Three-dimensional blood flow imaging method based on blood flow velocity, medium, equipment and system

Abstract

The invention discloses a three-dimensional blood flow imaging method based on blood flow velocity, a medium, equipment and a system, and belongs to the technical field of imaging. The method comprises the following steps: acquiring an OCT original spectral signal of a measured object; the OCT original spectral signals are converted into OCT intensity signals, time integral speckle intensity is obtained through grouping integration, and pixels of the same space area are obtained to form a pixel set; calculating a time integral speckle contrast of each spatial position in the pixel set, and obtaining statistics of all pixels in the pixel area; calculating the blood flow velocity according to the statistical magnitude; the blood flow velocity of each spatial position is obtained, and a three-dimensional blood flow image is generated. According to the method, the time integral speckle contrast of each spatial position in the pixel set is calculated, and the blood flow velocity of the pixel area is calculated according to the time integral speckle contrast, so that the fluid velocity detection upper limit is greatly expanded, and the problem of low blood flow velocity measurement accuracy in the three-dimensional blood flow imaging graph is solved.

Description

Technical Field

[0001] The present invention relates to the field of imaging technology, and in particular to a three-dimensional blood flow imaging method, medium, equipment and system based on blood flow velocity. Background Art

[0002] Quantitative monitoring of three-dimensional hemodynamic activity is of great significance for understanding the biophysical and physiological basis of vascular diseases, including the occurrence and progression of a variety of diseases such as stroke, aneurysm and diabetic retinopathy. Optical coherence tomography (OCT), as a non-invasive, high-resolution imaging technique, is particularly suitable for the visualization of biological tissues, including their hemodynamic properties, making it an important tool in both research and clinical fields.

[0003] But in related technologies, Doppler OCT quantifies blood flow velocity by the principle of Doppler frequency shift. However, Doppler OCT is sensitive to the angle of the incident beam and it is difficult to accurately measure the flow velocity perpendicular to the beam. This angle sensitivity and the phase wrapping problem, especially at high flow rates, limit the accuracy and range of velocity measurement. Another method for blood flow velocity measurement is correlation-based OCT, such as dynamic scattered light DLSOCT. This method calculates microvascular blood flow velocity by analyzing the intensity correlation of backscattered light. By using wavelength division multiplexing, a single interferogram can be divided into multiple phase-correlated sub-interferograms, thereby improving the sensitivity of capillary blood flow imaging. Although DLSOCT has made significant progress in capillary imaging, with capillary blood flow velocities of 0.2 to 3.3 mm / s and retinal arteries and veins of 1.8 to 7.2 mm / s, quantitative measurement of blood flow velocities in a high dynamic range, such as the maximum velocity of the human middle cerebral artery up to 80 cm / s and the human retinal artery 200 mm / s, is still a challenge.

[0004] At present, there are also many aspects of research on blood flow velocity detection in three-dimensional blood flow imaging, such as the blood flow velocity determination disclosed in the invention patent with publication number: CN116600718A, including: for example, for use in deriving a measure of blood flow through a blood vessel. The method includes two stages. In the first stage, ultrasound data is used to collect multiple measurements of blood flow velocity at different radial positions across a blood vessel cross section. Based on this, a velocity profile (the velocity as a function of the measurement position across the blood vessel cross section) is determined. In the second stage, new ultrasound data is collected in one or more subsequent cardiac cycles, and blood flow velocity measurements are collected at a single position. A single blood flow velocity measurement preferably corresponds to the position of the maximum velocity. This single measurement result is combined with the derived personalized velocity profile to derive a measure of the average blood flow velocity in the blood vessel.

[0005] For example, the method for measuring blood flow velocity in a blood vessel disclosed in the invention patent publication with publication number: CN109512450A includes: injecting a quantitative contrast agent into the starting point of the blood flow direction in the target area; scanning the starting point of the blood flow direction in the target area with CT, acquiring an image at the starting point, and starting timing; performing a perfusion scan on the end point of the blood flow direction in the target area with CT, acquiring an image where the contrast agent first reaches the end point, and recording the time t; performing a spiral scan on the target area with CT to achieve three-dimensional reconstruction of the blood vessels in the target area; straightening the blood vessels in the target area and measuring the blood vessel length S of the target area through CT analysis and measurement software; and calculating the blood flow velocity V of the blood vessels in the target area through the blood vessel length S and time t.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, optical methods such as DLSOCT and Doppler OCT have high spatial resolution, but the accuracy of blood flow velocity measurement is low at high blood flow velocity; ultrasound, CT, etc. lack high spatial resolution, but can measure high-speed blood flow, and it is difficult to take into account both high spatial resolution and high-speed blood flow velocity detection. Under the premise of meeting high resolution, a three-dimensional blood flow image is generated according to the blood flow velocity, but at high blood flow velocity, the blood flow velocity detection result is easily inaccurate, which in turn makes the generated three-dimensional blood flow image inaccurate, so there is a problem of low accuracy of blood flow velocity measurement in the three-dimensional blood flow imaging image. Summary of the invention

