Method and apparatus for reducing cone beam artifacts using spatially varying weighting functions

• US 20070140535A1
• Filed: 12/19/2005
• Published: 06/21/2007
• Est. Priority Date: 12/19/2005
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Hsieh, Jiang, Li, Jianying, Okerlund, Darin, Vass, Melissa, Tang, Xiangyang, Londt, John
• Granted Patent: US 7,613,275 B2
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: A61B 6/027 characterised by the use of a particular data acquisition trajectory, e.g. helical or spiral
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/463 characterised by displaying multiple images or images and diagnostic data on one display
• CPC Class Codes: A61B 6/503 for diagnosis of the heart
• CPC Class Codes: A61B 6/583 using calibration phantoms

Abstract

A method for combining images acquired using helical half-scan imaging comprises identifying an image plane within an overlap region comprising data from first and second view streams representative of first and second cycles of acquired image data. The image plane comprises the same anatomical structure. First and second weighting functions are calculated for first and second images based on first and second tube positions of an x-ray tube. The first and second images correspond to the image plane and are from the first and second view streams, respectively. The first and second tube positions also correspond to the image plane. A weighted image is then formed based on the first and second weighting functions and the first and second images.

Claims

1. A method for combining images acquired using helical half-scan imaging, comprising:

identifying an image plane within an overlap region comprising data from first and second view streams representative of first and second cycles of acquired image data, the image plane comprising the same anatomical structure;

calculating first and second weighting functions for first and second images based on first and second tube positions of an x-ray tube, the first and second images corresponding to the image plane and being from the first and second view streams, respectively, the first and second tube positions corresponding to the image plane; and

forming a weighted image based on the first and second weighting functions and the first and second images.

2. The method of claim 1, the first and second weighting functions further comprising a pixel radial distance-dependent weighting function for minimizing the first and second weighting functions within a portion of the first and second images.

3. The method of claim 1, further comprising:

calculating lower weights for tube side areas within the first and second images closer to the x-ray tube; and

calculating higher weights for better image areas within the first and second images further from the x-ray tube, the first and second weighting functions being based on the lower and higher weights.

4. The method of claim 1, further comprising:

defining a center area based on the first and second images; and

calculating a pixel radial distance-dependent weighting function for the center area, the pixel radial distance-dependent weighting function minimizing change to the center area by the first and second weighting functions.

5. The method of claim 1, further comprising calculating a center view tube position based on at least one of an initial position of the x-ray tube at the beginning of a scan, first and second center views of the first and second cycles, and a total number of views per rotation, the first and second tube positions being further calculated based on the center view tube position.

6. The method of claim 1, wherein the first and second cycles comprising consecutive heart cycles.

7. The method of claim 1, further comprising identifying third and fourth images within the first and second view streams, respectively, the third and fourth images comprising the same anatomical structure with respect to each other, the third and fourth images forming a non-overlapped region.

8. A method for combining computed tomography (CT) images acquired using cone beam geometry, comprising:

identifying first and second datasets comprising at least a portion of like anatomical data, the first and second datasets being acquired within first and second cycles, respectively;

applying lower weighting functions to first areas of the first and second datasets and higher weighting functions to second areas of the first and second datasets, the first areas being closer to x-ray tube locations corresponding to the first and second datasets and the second areas being further from the x-ray tube locations; and

forming a combined image by combining the first and second datasets with the lower and higher weighting functions.

9. The method of claim 8, further comprising:

identifying an area of pixels within a center region of the first and second datasets; and

calculating a pixel radial distance-dependent weighting function for minimizing the lower and higher weighting functions within the center region.

10. The method of claim 8, further comprising:

calculating a center view tube position based on at least one of an initial position of an x-ray tube at the beginning of a scan, first and second center views of the first and second cycles, respectively, and a total number of views per rotation; and

calculating first and second tube positions of the x-ray tube corresponding to the first and second datasets and being based on the center view tube position, the first and second areas being further defined by the first and second tube positions.

11. The method of claim 8, further comprising identifying third and fourth datasets within the first and second cycles, respectively, the third and fourth datasets comprising the same anatomical data, the third and fourth datasets forming a non-overlapped region.

12. The method of claim 8, further comprising normalizing the lower and higher weighting functions.

13. The method of claim 8, further comprising:

identifying first and second detector center views and first and second x-ray tube initial positions based on the first and second cycles, respectively; and

calculating first and second center view tube positions based on the first and second detector center views and the first and second x-ray tube initial positions, the first and second areas being further based on the first and second center view tube positions.

14. The method of claim 8, further comprising:

identifying first and second detector center views based on the first and second cycles; and

calculating a first off-center distance between the first dataset and the first detector center view and a second off-center distance between the second dataset and the second detector center view, the lower and higher weighting functions being further based on the first and second off-center distances.

15. A system for combining images comprising like anatomical structure acquired using helical half-scan imaging, the system comprising:

a computer for receiving image data comprising at least two cycles of data, the computer configured to;

identify an image plane within an overlap region comprising data from first and second view streams representative of first and second cycles of image data, the image plane comprising substantially the same anatomical structure;

calculate first and second weighting functions for first and second images based on first and second tube positions of an x-ray tube, the first and second images corresponding to the image plane and being from the first and second view streams, respectively, the first and second tube positions corresponding to the image plane; and

form a weighted image based on the first and second weighting functions and the first and second images.

16. The system of claim 15, the computer further configured to:

calculate lower weights for tube side areas within the first and second images closer to the x-ray tube; and

calculate higher weights for better image areas within the first and second images further from the x-ray tube, the first and second weighting functions being based on the lower and higher weights.

17. The system of claim 15, the computer further configured to:

define a center area based on the first and second images; and

calculate a pixel radial distance-dependent weighting function for the center area, the pixel radial distance-dependent weighting function minimizing change to the center area by the first and second weighting functions.

18. The system of claim 15, the computer further configured to normalize the first and second weighting functions.

19. The system of claim 15, the computer further configured to:

identify first and second detector center views and first and second x-ray tube initial positions based on the first and second cycles, respectively; and

calculate first and second center view tube positions based on the first and second detector center views and the first and second x-ray tube initial positions, the first and second tube positions being based on the first and second center view tube positions.

20. The system of claim 15, the computer further configured to:

identify first and second detector center views based on the first and second cycles; and

calculate a first off-center distance between the first image and the first detector center view and a second off-center distance between the second image and the second detector center view, the first and second weighting functions being further based on the first and second off-center distances, respectively.