Method and system for virtual endoscopy with guidance for biopsy

• US 7,536,216 B2
• Filed: 10/12/2005
• Issued: 05/19/2009
• Est. Priority Date: 10/18/2004
• Status: Expired due to Fees

• Current Assignee: Charite-Universitatsmedizin Berlin, Siemens Medical Solutions USA Incorporated (Siemens AG)
• Original Assignee: Charite-Universitatsmedizin Berlin, Siemens Medical Solutions USA Incorporated (Siemens AG)
• Inventors: Weiner, Guido M., Geiger, Bernhard
• Primary Examiner(s): Le; Long V
• Assistant Examiner(s): Selkin; Saurel J
• Application Number:
  US11/249,003
• Publication Number:
  US 20060084860A1
• Time in Patent Office: 1,315 Days
• Field of Search: 600/407, 600/410, 600/416, 600/420, 600/425, 600/431, 128/822, 128/920, 382/128, 348/45
• US Class Current: 600/407
• CPC Class Codes: A61B 10/04 Endoscopic instruments
• CPC Class Codes: A61B 34/10 Computer-aided planning, simulation or modelling of surgical operations
• CPC Class Codes: G06T 15/08 Volume rendering
• CPC Class Codes: G06T 2210/41 Medical

Abstract

A method for virtual endoscopy in a lumen includes acquiring from a patient an imaging dataset exhibiting a lesion external to a lumen wall; deriving a three-dimensional (3-D) volume of the lesion; obtaining data on maximum bend characteristics for a given endoscope; and deriving projection criteria, based on the 3-D volume and the maximum bend, for projecting an endoluminal image of said lesion, indicating an optimal biopsy insertion point.

Claims

1. A method for virtual endoscopy in a lumen, comprising:

acquiring from a patient an imaging dataset including a lesion external to a lumen wall;

generating multiplanar reformations (MPRs) from said dataset;

drawing planar contours around said lesions in said MPRs;

deriving a three-dimensional (3-D) volume of said lesion from said contours;

determining distances from a reference point to the start and end of said lesion;

determining the distance from said reference point to a virtual endoscope in said lumen;

obtaining data on maximum bend characteristics for a said endoscope in a lumen of a given diameter;

deriving projection criteria, based on said 3-D volume, the lumen diameter, and said maximum bend characteristics;

projecting an endoluminal image of said lesion based on said projection criteria, said endoluminal image indicating an optimal biopsy insertion point; and

deriving corrected values for the foregoing distances allowing for said maximum bend characteristics.

2. A method as recited in claim 1, wherein said step of deriving projection criteria comprises deriving a set of surface normals to said lumen wall and changing the respective angles of said normals based on said 3-D volume, the lumen diameter, and said maximum bend characteristics for obtaining a set of modified normals.

3. A method as recited in claim 1, wherein said step of projecting an endoluminal image of said lesion comprises utilizing said set of modified normals to define said endoluminal image.

4. A method as recited in claim 1, wherein said step of projecting an endoluminal image comprises a step of indicating thereon the respective depths of said lesion corresponding to respective points on said endoluminal image.

5. A method as recited in claim 4, wherein said step of indicating respective depths comprises a step of determining said respective depths along respective ones of said set of modified normals.

6. A method as recited in claim 4, wherein said step of projecting an endoluminal image comprises a step of indicating thereon the respective depths of said lesion corresponding to respective points on said endoluminal image.

7. A method as recited in claim 4, wherein said step of indicating the respective depths of said lesion comprises one of

(a) shading said image to indicate depth and

(b) color coding said image to indicate depth.

8. A method as recited in claim 7, including a step outputting information on the rotational angle of said virtual endoscope.

9. A method as recited in claim 7, including a step performing a real endoscopy utilizing information from said virtual endoscopy, including identifying said reference point, utilizing said corrected values, and said respective depths for inserting a biopsy needle at said optimal biopsy insertion point.

10. A method as recited in claim 7, including a step aligning said biopsy needle along a respective modified normal at said optimal biopsy insertion point.

11. A method as recited in claim 8, including a step performing a real endoscopy utilizing information from said virtual endoscopy, including identifying said reference point, utilizing said corrected values, said information on the rotational angle, and said respective depths for inserting a biopsy needle at said optimal biopsy insertion point.

12. A method as recited in claim 1, wherein said step of acquiring from a patient an imaging dataset comprises acquiring a data set of the bronchial airway.

13. A method for virtual endoscopy in a lumen, comprising:

acquiring from a patient an imaging dataset exhibiting a lesion external to a lumen wall;

drawing planar contours around said lesion;

deriving a three-dimensional (3-D) volume of said lesion;

obtaining data on maximum bend characteristics for a given endoscope; and

deriving projection criteria, based on said 3-D volume and said maximum bend, for projecting an endoluminal image of said lesion indicating an optimal biopsy insertion point.