[0008] The embodiments of the present application solve the problem of low accuracy in measuring blood flow velocity in three-dimensional blood flow imaging images in the prior art by providing a three-dimensional blood flow imaging method, medium, device and system based on blood flow velocity, greatly expand the upper limit of fluid velocity detection, and improve the measurement accuracy of blood flow velocity in three-dimensional blood flow imaging images.

[0009] The embodiment of the present application provides a three-dimensional blood flow imaging method based on blood flow velocity, comprising the following steps: obtaining an original spectral signal step: obtaining an original spectral signal of optical coherence tomography in W×X×Y dimensions of the object under test, where W is the number of repeated A scans, X is the number of lateral scanning points, and Y is the number of lateral scanning points; forming a pixel set step: converting the spectral signal of the original spectral signal of optical coherence tomography into a structural image, obtaining an optical coherence tomography intensity signal in Z×W×X×Y dimensions, where Z is the number of pixels in the depth direction, and grouping and integrating to obtain a pixel set with dimensions of Z×N×X×Y. The time-integrated speckle intensity, N is the number of A-scans at the same position after group integration, and pixels in the same spatial area are obtained to form a pixel set; a statistical quantity calculation step: calculating the time-integrated speckle contrast of each spatial position in the pixel set, and obtaining the statistics of all pixels in the corresponding spatiotemporal sequence in the pixel area; a blood flow velocity calculation step: calculating the blood flow velocity of the pixel area according to the statistics; a blood flow imaging step: obtaining the blood flow velocity of each spatial position (z, x, y) in each pixel area of ​​the time-integrated speckle OCT image, and generating a three-dimensional blood flow imaging.

[0010] Furthermore, the step of acquiring the blood flow velocity of each pixel area of ​​the time-integrated speckle OCT image comprises repeating the step of composing a pixel set, the step of calculating a statistic and the step of calculating the blood flow velocity, acquiring and traversing the time-integrated speckle OCT image, and obtaining the blood flow velocity of each pixel area of ​​the time-integrated speckle OCT image.

[0011] Furthermore, the time-integrated speckle intensity is obtained by:

[0012]

[0013] Among them, I′ i is the time-integrated speckle intensity at any spatial position of the time-integrated speckle OCT image, i is the serial number of the second dimension in the time-integrated speckle intensity of Z×N×X×Y, i=1,2,…,N, N is the number of A scans at the same position after group integration, is the total number of serial numbers of the second dimension of the OCT image, I j is the original speckle intensity at any spatial position of the OCT image. For each spatial position, the OCT original speckle intensity data time series is divided into N groups, each group has M=W / N data points, that is, M is the number of data points in each group, and j is the sequence number of the second dimension in the original speckle intensity of Z×W×X×Y.

[0014] Furthermore, the method for obtaining the statistics is:

[0015]

[0016] Wherein, TIS is a statistic, namely, the statistic of the time-integrated speckle contrast, i is the serial number of the second dimension in the time-integrated speckle intensity of Z×N×X×Y, i=1,2,…,N, N is the number of A scans at the same position after group integration, and is the total number of serial numbers of the second dimension of the OCT image.

[0017] Furthermore, the method for obtaining the blood flow velocity is as follows: obtaining statistics and correlation loss, and using the incomplete gamma function to obtain the transit variable; obtaining the transit variable, the lateral resolution of the optical coherence tomography system, the OCT scanning interval, and the number of A scans at the same position after group integration, and obtaining the blood flow velocity. The specific calculation formula is:

[0018]

[0019] Where TIS is a statistic, T is the OCT scanning interval, v is the blood flow velocity, x is the transition variable, LR is the lateral resolution of the optical coherence tomography system, β is the coherence loss, π is the circumference, is the incomplete gamma function and t is the integration variable.

[0020] Furthermore, the method for acquiring the original spectral signal of optical coherence tomography in the W×X×Y dimension includes: spectral domain optical coherence tomography, swept frequency optical coherence tomography, line scanning optical coherence tomography, full-field optical coherence tomography and parallel point scanning optical coherence tomography.

[0021] Furthermore, the specific steps of generating three-dimensional blood flow imaging are: obtaining the blood flow velocity at each spatial position (z, x, y) by calculating the blood flow velocity; generating a three-dimensional flow velocity distribution diagram or a blood flow velocity frontal projection diagram according to the blood flow velocities at all spatial positions.

