# US 8,542,901 B2 · Method for correcting motion artifacts in magnetic resonance images

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- Number: US 8,542,901 B2
- Document ID: US8542901B2
- Status: Granted
- Kind: B2
- Application number: 12/632,240
- Filed: 2009-12-07
- Published: 2010-06-10
- Granted: 2013-09-24
- Earliest claimed: 2008-12-05
- Assignees: Siemens Aktiengesellschaft
- Applicants: Ti-chiun Chang; Tong Fang; Michelle Xiaohong Yan
- Inventors: Ti-chiun Chang; Tong Fang; Michelle Xiaohong Yan
- Web page: https://patentgrep.com/patent/US8542901B2

## CPC

- A61B 6/03: Computed tomography [CT]
- A61B 5/055: involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
- A61B 5/7207: {of noise induced by motion artifacts}
- 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 33/56509: {due to motion, displacement or flow, e.g. gradient moment nulling (G01R33/567 takes precedence)}
- 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

For resonance image data of an imaged subject, a method that first detects and estimates the dominant motions of k-space data (i.e., the motion vectors) and then constructs a graphical model for each estimated motion vector. The segments of the k-space that are determined to be corrupted by motion are restored by minimizing the energy associated with the corresponding graphical model. Consequently, the MR image of the imaged subject becomes free of motion artifacts.

## Claims

### Claim 1

A method for compensating for motion by a subject in a magnetic resonance image, comprising:
a. obtaining magnetic resonance image data from the imaged subject:
b. estimating the dominant motion in the image data;
c. constructing a graphical model for the estimated dominant motion; and
d. optimizing the graphical model so that the image data corrupted by the motion of the imaged subject is corrected.

### Claim 2 (depends on 1)

The method of claim 1, wherein the image data is k-space data.

### Claim 3 (depends on 2)

The method of claim 2, wherein the estimating step comprises estimating the motion vectors in the k-space data.

### Claim 4 (depends on 3)

The method of claim 3, wherein the constructing step comprises constructing a graphical model for each estimated motion vector.

### Claim 5 (depends on 4)

The method of claim 4, wherein the optimizing step comprises a) minimizing the energy associated with the graphical models to obtain the motion vectors corrupted by the motion of the subject and b) compensating for the corrupted motion vectors, said minimizing and compensating steps being repeated until no corrupted motion vectors are obtained.

### Claim 6 (depends on 5)

The method of claim 5, wherein the minimizing step comprises applying the iterated conditional modes algorithm to the graphical models to determine the motion vectors corrupted by the motion of the subject.

### Claim 7

A method of producing a magnetic resonance image of a subject with compensated motion artifacts, comprising:
a. obtaining magnetic resonance image data of the imaged subject, said image data containing motion artifacts;
b. transforming the magnetic resonance image data into k-space data;
c. determining motion vectors in the k-space data likely causing motion artifacts;
d. constructing a graphical model for each motion vector;
e. determining, from the graphical models, the motion vectors that are causing motion artifacts; and
f. compensating for the motion vectors that are causing motion artifacts.

### Claim 8 (depends on 7)

The method of claim 7, wherein step c) comprises:
a. constructing a gradient magnitude map of the k-space data; and
b. estimating the motion vectors from the gradient magnitude map.

### Claim 9 (depends on 8)

The method of claim 8, wherein the constructing a gradient magnitude map step comprises constructing a low gradient magnitude map.

### Claim 10 (depends on 9)

The method of claim 9, wherein the constructing a low gradient magnitude map comprises constructing a gradient magnitude map of the k-space data; locating edge fragments in the k-space with high gradient magnitudes; and re-constructing the gradient magnitude map without edge fragments with high gradient magnitudes.

### Claim 11 (depends on 10)

The method of claim 10, wherein the estimating step comprises applying a normalized cross correlation algorithm for the low gradient magnitude map to determine motion vectors in the k-space data likely causing motion artifacts.

### Claim 12 (depends on 9)

The method of claim 9, wherein the estimating step comprises applying a normalized cross correlation algorithm for the low gradient magnitude map to determine motion vectors in the k-space data likely causing motion artifacts.

### Claim 13 (depends on 12)

The method of claim 12, wherein step d) comprises constructing each graphical model with the normalized gradient squared as the image quality metric.

### Claim 14 (depends on 13)

The method of claim 13, wherein step e) comprises applying the iterated conditional modes algorithm to the graphical models to determine the motion vectors that are causing motion artifacts.

### Claim 15 (depends on 14)

The method of claim 14, wherein the compensating step comprises applying to the k-space data inverse motion vectors for the motion vectors that are causing motion artifacts.

### Claim 16 (depends on 7)

The method of claim 7, wherein step d) comprises constructing each graphical model with the normalized gradient squared as the image quality metric.

### Claim 17 (depends on 7)

The method of claim 7, wherein step e) comprises applying the iterated conditional modes algorithm to the graphical models to determine the motion vectors that are causing motion artifacts.

### Claim 18 (depends on 7)

The method of claim 7, wherein the compensating step comprises applying to the k-space data inverse motion vectors for the motion vectors that are causing motion artifacts.

### Claim 19 (depends on 7)

The method of claim 7, further comprising repeating steps e) and f) until no further motion vectors that are causing motion artifacts are determined.

### Claim 20

A magnetic resonance imaging apparatus, comprising:
a. means for obtaining magnetic resonance image data of an imaged subject, said image data containing motion artifacts;
b. means for transforming the magnetic resonance image data into k-space data;
c. means for estimating the dominant motion in the k-space data;
d. means for constructing a graphical model for the estimated dominant motion; and
e. means for optimizing the graphical model so that the k-space data containing the motion artifacts is corrected.