Optical coherence tomography speckle velocimetry

Abstract

Blood flow information, including blood flow velocity information, is extracted from speckle information of a time series of structural optical coherence tomography (OCT) images. Flow information can be determined from high-frequency information of the OCT images, a speckle density of the OCT images, from a co-occurrence matrix applied to the OCT image, from a machine learning analysis of an input OCT image, or the like. A flow profile analogous to a pulse waveform is then generated as a time series of the extracted flow information.

Description

OPTICAL COHERENCE TOMOGRAPHY SPECKLE VELOCIMETRYCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims the benefit of U.S. Provisional Patent Application No. 63 / 642,103 filed May 3, 2024, the entirety7of which is incorporated by reference herein.BACKGROUND

[0002] Optical coherence tomography (OCT) is a non-invasive imaging technique, often used in ophthalmology. OCT relies on principles of interferometry7to image and collect information about an object (such as the eye of a subject). Particularly, light from a source is split into a sample arm where it is reflected by the object being imaged, and reference arm where it is reflected by a reference object such as a mirror. The reflected lights are then combined in a detection arm in a manner that produces an interference pattern that is detected by spectrometer, photodiode(s) or the like. The detected interference signal is processed to reconstruct the object and generate structural OCT images.

[0003] More particularly, structural OCT images and volumes are generated by combining numerous depth profiles (A-lines, e.g. along a Z-depth direction at an X-Y location) into a single cross-sectional image (B-scan, e.g., as an X-Z or Y-Z plane), and combining numerous B-scans into a volume. These depth profiles are generated by scanning along the X and Y directions. En-face images in the X-Y plane may be generated by flattening a volume in all or a portion of the Z-depth direction, and C-scan images may be generated by extracting slices of a volume at a given depth. In other methods, OCT images are acquired in an en-face manner in the X-Y plane with the Z-depth acquired sequentially. Cross-sectional images in the X-Z or Y-Z planes may be generated from the acquired volume.

[0004] In some applications, OCT imaging may be used to determine blood flow properties, such as velocity. One technique for doing so is Doppler OCT, which measures the Doppler shifts caused when blood cells scatter the OCT light beam. However, Doppler OCT requires extracting phase information from raw spectral data and determining Doppler phase shifts. These processes can be complicated and difficult to efficiently perform.BRIEF SUMMARY

[0005] According to one example of the present disclosure, a method comprises: capturing optical coherence tomography (OCT) data from an object; generating a first plurality of structural OCT images from a first location of the object based on the captured OCT data;extracting flow information (i.e., a qualitative profile of vascular flow) from individual ones of the first plurality of structural OCT images: and generating a first time-series flow profile of the first location of the object, wherein the first flow profile is a relationship between the extracted flow information and a timing of the captured OCT data from which the corresponding individual one of the first plurality of structural OCT images was generated.

[0006] In various embodiments of the above example, extracting flow information comprises applying a high frequency filter to frequency information of the individual one of the first plurality of structural OCT images, thereby producing high frequency information, and the flow information corresponds to the high frequency information; extracting flow information further comprises: applying a low frequency filter to the frequency information of the individual one of the first plurality of structural OCT images, thereby producing low frequency information, wherein the flow information is a ratio of the high frequency information to the low frequency information; extracting flow information comprises: applying two-dimensional Fourier transform to the individual one of the first plurality of structural OCT images, thereby producing the frequency information; the extracted flow information is a speckle density of the individual one of the first plurality of structural OCT images; extracting flow information comprises: applying a co-occurrence matrix to the first plurality of structural OCT images, and determining a correlation among the first plurality of structural OCT images based on the cooccurrence matrix; extracting flow information comprises: inputting the individual one of the first plurality of structural OCT images to a machine learning system trained to output flow information based on an input structural OCT image; extracting flow information and generating the time-series flow profile comprises: inputting the first plurality of structural optical coherence tomography (OCT) images as a time series to a machine learning system trained to output the flow profile based on an input time series of structural OCT images; the OCT data is captured for a time 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 the flow information from a plurality of regions of the individual one 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 the flow map for at least two of the first plurality of structural OCT images, generating a flow video from the generated flow maps, and displaying the flow video; 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 a known distance from the first cross-sectional location, wherein the method further comprises: generating a second pluralityof 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 of the object based on the known distance and the determined time difference; the OCT data is alternately captured between the first cross-sectional location and the second cross-sectional location; the time difference is between local maxima or local minima of the first and second flow profiles; the method further comprises: applying a stimulus to the object, and determining a change to the first flow profile in response to application of the stimulus; the applied stimulus is pressure; the flow information is extracted from a region of interest identified in one of the first plurality of structural OCT images, and which is registered to the other first plurality of structural OCT images; the region of interest corresponds to an area of blood flow, and is automatically identified; and / or the object is an eye.BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

