OCT speckle velocimetry

Abstract

Blood flow information is extracted from the speckle information of a time series of structured optical coherence tomography (OCT) images. The flow information can be determined from high frequency information of the OCT images, speckle density of the OCT images, a co-occurrence matrix applied to the OCT images, machine learning analysis of the input OCT images, etc. A flow profile resembling a pulse waveform is then generated as a time series of the extracted flow information.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application No. 63 / 269,298, filed March 14, 2022, which is incorporated by reference in its entirety. [Background technology]

[0002] Optical coherence tomography (OCT) is a non-invasive imaging technique that is frequently used in ophthalmology. OCT uses the principles of interferometry to image and collect information about an object (such as a subject's eye). Specifically, light from a light source is split into a sample arm where it is reflected by the object being imaged, and a reference arm where it is reflected by a reference object, such as a mirror. The reflected light is then combined in the detection arm to produce an interference pattern that is detected, for example, by a spectrometer or photodiode. The detected interference signal is processed to reconstruct the object and generate a structural OCT image.

[0003] More specifically, structural OCT images and volumes are generated by combining multiple depth profiles (A-lines, e.g., along the Z-depth direction at XY locations) into a single cross-sectional image (as B-scans, e.g., in the XZ or YZ planes) and by combining multiple B-scans into a volume. These depth profiles are generated by scanning along the X and Y directions. En face images in the XY plane can be generated by flattening all or part of the volume in the Z-depth direction, and C-scan images may be generated by extracting slices of the volume at a given depth. In other methods, OCT images are acquired en face in the XY plane at successively acquired Z-depths in sequence. Cross-sectional images in the XZ or YZ planes can be generated from the acquired volumes.

[0004] In some applications, OCT imaging can be used to determine blood flow characteristics such as velocity. One technique for doing so is Doppler OCT, which measures the Doppler shift that occurs when blood cells scatter the OCT light beam. However, Doppler OCT requires extracting phase information from the raw spectral data to determine the Doppler phase shift. These processes can be complex and difficult to perform efficiently. Summary of the Invention [Means for solving the problem]

[0005] According to one example of the present disclosure, a method comprises capturing optical coherence tomography (OCT) data from a subject; generating a first plurality of structural OCT images from a first location of the subject based on the captured OCT data; extracting flow information from each image of the first plurality of structural OCT images; and generating a first time series flow profile of the first location of the subject, the first flow profile being a relationship between the extracted flow information and timing of the captured OCT data from which a corresponding each image of the first plurality of structural OCT images was generated.

