Method and system for detecting blood flow velocity fluctuations based on octa

Abstract

The application belongs to the technical field of image processing. A method and system for detecting blood flow velocity fluctuation based on OCTA are provided. First and second positions are selected in a non-branching area of a blood vessel for back-and-forth B-scan scanning, and two groups of multi-frame B-scan image sequences are obtained. Corresponding first and second blood flow signals are generated accordingly. Dynamic time warping is used to perform nonlinear alignment on the two blood flow signals to obtain an optimal alignment path. The local lag amount at each time point is calculated according to the path. Finally, the instantaneous fluctuation of the blood flow velocity is calculated in combination with the blood vessel length between the two positions and the single B-scan scanning time. The application effectively overcomes the poor adaptability of traditional methods to non-stationary blood flow through skeleton guide positioning, double-point synchronous sampling and nonlinear signal alignment, and realizes high-sensitivity and high-time-resolution dynamic quantification of blood flow velocity at the scale of microvessels.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and specifically to a method and system for detecting blood flow velocity fluctuations based on OCTA. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] The retina is a highly specialized photosensitive tissue in the central nervous system, and its normal visual function depends on a continuous and dynamically regulated blood supply. The retina is composed of multiple layers of nerve cells, each with significantly different metabolic needs. The micron-scale capillary network is the key channel for delivering oxygen and nutrients. Because the inner diameter of capillaries (typically 5-10 micrometers) is similar to that of red blood cells, red blood cells often pass through them in a single file, exhibiting a distinctly discontinuous flow characteristic: gaps exist between red blood cell clusters, and the flow process is accompanied by frequent accelerations, decelerations, and even brief pauses. This non-steady-state blood flow pattern causes significant fluctuations in blood flow velocity on a millisecond to second timescale. Therefore, obtaining only the average blood flow velocity is insufficient to meet the needs of accurate assessment of microcirculatory function. There is an urgent need for a technique that can capture instantaneous dynamic changes in blood flow velocity to reveal early microvascular dysfunction and assess the pathological progression of diseases such as diabetic retinopathy or glaucoma.

[0004] Currently, mainstream OCTA methods for blood flow velocity detection mainly include Doppler OCT and cross-correlation-based time delay estimation. Doppler OCT relies on precise knowledge of the angle between the blood flow direction and the probe beam, and assumes that the velocity is constant within the sampling window, making it difficult to apply to capillary networks with complex directions and drastic velocity changes. While cross-correlation methods can estimate the average transmission time between two points, their effectiveness depends on the assumption that the signal is periodic or stationary, i.e., the blood flow waveform is highly repetitive in different scanning cycles. However, the intermittent perfusion of red blood cells in capillaries leads to highly non-stationary and non-periodic blood flow signals in time, causing the peak of the cross-correlation function to be blurred or shifted, failing to accurately reflect the true local transmission delay. These methods essentially cannot analyze the instantaneous velocity evolution of discontinuous blood flow in a single perfusion event, resulting in a severe deficiency in characterizing hemodynamic fluctuations. Therefore, there is an urgent need for a new method that does not rely on the assumption of signal stationarity and can extract fine time delay information from asynchronous and irregular blood flow signals to achieve high-fidelity quantification of capillary blood flow velocity fluctuations. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for detecting blood flow velocity fluctuations based on OCTA. By scanning different locations of the same blood vessel back and forth, OCTA blood flow light and dark codes at two locations are obtained. Cross-correlation coefficients are calculated on the obtained light and dark codes, and the blood flow direction is determined based on the overall cross-correlation. Dynamic time adjustment is performed on the light and dark signals to achieve better and more complete alignment. By tracing back the aligned path, the real-time lag of each point is obtained, thereby achieving accurate detection of blood flow velocity fluctuations.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a method for detecting blood flow velocity fluctuations based on OCTA.

[0008] A method for detecting blood flow velocity fluctuations based on OCTA includes:

[0009] Obtain OCTA angiography images of the target blood vessel and extract the vascular skeleton of the target blood vessel from the OCTA angiography images;

[0010] Based on the vascular skeleton, the unbranched region of the target blood vessel is scanned back and forth at the first and second positions to obtain a multi-frame B-scan image sequence corresponding to the first and second positions respectively.

[0011] Based on a multi-frame B-scan image sequence, a first blood flow signal corresponding to a first position and a second blood flow signal corresponding to a second position are generated;

[0012] Dynamic time warping and alignment of the first and second blood flow signals are performed to obtain the optimal alignment path;

[0013] Based on the optimal alignment path, calculate the local hysteresis between the first blood flow signal and the second blood flow signal at each time point;

[0014] The blood flow velocity fluctuation of the target vessel is calculated based on the local hysteresis, the vessel length between the first and second positions, and the scanning time of a single B-scan.

[0015] In one implementation of the first aspect of the present invention, before generating the first blood flow signal and the second blood flow signal, the method further includes: performing high-pass filtering on a multi-frame B-scan image sequence to remove noise signals; and arranging the high-pass filtered B-scan images in chronological order to form a high-pass filtered B-scan image sequence.

