MRI method for retrospective motion correction with interleaved radial acquisition

Patent metadata

Field	Value
Publication number	US-9658304-B2
Application number	US-201113810456-A
Country	US
Kind code	B2
Filing date	Jul 21, 2011
Priority date	Jul 22, 2010
Publication date	May 23, 2017
Grant date	May 23, 2017

Abstract

A motion-corrected magnetic resonance imaging method comprises: sequentially acquiring a plurality of interleaved magnetic resonance radial acquisition datasets using a magnetic resonance scanner; reconstructing each magnetic resonance radial acquisition dataset into a corresponding image to generate a set of images, the reconstructing including expanding radial k-space lines of the magnetic resonance radial acquisition dataset into corresponding radial bands in k-space using a generalized auto-calibrating partially parallel acquisition (GRAPPA) operator; selecting a reference image from the set of images; performing three-dimensional spatial registration of each image of the set of images except the reference image with respect to the reference image to generate a spatially registered set of images; and combining the spatially registered set of images to generate a motion corrected image.

First claim

Having thus described the preferred embodiments, the invention is now claimed to be:

1. A method comprising:
sequentially acquiring a plurality of interleaved magnetic resonance radial acquisition datasets using a magnetic resonance scanner;
reconstructing each magnetic resonance radial acquisition dataset into a corresponding image to generate a set of images, the reconstructing including expanding each radial k-space line into a radial band in k-space;
spatially registering the set of images; and
combining the spatially registered set of images to generate a motion-corrected image.

2. The method as set forth in claim 1 , wherein:
each magnetic resonance radial acquisition dataset includes n radial k-space lines and has n-fold rotational symmetry in k-space where n is a positive integer greater than one, and
the plurality of interleaved magnetic resonance radial acquisition datasets comprises N interleaved radial acquisition datasets and includes N×n radial k-space lines and has N×n fold rotational symmetry in k-space where N is a positive integer greater than one.

3. The method as set forth in claim 2 , wherein n is an even integer.

4. The method as set forth in claim 1 , wherein the reconstructing of each magnetic resonance radial acquisition dataset comprises:
expanding each radial k-space line into a radial band in k-space using partial parallel imaging (PPI) that utilizes information provided by simultaneous data acquisition using a plurality of receive elements with different coil sensitivities to extrapolate the acquired k-space data.

5. The method as set forth in claim 1 , wherein the reconstructing of each magnetic resonance radial acquisition dataset comprises:
expanding each radial k-space line into a radial band in k-space using a generalized auto-calibrating partially parallel acquisition (GRAPPA) operator.

6. The method as set forth in claim 1 , wherein the reconstructing of each magnetic resonance radial acquisition dataset comprises:
reconstructing the magnetic resonance radial acquisition dataset using the GRAPPA operator for wider radial band (GROWL) algorithm.

7. The method as set forth in claim 1 , wherein the spatial registering comprises:
selecting a reference image from the set of images; and
spatially registering each image of the set of images except the reference image with the reference image to generate a spatially registered set of images.

8. The method as set forth in claim 7 , wherein the selecting comprises:
selecting the reference image as the image having lowest entropy.

9. The method as set forth in claim 7 , wherein the spatial registering of each image of the set of images with the reference image comprises:
determining relative motion between the image and the reference image at each of a plurality of spaced-apart regions of interest.

10. The method as set forth in claim 9 , wherein the relative motion between the image and the reference image at each region of interest is determined using image correlation.

11. The method as set forth in claim 7 , wherein the spatial registering of each image of the set of images with the reference image comprises:
spatially registering the image with the reference image in three dimensions.

12. The method as set forth in claim 1 , wherein the combining comprises:
additively combining the spatially registered set of images in image space to generate the motion-corrected image.

13. The method as set forth in claim 1 , further comprising:
displaying or printing the motion-corrected image.

14. The method as set forth in claim 1 , wherein the reconstructing, spatial registering, and combining are performed by a digital processor.

15. A digital processor configured to perform a method as set forth in claim 1 .

16. A non-transitory storage medium storing instructions executable by a digital processor to perform a method as set forth in claim 1 .

17. A method comprising:
sequentially acquiring a plurality of interleaved magnetic resonance radial acquisition datasets using a magnetic resonance scanner;
reconstructing each magnetic resonance radial acquisition dataset into a corresponding image to generate a set of images, the reconstructing including expanding radial k-space lines of the magnetic resonance radial acquisition dataset into corresponding radial bands in k-space using a generalized auto-calibrating partially parallel acquisition (GRAPPA) operator;
selecting a reference image from the set of images;
performing three-dimensional spatial registration of each image of the set of images except the reference image with respect to the reference image to generate a spatially registered set of images; and
combining the spatially registered set of images to generate a motion-corrected image.

18. The method as set forth in claim 17 , wherein the performing of three-dimensional spatial registration of each image comprises:
determining three-dimensional motion by regional correlation applied to a plurality of regions of interest.

19. A method comprising:
sequentially acquiring a plurality of interleaved magnetic resonance radial acquisition datasets using a magnetic resonance scanner;
reconstructing each magnetic resonance radial acquisition dataset into a corresponding image to generate a set of images;
selecting a reference image from the set of images as the image having lowest entropy;
performing spatial registration of each image of the set of images except the reference image with respect to the reference image to generate a spatially registered set of images; and
combining the spatially registered set of images to generate a motion-corrected image.

20. The method as set forth in claim 19 , wherein the reconstructing includes expanding radial k-space lines of the magnetic resonance radial acquisition dataset into corresponding radial bands in k-space using a generalized auto-calibrating partially parallel acquisition (GRAPPA) operator.

Assignees

• Lin Wei
• Huang Feng
• Koninl Philips Electronics Nv

Inventors

• Lin Wei
• Huang Feng

Classifications

• G01R33/56509

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

• G01R33/4818 Primary

  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

• G01R33/4824

  using a non-Cartesian trajectory · CPC title

• G01R33/561

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

• G01R33/5611

  Parallel magnetic resonance imaging, e.g. sensitivity encoding [SENSE], simultaneous acquisition of spatial harmonics [SMASH], unaliasing by Fourier encoding of the overlaps using the temporal dimension [UNFOLD], k-t-broad-use linear acquisition speed-up technique [k-t-BLAST], k-t-SENSE (structural details of arrays of sub-coils G01R33/3415) · CPC title