[0007] Figure 1 illustrates an example optical coherence tomography (OCT) system.

[0008] Figure 2 illustrates an example method according to the present disclosure.

[0009] Figure 3 illustrates an example method for extracting blood flow information based on frequency.

[0010] Figure 4A illustrates a first example method for extracting blood flow information based on speckle density.

[0011] Figure 4B illustrates a second example method for extracting blood flow information based on speckle density.

[0012] Figure 5 illustrates an example method for extracting blood flow information based on a co-occurrence matrix.

[0013] Figure 6A comparatively illustrates a flow profile determined according to the present disclosure with a corresponding structural OCT signal from which the flow profile was determined.

[0014] Figure 6B comparatively illustrates a blood flow map and structural OCT image for the frames identified in Fig. 6A.

[0015] Figure 6C illustrates a flow profile generated by evaluating the temporal change of speckle.

[0016] Figure 7 illustrates an example method for determining flow velocity.

[0017] Figure 8A illustrates an en-face image identifying scan locations for determining a flow velocity.

[0018] Figure 8B illustrates flow profiles of the locations indicated in Fig. 8A, and the determination of flow velocity therefrom.

[0019] Figure 9 illustrates an example method of glaucoma testing.

[0020] Figure 10 illustrates the relative dimensions of a time series of structural OCT B-scan images for application in convolution techniques.DETAILED DESCRIPTION OF THE DRAWING

[0021] Based on the foregoing deficiencies, the present disclosure relates to determining blood flow information from optical coherence tomography (OCT) images without the need for complex processing. More particularly, the present disclosure relates to systems and methods for determining blood flow information from structural OCT images without a Doppler or like phase-related analysis. Still more particularly, the present disclosure determines blood flow information based on 0020] information in the structural OCT images.

[0022] In other words, the present disclosure relates to a method and system for analyzing OCT data to detect and quantify dynamic biological processes, particularly blood flow within tissues. These techniques overcome limitations of conventional OCT analysis by analyzing temporal changes in speckle patterns, along with spatial characteristics of the speckle observed in stationary tissue as compared with moving tissue, as in blood flow. These factors may facilitate direct observation and quantification without the need for Doppler OCT, phase variance analysis, or auxiliary imaging modalities. Particularly in areas experiencing blood flow, speckles tend to be smaller, more numerous, exhibit a vertical orientation, and change more rapidly than in stationary tissue. These features help differentiate betw een dynamic and static tissue elements. By leveraging temporal dynamics and distinct characteristics of speckle patterns associated with blood flow, coupled with image processing and machine learning techniques, the present disclosure may aid clinical diagnostics, research, and monitoring of vascular health and tissue perfusion, providing insights into dynamic biological processes.

[0023] For example, the amount of speckles showing smaller and more vertical orientations can be quantified. The mean value of this quantification within a selected vascular lumen, which is the region of interest (ROI). can be determined to produce a relative flow velocity value, termed the Relative Flow Value (RFV), which value is indicative of the velocity of the blood flowing through the vascular lumen. This RFV can be measured in a controlled phantom experiment to determine the relationship between the RFV and the absolute flow' speed for different fluids, such as blood. The relationship between the RFV and the absolute flow speed inside a controlled phantom can be used to inform us of the flow speeds inside in vivo vessels.

