Adaptive mask boundary correction in a cone beam imaging system

• US 6,084,937 A
• Filed: 09/28/1998
• Issued: 07/04/2000
• Est. Priority Date: 07/27/1998
• Status: Expired due to Term

• Current Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Original Assignee: Siemens Corporate Research Incorporated (Siemens AG)
• Inventors: Tam, Kwok C., Bani-Hashemi, Ali
• Primary Examiner(s): BRUCE, DAVID VERNON
• Application Number:
  US09/162,303
• Time in Patent Office: 645 Days
• Field of Search: 378/4, 378/8, 378/15, 378/94
• 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 6/466 adapted to display 3D data
• CPC Class Codes: G06T 11/006 Inverse problem, transformation from projection-space into object-space, e.g. transform methods, back-projection, algebraic methods
• CPC Class Codes: G06T 2211/421 Filtered back projection [FBP]
• CPC Class Codes: Y10S 378/901 Computer tomography program or processor

Abstract

A method and apparatus for three dimensional (3D) computerized tomographic (CT) imaging of a region-of-interest (ROI) in an object, wherein image reconstruction processing is applied to a plurality of sets of 2D cone beam projection data, each set being acquired on a 2D detector at a corresponding plurality of scan path source positions. A first image reconstruction processing step comprises applying a mask to each set of the projection data so that data inside the boundaries of each mask form a corresponding plurality of masked 2D data sets. Next, the data inside each masked 2D data set is filtered along a plurality of parallel lines formed therein, to generate a corresponding plurality of filtered 2D data sets. Each filtered 2D data set corresponds to a calculation of a first estimate of Radon derivative data determined from a given set of the 2D cone beam projection data. The next step comprises adaptively developing 2D correction data for each of the first estimates of Radon derivative data. The final step comprises combining each filtered 2D data set and the adaptively determined 2D correction data calculated therefore, in a weighted 3D backprojection manner into a 3D space, thereby reconstructing a 3D image of the ROI in the object.

Claims

1. A method for three dimensional (3D) computerized tomographic (CT) imaging of a region-of-interest (ROI) in an object, comprising:

acquiring a plurality of sets of 2D cone beam projection data by irradiating the object with energy from a cone beam source that is directed toward a 2D detector at a corresponding plurality of scan path source positions located about the object;

applying a mask to each set of the 2D cone beam projection data to form a corresponding plurality of masked 2D data sets;

processing the 2D cone beam projection data inside the mask boundaries of each masked 2D data set to develop a corresponding plurality of processed 2D data sets, each processed 2D data set corresponding to a calculation of a first estimate of Radon derivative data determined for a given set of the 2D cone beam projection data;

adaptively developing a predetermined limited amount of 2D correction data for each of the first estimates of Radon derivative data; and

combining each processed 2D data set and the 2D correction data adaptively developed therefore, in a weighted 3D backprojection manner into a common 3D space, thereby reconstructing in said 3D space a 3D image of the ROI in the object.

2. The method of claim 1, wherein the processing of the 2D cone beam projection data inside the mask boundaries comprises Feldkamp ramp filtering of said data along a plurality of parallel lines formed therein.

3. The method of claim 1, wherein the adaptiveness of the predetermined limited amount of 2D correction data that is developed is based on one of a desired degree of image reconstruction accuracy or a desired image reconstruction speed.

4. The method of claim 1, wherein the developing of said 2D correction data includes adaptively processing portions of the given set of 2D cone beam projection data that are adjacent boundaries of the mask in a corresponding one of the masked 2D data sets.

5. The method of claim 1, wherein the processing step corresponds to determining line integral derivatives for the 2D cone beam projection data in the masked 2D data sets that lies along a plurality of line segments L(r,θ

) having their end points bound by the mask in each of the masked 2D data sets.

6. The method of claim 5, wherein the developing of said 2D correction data includes calculating Δ

L(r,θ

) derivative data corresponding to the difference between line integrals calculated for pairs of line segments Δ

L(r,θ

) that are adjacent top and bottom boundaries of each mask, which pairs of line segments Δ

L(r,θ

) result from equal and opposite orthogonal translation of the line segments L(r,θ

) and extend outside and inside, respectively, of the top and bottom boundaries of the mask.

7. The method of claim 6, wherein the developing of said 2D correction data includes backprojecting the Δ

L(r,θ

) derivative data into a 2D space along a corresponding backprojection direction (θ

).

8. The method of claim 6, wherein the developing of said 2D correction data includes processing an adaptively limited range of angles (θ

) for the calculated Δ

L(r,θ

) derivative data, the adaptiveness of said range being based on one of a desired degree of image reconstruction accuracy or a desired image reconstruction speed.

9. The method of claim 7, wherein the backprojecting of the Δ

L(r,θ

) derivative data is along an adaptively limited range of backprojection directions (θ

), the adaptiveness of said limited range being based on one of a desired degree of image reconstruction accuracy or a desired image reconstruction speed.

10. The method of claim 6, wherein the developing of said 2D correction data includes backprojecting the Δ

L(r,θ

) derivative data into a 2D space having a size that is adaptively determined.

11. The method of claim 10, wherein the adaptiveness of said size determination during the backprojecting of the Δ

L(r,θ

) derivative data is based on one of a desired degree of image reconstruction accuracy or a desired image reconstruction speed.

12. The method of claim 4, wherein the developing of said 2D correction data includes multiplying portions of the 2D cone beam projection data that intersect top and bottom boundaries of the mask by a pixel spread function table that has been adaptively predetermined for said portions of the 2D cone beam projection data.

13. The method of claim 12, wherein the adaptiveness of the pixel spread function table is based on one of a desired degree of image reconstruction accuracy or a desired image reconstruction speed.