[0006] In various embodiments of the above example, extracting the flow information comprises applying a high frequency filter to frequency information of each image of the first plurality of structural OCT images, thereby generating high frequency information, the flow information corresponding to the high frequency information; extracting the flow information comprises applying a low frequency filter to frequency information of each image of the first plurality of structural OCT images, thereby generating low frequency information, the flow information being a ratio of the high frequency information to the low frequency information; extracting the flow information comprises applying a two-dimensional Fourier transform to each image of the first plurality of structural OCT images, thereby generating frequency information; the extracted flow information is the speckle density of each image of the first plurality of structural OCT images; extracting the flow information comprises applying a co-occurrence matrix to the first plurality of structural OCT images and determining correlations between the first plurality of structural OCT images based on the co-occurrence matrix; extracting the flow information comprises outputting the flow information based on the input structural OCT images. extracting flow information and generating a time series flow profile comprises inputting a first plurality of optical coherence tomography (OCT) images as a time series to a machine learning system trained to output a flow profile based on the time series of the input structural OCT images; the OCT data is captured over a period comprising a plurality of cardiac cycles; the method further comprises displaying the first flow profile as a time series graph; the method further comprises extracting flow information from a plurality of regions of each image of the first plurality of structural OCT images, generating a flow map of the extracted flow information over the plurality of regions, and displaying the flow map; the method further comprises generating a flow map for at least two of the first plurality of structural OCT images, generating a flow video from the generated flow map, and displaying the flow video;The first location is a cross-sectional location, and OCT data is captured from the first cross-sectional location and from a second cross-sectional location that is a known distance from the first cross-sectional location, the method further comprising generating a second plurality of structural OCT images from the second cross-sectional location of the object, generating a second time series flow profile of the second cross-sectional location of the object, determining a time difference between the first flow profile and the second flow profile, and determining a flow velocity in the object based on the known distance and the determined time difference; the OCT data is captured from the first cross-sectional location and the second cross-sectional location. the time difference is between a local maximum or minimum in the first flow profile and the second flow profile; the method further comprises applying a stimulus to the subject and determining a change to the first flow profile responsive to application of the stimulus; the applied stimulus is a pressure; flow information is extracted from a region of interest identified in one of the first plurality of structural OCT images and registered to another of the first plurality of structural OCT images; the region of interest corresponds to a region of blood flow and is automatically identified; and / or the subject is an eye; [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 illustrates an example of an optical coherence tomography (OCT) system. [Diagram 2] FIG. 2 illustrates an example of a method according to the present disclosure. [Diagram 3] FIG. 3 shows an example of a method for extracting blood flow information based on frequency. [Figure 4A] FIG. 4A shows a first example of a method for extracting blood flow information based on speckle density. [Figure 4B] FIG. 4B shows a second example of a method for extracting blood flow information based on speckle density. [Diagram 5] FIG. 5 shows an example of a method for extracting blood flow information based on a co-occurrence matrix. [Figure 6A] FIG. 6A shows a comparison of a flow profile determined in accordance with the present disclosure and the corresponding structural OCT signal used to determine the flow profile. [Figure 6B] FIG. 6B shows a comparison of the blood flow map and structural OCT image for the frame identified in FIG. 6A. [Figure 7] FIG. 7 shows an example of a method for determining the flow rate. [Figure 8A] FIG. 8A shows an en face image identifying scan locations for determining flow velocity. [Figure 8B] FIG. 8B shows the flow profile at the location shown in FIG. 8A and the determination of flow velocity therefrom. [Figure 9] FIG. 9 illustrates an example method for testing for glaucoma. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] In light of the above-mentioned problems, the present disclosure relates to determining blood flow information from optical coherence tomography (OCT) images without complex processing. More specifically, the present disclosure relates to a system and method for determining blood flow information from structural OCT images without Doppler or similar phase-related analysis. Even more specifically, the present disclosure relates to determining blood flow information based on speckle information in structural OCT images.

[0009] An example of an OCT system 100 as disclosed herein is shown in FIG. 1. As mentioned above, the system 100 includes a light source 101. Light generated by the light source 101 is split, for example, by a beam splitter (as part of the interferometer optics 108) and sent to a reference arm 104 and a sample arm 106. The light in the sample arm 106 is backscattered or otherwise reflected from an object, such as the retina of an eye 112. The light in the reference arm 104 is backscattered or otherwise reflected by a mirror 110 or similar object. The light from the sample arm 106 and the reference arm 104 are recombined in the optics 108 and a corresponding interference signal is detected by the detector 102. As used herein, "speckle" in an OCT image is understood to mean a characteristic interference pattern generated by backscattered OCT light from a turbid medium, such as tissue microstructure. The detector 102 may be a spectrometer, a photodetector, or any other light detection device. The detector 102 outputs electrical signals corresponding to the interference signals to one or more processors 114. The processors 114 process the electrical signals into OCT signal data, generate corresponding structural images, and / or further analyze the data and images. In particular, the processors 114 may implement any or all aspects of the present disclosure.

[0010] The processor 114 may also be associated with an input / output interface (not shown), including a display for outputting processed images or information related to the analysis of those images. The input / output interface may also include hardware such as buttons, keys, or other controls for receiving user input to the system. In some embodiments, the processor 114 may also be used to control the light source and imaging process.

[0011] An example of a method of the present disclosure is generally shown in FIG. 2, where the method begins with generating a time series of OCT structural images at a location of an imaged object. In other words, the OCT system 100 of FIG. 1 images the object according to a scan pattern that generates OCT data from the same location multiple times. For example, the OCT system 100 can repeatedly scan a single cross-sectional plane location of the object to generate multiple B-scans of that cross-sectional plane location at different times. In other embodiments, the OCT system 100 can repeatedly scan a region of the object to generate multiple volumes of the object from which B-scans at different times can be derived.

