METHOD AND SYSTEM FOR ITERATIVE RECONSTRUCTION

• US 20080118020A1
• Filed: 11/17/2006
• Published: 05/22/2008
• Est. Priority Date: 11/17/2006
• Status: Active Grant

• Current Assignee: General Electric Company, Purdue Research Foundation (Purdue University), University of Notre DAME Du Lac
• Original Assignee: General Electric Company
• Inventors: Yu, Zhou, Bouman, Charles Addison, Basu, Samit Kumar, Sauer, Ken David, Hsieh, Jiang, De Man, Bruno Kristiaan Bernard, Thibault, Jean-Baptiste Daniel Marie
• Granted Patent: US 8,175,115 B2
• Time in Patent Office: Days
• 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

Abstract

A method for reconstructing an image of an object, the image comprising a plurality of image elements, is disclosed. The method includes accessing image data associated with the plurality of image elements, applying a first algorithm to the plurality of image elements, selecting a spatially non-homogenous set of the plurality of image elements, and applying an iterative algorithm to the set of image elements to reduce an amount of time necessary for reconstructing the image, or to improve an image quality at a fixed computation time, or both.

Claims

1. ) A method for reconstructing an image of an object, the image comprising a plurality of image elements, the method comprising:

accessing image data associated with the plurality of image elements;

applying a first algorithm to the plurality of image elements;

selecting a spatially non-homogenous set of the plurality of image elements; and

applying an iterative algorithm to the set of image elements to reduce an amount of time necessary for reconstructing the image, or to improve an image quality at a fixed computation time, or both.

2. ) The method of claim 1, wherein the applying the first algorithm comprises at least one of:

applying an ordered subsets algorithm;

applying a filtered backprojection algorithm;

applying a conjugate gradient algorithm; and

applying a homogeneous iterative coordinate descent algorithm.

3. ) The method of claim 1, wherein the applying the iterative algorithm comprises:

applying a non-homogeneous iterative coordinate descent algorithm.

4. ) The method of claim 1, wherein:

the applying the first algorithm is in response to determining that the plurality of image elements do not meet a convergence criteria.

5. ) The method of claim 1, further comprising:

processing the image data to create an image element selection map, the selection map comprising a plurality of magnitudes associated with the plurality of image elements, the processing comprising at least one of the following;

processing the image data based upon a history of response of the plurality of image elements to at least one of the applying the first algorithm, and the applying the iterative algorithm;

processing the image data based upon frequency information associated with the image data preceding at least one of the applying the first algorithm, and the applying the iterative algorithm;

processing the image data based upon a backprojection of the error sinogram; and

processing the image data based upon how recently each pixel was last updated;

sorting the plurality of magnitudes within the image element selection map;

wherein the selecting comprises selecting the set of image elements corresponding to the sorted image element selection map.

6. ) The method of claim 5, wherein the sorting the plurality of magnitudes comprises at least one of:

sorting the plurality of magnitudes, relating to the processing the image data based upon the history of response of the plurality of image elements, based upon an absolute value of change resulting from a previous application of one of the first algorithm and the iterative algorithm;

sorting the plurality of magnitudes, relating to the processing the image data based upon the history of response of the plurality of image elements, based upon low-pass filtering a value of a change resulting from a previous application of one of the first algorithm and the iterative algorithm; and

sorting the plurality of magnitudes, relating to the processing the image data based upon the history of response of the plurality of image elements, based upon a value of change related to more than two previous applications of one of the first algorithm and the iterative algorithm.

7. ) The method of claim 5, wherein:

the selecting comprises selecting the set of image elements corresponding to the sorted plurality of magnitudes in accordance with a defined magnitude criteria.

8. ) The method of claim 7, wherein:

the applying the iterative algorithm to the set of image elements is in response to determining that an iteration criteria has not been met.

9. ) The method of claim 8, further comprising:

separating the plurality of image elements into a set of subgroups; and

selecting a subgroup of the set of subgroups;

wherein the applying the first algorithm comprises applying the first algorithm to the selected subgroup of the set of subgroups; and

wherein the processing comprises filtering to make available data not within the subgroup.

