Use of dynamic forward scattering signals for flow quantification based on optical coherence tomography

Abstract

1. An apparatus for measuring blood flow parameters in a blood vessel, comprising: an interferometric data collection device comprising a light source and a sensor connected to a controller, the controller comprising: 11. The apparatus of claim 10, wherein the apparatus is configured to: direct the light source toward a proximal side of a blood vessel; acquire interference data from tissue adjacent and exterior to a distal side of the blood vessel opposite the proximal side; determine a signal modulation ratio based on the interference data; and estimate a blood flow parameter within the blood vessel based on the signal modulation ratio.

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims the priority of U.S. Provisional Application No. 63 / 230,791, filed on August 8, 2021, the entire disclosure of which is incorporated herein by reference.

[0002] Description of Research Funded by the Federal Government This invention was made with government support under Grant No. P41EB015903 awarded by the National Institutes of Health / National Institute of Biomedical Imaging and Bioengineering. The government has certain rights in the invention.

Background Art

[0003] Using optical coherence tomography (OCT), the blood flow within individual blood vessels can be quantified. Many approaches have been described, and they generally are based on a common principle, namely that the movement of scatterers (such as red blood cells (RBCs)) induces modulation of the OCT signal, and the rate of this modulation is proportional to the flow velocity of the scatterers. Thus, OCT flowmetry systems are designed to first measure the signal modulation rate (by some measurement criterion) and then relate this rate to flow parameters (such as velocity and volumetric flow rate). However, each of these steps has challenges that must be overcome to achieve robust OCT - based flow measurement.

Summary of the Invention

[0004] Therefore, new systems, methods, and media for measuring flow parameters are desired.

[0005] Vascular and hemodynamic abnormalities are closely linked to the pathogenesis and progression of many eye diseases. As a result, numerous methods have been proposed to qualitatively visualize vascular structure and use "vascular density" as an alternative measure to perfusion. However, existing techniques cannot reliably measure blood flow through the vascular system.

[0006] We observed that OCT time-series measurements in static tissue beneath blood vessels possess analyzable dynamic characteristics that can be correlated with actual blood flow. This approach is called "dynamic forward scattering (DFS)" because the optical signal we focus on is modulated by photons forward-scattered from red blood cells within the vessel. Through various experiments and developments, we obtained data confirming that these signals allow for robust measurements of blood flow without artifacts that would interfere with the direct analysis of signals coming from inside the vessel via a single backscatter.

[0007] We note that the DFS approach has unique advantages for evaluating flow within the choroid. The choroid is characterized by its very weak signal when probed with OCT. The sclera, the layer beneath the choroid, is highly scatterable, homogeneous, and nearly avascular, making the disclosed procedure particularly well suited for use in this tissue. Signals from the sclera are brighter than those from the choroidal vascular lumen, with image transmittances sometimes reaching several hundred microns. This leads to two results when we analyze scleral OCT signals to calculate choroidal blood flow: (i) the analyzed scleral voxels may have a higher signal-to-noise ratio (SNR) than choroidal blood voxels; and (ii) there may be more voxels available for analysis.

[0008] The approach disclosed herein enables OCT-based blood flow measurement by focusing on the dynamics of the signal captured by forward-scattered light. In contrast, to our knowledge, all existing OCT flow measurement methods focus on OCT measurement of a single backscatter within a blood vessel. We have developed a concept and framework that utilizes all available measurements from both inside and outside the vascular lumen (particularly below the vessel) that contain information about blood flow, in order to provide a reliable estimate of blood flow.

[0009] Embodiments of the disclosed procedure may be used as part of a protocol for evaluating posterior segment blood flow using any optical coherence tomography (OCT) system. Embodiments of this procedure can be implemented on conventional OCT systems, such as those used in clinics. The acquisition protocol (within the software) can be modified to acquire data necessary for quantitative blood flow assessment, and various analyses can be performed in post-processing.

[0010] Accordingly, in various embodiments, the present disclosure provides an apparatus for measuring intravascular blood flow parameters. This may include an interference data acquisition device comprising a light source and a sensor connected to a controller. The controller may be configured to direct the light source towards the proximal side of the blood vessel, acquire interference data from tissue adjacent to and outside the distal side of the blood vessel opposite to the proximal side, determine the signal modulation rate based on the interference data, and estimate the intravascular blood flow parameters based on the signal modulation rate.

[0011] In some embodiments, the signal modulation rate may include the non-correlation rate. In other embodiments, the signal modulation rate may include, or be based on, a measurement of the temporal cross-correlation or temporal covariance of the signals. In some embodiments, the light source may be oriented substantially perpendicular to the central axis of the blood vessel. The central axis of the blood vessel is substantially parallel to the axis through which the substance (e.g., blood) flows within the blood vessel.

[0012] In one embodiment, the interference data acquisition device may include an optical coherence tomography (OCT) scanner. Accordingly, the controller may be configured to acquire OCT data from tissue adjacent to and outside the distal side of the blood vessel opposite to the proximal side when acquiring the interference data, and the controller may be configured to determine the signal modulation rate based on the OCT data when determining the signal modulation rate based on the interference data.

[0013] In various embodiments, the OCT data may be based on the forward scattering of light from the light source by blood flowing into the tissue adjacent to and outside the distal side of the blood vessel. Accordingly, the controller may be configured to estimate the blood flow parameters within the blood vessel based on the cumulative flow rate throughout the entire blood vessel when estimating the blood flow parameters within the blood vessel, that is, the blood flow parameters do not need to measure depth-decomposed flow within the blood vessel.

[0014] In some embodiments, the blood vessels and the tissue may be located in the retina of the subject. In some embodiments, the tissue may include at least one of scleral tissue or retinal pigment epithelium (RPE) tissue adjacent to and outside the distal side of the blood vessels.

[0015] In certain embodiments, the blood flow parameters include at least one of the following: flow speed (e.g., in mm / second), velocity (e.g., in mm / second, along with the relevant spatial direction), or flux (e.g., in mL / second, or the number of RBCs passing through the imaging beam per second).

[0016] In various embodiments, the controller may be configured to acquire backscatter interference data from inside the blood vessel when acquiring interference data from tissue adjacent to and outside the distal side of the blood vessel opposite to the proximal side; the controller may be configured to determine the backscatter signal modulation rate when determining the signal modulation rate based on the interference data; and the controller may be configured to estimate the blood flow parameters within the blood vessel when estimating the blood flow parameters within the blood vessel based on the signal modulation rate. Thus, in such embodiments, for example, the backscatter interference data may be used together with the forward scattering interference data to estimate the blood flow parameters.

