Using dynamic forward-scatter signals for optical coherence tomography-based flow quantification

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 priority to U.S. Provisional Application No. 63 / 230,791, filed August 8, 2021, the disclosure of which is incorporated herein by reference in its entirety.

[0002] STATEMENT REGARDING FEDERALLY FUNDED RESEARCH 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 technology]

[0003] Optical coherence tomography (OCT) can be used to quantify blood flow in individual blood vessels. Many approaches have been described, and they are generally based on a common principle: the movement of scatterers (e.g., red blood cells (RBCs)) induces a modulation of the OCT signal, and the rate of that modulation is proportional to the flow velocity of the scatterers. OCT flowmetry systems are therefore designed to first measure the signal modulation rate (by some metric) and then relate this rate to a flow parameter (e.g., velocity or volumetric flow rate). However, each of these steps has challenges that must be overcome to achieve robust OCT-based flow measurements. Summary of the Invention

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

[0005] Vascular and hemodynamic abnormalities are closely related to the pathogenesis and progression of many ocular diseases. As a result, numerous methods have been proposed to qualitatively visualize vascular structures and use "vascular density" as a surrogate measure of perfusion. However, existing techniques cannot reliably measure blood flow through the vasculature.

[0006] We observed that OCT time-series measurements in static tissues beneath blood vessels have dynamic properties that can be analyzed and related to actual blood flow. This approach is called "dynamic forward scattering" (DFS) because the optical signals we are interested in are modulated by photons forward scattered from red blood cells inside the vessels. Through various experiments and developments, we obtained data confirming that these signals allow robust measurements of blood flow without artifacts that would prevent direct analysis of the signal coming from inside the vessels via a single backscatter.

[0007] We note that the DFS approach has unique advantages for flow assessment in the choroid. The choroid is characterized by a very weak signal when probed with OCT. The layer beneath the choroid, the sclera, is highly scattering, homogeneous, and nearly avascular, making the disclosed procedure particularly well suited for use with this tissue. The signal from the sclera is brighter than the choroidal vascular lumen, and the image transmission can be hundreds of microns. This has two consequences when we analyze the scleral OCT signal 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) many more voxels may be available for analysis.

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

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

[0010] Thus, in various embodiments, the present disclosure provides an apparatus for measuring a blood flow parameter in a blood vessel, which may include an interferometric data collection device comprising a light source and a sensor coupled to a controller, which may be configured to direct the light source toward a proximal side of a blood vessel, acquire interferometric 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 interferometric data, and estimate a blood flow parameter in the blood vessel based on the signal modulation ratio.

[0011] In some embodiments, the signal modulation rate may include a decorrelation rate. In other embodiments, the signal modulation rate may include or be based on a measure of signal temporal cross-correlation or temporal covariance. In some embodiments, the light source may be oriented substantially orthogonal to a central axis of the blood vessel. The central axis of the blood vessel is approximately parallel to an axis along which material (e.g., blood) flows within the blood vessel.

[0012] In some embodiments, the coherence data collection device may comprise an optical coherence tomography device, and thus the controller, when acquiring the coherence data, may be further configured to acquire OCT data from tissue adjacent and exterior to the distal side of the blood vessel opposite the proximal side, and the controller, when determining the signal modulation ratio based on the coherence data, may be further configured to determine the signal modulation ratio based on the OCT data.

[0013] In various embodiments, the OCT data may be based on forward scattering of light from the light source by blood flowing into the tissue adjacent and outside the distal side of the blood vessel. Thus, when estimating the blood flow parameters in the blood vessel, the controller may be further configured to estimate the blood flow parameters in the blood vessel based on an integrated flow rate throughout the blood vessel, i.e., the blood flow parameters may not measure depth-resolved flow in the blood vessel.

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

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

[0016] In various embodiments, when acquiring the interference data from tissue adjacent and outside the distal side of the blood vessel opposite the proximal side, the controller may be further configured to acquire backscattered interference data from inside the blood vessel, when determining the signal modulation ratio based on the interference data, the controller may be further configured to determine a backscattered signal modulation ratio based on the backscattered interference data, and when estimating a blood flow parameter in the blood vessel based on the signal modulation ratio, the controller may be further configured to estimate the blood flow parameter in the blood vessel based on the backscattered signal modulation ratio. Thus, in such embodiments, for example, backscattered interference data may be used in conjunction with forward scattered interference data to estimate a blood flow parameter.

[0017] In some embodiments, the present disclosure provides a method for measuring a blood flow parameter in a blood vessel, the method comprising: providing an interferometric data collection device comprising a light source and a sensor connected to a controller; directing the light source toward a proximal side of a blood vessel using the controller; acquiring interferometric data from tissue adjacent a distal side of the blood vessel opposite the proximal side using the controller; determining a signal modulation ratio based on the interferometric data using the controller; and estimating a blood flow parameter in the blood vessel based on the signal modulation ratio using the controller.

[0018] In some embodiments, determining the signal modulation rate may further include determining a decorrelation rate based on the interference data, and estimating a blood flow parameter in the blood vessel may further include estimating the blood flow parameter in the blood vessel based on the decorrelation rate.