[0016] In one implementation of the first aspect of the present invention, generating a first blood flow signal corresponding to a first position and a second blood flow signal corresponding to a second position includes: calculating the sum of the brightness values ​​of all pixels in each frame of the filtered B-scan image sequence to obtain the total signal value of the frame; combining the total signal values ​​of each frame corresponding to the first position in chronological order to form the first blood flow signal; and combining the total signal values ​​of each frame corresponding to the second position in chronological order to form the second blood flow signal.

[0017] As a further limitation of the first aspect of the present invention, generating a first blood flow signal corresponding to a first position and a second blood flow signal corresponding to a second position further includes: performing Hu moment calculation on the red blood cell pattern in a multi-frame B-scan image sequence to obtain Hu moment features; and using the Hu moment features as auxiliary features, together with the first blood flow signal and the second blood flow signal, for dynamic time warping alignment.

[0018] In one implementation of the first aspect of the present invention, dynamic time warping and alignment of the first blood flow signal and the second blood flow signal includes: constructing a distance matrix, wherein each element of the distance matrix represents the first blood flow signal at a given time point. The difference between the signal value of the first blood flow signal and the signal value of the second blood flow signal at time point j; based on the distance matrix, a cumulative cost matrix is ​​constructed through dynamic programming; by backtracking from the end point of the cumulative cost matrix to the starting point, the optimal alignment path that minimizes the cumulative cost is obtained.

[0019] As a further limitation of the first aspect of the present invention, backtracking from the end point of the cumulative cost matrix to the starting point includes: at the current position, selecting the position with the smallest cumulative cost among the three predecessor positions from the previous moment as the previous point on the backtracking path; repeating the above selection process until backtracking to the starting point of the cumulative cost matrix.

[0020] In one implementation of the first aspect of the present invention, before performing back-and-forth scanning, the method further includes: combining the connectivity information of the vascular skeleton and the flashing point positions of the first position and the second position in the B-scan image to determine whether the first position and the second position belong to the same capillary; and performing back-and-forth scanning only when the determination result is yes.

[0021] In one implementation of the first aspect of the present invention, the calculation of the local hysteresis between the first blood flow signal and the second blood flow signal at each time point according to the optimal alignment path includes: for each pair of matching points in the optimal alignment path, calculating the difference between the time index of the matching point in the first blood flow signal and the time index of the second blood flow signal as the local hysteresis corresponding to the matching point.

[0022] In one implementation of the first aspect of the present invention, before calculating the blood flow velocity fluctuation, the method further includes: determining the blood flow direction based on the overall cross-correlation coefficient of the first blood flow signal and the second blood flow signal; wherein, if the hysteresis corresponding to the peak value of the overall cross-correlation coefficient is positive, the blood flow direction is from the first position to the second position; if it is negative, the blood flow direction is from the second position to the first position.

[0023] Secondly, the present invention provides a system for detecting blood flow velocity fluctuations based on OCTA.

[0024] A system for detecting blood flow velocity fluctuations based on OCTA includes:

[0025] The image acquisition unit is configured to: acquire OCTA angiography images of the target blood vessel and extract the vascular skeleton of the target blood vessel from the OCTA angiography images;

[0026] The scanning control unit is configured to: perform back-and-forth scanning of the unbranched region of the target blood vessel at a first position and a second position based on the vascular skeleton, thereby obtaining a multi-frame B-scan image sequence corresponding to the first position and the second position respectively;

[0027] The signal generation unit is configured to generate a first blood flow signal corresponding to a first position and a second blood flow signal corresponding to a second position based on a multi-frame B-scan image sequence.

[0028] The signal alignment unit is configured to perform dynamic time warping alignment on the first blood flow signal and the second blood flow signal to obtain the optimal alignment path.

[0029] The hysteresis calculation unit is configured to calculate the local hysteresis between the first blood flow signal and the second blood flow signal at each time point based on the optimal alignment path.

[0030] The velocity calculation unit is configured to calculate the blood flow velocity fluctuation of the target vessel based on the local hysteresis, the vessel length between the first and second positions, and the scan time of a single B-scan.

[0031] In one implementation of the second aspect of the present invention, before generating the first blood flow signal and the second blood flow signal, the method further includes:

[0032] High-pass filtering is applied to a multi-frame B-scan image sequence to remove noise signals;

[0033] Arrange the high-pass filtered B-scan images in chronological order to form a high-pass filtered B-scan image sequence;

[0034] The signal generation unit generates a first blood flow signal corresponding to a first position and a second blood flow signal corresponding to a second position, including:

[0035] For each frame of the high-pass filtered B-scan image sequence, the sum of the brightness values ​​of all pixels is calculated to obtain the total signal value of that frame.

