Image space correction for multi-slice helical reconstruction with z-smoothing

• US 6,600,802 B1
• Filed: 04/01/2002
• Issued: 07/29/2003
• Est. Priority Date: 04/01/2002
• Status: Expired due to Term

• Current Assignee: GE Medical Systems Global Technology Company LLC (GE Healthcare Technologies, Inc.)
• Original Assignee: GE Medical Systems Global Technology Company LLC (GE Healthcare Technologies, Inc.)
• Inventors: Hsieh, Jiang
• Primary Examiner(s): Bruce, David V.
• Application Number:
  US10/113,159
• Time in Patent Office: 484 Days
• Field of Search: 378/4, 378/8, 378/15, 378/19, 378/62, 378/901
• US Class Current: 378/4
• 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 6/4085 Cone-beams
• CPC Class Codes: Y10S 378/901 Computer tomography program or processor

Abstract

A method for facilitating a reduction of artifacts includes generating a first image of an object with a scanning system in native mode, generating a second image of the object with the scanning system with z-smoothing greater than any z-smoothing performed in native mode, and generating an artifact-candidate image by taking the difference between the first image and the second image.

Claims

1. A method for facilitating a reduction of artifacts, said method comprising:

generating a first image of an object with a scanning system in native mode;

generating a second image of the object with the scanning system with z-smoothing greater than any z-smoothing performed in native mode; and

generating an artifact-candidate image by taking the difference between the first image and the second image.

2. A method in accordance with claim 1 said generating a second image comprises generating a second image of the object with the scanning system with z-smoothing and a broader slice-sensitivity profile than the first image.

3. A method in accordance with claim 1 said generating a second image comprises generating a second image of the object with the scanning system with z-smoothing and a broader slice-sensitivity profile than the first image by no more than twenty percent.

4. A method in accordance with claim 1 said generating a second image comprises generating a second image of the object with the scanning system with z-smoothing and a broader slice-sensitivity profile than the first image by no more than twenty percent and no less than ten percent.

5. A method in accordance with claim 1 said generating a second image comprises generating a second image of the object with the scanning system with z-smoothing and a broader slice-sensitivity profile than the first image by approximately thirteen percent.

6. A method in accordance with claim 1 further comprising removing high-frequency content and other object related features from the generated artifact-candidate image to generate an artifact image.

7. A method in accordance with claim 6 wherein said removing high-frequency content comprises removing all objects smaller than a pre-defined threshold size.

8. A method in accordance with claim 1 further comprising utilizing a mask image based on a pre-determined set of conditions to correct the artifact-candidate image.

9. A method in accordance with claim 6 further comprising utilizing a mask image based on a pre-determined set of conditions to correct the artifact image.

10. A method in accordance with claim 9 further comprising generating a mask image m(x,y) using at least one of a m_o(x,y) and a m_s(x,y) according to:

$m_o<br><br>\left(x,y\right)=\{\begin{array}{cc}0,& I_o<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{low}}\\ \frac{I_o<br><br>\left(x,y\right)-t_{\mathrm{low}}}{t_{\mathrm{high}}-t_{\mathrm{low}}},& t_{\mathrm{low}<<br><br>}<br><br>I_o<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{high}}\\ 1,& I_o<br><br>\left(x,y\right)><br><br>t_{\mathrm{high}}\end{array}<br><br><br><br>m_s<br><br>\left(x,y\right)=\{\begin{array}{cc}0,& I_s<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{low}}\\ \frac{I_s<br><br>\left(x,y\right)-t_{\mathrm{low}}}{t_{\mathrm{high}}-t_{\mathrm{low}}},& t_{\mathrm{low}<<br><br>}<br><br>I_s<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{high}}\\ 1,& I_s<br><br>\left(x,y\right)><br><br>t_{\mathrm{high}}\end{array}$

where the mask m(x,y)=m_o(x,y)m_s(x,y), where m_o(x,y) represents a mask for the first image, m_s(x,y) represents a mask for the second image, t_low and t_high are pre-determined thresholds, I_o represents the first image, and I_s represents the second image.