[0024] An example OCT system 100 such as that of the present disclosure is illustrated in Fig. 1. As discussed above, the system 100 includes a light source 101. The light generated by the light source 101 is split by, for example, a beam splitter (as part of 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 off 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 like object. Light from the sample arm 106 and the reference arm 104 is recombined at the optics 108 and a corresponding interference signal is detected by a detector 102. Herein, the ‘'speckle” of an OCT image is understood to mean the characteristic interference pattern produced by the backscattered OCT light from a turbid media such as tissue microstructure. The detector 102 can be a spectrometer, photo detector, or any other light detecting device. The detector 102 outputs an electrical signal corresponding to the interference signal to one or more processors 114. The processor(s) 114 may process the electrical signal into OCT signal data, generate corresponding structural images, and / or further analyze the data and images. Particularly, the processor(s) 114 may include components, as illustrated in Fig. 1, which may implement any or all aspects of the present disclosure.

[0025] The processor(s) 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 inputs to the system. In some embodiments, the processor 114 may also be used to control the light source and imaging process.

[0026] An example method of the present disclosure is illustrated generally in Fig. 2. Therein, the method begins by generating a time-series of OCT structural images at a location of the object being imaged. In other words, the OCT system 100 of Fig. 1 images the object according to a scan pattern that produces OCT data from the same location at a plurality of times. For example, the OCT system 100 may repeatedly scan a single cross-sectional plane location of the object so as to generate a plurality of B-scans of the cross-sectional plane location at different times. In other embodiments, the OCT system 100 may repeatedly scan an area of the object so as to generate a plurality of volumes of the object, from which B-scans at different times can be derived.

[0027] For analyzing blood flow, the scanning protocol of the OCT system 100 may collect data for a long enough period of time to capture one or more cardiac pulses. In other words, the OCT data may be collected for a time on the order of a few seconds. According to one example, OCT data is collected for between two and three seconds. Further, the rate and densityof 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 great enough to obtain at least 50 repeated structural OCT B-scans per second at a common cross-sectional plane location. According to one example, each structural OCT B- scan is about 1 mm wide with between 500 to 1024 A-lines of information.

[0028] During OCT data capture, the object may be tracked to ensure movement is limited during the capture period. If movement is too great, thereby causing too much noise or an inability to register the resulting OCT B-scans or volumes, the scanning protocol may be reset.

[0029] Following OCT data capture and structural image generation, blood flow information is extracted from the individually generated structural OCT images. Depending on the embodiment, blood flow information may be extracted from an entire OCT B-scan, or only from a region of interest (ROI). If extracted from an ROI, the ROI may be determined manually or automatically. The ROI may be manually determined by a clinician selecting a region of an OCT B-scan corresponding to an area of blood flow or vasculature. Measurements of the vasculature, such as its size, may also be measured manually by a clinician from the structural OCT images. Such a region and measurements may instead be automatically determined by a variety of methods. For example, the region of vasculature (and thus blood flow) may be identified by segmentation techniques, machine learning techniques, thresholding techniques (blood flow and vasculature having a greater intensity and variation), and the like. Further, additional ROIs may be identified for other vessels.

[0030] As noted above, each of the repeated B-scans at the common cross-sectional plane location may be registered to each other. Therefore, the ROI may only be identified in a B-scan image and then extrapolated to the other repeated B-scan images at that location. In the event that images are acquired in an en-face manner in the X-Y plane, those X-Y plane images may also be registered to each other. Thus, the ROI would only need to be identified in a single X- Y plane image.

[0031] Blood flow information may be extracted from individual OCT B-scans in a variety of ways. Generally, the techniques for extracting blood flow information from structural OCT B-scans are based on the recognition that blood flow produces greater speckle variation than static structural tissue.

[0032] A first example method for extracting blood flow information based on frequency analysis is illustrated in Fig. 3. As seen therein, an ROI is identified within an area of an OCT structural image corresponding to blood flow and vasculature (as indicated by the higher intensity signal and higher density’ speckle pattern). The ROI is then transformed (e.g..according to a 2D Fourier transform) to produce 2D frequency information of the ROI, which may then be frequency filtered. In other words, high and low frequency filters are applied to frequency information of the transformed ROI. The cutoff frequency of the filters may be determined based on a size of the ROI (or 2D Fourier transform region) in relation to the size of blood vessel speckles under analysis. The resulting signal is then statistically combined into a single value to produce a high frequency signal FH, and a low frequency signal FL. According to the example of Fig. 3. the statistical combination is an average.

