Methods and apparatus for optical coherence tomography scanning

• US 7,884,945 B2
• Filed: 01/20/2006
• Issued: 02/08/2011
• Est. Priority Date: 01/21/2005
• Status: Active Grant

• Current Assignee: Massachusetts Institute of Technology
• Original Assignee: Massachusetts Institute of Technology
• Inventors: Huber, Robert, Wojtkowski, Maciej, Ko, Tony, Fujimoto, James, Srinivasan, Vivek
• Primary Examiner(s): Chowdhury; Tarifur
• Assistant Examiner(s): COOK, JONATHON
• Application Number:
  US11/336,427
• Publication Number:
  US 20060187462A1
• Time in Patent Office: 1,845 Days
• Field of Search: 356/479, 356/497
• US Class Current: 356/497
• CPC Class Codes: A61B 1/00172 with means for scanning
• CPC Class Codes: A61B 3/102 for optical coherence tomography [OCT]
• CPC Class Codes: A61B 5/0066 Optical coherence imaging
• CPC Class Codes: A61B 5/0073 by tomography, i.e. reconstruction of 3D images from 2D projections A61B5/0066 takes precedence
• CPC Class Codes: A61B 5/4842 Monitoring progression or stage of a disease
• CPC Class Codes: A61B 5/6852 Catheters
• CPC Class Codes: G01N 21/4795 spatially resolved investigating of object in scattering medium in vivo A61B

Abstract

In one aspect, the invention relates to a method of acquiring optical coherence tomographic data from a sample. The method includes the steps of scanning a first location on the sample to obtain a first set of optical coherence tomographic data, scanning a second location on the sample to obtain a second set of optical coherence tomographic data, and defining a fiducial position relative to a location on the sample using one of the two sets of optical coherence tomographic data. In one embodiment, the first set of optical coherence tomographic data is survey data. However, in another embodiment the first set of optical coherence tomographic data is sample measurement data.

Claims

1. A method of processing optical coherence tomography (OCT) data obtained from a sample, the method comprising:

analyzing at least a first subset of an OCT data set to identify landmark region data;

placing the OCT data set in correspondence with the landmark region data; and

processing at least a second subset of the OCT data set based on the correspondence of the OCT data set to the landmark region data, the OCT data set being derived from measurements of backscattering or back-reflection as a function of optical depth within the sample.

2. The method of claim 1 further comprising selecting another subset of data from the OCT data set for processing based on the correspondence of the OCT data set.

3. The method of claim 1 further comprising generating an image using the OCT data set.

4. The method of claim 3 wherein the image is an en face image.

5. The method of claim 1 further comprising generating numerical measurements regarding portions of elements in the sample using the OCT data set.

6. The method of claim 5 wherein the elements are constituents of a human eye.

7. The method of claim 1 further comprising correcting for motion error using the landmark region data.

8. The method of claim 1 wherein the sample is an eye, the second subset of data includes a circumpapillary scan, and the OCT data set includes data from a peripapillary region.

9. The method of claim 1 further comprising longitudinally monitoring changes in the second subset of data at different points in time.

10. The method of claim 9 further including monitoring a retinal nerve fiber layer thickness over time.

11. The method of claim 1 wherein at least a portion of the OCT data set is a three dimensional data set generated by a raster scan and analyzing at least the first subset of the OCT data includes transforming three dimensional OCT data to two dimensional data.

12. The method of claim 1 wherein the landmark region data includes a representation of an optic disc.

13. The method of claim 12 wherein processing includes measuring retinal nerve fiber layer thickness registered to a center of the optic disc.

14. The method of claim 1 wherein the landmark region data includes a representation of a macula.

15. The method of claim 14 wherein processing includes measuring retinal thickness registered to a center of the macula.

16. The method of claim 1 wherein the landmark region data includes a representation of a fovea.

17. The method of claim 1 wherein the first subset of the OCT data set includes data from the second subset of the OCT data set.

18. The method of claim 1 wherein the first subset of the OCT data set includes data other than data in the second subset of the OCT data set.

19. The method of claim 1 wherein the OCT data set is derived from Fourier domain measurements of backscattering or back-reflection.

20. A method of processing optical coherence tomography (OCT) data obtained from a sample, the method comprising:

analyzing at least a first subset of an OCT data set to identify landmark region data;

registering the OCT data set by aligning the OCT data set using the landmark region data; and

processing at least a second subset of the OCT data set in response to registration of the OCT data set, the OCT data set being derived from measurements of backscattering or back-reflection as a function of optical depth within the sample.

21. The method of claim 20 further comprising selecting another subset of data from the OCT data set for processing in response to the registration of the OCT data set.

22. The method of claim 20 further comprising generating an image using the OCT data set.

23. The method of claim 22 wherein the image is an en face image.

24. The method of claim 20 further comprising generating numerical measurements regarding portions of elements in the sample using the OCT data set.

