MRI streak-artifact suppression, system and method

Patent metadata

Field	Value
Publication number	US-11860256-B2
Application number	US-202217659712-A
Country	US
Kind code	B2
Filing date	Apr 19, 2022
Priority date	Apr 19, 2021
Publication date	Jan 2, 2024
Grant date	Jan 2, 2024

Abstract

A method for producing a streak-suppressed MR image of a subject includes (i) generating an interference correlation matrix from M coil images, (ii) producing eigenvectors of the interference correlation matrix, and (iii) determining, from the subspace-eigenvectors, a projection matrix of the interference null space. The subspace-eigenvectors include a plurality of subspace-eigenvectors that span an interference subspace and a plurality of null-space-eigenvectors that span an interference null space. The method also includes generating, from N coil images derived from a respective one of N MR signals, N streak-suppressed multi-coil images by either (i) preprocessing the N coil images with the projection matrix and applying an image-reconstruction technique to each of the resultant N preprocessed coil images, or (ii) applying an image-reconstruction technique to each of the N coil images to obtain N reconstructed coil images and post-processing the resultant N reconstructed coil images with the projection matrix.

First claim

What is claimed is:

1. A method for producing a streak-suppressed magnetic resonance (MR) image of a subject, the method comprising:
generating an interference correlation matrix from M coil images, each of the M coil images having been derived from a respective one of M MR signals, each of the M MR signals having been detected by a respective one of a phased array of M coils of magnetic resonance imaging scanner;
producing eigenvectors of the interference correlation matrix, the eigenvectors including a plurality of subspace eigenvectors that span an interference subspace and a plurality of null-space eigenvectors that span an interference null space;
determining, from the plurality of subspace-eigenvectors, a projection matrix of the interference null space; and
generating, from N coil images derived from a respective one of N MR signals, each detected by a respective one of the phased array of M coils, N streak-suppressed multi-coil images by either (i) preprocessing the N coil images with the projection matrix and applying an image-reconstruction technique to each of the resultant N preprocessed coil images, or (ii) applying an image-reconstruction technique to each of the N coil images to obtain N reconstructed coil images and post-processing the resultant N reconstructed coil images with the projection matrix;
each of M and N being a positive integer.

2. The method of claim 1 , the M coil images including the N coil images.

3. The method of claim 1 , the M coil images not including the N coil images.

4. The method of claim 1 , wherein generating the N streak-suppressed multi-coil images from the N coil images comprises:
preprocessing each of the N coil images with the projection matrix to yield a respective one of the N preprocessed coil images; and
applying an image-reconstruction technique to the N preprocessed coil images to yield the N streak-suppressed multi-coil images.

5. The method of claim 4 , applying an image-reconstruction technique comprising, for each preprocessed coil image,
determining a streak-suppressed multi-coil image that minimizes, to a minimization tolerance, a difference between (i) a product of a modal matrix of subspace-eigenvectors and the streak-suppressed multi-coil image and (ii) the preprocessed coil image.

6. The method of claim 1 , wherein generating the N streak-suppressed multi-coil images from the N coil images comprises:
applying an image-reconstruction technique to the N coil images to yield N multi-coil complex images, and
post-processing the N multi-coil complex images with the projection matrix to yield the N streak-suppressed multi-coil images.

7. The method of claim 1 , the projection matrix being proportional to I−Q r H DQ r , where I is an identity matrix, Q r is a matrix of a number r eigenvectors that span the interference subspace, Q r H is the Hermitian transpose of Q r , and D is a diagonal matrix.

8. The method of claim 1 , further comprising combining the N streak-suppressed multi-coil images to yield the streak-suppressed MR image.

9. The method of claim 1 , in said step of generating, the M MR signals originating in a first plurality of voxels of an imaging volume corresponding to an artifact-region of a coil image corrupted by an artifact, and coordinates (x, y) correspond to a location within a cross-sectional plane of the subject, the imaging volume including at least part of the subject.

10. A magnetic resonance imaging system comprising circuitry that executes the method of claim 4 .

11. The magnetic resonance imaging system of claim 10 , the circuitry including one of an application-specific integrated circuit and a field-programmable gate array.

12. A magnetic resonance imaging system of claim 10 , the circuitry comprising:
a processor; and
a memory storing machine-readable instructions, that when executed by the processor, control the processor to execute the method of claim 4 .

13. A magnetic resonance imaging system comprising circuitry that executes the method of claim 6 .

14. The magnetic resonance imaging system of claim 13 , the circuitry including one of an application-specific integrated circuit and a field-programmable gate array.

15. A magnetic resonance imaging system of claim 13 , the circuitry comprising:
a processor; and
a memory storing machine-readable instructions, that when executed by the processor, control the processor to execute the method of claim 6 .

Assignees

• Univ Arizona

Inventors

• Fu Zhiyang
• Altbach Maria I
• Bilgin Ali

Classifications

• G06T12/30

  Image post-processing, e.g. metal artefact correction · CPC title

• G06T12/10

  Image preprocessing, e.g. calibration, positioning of sources or scatter correction · CPC title

• G01R33/5608 Primary

  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) · CPC title

• G06T11/005

  Physics · mapped topic

• G06T11/008

  Physics · mapped topic