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

[0020] In certain embodiments, the coherence data collection device may comprise an optical coherence tomography device, and acquiring the coherence data may further include acquiring OCT data from tissue adjacent and exterior to the distal side of the blood vessel opposite the proximal side, and determining the signal modulation ratio based on the coherence data may further include determining the signal modulation ratio 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 and exterior to the distal side of the blood vessel. In some embodiments, estimating the blood flow parameters in the blood vessel may further include estimating the blood flow parameters in the blood vessel based on an integrated flow rate throughout the blood vessel.

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

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

[0023] In one embodiment, obtaining the interference data from tissue adjacent and outside the distal side of the blood vessel opposite the proximal side may further include obtaining backscattered interference data from inside the blood vessel, determining a signal modulation ratio based on the interference data may further include determining a backscattered signal modulation ratio based on the backscattered interference data, and estimating a blood flow parameter within the blood vessel based on the signal modulation ratio may further include estimating the blood flow parameter within the blood vessel based on the backscattered signal modulation ratio.

[0024] In various embodiments, the present disclosure provides an apparatus for measuring a flow parameter within a vessel, the apparatus comprising an interferometric data collection device comprising a light source and a sensor connected to a controller, the controller configured to direct the light source toward a proximal side of a vessel, acquire interferometric data from a sample adjacent and outside a distal side of the vessel opposite the proximal side, determine a signal modulation rate based on the interferometric data, and estimate a flow parameter within the vessel based on the signal modulation rate.

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

[0026] In certain embodiments, the interferometric data collection device may comprise an optical coherence tomography device, and the controller, when acquiring the interferometric data, may be further configured to acquire OCT data from a sample adjacent and outside the distal side of the vessel opposite the proximal side, and when determining the signal modulation ratio based on the interferometric data, the controller may be further configured to determine the signal modulation ratio based on the OCT data. In some embodiments, the OCT data may be based on forward scattering of light from the light source by a material flowing into the sample adjacent and outside the distal side of the vessel. In some embodiments, the controller, when estimating the flow parameters in the vessel, may be further configured to estimate the flow parameters in the vessel based on an integrated flow rate through the vessel.

[0027] In some embodiments, the vessel and the sample may be placed in a biological tissue. In other embodiments, the vessel and the sample may be placed in an ex vivo or in vitro prepared biological tissue, 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.

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

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

[0030] In various embodiments, the present disclosure may provide a method for measuring a flow parameter in a vessel, the method including providing an interferometric data collection device comprising a light source and a sensor connected to a controller, directing the light source toward a proximal side of a vessel using the controller, acquiring interferometric data from a sample adjacent a distal side of the vessel opposite the proximal side using the controller, determining a signal modulation rate based on the interferometric data using the controller, and estimating a flow parameter in the vessel based on the signal modulation rate using the controller.

[0031] In some embodiments, determining the signal modulation rate may further include determining a decorrelation rate based on the interference data, and estimating a flow parameter within the vessel may further include estimating the flow parameter within the vessel based on the decorrelation rate.

[0032] In some embodiments, directing the light source toward a proximal side of the vessel may further include directing the light source toward the proximal side of the vessel in an orientation substantially perpendicular to a central axis of the vessel.

[0033] In certain embodiments, the interferometric data collection device may comprise an optical coherence tomography device, and acquiring the interferometric data may further include acquiring OCT data from a sample adjacent and outside the distal side of the vessel opposite the proximal side, and determining the signal modulation ratio based on the interferometric data may further include determining the signal modulation ratio 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 and outside the distal side of the vessel. In various embodiments, estimating the flow parameters within the vessel may further include estimating the flow parameters within the vessel based on an integrated flow rate throughout the vessel.

[0034] In some embodiments, the vessel and the sample may be placed in a biological tissue. In other embodiments, the vessel and the sample may be placed in an ex vivo or in vitro prepared biological tissue, 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 flow rate (e.g., in mm / sec), velocity (e.g., in mm / sec with associated spatial direction), or flux (e.g., in mL / sec or in number of RBCs passing through the imaging beam per second).

[0036] In some embodiments, obtaining the interference data from the sample adjacent and outside the distal side of the vessel opposite the proximal side may further include obtaining backscattered interference data from inside the vessel, determining a signal modulation ratio based on the interference data may further include determining a backscattered signal modulation ratio based on the backscattered interference data, and estimating a flow parameter within the vessel based on the signal modulation ratio may further include estimating the flow parameter within the vessel based on the backscattered signal modulation ratio.

[0037] Various objects, features, and advantages of the disclosed subject matter may be more fully understood by reference to the following detailed description of the disclosed subject matter when considered in conjunction with the following drawings, in which like reference numerals identify like elements and in which: [Brief description of the drawings]