25. The method of claim 24 wherein the elements are constituents of a human eye.

26. The method of claim 20 further comprising correcting for motion error using the landmark region data.

27. The method of claim 20 wherein the sample is an eye, the second subset of data includes a circumpapillary scan, and the OCT data set includes data from a peripapillary region.

28. The method of claim 20 further comprising longitudinally monitoring changes in the second subset of data at different points in time.

29. The method of claim 28 further including monitoring a retinal nerve fiber layer thickness over time.

30. The method of claim 20 wherein at least a portion of the OCT data set is a three dimensional data set generated by a raster scan and analyzing at least the first subset of the OCT data includes transforming three dimensional OCT data to two dimensional data.

31. The method of claim 20 wherein the landmark region data includes a representation of an optic disc.

32. The method of claim 31 wherein processing includes measuring retinal nerve fiber layer thickness registered to a center of the optic disc.

33. The method of claim 20 wherein the landmark region data includes a representation of a macula.

34. The method of claim 33 wherein processing includes measuring retinal thickness registered to a center of the macula.

35. The method of claim 20 wherein the landmark region data includes a representation of a fovea.

36. The method of claim 20 wherein the first subset of the OCT data set includes data from the second subset of the OCT data set.

37. The method of claim 20 wherein the first subset of the OCT data set includes data other than data in the second subset of the OCT data set.

38. The method of claim 20 wherein the OCT data set is derived from Fourier domain measurements of backscattering or back-reflection.

39. A method of processing optical coherence tomography (OCT) data obtained from a sample, the method comprising:

analyzing at least a first subset of an OCT data set to identify landmark region data;

registering the OCT data set by identifying a position of the OCT data set relative to a location of the landmark region data; and

processing at least a second subset of the OCT data set in response to registration of the OCT data set, the OCT data set being derived from Fourier-domain measurements of backscattering or back-reflection as a function of optical depth within the sample.

40. The method of claim 39 further comprising selecting another subset of data from the OCT data set for processing in response to the registration of the OCT data set.

41. The method of claim 39 further comprising generating an image using the OCT data set.

42. The method of claim 41 wherein the image is an en face image.

43. The method of claim 39 further comprising generating numerical measurements regarding portions of elements in the sample using the OCT data set.

44. The method of claim 43 wherein the elements are constituents of a human eye.

45. The method of claim 39 further comprising correcting for motion error using the landmark region data.

46. The method of claim 39 wherein the sample is an eye, the second subset of data includes a circumpapillary scan, and the OCT data set includes data from a peripapillary region.

47. The method of claim 39 further comprising longitudinally monitoring changes in the second subset of data at different points in time.

48. The method of claim 47 further including monitoring a retinal nerve fiber layer thickness over time.

49. The method of claim 39 wherein at least a portion of the OCT data set is a three dimensional data set generated by a raster scan and analyzing at least the first subset of the OCT data includes transforming three dimensional OCT data to two dimensional data.

50. The method of claim 39 wherein the landmark region data includes a representation of an optic disc.

51. The method of claim 50 wherein processing includes measuring retinal nerve fiber layer thickness registered to a center of the optic disc.

52. The method of claim 39 wherein the landmark region data includes a representation of a macula.

53. The method of claim 52 wherein processing includes measuring retinal thickness registered to a center of the macula.

54. The method of claim 39 wherein the landmark region data includes a representation of a fovea.

55. The method of claim 39 wherein the first subset of the OCT data set includes data in the second subset of the OCT data set.

56. The method of claim 39 wherein the first subset of the OCT data set includes data other than data in the second subset of the OCT data set.

57. A method of processing optical coherence tomography (OCT) data, the method comprising:

obtaining an OCT data set from a sample, at least a portion of the OCT data set being obtained by raster scanning the sample;

analyzing at least a first subset of the OCT data set to identify landmark region data;

registering the OCT data set by identifying a position of the OCT data set relative to a location of the landmark region data; and

processing at least a second subset of the OCT data set to obtain at least one measurement associated with a part of the sample whose location is determined with respect to the landmark region data, the OCT data set being derived from Fourier-domain measurements of backscattering or back-reflection as a function of optical depth within the sample.

58. The method of claim 57 wherein the first subset of the OCT data set includes data in the second subset of the OCT data set.

59. The method of claim 57 wherein the first subset of the OCT data set includes data other than data in the second subset of the OCT data set.

60. The method of claim 57 wherein the OCT data set includes three-dimensional data and wherein analyzing at least the first subset of the OCT data set includes transforming three-dimensional OCT data to two-dimensional data.

61. The method of claim 60 wherein the two-dimensional data includes an en face image.

62. The method of claim 57 wherein the OCT data set includes three-dimensional data and wherein registering the OCT data set includes transforming three-dimensional OCT data into two-dimensional data.

63. The method of claim 62 wherein the two-dimensional data includes an en face image.