[0022] The embodiment of the present application also provides a computer-readable storage medium for storing a program, and when the program is executed by a processor, the three-dimensional blood flow imaging method based on blood flow velocity is implemented.

[0023] An embodiment of the present application also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor executes the three-dimensional blood flow imaging method based on blood flow velocity.

[0024] The embodiment of the present application also provides a three-dimensional blood flow imaging system based on blood flow velocity, which is used to implement a three-dimensional blood flow imaging method based on blood flow velocity, including: a light emitting module, a synchronization control module, a collection module and an information processing module, wherein the light emitting module is connected to the synchronization control module, the information processing module is connected to the collection module, and the collection module is connected to the synchronization control module and the light emitting module respectively; the light emitting module is used to emit laser light to the object to be measured and is also used to receive a control signal sent by the synchronization control module; the synchronization control module is used to send a control signal to the light emitting module, and the control signal is used to control the scanning galvanometer to make the laser Light is irradiated at a given position of the object to be measured; an acquisition module is used to collect the interference signal formed by the laser signal reflected by the object to be measured and the reference signal from the light emitting module to obtain W×X×Y optical coherence tomography original spectrum signals, and is also used to obtain the historical signal record of controlling the scanning galvanometer from the synchronization control module, extract the signal frequency, signal position, and signal emission time according to the historical signal record, and is also used to send the W×X×Y optical coherence tomography original spectrum signals to the information processing module; the information processing module is used to receive the W×X×Y optical coherence tomography original spectrum signals sent by the acquisition module.

[0025] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0026] 1. By calculating the time-integrated speckle contrast ratio of each spatial position in the pixel set and calculating the blood flow velocity in the pixel area based on the time-integrated speckle contrast ratio, the upper limit of fluid velocity detection is greatly expanded, the accuracy of measuring blood flow velocity is improved, and the problem of low accuracy of blood flow velocity measurement in three-dimensional blood flow imaging in the prior art is effectively solved.

[0027] 2. By expanding the OCT blood flow velocity detection range in multiple dimensions, including the time dimension, spatial depth dimension, spatial lateral dimension, and space-time dimension, it is possible to accurately measure blood flow velocity even in areas with high blood flow velocity.

[0028] 3. Traverse all spatial positions in the time-integrated speckle OCT image, obtain the blood flow velocity at each spatial position (z, x, y) in each pixel area of ​​the time-integrated speckle OCT image, generate three-dimensional blood flow imaging, and thus ensure that the three-dimensional blood flow image corresponding to the pixel area of ​​blood flow detection is generated completely. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A flow chart of a three-dimensional blood flow imaging method based on blood flow velocity provided in an embodiment of the present application;

[0030] Figure 2 A structural diagram of a three-dimensional blood flow imaging system based on blood flow velocity provided in an embodiment of the present application;

[0031] Figure 3 is a schematic diagram of a method for constructing time-integrated speckle contrast statistics and calculating blood flow velocity according to an embodiment of the present invention;

[0032] Figure 4 (a)-(c) are schematic diagrams showing the comparison results of the method (time-integrated speckle contrast) of the embodiment of the present invention and the non-time-integrated speckle contrast method in model tests;

[0033] Figure 5 (a)-(c) are schematic diagrams showing the comparison of the results of the method of the embodiment of the present invention and dynamic light scattering OCT in a model test, and the feasibility of the method of the embodiment of the present invention in a model test for verifying the high flow rate measurement;

[0034] Figure 6 (a)-(c) are schematic diagrams comparing the relative changes in blood flow velocity after middle artery embolism in the mouse cerebral cortex using the method of an embodiment of the present invention and dynamic light scattering OCT. DETAILED DESCRIPTION

[0035] The embodiments of the present application solve the problem of low accuracy in measuring blood flow velocity in three-dimensional blood flow imaging images in the prior art by providing a three-dimensional blood flow imaging method, medium, device and system based on blood flow velocity. By calculating the time-product speckle contrast ratio of each spatial position in a pixel set and calculating the blood flow velocity of the pixel area based on the time-product speckle contrast ratio, the upper limit of fluid velocity detection is greatly expanded, and the accuracy of blood flow velocity measurement in three-dimensional blood flow imaging images is improved.

