Systems and methods of accelerated propeller magnetic resonance imaging

Patent metadata

Field	Value
Publication number	US-11953574-B2
Application number	US-202217715617-A
Country	US
Kind code	B2
Filing date	Apr 7, 2022
Priority date	Apr 7, 2022
Publication date	Apr 9, 2024
Grant date	Apr 9, 2024

Abstract

A magnetic resonance (MR) imaging acceleration method is provided. The method includes applying, by an MR system, a pulse sequence having a k-space trajectory of a plurality of blades being rotated in k-space, each blade including a plurality of views, wherein the k-space trajectory has an undersampling pattern in the k-space. The method also includes receiving k-space data of a subject acquired by the pulse sequence, reconstructing MR images of the subject based on the k-space data using compressed sensing, and outputting the reconstructed images.

First claim

What is claimed is:

1. A magnetic resonance (MR) imaging acceleration method, comprising:
applying, by an MR system, a pulse sequence having a k-space trajectory of a plurality of blades being rotated ink-space, each blade including a plurality of views, wherein the k-space trajectory has an undersampling pattern in the k-space, wherein applying a pulse sequence further comprises:
reducing a number of views in a blade in a view undersampling pattern while maintaining a size of the blade;

receiving k-space data of a subject acquired by the pulse sequence;
reconstructing MR images of the subject based on the k-space data using compressed sensing; and
outputting the reconstructed images.

2. The method of claim 1 , wherein a first blade has a first view undersampling pattern, and a second blade has a second view undersampling pattern different from the first view undersampling pattern.

3. The method of claim 1 , wherein applying a pulse sequence further comprises:
reducing blade numbers in a kx-ky plane in a blade undersampling pattern.

4. The method of claim 3 , wherein angles between neighboring blades are golden angles.

5. The method of claim 3 , wherein angles between neighboring blades are randomized.

6. The method of claim 1 , wherein reconstructing MR images further comprises:
correcting phases by:
for each blade of the plurality of blades,
generating a phase image based on blade k-space data using nonuniform Fourier transform;
generating a phase corrected blade image based on the phase image; and
generating phase corrected blade k-space data based on the phase corrected blade image; and

reconstructing MR images based on phase corrected blade k-space data of the plurality of blades.

7. The method of claim 6 , wherein generating a phase image further comprises generating the phase image based on k-space data of views in a central belt region.

8. The method of claim 1 , wherein reconstructing MR images further comprises:
correcting motion by:
for each blade of the plurality of blades,
reconstructing a blade image based on blade k-space data using nonuniform Fourier transform;
computing a cross-correlation coefficient between the blade image and a reference image; and
determining whether the blade is motion corrupted by comparing the cross-correlation coefficient with a threshold; and

post-processing the motion-corrupted blade.

9. The method of claim 8 , wherein correcting motion further comprises generating the reference image based on blade images of the plurality of blades as an averaged image of the blade images corresponding to the plurality of blades.

10. The method of claim 8 , wherein post-processing the motion-corrupted blade further comprises applying a weight to blade k-space data of the motion-corrupted blade.

11. The method of claim 8 , wherein post-processing the motion-corrupted blade further comprises discarding blade k-space data of the motion-corrupted blade in reconstructing MR images.

12. The method of claim 1 , wherein reconstructing MR images further comprises:
generating an initial image of compressed sensing by applying nonuniform Fourier transform to the k-space data weighted by Voronoi diagrams of a sampling pattern of the k-space trajectory.

13. A magnetic resonance (MR) imaging acceleration system, comprising an imaging acceleration computing device, the imaging acceleration computing device comprising at least one processor in communication with at least one memory device, and the at least one processor programmed to:
receive k-space data of a subject acquired by a pulse sequence having a k-space trajectory of a plurality of blades being rotated in k-space, each blade including a plurality of views, wherein the k-space trajectory has an undersampling pattern, wherein in the pulse sequence, a number of views in a blade is reduced in a view undersampling pattern while maintaining a size of the blade;
reconstruct MR images of the subject based on the k-space data using compressed sensing; and
output the reconstructed images.

14. The system of claim 13 , wherein a first blade has a first view undersampling pattern, and a second blade has a second view undersampling pattern different from the first view undersampling pattern.

15. The system of claim 13 , wherein a number of blades in a kx-ky plane is reduced, and angles between neighboring blades are golden angles.

16. The system of claim 13 , wherein the at least one processor IS further programmed to:
correct phases by:
for each blade of the plurality of blades,
generating a phase image based on blade k-space data using nonuniform Fourier transform;
generating a phase corrected blade image based on the phase image; and
generating phase corrected blade k-space data based on the phase corrected blade image; and

reconstruct MR images based on phase corrected blade k-space data of the plurality of blades.

17. The system of claim 13 , wherein the at least one processor IS further programmed to:
correct motion by:
for each blade of the plurality of blades,
reconstructing a blade image based on blade k-space data using nonuniform Fourier transform;
computing a cross-correlation coefficient between the blade image and a reference image; and
determining whether the blade is motion corrupted by comparing the cross-correlation coefficient with a threshold; and

post-processing the motion-corrupted blade.

18. The system of claim 17 , wherein the at least one processor is further programmed to:
generate the reference image based on blade images of the plurality of blades as an averaged image of the blade images corresponding to the plurality of blades; and
post-process the motion-corrupted blade by applying a weight to blade k-space data of the motion-corrupted blade.

19. The system of claim 13 , wherein the at least one processor is further programmed to:
generate an initial image of compressed sensing by applying nonuniform Fourier transform to the k-space data weighted by Voronoi diagrams of a sampling pattern of the k-space trajectory.

Assignees

• Ge Prec Healthcare Llc

Inventors

• Jain Nitin
• Sundaresan Rajagopalan
• Agarwal Harsh
• Venkatesan Ramesh

Classifications

• G01R33/561 Primary

  by reduction of the scanning time, i.e. fast acquiring systems, e.g. using echo-planar pulse sequences · CPC title

• G01R33/4818

  MR characterised by data acquisition along a specific k-space trajectory or by the temporal order of k-space coverage, e.g. centric or segmented coverage of k-space · CPC title

• G01R33/56509

  due to motion, displacement or flow, e.g. gradient moment nulling (G01R33/567 takes precedence) · CPC title

• G01R33/48 Primary

  NMR imaging systems · CPC title

• G01R33/4824

  using a non-Cartesian trajectory · CPC title