[0017] In some embodiments, the present disclosure provides a method for measuring intravascular blood flow parameters, comprising: an interference data acquisition device comprising a light source and a sensor connected to a controller; using the controller to direct the light source to the proximal side of the blood vessel; using the controller to acquire interference data from tissue adjacent to the distal side of the blood vessel opposite to the proximal side; using the controller to determine the signal modulation rate based on the interference data; and using the controller to estimate intravascular blood flow parameters based on the signal modulation rate.

[0018] In one embodiment, determining the signal modulation rate may further include determining the non-correlation rate based on the interference data, and estimating the blood flow parameters within the blood vessel may further include estimating the blood flow parameters within the blood vessel based on the non-correlation rate.

[0019] In various embodiments, directing the light source towards the proximal side of the blood vessel may further include directing the light source towards the proximal side of the blood vessel in a direction substantially perpendicular to the central axis of the blood vessel.

[0020] In certain embodiments, the interference data acquisition device may include an optical coherence tomography (OCT) scanner, and acquiring the interference data may further include acquiring OCT data from tissue adjacent to and outside the distal side of the blood vessel opposite to the proximal side, and determining the signal modulation rate based on the interference data may further include determining the signal modulation rate based on the OCT data. In some embodiments, the OCT data may be based on forward scattering of light from the light source by blood flowing into the tissue adjacent to and outside the distal side of the blood vessel. In some embodiments, estimating the blood flow parameters within the blood vessel may further include estimating the blood flow parameters within the blood vessel based on the cumulative flow rate throughout the blood vessel.

[0021] In various embodiments, the blood vessels and tissues may be located in the retina of the subject. In some embodiments, the tissues may include at least one of scleral tissue or retinal pigment epithelium (RPE) tissue adjacent to and outside the distal side of the blood vessels.

[0022] In some embodiments, the blood flow parameters may include at least one of the following: flow velocity (e.g., in mm / second), velocity (e.g., in mm / second, along with the relevant spatial direction), or flux (e.g., in mL / second or the number of RBCs passing through the imaging beam per second).

[0023] In one embodiment, obtaining the interference data from tissue adjacent to and outside the distal side of the blood vessel on the side opposite to the proximal side may further include obtaining backward scattering interference data from inside the blood vessel. Determining the signal modulation rate based on the interference data may further include determining the backward scattering signal modulation rate based on the backward scattering interference data. Estimating the blood flow parameters in the blood vessel based on the signal modulation rate may further include estimating the blood flow parameters in the blood vessel based on the backward scattering signal modulation rate.

[0024] In various embodiments, the present disclosure provides an apparatus for measuring flow parameters in a vessel, comprising an interference data collection device including a light source and a sensor connected to a controller. The controller is configured to direct the light source towards the proximal side of the vessel, obtain interference data from a sample adjacent to and outside the distal side of the vessel on the side opposite to the proximal side, determine a signal modulation rate based on the interference data, and estimate the flow parameters in the vessel based on the signal modulation rate.

[0025] In some embodiments, the signal modulation rate may include a decorrelation rate. In one embodiment, the light source may be directed in a direction substantially orthogonal to the central axis of the vessel.

[0026] In certain embodiments, the interference data collection device may include an optical coherence tomography (OCT) device. The controller may be further configured to obtain OCT data from a sample adjacent to and outside the distal side of the vessel, opposite to the proximal side, when obtaining the interference data. The controller may be further configured to determine the signal modulation rate based on the OCT data when determining the signal modulation rate based on the interference data. In some embodiments, the OCT data may be based on the forward scattering of light from the light source by a material flowing into the sample adjacent to and outside the distal side of the vessel. In one embodiment, the controller may be further configured to estimate the flow parameter in the vessel based on the integrated flow rate across the entire vessel when estimating the flow parameter in the vessel.

[0027] In some embodiments, the vessel and the sample may be disposed in biological tissue. In other embodiments, the vessel and the sample may be disposed within biological tissue prepared ex vivo or in vitro, such as a tumor spheroid. In other embodiments, the vessel and the sample may be disposed within an engineering construct, such as a flow phantom or a microfluidic platform.

[0028] In various embodiments, the flow parameter may include at least one of a flow velocity (e.g., in mm / sec), a velocity (e.g., in mm / sec along with the associated spatial direction), or a flux (e.g., in mL / sec or the number of RBCs passing through the imaging beam per second).

[0029] In certain embodiments, the controller may be configured to acquire backscatter interference data from inside the vessel when acquiring interference data from a sample adjacent to and outside the distal side of the vessel opposite to the proximal side, the controller may be configured to determine the backscatter signal modulation rate when determining the signal modulation rate based on the interference data, and the controller may be configured to estimate the flow parameters inside the vessel based on the backscatter signal modulation rate when estimating the flow parameters inside the vessel based on the signal modulation rate.

[0030] In various embodiments, the present disclosure may provide a method for measuring flow parameters in a vessel, comprising: providing an interference data acquisition device comprising a light source and a sensor connected to a controller; using the controller to direct the light source to the proximal side of the vessel; using the controller to acquire interference data from a sample adjacent to the distal side of the vessel opposite to the proximal side; using the controller to determine the signal modulation rate based on the interference data; and using the controller to estimate the flow parameters in the vessel based on the signal modulation rate.

[0031] In some embodiments, determining the signal modulation rate may further include determining the non-correlation rate based on the interference data, and estimating the flow parameters in the Bessel may further include estimating the flow parameters in the Bessel based on the non-correlation rate.

[0032] In one embodiment, directing the light source towards the proximal side of the vessel may further include directing the light source towards the proximal side of the vessel in a direction substantially perpendicular to the central axis of the vessel.

[0033] In certain embodiments, the interference data acquisition device may include an optical coherence tomography (OCT) scanner, and acquiring the interference data may further include acquiring OCT data from a sample adjacent to and outside the distal side of the vessel opposite to the proximal side, and determining the signal modulation rate based on the interference data may further include determining the signal modulation rate based on the OCT data. In some embodiments, the OCT data may be based on forward scattering of light from the light source by material flowing into the sample adjacent to and outside the distal side of the vessel. In various embodiments, estimating the flow parameters in the vessel may further include estimating the flow parameters in the vessel based on the integrated flow rate throughout the vessel.

[0034] In some embodiments, the vessel and the sample may be placed in biological tissue. In other embodiments, the vessel and the sample may be placed in biological tissue prepared ex vivo or in vitro, such as a tumor spheroid. In other embodiments, the vessel and the sample may be placed in an engineering construct, such as a flow phantom or a microfluidic platform.

