Method and apparatus for automated placement of scanned laser capsulorhexis incisions

• US 9,495,743 B2
• Filed: 08/04/2014
• Issued: 11/15/2016
• Est. Priority Date: 01/22/2010
• Status: Active Grant

• Current Assignee: AMO Development LLC (Johnson & Johnson)
• Original Assignee: OptiMedica Corporation (Johnson & Johnson)
• Inventors: Angeley, David G., Gooding, Philip, Woodley, Bruce, Marcellino, George
• Primary Examiner(s): Jackson, Gary
• Assistant Examiner(s): LUAN, SCOTT
• Application Number:
  US14/450,587
• Publication Number:
  US 20140341451A1
• Time in Patent Office: 834 Days
• US Class Current: 1/1
• CPC Class Codes: A61B 3/102 for optical coherence tomography [OCT]
• CPC Class Codes: A61B 3/107 for determining the shape or measuring the curvature of the cornea
• CPC Class Codes: A61B 3/145 by video means
• CPC Class Codes: A61B 5/0066 Optical coherence imaging
• CPC Class Codes: A61F 2009/00851 Optical coherence topography [OCT]
• CPC Class Codes: A61F 2009/00865 Sclera
• CPC Class Codes: A61F 2009/0087 Lens
• CPC Class Codes: A61F 2009/00872 Cornea
• CPC Class Codes: A61F 2009/00876 Iris
• CPC Class Codes: A61F 2009/00889 Capsulotomy
• CPC Class Codes: A61F 2009/00897 Scanning mechanisms or algorithms
• CPC Class Codes: A61F 9/008 using laser
• CPC Class Codes: A61F 9/00825 for photodisruption
• CPC Class Codes: A61F 9/0084 Laser features or special beam parameters therefor
• CPC Class Codes: A61F 9/009 Auxiliary devices making contact with the eyeball and coupling in laser light, e.g. goniolenses apparatus for eye examination A61B3/0008
• CPC Class Codes: G06T 2207/10101 Optical tomography; Optical coherence tomography [OCT]
• CPC Class Codes: G06T 2207/30041 Eye; Retina; Ophthalmic
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/13 Edge detection
• CPC Class Codes: G06T 7/149 involving deformable models, e.g. active contour models

Abstract

Systems and methods are described for cataract intervention. In one embodiment a system comprises a laser source configured to produce a treatment beam comprising a plurality of laser pulses; an integrated optical system comprising an imaging assembly operatively coupled to a treatment laser delivery assembly such that they share at least one common optical element, the integrated optical system being configured to acquire image information pertinent to one or more targeted tissue structures and direct the treatment beam in a 3-dimensional pattern to cause breakdown in at least one of the targeted tissue structures; and a controller operatively coupled to the laser source and integrated optical system, and configured to adjust the laser beam and treatment pattern based upon the image information, and distinguish two or more anatomical structures of the eye based at least in part upon a robust least squares fit analysis of the image information.

Claims

1. A method for locating a boundary of at least one anatomical structure of an eye of a patient comprising:

providing image information pertinent to at least one anatomical structure of the eye;

processing the image information to identify a first set of candidate edge locations for the at least one anatomical structure of the eye, the first set of candidate edge locations being disposed in a first boundary surface portion of the at least one anatomical structure,

using a least-squares technique to fit a first mathematical surface model to the first set of the candidate edge locations,

processing the image information based on proximity to the first mathematical surface model to identify a second set of candidate edge locations for the at least one anatomical structure, the second set of candidate locations being disposed in a second boundary surface portion of the at least one anatomical structure that is different from the first boundary surface portion, and

using a least-squares technique to fit a second mathematical surface model to the combined first and second sets of candidate edge locations; and

locating a boundary of the at least one anatomical feature based on the fit of the least-squares technique.

2. The method of claim 1, wherein the at least one anatomical structure comprises a cornea.

3. The method of claim 1, wherein the at least one anatomical structure comprises a sclera.

4. The method of claim 1, wherein the at least one anatomical structure comprises a limbus.

5. The method of claim 1, wherein the at least one anatomical structure comprises an iris.

6. The method of claim 1, wherein the at least one anatomical structure comprises a lens.

7. The method of claim 1, wherein the at least one anatomical structure comprises a lens capsule.

8. The method of claim 1, further comprising performing a series of three or more least squares least-squares fit analyses, and separately processing the image information to identify additional candidate edge locations so that a greater number of candidate edge locations are used in each successive least squares least-squares fit analysis.

9. The method of claim 1, wherein at least one of the first and second mathematical surface models is a spherical surface.

10. The method of claim 1, wherein at least one of the first and second mathematical surface models is an aspherical surface.

11. The method of claim 1, further comprising locating two or more boundaries between a corresponding three or more anatomical structures of the eye.

12. The method of claim 11, wherein the boundaries include the intersection between a cornea of the eye and a sclera of the eye.

13. The method of claim 11, wherein the boundaries include the intersection between a cornea of the eye and an iris of the eye.

14. The method of claim 11, wherein the boundaries include the intersection between a lens of the eye and an iris of the eye.

15. The method of claim 1, further comprising utilize one or more weighted candidate edge locations of a least squares least-squares fit analysis to locate an anatomical structure of the eye.

16. The method of claim 1, wherein the second boundary surface portion completely surrounds the first boundary surface portion.

17. The method of claim 1, wherein the image information comprises an A-Scan comprising at least one of a line, sequence, and column of a plurality of pixels.

18. The method of claim 17, wherein the candidate edge locations are a set of XYZ triples comprising an X coordinate, a Y coordinate, and a Z coordinate representing the location of an edge pixel from amongst the plurality of pixels.

19. The method of claim 18, further comprising performing a total of three or more least squares fit analyses, and separately processing the image information with additional candidate edge locations so that a greater number of candidate edge locations are used in each successive least squares least-squares fit analysis.

20. The method of claim 19, wherein a boundary is located based on a weighting obtained from the least-squares technique.