Methods and apparatus for weighting projection data

• US 6,654,442 B2
• Filed: 03/29/2002
• Issued: 11/25/2003
• Est. Priority Date: 03/29/2002
• 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:
  US10/113,532
• Publication Number:
  US 20030185337A1
• Time in Patent Office: 606 Days
• Field of Search: 378/4, 378/8, 378/15, 378/901
• US Class Current: 378/15
• CPC Class Codes: A61B 6/027 characterised by the use of a particular data acquisition trajectory, e.g. helical or spiral
• CPC Class Codes: G06T 11/005 Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating

Abstract

A method for weighting projection data includes selecting a region that includes a plurality of projection data samples, dividing the region into a plurality of equally sized sub-regions, and weighting the equally sized sub-regions using a location dependent z-smoothing weighting function.

Claims

1. A method for weighting projection data, said method comprising:

selecting a region that includes a plurality of projection data samples;

dividing the region into a plurality of equally sized sub-regions; and

weighting the equally sized sub-regions using a location dependent z-smoothing weighting function.

2. A method in accordance with claim 1 wherein selecting a region that includes a plurality of projection data samples comprises selecting a region that includes a plurality of projection data samples that are within 2π

/N in projection angle β

on either side of a plane of reconstruction.

3. A method in accordance with claim 1 further comprising defining a boundary of the sub-regions in accordance with:

${\mathrm{\beta <br><br>}}_{n,k}={\mathrm{\beta <br><br>}}_0+\frac{\left(2<br><br>k-M-1+n\right)<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\pi <br><br>}}{N};<br><br>$

where;

n=−

2, −

1, 0, 1, and 2; and

k=1, . . . ,M.; and

wherein;

β

₀ is a projection angle of a center view;

k is a single detector row;

M is a number of detector rows;

n is a sub-region index; and

N is a helical pitch.

4. A method in accordance with claim 1 further comprising selecting a weighting function that is an even function of a fan angle γ

, parallel to the sub-regions and a summation of the weighting is unity.

5. A method in accordance with claim 4 wherein selecting a weighting function comprises selecting a weighting function in accordance with:

6. A method in accordance with claim 1 wherein weighting the equally sized sub-regions using a location dependent z-smoothing weighting function comprises weighting the equally sized sub-regions using a location dependent z-smoothing weighting function in accordance with:

7. A method for weighting projection data, said method comprising:

selecting a region that includes a plurality of projection data samples that are within 2π

/N in projection angle β

on either side of a plane of reconstruction;

dividing the region into a plurality of equally sized sub-regions;

defining a boundary of the sub-regions in accordance with;

${\mathrm{\beta <br><br>}}_{n,k}={\mathrm{\beta <br><br>}}_0+\frac{\left(2<br><br>k-M-1+n\right)<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\pi <br><br>}}{N};<br><br>$

 

where;

n=−

2, −

1, 0, 1, or 2; and

k=1, . . . ,M; and

wherein;

β

₀ is a projection angle of a center view;

k is a single detector row;

M is a number of detector rows;

n is a sub-region index; and

N is a helical pitch; and

weighting the equally sized sub-regions using a location dependent z-smoothing weighting function in accordance with;

8. A computer for weighting projection data acquired using a medical imaging system, wherein said imaging system comprises a radiation source and a detector, said computer programmed to:

select a region that includes a plurality of projection data samples;

divide the region into a plurality of equally sized sub-regions; and

weight the equally sized sub-regions using a location dependent z-smoothing weighting function.

9. A computer in accordance with claim 8 wherein to select a region that includes a plurality of projection data samples, said computer further programmed to select a region that includes a plurality of projection data samples that are within 2π

/N in projection angle β

on either side of a plane of reconstruction.

10. A computer in accordance with claim 8, said computer further programmed to define a boundary of the sub-regions in accordance with:

${\mathrm{\beta <br><br>}}_{n,k}={\mathrm{\beta <br><br>}}_0+\frac{\left(2<br><br>k-M-1+n\right)<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\pi <br><br>}}{N};<br><br>$

where;

n=−

2, −

1, 0, 1, and 2; and

k=1, . . . ,M.; and

wherein;

β

₀ is a projection angle of a center view;

k is a single detector row;

M is a number of detector rows;

n is a sub-region index; and

N is a helical pitch.

11. A computer in accordance with claim 8, said computer further programmed to select a weighting function that is an even function of a fan angle γ

parallel to the sub-regions and a summation of the weighting is unity.

12. A computer in accordance with claim 11 wherein to select a region that includes a plurality of projection data samples, said computer further programmed to select a weighting function in accordance with:

 

w_k(γ

,β

)=w_k(−

γ

,β

−

γ

)

13. A computer in accordance with claim 8 wherein to weight the equally sized sub-regions using a location dependent z-smoothing weighting function, said computer further programmed to weight the equally sized sub-regions using a location dependent z-smoothing weighting function in accordance with:

14. A computed tomographic (CT) imaging system for weighting projection data, said CT system comprising:

a radiation source;

a detector array; and

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

select a region that includes a plurality of projection data samples;

divide the region into a plurality of equally sized sub-regions; and

weight the equally sized sub-regions using a location dependent z-smoothing weighting function.

19. A CT system in accordance with claim 14 wherein to weight the equally sized sub-regions using a location dependent z-smoothing weighting function, said computer further configured to weight the equally sized sub-regions using a location dependent z-smoothing weighting function in accordance with:

15. A CT system in accordance with 14, said computer further configured to select a region that includes a plurality of projection data samples that are within 2π

/N in projection angle β

on either side of a plane of reconstruction.

16. A CT system in accordance with 14, said computer further configured to define a boundary of the sub-regions in accordance with:

${\mathrm{\beta <br><br>}}_{n,k}={\mathrm{\beta <br><br>}}_0+\frac{\left(2<br><br>k-M-1+n\right)<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\pi <br><br>}}{N};<br><br>$

where;

n=−

2, −

1, 0, 1, and 2; and

k=1, . . . ,M.; and

wherein;

β

₀ is a projection angle of a center view;

k is a single detector row;

M is a number of detector rows;

n is a sub-region index; and

N is a helical pitch.

17. A CT system in accordance with 14, said computer further configured to select a weighting function that is an even function of a fan angle γ

parallel to the sub-regions and a summation of the weighting is unity.

18. A CT system in accordance with claim 17 wherein to select a region that includes a plurality of projection data samples, said computer further configured to select a weighting function in accordance with:

20. A computed tomographic (CT) imaging system for weighting projection data, said CT system comprising:

a radiation source;

a detector array; and

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

select a region that includes a plurality of projection data samples that are within 2π

/N in projection angle β

on either side of a plane of reconstruction;

divide the region into a plurality of equally sized sub-regions;

define a boundary of the sub-regions in accordance with;

${\mathrm{\beta <br><br>}}_{n,k}={\mathrm{\beta <br><br>}}_0+\frac{\left(2<br><br>k-M-1+n\right)<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\pi <br><br>}}{N};<br><br>$

 

where;

n=−

2, −

1, 0, 1, and 2; and

k=1, . . . ,M; and

wherein;

β

₀ is a projection angle of a center view;

k is a single detector row;

M is a number of detector rows;

n is a sub-region index; and

N is a helical pitch; and

weight the equally sized sub-regions using a location dependent z-smoothing weighting function in accordance with;