[0012] To analyze blood flow, the scanning protocol of the OCT system 100 may collect data for a time period long enough to capture one or more heart beats. In other words, the OCT data may be collected for a time period on the order of a few seconds. In one example, the OCT data is collected for 2 to 3 seconds. Furthermore, the rate and density of the OCT data capture may be oversampled to improve the amount of speckle information in each resulting structural OCT image. For example, the OCT system 100 may operate at a frequency sufficient to obtain at least 50 repeated structural OCT B-scans per second at a common cross-sectional plane location. In one example, each structural OCT B-scan is approximately 1 mm wide with 500 to 1024 A-lines of information.

[0013] During capture of OCT data, the subject may be tracked to limit movement during the capture period, and if there is too much movement, causing too much noise or failure to register the resulting OCT B-scan or volume, the scan protocol may be reset.

[0014] Following OCT data capture and structural image generation, blood flow information is extracted from the separately generated structural OCT image. Depending on the embodiment, blood flow information may be extracted from the entire OCT B-scan or only from the region of interest (ROI). If extracted from the ROI, the ROI may be determined manually or automatically. The ROI may be determined manually by the clinician by selecting an area of ​​the OCT B-scan that corresponds to an area of ​​blood flow or vasculature. Vasculature measurements, such as vasculature size, may be measured manually by the clinician from the structural OCT image. In alternative examples, such areas and measurements may be determined automatically by various methods. For example, areas of vasculature (and therefore blood flow) may be identified by segmentation techniques, machine learning techniques, thresholding techniques (where blood flow and vasculature have greater intensity and variability), etc. Additionally, additional ROIs may be identified for other vessels.

[0015] As described above, each of the repeated B-scans at a common cross-sectional plane location may be registered with each other. Thus, the ROI may be identified only in the B-scan image and then extrapolated to the other repeated B-scan images at that location. If the images are acquired face-to-face in the XY plane, the XY plane images may also be registered with each other. Thus, the ROI need only be identified in a single XY plane image.

[0016] Blood flow information can be extracted from individual OCT B-scans in a variety of ways. In general, techniques for extracting blood flow information from structural OCT B-scans are based on the recognition that blood flow produces larger speckle fluctuations than static structural tissue.

[0017] A first example of a method for extracting blood flow information based on frequency analysis is shown in FIG. 3. As can be seen there, an ROI is identified within a region of the OCT structural image that corresponds to blood flow and vasculature (as indicated by high intensity signal and high density speckle pattern). The ROI is then transformed (e.g. according to a 2D Fourier transform) to generate 2D frequency information of the ROI, which can then be frequency filtered. In other words, a high frequency filter and a low frequency filter are applied to the frequency information of the transformed ROI. The cutoff frequency of the filters can be determined based on the size of the ROI (or the 2D Fourier transform region) relative to the size of the vascular speckle under analysis. The resulting signals are then statistically combined into one value to produce a high frequency signal F H and low frequency signal F L According to the example in Figure 3, the statistical combination is the average.

[0018] In general, a high frequency signal F H represents the blood flow, and the low frequency signal F L may be understood to represent stationary tissue. Thus, in some embodiments, the high frequency signal F H However, in some embodiments, only the high frequency signal F H (For example, a low-frequency signal F L Normalizing the high frequency signal F (to ) helps account for natural variations in the imaged object and the OCT system 100, and may thereby improve the quality of the extracted flow information. H and a low frequency signal F L It may be determined as the ratio of

[0019] According to another technique, flow information can be extracted based on speckle density. A first example of such a technique is shown in FIG. 4A. According to the example of FIG. 4, a threshold can be applied to the pixels in the ROI. A binary map of the ROI can then be generated based on the applied threshold. For example, all pixels greater than the threshold can be set to a value of 1 and all pixels less than the threshold can be set to a value of 0. The speckle density can be determined as the ratio of the number of pixels with a value of 1 to the total number of pixels in the ROI or the number of pixels with a value of 0. Thus, the flow information can be considered as equal to or a function of the speckle density.

[0020] A second example of a technique for determining speckle density is shown in FIG. 4B. According to this method, an edge filter is first applied to the structural OCT B-scan to identify the edges of the speckles. An averaging filter is then applied to the speckle edges (the output of the edge filter) and the structural OCT B-scan. Applying the averaging filter creates edge intensities and average intensities, respectively, of the structural OCT B-scan. The averaging filter preferably has a size close to the size of the blood vessels in the structural OCT B-scan that are used to determine blood flow information. Finally, since speckle density is proportional to the density of visible edges, speckle density can be determined as a ratio between edge intensity and average intensity (e.g. edge intensity divided by average intensity). As mentioned above, flow information can be considered as a function of speckle density (e.g. ratio) or speckle density.

