Image segmentation for DRR generation and image registration

• US 20080037843A1
• Filed: 08/11/2006
• Published: 02/14/2008
• Est. Priority Date: 08/11/2006
• Status: Abandoned Application

• Current Assignee: Accuray Incorporated
• Original Assignee: Accuray Incorporated
• Inventors: Maurer, Calvin R., Wang, Hongwu, Fu, Dongshan
• Application Number:
  US11/502,699
• Publication Number:
  US 20080037843A1
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: G06T 15/08 Volume rendering
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 2210/41 Medical
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/38 Registration of image sequences

Abstract

A system, method and apparatus for enhancing 2D-3D registration with digitally reconstructed radiographs derived from segmented spine data.

Claims

1. A method, comprising:

segmenting a volume of interest (VOI) from three-dimensional (3D) imaging data to obtain a segmented VOI, wherein the 3D imaging data includes a pathological anatomy; and

generating digitally reconstructed radiographs (DRRs) from 3D transformations of the segmented VOI in each of two or more projections.

2. The method of claim 1, further comprising:

comparing a DRR in each projection with a corresponding two-dimensional (2D) in-treatment image to produce a similarity measure in each projection; and

computing a 3D rigid transformation corresponding to a maximum similarity measure in each projection.

3. The method of claim 2, wherein the maximum similarity measure corresponds to registration between the DRR in each projection and the corresponding 2D in-treatment image, further comprising computing the 3D rigid transformation from a transformation between the DRR in each projection and the corresponding 2D in-treatment image.

4. The method of claim 2, wherein the similarity measure in each projection comprises a vector displacement field between the DRR and the corresponding 2D in-treatment image.

5. The method of claim 4, further comprising determining an average rigid transformation of the segmented VOI from the 2D displacement field in each projection.

6. The method of claim 5, further comprising:

conforming relative positions of the pathological anatomy and a radiation treatment source to a radiation treatment plan.

7. The method of claim 2, wherein computing the 3D rigid transformation comprises:

computing a similarity measure between a first DRR in each projection and a corresponding 2D in-treatment image; and

selecting a transformation of the 3D segmented region from the similarity measure that generates a second DRR in each projection having an increased similarity measure with the corresponding 2D in-treatment image.

8. The method of claim 7, further comprising:

selecting a transformation of the 3D segmented region data that produces the greatest similarity measure in each projection.

9. The method of claim 2, wherein computing the 3D rigid transformation comprises:

computing a similarity measure between each of a plurality of DRRs in each projection and a corresponding 2D in-treatment image, wherein each DRR in a projection corresponds to a different 3D transformation of the segmented VOI.

10. The method of claim 9, further comprising:

selecting a transformation of the segmented VOI that produces the greatest similarity measure in each projection.

11. The method of claim 10, further comprising

determining 3D coordinates of the pathological anatomy from the transformation of the segmented VOI that produces the greatest similarity measure in each projection.

12. The method of claim 11, further comprising:

positioning a radiation treatment beam source using the 3D coordinates of the pathological anatomy such that a radiation beam emitted from the radiation treatment beam source is focused onto the pathological anatomy.

13. The method of claim 11, further comprising:

positioning a patient using the 3D coordinates of the pathological anatomy such that a radiation beam emitted from a radiation treatment beam source is focused onto the pathological anatomy.

14. The method of claim 1, wherein the VOI comprises a set of 2D contours in one or more views of the 3D imaging data.

15. The method of claim 1, wherein segmenting the VOI comprises generating a 3D voxel mask, wherein the voxel mask is configured to delineate the segmented region and to exclude all anatomical structures external to the segmented region.

16. The method of claim 15, wherein the 3D voxel mask is generated from a set of 2D contours.

17. The method of claim 15, wherein the 3D voxel mask comprises a plurality of multiple-bit voxel masks, wherein each bit in a multiple-bit voxel mask corresponds to a different VOI.

18. The method of claim 1, further comprising obtaining the 3D imaging data from a medical imaging system.

19. The method of claim 1, wherein the 3D imaging data comprises one or more of computed tomography (CT) image data, magnetic resonance (MR) image data, positron emission tomography (PET) image data and 3D rotational angiography (3DRA) image data for treatment planning.

20. The method of claim 1, wherein the 3D segmented region is the spine.

21. The method of claim 1, wherein the 3D segmented region is the cranium.

22. The method of claim 1, wherein the corresponding two-dimensional (2D) in-treatment image comprises an in-treatment x-ray image.

23. An article of manufacturing, comprising:

a machine-accessible medium including data that, when accessed by a machine, cause the machine to perform operations comprising;

segmenting a volume of interest (VOI) from three-dimensional (3D) imaging data to obtain a segmented VOI, wherein the 3D imaging data includes a pathological anatomy; and

generating digitally reconstructed radiographs (DRRs) from 3D transformations of the segmented VOI in each of two or more projections.

24. The article of manufacture of claim 23, wherein the machine-accessible medium further includes data that cause the machine to perform operations, comprising:

comparing a DRR in each projection with a corresponding two-dimensional (2D) in-treatment image to produce a similarity measure in each projection; and

computing a 3D rigid transformation corresponding to a maximum similarity measure in each projection.

25. The article of manufacture of claim 24, wherein the maximum similarity measure corresponds to registration between the DRR in each projection and the corresponding 2D in-treatment image, further comprising computing the 3D rigid transformation from a transformation between the DRR in each projection and the corresponding 2D in-treatment image.

