SYSTEMS AND METHODS FOR METAL ARTIFACT REDUCTION

• US 20160117850A1
• Filed: 10/23/2015
• Published: 04/28/2016
• Est. Priority Date: 10/24/2014
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: De Man, Bruno Kristiaan Bernard, Jin, Yannan, Wang, Ge, Xi, Yan
• Granted Patent: US 9,655,580 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 6/03 Computed tomography [CT] echo-tomography A61B8/14
• CPC Class Codes: A61B 6/482 involving multiple energy imaging
• CPC Class Codes: A61B 6/5205 involving processing of raw data to produce diagnostic data
• CPC Class Codes: A61B 6/5258 involving detection or reduction of artifacts or noise
• CPC Class Codes: G06T 11/005 Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/10144 Varying exposure
• CPC Class Codes: G06T 2207/20221 Image fusion; Image merging
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 2211/408 Dual energy
• CPC Class Codes: G06T 5/50 using two or more images, e.g. averaging or subtraction
• CPC Class Codes: G06T 5/77 Retouching; Inpainting; Scratch removal

Abstract

A method includes receiving, with at least one processor, a first projection dataset corresponding to X-rays at a first energy level projected towards a subject at a first set of view angles and receiving, with the at least one processor, a second projection dataset corresponding to X-rays at a second energy level projected towards the subject at a second set of view angles. The method further includes identifying, with the at least one processor, a metal trace from at least one of the first projection dataset and the second projection dataset. Moreover, the method includes converting, with the at least one processor, at least a portion of the first projection dataset to a pseudo dataset at the second energy level. The method also includes generating, with the at least one processor, a final image of the subject based on the second projection dataset, the pseudo dataset, and the metal trace.

Claims

1. A method, comprising:

receiving, with at least one processor, a first projection dataset corresponding to X-rays at a first energy level projected towards a subject at a first set of view angles;

receiving, with the at least one processor, a second projection dataset corresponding to X-rays at a second energy level projected towards the subject at a second set of view angles;

identifying, with the at least one processor, a metal trace from at least one of the first projection dataset and the second projection dataset;

converting, with the at least one processor, at least a portion of the first projection dataset to a pseudo dataset at the second energy level; and

generating, with the at least one processor, a final image of the subject based on the second projection dataset, the pseudo dataset, and the metal trace.

2. The method of claim 1, wherein the first set of view angles is different from the second set of view angles, and wherein a number of view angles in the first set is lower than a number of view angles in the second set.

3. The method of claim 1, wherein identifying the metal trace comprises:

reconstructing a first image based on the first projection dataset;

segmenting a metal mask from the first image; and

determining the metal trace in the first projection dataset based on the metal mask.

4. The method of claim 3, wherein converting at least the portion of the first projection dataset to the pseudo dataset at the second energy level comprises:

converting at least a portion of the first image to a pseudo image at the second energy level; and

generating the pseudo dataset at the second energy level based on the pseudo image at the second energy level.

5. The method of claim 3, wherein converting at least the portion of the first projection dataset to the pseudo dataset at the second energy level comprises:

segmenting one or more material regions from the first image; and

generating the pseudo dataset at the second energy level based on the one or more material regions and the first projection dataset using a higher order beam hardening correction technique.

6. The method of claim 3, wherein generating the final image of the subject comprises:

generating a final projection dataset by replacing one or more dexels in the second projection dataset with one or more dexels in the pseudo dataset based on the metal trace; and

reconstructing the final image of the subject based on the final projection dataset.

7. The method of claim 3, wherein converting at least the portion of the first projection dataset to the pseudo dataset at the second energy level comprises:

generating a cartoon image based on the first image;

converting at least a portion of the cartoon image to a pseudo image at the second energy level; and

generating the pseudo dataset at the second energy level based on the pseudo image at the second energy level.

8. The method of claim 7, wherein generating the final image of the subject comprises:

normalizing the second projection dataset based on the pseudo dataset;

generating a pre-final projection dataset by replacing one or more dexels in the normalized second projection dataset with one or more dexels in the pseudo dataset based on the metal trace;

generating a final projection dataset by de-normalizing the pre-final projection dataset based on the pseudo dataset; and

reconstructing the final image of the subject based on the final projection dataset.

9. The method of claim 3, wherein generating the final image of the subject comprises:

segmenting a neighborhood mask from the first image based on the metal mask;

identifying a neighborhood trace in the first projection dataset based on the neighborhood mask;

generating a final projection dataset by replacing one or more dexels in the second projection dataset with one or more dexels in the pseudo dataset based on the metal trace and the neighborhood trace; and

reconstructing the final image of the subject based on the final projection dataset.

10. The method of claim 1, wherein generating the final image of the subject comprises:

reconstructing a first image based on the first projection dataset;

converting at least a portion of the first image to a pseudo image at the second energy level; and

generating the final image of the subject based on the pseudo image at the second energy level and the second projection dataset using an iterative reconstruction technique, wherein the iterative reconstruction technique is configured to down-weight one or more dexels in the second projection dataset based on the metal trace.