[0036] The total signal values ​​of each frame image corresponding to the first position are combined in chronological order to form the first blood flow signal;

[0037] The total signal values ​​of each frame image corresponding to the second position are combined in chronological order to form the second blood flow signal;

[0038] Hu moments were calculated on the red blood cell patterns in a multi-frame B-scan image sequence to obtain Hu moment features;

[0039] The Hu moment feature is used as an auxiliary feature, and together with the first blood flow signal and the second blood flow signal, it is used for dynamic time warping alignment.

[0040] In one implementation of the second aspect of the present invention, the signal alignment unit performs dynamic time-warping alignment on the first blood flow signal and the second blood flow signal, including:

[0041] Construct a distance matrix, where each element of the distance matrix represents the first blood flow signal at a given time point. The difference between the signal value of the second blood flow signal and the signal value of the second blood flow signal at time point j;

[0042] Based on the distance matrix, a cumulative cost matrix is ​​constructed using dynamic programming.

[0043] By tracing back from the end point of the cumulative cost matrix to the beginning point, we obtain the optimal alignment path that minimizes the cumulative cost.

[0044] In one implementation of the second aspect of the present invention, the scanning control unit further includes, before performing a back-and-forth scan:

[0045] By combining the connectivity information of the vascular skeleton and the blinking point positions of the first and second positions in the B-scan image, it is determined whether the first and second positions belong to the same capillary.

[0046] A back-and-forth scan is only performed if the determination result is yes.

[0047] In one implementation of the second aspect of the present invention, the hysteresis calculation unit calculates the local hysteresis between the first blood flow signal and the second blood flow signal at each time point according to the optimal alignment path, including:

[0048] For each pair of matching points in the optimal alignment path, the difference between the time index of the matching point in the first blood flow signal and the time index in the second blood flow signal is calculated as the local hysteresis corresponding to that matching point.

[0049] Thirdly, the present invention provides a computer device, comprising: a processor and a computer-readable storage medium;

[0050] A processor, adapted to execute computer programs;

[0051] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for detecting blood flow velocity fluctuations based on OCTA according to the first aspect of the present invention.

[0052] Fourthly, the present invention provides a computer-readable storage medium storing a computer program adapted to be loaded by a processor and executed by the method for detecting blood flow velocity fluctuations based on OCTA according to the first aspect of the present invention.

[0053] Fifthly, the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the method for detecting blood flow velocity fluctuations based on OCTA according to the first aspect of the present invention.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] This invention employs rapid back-and-forth B-scan scanning (i.e., brightness-mode scan) at two adjacent locations within a non-bifurcation region of the target blood vessel to acquire two time-interleaved blood flow signals. Dynamic time warping (DTW) is then used to align these two signals, thereby accurately extracting local time lag. This method effectively overcomes the limitations of traditional linear alignment methods such as cross-correlation when dealing with non-stationary and irregular blood flow fluctuations, and can adapt to complex physiological conditions such as discontinuous red blood cell cluster movement and disordered pulsation rhythms. Based on this, and combining known spatial distance and scan time parameters, the local lag is converted into instantaneous blood flow velocity changes, achieving highly sensitive capture of dynamic fluctuations in blood flow velocity at the microvascular scale. The entire process, from image acquisition, skeleton-guided localization, dual-point synchronous sampling to nonlinear signal alignment and velocity calculation, forms a closed-loop and logically tight technical chain, significantly improving the temporal resolution and physiological realism of the OCTA system in the quantitative analysis of microcirculatory hemodynamics.

[0056] This invention introduces high-pass filtering and normalization preprocessing before blood flow signal generation, effectively suppressing interference from static tissue residue, system noise, and uneven illumination in OCTA images. By integrating and normalizing the B-scan sequence frame by frame, the first and second blood flow signals are made comparable in amplitude and baseline, providing high-quality input for subsequent dynamic time warping alignment. This preprocessing mechanism ensures that the DTW algorithm is unaffected by overall brightness shifts or amplitude differences when calculating local similarity between signals, thereby improving the reliability of the optimal alignment path, avoiding misjudgments of hysteresis caused by signal distortion, and ultimately ensuring the accuracy and stability of the final blood flow velocity fluctuation calculation results.

[0057] Before performing rapid dual-position scanning, this invention verifies whether the selected first and second positions belong to the same capillary by combining the topological connectivity of the vascular skeleton and the spatial consistency of scintillation points in the B-scan image. This judgment mechanism effectively prevents inconsistencies in blood flow signal sources caused by misselection across blood vessels, avoiding incorrect pairing and alignment of signals from different blood vessels or even different blood flow directions for velocity calculation. By ensuring that the two sampling points are located on the same blood flow path, the physical correspondence between local hysteresis and actual blood flow propagation time is fundamentally guaranteed, significantly improving the applicability and robustness of the method in complex microvascular networks.

[0058] This invention directly uses the optimal alignment path obtained from dynamic time warping for the extraction of point-by-point local hysteresis, rather than relying solely on the global maximum correlation peak. This design enables the method to characterize the non-uniform variations in blood flow velocity within a single heartbeat cycle or on shorter timescales, such as transient phenomena like acceleration, deceleration, or brief pauses. Compared to traditional methods that only output average velocity, this approach preserves the temporal details of blood flow fluctuations, providing richer dynamic information dimensions for studying microcirculation regulation mechanisms, early identification of blood perfusion abnormalities, or assessment of vascular function, thus enhancing the clinical and research value of OCTA in functional microvascular imaging.