[0033] Generally, the high frequency signal FH can be understood to represent blood flow, while the low frequency signal FL represents static tissue. Therefore, in some embodiments, the high frequency signal FH alone may be determined and further processed, and may correspond to the flow information. However, in some embodiments, normalizing the high frequency signal FH (for example, to the low frequency signal F ) may help account for natural variations in the imaged object and the OCT system 100, thereby improving the quality of the extracted flow information. In other words, the flow information may be determined as the ratio between the high and low frequency signals FH, FL.

[0034] According to another technique, flow information may be extracted based on a speckle density. A first example of such a technique is illustrated in Fig. 4A. According to the example of Fig. 4, a threshold may be applied to the pixels within the ROI. A binary map of the ROI may then be generated based on an applied threshold. For example, all pixels greater than the threshold may be set to a value of 1, while all pixels less than threshold may be set to a value of 0. A speckle density may then be determined as a ratio of the number of pixels having a value of 1 to the total number of pixels of the ROI, or to the number of pixels having a value of 0. The flow information may thus be considered equal to or a function of speckle density.

[0035] A second example technique for determining speckle density is illustrated in Fig. 4B. According to the method therein, an edge filter is first applied to a structural OCT B-scan, which identifies edges of the speckles. An averaging filter is then applied to the speckle edges (the output of the edge filter) and to the structural OCT B-scan. Applying the averaging filter produces an edge strength and a mean intensity of the structural OCT B-scan, respectively. The averaging filter preferably has size that is close to the size of the blood vessel in the structural OCT B-scan from which the blood flow information is being determined. Finally, because speckle density is proportional to the density of visible edges, the speckle density can be determined as a ratio between the edge strength and the mean intensity (e.g., edge strength divided by mean intensity). As above, the flow information may be considered the speckle density (e.g., the ratio) or a function of the speckle density.

[0036] In one particular embodiment, this edge filter may be a Sobel operator. With this technique, each pixel in a structural OCT B-scan image (or within a region of interest in the image) is convolved with a kernel in the X-Z domain, such as the following 3x3 matrix.

[0037] In still other embodiments, speckle density could be determined by applying a threshold intensity to the speckle edges. In other words, the speckle density may be considered the number of speckle edges exceeding a threshold intensity. However, normalizing (e.g., to the mean intensity' as discussed above) helps retain edge strength information at different locations.

[0038] In still other techniques, flow information may be determined by applying a cooccurrence matrix to the ROI. The co-occurrence matrix creates a dependence matrix by determining how often a pixel with pixel intensity value I occurs in the adjacent frame to a pixel with the value / . Each element (7, j) in the dependence matrix specifies the number of times that the pixel with a value I occurred in the adjacent frame to a pixel with value j. The flow information is then determined by applying the correlation of a pixel to its neighbor frames on the co-occurrence matrix. An example application of such a co-occurrence matrix application is illustrated in Fig. 5. The size of a moving window applied on temporally adjacent structural OCT B-scan images is first determined. According to one example, the moving indow size is four. A co-occurrence matrix is then applied to the structural OCT B-scan image data inside this analysis window. The flow information is then determined by applying a correlation property7on the co-occurrence matrix.

[0039] In a similar technique, and with reference to Fig. 10, the change in speckle (e.g., as determined by edge detection) between structural OCT B-scan images at a common location can be considered over a time T (e.g., considering temporally adjacent OCT B-scan images). This accentuates changes between consecutive frames. The application of subsequent convolution kernels further emphasizes vertical edges that correlate with blood flow', distinguishing them from the background tissue.

[0040] As suggested above, quantification of flow can be achieved by evaluating the edges in a region of interest through image processing techniques. Edge detection techniques can be crafted to result in brighter pixels when an edge is detected. The edges in turn are related to the flow velocity. In one example, the mean grayscale value of the detected edges within regionsof interest is evaluated, since the detection method highlights temporal changes, speckle density, and orientation of the speckle in the region of interest. The mean grayscale value produced is correlated to flow velocity. Where the region of interest is the internal area of a blood vessel, the volume of flow can be calculated.

