonlinearity corrected displacement data comprise gradient nonlinearity-corrected displacement images generated by:
factoring the motion-encoded magnetic resonance data into a plurality of subsets, wherein each subset contains motion-encoded magnetic resonance data acquired using a different amplitude of motion encoding;
reconstructing a plurality of displacement images, each displacement image being reconstructed from a different one of the plurality of subsets; and
correcting the plurality of displacement images for gradient nonlinearity using the gradient waveform data and the spatial response function data.

### Claim 8 (depends on 1)

The method as recited in claim 1, wherein the pulse sequence used to acquire the motion-encoded magnetic resonance data implements the at least one motion-encoding gradient in a monodirectional motion encoding scheme.

### Claim 9 (depends on 1)

The method as recited in claim 1, wherein the pulse sequence used to acquire the motion-encoded magnetic resonance data implements the at least one motion-encoding gradient in a tetrahedral motion encoding scheme.

### Claim 10 (depends on 9)

The method as recited in claim 9, wherein the tetrahedral motion encoding scheme is a Hadamard motion encoding scheme.

### Claim 11 (depends on 1)

The method as recited in claim 1, wherein the pulse sequence used to acquire the motion-encoded magnetic resonance data implements the at least one motion-encoding gradient in a sample interval modulation (SLIM) motion encoding scheme.

### Claim 12 (depends on 1)

The method as recited in claim 1, wherein the pulse sequence used to acquire the motion-encoded magnetic resonance data includes at least one first gradient moment nulled motion-encoding gradient.

### Claim 13 (depends on 1)

The method as recited in claim 1, wherein the image that depicts mechanical properties of the subject depicts at least one of stiffness, storage modulus, loss modulus, damping ratio, and poroelastic parameters.

### Claim 14 (depends on 1)

The method as recited in claim 1, wherein the gradient nonlinearity corrected displacement data are computed based in part on a Taylor series expansion of the spatial response function data.

### Claim 15 (depends on 1)

The method as recited in claim 1, wherein the gradient nonlinearity corrected displacement data are computed based in part on a Chebyshev polynomial expansion of the spatial response function data.

### Claim 16 (depends on 1)

The method as recited in claim 1, wherein the gradient nonlinearity corrected displacement data are computed and the image that depicts mechanical properties of the subject is generated by inputting the motion-encoded magnetic resonance data, the gradient waveform data, and the spatial response function data to an iterative model-based estimation algorithm, generating output as the image that depicts mechanical properties of the subject.