[0021] In yet another embodiment, the speckle density can be determined by applying a threshold intensity to the speckle edges. In other words, the speckle density can be considered as the number of speckle edges that exceed a threshold intensity. However, normalization (e.g., the average intensity as described above) helps to retain the information of edge intensity at various locations.

[0022] In yet another technique, the flow information may be determined by applying a co-occurrence matrix to the ROI. The co-occurrence matrix creates a dependency matrix by determining how often a pixel with pixel intensity value i occurs in a frame adjacent to a pixel with value j. Each element (i,j) of the dependency matrix specifies the number of times a pixel with value i occurs in a frame adjacent to a pixel with value j. The flow information is then determined by applying pixel correlations to adjacent frames on the co-occurrence matrix. An example of the application of such a co-occurrence matrix is ​​shown in FIG. 5. First, the size of a moving window applied to the temporally adjacent structural OCT B-scan images is determined. In one example, the size of the moving window is 4. The co-occurrence matrix is ​​then applied to the structural OCT B-scan image data within this analysis window. The flow information is then determined by applying the correlation attributes on the co-occurrence matrix.

[0023] In yet another technique, the flow may be determined by a machine learning system. Such a machine learning system may be trained to output one or more values ​​representing flow information based on an input structural ROI, OCT B-scan, or OCT volume. For example, the machine learning system may be trained in a supervised manner based on input structural OCT image information and corresponding ground truth flow information. The ground truth flow information may be determined, for example, according to one of the techniques described above. The ground truth flow information may additionally or alternatively be determined by other techniques, such as Doppler OCT or non-OCT analysis techniques. Thus, the machine learning system is trained to recognize a relationship between structural speckle signals and corresponding flow information.

[0024] In yet other techniques, flow may be determined by applying known ultrasound and laser speckle techniques such as speckle decorrelation, speckle contrast, and speckle autocorrelation. Each of these techniques may be applied to individual structural OCT B-scans and / or temporally adjacent structural OCT B-scan images. In yet other techniques, flow may be determined by applying known amplitude-based OCT angiography techniques such as speckle variance, amplitude-decorrelation, or split spectrum amplitude-decorrelation. As noted above, each of these techniques may be applied to individual structural OCT B-scans and / or temporally adjacent structural OCT B-scan images.

[0025] Returning to FIG. 2, once flow information for multiple repeated OCT B-scan images (or other images at a common location) has been identified, a flow profile can be determined as a time series plot of the flow information. In other words, the high frequency signal F H and a low frequency signal F L The ratio between, speckle density, or similar flow-related information values ​​are plotted according to a time series in which the relative time of each flow-related information value corresponds to the time at which the repeated OCT images were captured.

[0026] An example of a flow profile is shown in FIG. 6A along with the corresponding structural OCT signal. The structural OCT signal represents the average pixel intensity within the ROI form which the flow information of the flow profile was extracted. As shown in the figure, the flow profile resembles a conventional pulse waveform. In fact, a double notch is roughly seen even between image frames 15 and 20. In contrast, the structural OCT signal is relatively flat and is not associated with a pulse wave.

[0027] Figure 6A further identifies four image frames (9, 12, 29, and 36) shown in Figure 6B. More specifically, Figure 6B shows the blood flow map for each frame along with the original structural OCT image for each frame. The blood flow map is a map of blood flow information extracted for the entire region of the structural OCT image, and the time series signal shown in Figure 6A represents only the ROI recorded in the image frame of Figure 6B.

[0028] Comparing the flow profile in Figure 6A with the blood flow map in Figure 6B, the pixel intensity (and therefore the extracted blood flow information) in the region corresponding to the ROI is relatively low (dark) in frames 9 and 29, but relatively high (bright) in frames 12 and 36. In contrast, there is little or no discernible difference in the ROI in the structural OCT image in any frame.

[0029] In some embodiments, the flow profile may be generated by a machine learning system, for example, a machine learning system may be trained to output a flow profile based on a time series of input structural OCT images.