[0035] In certain embodiments, the flow parameters may include at least one of the following: flow velocity (e.g., in mm / second), velocity (e.g., in mm / second, along with the relevant spatial direction), or flux (e.g., in mL / second or the number of RBCs passing through the imaging beam per second).

[0036] In one embodiment, obtaining the interference data from a sample adjacent to and outside the distal side of the vessel opposite to the proximal side may further include obtaining backscatter interference data from inside the vessel, determining the signal modulation rate based on the interference data may further include determining the backscatter signal modulation rate based on the backscatter interference data, and estimating the flow parameters in the vessel based on the signal modulation rate may further include estimating the flow parameters in the vessel based on the backscatter signal modulation rate.

[0037] The various purposes, features, and advantages of the disclosed subject matter can be more fully understood by referring to the following detailed description of the disclosed subject matter, when considered in relation to the following drawings in which similar reference numbers identify similar elements. [Brief explanation of the drawing]

[0038] [Figure 1] Panel (a) in Figure 1 shows the shapes of the dynamically back-scattered (DBS) and dynamically forward-scattered (DFS) signals from the Bessel. Panel (b) shows a diagram of the flow phantom, with a polystyrene flow tube having an inner diameter of 125 μm and a Teflon® static scatterer positioned beneath the tube. Panel (c) shows the OCT structural image of the tube phantom, where marked areas indicate DBS voxels prone to artifacts caused by axial gradient effects (double circles) and multiple scattering (squares). Panel (d) shows the estimated non-correlation rate (ρ^(correctly a hat over ρ; the same applies below)) (dark dots represent the lowest value, bright dots represent the highest value, and are represented by viridis scale colors), with each voxel within the DBS and DFS regions overlaid on the structural image. In these data, the pump flow rate was set to 50 μL / min, and the flow angle was α = 96.83°. [Figure 2]Panel (a) in Figure 2 shows a visualization of the uncorrelated estimates (ρ^) of DBS and DFS for two Doppler angles. Panel (b) shows the lateral ρ^ profile at the location marked by the white line in Panel (a). Panel (c) shows the ROI of the mean uncorrelatedness. Panel (d) shows the mean ρ^ (normalized to ρ^0, value at a Doppler angle of 90°) and DFS ROI (bottom plot) within the two DBS ROIs (entire lumen and 66% of the inner diameter of the upper half of the tube, top plot). [Figure 3] Panel (a) in Figure 3 shows the DFS uncorrelation rate (ρ^, mean DFS ROI) plotted against the flow rate derived from the Doppler in the tube. Panel (b) shows ρ^ normalized to a 60% hematocrit level. [Figure 4] Panel (a) of Figure 4 shows a scanning laser ophthalmoscopic image of the retina indicating the scanning location used for DFS signal measurement beneath retinal vessels (top of image, indicated by short thick lines) and across multiple choroidal vessels (bottom of image, indicated by long double-headed arrows). Scale bar = 1 mm. Panel (b) shows a film strip of structure (top) and uncorrelatedness parameter (ρ^) (bottom). Panel (c) shows the lateral flow profile across DBS (top) and DFS (bottom) ROIs. Panel (d) shows the mean value of ρ^ within the DFS ROI, which is plotted over time and shows cardiac pulsatileness. Vertical lines indicate periods containing four time points (indicated by dots between the vertical lines), and cross-sections of the flow are shown in panel (b). [Figure 5]Panel (a) of Figure 5 shows a cross-sectional image of the imaged lines shown in Figure 4, where the boundary between the RPE and the choriosclera is indicated by a dotted line in panel (a) (magenta line at the bottom of the image). Panel (b) shows the uncorrelatedness (ρ^) image (using the rainbow scale as in Figure 1), where the locations of four choroidal vessels are selected and marked with colored triangles, as in panel (a). Panel (c) shows the flow dynamics in the selected vessels, calculated by the mean ρ^ at depth on the sclera. Panel (d) shows the uncorrelatedness profile calculated by depth (on the sclera) and time-averaged ρ^. Asterisks (*) indicate lateral regions where signal uncorrelatedness exists due to larger retinal vessels. [Figure 6] Figure 6 shows the linear phase (Doppler) profile inside the tube and the lateral flow profile at different flow angles. Panel (a) shows the linear Doppler phase indicating axial flow velocity, and the Doppler phase is unaffected by the velocity gradient effect at the end of the tube. Panel (b) shows the uncorrelation rates inside the tube (top) and inside the DFS (bottom). The first two columns show the same plots as shown in panel (b) of Figure 1. [Figure 7] Figure 7 shows the linear trend of flow rates at various flow angles, with the DFS uncorrelation rate (averaged across the entire DFS ROI in panel (c) of Figure 2) for all flow angles outside the range of a 3° deviation from α = 90°. The horizontal axis is the flow rate derived from Doppler. [Figure 8] Figure 8 provides a diagram of an interference spectroscopy system that may be used in conjunction with various embodiments disclosed herein, where panel (A) shows a Mach-Zehnder interferometer that can be implemented using a free-space optical system, and panel (B) shows the fiber arrangement. [Figure 9] Figure 9 shows an example of a system for measuring flow parameters according to several embodiments of the disclosed subject. [Figure 10] Figure 10 shows examples of hardware that can be used to implement computing devices and servers according to several embodiments of the disclosed subject matter. [Figure 11]Figure 11 shows an example of a process for measuring flow parameters according to several embodiments of the disclosed subject matter. [Modes for carrying out the invention]

[0039] According to some embodiments of the disclosed subject matter, a mechanism (which may include a system, method, and medium) for measuring flow parameters is provided.

[0040] To the best of the inventors' knowledge, all existing optical coherence tomography approaches for quantifying blood flow (whether Doppler-based or uncorrelated) analyze light backscattered by moving red blood cells (RBCs). This application discloses the advantages of performing these measurements instead based on light forward-scattered by RBCs, that is, by observing the signal backscattered from beneath (i.e., distal to) the blood vessel. We experimentally demonstrate herein that flow measurement based on forward scattering is unaffected by the orientation of the blood vessel, which is nearly perpendicular to the imaging beam. Furthermore, we also provide a proof-of-principle demonstration of dynamic forward-scattering (DFS) flow measurement in human retinal and choroidal blood vessels. Nevertheless, although many of the examples provided herein are based on data obtained from posterior intraocular blood vessels, the disclosed procedure can be performed on a wide variety of tissues and materials, including skin, brain, gastrointestinal tissue, nerves, precancerous lesions, and cancerous lesions.

