Method and system for expanding axial coverage in iterative reconstruction in computer tomography (CT)
View Patent ↗The image generation method and system generates an image using a predetermined iterative reconstruction technique from cone beam data that has been expanded by adding additional data, and an instance of the iteration process is weighted according to a corresponding validation weight during the reconstruction. Optionally, an instance of the iteration process is weighted according to a combination of weights during the reconstruction. The predetermined combination of the weights includes axial weights based upon a validity value of the expanded data and statistical weights.
1. A method of iterative image reconstruction in computed tomography, comprising the steps of:
a) expanding measured data by adding additional data along a predetermined axis that is parallel to an axis of rotation according to the measured data to generate expanded data;
b) generating a validity weight based upon a predetermined function for weighting the expanded data at each of projection bins; and
c) iteratively reconstructing an image volume with full axial coverage using the expanded data and the validity weight according to a predetermined iterative reconstruction technique.
2. The method of iterative image reconstruction in computed tomography according to claim 1 wherein the additional data is a predetermined number of rows of identical data that corresponds to one of edge rows.
3. The method of iterative image reconstruction in computed tomography according to claim 1 wherein the additional data is extrapolated.
4. The method of iterative image reconstruction in computed tomography according to claim 1 wherein the predetermined function has an axial weight component.
5. The method of iterative image reconstruction in computed tomography according to claim 1 wherein the predetermined function has a combination of an axial weight component and a statistical weight component.
6. The method of iterative image reconstruction in computed tomography according to claim 1 wherein a seed in said step c) is a reconstructed analytic filtered-backprojection image based upon the expanded data.
7. The method of iterative image reconstruction in computed tomography according to claim 1 wherein the measured data is obtained along a circular source trajectory.
8. The method of iterative image reconstruction in computed tomography according to claim 1 wherein the measured data is obtained along a helical source trajectory.
9. The method of iterative image reconstruction in computed tomography according to claim 1 wherein the predetermined iterative reconstruction technique includes algebraic reconstruction technique (ART) type algorithms, simultaneous algebraic reconstruction technique (SART) type algorithms, ordered-subset simultaneous algebraic reconstruction technique (OS-SART) type algorithms, simultaneous iterative reconstruction technique (SIRT) type algorithms, expectation maximization (EM) type algorithms and penalized least square type algorithms (PLS).
10. The method of iterative image reconstruction in computed tomography according to claim 1 wherein the predetermined iterative reconstruction technique is ordered subset simultaneous algebraic reconstruction technique (OS-SART), which is weighted by the validity weight as denoted by d i for ith measured projection of the measured projection data as:
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where x j (n+1) is an updated image from an old image x j (n) , λ n is a relaxation factor, a i,j is an element of the system matrix, and b i is after-log data of the expanded data at an ith detector bin.
11. A system for iteratively reconstructing an image in computed tomography, comprising:
an axial artifact reduction device for expanding measured data by adding additional data along a predetermined axis that is parallel to an axis of rotation according to the measured data to generate expanded data, said axial artifact reduction device generating a validity weight based upon a predetermined function for weighting the expanded data at each of projection bins; and
a reconstruction device connected to said axial artifact reduction device for iteratively reconstructing an image volume with full axial coverage using the expanded data and the validity weight according to a predetermined iterative reconstruction technique.
12. The system for iteratively reconstructing an image in computed tomography according to claim 11 wherein the additional data is a predetermined number of rows of identical data that corresponds to one of edge rows.
13. The system for iteratively reconstructing an image in computed tomography according to claim 11 wherein the additional data is extrapolated.
14. The system for iteratively reconstructing an image in computed tomography according to claim 11 wherein the predetermined function has an axial weight component.
15. The system for iteratively reconstructing an image in computed tomography according to claim 11 wherein the predetermined function has a combination of an axial weight component and a statistical weight component.
16. The system for iteratively reconstructing an image in computed tomography according to claim 11 wherein a seed for the predetermined iterative reconstruction technique is a reconstructed analytic filtered-backprojection image based upon the expanded data.
17. The system for iteratively reconstructing an image in computed tomography according to claim 11 wherein the measured data is obtained along a circular source trajectory.
18. The system for iteratively reconstructing an image in computed tomography according to claim 11 wherein the measured data is obtained along a helical source trajectory.
19. The system for iteratively reconstructing an image in computed tomography according to claim 11 wherein the predetermined iterative reconstruction technique includes algebraic reconstruction technique (ART) type algorithms, simultaneous algebraic reconstruction technique (SART) type algorithms, ordered-subset simultaneous algebraic reconstruction technique (OS-SART) type algorithms, simultaneous iterative reconstruction technique (SIRT) type algorithms, expectation maximization (EM) type algorithms and penalized least square type algorithms (PLS).
20. The system for iteratively reconstructing an image in computed tomography according to claim 11 wherein the predetermined iterative reconstruction technique is ordered subset simultaneous algebraic reconstruction technique (OS-SART), which is weighted by the validity weight as denoted by d i for ith measured projection of the measured projection data as:
x
j
(
n
+
1
)
=
x
j
(
n
)
+
λ
n
(
∑
i
a
i
,
j
(
d
i
(
b
i
-
∑
k
a
i
,
k
x
k
(
n
)
)
∑
k
a
i
,
k
)
∑
i
a
i
,
j
d
i
)
where x j (n+1) is an updated image from an old image x j (n) , λ n is a relaxation factor, a i,j is an element of the system matrix, and b i is after-log data of the expanded data at an ith detector bin.