14. A method as recited in claim 13, wherein said step of deriving projection criteria comprises deriving a set of normals to said lumen wall and changing the respective angles of said normals based on said 3-D volume, the lumen diameter, and said maximum bend characteristics for obtaining a set of modified normals.

15. A method as recited in claim 14, wherein said step of projecting an endoluminal image of said lesion comprises utilizing said set of modified normals to define said endoluminal image.

16. A method as recited in claim 13, wherein said step of projecting an endoluminal image comprises a step of indicating thereon the respective depths of said lesion corresponding to respective points on said endoluminal image.

17. A method as recited in claim 15, wherein said step of indicating respective depths comprises a step of determining said respective depths along respective ones of said set of modified normals.

18. A method as recited in claim 15, wherein said step of projecting an endoluminal image comprises a step of indicating thereon the respective depths of said lesion corresponding to respective points on said endoluminal image.

19. A method as recited in claim 15, wherein said step of indicating the respective depths of said lesion comprises one of

(a) shading said image to indicate depth and

(b) color coding said image to indicate depth.

20. A method as recited in claim 19, including a step outputting information on the rotational angle of said virtual endoscope.

21. A method as recited in claim 19, including

determining distances from a reference point to the start and end of said lesion;

and

determining the distance from said reference point to a virtual endoscope in said lumen.

22. A method as recited in claim 21, including a step of performing a real endoscopy utilizing information from said virtual endoscopy, including identifying said reference point, utilizing said corrected values, and said respective depths for inserting a biopsy needle at said optimal biopsy insertion point.

23. A method as recited in claim 22, including a step aligning said biopsy needle along a respective modified normal at said optimal biopsy insertion point.

24. A method as recited in claim 21, including a step of utilizing an endoscope having the characteristics of said virtual endoscope.

25. A method as recited in claim 22, including a step of performing a real endoscopy utilizing information from said virtual endoscopy, including identifying said reference point, utilizing said corrected values, said information on the rotational angle, and said respective depths for inserting a biopsy needle at said optimal biopsy insertion point.

26. A method as recited in claim 13 wherein said step of acquiring from a patient an imaging dataset comprises acquiring a data set of the bronchial airway.

27. A system for virtual endoscopy in a lumen, comprising:

means for acquiring from a patient an imaging dataset exhibiting a lesion external to a lumen wall;

generating multiplanar reformations (MPRs) from said dataset;

drawing planar contours around said lesion;

means for deriving a three-dimensional (3-D) volume of said lesion;

means for obtaining data on maximum bend characteristics for a given endoscope; and

means for deriving projection criteria, based on said 3-D volume and said maximum bend, for projecting an endoluminal image of said lesion, indicating an optimal biopsy insertion point.

28. A method for virtual endoscopy in a lumen, comprising:

acquiring a dataset from a patient using an imaging technique such as CT or MRI;

generating multiplanar reformations (MPRs) from said dataset, comprising two-dimensional (2D) sections;

displaying a virtual endoscopic view utilizing said dataset;

drawing planar contours around lesions in said MPRs;

calculating a 3-D volume from the planar contours;

displaying the 3-D volume in an endoluminal view;

selecting a reference point;

utilizing software to display the distances between said reference point to the start and endpoints of a lesion and to a virtual endoscope position;

utilizing software to display the maximal bending angle of an endoscope in a selected insertion area, a corrected distance to said reference point after bending, and to project an insertion point onto the endoluminal wall in said selected insertion area, and optionally, to display rotational information; and

guiding a real endoscope in a patient to said insertion point using information from said virtual bronchoscopy.

29. A computer program product comprising a computer useable medium having computer program logic recorded thereon for program code for performing a method for virtual endoscopy in a lumen, comprising:

acquiring from a patient an imaging dataset exhibiting a lesion external to a lumen wall;

deriving a three-dimensional (3-D) volume of said lesion;

obtaining data on maximum bend characteristics for a given endoscope; and

deriving projection criteria, based on said 3-D volume and said maximum bend, for projecting an endoluminal image of said lesion, indicating an optimal biopsy insertion point.

30. A method for virtual endoscopy within a cavity, comprising:

acquiring from said cavity an imaging dataset including a feature of interest external to a cavity wall;

drawing contours around said feature;

deriving a three-dimensional (3-D) volume of said feature from said contours;

determining distances from a reference point to the start and end of said feature;

determining the distance from said reference point to a virtual endoscope in said cavity;

obtaining data on maximum bend characteristics for said endoscope corresponding to said cavity dimensions;

deriving projection criteria, based on said 3-D volume, said cavity dimensions, and said maximum bend characteristics;

projecting an endoluminal image of said feature based on said projection criteria, said endoluminal image indicating an optimal access point; and

deriving corrected values for the foregoing distances allowing for said maximum bend characteristics.