[0038] [Figure 1] Panel (a) of Figure 1 shows the shape of the dynamically back-scattered (DBS) and dynamically forward-scattered (DFS) signals from a vessel. Panel (b) shows a diagram of a flow phantom, with a polystyrene flow tube with an inner diameter of 125 μm and a static Teflon scatterer placed under the tube. Panel (c) shows an OCT structural image of the tube phantom, with the marked areas indicating DBS voxels prone to artifacts caused by axial gradient effects (double circle line) and multiple scattering (square line). Panel (d) shows the estimated decorrelation ratio (ρ^) (represented by viridis scale colors, with dark points being the lowest and light points being the highest values) with each voxel in the DBS and DFS regions overlaid on the structural image. For these data, the pump flow rate was set at 50 μL / min and the flow angle was α=96.83°. [Diagram 2]Panel (a) of Figure 2 shows the visualization of decorrelation estimates (ρ^) for DBS and DFS for two Doppler angles. Panel (b) shows the lateral ρ^ profile for the location marked by the white line in panel (a). Panel (c) shows the ROIs for the mean decorrelation ratio. Panel (d) shows the mean ρ^ (normalized to ρ^0, at a Doppler angle of 90°) within the two DBS ROIs (full lumen and 66% of the inner diameter of the upper half of the tube, top plots) and the DFS ROI (bottom plots). [Diagram 3] Panel (a) of Fig. 3 shows the DFS decorrelation ratio (ρ̂, averaged over the DFS ROI) plotted against the Doppler-derived flow rate within the tube. Panel (b) shows ρ̂ normalized to a hematocrit level of 60%. [Figure 4] Panel (a) of Figure 4 shows a scanning laser ophthalmoscope image of the retina showing the scanning locations used for DFS signal measurements beneath the retinal vessels (top of image, indicated by short bold lines) and across several choroidal vessels (bottom of image, indicated by long double-arrowed lines). Scale bar = 1 mm. Panel (b) shows the film strips of structure (top) and decorrelation rate parameter (ρ̂) (bottom). Panel (c) shows the transverse flow profile across the DBS (top) and DFS (bottom) ROIs. Panel (d) shows the average value of ρ̂ within the DFS ROI, which is plotted over time to indicate cardiac pulsatility. The vertical lines indicate a period (indicated by the dots between the vertical lines) encompassing four time points, whose flow cross-sections are shown in panel (b). [Diagram 5]Panel (a) of Figure 5 shows a cross-sectional structural image of the imaged line shown in Figure 4; in panel (a) (magenta line at the bottom of the image), the border between the RPE and choriosclera is indicated by a dotted line. Panel (b) shows a decorrelation ratio (ρ^) image (using a rainbow scale as in Figure 1), where, as in panel (a), the locations of four choroidal vessels have been selected and marked with colored triangles. Panel (c) shows the flow dynamics in selected vessels, calculated by the average ρ^ over depth on the sclera. Panel (d) shows the decorrelation ratio profile calculated by the depth (on the sclera) and time average ρ^. The asterisk (*) indicates the lateral region where signal decorrelation exists due to the larger vessels of the retina. [Figure 6] Figure 6 shows the linear phase (Doppler) profiles inside the tube, showing the transverse flow profile at different flow angles. Panel (a) shows the linear Doppler phase, which indicates the axial velocity, and the Doppler phase is not affected by the velocity gradient effect at the end of the tube. Panel (b) shows the decorrelation rate 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 at various flow angles, the DFS decorrelation ratio (averaged over the entire DFS ROI in panel (c) of Figure 2) for all flow angles outside the range of 3° deviation from α=90°. The horizontal axis is Doppler-derived flow. [Figure 8] FIG. 8 provides an illustration of an interferometry system that may be used in conjunction with various embodiments disclosed herein, where panel (A) shows a Mach-Zehnder interferometer that may be implemented using free-space optics and panel (B) shows a fiber arrangement. [Figure 9] FIG. 9 illustrates an example of a system for measuring flow parameters according to some embodiments of the disclosed subject matter. [Figure 10] FIG. 10 illustrates example hardware that can be used to implement computing devices and servers according to some embodiments of the disclosed subject matter. [Figure 11]FIG. 11 illustrates an example process for measuring a flow parameter according to some embodiments of the disclosed subject matter. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0039] According to some embodiments of the disclosed subject matter, mechanisms (which may include systems, methods, and mediums) for measuring flow parameters are provided.

[0040] To the best of the inventors' knowledge, all existing optical coherence tomography approaches for quantifying blood flow (whether Doppler-based or decorrelation-based) analyze light backscattered by materials such as moving red blood cells (RBCs). The present application discloses the advantages of making these measurements instead based on light forward-scattered by RBCs, i.e., by observing the signal backscattered from below (i.e., distal) the blood vessel. We experimentally show herein that forward-scattering-based flow measurements are insensitive to vessel orientations that are nearly orthogonal to the imaging beam. Furthermore, we also provide a proof-of-principle demonstration of dynamic forward-scattering (DFS) flow measurements in human retinal and choroidal vessels. Nevertheless, although many of the examples provided herein are based on data obtained from blood vessels in the posterior eye, the disclosed procedures can be performed on a wide variety of tissues and materials, including skin, brain, gastrointestinal tissue, nerve, precancerous lesions, and cancerous lesions.

[0041] Optical coherence tomography can be used to quantify blood flow in individual blood vessels. Many approaches have been described, but all are generally based on a common principle: the movement of scatterers (e.g., red blood cells (RBCs)) induces a modulation of the OCT signal, and the rate of that modulation is proportional to the flow velocity of the scatterers. OCT flowmetry systems are therefore designed to first measure the signal modulation rate (by some metric) and then relate this rate to a flow parameter (e.g., velocity or volumetric flow rate). Each of these steps has challenges that must be overcome to achieve robust OCT-based flow measurements.