10. ) The method of claim 1, wherein the selecting the set of the plurality of image elements comprises:

selecting a spatially non-homogeneous set of groups of the plurality of image elements.

11. ) The method of claim 10, wherein the selecting the set of groups of the plurality of image elements comprises:

selecting a set of groups of the plurality of image elements aligned along a z axis.

12. ) The method of claim 1, further comprising:

defining a positivity constraint to identify image elements disposed within the object;

wherein the selecting comprises selecting the spatially non-homogenous set of the plurality of image elements that meet the positivity constraint.

13. ) A program storage device readable by a computer, the device embodying a program or instructions executable by the computer to perform the method of claim 1.

14. ) An imaging system comprising:

a controller;

a source in signal communication with the controller, the source generating imaging energy in response to a control signal from the controller;

a detector array configured to receive the imaging energy and make available image data in response thereto;

a translating table configured for receiving an object thereon and operable to translate in relation to the source and the detector array; and

an image reconstructor electrically coupled to the detector array and the controller, the image reconstructor responsive to the controller and receptive of the image data to reconstruct an image comprising a plurality of image elements via a technique comprising the application of a first algorithm to the plurality of image elements, selection of a spatially non-homogenous set of the plurality of image elements, and application of an iterative algorithm to the set of image elements to reduce an amount of time necessary for reconstructing the image.

15. ) The system of claim 14, wherein:

the iterative algorithm comprises a non-homogenous iterative coordinate descent algorithm; and

the first algorithm comprises at least one of a penalized weighted least squared ordered subsets, a conjugate gradient algorithm, and a homogenous iterative coordinate descent algorithm.

16. ) The system of claim 14, wherein:

the image reconstructor is receptive of a convergence criteria; and

the image reconstructor applies the first algorithm in response to determining that the plurality of image elements do not meet the convergence criteria.

17. ) The system of claim 14, wherein:

the image reconstructor is responsive to the controller to process the image data to create an image element selection map, the selection map comprising a plurality of magnitudes associated with the plurality of image elements, via at least one of the following;

a history of response of the plurality of image elements to the application of at least one of the first algorithm and the iterative algorithm;

frequency information associated with the image data preceding the application of at least one of the first algorithm and the iterative algorithm;

a backprojection of the error sinogram;

a weighted backprojection of the error sinogram; and

a filtered backprojection of the error sinogram; and

the image reconstructor is responsive to the controller to sort the plurality of magnitudes within the image element selection map and select the set of image elements corresponding to the sorted image element selection map.

18. ) The system of claim 17, wherein:

the image reconstructor is responsive to the controller to sort the plurality of magnitudes within the image element selection map relating to the history of response of the plurality of image elements via at least one of;

an absolute value of change resulting from a previous application of one of the first algorithm and the iterative algorithm;

application of a low-pass filter to a value of a change resulting from a previous application of one of the first algorithm and the iterative algorithm; and

a value of change related to more than two previous applications of one of the first algorithm and the iterative algorithm;

wherein the image reconstructor selects the set of image elements corresponding to the sorted plurality of magnitudes in accordance with a defined magnitude criteria.

19. ) The system of claim 18, wherein:

the image reconstructor applies the iterative algorithm to the set of image elements in response to determining that an iteration criteria has not been met.

20. ) The system of claim 19, wherein:

the image reconstructor is responsive to the controller to separate the plurality of image elements into a set of subgroups, apply the first algorithm to a subgroup of the set of subgroups, and to apply filtering to the image element selection map to make available data not within the subgroup.

21. ) The system of claim 14, wherein:

the image reconstructor is responsive to the controller to select a set of groups of the plurality of image elements aligned along a z axis.

22. ) The system of claim 14, wherein:

wherein the image reconstructor is receptive of a positivity constraint to identify image elements disposed within the object, and selects the spatially non-homogenous set of the plurality of image elements that meet the positivity constraint.

23. ) The system of claim 14, wherein:

the imaging system comprises a computed tomography (CT) imaging system.