[0059] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0060] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0061] Figure 1 A flowchart illustrating a method for detecting blood flow velocity fluctuations based on OCTA, provided as an exemplary embodiment of the present invention;

[0062] Figure 2 A schematic diagram illustrating the application of the DTW algorithm for nonlinear alignment of signals, provided as an exemplary embodiment of the present invention;

[0063] Figure 3 A schematic diagram of the principle of a system for detecting blood flow velocity fluctuations based on OCTA, provided as an exemplary embodiment of the present invention;

[0064] Figure 4 A schematic diagram of a computer device provided for an exemplary embodiment of the present invention. Detailed Implementation

[0065] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0066] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0067] like Figure 1 As shown, this implementation proposes a method for detecting blood flow velocity fluctuations based on OCTA, including the following process:

[0068] S101: Obtain OCTA angiography images of the target blood vessel and extract the vascular skeleton of the target blood vessel from the OCTA angiography images;

[0069] S102: Based on the vascular skeleton, the unbranched region of the target blood vessel is scanned back and forth at the first and second positions to obtain a multi-frame B-scan image sequence corresponding to the first and second positions respectively.

[0070] S103: Based on the multi-frame B-scan image sequence, generate a first blood flow signal corresponding to the first position and a second blood flow signal corresponding to the second position;

[0071] S104: Perform dynamic time warping and alignment on the first blood flow signal and the second blood flow signal to obtain the optimal alignment path;

[0072] S105: Calculate the local hysteresis between the first blood flow signal and the second blood flow signal at each time point based on the optimal alignment path;

[0073] S106: Calculate the blood flow velocity fluctuation of the target vessel based on the local hysteresis, the vessel length between the first and second positions, and the scanning time of a single B-scan.

[0074] Specifically, in this implementation, firstly, high-speed OCTA is used to capture angiographic images, and the vascular skeleton is extracted from the OCTA angiographic images. Then, based on the vascular skeleton information, a fast back-and-forth scanning protocol is used to perform high-precision scanning of capillary regions without bifurcations. This scanning strategy avoids complex flow interference in bifurcation regions. To further ensure that the scanned area originates from the same capillary, this invention combines the extracted vascular skeleton connectivity information with the positions of B-scan scintillation points at two scanning locations, comparing their relative positions and dynamic change characteristics to determine whether they belong to the same capillary. Next, the En-OCTA blood flow signals from the two locations confirmed to originate from the same capillary are processed.

[0075] The key steps of signal processing include: encoding the OCTA signal into sub-pixel level to improve temporal resolution and signal detail resolution; then, extracting cross-sectional images of capillaries from repeated acquisitions at each scan location and arranging them in chronological order to generate two consecutive blood flow signals, which are named signal1 (i.e., the first blood flow signal) and signal2 (i.e., the second blood flow signal), respectively.

[0076] First, the obtained B-scan image signal is subjected to high-pass filtering to remove noise based on the brightness value of each pixel:

[0077] (1);

[0078] in, Represents the signal threshold; The image represents the first The pixel brightness value at that location; Represents the x-coordinate of a pixel; Represents the ordinate of a pixel; Represents the coordinates in the image after high-pass filtering. The pixel brightness value at that location.

[0079] The brightness signal value of each B-scan image is obtained by summing the brightness values ​​of each filtered B-scan image:

[0080] (2);

[0081] in, Representing the The brightness signal value of each B-scan.

[0082] get The total luminance signal of the B-scan at position (i.e., the first position) )and The total luminance signal of the B-scan at position (i.e., the second position) ).

[0083] Calculate the mean of each signal column separately:

[0084] (3);

[0085] in, The first position represents the first A B-scan brightness signal, The second position represents the first A B-scan brightness signal, This represents the number of B-scans.

[0086] Calculate the overall standard deviation for each signal column separately:

[0087] (4);

[0088] For the two obtained total signals , Normalize the brightness value sequence:

[0089] (5);

[0090] in, This represents the mean of each signal. This represents the overall standard deviation of each signal. Representing the first position The signal values ​​after B-scan normalization Representing the second position The signal values ​​after B-scan normalization.

[0091] Calculate the slope of each column of adjacent signals, including:

[0092] (6);

[0093] in, The first position represents the The slope of adjacent signals in a B-scan The second position The slope of adjacent signals of each B-scan.

[0094] calculate and The overall mean:

[0095] (7);

[0096] Calculate the overall standard deviation for each signal. and :

[0097] (8);

[0098] Normalization yields:

[0099] (9);

[0100] in, and This represents the normalized population standard deviation.

[0101] Hu moment calculation for red blood cell patterns:

[0102] (10);

[0103] in, represent Order geometric moments.