[0042] In this work, we disclose a strategy to overcome the central challenge in the second step, i.e., the calculation of flow from the measured signal modulation rate. It is well established that for blood vessels that are oriented almost perpendicular to the OCT beam (Doppler angle α = ≈90°), the calculation of flow can be unreliable, which is particularly problematic for measurements of blood vessels in the retina, which are mostly orthogonal to the OCT beam. The root cause of this unreliability is the highly anisotropic phase response of the backscatter to axial and lateral motions. Axial motions on the scale of half the wavelength of light cause a full 2π phase modulation, whereas lateral motions need to be on the order of the imaging resolution (typically 10–20 μm) to achieve a similar expected phase response. This is, of course, why Doppler-based methods, which by definition operate on the signal phase, measure axial motion. It is less clear how this affects decorrelation-based methods, including decorrelation-based methods that operate on the OCT intensity signal. Here, it is shown that changes in phase response resulting from intravoxel gradients of axial motion lead to rapid decorrelation of OCT signals. This is true whether complex-valued or intensity signals are manipulated. Although the disclosed examples are directed to determining decorrelation rates, in various embodiments, other types of analysis can be performed on the flow-related DFS signals, such as power spectrum analysis or model-based statistical inference. In other embodiments, the signal modulation rate may include or be based on a measure of the temporal cross-correlation or temporal covariance of the signals.

[0043] When known axially biased methods are applied to vessels with Doppler angles 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 the signal modulation is highly dependent on the relatively small axial component of velocity. To calculate 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 procedures 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 with associated spatial direction), or flux (e.g., in mL / sec or in number of RBCs passing through the imaging beam per second).

[0044] The calculation of total flow rate becomes more stable for vessels with Doppler angles close to 90° if we can make the response to the scatterer motion isotropic or invert the anisotropy to lateral motion. It turns out that the light forward-scattered by the moving scatterer has the latter property. 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 Fig. 1(a). Of course, since OCT is a reflective imaging technique, we cannot measure DFS light directly. However, we can indirectly measure DFS by observing the signal that is backscattered from underneath the vessel and that necessarily passes through the vessel twice. Thus, as long as the light source is directed towards the proximal side of the vessel, the interferometric data is acquired from tissue or other material in the area 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 overlying vascular flow, we constructed a blood flow phantom as shown in Figure 1(b)-Figure 1(c) (all measurements in this study use blood as an exemplary flow medium). We characterized the decorrelation rate of the signal in the voxels below the flow tube (Figure 1(d)). To quantify the decorrelation rate, we modeled the autocorrelation / autocovariance of the DFS signal as follows:

number

[0046] where τ is the delay between measurements. The function g (1) (τ) is the complex autocorrelation function. We estimated the value of ρ using complex-valued OCT data. Briefly, we used a maximum-likelihood-based statistical framework. Alternatively, methods that fit a complex autocorrelation function to the calculated autocorrelation coefficients of the measurements can also be used, i.e., the DFS approach is not tied to a specific analytical framework.

[0047] The decorrelation rate can be measured by calculating the autocovariance of the measured time signals, fitting these autocovariance data to equation (1) and finding the value of ρ that results in the best fit. Numerical techniques such as optimization can be used to perform this fitting. The model provided by equation (1) can be modified to have a different functional form for the decorrelation rate parameter ρ or a different functional form for the delay τ. The measured data can be complex-valued, in which case the autocovariance will be a complex-valued output. Alternatively, the amplitude or the squared amplitude of the measured data (both real values) can be used to calculate a real-valued autocovariance measure. Thus, decorrelation models or signal modulation models such as those used in equation (1) can be written for real-valued or complex-valued measurements and an appropriate model can be used for fitting.

[0048] Additional indices can be calculated from time series measurements taken outside the blood vessel to estimate flow characteristics. These include power spectrum analysis, where power localized at lower RF frequencies is associated with slower flow, while power spread more broadly across RF frequencies is associated with faster flow. Other techniques and methods that measure the dynamic properties of time series OCT measurements and are known to those skilled in the art can also be used.

[0049] Furthermore, we note that decorrelation analysis in this application refers to a generalized measurement of the speed of a single fluctuation, and is not limited to any particular methodology and definition of decorrelation or decorrelation rate.

[0050] It is important to emphasize that equation (1) is an assumed statistical model of the DFS signal. Although many efforts have been made to define statistical models for DBS signals, no such body of work exists for DFS signals. Thus, the DFS signal may follow a functional dependence on τ that differs from that of equation (1). Furthermore, even if we assume equation (1) to be the correct statistical model for the DFS signal, it is unclear how ρ relates to the flow properties of the overlying vessels. For example, ρ may be proportional to peak velocity, but may instead be related to RBC flux. These are important questions that need to be answered in the widespread development of DFS-based flow quantification strategies, which may require significant effort. Here, we sought to confirm the motivating principle behind our approach that the DFS decorrelation rate is insensitive to α around α = 90°. For this purpose the assumed model is sufficient.

[0051] Imaging was performed using an M-mode B-scan protocol, in which 128 A-lines were recorded at each of 100 locations spanning 300 μm (100(x)x128(t) A-lines per frame). Ten repeated B-scan frames were acquired at each location. Imaging was performed at 19 different locations, each providing a different Doppler angle between 80° and 100°. We calculated estimates of ρ, denoted ρ^, for each voxel in the DBS region (within the tube) and the DFS region (below the tube). All data in this study were acquired using a swept-source OCT system (central wavelength 1060 nm, 100 kHz A-line rate). The equipment and procedures for developing and using OCT systems, flow phantoms, beam scanning protocols, and calibration and removal of bulk motion are known to those skilled in the art.