[0041] Optical coherence tomography (OCT) allows for the quantification of blood flow within individual blood vessels. While many approaches have been described, they all generally rely on a common principle: the movement of scatterers (such as red blood cells (RBCs)) induces modulation of the OCT signal, and the rate of this modulation is proportional to the flow velocity of the scatterers. Therefore, OCT flowmetry systems are designed to first measure the signal modulation rate (by some metric) and then correlate this rate to flow parameters (such as velocity or volumetric flow rate). Each of these steps presents challenges that must be overcome to achieve robust OCT-based flowmetry.

[0042] In this study, we disclose a strategy to overcome a central challenge in the second step: calculating flow from the measured signal modulation rate. It is established that flow calculations can be unreliable in vessels oriented nearly perpendicular to the OCT beam (Doppler angle α = approximately 90°), which is particularly problematic in measuring retinal vessels that are largely orthogonal to the OCT beam. The root cause of this unreliability is the highly anisotropic phase response of backscatter to axial and transverse motion. Axial motion on a scale of half the wavelength of light causes full 2π phase modulation, while transverse motion needs to be on the order of imaging resolution (typically 10-20 μm) to achieve a similar expected phase response. Of course, this is why Doppler-based methods, which by definition act on the signal phase, measure axial motion. How this affects uncorrelated-based methods, including uncorrelated-based methods that act on the OCT intensity signal, is not as clear. Here, it is shown that changes in phase response resulting from the voxel gradient of axial motion lead to rapid decorrelation of the OCT signal. This holds true regardless of whether complex-valued or intensity signals are being manipulated. While the disclosed example deals with determining the decorrelation rate, in various embodiments other types of analysis, such as power spectral analysis or model-based statistical inference, can be performed on flow-related DFS signals. In other embodiments, the signal modulation rate may include, or be based on, a measurement of the temporal cross-correlation or temporal covariance of the signal.

[0043] When known axially biased methods are applied to vessels with a Doppler angle close to 90°, the signal modulation rate disproportionately measures the relatively small axial component of velocity, making flow rate calculations from the signal modulation rate unreliable. This is because signal modulation heavily depends on the relatively small axial component of velocity. To calculate the total flow rate using these axially biased methods, it is necessary to scale up the measurements using geometric factors. Without precise knowledge of α, the resulting calculated total flow rate is unreliable. In various embodiments, the disclosed procedure can be used to measure various flow parameters (e.g., blood flow parameters), including flow velocity (e.g., in mm / sec), velocity (e.g., in mm / sec, along with the relevant spatial direction), or flux (e.g., in mL / sec or the number of RBCs passing through the imaging beam per second).

[0044] We can more reliably calculate total flow for vessels with Doppler angles close to 90° if we can make the response to the movement of the scatterer isotropic or reverse the anisotropy to lateral movement. We can see that light forward-scattered by a moving scatterer has the latter properties. This can be seen in the conceptual diagram of the photon paths of dynamically back-scattered (DBS) and dynamically forward-scattered (DFS) light shown in Figure 1(a). Of course, since OCT is a reflective imaging technique, we cannot directly measure DFS light. However, we can indirectly measure DFS by observing the signal that is back-scattered from below the vessel and has inevitably passed through the vessel twice. Thus, as long as the light source is directed towards the proximal side of the vessel, interference data is acquired from tissue or other material in the region adjacent to (and outside of) the distal side of the vessel opposite the proximal side.

[0045] To characterize the properties of DFS and its fidelity as a measure of upper vascular flow, we constructed a blood flow phantom as shown in Figures 1(b)–1(c) (all measurements in this study use blood as the exemplary fluid medium). We characterized the noncorrelation of signals in voxels below the flow tube (Figure 1(d)). To quantify the noncorrelation, we modeled the autocorrelation / autocovariance of the DFS signal as follows:

number

[0046] However, τ is the delay between measurements. Function g (1) (τ) is the complex autocorrelation function. We estimated the value of ρ using complex-valued OCT data. In short, we used a maximum-likelihood-based statistical framework. Alternatively, we could also use a method that fits the complex autocorrelation function to the calculated autocorrelation coefficient of the measurements; in other words, the DFS approach is not tied to a specific analytical framework.

[0047] The uncorrelation rate can be measured by calculating the autocovariance of the measured time signal, fitting these autocovariance data to equation (1), and finding the value of ρ that yields the best fit. Numerical methods such as optimization can be used to perform this fitting. The model provided by equation (1) can be modified to have different functional forms with respect to the uncorrelation rate parameter ρ or different functional forms with respect to the delay τ. The measured data can take complex values, in which case the autocovariance will be a complex value output. Alternatively, the amplitude or square of the amplitude (both real values) of the measured data can be used to calculate a real-value autocovariance measurement. Thus, uncorrelation models or signal modulation models, such as those used in equation (1), can be described for real-value or complex-value measurements, and an appropriate model can be used for fitting.

[0048] To estimate flow characteristics, additional indices can be calculated from time-series measurements acquired outside the blood vessel. These include power spectral analysis, in which power localized at lower RF frequencies is associated with slower flow, while power spread more broadly across the entire RF frequency is associated with faster flow. The dynamic characteristics of time-series OCT measurements can be measured, and other techniques and methods known to those skilled in the art can be further utilized.

[0049] Furthermore, we note that the non-correlation analysis in this application refers to a generalized measurement of the rate of a single variation and is not limited to any specific methodology or definition of non-correlation or non-correlation rate.

[0050] It is important to emphasize that equation (1) is an assumed statistical model of the DFS signal. While many efforts have been made to define a statistical model of the DBS signal, no such series of studies exists for the DFS signal. Therefore, the DFS signal may follow a functional dependence on τ that is different from the functional dependence of equation (1). Furthermore, even if we assume that equation (1) is a correct statistical model of the DFS signal, it remains unclear how ρ relates to the flow characteristics of the upper vessels. For example, ρ may be proportional to the peak velocity, or it may be related to the RBC flux instead. These are important questions that need to be answered in the broad development of DFS-based flow quantification strategies, and may require considerable effort. Here, we sought to confirm the motivational principle behind the approach that the DFS uncorrelation rate is unaffected by α around α = 90°. The assumed model is sufficient for this goal.

[0051] Imaging was performed using an M-mode B-scan protocol, recording 128 A-lines at each of 100 locations over a 300 μm span (100(x) x 128(t) A-lines per frame). Ten repeating B-scan frames were acquired at each location. Imaging was performed at 19 different locations, each providing a different Doppler angle from 80° to 100°. We calculated estimates of ρ, denoted by ρ^, for each voxel in the DBS region (inside the tube) and the DFS region (outside the tube). All data in this study were acquired using a swept source OCT system (center wavelength 1060 nm, 100 kHz A-line rate). The OCT system, flow phantom, beam scan protocol, and apparatus and procedures for developing and using bulk motion calibration and elimination are known to those skilled in the art.