[0030] Although the above discussion has been directed to generating a flow profile at a common cross-sectional plane location, it is possible to determine flow rates based on flow profiles from two different cross-sectional plane locations.

[0031] With reference to FIG. 7, the first step in an example of a method for determining flow velocity includes simultaneously capturing OCT data from at least two different locations, the distance between which is known. In one example, the capture of OCT data alternates between the different locations such that the resulting B-scan images from each location are temporally interleaved. FIG. 8A shows an en face image in which two locations A, B are identified along a blood vessel therein. The locations A, B are separated by a distance d. In some embodiments, the OCT data may be captured according to circular scans having different radii. The difference in radius between each circular scan thus represents the known distance. These circular scans may be temporally interleaved in the same manner as described above.

[0032] Following capture of the OCT data, flow profiles of the data from each location are generated. These flow profiles may be generated from any of the methods described above. FIG. 8B shows flow profiles corresponding to locations A and B of FIG. 8A. The time Δt between these two flow profiles may then be determined. For example, as shown in FIG. 8B, the time Δt is determined between local maxima of the flow profiles. However, as an alternative example, the time may be determined between other common locations (such as local minima) of the flow profiles. The local maxima may be determined, for example, by identifying the frames in each cardiac cycle where the maxima occur. According to another embodiment, the derivative of the flow profile is determined, and then the frames where the derivative is equal to (or crosses) zero correspond to the local maxima and minima. The time Δt may then be determined based on the sampling rate at which the OCT data was captured. In other words, if OCT B-scans are captured at 50 frames per second, then a difference of 50 frames between local maxima of different flow profiles represents a difference of 1 second.

[0033] Once the time Δt is determined, the velocity may be determined according to the relationship velocity=d / Δt. The direction of flow may further be determined by whether the phase shift of the OCT signal between the two locations is positive or negative. The velocity may be determined multiple times between the same two locations and / or between multiple locations. The multiple determined velocities are then averaged (or combined according to another statistical determination) to identify a representative velocity. Similarly, the determined velocities may be compared between multiple patients to identify abnormalities, or between the same patient at multiple capture times to determine changes in the patient's condition.

[0034] Furthermore, any part of the above description may be incorporated with other analysis techniques and / or processes. For example, flow profiles may be averaged over several cardiac cycles or compared over a period of time (e.g., weeks, months, years) to monitor disease. In another example, the determined distance of blood flow may be used to distinguish arteries and veins in a structural image, since blood flow in veins and arteries may be considered opposite. Similarly, by comparing the determined velocities, veins and arteries are further differentiated, since blood flow in veins is slower than in arteries. In yet another example, the OCT data acquisition described above may be performed simultaneously with other physiological measurements, such as pulse oximetry, electrocardiogram, etc. Further analysis of the flow profiles herein may be based on additional physiological information captured by simultaneous measurements.

[0035] Moreover, any vascular biomarker of glaucomatous damage, such as impaired vascular flow or autoregulation, may be used to provide diagnostic information. Thus, by measuring changes in blood flow in response to external stimuli, it may be possible to detect glaucoma or quantify its severity. An example of such a method is shown in FIG. 9.

[0036] As seen in the example method of FIG. 9, an external stimulus is applied. In the example of a glaucoma test, such a stimulus can be an applied pressure (e.g., an air puff) or other stimulus that causes a change in intraocular pressure. A flow profile can then be determined for the patient according to any of the techniques described above. The resulting flow profile can be compared to a known healthy patient or a baseline flow profile previously determined for that patient. A change in the flow profile relative to the baseline may be indicative of a healthy eye, since the flow of the eye (and thus the blood vessels of the eye) is affected by the applied pressure. In contrast, no (or a relatively small) change in the flow profile relative to the baseline may be indicative of glaucoma, since the blood flow and blood vessels do not respond to the applied pressure. The above analysis may also be applied vice versa, depending on the applied external stimulus.

[0037] While the above examples relate to pressures applied to test for glaucoma, it should be understood that any external stimulus and test can be utilized. In other words, the flow profile can be determined in accordance with the present disclosure before, during, or after application of any external stimulus. The response of the flow profile to the stimulus can then be analyzed for diagnostic or similar purposes.