[0104] Calculate the centroid of the image moments:

[0105] (11);

[0106] (12);

[0107] in, correspond Dimensional order correspond The order of the dimension takes values ​​of 0, 1, 2, 3, ...; The zeroth moment represents the sum of all gray values; represent( )th order geometric moment; Representative image Direction center coordinates; Representative image Direction center coordinates.

[0108] Calculate the central moments of the image:

[0109] (13);

[0110] in, These represent different moments of the image about the centroid.

[0111] Calculate the normalized central moments of the image:

[0112] (14);

[0113] in, The central moment represents the scale-normalized value.

[0114] Hu moment calculation, specifically, includes:

[0115] (15);

[0116] (16);

[0117] (17);

[0118] (18);

[0119] (19);

[0120] (20);

[0121] (twenty one);

[0122] According to formulas (15)-(21), calculate the first position and the second position respectively. Invariant Moments and And further, the two total Hu moments are obtained, namely and :

[0123] (twenty two);

[0124] The two obtained total Hu moments are normalized, specifically including:

[0125] and The overall mean is as follows: and ,include:

[0126] (twenty three);

[0127] Calculate the overall standard deviation for each signal. and :

[0128] (twenty four);

[0129] Normalization yields and ,include:

[0130] (25);

[0131] The total characteristic signal is and ,include:

[0132] (26);

[0133] Therefore, the blood flow code signal1 at scan location 1 (i.e., B1) The blood flow code signal2 at scan location 2 (i.e., B2) )

[0134] To calculate the time lag between the two signals, a cross-correlation method is used to analyze signal1 and signal2. The cross-correlation function is... The definition is as follows:

[0135] (27);

[0136] Then, the calculated cross-correlation function is normalized to better compare the cross-correlation between different blood vessels. Correlation coefficient:

[0137] (28);

[0138] in, This indicates signal1, corresponding to scan position 1 (i.e., B1). Blood flow coding at any given moment; This indicates signal2, corresponding to scan position 2 (i.e., B2). The blood flow encoding at each time point, where N represents the number of scans at each location, and τ is the hysteresis, representing the lag between two signals, expressed in frames per scan. This indicates the similarity between signals s1 and s2 under the hysteresis τ. The larger the value, the more the two signals lag. The stronger the similarity, the better.

[0139] By calculating the cross-correlation coefficients under different lags, a preliminary reliable lag is obtained to determine the matching threshold range in the subsequent DTW algorithm.

[0140] The hysteresis to be solved includes the overall blood flow velocity direction and the average blood flow velocity, using the formula:

[0141] (29)

[0142] Where L represents the capillary length between the two scanning positions. This is the time required for one B-scan. Importantly, The sign can be positive or negative, indicating the direction of flow. A positive value indicates that signal1 leads signal2. That is, the blood flow direction is from position Bscan1 to position Bscan2, while a negative value... This indicates that signal1 lags behind signal2. In other words, the blood flow direction is from position Bscan2 to position Bscan1.

[0143] In practice, blood flow velocity often exhibits a nonlinear shift, primarily because changes in blood flow over different time periods are not always linear. For example, blood flow velocity can be influenced by various physiological and pathological factors, causing fluctuations within a short period. Traditional linear shift methods (such as determining hysteresis by calculating the peak value of the maximum cross-correlation coefficient through cross-correlation) typically assume a constant time difference between signals, failing to effectively capture such localized temporal variations. Therefore, relying solely on linear alignment techniques may lead to error accumulation when analyzing dynamic changes in blood flow, thus affecting the accurate measurement of blood flow velocity.

[0144] To overcome this limitation, this invention employs a nonlinear signal alignment method based on the Dynamic Time Warping (DTW) algorithm. The DTW algorithm flexibly stretches and compresses signals along the time axis, enabling nonlinear alignment of two signals within a local range. This method can adapt to the complexity of blood flow velocity variations over time, exhibiting greater flexibility and accuracy than traditional methods, particularly in capturing dynamic fluctuations, rhythmic changes, and sudden fluctuations. Figure 2 The diagram illustrates the application of the DTW algorithm for nonlinear signal alignment, where two signals can achieve optimal matching at various time points by dynamically adjusting the time interval, fully taking into account the temporal variation of blood flow velocity.

[0145] First, construct a distance matrix D to represent the difference between signal 1 and signal 2 at each point, defined as:

[0146] (30);

[0147] and These represent signal1 and signal2 at time points respectively. and Blood flow characteristic values, This represents the element in the i-th row and j-th column of the distance matrix D.

[0148] (31);

[0149] Each This represents the local difference between signal1 and signal2 at a specific point in time, in order to find the shortest path.

[0150] Construct a cumulative cost matrix C, where, Indicates starting from the origin Go to current location The minimum cumulative distance is calculated recursively using dynamic programming:

[0151] (32);

[0152] in, This represents a penalty cost, used to prevent excessive off-diagonal movement.