[0041] For example, each pixel in a temporal series of OCT B-scan images can be convolved with a first kernel in the T-Z domain. The resulting values may then be convolved with a second kernel in the X-Z domain. One example of the first kernel may be:And one example of the second kernel may be:

[0042] In some embodiments, a fundus image can be simultaneously obtained with the structural OCT B-scan images to provide a marker indicating the location of the scan obtained. From this image the angle of crossing of the vessel with the scan direction can be calculated, thus offering a method of correcting the distortion of the cross-sectional area by scan lines that are not perpendicular to the vessel orientation.

[0043] In still other techniques, 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 a corresponding ground truth flow information. The ground truth flow information may be determined, for example, according to one of the above-described techniques. The ground truth flow information may additionally or alternatively be determined by other techniques, such as Doppler OCT or even non-OCT analysis techniques. Accordingly, the machine learning system is trained to recognize a relationship between a structural specklesignal and corresponding flow information. Machine learning can incorporate the speckle size, orientation, and changes over time in the retina to determine flow characteristics. Manual or automatic segmentation of the structural OCT, flow data, or both can be used to select regions of interest corresponding to the lumens of vessels.

[0044] In still 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 could be applied on individual structural OCT B-scans and / or on temporally adjacent structural OCT B-scan images. In still 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 above, each of these techniques could be applied on individual structural OCT B-scans and / or on temporally adjacent structural OCT B-scan images.

[0045] Returning to Fig. 2, once flow information is identified for a plurality of repeated OCT B-scan images (or other images at a common location), a flow profile may be determined as a time-series plot of the flow information. In other words, the ratio between the high and low frequency signals E , E , the speckle density, or like flow-related information values are plotted according to a time series in which the relative time for each flow-related information value corresponds to a time at which the corresponding repeated OCT image was captured.

[0046] An example flow profile is illustrated in Fig. 6A along with a corresponding structural OCT signal. The structural OCT signal represents an average pixel intensity within the ROI form which the flow information of the flow profile was extracted. As can be seen, the flow profile is similar to a traditional pulse w aveform. Indeed, the dicrotic notch is even visible roughly between image frames 15 and 20. In contrast, the structural OCT signal is relatively flat and bears no relationship to a pulse wave.

[0047] Fig. 6A further identifies four image frames (9, 12, 29, and 36) which are illustrated in Fig. 6B. More particularly, Fig. 6B illustrates the original structural OCT image for each frame along with a blood flow map for each frame. The blood flow map is a map of extracted flow information for the entire area of the structural OCT image, while the time-series signals shown in Fig. 6A only represent the ROI noted in the image frames in Fig. 6B.

[0048] Comparing the flow profile in Fig. 6A with the blood flow' maps of Fig. 6B, the pixel intensity (and thus the extracted blood flow' information) of the area 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-to-no discernible difference in the ROI in the structural OCT images at any frame.

[0049] It is noted that the flow profile may be determined merely by evaluating the change over time in the speckle, without regard to any 2D pixel information to get this flow chart. An example of such a flow profile is shown in Fig. 6C, with the profile of the artery having a larger magnitude than that of the vein.

[0050] In some embodiments, a 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 an input time-series of structural OCT images.

[0051] While the above description relates to generating flow profiles at a common cross- sectional plane location, it is possible to determine a flow velocity based on flow profiles from two different cross-sectional plane locations.

[0052] With reference to Fig. 7, the first step of an example method for determining flow velocity involves simultaneously capturing OCT data from at least two different locations having a known distance therebetween. According to one example, the OCT data capture occurs alternately betw een the different locations such that the resulting B-scan images from each location are temporally interleaved. Fig. 8A illustrates 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, OCT data may be captured according to circular scans having different radii. The difference in radius between each circular scan thus represents a known distance. These circular scans could be temporally interleaved in the same manner discussed above.

