Measurement of the lipid and aqueous layers of a tear film

Patent metadata

Field	Value
Publication number	US-9615735-B2
Application number	US-201414548067-A
Country	US
Kind code	B2
Filing date	Nov 19, 2014
Priority date	Jan 31, 2014
Publication date	Apr 11, 2017
Grant date	Apr 11, 2017

Abstract

Systems and methods for determining thickness of lipid and aqueous layers of a tear film in which a spectrum array is generated from optical coherence tomography and input into a statistical estimator, which determines the thickness of the lipid and/or aqueous layers at a nanometer resolution based on the inputted spectrum and other information, such as information about a laser intensity noise, Poisson noise, and dark noise associated with the OCT.

First claim

The invention claimed is:

1. A method of determining thickness of lipid and aqueous layers of a tear film, the method comprising:
directing light from a light source to an eye, the eye having a tear film including a lipid layer and an aqueous layer;
collecting light at a light detector, the collected light including back-reflected light from the eye;
generating a spectrum array based on the light collected at the light detector;
inputting the spectrum array into a statistical estimator comprising a processor and a memory;
at the statistical estimator, determining an estimate of a lipid layer thickness and an estimate of an aqueous layer thickness for the lipid and aqueous layers based on a statistical likelihood of the inputted spectrum array being generated by the estimated lipid layer thickness and the estimated aqueous layer thickness out of different possible combinations of potential lipid layer thicknesses and potential aqueous layer thicknesses;
wherein collecting light at the light detector comprises collecting light at a spectrometer;
wherein the light source and the spectrometer are components of an optical coherence tomography system, the optical coherence tomography system comprising an axial point spread function of 2 μm or less for a corneal epithelium.

2. The method of claim 1 , wherein the estimated lipid and aqueous layer thicknesses are determined at a nanometer scale.

3. The method of claim 1 , wherein the light source and the spectrometer are components of an optical coherence tomography system, the optical coherence tomography system comprising an axial point spread function of between 0.75 μm and 1.25 μm for a corneal epithelium.

4. The method of claim 1 , wherein the generated spectrum array comprises an array with a plurality of elements in which at least some of the elements are each proportional to a number of electrons accumulated at a location on the light detector over a time segment.

5. The method of claim 4 , wherein the statistical estimator estimates the lipid and aqueous layer thicknesses based on the inputted spectrum array and at least one of a quantified intensity noise of the light source, a quantified Poisson noise of the light detector, and a quantified dark noise of the detector.

6. The method of claim 4 , wherein the statistical estimator estimates the lipid and aqueous layer thicknesses based on the inputted spectrum array, an intensity noise of the light source, a Poisson noise of the light detector, and a dark noise of the detector.

7. The method of claim 1 , wherein the optical coherence tomography system further comprises a beam splitter, a reference arm, and a sample arm.

8. The method of claim 7 , wherein the light source is a broadband source.

9. The method of claim 1 , wherein the optical coherence tomography system is a micron axial resolution optical coherence tomography component.

10. A method of determining thickness of lipid and aqueous layers of a tear film, the method comprising:
directing light from a light source to an eye, the eye having a tear film including a lipid layer and an aqueous layer;
collecting light at a light detector, the collected light including back-reflected light from the eye;
generating a spectrum array based on the light collected at the light detector;
inputting the spectrum array into a statistical estimator comprising a processor and a memory;
at the statistical estimator, determining an estimate of a lipid layer thickness and an estimate of an aqueous layer thickness for the lipid and aqueous layers based on a statistical likelihood of the inputted spectrum array being generated by the estimated lipid layer thickness and the estimated aqueous layer thickness out of different possible combinations of potential lipid layer thicknesses and potential aqueous layer thicknesses;
wherein collecting light at the light detector comprises collecting light at a spectrometer;
wherein the light source and the spectrometer are components of an optical coherence tomography system, the optical coherence tomography system comprising an axial point spread function of between 0.75 μm and 1.25 μm for a corneal epithelium.

11. The method of claim 10 , wherein the estimated lipid and aqueous layer thicknesses are determined at a nanometer scale.

12. The method of claim 10 , wherein the generated spectrum array comprises an array with a plurality of elements in which at least some of the elements are each proportional to a number of electrons accumulated at a location on the light detector over a time segment.

13. The method of claim 12 , wherein the statistical estimator estimates the lipid and aqueous layer thicknesses based on the inputted spectrum array and at least one of a quantified intensity noise of the light source, a quantified Poisson noise of the light detector, and a quantified dark noise of the detector.

14. The method of claim 12 , wherein the statistical estimator estimates the lipid and aqueous layer thicknesses based on the inputted spectrum array, an intensity noise of the light source, a Poisson noise of the light detector, and a dark noise of the detector.

15. A method of determining thickness of lipid and aqueous layers of a tear film, the method comprising:
directing light from a light source to an eye, the eye having a tear film including a lipid layer and an aqueous layer;
collecting light at a light detector, the collected light including back-reflected light from the eye;
generating a spectrum array based on the light collected at the light detector;
inputting the spectrum array into a statistical estimator comprising a processor and a memory;
at the statistical estimator, determining an estimate of a lipid layer thickness and an estimate of an aqueous layer thickness for the lipid and aqueous layers based on a statistical likelihood of the inputted spectrum array being generated by the estimated lipid layer thickness and the estimated aqueous layer thickness out of different possible combinations of potential lipid layer thicknesses and potential aqueous layer thicknesses;
wherein the generated spectrum array comprises an array with a plurality of elements in which at least some of the elements are each proportional to a number of electrons accumulated at a location on the light detector over a time segment.

16. The method of claim 15 , wherein collecting light at the light detector comprises collecting light at a spectrometer; wherein the light source and the spectrometer are components of an optical coherence tomography system, the optical coherence tomography system further comprising a beam splitter, a reference arm, and a sample arm.

17. The method of claim 16 , wherein the optical coherence tomography system is a micron axial resolution optical coherence tomography component.

18. The method of claim 15 , wherein the light source is a broadband source.

19. The method of claim 15 , wherein the statistical estimator estimates the lipid and aqueous layer thicknesses based on the inputted spectrum array and at least one of a quantified intensity noise of the light source, a quantified Poisson noise of the light detector, and a quantified dark noise of the light detector.

20. The method of claim 15 , wherein the statistical estimator estimates the lipid and aqueous layer thicknesses based on the inputted spectrum array, an intensity noise of the light source, a Poisson noise of the light detector, and a dark noise of the detector.

Assignees

• Univ Rochester
• Univ Arizona

Inventors

• Huang Jinxin
• Rolland Jannick P
• Clarkson Eric
• Kupinski Matthew

Classifications

• A61B3/0025

  characterised by electronic signal processing, e.g. eye models · CPC title

• A61B3/102

  for optical coherence tomography [OCT] · CPC title

• A61B3/101 Primary

  for examining the tear film · CPC title

• A61B3/113

  for determining or recording eye movement · CPC title