[0052] The decorrelation rate of DFS is not affected by the Doppler angle

[0053] Figure 2(a) shows example frames of the estimated decorrelation rate coefficient ρ̂ for both 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 decorrelation rate of the DBS signal at 80.8°. The increase in decorrelation in the lower region of the lumen is presumed to be caused by multiple scattering (effectively DFS), which is further explained below. It is clear from Figure 2(a) that the Doppler angle affects ρ̂ in the DBS region but not significantly in the DFS region.

[0054] To visualize more clearly how the Doppler angle affects the DBS and DFS signals, the transverse profiles of ρ̂ are shown in Fig. 2(b) for the indicated rows of 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 transverse profile of ρ̂ is at least approximately parabolic and does not change between the two Doppler angles. For reference, the Doppler linear phase shifts of these measurements are shown in Fig. 6.

[0055] To quantify the decorrelation properties across 19 flow angles, we calculated the average ρ^ within the ROIs defined in Fig. 2(c) for a fixed flow rate of 60 μl / min. Two ROIs were defined in the DBS region, one equal to the full lumen and one excluding (i) the edges where the velocity gradient is highest, and (ii) the lower tube region where multiple scattering is prominent. The DFS ROI covers a large area below the tube (purple). Fig. 2(d) shows these measurements normalized to measurements taken at a Doppler angle of 90°. Note the strong quadratic dependence of decorrelation on Doppler angle for the full lumen DBS ROI, and the reduced quadratic dependence on Doppler angle for the reduced tube ROI. The DFS signal shows no evidence of a quadratic dependence on Doppler angle. A minimal linear dependence on Doppler angle is seen, but this is more likely a measurement artifact than a true response. We conclude this because the mechanism generating the asymmetry around a Doppler angle of 90° is unclear and a similar linear trend is observed in DBS measurements.

[0056] DFS decorrelation rate linearly proportional to flow velocity

[0057] Next, we used a flow phantom to verify that the decorrelation rate of the DFS region changes linearly with changes in flow velocity while all other characteristics (e.g., tube geometry) are held constant. Using a Doppler angle of 83.5°, we calculated the average ρ^ of the DFS ROI for various pump flow settings. These measurements are shown in Figure 3(a) and the Doppler analysis of the DBS signal (F Doppler ) as a function of flow rate, as derived by the DBS signal. As expected, the trend is very linear. There is also a small y-axis offset. We repeated this analysis for all Doppler angles above 3° from 90° (to ensure a reliable Doppler measurement with the DBS signal) and observed the same trend with nearly identical slopes and offsets (Figure 7). The cause of the non-zero y-axis intercept is unclear. Possible causes include Brownian motion and measurement noise.

[0058] DFS decorrelation rate is more strongly related to flow velocity than to RBC flux

[0059] As mentioned before, the statistical (autocorrelation or autocovariance) model of the DFS signal is not accounted for. Thus, we do not know how to relate the decorrelation rate to the flow properties of the overlying vessel. For example, unlike the DBS signal, the DFS signal may depend on both the forward-scattered RBC motion and the number of RBCs with which the light has interacted. The latter means that the DFS signal is in part measuring RBC flux. We tested this by comparing ρ^ when blood was variously diluted from 20% to 60% hematocrit level (Figs. 1-3 used 60% hematocrit). If the DFS decorrelation rate is significantly affected by RBC flux, we would see that ρ^ decreases dramatically at lower hematocrit values. In this analysis, we equate ρ^ with the Doppler-derived flow (F Doppler ) to remove the effects of pump variation. At each hematocrit level, we averaged measurements over all Doppler angles greater than 3° from 90°.

[0060] Figure 3(b) shows the results. The normalized decorrelation ratio (ρ̂ / F Doppler ) are 1.126, 1.1, 1.154, and 1.04 for hematocrits of 20, 30, 40, and 50%, respectively. There is no observable trend for lower ρ^ at lower hematocrits. Indeed, a slight increase was observed at lower hematocrits, which may be explained by secondary factors (e.g., the effect of hematocrit on the SNR of the DFS signal). These results do not exclude an RBC flux dependence in the DFS decorrelation rate, but indicate that such dependence is likely to be much smaller than that of flow velocity.

[0061] Application to retinal and choroidal blood vessels

[0062] Finally, we demonstrate that the DFS-based technique can be applied to larger retinal and choroidal vessels in human subjects. First, we measured the decorrelation ratio associated with the DFS signal beneath the retinal vessels (location indicated by the short thick line at the top of the image) as shown in Fig. 4(a). Because the retinal pigment epithelium (RPE) is avascular and highly scattering, 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 B-scans were repeated for approximately 10 s to capture pulsatility. Structural and decorrelation ratio (ρ^) images at the four time points, along with the DFS ROI, are shown in Fig. 4(b).

[0063] In Fig. 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 across three depth lines at the specified locations for the DBS signal. As observed in the flow phantom, the lateral decorrelation profile from DFS is more parabolic than the profile associated with DBS. The DFS profile is less noisy, which may be a result of more extensive averaging (13 depth points for the DFS ROI vs. 3 depth points for DBS). The average value of ρ^ across the ROI is plotted as a function of time in Fig. 4(d), showing the expected pulsatile nature. As a confirmation, the ratio of maximum to minimum ρ^ across the cardiac cycle was 2.7, consistent with previous reports.