[0053] Following the capture of OCT data, flow profiles for the data from each location are generated. These flow profiles may be generated from any of the above-discussed methods. Fig. 8B illustrates flow profiles corresponding to the locations A, B of Fig. 8A. A time At between these two flow profiles can then be determined. For example, as illustrated in Fig. 8B, the time At is determined between local maxima of the flow profiles; however, the time may instead be determined between other common locations of the flow profiles (e.g., local minima). The local maxima may be determined, for example, by identifying the frame at which a maximum value in each cardiac cycle occurs. According to other examples, a derivative of the flow profile may be determined and then the frames at which the derivative equals (or crosses) zero correspond to local maxima and minima. The time At may then be determined based on the sampling rate at which OCT data w as captured. In other w ords, if OCT B-scans are captured at 50 frames per second, a difference of 50 frames between local maxima of the different flow profiles represents a 1 second difference.

[0054] Once the time At is determined, a velocity may be determined according to the relationship velocity = d / At . A direction of the flow may be further determined according to whether a phase shift of the OCT signal between the two locations is positive for negative. This velocity may be determined multiple times between the same two locations and / or between multiple locations. The plurality of determined velocities may then be averaged (or combined according to another statistical determination) in order to identify a representative velocity. Similarly, determined velocities can be compared between a plurality of patients to identify’ abnormalities, or between the same patient at multiple capture times to determine a change in the patient’s condition.

[0055] In another embodiment, the in vivo flow velocity inside a blood vessel can be estimated following the procedure described above in paragraph

[0022] , The measured Relative Flow Value (RFV) and the actual flow velocity (for human blood), in certain conditions, follow a power law relationship. In one example, this power law relationship was determined empirically and may be described by the equation: Relative Flow Value = 169.6 * (Actual Flow in mm / second)264, which allows one to calculate an actual flow rate.

[0056] Still further, any part of the above description may be incorporated with other analysis techniques and / or processing. For example, the flow profiles may be averaged over several cardiac cycles or compared over a period of time (e.g., weeks, months, years) to monitor disease progression. In other examples, because blood flow in veins and arteries may be considered opposite, the determined direction and distance of blood flow may be used to distinguish arteries from veins in the structural images. Similarly, because blood flow is slower in veins than arteries, veins and arteries could further be distinguished by comparison of determined velocity. In still other examples, the above OCT data acquisition could be performed concurrently with other physiological measurements, such as pulse oximetry, electrocardiography, and the like. Further analysis of the flow profiles herein may be based on additional physiological information captured by the concurrent measurements.

[0057] The structural OCT images can provide anatomical information such as the vessel diameters and the vessel wall thickness. The blood flow volume can be calculated using the flow velocity over time and the vessel diameter. Knowing the blood flow volume allows the pressure drop to be calculated using the known blood viscosity value. As the flow rate changed during systole and diastole, one can use the differing values to calculate a surrogate marker of vessel compliance. The vessel wall thickness and its compliance can be used as an indication of the overall health status of the local and systemic blood vessels for the person.

[0058] Additionally, any vascular biomarkers of glaucomatous damage such as impaired vascular flow and autoregulation may be used to provide diagnostic information. Accordingly, it may be possible to detect glaucoma, or quantify its severity, by measuring changes in blood flow in response to an external stimulus. An example of such a method is illustrated in Fig. 9.

[0059] As seen in the example method of Fig. 9, an external stimulus is applied. In the example of glaucoma testing, such a stimulus may be an applied pressure (e.g., an air puff) or other stimulus that produces a change in intraocular pressure. A flow profile may then be determined for the patient according to any of the above techniques. The resulting flow profile may be compared to known healthy patients or a previously determined baseline flow profile for that patient. Changes in the flow profile relative to the baseline may be indicative of a healthy eye, as flow in the eye (and thus the vessels of the eye) are affected by the applied pressure. In contrast, no (or relatively minimal) change in flow profile relative to the baseline may be indicative of glaucoma since the blood flow and vessel are not responsive to the applied pressure. The above analysis may also be applied vice versa depending on the applied external stimulus.