[0036] The technical solution in the embodiment of the present application is to solve the problem of low accuracy in measuring blood flow velocity in the above three-dimensional blood flow imaging image. The overall idea is as follows:

[0037] The OCT (optical coherence tomography) system is used to obtain the W×X×Y-dimensional OCT raw spectral signal, which is converted into the Z×W×X×Y-dimensional OCT intensity signal, and then grouped and integrated to obtain the Z×N×X×Y-dimensional time-product speckle intensity. The pixels in the same spatial area are obtained to form a pixel set, and the time-product speckle contrast of each spatial position in the pixel set is calculated to obtain the statistics of all pixels in the corresponding space-time sequence in the pixel area. The blood flow velocity is inferred based on the statistics, and three-dimensional blood flow imaging is performed based on the blood flow velocity at all spatial positions, which greatly expands the upper limit of fluid velocity detection and improves the accuracy of measuring blood flow velocity.

[0038] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0039] like Figure 1The flowchart of the three-dimensional blood flow imaging method based on blood flow velocity provided in the embodiment of the present application, the embodiment of the present application provides a three-dimensional blood flow imaging method based on blood flow velocity, comprising the following steps: S1, the step of obtaining the original spectral signal: obtaining the original spectral signal of optical coherence tomography (OCT) in the W×X×Y dimensions of the object under test, where W is the number of repeated A scans. A scan (Axial scan) refers to a single one-dimensional signal data collected along the propagation direction of the light beam (i.e., the depth direction). Through interference signal analysis, the A scan provides distribution information of reflectivity changes with depth, reflecting the structure at different depths inside the tissue. A scans at multiple different positions can be arranged horizontally to form a two-dimensional or three-dimensional tomographic image, which is used to display the internal structure of the sample. X is the number of lateral scanning points, and Y is the number of lateral scanning points.

[0040] Furthermore, methods for acquiring original spectral signals of optical coherence tomography in W×X×Y dimensions of the object under test include: spectral domain optical coherence tomography, swept frequency optical coherence tomography, line scanning optical coherence tomography, full-field optical coherence tomography and parallel point scanning optical coherence tomography.

[0041] S2, step of forming a pixel set: converting the spectral signal of the original spectral signal of the optical coherence tomography into a structural image through Fourier transform, obtaining the optical coherence tomography intensity signal of the dimension Z×W×X×Y, where Z is the number of pixels in the depth direction, and performing group integration to obtain the time-integrated scattered speckle intensity of the dimension Z×N×X×Y, where N is the number of A-scans at the same position after group integration, and obtaining pixels in the same spatial area to form a pixel set.

[0042] Furthermore, the time-integrated speckle intensity is obtained as follows:

[0043]

[0044] Among them, I′ i is the time-integrated speckle intensity at any spatial position in the time-integrated speckle OCT image. The time-integrated speckle intensity refers to the signal intensity within the same integration time. i is the serial number of the second dimension in the time-integrated speckle intensity of Z×N×X×Y, i=1,2,…,N, N is the number of A scans at the same position after group integration, and is the total number of serial numbers of the second dimension of the OCT image, I j is the original speckle intensity at any spatial position of the OCT image. For each spatial position, the OCT original speckle intensity data time series is divided into N groups, each group has M = W / N data points, that is, M is the number of data points in each group, and j is the sequence number of the second dimension in the original speckle intensity of Z×W×X×Y.

[0045] In this embodiment, the acquired S×W×X×Y spectral data is Fourier transformed in the first dimension, where S is the number of sampling points in the spectral signal, to obtain Z×W×X×Y optical coherence tomography intensity signal data. Then, it is evenly divided into N groups in the second dimension (time dimension). For each spatial position, each group contains M speckle intensity data, and an average operation is performed to obtain time-integrated speckle intensity data of size Z×N×X×Y. The pixels in the same spatial region in the N frames of OCT time-integrated speckle images are combined into a pixel set, for example, denoted as {I1 ′ ,I2 ′ ,…,I ′ N}, where I i ′ is the i-th intensity data of the second dimension of the time-integrated speckle intensity in the spatial region. The same spatial region can be understood as a spatial region with the same position and size in the time-integrated speckle image.

[0046] S3, statistical calculation step: calculate the time product speckle contrast ratio of each spatial position in the pixel set to obtain the statistics of all pixels in the corresponding spatiotemporal sequence in the pixel area. The time product speckle contrast ratio refers to the ratio of the variance of the time product speckle intensity to the square of its mean.

[0047] Furthermore, the method for obtaining statistics is:

[0048]

[0049] Wherein, TIS is a statistic, namely, the statistic of the time-integrated speckle contrast, i is the serial number of the second dimension in the time-integrated speckle intensity of Z×N×X×Y, i=1,2,…,N, N is the number of A scans at the same position after group integration, and is the total number of serial numbers of the second dimension of the OCT image.

