Analysis and visualization of oct angiography data

Abstract

Methods for analyzing and visualizing OCT angiography data are presented. In one embodiment, an automated method for identifying the foveal avascular zone in a two dimensional en face image generated from motion contrast data is presented. Several 3D visualization techniques are presented including one in which a particular vessel is selected in a motion contrast image and all connected vessels are highlighted. A further embodiment includes a stereoscopic visualization method. In addition, a variety of metrics for characterizing OCT angiography image data are described.

1 . (canceled) 
     
     
         2 . A method of analyzing OCT data of an eye of a patient, said method comprising:
 collecting OCT data over a plurality of transverse locations of the eye of the patient, wherein at least some of the OCT measurements are obtained at approximately the same transverse locations separated in time by a predetermined interval;   applying an OCT angiography processing technique to the OCT data set to generate a motion contrast data set, said motion contrast data set indicating changes in the OCT measurements at approximately the same locations during the predetermined interval;   determining a metric from the motion contrast data set;   comparing the determined metric to a database of normal eyes or eyes with abnormal pathology to evaluate the health of the eye; and   storing or displaying the results of the comparison or a further analysis thereof.   
     
     
         3 . The method as recited in  claim 2 , further comprising generating a two-dimensional en face vasculature from the motion contrast data set and wherein the metric is determined from the en face vasculature image. 
     
     
         4 . The method as recited in  claim 3 , further comprising identifying a layer in the eye within the OCT data set and using the identified layer as a boundary in generating the en face vasculature image. 
     
     
         5 . The method as recited in  claim 4 , wherein the layer is a retinal layer. 
     
     
         6 . The method as recited in  claim 4 , wherein the layer is a choroidal layer. 
     
     
         7 . A method as recited in  claim 3 , wherein the two-dimensional en face vasculature image is generated by assigning single representative intensity values over a depth range in the motion contrast data set 
     
     
         8 . The method as recited in  claim 5 , wherein the en face vasculature image is generated by summing the intensity values over a depth range . 
     
     
         9 . The method as recited in  claim 2 , wherein the metric is selected from the group consisting of: foveal avascular zone (FAZ) area, capillary density, capillary volume, vessel tortuosity, vessel regularity, vessel segment length, vessel total crossings, the number of bifurcations, bifurcation angles, vessel width parameters, ratio of small to large vessels, capillary density, capillary diameter, capillary density ratio between arteriole and venous capillaries, ratio of vessel diameters of parent and daughter vessels and inter-capillary distance. 
     
     
         10 . The method as recited in  claim 4 , further comprising repeating the steps to determine a second metric using a second boundary in the motion contrast data and defining a third metric relating the first and second metrics. 
     
     
         11 . The method as recited in  claim 8 , wherein the third metric is the ratio of the first and second metrics or the difference of the first and second metrics. 
     
     
         12 . A method of analyzing OCT data of an eye of a patient, said method comprising:
 (a) collecting OCT data over a plurality of transverse locations of the eye of the patient at a first patient visit, wherein at least some of the OCT measurements are obtained at approximately the same transverse locations separated in time by a predetermined interval;   (b) applying an OCT angiography processing technique to the OCT data set to generate a motion contrast data set, said motion contrast data set indicating changes in the OCT measurements at approximately the same locations during the predetermined interval;   (c) generating a two-dimensional en face vasculature image from the motion contrast data set;   (d) repeating steps (a)-(c) at a second patient visit and comparing the en face vasculature images from the two visits to identify changes in the eye occurring between the two visits; and   (e) storing or displaying the results of the comparison or a further analysis thereof.   
     
     
         13 . The method as recited in  claim 12 , further comprising identifying a layer in the eye within the OCT data set and using the identified layer as a boundary in generating the en face vasculature image. 
     
     
         14 . The method as recited in  claim 13 , wherein the layer is a retinal layer. 
     
     
         15 . The method as recited in  claim 13 , wherein the layer is a choroidal layer. 
     
     
         16 . A method as recited in  claim 12 , wherein the two-dimensional en face vasculature image is generated by assigning single representative intensity values over a depth range in the motion contrast data set 
     
     
         17 . The method as recited in  claim 16 , the en face vascular image is generated by summing intensity values over the depth range . 
     
     
         18 . The method as recited in  claim 12 , wherein the results of the comparison reveals changes associated with progressive diseases. 
     
     
         19 . The method as recited in  claim 12 , further comprising determining a characteristic metric for each of the two-dimensional en face vasculature images from the two visits and the comparing step involves comparing the two metrics. 
     
     
         20 . The method as recited in  claim 12 , wherein the metric is selected from the group consisting of: foveal avascular zone (FAZ) area, capillary density, capillary volume, vessel tortuosity, vessel regularity, vessel segment length, vessel total crossings, the number of bifurcations, bifurcation angles, vessel width parameters, ratio of small to large vessels, capillary density, capillary diameter, capillary density ratio between arteriole and venous capillaries, ratio of vessel diameters of parent and daughter vessels and inter-capillary distance.