[0060] While the above example relates to an applied pressure for testing glaucoma, it should be understood that any external stimulus and testing could be utilized. In other words, a flow profile may be determined according to the present disclosure before, during, or after the application of any external stimulus. Response of the flow profile to the stimulus may then be analyzed for diagnostic or like purposes.

[0061] Returning again to Fig. 2, with the flow profile known, the extracted flow information may be further analyzed and / or displayed. Because the above-discussed flow profile corresponds to a pulse waveform, such an analysis can include any known techniques for analyzing pulse waveforms. Such techniques may include those for determining heart rate, rise time, flow skew, blood pressure, cardiac function, vessel stiffness, and the like.

[0062] Regarding displays, display of the extracted flow information may include the flow profile itself, the blood flow maps, and / or the structural OCT images. The extracted flow information may be mapped to pixel intensity and / or color in the blood flow maps. In some embodiments, the blood flow maps may be shown as videos rather than static images.

[0063] While various features are presented above, it should be understood that the features may be used singly or in any combination thereof. Further, it should be understood that variations and modifications may occur to those skilled in the art to which the claimed examples pertain.

Claims

WHAT IS CLAIMED IS:

1. A method for analyzing optical coherence tomography (OCT) data comprising: capturing OCT data from an object; generating a sequence of structural OCT images from a specified location within the object over time based on the captured OCT data; and extracting flow information from the sequence of structural OCT images by analyzing temporal and spatial changes of the structural OCT images.

2. The method of claim 1 , wherein areas of blood flow are determined by identifying speckle that is smaller, more numerous, having a vertical orientation, and / or having a higher rate of change relative to that of stationary tissue.

3. The method of any of claims 1-2, further comprising: quantifying blood flow volume based on the extracted flow velocity information and corrected vessel cross-section area.

4. The method of any of claims 1-2, further comprising: quantifying blood flow velocity based on the extracted flow information.

5. The method of claim 4, wherein the blood flow velocity is empirically quantified.

6. The method of claim 5, wherein the blood flow velocity is a function of an empirically derived relative flow value.

7. The method of claim 6, wherein the blood flow velocity follows a power law relationship.

8. The method of any of claims 1-7, wherein analyzing temporal changes in speckle patterns comprises: applying detection techniques along the temporal dimension to highlight significant changes between consecutive structural OCT images; and using convolution kernels to accentuate vertical edges associated with the directional characteristics of blood flow.

9. The method of claim 8, further comprising: inputting the sequence of structural OCT images to a machine learning system trained to enhance the detection of vertical edges and to differentiate between speckle patterns associated with blood flow and stationary7tissue.

10. The method of claim 9. wherein the machine learning system is trained with comparative information from Doppler OCT and / or model systems using known flow velocities through capillary' tubing with diameters that have physiologic relevance.

11. The method of claim 9 or 10, wherein the machine learning system is trained with datasets annotated with known flow rates correlated to detected speckle patterns.

12. The method of any of claim 9-11, wherein the machine learning system is further trained to: automate a selection of regions of interest based on the presence of speckle characteristics indicative of blood flow ; and output quantified flow information based on the analysis of speckle patterns within the selected regions of interest.

13. The method of any of claims 1-12, wherein the OCT data is captured for a duration sufficient to encompass multiple cardiac cycles, allowing for the analysis of blood flow variations over time.

14. The method of any of claims 1-13, further comprising: displaying the quantified blood flow information as a time-series graph, illustrating the relationship between the extracted flow information and the timing of the captured OCT data.

15. A method comprising: capturing optical coherence tomography (OCT) data from an obj ect; generating a plurality of structural OCT images from a first location of the object based on the captured OCT data; determining a change between the plurality' of structural OCT images; andgenerating a first time-series flow profile of the first location of the object based on the determined change.

16. The method of claim 15, wherein the change is a change in speckle characteristics of the structural OCT images.

17. A system for extracting blood flow information of the retina of an eye, the system comprising: an optical coherence tomography (OCT) device for generating a plurality of structural OCT images of a location of the retina; a processor for extracting flow information from individual ones of the structural OCT images and generating a time-series flow profile of blood flow at the location of the retina.

18. The system of claim 17, wherein the flow information includes a relative flow value indicative of a velocity of blood flow at the location of the retina.

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