[0052] The non-correlation rate of DFS is not affected by the Doppler angle.

[0053] Figure 2(a) shows example frames of the estimated uncorrelation coefficient ρ^ for both the DBS and DFS regions overlaid on the intensity frame, and for Doppler angles of 80.8° and 89.2°. The flow rate for both measurements was 60 μl / min. Note the contribution of the velocity gradient to the uncorrelation of the DBS signal at 80.8°. The increase in uncorrelation in the lower region of the lumen is presumed to be caused by multiple scattering (essentially DFS). This will be explained further below. From Figure 2(a), it is clear that the Doppler angle affects ρ^ in the DBS region but has little effect in the DFS region.

[0054] To more clearly visualize how the Doppler angle affects the DBS and DFS signals, Figure 2(b) shows the lateral profiles of ρ^ for the indicated rows in the DBS (top row) and DFS (bottom row) regions for measurements at 80.8° (left column) and 89.2° (right column). For the DFS signal, the lateral profile of ρ^ is at least nearly parabolic and does not change between the two Doppler angles. For reference, Figure 6 shows the Doppler linear phase shift for these measurements.

[0055] To quantify the uncorrelated characteristics across 19 flow angles, we calculated the average ρ^ within the ROI defined in Figure 2(c) for a fixed flow rate of 60 μl / min. Two ROIs are defined in the DBS region: one equal to the total lumen and the other excluding (i) the edge with the highest velocity gradient and (ii) the lower tube region where multiple scattering is prominent. The DFS ROI covers a broad area below the tube (purple). Figure 2(d) shows these measurements normalized to the measurements obtained at a 90° Doppler angle. Note the strongly uncorrelated quadratic dependence of the total lumen DBS ROI with respect to the Doppler angle, and the decreased quadratic dependence with respect to the Doppler angle for the case of a reduced tube ROI. The DFS signal shows no evidence of a quadratic dependence with respect to the Doppler angle. A minimal linear dependence with respect to the Doppler angle is observed, but this is more likely to be a measurement artifact than a true response. We conclude this because the mechanism that generates asymmetry around a Doppler angle of 90° is unknown, and a similar linear trend is observed in DBS measurements.

[0056] DFS noncorrelation rate linearly proportional to flow velocity

[0057] Next, we used a flow phantom to confirm that the uncorrelation rate of the DFS region changed linearly with respect to the change in flow velocity, while all other characteristics (such as tube shape) remained constant. Using a Doppler angle of 83.5°, we calculated the average ρ^ of the DFS ROI for various pump flow rate settings. These measurements are shown in Figure 3(a) with Doppler analysis of the DBS signal (F Doppler The graph is plotted as a function of the flow rate derived by ). As expected, the trend is very linear. There is also a small Y-axis offset. This analysis was repeated for all Doppler angles from 90° to over 3° (to ensure reliable Doppler measurements with the DBS signal), and the same trend with nearly identical slope and offset was observed (Figure 7). The reason for the non-zero y-intercept is unknown. Possible causes include Brownian motion and measurement noise.

[0058] DFS non-correlation is more strongly correlated with flow velocity than with RBC flux.

[0059] As mentioned earlier, the statistical (autocorrelation or autocovariance) model of the DFS signal is not explained. Therefore, we do not know how the noncorrelation rate relates to the flow characteristics of the upper vascular layer. For example, unlike the DBS signal, the DFS signal may depend on both the movement of forward-scattered red blood cells and the number of red blood cells that interact with the light. The latter means that the DFS signal partially measures RBC flux. To test this, we compared ρ^ when the blood was diluted in various ways so that the hematocrit level ranged from 20% to 60% (Figures 1-3 used 60% hematocrit). If the DFS noncorrelation rate is greatly influenced by RBC flux, we can see that ρ^ decreases dramatically as the hematocrit value decreases. In this analysis, we denote ρ^ as the flow (F) derived from the Doppler. Doppler The effects of pump fluctuations were removed by normalizing against the specified value. At each hematocrit level, we averaged the measurements across all Doppler angles from 90° to over 3°.

[0060] Figure 3(b) shows the results. Normalized non-correlation rate (ρ^ / F) relative to hematocrit 60%. Doppler The values ​​for ρ^ at hematocrits of 20%, 30%, 40%, and 50% are 1.126, 1.1, 1.154, and 1.04, respectively. There is no observable trend of lower ρ^ at lower hematocrit levels. This may be explained by secondary factors (such as the effect of hematocrit on the SNR of the DFS signal), but in fact, a slight increase was observed at lower hematocrit levels. These results do not rule out RBC flux dependence within the DFS uncorrelatedness, but suggest that such dependence is likely to be much smaller than the dependence on flow velocity.

[0061] Application to retinal and choroidal blood vessels

[0062] Finally, we demonstrate that DFS-based techniques can be applied to larger retinal and choroidal vessels in human subjects. First, we measured the uncorrelatedness associated with the DFS signal beneath the retinal vessels (indicated by the short thick line at the top of the image), as shown in Figure 4(a). Because the retinal pigment epithelium (RPE) is avascular and highly scatterable, we used this tissue as a static reporter for the DFS signal. Data were acquired using the same M-mode B-scan protocol used for flow phantom imaging, except that the B-scan was repeated for approximately 10 seconds to capture pulsation. Structural and uncorrelatedness (ρ^) images at four time points, along with the DFS ROI, are shown in Figure 4(b).

[0063] In Figure 4(c), we plot the lateral profiles of ρ^ for the DFS and DBS signals by averaging ρ^ across the entire ROI depth for the DFS signal and averaging ρ^ across three depth lines at specified locations for the DBS signal. As observed in the flow phantom, the lateral uncorrelated profile from DFS is more parabolic than the profile associated with DBS. The DFS profile is less noisy, which may be a result of broader averaging (13 depth points for the DFS ROI compared to 3 for DBS). The average ρ^ across the entire ROI is plotted as a function of time in Figure 4(d), showing the expected pulsatileness. For confirmation, the ratio of the maximum to minimum ρ^ over the entire cardiac cycle was 2.7, consistent with previous reports.

