Method and system for diffusion magnetic resonance imaging

Patent metadata

Field	Value
Publication number	US-11372071-B2
Application number	US-202017088630-A
Country	US
Kind code	B2
Filing date	Nov 4, 2020
Priority date	Mar 29, 2019
Publication date	Jun 28, 2022
Grant date	Jun 28, 2022

Abstract

A method may include obtaining a plurality of groups of imaging data. Each group of the plurality of groups of imaging data may be generated based on MR signals acquired by an MR scanner via scanning a subject using a diffusion sequence. The method may also include determining one or more correction coefficients associated with an error caused by the diffusion sequence for each group of the plurality of groups of imaging data. The method may also include determining, based on the one or more correction coefficients corresponding to the each group of the plurality of groups of imaging data, a plurality of groups of corrected imaging data. The method may further include determining averaged imaging data by averaging the plurality of groups of corrected imaging data in a complex domain and generating, based on the averaged imaging data, an MR image.

First claim

What is claimed is:

1. A system for magnetic resonance imaging (MRI), comprising:
at least one storage device storing executable instructions, and
at least one processor in communication with the at least one storage device, when executing the executable instructions, causing the system to perform operations including:
obtaining a plurality of groups of imaging data, each group of the plurality of groups of imaging data being generated based on MR signals acquired by an MR scanner via scanning a subject using a diffusion sequence;
for each group of the plurality of groups of imaging data, determining one or more correction coefficients, wherein the one or more correction coefficients include a phase correction coefficient, and wherein to determine one or more correction coefficients, the at least one processor is directed to cause the system to perform additional operations including:
determining one group of the plurality of groups of imaging data as reference imaging data;
determining phase difference data between the each group of the plurality of groups of imaging data and the reference imaging data; and
determining, based on the phase difference data, the phase correction coefficient;

determining, based on the one or more correction coefficients corresponding to the each group of the plurality of groups of imaging data, a plurality of groups of corrected imaging data;
determining averaged imaging data by averaging the plurality of groups of corrected imaging data in a complex domain; and
generating, based on the averaged imaging data, an MR image.

2. The system of claim 1 , wherein the one or more correction coefficients further include a magnitude correction coefficient configured to correct a magnitude error.

3. The system of claim 2 , wherein to determine one or more correction coefficients, the at least one processor is directed to cause the system to perform additional operations including:
determining one group of the plurality of groups of imaging data as reference imaging data;
determining similarity data between the each group of the plurality of groups of imaging data and the reference imaging data; and
determining, based on the similarity data, the magnitude correction coefficient.

4. The system of claim 3 , wherein to determine one group of the plurality of groups of imaging data as reference imaging data, the at least one processor is directed to cause the system to perform additional operations including:
identifying the one group of imaging data that corresponds to a maximum magnitude among the plurality of groups of imaging data as the reference imaging data.

5. The system of claim 3 , wherein to determine one group of the plurality of groups of imaging data as reference imaging data, the at least one processor is directed to cause the system to perform additional operations including:
determining an average of magnitude data associated with the plurality of groups of imaging data; and
designating the average of the magnitude data associated with the plurality of groups of imaging data as the reference imaging data.

6. The system of claim 3 , wherein to determine, based on the similarity data, the magnitude correction coefficient, the at least one processor is further configured to cause the system to perform additional operations including:
performing a lowpass filtering operation on the similarity data; and
determining, based on the filtered similarity data, the magnitude correction coefficient.

7. The system of claim 1 , wherein to determine, based on the one or more correction coefficients corresponding to the each group of the plurality of groups of imaging data, a plurality of groups of corrected imaging data, the at least one processor is further configured to cause the system to perform additional operations including:
performing a dot product between the each group of the plurality of groups of imaging data and the one or more corresponding correction coefficients in an image domain.

8. The system of claim 1 , wherein to determine, based on the one or more correction coefficients corresponding to the each group of the plurality of groups of imaging data, a plurality of groups of corrected imaging data, the at least one processor is further configured to cause the system to perform additional operations including:
performing a convolution operation between the each group of the plurality of groups of imaging data and the one or more corresponding correction coefficients in a k-space domain.

9. The system of claim 1 , wherein to determine one group of the plurality of groups of imaging data as reference imaging data, the at least one processor is directed to cause the system to perform additional operations including:
identifying the one group of the plurality of groups of imaging data that corresponds to a maximum magnitude among the plurality of groups of imaging data as the reference imaging data.

10. The system of claim 1 , wherein to determine, based on the phase difference data, the phase correction coefficient, the at least one processor is further configured to cause the system to perform additional operations including:
performing a lowpass filtering operation on the phase difference data to obtain filtered phase difference data; and
designating the filtered phase difference data as the phase correction coefficient.

11. The system of claim 1 , wherein the diffusion sequence includes at least one diffusion block associated with a diffusion gradient and at least one imaging block associated with one or more scanning parameters, the imaging block being arranged subsequent to the diffusion block in the diffusion sequence.

12. A system for magnetic resonance imaging (MRI), comprising:
at least one storage device storing executable instructions, and
at least one processor in communication with the at least one storage device, when executing the executable instructions, causing the system to perform operations including:
obtaining a plurality of groups of imaging data, each group being generated via scanning a subject using a diffusion sequence;
for each group of the plurality of groups of imaging data, determining a weighting coefficient, wherein the weighting coefficient is determined at least based on a phase correction coefficient, and wherein to determine the weighting coefficient, the at least one processor is directed to cause the system to perform additional operations including:
determining one group of the plurality of groups of imaging data as reference imaging data;
determining phase difference data between the each group of the plurality of groups of imaging data and the reference imaging data; and
determining, based on the phase difference data, the phase correction coefficient;

determining corrected imaging data by weighting the plurality of groups of imaging data based on the weighting coefficient; and
generating a diffusion image of the subject based on the corrected imaging data.

13. The system of claim 12 , wherein the weighting coefficient is further determined based on a magnitude correction coefficient configured to correct a magnitude error of each group of imaging data.

14. The system of claim 13 , wherein the magnitude correction coefficient is determined based on reference imaging data, and wherein
the reference imaging data is a group of imaging data that corresponds to a maximum magnitude among the plurality of groups of imaging data, or
the reference imaging data is an average of magnitude data associated with the plurality of groups of imaging data.

15. The system of claim 14 , wherein the weighting coeffic

Assignees

• Shanghai United Imaging Healthcare Co Ltd

Inventors

• Li Guobin
• Liu Nan
• Li Zhaopeng

Classifications

• A61B5/055

  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging · CPC title

• G01R33/56341

  Diffusion imaging · CPC title

• G01R33/56545 Primary

  caused by finite or discrete sampling, e.g. Gibbs ringing, truncation artefacts, phase aliasing artefacts · CPC title

• G01R33/565 Primary

  Correction of image distortions, e.g. due to magnetic field inhomogeneities · CPC title

• G01R33/5608

  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) · CPC title