Method and apparatus for reconstructing magnetic resonance image data

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 the transform sparsity component of the optimization problem.

2. The 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. The 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. The method as claimed in claim 3 , wherein the determination of the difference comprises determining the I2-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. The 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. The 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. The method as claimed in claim 6 , wherein the transform sparsity component comprises determining the I1-norm of the result of the finite differences operation performed on the motion-corrected reconstructed MR image data.

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

9. The 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. The method as claimed in claim 1 , wherein the optimization problem is of the form

x

^

=

arg

⁢

⁢

min

x

⁢

f

⁡

(

x

)

,

wherein f(x) is a function defined as {data consistency component+α(transform sparsity component)}, wherein α is a regularization parameter.

11. The 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. The 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. The 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 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 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 the transform sparsity component of the optimization problem.