[0038] Returning again to Figure 2, with knowledge of the flow profile, the extracted flow information may be further analyzed and / or displayed. Because the flow profile described above corresponds to a pulse waveform, such analysis may include known techniques for analysis of pulse waveforms. Such techniques may include those for determining heart rate, rise time, flow skewness, blood pressure, cardiac function, vascular stiffness, etc.

[0039] With regard to display, the display of the extracted flow information may include the flow profile itself, a flow map, and / or a structural OCT image. The extracted flow information may be mapped to pixel intensity and / or color in the flow map. In some embodiments, the flow map may be shown as a video rather than a still image.

[0040] While there are various features described above, it should be understood that the features may be used alone or in any combination thereof. It should also be understood that variations and modifications may occur to those skilled in the art to which the claimed examples pertain.

Claims

1. capturing optical coherence tomography (OCT) data from the subject; generating a first plurality of structural OCT images from a first location of the object based on the captured OCT data; extracting flow information from each image of the first plurality of structural OCT images; and generating a first time-series flow profile for the first location of the object, the first time-series flow profile being a relationship between the extracted flow information and timing of the captured OCT data from which a corresponding individual image of the first plurality of structural OCT images was generated; A method comprising:

2. extracting flow information comprises applying a high frequency filter to frequency information of the individual images of the first plurality of structural OCT images, thereby generating high frequency information; The method of claim 1 , wherein the flow information corresponds to the high frequency information.

3. extracting flow information comprises applying a low frequency filter to the frequency information of the individual images of the first plurality of structural OCT images, thereby generating low frequency information; The method of claim 2 , wherein the flow information is a ratio of the high frequency information to the low frequency information.

4. Extracting flow information involves: applying a two-dimensional Fourier transform to each of the first plurality of structural OCT images, thereby generating the frequency information; The method of claim 2 comprising:

5. The method of claim 1 , wherein the extracted flow information is speckle density of the individual images of the first plurality of structural OCT images.

6. Extracting the flow information includes: applying a co-occurrence matrix to the first plurality of structural OCT images; and determining correlations between the first plurality of structural OCT images based on the co-occurrence matrix; The method of claim 1 , comprising:

7. Extracting flow information includes inputting each of the first plurality of structural OCT images to a machine learning system trained to output flow information based on input structural OCT images; The method of claim 1 , comprising:

8. Extracting flow information and generating the time series flow profile includes: inputting the first plurality of structural OCT images as a time series into a machine learning system trained to output the time series flow profile based on the input time series of structural OCT images; The method of claim 1 , comprising:

9. The method of claim 1 , wherein the OCT data is captured over a period comprising multiple cardiac cycles.

10. The method includes displaying the first time series flow profile as a time series graph; The method of claim 1 further comprising:

11. The method comprises: extracting the flow information from a plurality of regions of each of the first plurality of structural OCT images; generating a flow map of the extracted flow information across the plurality of regions; and displaying the flow map; The method of claim 1 further comprising:

12. The method comprises: generating the flow map for at least two of the first plurality of structural OCT images; generating a flow video from the generated flow map; and displaying the flow video; The method of claim 11 further comprising:

13. the first location is a cross-sectional location, and the OCT data is captured from the first cross-sectional location and from a second cross-sectional location that is a known distance from the first cross-sectional location; The method comprises: generating a second plurality of structural OCT images from the second cross-sectional location of the object; generating a second time series flow profile of the second cross-sectional location of the object; determining a time difference between the first time series flow profile and the second time series flow profile; and determining a flow velocity at the object based on the known distance and the determined time difference; The method of claim 1 further comprising:

14. The method of claim 13 , wherein the OCT data is captured alternately between the first cross-sectional location and the second cross-sectional location.

15. The method of claim 13 , wherein the time difference is the distance between a maximum or minimum in the first time-series flow profile and the second time-series flow profile.

16. The method comprises: applying a stimulus to the subject; and determining a change to the first time series flow profile in response to application of the stimulus; The method of claim 1 further comprising:

17. 17. The method of claim 16, wherein the applied stimulus is pressure.

18. The method of claim 1 , wherein the flow information is extracted from a region of interest identified in one of the first plurality of structural OCT images and registered to another of the first plurality of structural OCT images.

19. The method of claim 18 , wherein the region of interest corresponds to a region of blood flow and is automatically identified.

20. The method of claim 1 , wherein the object is an eye.

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