31. A method as recited in claim 30, wherein said step of deriving projection criteria comprises deriving a set of surface normals to said cavity wall and changing the respective angles of said normals based on said 3-D volume, said cavity dimensions, and said maximum bend characteristics for obtaining a set of modified normals.

32. A method as recited in claim 31, wherein said step of projecting an endoluminal image of said feature comprises utilizing said set of modified normals to define said endoluminal image.

33. A method as recited in claim 30, wherein said step of projecting an endoluminal image comprises a step of indicating thereon the respective depths of said feature corresponding to respective points on said endoluminal image.

34. A method as recited in claim 33, wherein said step of indicating respective depths comprises a step of determining said respective depths along respective ones of said set of modified normals.

35. A method as recited in claim 33, wherein said step of projecting an endoluminal image comprises a step of indicating thereon the respective depths of said feature corresponding to respective points on said endoluminal image.

36. A method as recited in claim 33, wherein said step of indicating the respective depths of said feature comprises one of

(a) shading said image to indicate depth and

(b) color coding said image to indicate depth.

37. A method as recited in claim 36, including a step outputting information on the rotational angle of said virtual endoscope.

38. A method as recited in claim 36, including a step performing a real endoscopy utilizing information from said virtual endoscopy, including identifying said reference point, utilizing said corrected values, and said respective depths for inserting a tool at said optimal insertion point.

39. A method as recited in claim 38 including a step aligning said tool along a respective modified normal at said optimal insertion point.

40. A method as recited in claim 37, including a step performing a real endoscopy utilizing information from said virtual endoscopy, including identifying said reference point, utilizing said corrected values, said information on the rotational angle, and said respective depths for inserting a tool at said optimal insertion point.

41. A method for virtual endoscopy in a cavity, comprising:

acquiring an imaging dataset exhibiting a feature of interest external to a wall of said cavity;

deriving a three-dimensional (3-D) volume of said feature of interest;

obtaining data on maximum bend characteristics for a given endoscope; and

deriving projection criteria, based on said 3-D volume and said maximum bend, for projecting an endoluminal image of said feature of interest.

42. A method as recited in claim 41, wherein said step of deriving projection criteria comprises deriving a set of normals to said lumen wall and changing the respective angles of said normals based on said 3-D volume, the cavity diameter, and said maximum bend characteristics for obtaining a set of modified normals.

43. A method as recited in claim 41, wherein said step of projecting an endoluminal image of said feature of interest comprises utilizing said set of modified normals to define said endoluminal image.

44. A method as recited in claim 41, wherein said step of projecting an endoluminal image comprises a step of indicating thereon the respective depths of said feature of interest corresponding to respective points on said endoluminal image.

45. A method as recited in claim 44, wherein said step of indicating respective depths comprises a step of determining said respective depths along respective ones of said set of modified normals.

46. A method as recited in claim 44, wherein said step of projecting an endoluminal image comprises a step of indicating thereon the respective depths of said feature of interest corresponding to respective points on said endoluminal image.

47. A method as recited in claim 44, wherein said step of indicating the respective depths of said feature of interest comprises one of

(a) shading said image to indicate depth;

(b) color coding said image to indicate depth; and

(c) shading and color coding said image to indicate depth.

48. A method as recited in claim 47, including a step outputting information on the rotational angle of said virtual endoscope.

49. A method as recited in claim 47, including

determining distances from a reference point to the start and end of said feature of interest;

and

determining the distance from said reference point to a virtual endoscope in said lumen.

50. A method as recited in claim 49, including a step of performing a real endoscopy utilizing information from said virtual endoscopy, including identifying said reference point, utilizing said corrected values, and said respective depths for inserting a tool.

51. A method as recited in claim 50, including a step of aligning said tool along a respective modified normal at said optimal insertion point.

52. A method as recited in claim 49, including a step of utilizing an endoscope having the characteristics of said virtual endoscope.

53. A method as recited in claim 49, including a step of performing a real endoscopy utilizing information from said virtual endoscopy, including identifying said reference point, utilizing said corrected values, said information on the rotational angle, and said respective depths for inserting a tool at said optimal tool insertion point.

54. A method as recited in claim 41, wherein said step of acquiring an imaging dataset comprises acquiring a data set of said cavity.

55. A method for virtual endoscopy in a lumen, comprising:

deriving a three-dimensional (3-D) volume of a lesion;

obtaining data on maximum bend characteristics for a given endoscope; and

deriving projection criteria, based on said 3-D volume and said maximum bend, for projecting an endoluminal image of said lesion indicating an optimal biopsy insertion point.