[0064] Next, we applied this technique to the choroid. Quantifying blood flow within the choroid is extremely difficult due to a combination of factors including the complex and dense vascular structure, reduced signal quality due to high scattering RPE, and low SNR. We used an extended M-mode B scan protocol to image 3 mm lines with 256 A lines and 100 locations (30 μm intervals) per location (Figure 5(a)). For choroidal vessels, we analyzed the DFS signal within the sclera. The boundary between the choroid and sclera is shown in Figure 5(a). Figure 5(b) shows the discorrelation rate across the entire image, indicating a clear signal beneath the estimated choroidal vessels. We selected four locations corresponding to estimated choroidal vessels (indicated by colored triangles below the images in Figures 5(a) and 5(b)) and plotted the mean ρ^ across the entire sclera as a function of time (Figure 5(c)). Although the temporal resolution of these measurements is limited (approximately 0.25 sec), we observed cardiac pulsation in these vessels. Figure 5(d) shows the time-averaged flow profile over a 3 mm scan length, calculated by averaging ρ^ over time across the depth of the sclera. While these data are preliminary, they suggest that they may provide a means of quantifying flow within choroidal vessels, a challenge for which existing solutions are very limited.

[0065] This study reveals that DFS signals, known to be the cause of "shadows" or "tails" reported to appear beneath vessels in OCT angiography, should be considered a reliable source of flow information, especially for vessels with Doppler angles close to 90°. While insensitivity to Doppler angle was the initial motivation for this study, the DFS approach has several additional advantages worth discussing. First, as we have explained elsewhere, it is clear in Figure 2(a) that DBS signals are affected by multiple scattering. We have called these signals DBS, but more precisely DBS+DFS. When analyzing them, we need to deal with signals modulated by the mixing of the two processes. In contrast, signals measured beneath vessels (from static scatterers) are pure DFS signals, which may make them easier to model and interpret. Second, in some applications, the DFS approach may have the advantage of providing more independent signal dynamics measurements than the DBS approach. For example, consider a limited set of DBS voxels located inside choroidal vessels compared to a larger set of DFS voxels in the sclera beneath the choroidal vessels. Thirdly, our data suggest that the DFS signal is uncorrelated at approximately twice the rate of the DBS signal from the center of the lumen. This allows for shorter time-series measurements, thus accelerating flow imaging. Finally, we note that the DFS approach can be introduced concurrently with the DBS approach. The difference lies in the signal analysis. Thus, this approach can be considered primary for vessels that are nearly or substantially orthogonal to the direction of the interference source, and secondary as an accessory to existing methods otherwise.

[0066] A limitation of the DFS approach is that it does not measure depth-resolved flow within vessels. Each DFS voxel provides a single metric reporting the integrated flow on the voxel. This can lead to ambiguity when multiple vessels cross the beam's path. The extent to which this limits the usefulness of this approach may depend on the application. For example, in the choroid, it is not always clear to correlate the scleral DFS properties with a single choroidal vessel. However, this limitation should be considered in light of the current lack of a viable approach to quantify flow within the choroid. Because retinal vascular structures are relatively sparse, mapping DFS signals within the RPE to the relevant vessels may not be too difficult. A further limitation of the DFS approach is that, due to the limited DFS signal, it may not be applicable to capillaries. Follow-up studies are needed to investigate the range of vessel diameters over which DFS can be used.

[0067] We also note that there are some limitations to the methods used in this work. The Doppler angle was modified by imaging the tube at various locations, which may have introduced secondary changes (beam resolution / aberration) that interfered with the measurements. This may have resulted in the small linear dependence of ρ^ on the Doppler angle observed, for example, in Figure 2(d). A single tube diameter was used in the flow phantom study, and this diameter was close to the upper limit of the relevant range for retinal and choroidal vessels. We expect the angle dependence of the DBS signal to be more pronounced in smaller vessels due to the larger flow gradient, but this and the effect of vessel diameter on the DFS signal need further investigation. Finally, as previously stated, this study operated using a assumed statistical model of the DFS signal. In various embodiments, other models may be developed by experimental, numerical, or analytical methods and applied to data collected using the disclosed procedures.

[0068] Figure 8 provides a diagram of an interference spectroscopy system that can be used in conjunction with various embodiments disclosed herein. Figure 8 shows a Mach-Zehnder interferometer (Figure 8A) or a fiber arrangement (Figure 8B) that can be implemented using a free-space optical system. Other types of interferometers (such as Michelson interferometers) can also be applied. The light source LS in either Figure 8A or 8B may be a wavelength-swept laser source, a wavelength-swept light source, a time-stepped optical frequency comb source, or a time-stepped discrete optical frequency source. The beam B9 emitted from the LS is led to the interferometer input, where it is split into two paths of approximately equal length using a beam splitter (BS3). B10 is directed to a sample S (e.g., the retina of a subject's eye). Light from the object of interest is directed to the interferometer output (B11). In the reference arm, beam B12 is optionally directed to a phase modulator (PM), which can be an electro-optic phase modulator or an acousto-optic frequency shifter. The beam after the PM (i.e., beam B13) is coupled by BS4 and then directed to the interferometer output to interfere with beam B11. The output beam B14 is then detected by detector D (e.g., a photodiode). Alternatively, a fiber-based interferometer, as shown in Figure 8B, facilitates balanced detection by a phase shift of π between output beams B14 and B15. The detected signal is sampled at a sampling rate f using a data acquisition and processing system (which may include a data acquisition board or a real-time oscilloscope (DAQ)). SThe images are then digitized. Several wavelength sweeps (A1, A2, ..., An) can be acquired to form a two-dimensional or three-dimensional image. In various embodiments, the sample arm S of the interferometer may be integrated with a patient / subject interface (e.g., a lens or probe) to facilitate direct light irradiation of the patient's or subject's tissue and reception of light from the tissue, for example, from the retina. The wavelength-resolved measurements provided by this interferometer system can be processed in a computer system using the discrete Fourier transform to generate depth-resolved measurements of the sample's reflectivity.

[0069] An alternative interferometer system that can be used in conjunction with various embodiments of the present invention may be based on the Mach-Zehnder system shown in Figure 8, but the detector and DAQ system are replaced with a spectrometer. The spectrometer may include an optical grating and a line-scan camera. The light source LS may be a broadband light source such as a superluminescent diode, an LED light source, a supercontinuum light source, or another light source that provides broadband light output. The wavelength-resolved measurements provided by this interferometer system can be processed in a computer system using a discrete Fourier transform to generate depth-resolved measurements of the sample's reflectivity.

[0070] Further interferometer systems that can be used in conjunction with various embodiments of the present invention may be based on the Mach-Zehnder system of Figure 8, but the light source LS may be a broadband light source such as a superluminescent diode, an LED light source, a supercontinuum light source, or another light source that provides broadband optical output. The detector and DAQ measure low-coherence interference fringes according to known time-domain OCT methods to measure reflectance at a single depth point. The reference path B12 may include a variable optical delay for scanning the position of this depth-resolved measurement. A phase modulator PM can be used to generate an inference signal encoded at a specific carrier frequency determined by the signal supplied to the phase modulator. Alternatively, the phase modulator may be an acousto-optic frequency shift provided by an RF signal of frequency Fao, positioning the interference signal at the RF frequency Fao.

