Acquistion and analysis techniques for improved outcomes in optical coherence tomography angiography

Patent metadata

Field	Value
Publication number	US-9700206-B2
Application number	US-201615015587-A
Country	US
Kind code	B2
Filing date	Feb 4, 2016
Priority date	Feb 5, 2015
Publication date	Jul 11, 2017
Grant date	Jul 11, 2017

Abstract

Methods for improved acquisition and processing of optical coherence tomography (OCT) angiography data are presented. One embodiment involves improving the acquisition of the data by evaluating the quality of different portions of the data to identify sections having non-uniform acquisition parameters or non-uniformities due to opacities in the eye such as floaters. The identified sections can then be brought to the attention of the user or automatically reacquired. In another embodiment, segmentation of layers in the retina includes both structural and flow information derived from motion contrast processing. In a further embodiment, the health of the eye is evaluating by comparing a metric reflecting the density of vessels at a particular location in the eye determined by OCT angiography to a database of values calculated on normal eyes.

First claim

What is claimed is:

1. A method to evaluate the health of an eye surrounding the fovea using optical coherence tomography (OCT) angiography, comprising:
acquiring a cluster of circular scans at a fixed distance from the fovea;
processing the cluster of circular scans to generate vasculature information of the region of the eye;
calculating a metric based upon the density of vessels as a function of distance from the fovea or foveal avascular zone using the generated vasculature information;
comparing the calculated metric with a collection of metrics calculated from OCT angiography data acquired on eyes having normal vasculature, said comparing step to evaluate the health of the eye; and
storing or displaying the results of the comparison or a further analysis thereof.

2. A method as recited in claim 1 , wherein the metric is calculated based on a skeletonized image of the vasculature.

3. A method as recited in claim 1 , wherein the metric is calculated based on an average of the vessel density around an ellipse centered on the fovea.

4. A method for acquiring wide field optical coherence tomography (OCT) angiography images of the eye comprising:
acquiring an OCT angiography dataset over a region of the eye, said region comprising multiple sub-regions;
determining a quality metric for the dataset in each of the sub-regions;
comparing the quality metrics to a predetermined threshold to identify any sub-regions of sub-optimal data;
acquiring replacement data for any identified sub-regions; and
generating an image from the acquired dataset, including the replacement data.

5. A method as recited in claim 4 , wherein the OCT angiography data set is acquired for each sub-region individually and there is a partial overlap between the data for each sub-region.

6. A method as recited in claim 5 , further comprising stitching the data for each sub-region together to provide a wide field of view image.

7. A method as recited in claim 4 , further comprising displaying the image to the user indicating the locations of the sub-optimal data and prompting the user to acquire the replacement data.

8. A method as recited in claim 4 , wherein the replacement data is acquired automatically.

9. A method as recited in claim 4 , further comprising processing the OCT angiography dataset to generate vasculature information and wherein the quality metrics are determined on the vasculature information.

10. A method for identifying retinal layers in optical coherence tomography data of an eye of a patient, said method comprising:
collecting OCT data over a region of the eye, said data comprising multiple B-scans taken at approximately the same locations in the region;
generating structural information from the data by processing the B-scans;
generating motion contrast information from the data by comparing the multiple B-scans taken at approximately the same locations;
identifying one or more retinal layers in the eye using both the structural and motion contrast information; and
storing or displaying the resulting layer identification or a further processing thereof.

11. A method as recited in claim 10 , in which the layer identification involves creating a cost function based on the structural and motion contrast information.

12. A method as recited in claim 11 , further comprising using a graph-based method to determine the location of the layer based on the cost function.

13. A method as recited in claim 11 , in which the layer identification comprises identifying the layer in either the structural or motion contrast information and then using the other information to refine the identification.

14. A method as recited in claim 11 in which the structural information is generated by averaging the B-scans taken at approximately the same locations.

Assignees

• Zeiss Carl Meditec Inc

Inventors

• An Lin
• Durbin Mary K
• KUBACH Sophie
• Sharma Utkarsh

Classifications

• G06T2207/20044

  Skeletonization; Medial axis transform · CPC title

• A61B3/1233 Primary

  for measuring blood flow, e.g. at the retina · CPC title

• G06T2207/10101

  Optical tomography; Optical coherence tomography [OCT] · CPC title

• A61B3/102 Primary

  for optical coherence tomography [OCT] · CPC title

• G06T7/0014

  using an image reference approach · CPC title