11. A method in accordance with claim 10 wherein to correct the artifact image, said method further comprises generating a corrected image according to:

I_c(x,y)=I_o(x,y)−

s×

m(x,y)×

I_n(x,y), where s is a scaling factor, I_c represents the corrected image, and I_n represents the artifact image.

12. A method for facilitating a reduction of artifacts, said method comprising:

generating a first image I_o of an object with a scanning system in native mode;

generating a second image I_s of the object with the scanning system with z-smoothing greater than any z-smoothing performed in native mode;

generating an artifact-candidate image I_a by taking the difference between the first image and the second image;

removing high-frequency content and object related features from the generated artifact-candidate image to generate an artifact image I_n by removing all objects that are at least one of smaller than a pre-defined threshold size and match a pre-defined feature;

generating a mask image m(x,y) using at least one of a m_o(x,y) and a m_s(x,y) according to;

$m_o<br><br>\left(x,y\right)=\{\begin{array}{cc}0,& I_o<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{low}}\\ \frac{I_o<br><br>\left(x,y\right)-t_{\mathrm{low}}}{t_{\mathrm{high}}-t_{\mathrm{low}}},& t_{\mathrm{low}<<br><br>}<br><br>I_o<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{high}}\\ 1,& I_o<br><br>\left(x,y\right)><br><br>t_{\mathrm{high}}\end{array}<br><br><br><br>m_s<br><br>\left(x,y\right)=\{\begin{array}{cc}0,& I_s<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{low}}\\ \frac{I_s<br><br>\left(x,y\right)-t_{\mathrm{low}}}{t_{\mathrm{high}}-t_{\mathrm{low}}},& t_{\mathrm{low}<<br><br>}<br><br>I_s<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{high}}\\ 1,& I_s<br><br>\left(x,y\right)><br><br>t_{\mathrm{high}}\end{array}$

where the mask m(x,y)=m_o(x,y)m_s(x,y), where m_o(x,y) represents a mask for the first image, m_s(x,y) represents a mask for the second image, t_low and t_high are pre-determined thresholds; and

generating a corrected image I_c according to;

13. A computer programmed to:

generate a first image of an object with a scanning system in native mode;

generate a second image of the object with the scanning system with z-smoothing greater than any z-smoothing performed in native mode; and

generate an artifact-candidate image by taking the difference between the first image and the second image.

14. A computer in accordance with claim 13 further programmed to generate a second image of the object with the scanning system with z-smoothing and a broader slice-sensitivity profile than the first image.

15. A computer in accordance with claim 13 further programmed to generate a second image of the object with the scanning system with z-smoothing and a broader slice-sensitivity profile than the first image by no more than twenty percent.

16. A computer in accordance with claim 13 further programmed to generate a second image of the object with the scanning system with z-smoothing and a broader slice-sensitivity profile than the first image by no more than twenty percent and no less than ten percent.

17. A computer in accordance with claim 13 further programmed to generate a second image of the object with the scanning system with z-smoothing and a broader slice-sensitivity profile than the first image by approximately thirteen percent.

18. A computer in accordance with claim 13 further programmed to remove high-frequency content and other object related features from the generated artifact-candidate image to generate an artifact image.

19. A computer in accordance with claim 18 further programmed to remove all objects that are at least one of smaller than a pre-defined threshold size and match a pre-defined feature.

20. A computer in accordance with claim 13 further programmed to utilize a mask image based on a pre-determined set of conditions to correct the artifact-candidate image.

21. A computer in accordance with claim 13 further programmed to utilize a mask image based on a pre-determined set of conditions to correct the artifact image.

22. A computer in accordance with claim 21 further programmed to generate a mask image m(x,y) using at least one of a m_o(x,y) and a m_s(x,y) according to:

$m_o<br><br>\left(x,y\right)=\{\begin{array}{cc}0,& I_o<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{low}}\\ \frac{I_o<br><br>\left(x,y\right)-t_{\mathrm{low}}}{t_{\mathrm{high}}-t_{\mathrm{low}}},& t_{\mathrm{low}<<br><br>}<br><br>I_o<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{high}}\\ 1,& I_o<br><br>\left(x,y\right)><br><br>t_{\mathrm{high}}\end{array}<br><br><br><br>m_s<br><br>\left(x,y\right)=\{\begin{array}{cc}0,& I_s<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{low}}\\ \frac{I_s<br><br>\left(x,y\right)-t_{\mathrm{low}}}{t_{\mathrm{high}}-t_{\mathrm{low}}},& t_{\mathrm{low}<<br><br>}<br><br>I_s<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{high}}\\ 1,& I_s<br><br>\left(x,y\right)><br><br>t_{\mathrm{high}}\end{array}$

where the mask m(x,y)=m_o(x,y)m_s(x,y), where m_o(x,y) represents a mask for the first image, m_s(x,y) represents a mask for the second image, t_low and t_high are pre-determined thresholds, I_o represents the first scan, and I_s represents the second image.