[0050] In this embodiment, a time window is used to calculate the time-integrated speckle contrast ratio, and the statistic can be calculated by the following formula:

[0051]

[0052] Among them, TIS t is the statistic, t represents the statistic in the time window, and i represents the time index. The second dimension of the 4D OCT image can be regarded as the time dimension because it is a sequence formed by repeated scans of the same position. The other three dimensions (1, 3, 4) belong to the spatial dimensions, namely the depth position, the lateral position, and the lateral position.

[0053] In this embodiment, a spatial window is used to calculate the time product speckle contrast ratio, and the statistic can be calculated by the following formula:

[0054]

[0055] in, is a statistic, s represents statistics in the spatial window, N z represents the spatial depth window size, z represents the spatial depth position, and k represents the depth position index.

[0056] In this embodiment, a time-space window is used to calculate the time-integrated speckle contrast ratio, and the statistic can be calculated by the following formula:

[0057]

[0058] in, is a statistic, and st represents statistics in a time and space window.

[0059] In this embodiment, a time-space window is used to calculate the time-integrated speckle contrast ratio, and the statistic can be calculated by the following formula:

[0060]

[0061] in, is a statistic, st represents statistics in the time and space window, N x Indicates the horizontal window size, x indicates the horizontal position, and l indicates the horizontal position index

[0062] S4, blood flow velocity calculation step: calculate the blood flow velocity of the pixel area according to the statistic.

[0063] Furthermore, the blood flow velocity is obtained by: obtaining statistics and correlation loss, and then combining the incomplete gamma function to obtain the transit variable; obtaining the transit variable, the lateral resolution of the optical coherence tomography system, the OCT scanning interval, and the number of A scans at the same position after group integration, and then obtaining the blood flow velocity. The specific calculation formula is:

[0064]

[0065] Among them, TIS is the statistic, T is the OCT scanning interval, v is the blood flow velocity, x is the transition variable, LR is the lateral resolution of the optical coherence tomography system, β is the coherence loss, and the OCT image of the static scattering phantom is obtained and the value of its TIS is calculated, which is the coherence loss. π is the circumference of a circle, is the incomplete gamma function and t is the integration variable.

[0066] S5, blood flow imaging step: repeat steps S2-S4, traverse all spatial positions in the time-integrated speckle OCT image, obtain the blood flow velocity at each spatial position (z, x, y) in each pixel area of ​​the time-integrated speckle OCT image, and generate three-dimensional blood flow imaging, where y is the spatial lateral position.

[0067] Furthermore, the blood flow velocity of each pixel area of ​​the time-integrated speckle OCT image is obtained: the steps of composing a pixel set, calculating a statistic and calculating the blood flow velocity are repeated, the time-integrated speckle OCT image is obtained and traversed, and the blood flow velocity of each pixel area of ​​the time-integrated speckle OCT image is obtained.

[0068] Furthermore, the specific steps of generating three-dimensional blood flow imaging are: obtaining the blood flow velocity at each spatial position (z, x, y) by calculating the blood flow velocity; generating a three-dimensional flow velocity distribution diagram or a blood flow velocity frontal projection diagram according to the blood flow velocities at all spatial positions.

[0069] In this embodiment, for example, the ergodic speckle three-dimensional blood flow imaging method includes the following steps:

[0070] Step 1: Obtain the blood flow velocity at each spatial position (z, x, y).

[0071] Step 2: Generate a three-dimensional flow velocity distribution map or a blood flow velocity frontal projection map according to the blood flow velocities at all spatial positions.

[0072] In the above embodiment, the present invention calculates the time-integrated speckle contrast ratio of each spatial position in the pixel set, and calculates the blood flow velocity of the pixel area according to the time-integrated speckle contrast ratio, such as Figure 3 As shown, Figure 3 This is a schematic diagram of a method for constructing a time-integrated speckle contrast statistic and calculating blood flow velocity in an embodiment of the present invention. Compared with the DLSOCT (dynamic light scattering optical coherence tomography) method, at the same A-scan frequency, the upper limit of fluid velocity detection is greatly expanded, and the accuracy of measuring blood flow velocity is improved, especially for arterial blood vessel areas with fast blood flow velocity. The above DLSOCT method realizes non-invasive measurement of blood flow velocity in biological tissues by accurately measuring light scattering phase changes.

[0073] The three-dimensional blood flow imaging method based on blood flow velocity of the embodiment of the present invention can be used to estimate the changes in cerebral cortical blood flow, skin blood flow, blood flow velocity of biological tissues, etc. For example, to study the changes in cerebral cortical blood flow caused by neural activities of experimental animals such as rats, mice, rabbits, and monkeys, and to diagnose the changes in blood flow in the retinal blood vessels of the human eye caused by diabetes.