[0153] After completing the cumulative cost matrix The optimal alignment path P is determined by backtracking from the end point of matrix C back to the starting point. This is achieved by selecting the current point at each step. Three possible precursor points , and The path with the lowest cumulative cost is used to backtrack back to the starting point (1,1), thus constructing the optimal alignment path covering the entire signal sequence. This is achieved by optimizing the path... The lag is calculated for each pair of matching points. Furthermore, by combining formula (27), the dynamic changes in blood flow velocity can be further solved.

[0154] Figure 3 A system for detecting blood flow velocity fluctuations based on OCTA is shown, comprising:

[0155] The image acquisition unit 301 is configured to: acquire an OCTA angiography image of the target blood vessel and extract the vascular skeleton of the target blood vessel from the OCTA angiography image;

[0156] The scanning control unit 302 is configured to: perform back-and-forth scanning of the unbranched region of the target blood vessel at a first position and a second position based on the vascular skeleton, thereby obtaining a multi-frame B-scan image sequence corresponding to the first position and the second position respectively;

[0157] The signal generation unit 303 is configured to generate a first blood flow signal corresponding to a first position and a second blood flow signal corresponding to a second position based on a multi-frame B-scan image sequence.

[0158] The signal alignment unit 304 is configured to perform dynamic time-warped alignment on the first blood flow signal and the second blood flow signal to obtain the optimal alignment path.

[0159] The hysteresis calculation unit 305 is configured to calculate the local hysteresis between the first blood flow signal and the second blood flow signal at each time point according to the optimal alignment path.

[0160] The velocity calculation unit 306 is configured to calculate the blood flow velocity fluctuation of the target vessel based on the local hysteresis, the vessel length between the first and second positions, and the scanning time of a single B-scan.

[0161] It is understood that the aforementioned units can be individually or entirely merged into one or more other units, or some of the units can be further divided into multiple functionally smaller units. This achieves the same operation without affecting the technical effects of the embodiments of the present invention. The aforementioned units are based on logical functional division. In practical applications, the function of one unit can be implemented by multiple units, or the function of multiple units can be implemented by one unit. In other embodiments of the present invention, the system may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented collaboratively by multiple units.

[0162] According to another embodiment of the present invention, the system of this embodiment can be constructed by running a computer program (including program code) capable of performing the steps involved in the corresponding method of the present invention on a general-purpose computing device, such as a computer, which includes processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable recording medium, loaded into the aforementioned computing device through the computer-readable recording medium, and run therein.

[0163] Figure 4 A computer device is shown, which includes a processor 401, a communication interface 402, and a computer-readable storage medium 403. The processor 401, communication interface 402, and computer-readable storage medium 403 can be connected via a bus or other means.

[0164] The communication interface 402 is used to receive and send data. The computer-readable storage medium 403 can be stored in the memory of the electronic device. The computer-readable storage medium 403 is used to store computer programs, which include program instructions. The processor 401 is used to execute the program instructions stored in the computer-readable storage medium 403.

[0165] The processor 401 is the computing and control core of the electronic device. It is suitable for implementing one or more instructions, specifically for loading and executing one or more instructions to achieve the corresponding method flow or corresponding function.

[0166] Processor 401 is configured to perform the following procedure:

[0167] Obtain OCTA angiography images of the target blood vessel and extract the vascular skeleton of the target blood vessel from the OCTA angiography images;

[0168] Based on the vascular skeleton, the unbranched region of the target blood vessel is scanned back and forth at the first and second positions to obtain a multi-frame B-scan image sequence corresponding to the first and second positions respectively.

[0169] Based on a multi-frame B-scan image sequence, a first blood flow signal corresponding to a first position and a second blood flow signal corresponding to a second position are generated;

[0170] Dynamic time warping and alignment of the first and second blood flow signals are performed to obtain the optimal alignment path;

[0171] Based on the optimal alignment path, calculate the local hysteresis between the first blood flow signal and the second blood flow signal at each time point;

[0172] The blood flow velocity fluctuation of the target vessel is calculated based on the local hysteresis, the vessel length between the first and second positions, and the scanning time of a single B-scan.

[0173] This invention also provides a computer-readable storage medium, which is a memory device in an electronic device for storing programs and data. It is understood that the computer-readable storage medium here may include both built-in storage media in the electronic device and extended storage media supported by the electronic device. The computer-readable storage medium provides storage space for storing the processing system of the electronic device.

[0174] Furthermore, this storage space also contains one or more instructions suitable for loading and execution by the processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory; alternatively, it can also be at least one computer-readable storage medium located remotely from the aforementioned processor.

[0175] In one embodiment, the computer-readable storage medium stores one or more instructions; the processor loads and executes the one or more instructions stored in the computer-readable storage medium to perform the following process:

[0176] Obtain OCTA angiography images of the target blood vessel and extract the vascular skeleton of the target blood vessel from the OCTA angiography images;

[0177] Based on the vascular skeleton, the unbranched region of the target blood vessel is scanned back and forth at the first and second positions to obtain a multi-frame B-scan image sequence corresponding to the first and second positions respectively.