23. A computer in accordance with claim 22 further programmed to generate a corrected image according to:

I_c(x,y)=I_o(x,y)−

s×

m(x,y)×

I_n(x,y), where s is a scaling factor, I_c represents the corrected image, and I_n represents the artifact image.

24. A computer programmed to:

generate a first image I_o of an object with a scanning system in native mode;

generate a second image I_s of the object with the scanning system with z-smoothing greater than any z-smoothing performed in native mode;

generate an artifact-candidate image I_a by taking the difference between the first image and the second image;

remove high-frequency content from the generated artifact-candidate image to generate an artifact image I_n by removing all objects that are at least one of smaller than a pre-defined threshold size and match a pre-defined feature;

generate a mask image m(x,y) using at least one of a m_o(x,y) and a m_s(x,y) according to;

$m_o<br><br>\left(x,y\right)=\{\begin{array}{cc}0,& I_o<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{low}}\\ \frac{I_o<br><br>\left(x,y\right)-t_{\mathrm{low}}}{t_{\mathrm{high}}-t_{\mathrm{low}}},& t_{\mathrm{low}<<br><br>}<br><br>I_o<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{high}}\\ 1,& I_o<br><br>\left(x,y\right)><br><br>t_{\mathrm{high}}\end{array}<br><br><br><br>m_s<br><br>\left(x,y\right)=\{\begin{array}{cc}0,& I_s<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{low}}\\ \frac{I_s<br><br>\left(x,y\right)-t_{\mathrm{low}}}{t_{\mathrm{high}}-t_{\mathrm{low}}},& t_{\mathrm{low}<<br><br>}<br><br>I_s<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{high}}\\ 1,& I_s<br><br>\left(x,y\right)><br><br>t_{\mathrm{high}}\end{array}$

where the mask m(x,y)=m_o(x,y)m_s(x,y), where m_o(x,y) represents a mask for the first image, m_s(x,y) represents a mask for the second image, t_low and t_high are predetermined thresholds; and

generate a corrected image I_c according to;

25. A computed tomographic (CT) imaging system for reconstructing an image of an object, said imaging system comprising:

a detector array;

at least one radiation source; and

a computer coupled to said detector array and said radiation source, said computer configured to;

generate a first image of an object with a scanning system in native mode;

generate a second image of the object with the scanning system with z-smoothing greater than any z-smoothing performed in native mode; and

generate an artifact-candidate image by taking the difference between the first image and the second image.

26. A CT imaging system in accordance with claim 25 wherein said computer is further programmed to generate a second image of the object with the scanning system with z-smoothing greater than any z-smoothing performed in native mode and a broader slice-sensitivity profile than the first image.

27. A CT imaging system in accordance with claim 25 wherein said computer is further programmed to generate a second image of the object with the scanning system with z-smoothing greater than any z-smoothing performed in native mode and a broader slice-sensitivity profile than the first image by no more than twenty percent.

28. A CT imaging system in accordance with claim 25 wherein said computer is further programmed to generate a second image of the object with the scanning system with z-smoothing greater than any z-smoothing performed in native mode and a broader slice-sensitivity profile than the first image by no more than twenty percent and no less than ten percent.

29. A CT imaging system in accordance with claim 25 wherein said computer is further programmed to generate a second image of the object with the scanning system with z-smoothing greater than any z-smoothing performed in native mode and a broader slice-sensitivity profile than the first image by approximately thirteen percent.

30. A CT imaging system in accordance with claim 25 wherein said computer is further programmed to remove high-frequency content and other object related features from the generated artifact-candidate image to generate an artifact image.