[0074] The embodiment of the present application also provides a computer-readable storage medium for storing a program, which, when executed by a processor, implements a three-dimensional blood flow imaging method based on blood flow velocity.

[0075] An embodiment of the present application also provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions that can be executed by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor executes a three-dimensional blood flow imaging method based on blood flow velocity.

[0076] like Figure 2 As shown, it is a structural diagram of a three-dimensional blood flow imaging system based on blood flow velocity provided in an embodiment of the present application. The embodiment of the present application also provides a three-dimensional blood flow imaging system based on blood flow velocity, which is used to implement a three-dimensional blood flow imaging method based on blood flow velocity, including: a light emitting module, a synchronization control module, a collection module and an information processing module, the light emitting module is connected to the synchronization control module, the information processing module is connected to the collection module, and the collection module is respectively connected to the synchronization control module and the light emitting module; the light emitting module is used to emit laser to the object to be measured and is also used to receive a control signal sent by the synchronization control module; the synchronization control module is used to send a control signal to the light emitting module to control The signal is used to control the scanning galvanometer so that the laser is irradiated at a given position of the object to be measured; the acquisition module is used to collect the interference signal formed by the laser signal reflected by the object to be measured and the reference signal from the optical emission module to obtain W×X×Y optical coherence tomography original spectrum signals, and is also used to obtain the historical signal record of controlling the scanning galvanometer from the synchronization control module, extract the signal frequency, signal position, and signal emission time according to the historical signal record, and send W×X×Y optical coherence tomography original spectrum signals to the information processing module; the information processing module is used to receive W×X×Y optical coherence tomography original spectrum signals sent by the acquisition module. Among them, according to the extracted signal frequency, signal position, and signal emission time, combined with the signal reflection time obtained from the optical emission module, dynamic blood flow velocity data can be accurately extracted in blood flow velocity imaging, greatly improving the imaging accuracy, facilitating the statistical analysis of the laser emission situation, and further optimizing the scanning strategy and imaging quality. For example, by analyzing the signal frequency, the system can distinguish the blood flow characteristics at different depths to avoid the imaging effect being affected by the overlap of reflected waves.

[0077] In this embodiment, the light emitting module emits laser light to the object to be measured, the synchronous control module controls the scanning galvanometer to irradiate the laser light at a given position of the object to be measured, and the acquisition module acquires the reflected interference signal to obtain W×X×Y OCT original spectral images. The information processing module executes a three-dimensional blood flow imaging method based on blood flow velocity, obtains the blood flow velocity of each pixel area, and generates a three-dimensional flow velocity distribution map or a blood flow velocity front projection map.

[0078] In summary, the method and system for calculating blood flow velocity provided by the present invention have the advantages of wide measurement range and high accuracy, especially for high blood flow velocity areas, can effectively solve the limitations of traditional OCT velocity detection methods, and have broad application prospects.

[0079] Example 1

[0080] like Figure 4 , Figure 5 As shown, Figure 4 (a)-(c) are schematic diagrams showing the comparison results of the method (time-integrated speckle contrast) of the embodiment of the present invention and the non-time-integrated speckle contrast method in model tests. Figure 5 (a)-(c) are schematic diagrams showing the comparison results of the method according to an embodiment of the present invention and dynamic light scattering OCT in a model test, and verifying the feasibility of high flow rate measurement in a model test using the method according to an embodiment of the present invention.

[0081] In the flow phantom experiment, a microsyringe (100 μL) was used to inject 1% fat emulsion solution into a capillary glass tube with an inner diameter of 300 μm. The microsyringe was fixed on a precision injection pump, and the syringe and the glass tube were connected by a silicone hose.

[0082] The flow rate of 1% fat emulsion solution in a capillary glass tube with an inner diameter of 300 μm was controlled by a microinjection pump. The flow rate of the injection pump was set to 8.5-170 μL / min. The average speed of the fat emulsion solution in the glass tube was 2-40 mm / s. There were 20 groups of different average speeds, and the average speed interval of each group was 2 mm / s. After the syringe was pushed steadily, the swept-frequency OCT system collected the fluid cross-section data of the fat emulsion solution with a repeated A-scan strategy. Each point was scanned repeatedly 100 times, and each section contained 512 points.

[0083] For each point, the 100 A-scans were divided into 1 group, each group containing 100 A-scans. The time-integrated speckle intensity data were then generated by averaging these 100 A-scans in groups.

