System and method for removing gibbs artifact in medical imaging system

1. A method of image processing comprising:

setting k-space comprising a first part and a second part, wherein the first part is divided into a first region and a second region;

filling, in the first part of the k-space, a matrix comprising data;

applying a filter on the matrix in the first part to produce a filtered data matrix in the first part;

padding the second part of the k-space with padded data;

performing, based on a constraint, a plurality of iterations of an objective function for a target array in image domain, wherein the objective function is based on a total variation of the target array, and wherein the constraint is given by setting filtered data in the second region of the first part of the k-space to be invariant during the plurality of iterations, or a changing extent of filtered data in the second region be less than a changing extent of filtered data in the first region during the plurality of iterations; and

reconstructing an image based on the target array.

2. The method of claim 1 , wherein the objective function is based on a first function of the target array, the filtered data matrix in the first part, and the padded data in the second part of the k-space.

3. The method of claim 1 , wherein the matrix comprising data is undersampled.

4. The method of claim 1 , wherein the second part is outside of the first part.

5. The method of claim 1 , wherein the filter is based on a plurality of one-dimensional filters orthogonal to one another.

6. The method of claim 1 , wherein the padding is zero padding.

7. The method of claim 1 , wherein the total variation is based on a first order derivative of the target array of data in image domain.

8. The method of claim 1 , wherein the total variation is based on a second order derivative of the target array of data in image domain.

9. The method of claim 2 , wherein the first function is based on an L-2 norm function.

10. The method of claim 1 , wherein the constraint is given as a term of the objective function based on the Fourier transform of the target array and the filtered data in the second region of the first part of the k-space.

11. The method of claim 10 , wherein the constraint is based on an L-2 norm function.

12. The method of claim 10 , wherein the constraint strength is tuned by setting a coefficient for the term in the objective function.

13. A non-transitory computer readable medium comprising executable instructions that, when executed by at least one processor, cause the at least one processor to effectuate a method comprising:

instructions for causing a computer to implement a method, the method further comprising:

setting k-space comprising a first part and a second part, wherein the first part is divided into a first region and a second region;

filling, in the first part of the k-space, a matrix comprising data;

applying a filter on the matrix in the first part to produce a filtered data matrix in the first part;

padding the second part of the k-space with padded data;

performing, based on a constraint, a plurality of iterations of an objective function for a target array in image domain, wherein the objective function is based on a total variation of the target array, and wherein the constraint is given by setting filtered data in the second region of the first part of the k-space to be invariant during the plurality of iterations, or a changing extent of filtered data in the second region be less than a changing extent of filtered data in the first region during the plurality of iterations; and

reconstructing an image based on the target array.

14. A system comprising:

a storage device storing a set of instructions; and

at least one processor in communication with the storage device, wherein when executing the instructions, the at least one processor is configured to cause the system to:

specify a first part and a second part of k-space, the first part comprising a first region and a second region, the second part being outside of the first part, wherein the first part is divided into a first region and a second region;

fill a matrix comprising data in the first part of the k-space;

act on the matrix in the first part to produce a filtered data matrix in the first part;

pad the second part of the k-space with padded data;

perform, based on a constraint, a plurality of iterations of an objective function for a target array in image domain, wherein the objective function is based on a total variation of the target array, and wherein the constraint is given by setting filtered data in the second region of the first part of the k-space to be invariant during the plurality of iterations, or a changing extent of filtered data in the second region be less than a changing extent of filtered data in the first region during the plurality of iterations; and

reconstruct an image based on the target array.

15. The system of claim 14 , wherein the objective function is based on a first function of the Fourier transform of the target array, the filtered data matrix in the first part, and the padded data in the second part of the k-space.

16. The system of claim 15 , wherein the first function is based on an L-2 norm function.

17. The system of claim 14 , wherein the constraint is given as a term of the objective function based on the Fourier transform of the target array and the filtered data in the second region of the first part of the k-space.

18. The system of claim 17 , wherein the constraint strength is tuned by setting a coefficient for the term in the objective function.