IP Library Granted Patent US 8,958,660
Granted Patent B2
US 8,958,660 · App. 13/531,255 · Granted Feb 17, 2015

Method and apparatus for iterative reconstruction

Inventors: Debashish Pal (Waukesha, WI); Donghwan Kim (Ann Arbor, MI); Jang Hwan Cho (Ann Arbor, MI); Jeffrey A. Fessler (Ann Arbor, MI); Jean-Baptiste Thibault (Milwaukee, WI); Zhou Yu (Waukesha, WI); Somesh Srivastava (Waukesha, WI); Lin Fu (Niskayuna, NY); Bruno Kristiaan Bernard De Man (Clifton Park, NY)
Assignees: General Electric Company; University of Michigan
G06T11/003G06T11/006G06T2211/408G06T2211/424
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Quick Facts
Patent No.
US 8,958,660
App. No.
13/531,255
Granted
Feb 17, 2015
Kind
B2
Abstract

A method is provided for iteratively reconstructing an image of an object. The method includes accessing measurement data associated with the image, and using a simultaneous algorithm to reconstruct the image. Using the simultaneous algorithm to reconstruct the image includes determining a scaling factor that is voxel-dependent, and applying the voxel-dependent scaling factor to a gradient of an objective function to reconstruct the image.

Claims (27)

1. A method for iteratively reconstructing an image of an object, the method comprising:

accessing measurement data associated with the image, the measurement data obtained from a detector array; and

using, with a computer operably coupled to the detector array, a simultaneous algorithm to reconstruct the image, wherein using the simultaneous algorithm to reconstruct the image comprises:

determining, with the computer, a scaling factor that is voxel-dependent; and

applying, with the computer, the voxel-dependent scaling factor to a gradient of an objective function to reconstruct the image.

2. The method of claim 1 , wherein determining a scaling factor that is voxel-dependent comprises computing the scaling factor directly based on acquisition geometry.

3. The method of claim 1 , wherein determining a scaling factor that is voxel-dependent comprises computing the scaling factor directly based on acquisition geometry, the measurement data being non-uniformly sampled in a three-dimensional (3D) acquisition geometry.

4. The method of claim 1 , wherein determining a scaling factor that is voxel-dependent comprises generating an coefficient map and using the coefficient map to compute the voxel-dependent scaling factor.

5. The method of claim 4 , wherein generating an coefficient map comprises transforming an update obtained from a difference of one of at least two iterations or at least two sub-iterations.

6. The method of claim 4 , wherein generating an coefficient map comprises generating an coefficient map that is locally smooth and is capable of being low-pass filtered.

7. The method of claim 4 , wherein generating an coefficient map comprises obtaining the coefficient map from an initial image.

8. The method of claim 4 , wherein generating an coefficient map comprises augmenting the coefficient map with at least one of prior information about an initial image, a feature of a current image, or acquisition geometry.

9. The method of claim 1 , wherein applying the voxel-dependent scaling factor to a gradient of an objective function to reconstruct the image comprises scaling the gradient using the voxel-dependent scaling factor.

10. The method of claim 9 , wherein the scaling is at least one of obtained using A T WA or linked to contribution of subsets to every voxel.

11. The method of claim 1 , wherein applying the voxel-dependent scaling factor to a gradient of an objective function to reconstruct the image comprises computing a relaxation factor for each slice of the measurement data, the measurement data being non-uniformly sampled in a three-dimensional (3D) acquisition geometry wherein slices inside a region of interest (ROI) are sampled by a different number of views compared to slices outside the ROI.

12. The method of claim 1 , wherein applying the voxel-dependent scaling factor to a gradient of an objective function to reconstruct the image comprises updating the voxel-dependent scaling factor at a frequency that is reduced to every n-th iteration instead of every iteration.

13. The method of claim 4 , wherein the coefficient map is down-sampled.

14. An imaging system comprising:

a detector array; and

a computer coupled to the detector array, the computer configured to:

access measurement data associated with an image of an object; and

use a simultaneous algorithm to reconstruct the image, wherein the computer is configured to use the simultaneous algorithm to reconstruct the image by determining a scaling factor that is voxel-dependent and applying the voxel-dependent scaling factor to a gradient of an objective function to reconstruct the image.

15. The imaging system of claim 14 , wherein the computer is configured to determine the scaling factor that is voxel-dependent by computing the scaling factor directly based on acquisition geometry.

16. The imaging system of claim 14 , wherein the computer is configured to determine the scaling factor that is voxel-dependent by generating an coefficient map and using the coefficient map to compute the voxel-dependent scaling factor.

17. The imaging system of claim 16 , wherein the computer is configured to generate the coefficient map by transforming an update obtained from a difference of one of at least two iterations or at least two sub-iterations, the transformation being one of discrete or continuous.

18. The imaging system of claim 14 , wherein the computer is configured to apply the voxel-dependent scaling factor to the gradient of the objective function by scaling the gradient using the voxel-dependent scaling factor.

19. The imaging system of claim 14 , wherein the computer is configured to apply the voxel-dependent scaling factor to the gradient of the objective function by computing a relaxation factor for each slice of the measurement data, the measurement data being non-uniformly sampled in a three-dimensional (3D) acquisition geometry wherein slices inside a region of interest (ROI) are sampled by a different number of views compared to slices outside the ROI.

Assignments (3)
CONFIRMATORY LICENSE Recorded Jun 3, 2013
From: UNIVERSITY OF MICHIGAN
To: NATIONAL INSTITUTES OF HEALTH (NIH), U.S. DEPT. OF HEALTH AND HUMAN SERVICES (DHHS), U.S. GOVERNMENT
Reel/Frame 030537/0337 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 19, 2013
From: FESSLER, JEFFREY A.; CHO, JANG HWAN; KIM, DONGHWAN
To: THE REGENTS OF THE UNIVERSITY OF MICHIGAN
Reel/Frame 030254/0394 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 17, 2013
From: PAL, DEBASHISH; THIBAULT, JEAN-BAPTISTE; YU, ZHOU; SRIVASTAVA, SOMESH; FU, LIN; DE MAN, BRUNO KRISTIAAN BERNARD
To: GENERAL ELECTRIC COMPANY
Reel/Frame 030236/0084 →
Continuity (1)
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