[0084] exist Figure 5 In (a), although the speed measured by DLSOCT in the slow range The actual flow rate It shows a linear relationship, but the measurement curve tends to saturation at faster flow rates. Figure 5 (b) shows the velocity measured by TISOCT The actual flow rate The positive proportional relationship between the center and the repeatability of the velocity measured by TISOCT is the key to the accuracy of the measurement. Due to the shear thinning characteristics of non-Newtonian fluids, the cross-sectional velocity distribution of fat emulsion fluids shows a trend of decreasing from the center to the periphery at different fluid velocities. Therefore, the center velocity of the fluid may exceed the detection range of DLSOCT, which makes Slightly less than Figure 5 The horizontal and vertical axes in (a) and (b) represent the average velocity of the entire cross-section of the fat emulsion fluid in the capillary. Since the actual velocity of each point in the fluid is unknown, and the fluid flow rate is constant for each fixed average velocity, the velocity of the peripheral area of ​​the fluid flow is corrected using the profile equation before calculating the average velocity.

[0085] In order to further verify the feasibility of high flow rate measurement, the average velocity of the fat emulsion solution in the glass tube was set to 80-280 mm / s, with a total of 5 groups, and the average velocity interval of each group was 40 mm / s. Figure 5 (c) shows the velocity measured by TISOCT The actual flow rate The proportional relationship between them and the repeatability of the velocity measured by TISOCT.

[0086] Example 2

[0087] like Figure 6 As shown, Figure 6 (a)-(c) are schematic diagrams showing the results of comparing the relative changes in blood flow velocity after middle cerebral artery occlusion in the mouse cerebral cortex using the method of the embodiment of the present invention and dynamic light scattering OCT. In the animal experiment, 7-8 week old male C57BL / 6J mice were selected and placed supinely on a small animal surgical fixation board. DLSOCT and TISOCT methods were used to observe the changes in blood flow after middle cerebral artery occlusion (MCAO) in mice caused by photothrombosis.

[0088] The blood flow velocity of the main cerebral arteries of mice was greater than 50 mm / s.

[0089] The blood flow velocity and relative changes in the mouse cerebral cortex Figure 6 As shown in the figure. After ischemia, the relative change of cerebral blood flow velocity (rCBF) in the largest vessel MCA was obvious, while only the part of MCA illuminated by light, which causes thrombosis, showed a significant decrease. Because the velocity of the flowing vessel is still beyond the detection range of DLSOCT even after photothrombosis, it means that the velocity measured by DLSOCT is saturated.

[0090] Figure 6 (c) rCBF is greater than 20% of the physiological stroke threshold, rCBF TISOCT and rCBFDLSOCT However, the rCBF in the MCA (ROI1) with postischemic flow was TISOCT =-0.784, which is significantly greater than rCBF DLSOCT = -0.229 absolute value. This also occurred in the veins (ROI3, 4) after 24 hours of reperfusion. Because CBF was severely reduced, after occluding MCA ischemia (ROI2), rCBF DLSOCT =-0.726 close to rCBF TISOCT =-0.874.

[0091] The above experimental results show that the blood flow velocity calculation method of the present invention has significant linearity in the flow velocity range of 2 to 40 mm / s, verifying the high dynamic range and accuracy of the method. It also shows that the blood flow velocity calculation method of the present invention can obtain three-dimensional blood flow velocity information, providing more information for further analysis of hemodynamics.

[0092] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0094] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0096] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0097] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A three-dimensional blood flow imaging method based on blood flow velocity, characterized in that: The following steps are involved: The steps of obtaining the original spectral signal are as follows: obtaining the original spectral signal of the optical coherence tomography imaging of the object under test in the W×X×Y dimension, where W is the number of repeated A scans, X is the number of transverse scanning points, and Y is the number of lateral scanning points; The step of forming a pixel set is as follows: converting the spectral signal of the original spectral signal of the optical coherence tomography into a structural image, obtaining the optical coherence tomography intensity signal of the dimension Z×W×X×Y, where Z is the number of pixels in the depth direction, and performing group integration to obtain the time-integrated scattered speckle intensity of the dimension Z×N×X×Y, where N is the number of A scans at the same position after group integration, and obtaining pixels in the same spatial area to form a pixel set; Calculating statistics step: calculating the time product speckle contrast ratio of each spatial position in the pixel set to obtain statistics of all pixels in the corresponding spatiotemporal sequence in the pixel area; Calculating blood flow velocity step: calculating the blood flow velocity of the pixel area according to the statistic; Blood flow imaging step: obtaining the blood flow velocity at each spatial position (z, x, y) in each pixel area of ​​the time-integrated speckle OCT image to generate three-dimensional blood flow imaging.