[0178] Based on a multi-frame B-scan image sequence, a first blood flow signal corresponding to a first position and a second blood flow signal corresponding to a second position are generated;

[0179] Dynamic time warping and alignment of the first and second blood flow signals are performed to obtain the optimal alignment path;

[0180] Based on the optimal alignment path, calculate the local hysteresis between the first blood flow signal and the second blood flow signal at each time point;

[0181] The blood flow velocity fluctuation of the target vessel is calculated based on the local hysteresis, the vessel length between the first and second positions, and the scanning time of a single B-scan.

[0182] The present invention also provides a computer program product or computer program comprising computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the following process:

[0183] Obtain OCTA angiography images of the target blood vessel and extract the vascular skeleton of the target blood vessel from the OCTA angiography images;

[0184] Based on the vascular skeleton, the unbranched region of the target blood vessel is scanned back and forth at the first and second positions to obtain a multi-frame B-scan image sequence corresponding to the first and second positions respectively.

[0185] Based on a multi-frame B-scan image sequence, a first blood flow signal corresponding to a first position and a second blood flow signal corresponding to a second position are generated;

[0186] Dynamic time warping and alignment of the first and second blood flow signals are performed to obtain the optimal alignment path;

[0187] Based on the optimal alignment path, calculate the local hysteresis between the first blood flow signal and the second blood flow signal at each time point;

[0188] The blood flow velocity fluctuation of the target vessel is calculated based on the local hysteresis, the vessel length between the first and second positions, and the scanning time of a single B-scan.

[0189] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can implement the described functions using different methods for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0190] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic cable, digital cable) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data processing device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0191] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for detecting blood flow velocity fluctuations based on OCTA, characterized in that, include: Obtain OCTA angiography images of the target blood vessel and extract the vascular skeleton of the target blood vessel from the OCTA angiography images; Based on the vascular skeleton, the unbranched region of the target blood vessel is scanned back and forth at the first and second positions to obtain a multi-frame B-scan image sequence corresponding to the first and second positions respectively. Based on a multi-frame B-scan image sequence, a first blood flow signal corresponding to a first position and a second blood flow signal corresponding to a second position are generated; Dynamic time warping and alignment of the first and second blood flow signals are performed to obtain the optimal alignment path; Based on the optimal alignment path, calculate the local hysteresis between the first blood flow signal and the second blood flow signal at each time point; The blood flow velocity fluctuation of the target vessel is calculated based on the local hysteresis, the vessel length between the first and second positions, and the scanning time of a single B-scan. Dynamic time-warping and alignment of the first and second blood flow signals includes: Construct a distance matrix, where each element of the distance matrix represents the first blood flow signal at a given time point. The difference between the signal value of the second blood flow signal and the signal value of the second blood flow signal at time point j; Based on the distance matrix, a cumulative cost matrix is ​​constructed using dynamic programming. By tracing back from the end point of the cumulative cost matrix to the beginning point, we obtain the optimal alignment path that minimizes the cumulative cost. Tracing back from the end point of the cumulative cost matrix to the beginning point includes: At the current position, select the point with the smallest cumulative cost among the three preceding positions from the previous moment as the previous point on the backtracking path; Repeat the selection process described above until you return to the starting point of the cumulative cost matrix.

2. The method for detecting blood flow velocity fluctuations based on OCTA as described in claim 1, characterized in that, Before generating the first blood flow signal and the second blood flow signal, the process also includes: High-pass filtering is applied to a multi-frame B-scan image sequence to remove noise signals; The high-pass filtered B-scan images are arranged in chronological order to form a high-pass filtered B-scan image sequence.

3. The method for detecting blood flow velocity fluctuations based on OCTA as described in claim 2, characterized in that, Generating a first blood flow signal corresponding to a first position and a second blood flow signal corresponding to a second position includes: For each frame of the high-pass filtered B-scan image sequence, the sum of the brightness values ​​of all pixels is calculated to obtain the total signal value of that frame. The total signal values ​​of each frame image corresponding to the first position are combined in chronological order to form the first blood flow signal; The total signal values ​​of each frame corresponding to the second position are combined in chronological order to form the second blood flow signal.

4. The method for detecting blood flow velocity fluctuations based on OCTA as described in claim 1, characterized in that, Generating a first blood flow signal corresponding to a first position and a second blood flow signal corresponding to a second position further includes: Hu moments were calculated on the red blood cell patterns in a multi-frame B-scan image sequence to obtain Hu moment features; The Hu moment feature is used as an auxiliary feature, and together with the first blood flow signal and the second blood flow signal, it is used for dynamic time warping alignment.

5. The method for detecting blood flow velocity fluctuations based on OCTA as described in claim 1, characterized in that, Before performing the back-and-forth scan, it also includes: By combining the connectivity information of the vascular skeleton and the blinking point positions of the first and second positions in the B-scan image, it is determined whether the first and second positions belong to the same capillary. A back-and-forth scan is only performed if the determination result is yes.

