METHOD AND APPARATUS FOR RECONSTRUCTING MAGNETIC RESONANCE IMAGE DATA

• US 20190317172A1
• Filed: 04/12/2019
• Published: 10/17/2019
• Est. Priority Date: 04/12/2018
• Status: Active Grant

• Current Assignee: Siemens Healthineers AG (Siemens AG)
• Original Assignee: Siemens Healthcare Limited. (Siemens AG)
• Inventors: Botnar, Rene, Correia, Teresa, Neji, Radhouene, Prieto, Claudia
• Granted Patent: US 11,360,178 B2
• Time in Patent Office: Days
• CPC Class Codes: G01R 33/482 using a Cartesian trajectory
• CPC Class Codes: 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
• CPC Class Codes: G01R 33/5611 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 Class Codes: G01R 33/56509 due to motion, displacement or flow, e.g. gradient moment nulling G01R33/567 takes precedence
• CPC Class Codes: G01R 33/5676 Gating or triggering based on an MR signal, e.g. involving one or more navigator echoes for motion monitoring and correction
• CPC Class Codes: G06T 11/005 Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
• CPC Class Codes: G06T 2210/41 Medical

Abstract

In a method for reconstructing magnetic resonance (MR) image data from k-space data, k-space data of an image region of a subject are provided to a computer that is also provided with multiple navigator signals for the image region of the subject. The computer sorts the k-space data into multiple bins, the multiple bins representing different motion states of the subject. For each of the multiple bins, the computer executes a compressed sensing procedure to reconstruct the MR image data from the k-space data in the respective bin. Execution of the compressed sensing procedure includes solving an optimization problem comprising a data consistency component and a transform sparsity component. Motion information is incorporated by the computer into at least one of the data consistency component and the transform sparsity component of the optimization problem.

Claims

1. A method for reconstructing magnetic resonance (MR) image data from k-space data, the method comprising:

providing a computer with k-space data of an image region of a subject;

also providing the computer with a plurality of navigator signals for the image region of the subject;

in said computer, sorting the k-space data into a plurality of bins (103), the plurality of bins representing different motion states of the subject;

for each of the plurality of bins, executing a compressed sensing procedure in said computer to reconstruct the MR image data from the k-space data in the respective bin, execution of the compressed sensing procedure comprises solving an optimization problem comprising a data consistency component and a transform sparsity component; and

in said computer, incorporating motion information for the subject obtained from the plurality of navigator signals into at least one of the data consistency component and the transform sparsity component of the optimization problem.

2. A method as claimed in claim 1, further comprising, in said computer, correcting for motion within the plurality of bins using the motion information for the subject obtained from the plurality of navigator signals so as to generate motion corrected binned k-space data, wherein the data consistency component uses the motion corrected binned k-space data.

3. A method as claimed in claim 2, wherein the data consistency component of the optimization problem comprises a determination of the difference between the reconstructed MR image data transformed into k-space and the motion corrected binned k-space data.

4. A method as claimed in claim 3, wherein the determination of the difference comprises determining the l2-norm of the result of the subtraction of the motion corrected binned k-space data from the reconstructed MR image data transformed into k-space.

5. A method as claimed in claim 1, wherein the transform sparsity component of the optimization problem transforms the reconstructed MR image data to a motion-corrected domain using the motion information for the subject obtained from the plurality of navigator signals so as to generate motion-corrected reconstructed MR image data, and applies a sparsifying transform to the motion-corrected reconstructed MR image data.

6. A method as claimed in claim 5, wherein the sparsifying transform comprises performing a finite differences operation on the motion-corrected reconstructed MR image data.

7. A method as claimed in claim 6, wherein the transform sparsity component comprises determining the l1-norm of the result of the finite differences operation performed on the motion-corrected reconstructed MR image data.

8. A method as claimed in claim 1, wherein the optimization problem further comprises a spatial total variation function.

9. A method as claimed in claim 1, wherein both the data consistency component and the transform sparsity component of the optimization problem incorporate the motion information for the subject obtained from the plurality of navigator signals.

10. A method as claimed in claim 1, wherein the optimization problem is of the form

11. A method as claimed in claim 1, wherein sorting the k-space data into the plurality of bins comprises sorting the k-space data into the plurality of bins using the motion information for the subject obtained from the plurality of navigator signals.

12. A method as claimed in claim 1, wherein the compressed sensing procedure is a first reconstruction procedure, and wherein the method further comprises performing a second reconstruction procedure using the MR image data reconstructed using the first reconstruction procedure.

13. A method as claimed in claim 12, wherein performing the second reconstruction procedure comprises using the MR image data reconstructed using the first reconstruction procedure to determine one or more nonrigid motion fields, and further comprises, for each of the plurality of bins, using the one or more nonrigid motion fields to reconstruct the MR image data from the k-space data in the respective bin.

14. A method for operating a magnetic resonance (MR) image apparatus comprising an MR data acquisition scanner, said method comprising:

operating the MR data acquisition scanner to acquire k-space data from an image region of a subject in the MR data acquisition scanner, and to obtain a plurality of navigator signals from said image region;

providing the k-space data and the plurality of navigator signals to a computer and, in said computer, sorting the k-space data into a plurality of bins (103), the plurality of bins representing different motion states of the subject;

for each of the plurality of bins, executing a compressed sensing procedure in said computer to reconstruct the MR image data from the k-space data in the respective bin, execution of the compressed sensing procedure comprises solving an optimization problem comprising a data consistency component and a transform sparsity component; and

in said computer, incorporating motion information for the subject obtained from the plurality of navigator signals into at least one of the data consistency component and the transform sparsity component of the optimization problem.

15. A magnetic resonance (MR) apparatus comprising:

an MR data acquisition scanner comprising a gradient system, and an excitation system;

a computing system in communication with the excitation system, and the gradient system, and configured to receive the signals from the excitation system;

the computing being configured to execute program code in order to control the gradient system so as to apply a magnetic field gradient in said scanner and in order to control the excitation system so as to apply an excitation pulse to a subject in the scanner and to receive signals from the subject resulting from the excitation that represent k-space data of an image region of a subject, and to acquire a plurality of navigator signals for the image region of the subject;

the computing system being configured to sort the k-space data into a plurality of bins, the plurality of bins representing different motion states of the subject;

the computing system being configured to, for each of the plurality of bins, execute a compressed sensing procedure to reconstruct the MR image data from the k-space data in the respective bin, execution of the compressed sensing procedure comprising solving an optimization problem comprising a data consistency component and a transform sparsity component, and

the computing system is configured to incorporate motion information for the subject obtained from the plurality of navigator signals into at least one of the data consistency component and the transform sparsity component of the optimization problem.

16. A non-transitory, computer-readable data storage medium encoded with programming instructions, said storage medium being loaded into a computer and said programming instructions causing said computer to:

receive k-space data obtained from an image region of a subject situated in an MR data acquisition scanner;

also receive a plurality of navigator signals of the image region of the subject;

sort the k-space data into a plurality of bins, the plurality of bins representing different motion states of the subject;

for each of said plurality of bins, execute a compressed sensing procedure to reconstruct MR image data from the k-space data in the respective bin, the execution of the compressed sensing procedure comprising solving an optimization problem comprising a data consistency component and a transform sparsely component; and

incorporate motion information for the subject obtained from the plurality of navigator signals into at least one of the data consistency component and the transform sparsity component of the optimization problem.