11. The method of claim 1, wherein generating the final image of the subject comprises:

generating a monochromatic projection dataset at the second energy level based on the pseudo dataset; and

generating the final image of the subject based on the monochromatic projection dataset and the second projection dataset using an iterative reconstruction technique, wherein the iterative reconstruction technique is configured to down-weight one or more dexels in the second projection dataset based on the metal trace.

12. The method of claim 1, wherein generating the final image of the subject comprises:

generating a final projection dataset by performing guided projection completion in the second projection dataset based on the pseudo dataset; and

generating the final image of the subject based on the final projection dataset.

13. A system, comprising:

at least one processor;

a communication subunit configured to;

receive a first projection dataset corresponding to X-rays at a first energy level projected towards a subject at a first set of view angles; and

receive a second projection dataset corresponding to X-rays at a second energy level projected towards the subject at a second set of view angles;

a mask subunit communicatively coupled with the communication subunit and configured to identify a metal trace from at least one of the first projection dataset and the second projection dataset;

a conversion subunit communicatively coupled with the mask subunit and configured to convert at least a portion of the first projection dataset to a pseudo dataset at the second energy level; and

a final image subunit communicatively coupled with the conversion subunit and configured to generate a final image of the subject based on the second projection dataset, the pseudo dataset, and the metal trace,

wherein one or more of the communication subunit, the mask subunit, the conversion subunit, and the final image subunit are executable by the at least one processor.

14. The system of claim 13, wherein the first set of view angles is different from the second set of view angles and a number of view angles in the first set is lower than a number of view angles in the second set.

15. The system of claim 13, further comprising a reconstruction subunit configured to:

reconstruct a first image based on the first projection dataset; and

generate a cartoon image based on the first image.

16. The system of claim 15, wherein the mask subunit is further configured to segment at least one of a metal mask and a neighborhood mask from at least one of the first image and the cartoon image.

17. The system of claim 16, further comprising a reprojection subunit configured to:

identify the metal trace in the first projection dataset based on the metal mask; and

identify a neighborhood trace in the first projection dataset based on the neighborhood mask.

18. The system of claim 17, wherein the conversion subunit is further configured to generate the pseudo dataset at the second energy level based on one or more material regions in the first image, the metal trace, and the first projection dataset using a higher order beam hardening correction technique.

19. The system of claim 17, wherein the conversion subunit is further configured to:

convert at least one of at least a portion of the first image and at least a portion of the cartoon image to a pseudo image at the second energy level; and

generate the pseudo dataset at the second energy level based on the pseudo image at the second energy level.

20. The system of claim 18, wherein the final image subunit is further configured to:

generate a final projection dataset by replacing one or more dexels in the second projection dataset with one or more dexels in the pseudo dataset based on at least one of the metal trace and the neighborhood trace; and

reconstruct the final image of the subject based on the final projection dataset.

21. The system of claim 18, wherein the final image subunit is further configured to generate the final image of the subject based on the pseudo image at the second energy level and the second projection dataset using an iterative reconstruction technique, and wherein the iterative reconstruction technique is configured to down-weight one or more dexels in the second projection dataset based on the metal trace.

22. The system of claim 13, wherein the conversion subunit is further configured to generate a monochromatic projection dataset at the second energy level based on the pseudo dataset.

23. The system of claim 22, wherein the final image subunit is further configured to generate the final image of the subject based on the monochromatic projection dataset and the second projection dataset using an iterative reconstruction technique, and wherein the iterative reconstruction technique is configured to down-weight one or more dexels in the second projection dataset based on the metal trace.

24. An imaging system, comprising:

a computed tomography (CT) scanner configured to;

project X-rays at a first energy level towards a subject at a first set of view angles;

project X-rays at a second energy level towards the subject at a second set of view angles;

generate a first projection dataset corresponding to the X-rays at the first energy level;

generate a second projection dataset corresponding to the X-rays at the second energy level;

a controller communicatively coupled to the CT scanner and comprising;

at least one processor;

a communication subunit configured to receive the first projection dataset and the second projection dataset from the CT scanner;

a mask subunit communicatively coupled with the communication subunit and configured to identify a metal trace from at least one of the first projection dataset and the second projection dataset;

a conversion subunit communicatively coupled with the mask subunit and configured to convert at least a portion of the first projection dataset to a pseudo dataset at the second energy level; and

a final image subunit communicatively coupled with the conversion subunit and configured to generate a final image of the subject based on the second projection dataset, the pseudo dataset, and the metal trace,

wherein one or more of the communication subunit, the mask subunit, the conversion subunit, and the final image subunit are executable by the at least one processor.

25. A computer program product comprising a non-transitory computer readable medium encoding instructions that, in response to execution by at least one processor, cause the at least one processor to perform operations comprising:

receiving a first projection dataset corresponding to X-rays at a first energy level projected towards a subject at a first set of view angles;

receiving a second projection dataset corresponding to X-rays at a second energy level projected towards the subject at a second set of view angles;

identifying a metal trace from at least one of the first projection dataset and the second projection dataset;

converting at least a portion of the first projection dataset to a pseudo dataset at the second energy level; and

generating a final image of the subject based on the second projection dataset, the pseudo dataset, and the metal trace.