[0064] Next, we applied this technique to the choroid. A combination of factors such as the complex and dense vascular structure, the degradation of signal quality due to the highly scattering RPE, and low SNR makes quantification of blood flow in the choroid very challenging. We imaged 3 mm lines with 256 A-lines per location and 100 locations (30 μm spacing) using an extended M-mode B-scan protocol (Figure 5(a)). For choroidal vessels, we analyzed the DFS signal in the sclera. The boundary between the choroid and sclera is shown in Figure 5(a). Figure 5(b) shows the decorrelation rate across the image, demonstrating clear signals below the estimated choroidal vessels. We selected four locations (indicated by colored triangles below the images in Figures 5(a) and 5(b)) that matched the estimated choroidal vessels, and plotted the average of ρ^ across the entire sclera as a function of time (Figure 5(c)). Although the time resolution of these measurements is limited (approximately 0.25 s), we observed a cardiac pulsation of these vessels. The time-averaged flow profile over a 3 mm scan length, calculated by averaging ρ̂ over time across the scleral depth, is shown in Fig. 5(d). Although these data are preliminary, they suggest promise in providing a means to quantify flow in choroidal vasculature, a problem for which existing solutions are very limited.

[0065] This study reveals that DFS signals, known to be the cause of the “shadows” or “tails” reported to appear under vessels in OCT angiograms, should be considered as a reliable source of flow information, especially for vessels with Doppler angles close to 90°. Although insensitivity to Doppler angle was the initial motivation for this study, the DFS approach has some additional advantages that are worth discussing. First, we can clearly see in Fig. 2(a) that DBS signals suffer from multiple scattering, as explained elsewhere. We have called these signals DBS, but more accurately they are called DBS+DFS. When analyzing them, we need to deal with signals that are modulated by a mixture of two processes. In contrast, signals measured under vessels (from static scatterers) are pure DFS signals, which may be easier to model and interpret. Second, in some applications, the DFS approach may have the advantage of providing a more independent measure of signal dynamics than the DBS approach. For example, consider a limited set of DBS voxels located inside the choroidal vessels compared to a larger set of DFS voxels in the sclera below the choroidal vessels. Third, our data suggest that the DFS signal is decorrelated approximately twice as fast as the DBS signal from the center of the lumen. This can be used to enable shorter duration time series measurements, speeding up flow imaging. Finally, we note that the DFS approach can be deployed simultaneously with the DBS approach; the difference is in the signal analysis. Thus, this approach can be considered an adjunct to existing methods, primarily for vessels that are nearly or substantially perpendicular to the direction of the coherent light source, and secondary otherwise.

[0066] A limitation of the DFS approach is that it does not measure depth-resolved flow in blood vessels. Each DFS voxel provides a single metric that reports the integrated flow rate over the voxel. This can lead to ambiguities when multiple vessels cross the path of the beam. How much this limits the usefulness of this approach may depend on the application. For example, in the choroid, it is difficult to always unambiguously relate scleral DFS characteristics to a single choroidal vessel. However, this limitation must be considered in light of the current lack of a viable approach to quantify flow in the choroid. As the retinal vasculature is relatively sparse, it may not be too difficult to map the DFS signal in the RPE to the associated vessels. A further limitation of the DFS approach is that it may not be applicable to capillaries due to the limited DFS signal. Follow-up studies are needed to investigate the range of vessel diameters over which DFS can be used.

[0067] We also note that the method used in this work has some limitations. The Doppler angle was altered by imaging the tube at different locations, which may have caused secondary changes (beam resolution / aberrations) that confounded the measurements. This may have caused, for example, the small linear dependence of ρ on the Doppler angle observed in FIG. 2(d). A single tube diameter was used in the flow phantom study, which was near the upper end of the relevant range for retinal and choroidal vessels. We expect that the angular dependence of the DBS signal will be more severe in small vessels due to the larger flow gradients, but this and the effect of vessel diameter on the DFS signal need to be further studied. Finally, as mentioned above, this study worked with an assumed statistical model of the DFS signal. In various embodiments, other models may be developed by experimental, numerical, or analytical methods and applied to the data collected using the disclosed procedure.

[0068] FIG. 8 provides an illustration of an interferometry system that may be used in conjunction with various embodiments disclosed herein. FIG. 8 shows a Mach-Zehnder interferometer (FIG. 8A) or fiber arrangement (FIG. 8B) that may be implemented using free-space optics. Other types of interferometers (such as Michelson) are also applicable. The light source LS in either FIG. 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 LS is directed to the interferometer input, where it is split into two paths of approximately equal length using a beam splitter (BS3). B10 is directed to the sample S (e.g., the retina of the subject's eye). Light from the target object 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 PM (i.e., beam B13) is combined by BS4 and then directed to the interferometer output to interfere with beam B11. The output beam B14 is then detected by a detector D (e.g., a photodiode). Alternatively, a fiber-based interferometer as shown in FIG. 8B easily enables balanced detection due to a phase shift of π between output beams B14 and B15. The detected signals are then sampled at a sampling rate of f using a data acquisition and processing system, which may include a data acquisition board or a real-time oscilloscope (DAQ). SThe wavelengths are then digitized at 100 nm. Several wavelength sweeps (A1, A2, ..., An) can be acquired to form a two- 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) that facilitates direct illumination of the patient or subject's tissue and reception of light from the tissue, e.g., from the retina. The wavelength-resolved measurements provided by this interferometer system can be processed using a discrete Fourier transform in a computer system to generate depth-resolved measurements of the reflectance of the sample.