26. The article of manufacture claim 24, wherein the transformation between the DRR and the corresponding 2D in-treatment image is a 2D displacement field in each projection.

27. The article of manufacture claim 26, wherein the machine-accessible medium further includes data that cause the machine to perform operations, comprising:

determining an average rigid transformation of the segmented VOI from the 2D displacement field in each projection.

28. The article of manufacture of claim 27, wherein the machine-accessible medium further includes data that cause the machine to perform operations, comprising:

conforming relative positions of the pathological anatomy and a radiation treatment source to a radiation treatment plan.

29. The article of manufacture of claim 24, wherein computing the 3D rigid transformation comprises:

computing a similarity measure between a first DRR in each projection and a corresponding 2D in-treatment image; and

selecting a transformation of the 3D segmented region from the similarity measure that generates a second DRR in each projection having an increased similarity measure with the corresponding 2D in-treatment image.

30. The article of manufacture of claim 29, wherein the machine-accessible medium further includes data that cause the machine to perform operations, comprising:

selecting a transformation of the 3D segmented region data that produces the greatest similarity measure in each projection.

31. The article of manufacture of claim 24, wherein the machine-accessible medium further includes data that cause the machine to perform operations, comprising:

computing a similarity measure between each of a plurality of DRRs in each projection and a corresponding 2D in-treatment image, wherein each DRR in a projection corresponds to a different 3D transformation of the segmented VOI.

32. The article of manufacture of claim 31, wherein the machine-accessible medium further includes data that cause the machine to perform operations, comprising:

selecting a transformation of the segmented VOI that produces the greatest similarity measure in each projection.

33. The article of manufacture of claim 32, wherein the machine-accessible medium further includes data that cause the machine to perform operations, comprising:

determining 3D coordinates of the pathological anatomy from the transformation of the segmented VOI that produces the greatest similarity measure in each projection.

34. The article of manufacture of claim 33, wherein the machine-accessible medium further includes data that cause the machine to perform operations, comprising:

positioning a radiation treatment beam source using the 3D coordinates of the pathological anatomy such that a radiation beam emitted from the radiation treatment beam source is focused onto the pathological anatomy.

35. The article of manufacture of claim 33, wherein the machine-accessible medium further includes data that cause the machine to perform operations, comprising:

positioning a patient using the 3D coordinates of the pathological anatomy such that a radiation beam emitted from a radiation treatment beam source is focused onto the pathological anatomy.

36. The article of manufacture of claim 23, wherein the segmented VOI comprises a set of 2D contours in one or more views of the 3D imaging data.

37. The article of manufacture of claim 23, wherein segmenting the VOI comprises generating a 3D voxel mask, wherein the voxel mask is configured to delineate the VOI and to exclude all anatomical structures external to the VOI.

38. The article of manufacture of claim 37, wherein the 3D voxel mask is generated from a set of 2D contours.

39. The article of manufacture of claim 37, wherein the 3D voxel mask comprises a plurality of multiple-bit voxel masks, wherein each bit in a multiple-bit voxel mask corresponds to a different VOI.

40. The article of manufacture of claim 23, wherein the machine-accessible medium further includes data that cause the machine to perform operations, comprising obtaining the 3D imaging data from a medical imaging system.

41. The article of manufacture of claim 23, wherein the 3D imaging data comprises one or more of computed tomography (CT) image data, magnetic resonance (MR) image data, positron emission tomography (PET) image data and 3D rotational angiography (3DRA) image data for treatment planning.

42. The article of manufacture of claim 23, wherein the 3D segmented region is the spine.

43. The article of manufacture of claim 23, wherein the 3D segmented region is the cranium.

44. The article of manufacture of claim 23, wherein the corresponding two-dimensional (2D) in-treatment image comprises an in-treatment x-ray image.

45. A system, comprising:

a treatment planning system including a first processing device, wherein the first processing device is configured to segment a volume of interest (VOI) from three-dimensional (3D) scan data to obtain a segmented VOI, wherein the 3D imaging data includes a pathological anatomy, and wherein the first processing device is further configured to generate a plurality of digitally reconstructed radiographs (DRRs) from the segmented VOI in each of two or more projections; and

a treatment delivery system including a second processing device configured to compare one or more DRRs in each projection with a corresponding two-dimensional (2D) in-treatment image to generate a 2D displacement field in each projection.

46. The system of claim 45, further comprising an image acquisition system including a third processing device, wherein the third processing device is configured to obtain the 3D imaging data, and wherein the second processor is further configured to determine an average rigid transformation of the 3D image data and a 3D displacement of the pathological anatomy and to apply image-guided radiation treatment to the pathological anatomy.

47. The system of claim 46, wherein the first processing device, the second processing device and the third processing device are the same processing device.

48. The system of claim 46, wherein the first processing device, the second processing device and the third processing device are different processing devices.

49. An apparatus, comprising:

means for removing image artifacts from an imaged volume; and

means for generating a two-dimensional (2D) projection of the imaged volume without the image artifacts.

50. The apparatus of claim 49, wherein the image artifacts are motion artifacts.

51. The apparatus of claim 49, wherein the image artifacts are interference artifacts.

52. The apparatus of claim 49, further comprising means for registering the 2D projection of the imaged volume with a corresponding 2D in-treatment image to determine 2D-3D transformation between the 2D in-treatment image and the imaged volume.

53. The apparatus of claim 49, further comprising means for comparing the 2D projection of the imaged volume with a corresponding 2D in-treatment image to generate a similarity measure, wherein the similarity measure corresponds to a 3D transformation of the imaged volume.