6. The method for detecting blood flow velocity fluctuations based on OCTA as described in claim 1, characterized in that, Based on the optimal alignment path, the local hysteresis between the first and second blood flow signals at each time point is calculated, including: For each pair of matching points in the optimal alignment path, the difference between the time index of the matching point in the first blood flow signal and the time index in the second blood flow signal is calculated as the local hysteresis corresponding to that matching point.

7. The method for detecting blood flow velocity fluctuations based on OCTA as described in claim 1, characterized in that, Before calculating blood flow velocity fluctuations, the following is also included: The blood flow direction is determined based on the overall cross-correlation coefficient of the first and second blood flow signals; if the hysteresis corresponding to the peak value of the overall cross-correlation coefficient is positive, the blood flow direction is from the first position to the second position; if it is negative, the blood flow direction is from the second position to the first position.

8. A system for detecting blood flow velocity fluctuations based on OCTA, characterized in that, include: The image acquisition unit is configured to: acquire OCTA angiography images of the target blood vessel and extract the vascular skeleton of the target blood vessel from the OCTA angiography images; The scanning control unit is configured to: perform back-and-forth scanning of the unbranched region of the target blood vessel at a first position and a second position based on the vascular skeleton, thereby obtaining a multi-frame B-scan image sequence corresponding to the first position and the second position respectively; The signal generation unit is configured to generate a first blood flow signal corresponding to a first position and a second blood flow signal corresponding to a second position based on a multi-frame B-scan image sequence. The signal alignment unit is configured to perform dynamic time warping and alignment on the first blood flow signal and the second blood flow signal to obtain the optimal alignment path. The hysteresis calculation unit is configured to calculate the local hysteresis between the first blood flow signal and the second blood flow signal at each time point based on the optimal alignment path. The velocity calculation unit is configured to calculate the blood flow velocity fluctuation of the target vessel based on the local hysteresis, the vessel length between the first and second positions, and the scan time of a single B-scan. Dynamic time-warping and alignment of the first and second blood flow signals includes: Construct a distance matrix, where each element of the distance matrix represents the first blood flow signal at a given time point. The difference between the signal value of the second blood flow signal and the signal value of the second blood flow signal at time point j; Based on the distance matrix, a cumulative cost matrix is ​​constructed using dynamic programming. By tracing back from the end point of the cumulative cost matrix to the beginning point, we obtain the optimal alignment path that minimizes the cumulative cost. Tracing back from the end point of the cumulative cost matrix to the beginning point includes: At the current position, select the point with the smallest cumulative cost among the three preceding positions from the previous moment as the previous point on the backtracking path; Repeat the selection process described above until you return to the starting point of the cumulative cost matrix.

9. The system for detecting blood flow velocity fluctuations based on OCTA as described in claim 8, characterized in that, Before generating the first blood flow signal and the second blood flow signal, the process also includes: High-pass filtering is applied to a multi-frame B-scan image sequence to remove noise signals; Arrange the high-pass filtered B-scan images in chronological order to form a high-pass filtered B-scan image sequence; The signal generation unit generates a first blood flow signal corresponding to a first position and a second blood flow signal corresponding to a second position, including: For each frame of the high-pass filtered B-scan image sequence, the sum of the brightness values ​​of all pixels is calculated to obtain the total signal value of that frame. The total signal values ​​of each frame image corresponding to the first position are combined in chronological order to form the first blood flow signal; The total signal values ​​of each frame image corresponding to the second position are combined in chronological order to form the second blood flow signal; Hu moments were calculated on the red blood cell patterns in a multi-frame B-scan image sequence to obtain Hu moment features; The Hu moment feature is used as an auxiliary feature, and together with the first blood flow signal and the second blood flow signal, it is used for dynamic time warping alignment.

10. The system for detecting blood flow velocity fluctuations based on OCTA as described in claim 8, characterized in that, The scanning control unit also includes, before performing a back-and-forth scan: By combining the connectivity information of the vascular skeleton and the blinking point positions of the first and second positions in the B-scan image, it is determined whether the first and second positions belong to the same capillary. A back-and-forth scan is only performed if the determination result is yes.

11. The system for detecting blood flow velocity fluctuations based on OCTA as described in claim 8, characterized in that, In the hysteresis calculation unit, based on the optimal alignment path, the local hysteresis between the first blood flow signal and the second blood flow signal at each time point is calculated, including: For each pair of matching points in the optimal alignment path, the difference between the time index of the matching point in the first blood flow signal and the time index in the second blood flow signal is calculated as the local hysteresis corresponding to that matching point.

12. A computer device, characterized in that, include: Processor and computer-readable storage media; A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the method for detecting blood flow velocity fluctuations based on OCTA as described in any one of claims 1 to 7.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded by a processor and executed as described in any one of claims 1 to 7, for detecting blood flow velocity fluctuations based on OCTA.

14. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method for detecting blood flow velocity fluctuations based on OCTA as described in any one of claims 1 to 7.

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