[0071] The Mach-Zehnder interferometer configuration shown in Figure 8 and used in further embodiments of the interferometer system can be replaced with alternative architectures that provide light to at least the sample path and the reference path. These include Michelson interferometer systems and Mach-Zehnder systems in which at least one of the sample or reference path includes a bidirectional portion.

[0072] Computers and Optical Systems

[0073] Returning to Figure 9, an example 900 of a system for measuring flow parameters (e.g., a data acquisition and processing system) is shown by several embodiments of the disclosed subject. In some embodiments, a computing device 910 can perform at least part of the system for measuring flow parameters 904 and provide control signals to an interference data acquisition device 902. Additionally or alternatively, in some embodiments, the computing device 910 can communicate information regarding control signals to and from a server 920 via a communication network 906, and the server 920 can perform at least part of the system for measuring flow parameters 904. In some such embodiments, the server 920 can return information relating to the control signals of the system for measuring flow parameters 904 to the computing device 910 (and / or any other suitable computing device). This information may be transmitted to and / or presented to a user (e.g., a researcher, operator, clinician, etc.) and / or stored (e.g., as part of a research database or medical records related to a subject).

[0074] In some embodiments, the computing device 910 and / or server 920 may be any suitable computing device or combination of devices, such as a desktop computer, laptop computer, smartphone, tablet computer, wearable computer, server computer, or virtual machine running on a physical computing device. As described herein, the system for measuring the flow parameter 904 may present information regarding the control signal to a user (e.g., a researcher and / or physician).

[0075] In some embodiments, the communication network 906 may be any suitable communication network or combination of communication networks. For example, the communication network 906 may include a Wi-Fi network (which may include one or more wireless routers, one or more switches, etc.), a peer-to-peer network (e.g., a Bluetooth® network), a cellular network (e.g., a 3G network, 4G network, 5G network, etc., conforming to any suitable standard such as CDMA, GSM, LTE, LTE Advanced, WiMAX®, etc.), a wired network, etc. In some embodiments, the communication network 906 may be a local area network, a wide area network, a public network (e.g., the Internet), a private or semi-private network (e.g., an intranet of a company or university), any other suitable type of network, or any suitable combination of networks. Each communication link shown in Figure 9 may be any suitable communication link or combination of communication links, such as a wired link, an optical fiber link, a Wi-Fi link, a Bluetooth® link, or a cellular link.

[0076] Figure 10 shows an example of hardware 1000 that can be used to implement computing devices 910 and server 920 according to several embodiments of the disclosed subject matter. As shown in Figure 10, in some embodiments, computing device 910 may include a processor 1002, a display 1004, one or more inputs 1006, one or more communication systems 1008, and / or memory 1010. In some embodiments, the processor 1002 may be any suitable hardware processor or combination of processors, such as a central processing unit or graphics processing unit. In some embodiments, the display 1004 may include any suitable display device, such as a computer monitor, a touchscreen, or a television. In some embodiments, the input 1006 may include any suitable input device and / or sensor that can be used to receive user input, such as a keyboard, mouse, touchscreen, or microphone.

[0077] In some embodiments, the communication system 1008 may include any suitable hardware, firmware, and / or software for communicating information over the communication network 906 and / or any other suitable communication network. For example, the communication system 1008 may include one or more transceivers, one or more communication chips and / or chipsets, etc. In more specific examples, the communication system 1008 may include hardware, firmware, and / or software that can be used to establish Wi-Fi, Bluetooth®, cellular, Ethernet®, and the like.

[0078] In some embodiments, memory 1010 may include any suitable storage device that can be used to store instructions, values, etc., that can be used by the processor 1002 to present content using the display 1004 or to communicate with the server 920 via the communication system 1008. Memory 1010 may include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 1010 may include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid-state drives, one or more optical drives, etc. In some embodiments, memory 1010 may encode a computer program for controlling the operation of the computing device 910. In such embodiments, the processor 1002 may execute at least a portion of the computer program to present content (e.g., images, user interfaces, graphics, tables, etc.), receive content from the server 920, and transmit information to the server 920.

[0079] In some embodiments, the server 920 may include a processor 1012, a display 1014, one or more inputs 1016, one or more communication systems 1018, and / or memory 1020. In some embodiments, the processor 1012 may be any suitable hardware processor or combination of processors, such as a central processing unit or graphics processing unit. In some embodiments, the display 1014 may include any suitable display device, such as a computer monitor, touchscreen, or television. In some embodiments, the input 1016 may include any suitable input device and / or sensor that can be used to receive user input, such as a keyboard, mouse, touchscreen, or microphone.

[0080] In some embodiments, the communication system 1018 may include any suitable hardware, firmware, and / or software for communicating information over the communication network 906 and / or any other suitable communication network. For example, the communication system 1018 may include one or more transceivers, one or more communication chips and / or chipsets, etc. In more specific examples, the communication system 1018 may include hardware, firmware, and / or software that can be used to establish Wi-Fi, Bluetooth®, cellular, Ethernet®, and the like.

[0081] In some embodiments, memory 1020 may include any suitable storage device that can be used to store instructions, values, etc., which can be used, for example, for the processor 1012 to present content using the display 1014 or to communicate with one or more computing devices 910. Memory 1020 may include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 1020 may include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid-state drives, one or more optical drives, etc. In some embodiments, a server program for controlling the operation of server 920 may be encoded in memory 1020. In such embodiments, the processor 1012 executes at least a portion of the server program to send information and / or content (e.g., results of organizational identification and / or classification, user interface, etc.) to one or more computing devices 910, receives information and / or content from one or more computing devices 910, and receives instructions from one or more devices (e.g., personal computers, laptop computers, tablet computers, smartphones, etc.).

[0082] In some embodiments, any suitable computer-readable medium can be used to store instructions for performing the functions and / or processes described herein. For example, in some embodiments, the computer-readable medium may be transient or non-transient. For example, non-transient computer-readable media may include magnetic media (such as hard disks and floppy disks), optical media (such as compact discs, digital video discs, and Blu-ray discs), semiconductor media (such as RAM, flash memory, electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)), suitable media that are not transient or lack persistence during transmission, and / or any suitable tangible media. As another example, transient computer-readable media may include signals on a network in wires, conductors, optical fibers, circuits, or suitable media that are transient or lack persistence during transmission, and / or any suitable intangible media.

