System and method for reconstruction of X-ray images

• US 8,189,735 B2
• Filed: 07/22/2010
• Issued: 05/29/2012
• Est. Priority Date: 07/22/2010
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Khare, Kedar Bhalchandra, Eberhard, Jeffrey Wayne, Claus, Bernhard Erich Hermann
• Primary Examiner(s): Taningco, Alexander H
• Application Number:
  US12/841,815
• Publication Number:
  US 20120020448A1
• Time in Patent Office: 677 Days
• Field of Search: 378/4, 382/128, 382/131
• US Class Current: 378/4
• CPC Class Codes: G06T 11/006 Inverse problem, transformation from projection-space into object-space, e.g. transform methods, back-projection, algebraic methods
• CPC Class Codes: G06T 2211/424 Iterative
• CPC Class Codes: G06T 2211/436 Limited angle

Abstract

Systems and method for reconstruction of x-ray images are provided. One method includes acquiring a plurality of image views using an x-ray imaging system, the plurality of image views defining a limited tomographic dataset. The method also includes performing three-dimensional (3D) image reconstruction using the plurality of image views in an iterative reconstruction, wherein the iterative reconstruction includes forming a linear combination based on a plurality of previous iteration results. The method further includes displaying an image based on the image reconstruction, wherein the image includes clinically relevant high-frequency detail information.

Claims

1. A method for reconstructing images, the method comprising:

acquiring a plurality of image views using an x-ray imaging system, the plurality of image views defining a limited tomographic dataset;

performing three-dimensional (3D) image reconstruction using the plurality of image views in an iterative reconstruction, wherein the iterative reconstruction includes forming a linear combination based on a plurality of previous iteration results; and

displaying an image based on the image reconstruction, the image including clinically relevant high-frequency detail information.

2. A method in accordance with claim 1 further comprising using at least two prior iteration reconstruction results to update a convergence for the image reconstruction.

3. A method in accordance with claim 1, further comprising using an optimal gradient process for the iterative reconstruction.

4. A method in accordance with claim 3, wherein the optimal gradient process is a Nesterov optimal gradient process used to minimize a data consistency term for a cost function used in the iterative reconstruction.

5. A method in accordance with claim 1, wherein the iterative reconstruction includes a cost function minimization to a threshold level and wherein a value for a penalty weight for the cost function is automatically determined based on acquired image data including the plurality of image views.

6. A method in accordance with claim 5, wherein the automatic determination comprises using a statistical spread to set the weighting value.

7. A method in accordance with claim 1, wherein each iteration of the iterative reconstruction is defined by:

fⁿ⁺¹=P[gⁿ+τ

A^T(y−

A gⁿ)]

gⁿ=a(n)fⁿ+b(n)f<sup>n−

1</sup>

wherein P is a constraint operation, y is projection data corresponding to the projection views, A is a system operator, A^T is the backprojection operator, f is the image to be displayed, a and b are previous iteration results, g is the linear combination of a and b, and τ

is a scaling constant.

8. A method in accordance with claim 7, wherein P comprises a sparsification operation including at least one of thresholding in an image or other transform domain, total variation minimization, positivity and smoothness requirements.

9. A method in accordance with claim 1, wherein the image reconstruction comprises applying one of sparsity transforms and constraints.

10. A method in accordance with claim 1, further comprising determining when the image reconstruction has converged within a threshold, and wherein another iteration is performed if a result of the image reconstruction is not within the threshold and displaying the image if the result of the image reconstruction is within the threshold.

11. A method in accordance with claim 1, wherein the x-ray imaging system comprises operates in connection with one of a Positron Emission Tomography (PET) system or a Single Photon Emission Computed Tomography (SPECT) system.

12. A method in accordance with claim 1, wherein the iterative reconstruction forms a 3D image from the limited tomographic dataset, the 3D image having reduced artifacts.

13. A method in accordance with claim 12, wherein the limited tomographic dataset comprises between about 50 image views and about 100 image views.

14. A method in accordance with claim 12, wherein the 3D image formed from the limited tomographic dataset has an image quality less than and within an image margin of a full computed tomography (CT) scanner image quality.

15. A method in accordance with claim 1, wherein the imaging system acquiring the plurality of image views operates an x-ray source along a two-dimensional trajectory having laterally displaced trajectory points.

16. A method in accordance with claim 1, wherein the imaging system acquiring the plurality of image views operates an x-ray source along one of a circle, spiral, multiple line or sideways to an object trajectory.

17. A method in accordance with claim 1, further comprising using at least two of the previous iteration results for a subsequent update step when performing the 3D image reconstruction, the previous iteration results comprising prior image solutions.

18. A method in accordance with claim 1, further comprising using at least two of the previous iteration results for a subsequent update step when performing the 3D image reconstruction, the previous iteration results comprising prior image solutions not previously combined.

19. A method for reconstructing x-ray tomographic images, the method comprising:

acquiring a plurality of image views using an x-ray imaging system, the plurality of image views defining a limited tomographic dataset;

forming a linear combination of at least two reconstruction values for an iterative image reconstruction;

applying at least one sparsity transformation;

forward projecting the sparsity transformed linear combination and subtracting a result from data corresponding to the plurality of images views; and

displaying a three-dimensional (3D) image when a convergence level has been reached, the 3D image formed from the limited tomographic dataset having an image quality less than and within an image margin of a full computed tomography (CT) scanner image quality.

20. A method in accordance with claim 19, wherein the at least two reconstruction values comprise two prior image reconstruction iteration solutions or two initial values.

21. A method in accordance with claim 19, further comprising automatically selecting weighting values for a cost function for the linear combination.

22. A method in accordance with claim 19, further comprising backprojecting a residual error based on the forward projection if a threshold for the convergence level is not reached.

23. A method in accordance with claim 19, wherein the linear combination is based on:

fⁿ⁺¹=P[gⁿ+τ

A^T(y−

A gⁿ)]

g^n=a(n)fⁿ+b(n)f<sup>n−

1</sup>

wherein P is a constraint operation, y is projection data corresponding to the projection views, A is a system operator, A^T is the backprojection operator, f is the image to be displayed, a and b are previous iteration results, g is the linear combination of a and b, and τ

is a scaling constant.

24. A tomographic x-ray imaging system comprising:

an x-ray source;

an x-ray detector configured to detect x-rays impinging thereon after passing through an object;

a data acquisition system configured to acquire limited tomographic data from the x-ray detector;

an image reconstruction module configured to perform image reconstruction using the limited tomographic data in an iterative reconstruction, wherein the iterative reconstruction includes forming a linear combination based on a plurality of previous iteration results; and

a display for displaying an image based on the image reconstruction, wherein the image includes clinically relevant high-frequency detail information.

25. A tomographic x-ray imaging system in accordance with claim 24, further comprising automatically selecting weighting values for the image reconstruction.