2. The three-dimensional blood flow imaging method based on blood flow velocity as claimed in claim 1, characterized in that: The blood flow velocity of each pixel area of ​​the acquired time-integrated speckle OCT image is: The steps of forming a pixel set, calculating a statistic and calculating a blood flow velocity are repeated to acquire and traverse the time-integrated speckle OCT image, and obtain the blood flow velocity of each pixel region of the time-integrated speckle OCT image.

3. The three-dimensional blood flow imaging method based on blood flow velocity as claimed in claim 1, characterized in that: The time-integrated speckle intensity is obtained in the following manner: Among them, I′ i is the time-integrated speckle intensity at any spatial position of the time-integrated speckle OCT image, i is the serial number of the second dimension in the time-integrated speckle intensity of Z×N×X×Y, i=1,2,…,N, N is the number of A scans at the same position after group integration, is the total number of serial numbers of the second dimension of the time-integrated speckle OCT image, I j is the original speckle intensity at any spatial position of the OCT image. For each spatial position, the OCT original speckle intensity data time series is divided into N groups, each group has M=W / N data points, that is, M is the number of data points in each group, and j is the sequence number of the second dimension in the original speckle intensity of Z×W×X×Y.

4. The three-dimensional blood flow imaging method based on blood flow velocity as claimed in claim 1, characterized in that: The method for obtaining the statistics is: Wherein, TIS is a statistic, namely, the statistic of the time-integrated speckle contrast, i is the serial number of the second dimension in the time-integrated speckle intensity of Z×N×X×Y, i=1,2,…,N, N is the number of A scans at the same position after group integration, and is the total number of serial numbers of the second dimension of the OCT image.

5. The three-dimensional blood flow imaging method based on blood flow velocity as claimed in claim 1, characterized in that: The method for obtaining the blood flow velocity is: Obtain statistics and correlation loss, and use them in combination with the incomplete gamma function to find the intermediate variable; The transfer variables, the lateral resolution of the optical coherence tomography system, the OCT scanning interval, and the number of A scans at the same position after group integration are obtained, and the blood flow velocity is obtained based on this. The specific calculation formula is: Where TIS is a statistic, T is the OCT scanning interval, v is the blood flow velocity, x is the transition variable, LR is the lateral resolution of the optical coherence tomography system, β is the coherence loss, π is the circumference, is the incomplete gamma function and t is the integration variable.

6. The three-dimensional blood flow imaging method based on blood flow velocity as claimed in claim 1, characterized in that: The method for acquiring the original spectrum signal of optical coherence tomography in the W×X×Y dimension includes: Spectral domain optical coherence tomography, swept frequency optical coherence tomography, line scanning optical coherence tomography, full field optical coherence tomography and parallel point scanning optical coherence tomography.

7. The three-dimensional blood flow imaging method based on blood flow velocity as claimed in claim 1, characterized in that: The specific steps of generating three-dimensional blood flow imaging are: By calculating the blood flow velocity step, the blood flow velocity at each spatial position (z, x, y) is obtained; A three-dimensional flow velocity distribution map or a blood flow velocity frontal projection map is generated according to the blood flow velocities at all spatial positions.

8. A computer-readable storage medium for storing a program, characterized in that: When the program is executed by a processor, the three-dimensional blood flow imaging method based on blood flow velocity as described in any one of claims 1 to 7 is implemented.

9. An electronic device, characterized in that: include: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor executes the three-dimensional blood flow imaging method based on blood flow velocity as described in any one of claims 1-7.

10. A three-dimensional blood flow imaging system based on blood flow velocity, used to implement the three-dimensional blood flow imaging method based on blood flow velocity according to any one of claims 1 to 7, characterized in that: include: An optical transmission module, a synchronous control module, a collection module and an information processing module, wherein the optical transmission module is connected to the synchronous control module, the information processing module is connected to the collection module, and the collection module is respectively connected to the synchronous control module and the optical transmission module; The optical transmission module is used to transmit laser light to the object to be measured and also to receive control signals sent by the synchronization control module; A synchronous control module, used for sending a control signal to the light emitting module, wherein the control signal is used for controlling the scanning galvanometer to irradiate the laser at a given position of the object to be measured; An acquisition module is used to acquire the interference signal formed by the laser signal reflected by the measured object and the reference signal from the light emission module to obtain W×X×Y optical coherence tomography original spectrum signals, and is also used to obtain the historical signal record of controlling the scanning galvanometer from the synchronization control module, extract the signal frequency, signal position, and signal emission time according to the historical signal record, and is also used to send the W×X×Y optical coherence tomography original spectrum signals to the information processing module; The information processing module is used to receive W×X×Y optical coherence tomography original spectrum signals sent by the acquisition module.

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