31. A CT imaging system in accordance with claim 30 wherein said computer is further programmed to remove all objects that are at least one of smaller than a pre-defined threshold size and match a pre-defined feature.

32. A CT imaging system in accordance with claim 25 wherein said computer is further programmed to utilize a mask image based on a pre-determined set of conditions to correct the artifact-candidate image.

33. A CT imaging system in accordance with claim 25 wherein said computer is further programmed to utilize a mask image based on a pre-determined set of conditions to correct the artifact image.

34. A CT imaging system in accordance with claim 33 wherein said computer is further programmed to generate a mask image m(x,y) using at least one of a m_o(x,y) and a m_s(x,y) according to:

$m_o<br><br>\left(x,y\right)=\{\begin{array}{cc}0,& I_o<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{low}}\\ \frac{I_o<br><br>\left(x,y\right)-t_{\mathrm{low}}}{t_{\mathrm{high}}-t_{\mathrm{low}}},& t_{\mathrm{low}<<br><br>}<br><br>I_o<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{high}}\\ 1,& I_o<br><br>\left(x,y\right)><br><br>t_{\mathrm{high}}\end{array}<br><br><br><br>m_s<br><br>\left(x,y\right)=\{\begin{array}{cc}0,& I_s<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{low}}\\ \frac{I_s<br><br>\left(x,y\right)-t_{\mathrm{low}}}{t_{\mathrm{high}}-t_{\mathrm{low}}},& t_{\mathrm{low}<<br><br>}<br><br>I_s<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{high}}\\ 1,& I_s<br><br>\left(x,y\right)><br><br>t_{\mathrm{high}}\end{array}$

where the mask m(x,y)=m_o(x,y)m_s(x,y), where m_o(x,y) represents a mask for the first image, m_s(x,y) represents a mask for the second image, t_low and t_high are pre-determined thresholds, I_o represents the first scan, and I_s represents the second image.

35. A CT imaging system in accordance with claim 34 wherein said computer is further programmed to generate a corrected image according to:

I_c(x,y)=I_o(x,y)−

s×

m(x,y)×

I_n(x,y), where s is a scaling factor, I_c represents the corrected image, and I_n represents the artifact image.

36. A computed tomographic (CT) imaging system for reconstructing an image of an object, said imaging system comprising:

a detector array;

at least one radiation source; and

a computer coupled to said detector array and said radiation source, said computer configured to;

generate a first image I_o of an object with a scanning system in native mode;

generate a second image I_s of the object with the scanning system with z-smoothing greater than any z-smoothing performed in native mode;

generate an artifact-candidate image I_a by taking the difference between the first image and the second image;

remove high-frequency content and other object related features from the generated artifact-candidate image to generate an artifact image I_n by removing all objects that are at least one of smaller than a pre-defined threshold size and match a pre-defined feature;

generate a mask image m(x,y) using at least one of a m_o(x,y) and a m_s(x,y) according to;

$m_o<br><br>\left(x,y\right)=\{\begin{array}{cc}0,& I_o<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{low}}\\ \frac{I_o<br><br>\left(x,y\right)-t_{\mathrm{low}}}{t_{\mathrm{high}}-t_{\mathrm{low}}},& t_{\mathrm{low}<<br><br>}<br><br>I_o<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{high}}\\ 1,& I_o<br><br>\left(x,y\right)><br><br>t_{\mathrm{high}}\end{array}<br><br><br><br>m_s<br><br>\left(x,y\right)=\{\begin{array}{cc}0,& I_s<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{low}}\\ \frac{I_s<br><br>\left(x,y\right)-t_{\mathrm{low}}}{t_{\mathrm{high}}-t_{\mathrm{low}}},& t_{\mathrm{low}<<br><br>}<br><br>I_s<br><br>\left(x,y\right)\le <br><br>t_{\mathrm{high}}\\ 1,& I_s<br><br>\left(x,y\right)><br><br>t_{\mathrm{high}}\end{array}$

where the mask m(x,y)=m_o(x,y)m_s(x,y), where m_o(x,y) represents a mask for the first image, m_s(x,y) represents a mask for the second image, t_low and t_high are pre-determined thresholds; and

generate a corrected image I_c according to;