[0083] It should be noted that, as used herein, the term "mechanism" may encompass hardware, software, firmware, or any appropriate combination thereof.

[0084] Figure 11 shows an example 1100 of a process for measuring blood flow parameters in a blood vessel according to several embodiments of the disclosed subject matter. As shown in Figure 11, in 1102, process 1100 can provide an interference data acquisition device including a light source and a sensor coupled to a controller. In 1104, process 1100 can use the controller to direct the light source to the proximal side of the blood vessel. In 1106, process 1100 can use the controller to acquire interference data from tissue adjacent to and outside the distal side of the blood vessel opposite to the proximal side. In 1108, process 1100 can use the controller to determine the signal modulation rate based on the interference data. Finally, in 1110, process 1100 can use the controller to estimate blood flow parameters in the blood vessel based on the signal modulation rate.

[0085] It should be understood that the steps of the process shown in Figure 11 are not limited to the order and sequence shown and explained in the figure, but can be performed in any order or sequence. Furthermore, some of the steps of the process shown in Figure 11 can be performed substantially simultaneously or in parallel as needed to reduce waiting and processing times.

[0086] Accordingly, although the present invention has been described in relation to specific embodiments and examples, the present invention is not necessarily limited in this way, and many other embodiments, examples, uses, modifications, and departures from embodiments, examples, and uses are intended to be included in the claims appended herein.

Claims

1. A device for measuring flow parameters within a vessel, The device includes an interference data acquisition system equipped with a light source and a sensor connected to a controller, The aforementioned controller, The light source is directed towards the proximal side of the Bessel, Interference data is obtained from a sample located adjacent to and outside the distal side of the vessel, opposite to the proximal side. Based on the aforementioned interference data, the signal modulation rate is determined. The flow parameters within the Bessel are estimated based on the signal modulation rate. A device characterized by being configured in such a way.

2. The aforementioned signal modulation rate includes the non-correlation rate. The apparatus according to feature 1.

3. The light source is directed in a direction substantially perpendicular to the central axis of the Bessel. The apparatus according to feature 1.

4. The aforementioned interference data acquisition device includes an optical coherence tomography (OCT) device. The controller is configured to acquire OCT data from the sample located adjacent to and outside the distal side of the vessel, opposite to the proximal side, when acquiring the interference data. The controller is configured to determine the signal modulation rate based on the interference data, and further to determine the signal modulation rate based on the OCT data. The apparatus according to feature 1.

5. The OCT data is based on forward scattering of light from the light source by material flowing into the sample adjacent to and outside the distal side of the vessel. The apparatus according to feature 4.

6. The controller is configured to estimate the flow parameters within the vessel based on the integrated flow rate across the entire vessel, when estimating the flow parameters within the vessel. The apparatus according to feature 5.

7. The vessel and the sample are placed within a designed phantom that includes in vitro biological tissue, ex vivo biological tissue, fluid channels, flow phantom, or microfluidic platform. The apparatus according to feature 1.

8. The flow parameter includes at least one of flow velocity, speed, or flux. The apparatus according to feature 1.

9. The controller is configured to acquire interference data from a sample adjacent to and outside the distal side of the vessel, opposite to the proximal side, and to further acquire backscatter interference data from inside the vessel. The controller is configured to determine the signal modulation rate based on the interference data, and further to determine the backscatter signal modulation rate based on the backscatter interference data. The controller is configured to estimate the flow parameters in the Bessel based on the signal modulation rate, and further estimate the flow parameters in the Bessel based on the backscatter signal modulation rate. The apparatus according to feature 1.

10. The vessel includes a blood vessel, The aforementioned sample includes tissue. The apparatus according to feature 1.

11. The blood vessels and tissues are located within the retina of the subject. The apparatus according to feature 10.

12. The tissue comprises at least one of scleral tissue or retinal pigment epithelium (RPE) tissue adjacent to and outside the distal side of the blood vessel. The apparatus according to feature 11.

13. A method for measuring flow parameters in a vessel, We provide an interference data acquisition device that includes a light source and a sensor connected to a controller. Using the controller, the light source is directed towards the proximal side of the Bessel. Using the controller, interference data is acquired from a sample adjacent to the distal side of the vessel, opposite to the proximal side. Using the controller, the signal modulation rate is determined based on the interference data. Using the controller, the flow parameters in the Bessel are estimated based on the signal modulation rate. A method characterized by including the following.

14. Determining the signal modulation rate further includes determining the non-correlation rate based on the interference data, Estimating the flow parameters within the vessel further includes estimating the flow parameters within the vessel based on the non-correlation rate. The method according to the present invention, characterized by the present invention.

15. Orienting the light source towards the proximal side of the vessel further includes orienting the light source towards the proximal side of the vessel in a direction substantially perpendicular to the central axis of the vessel. The method according to the present invention, characterized by the present invention.

16. The aforementioned interference data acquisition device includes an optical coherence tomography (OCT) device. Acquiring the aforementioned interference data further includes acquiring OCT data from the sample located adjacent to and outside the distal side of the vessel on the opposite side from the proximal side, Determining the signal modulation rate based on the interference data further includes determining the signal modulation rate based on the OCT data. The method according to the present invention, characterized by the present invention.

17. The OCT data is based on forward scattering of light from the light source by material flowing into the sample adjacent to and outside the distal side of the vessel. The method according to 16, characterized by...

18. Estimating the flow parameters within the vessel further includes estimating the flow parameters within the vessel based on the cumulative flow rate across the entire vessel. The method according to feature 17.

19. The vessel and the sample are placed within a designed phantom that includes in vitro biological tissue, ex vivo biological tissue, fluid channels, flow phantom, or microfluidic platform. The method according to the present invention, characterized by the present invention.

20. The flow parameter includes at least one of flow velocity, speed, or flux. The method according to the present invention, characterized by the present invention.

21. Obtaining interference data from a sample located adjacent to and outside the distal side of the vessel on the opposite side from the proximal side further includes obtaining backscatter interference data from inside the vessel, Determining the signal modulation rate based on the interference data further includes determining the backscatter signal modulation rate based on the backscatter interference data, Estimating the flow parameters in the Bessel based on the signal modulation rate further includes estimating the flow parameters in the Bessel based on the backscatter signal modulation rate. The method according to the present invention, characterized by the present invention.

22. The vessel includes a blood vessel, The aforementioned sample includes tissue. The method according to the present invention, characterized by the present invention.

23. The blood vessels and tissues are located within the retina of the subject. The method according to the feature of 22.

24. The tissue comprises at least one of scleral tissue or retinal pigment epithelium (RPE) tissue adjacent to and outside the distal side of the blood vessel. The method according to the feature of 23.

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