[0069] An alternative interferometer system that may be used in conjunction with various embodiments of the present invention may be based on the Mach-Zehnder system of FIG. 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 a broadband light output. The wavelength-resolved measurements provided by this interferometer system may be processed using a discrete Fourier transform in a computer system to generate depth-resolved measurements of the reflectance of the sample.

[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 FIG. 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 a broadband light output. The detector and DAQ measure the low coherence interference fringes and measure the reflectivity at a single depth point according to known time-domain OCT methods. The reference path B12 may include a variable optical delay to scan the position of this depth-resolved measurement. The phase modulator PM may be used to generate an inference signal encoded at a specific carrier frequency determined by the signal provided to the phase modulator. Alternatively, the phase modulator may be an acousto-optical frequency shift provided by an RF signal at 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 interferometer system embodiments can be replaced with alternative architectures that provide light to at least the sample and reference paths. These include Michelson interferometer systems and Mach-Zehnder systems where at least one of the sample or reference paths includes a bidirectional portion.

[0072] Computer and Optical Systems

[0073] Returning to FIG. 9 , an example system (e.g., data collection and processing system) 900 for measuring flow parameters is illustrated in accordance with some embodiments of the disclosed subject matter. In some embodiments, a computing device 910 can execute at least a portion of a system for measuring flow parameters 904 and provide a control signal to the interferometric data collection device 902. Additionally or alternatively, in some embodiments, the computing device 910 can communicate information related to the control signal to or from a server 920 via a communications network 906, the server 920 can execute at least a portion of a system for measuring flow parameters 904. In some such embodiments, the server 920 can return information related to the control signal 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 and / or presented to a user (e.g., a researcher, an operator, a clinician, etc.) and / or may be stored (e.g., as part of a research database or medical record associated with the subject).

[0074] In some embodiments, the computing device 910 and / or the server 920 may be any suitable computing device or combination of devices, such as a desktop computer, a laptop computer, a smartphone, a tablet computer, a wearable computer, a server computer, a virtual machine run by a physical computing device, etc. As described herein, a system for measuring flow parameters 904 may present information related to the control signal to a user (e.g., a researcher and / or a 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, a 4G network, a 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., a corporate or university intranet), any other suitable type of network, or any suitable combination of networks. Each of the communication links illustrated in FIG. 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, a cellular link, etc.

[0076] FIG. 10 illustrates example hardware 1000 that can be used to implement a computing device 910 and a server 920 in accordance with some embodiments of the disclosed subject matter. As illustrated in FIG. 10, in some embodiments, the computing device 910 can 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 can be any suitable hardware processor or combination of processors, such as a central processing unit, a graphics processing unit, etc. In some embodiments, the display 1004 can include any suitable display device, such as a computer monitor, a touch screen, a television, etc. In some embodiments, the input 1006 can include any suitable input device and / or sensor that can be used to receive user input, such as a keyboard, a mouse, a touch screen, a microphone, etc.

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

[0078] In some embodiments, the memory 1010 may include any suitable storage device that may be used to store instructions, values, etc. that may 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. The memory 1010 may include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, the 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, the memory 1010 may be encoded with 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, transmit information to the server 920, etc.

[0079] In some embodiments, 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, processor 1012 may be any suitable hardware processor or combination of processors, such as a central processing unit, a graphics processing unit, etc. In some embodiments, display 1014 may include any suitable display device, such as a computer monitor, a touch screen, a television, etc. In some embodiments, input 1016 may include any suitable input device and / or sensor that can be used to receive user input, such as a keyboard, a mouse, a touch screen, a microphone, etc.

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

[0081] In some embodiments, the memory 1020 may include any suitable storage device that may be used to store instructions, values, etc. that may be used by the processor 1012, for example, to present content using the display 1014 or to communicate with one or more computing devices 910. The memory 1020 may include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, the 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, the memory 1020 may be encoded with a server program for controlling the operation of the server 920. In such an embodiment, the processor 1012 executes at least a portion of the server program to send information and / or content (e.g., organization identification and / or classification results, user interfaces, etc.) to the one or more computing devices 910, receive information and / or content from the one or more computing devices 910, and receive 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 may 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 transitory or non-transient. For example, non-transient computer-readable media may include magnetic media (hard disks, floppy disks, etc.), optical media (compact disks, digital video disks, Blu-ray disks, etc.), semiconductor media (RAM, flash memory, electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.), any suitable medium that is not transitory or lacks permanence during transmission, and / or any suitable tangible medium. As another example, a transitory computer-readable medium may include wires, conductors, optical fibers, circuits, or signals over a network in any suitable medium that is transitory or lacks permanence during transmission, and / or any suitable intangible medium.

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

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

[0085] It should be understood that the above steps of the process of Figure 11 are not limited to the order and sequence shown and described, but may be performed or taken in any order or sequence, and some of the above steps of the process of Figure 11 may be performed or taken substantially simultaneously or in parallel, if desired, to reduce latency and processing times.