14. The method of claim 12, wherein said pixel spread function table is adaptively predetermined by determining line integral derivatives for only a limited angular range (θ

) for line segments Δ

L(r,θ

).

15. The method of claim 14, wherein said pixel spread function table is adaptively predetermined by 2D backprojecting said determined line integral derivatives along only a limited angular range (θ

) of backprojection directions.

16. The method of claim 14, wherein said pixel spread function table is adaptively predetermined by 2D backprojecting said determined line integral derivatives into a 2D space having an adaptively determined size.

17. The method of claim 16, wherein the adaptiveness of the size of the 2D space is based on one of a desired degree of image reconstruction accuracy or a desired image reconstruction speed.

18. Apparatus for three dimensional (3D) computerized tomographic (CT) imaging of a region-of-interest (ROI) in an object, comprising:

a cone beam source for applying radiation energy to at least the ROI of the object;

a 2D area detector for detecting radiation energy;

means for defining a source scanning trajectory as a scan path traversed by the source;

a manipulator for causing the cone beam source, fixed relative to an area detector with both source and detector movably positioned relative to the object, to scan about the ROI in the object at a plurality of source positions in a direction along the scan path to cause said area detector to acquire a set of 2D cone beam projection data at each of said source positions;

a masking means for applying a mask to each set of the 2D projection data to form masked data sets;

estimating means for processing the 2D cone beam projection data inside the mask boundaries of each masked 2D data set along a plurality of parallel lines formed therein, to generate a corresponding plurality of estimated 2D data sets, each estimated 2D data set corresponding to a calculation of a first estimate of Radon derivative data determined for a given set of the 2D cone beam projection data;

an adaptive processor means for adaptively developing a predetermined limited amount of 2D correction data for each of the first estimates of Radon derivative data; and

3D backprojection means for combining each estimated 2D data set and the 2D correction data adaptively calculated therefore, in a weighted 3D backprojection manner into a common 3D space, thereby reconstructing in said 3D space a 3D image of the ROI in the object.

19. The apparatus of claim 18, wherein the estimating means comprises a ramp filter processor for ramp filtering the 2D cone beam projection data inside the mask boundaries of each masked 2D data along a plurality of parallel lines formed therein.

20. The apparatus of claim 18, wherein the adaptive processor develops the predetermined limited amount of 2D correction data based on one of a desired degree of image reconstruction accuracy or a desired image reconstruction speed.

21. The apparatus of claim 18, wherein adaptive processor develops the predetermined limited amount of 2D correction data by processing portions of the given set of 2D cone beam projection data that are adjacent boundaries of the mask in a corresponding one of the masked 2D data sets.

22. The apparatus of claim 21, wherein the estimating means determines line integral derivatives for the 2D cone beam projection data in each of the masked 2D data sets that lies along a plurality of line segments L(r,θ

), each line segment having its end points bound by the mask in each of the masked 2D data sets.

23. The apparatus of claim 22, wherein the adaptive processor calculates Δ

L(r,θ

) derivative data as said correction data, said derivative data corresponding to the difference between line integrals calculated for pairs of line segments Δ

L(r,θ

) that are adjacent top and bottom boundaries of each mask, which pairs of line segments Δ

L(r,θ

) result from equal and opposite orthogonal translation of the line segments L(r,θ

) and extend outside and inside, respectively, of the top and bottom boundaries of the mask.

24. The apparatus of claim 23, wherein the adaptive processor backprojects the Δ

L(r,θ

) derivative data into a 2D space along a corresponding backprojection direction (θ

).

25. The apparatus of claim 23, wherein the adaptive processor processes an adaptively limited range of angles (θ

) for the calculated Δ

L(r,θ

) derivative data, the adaptiveness of said range being based on a one of a desired degree of image reconstruction accuracy or a desired image reconstruction speed.

26. The apparatus of claim 24, wherein the adaptive processor backprojects the Δ

L(r,θ

) derivative data along an adaptively limited range of backprojection directions (θ

), the adaptiveness of said limited range being based on one of a desired degree of image reconstruction accuracy or a desired image reconstruction speed.

27. The apparatus of claim 24, wherein the adaptive processor backprojects the Δ

L(r,θ

) derivative data into an adaptively determined size for said 2D space.

28. The apparatus of claim 27, wherein the adaptive processor determines the size of said space based on one of a desired degree of image reconstruction accuracy or a desired image reconstruction speed.

29. The apparatus of claim 21, wherein the adaptive processor includes a memory for storing values for a pixel spread function table that has been adaptively predetermined for portions of the 2D cone beam projection data that intersect top and bottom boundaries of the mask, the adaptive processor multiplying portions of the 2D cone beam projection by the values of said pixel spread function table.

30. The apparatus of claim 29, wherein the adaptiveness of the pixel spread function table is based on one of a desired degree of image reconstruction accuracy or a desired image reconstruction speed.

31. The apparatus of claim 29, wherein the adaptiveness of the pixel spread function table is predetermined by determining line integral derivatives for only a limited angular range (θ

) for line segments Δ

L(r,θ

).

32. The apparatus of claim 31, wherein said pixel spread function table is adaptively predetermined by 2D backprojecting said determined line integral derivatives along only a limited angular range (θ

) of backprojection directions.

33. The apparatus of claim 31, wherein said pixel spread function table is adaptively predetermined by 2D backprojecting said determined line integral derivatives into a 2D space having an adaptively determined size.

34. The apparatus of claim 31, wherein said pixel spread function table adaptively predetermines the size of the 2D space based on one of a desired degree of image reconstruction accuracy or a desired image reconstruction speed.