Sparse iterative phase correction for MR partial fourier reconstruction
A method for sparse iterative phase correction for Magnetic Resonance (MR) partial Fourier reconstruction includes acquiring a partial Fourier k-space dataset using an MR scanner and estimating, by a control computer, a coil sensitivity map associated with the MR scanner from fully sampled k-space center. The control computer extracts a symmetrically sampled k-space center dataset from the partial Fourier k-space dataset and determines a low-resolution image based on the symmetrically sampled k-space center dataset and the coil sensitivity map. The control computer also determines phase corresponding to the low-resolution image. An iterative reconstruction process may then be applied to generate an image based on the partial Fourier k-space dataset. This iterative reconstruction process applies a Fast Iterative Shrinkage Thresholding Algorithm (FISTA) with phase correction based on the phase corresponding to the low-resolution image.
1. A method for sparse iterative phase correction for Magnetic Resonance (MR) partial Fourier reconstruction, the method comprising:
acquiring a partial Fourier k-space dataset using an MR scanner;
estimating, by a control computer, a coil sensitivity map associated with the MR scanner from fully sampled k-space center;
extracting, by the control computer, a symmetrically sampled k-space center dataset from the partial Fourier k-space dataset;
determining, by the control computer, a low-resolution image based on the symmetrically sampled k-space center dataset and the coil sensitivity map;
determining, by the control computer, phase corresponding to the low-resolution image; and
applying, by the control computer, an iterative reconstruction process to generate an image based on the partial Fourier k-space dataset, wherein the iterative reconstruction process applies a Fast Iterative Shrinkage Thresholding Algorithm (FISTA) with phase correction based on the phase corresponding to the low-resolution image.
2. The method of claim 1 , wherein the iterative reconstruction process uses the low-resolution image as an initial estimate for the image.
3. The method of claim 2 , wherein each respective iteration of the iterative reconstruction process comprises:
applying a proximal operator to determine an updated estimate for the image; and
replacing a phase term in the updated estimate from the image with the phase corresponding to the low-resolution image.
4. The method of claim 3 , wherein the proximal operator applies the Chambolle-Pock algorithm.
5. The method of claim 3 , wherein the proximal operator applies the Dykstra algorithm.
6. The method of claim 2 , wherein each respective iteration of the iterative reconstruction process comprises:
applying a proximal operator to determine an updated estimate for the image; and
if less than a pre-defined number of iterations of the iterative reconstruction process have been completed, replacing a phase term in the updated estimate from the image with the phase corresponding to the low-resolution image.
7. The method of claim 1 , wherein the partial Fourier k-space dataset is acquired in phase-encoding directions.
8. The method of claim 1 , wherein the partial Fourier k-space dataset is acquired in frequency-encoding directions.
9. An article of manufacture for sparse iterative phase correction for Magnetic Resonance (MR) partial Fourier reconstruction, the article of manufacture comprising a non-transitory, tangible computer-readable medium holding computer-executable instructions for performing a method comprising:
receiving a partial Fourier k-space dataset acquired using an MR scanner;
estimating a coil sensitivity map associated with the MR scanner from fully sampled k-space center;
extracting a symmetrically sampled k-space center dataset from the partial Fourier k-space dataset;
determining a low-resolution image from the symmetrically sampled k-space center dataset;
determining phase corresponding to the low-resolution image; and
applying an iterative reconstruction process to generate an image based on the partial Fourier k-space dataset, wherein the iterative reconstruction process applies a Fast Iterative Shrinkage Thresholding Algorithm (FISTA) with phase correction based on the phase corresponding to the low-resolution image.
10. The article of manufacture of claim 9 , wherein the iterative reconstruction process uses the low-resolution image as an initial estimate for the image.
11. The article of manufacture of claim 10 , wherein each respective iteration of the iterative reconstruction process comprises:
applying a proximal operator to determine an updated estimate for the image; and
replacing a phase term in the updated estimate from the image with the phase corresponding to the low-resolution image.
12. The article of manufacture of claim 11 , wherein the proximal operator applies the Chambolle-Pock algorithm.
13. The article of manufacture of claim 11 , wherein the proximal operator applies the Dykstra algorithm.
14. The article of manufacture of claim 10 , wherein each respective iteration of the iterative reconstruction process comprises:
applying a proximal operator to determine an updated estimate for the image; and
if less than a pre-defined number of iterations of the iterative reconstruction process have been completed, replacing a phase term in the updated estimate from the image with the phase corresponding to the low-resolution image.
15. The article of manufacture of claim 9 , wherein the partial Fourier k-space dataset is acquired in phase-encoding directions.
16. The article of manufacture of claim 9 , wherein the partial Fourier k-space dataset is acquired in frequency-encoding directions.
17. A system for sparse iterative phase correction for Magnetic Resonance (MR) partial Fourier reconstruction, the system comprising:
an imaging device comprising a plurality of coils configured to acquire a partial Fourier k-space dataset acquired using a magnetic resonance imaging device; and
a central computer configured to apply an iterative reconstruction process to generate an image based on the partial Fourier k-space dataset over a plurality of iterations, wherein the iterative reconstruction process applies a Fast Iterative Shrinkage Thresholding Algorithm (FISTA) with phase correction performed during at least a portion of the plurality of iterations.
18. The system of claim 17 , wherein phase correction is performed during each of the plurality of iterations.
19. The system of claim 17 , wherein the central computer is further configured to:
estimate a coil sensitivity map associated with the imaging device;
extract a symmetrically sampled k-space center dataset from the partial Fourier k-space dataset;
determining a low-resolution image based on the symmetrically sampled k-space center dataset and the coil sensitivity map; and
determine phase corresponding to the low-resolution image,
wherein the phase corresponding to the low-resolution image is used for the phase correction performed during each of the at least a portion of the plurality of iterations.
20. The system of claim 19 , wherein the central control computer is configured to use the low-resolution image as an initial estimate of the image in the iterative reconstruction process.