[0086] Thus, while the invention has been described in connection with particular embodiments and examples, the invention is not necessarily so limited, and many other embodiments, examples, uses, variations, and departures from the embodiments, examples, and uses are intended to be encompassed within the scope of the claims appended hereto.

Claims

1. An apparatus for measuring flow parameters within a vessel, comprising an interference data collection device having a light source and a sensor connected to a controller, wherein 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, opposite the proximal side; determine a signal modulation rate based on the interference data; and estimate the flow parameter within the vessel based on the signal modulation rate. The apparatus is characterized by being configured as described above.

2. The apparatus according to claim 1, wherein the signal modulation rate includes a decorrelation rate.

3. The apparatus according to claim 1, wherein the light source is directed in a direction substantially orthogonal to the central axis of the vessel.

4. The apparatus according to claim 1, wherein the interference data collection device comprises an optical coherence tomography (OCT) device, and the controller is configured to: obtain OCT data from the sample adjacent to and outside the distal side of the vessel, opposite the proximal side, when obtaining the interference data; and determine the signal modulation rate based on the OCT data when determining the signal modulation rate based on the interference data.

5. The apparatus according to claim 4, wherein the OCT data is based on 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.

6. The apparatus according to claim 5, wherein the controller is further configured to estimate the flow parameter within the vessel based on an integrated flow rate across the entire vessel when estimating the flow parameter within the vessel.

7. The apparatus according to claim 1, wherein the vessel and the sample are disposed within a designed phantom including in vitro biological tissue, ex vivo biological tissue, a fluid channel, a flow phantom, or a microfluidic platform.

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

9. ​ ​ ​ ​ ​ ​ ​ ​ When the controller acquires the interference data from a sample that is adjacent to and outside the distal side of the vessel on the side opposite to the proximal side, the controller is further configured to acquire backscattered interference data from inside the vessel. When the controller determines the signal modulation rate based on the interference data, the controller is further configured to determine a backscattered signal modulation rate based on the backscattered interference data. When the controller estimates the flow parameter in the vessel based on the signal modulation rate, the controller is further configured to estimate the flow parameter in the vessel based on the backscattered signal modulation rate. The apparatus according to claim 1, characterized in that.

10. The vessel includes a blood vessel. The sample includes tissue. The apparatus according to claim 1, characterized in that.

11. The blood vessel and the tissue are located within the retina of a subject. The apparatus according to claim 10, characterized in that.

12. The tissue includes at least one of scleral tissue or retinal pigment epithelium (RPE) tissue that is adjacent to and outside the distal side of the blood vessel. The apparatus according to claim 11, characterized in that.

13. A method for measuring a flow parameter in a vessel, comprising: providing an interference data collection device comprising a light source and a sensor connected to a controller; using the controller to direct the light source towards the proximal side of the vessel; using the controller to acquire interference data from a sample adjacent to the distal side of the vessel on the side opposite to the proximal side; using the controller to determine a signal modulation rate based on the interference data; and using the controller to estimate the flow parameter in the vessel based on the signal modulation rate. Providing an interference data collection device comprising a light source and a sensor connected to a controller. Using the controller to direct the light source towards the proximal side of the vessel. Using the controller to acquire interference data from a sample adjacent to the distal side of the vessel on the side opposite to the proximal side. Using the controller to determine a signal modulation rate based on the interference data. Using the controller to estimate the flow parameter in the vessel based on the signal modulation rate. A method characterized by including the above.

14. Determining the signal modulation rate further includes determining an uncorrelation rate based on the interference data. Estimating the flow parameter in the vessel further includes estimating the flow parameter in the vessel based on the uncorrelation rate. The method according to claim 13, characterized in that.

15. Directing the light source toward the proximal side of the vessel further includes directing the light source toward the proximal side of the vessel in a direction substantially orthogonal to the central axis of the vessel. The method according to claim 13, characterized in that.

16. The interference data acquisition device includes an optical coherence tomography (OCT) device. Acquiring the interference data further includes acquiring OCT data from the sample adjacent to and outside the distal side of the vessel on the side opposite to 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 claim 13, characterized in that.

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

18. Estimating the flow parameter in the vessel further includes estimating the flow parameter in the vessel based on the integrated flow rate across the entire vessel. The method according to claim 17, characterized in that.

19. The vessel and the sample are disposed in a designed phantom including in vitro biological tissue, ex vivo biological tissue, a fluid channel, a flow phantom, or a microfluidic platform. The method according to claim 13, characterized in that.

20. The flow parameter includes at least one of flow velocity, speed, or flux. The method according to claim 13, characterized in that.

21. Acquiring the interference data from the sample adjacent to and outside the distal side of the vessel on the side opposite to the proximal side further includes acquiring backscattered interference data from inside the vessel. Determining the signal modulation rate based on the interference data further includes determining the backscattered signal modulation rate based on the backscattered interference data. Estimating the flow parameter in the vessel based on the signal modulation rate further includes estimating the flow parameter in the vessel based on the backscattered signal modulation rate. The method according to claim 13, characterized in that.

22. The vessel includes a blood vessel. The sample includes tissue. The method according to claim 13, characterized in that...

23. The blood vessel and the tissue are located within the retina of a subject The method according to claim 22, characterized in that...

24. The tissue includes at least one of scleral tissue or retinal pigment epithelium (RPE) tissue that is adjacent to and outside the distal side of the blood vessel The method according to claim 23, characterized in that...

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