Optical coherence tomography angiography method, device, electronic device, and storage medium

Abstract

This application provides an optical coherence tomography angiography method, device, electronic device, and storage medium. The method includes the steps of: acquiring a time domain signal of a target region, which is N interference spectrum signals obtained by repeating A-scans N times on the target region; scaling each of the N interference spectrum signals based on K scales to obtain N×k scale-transformed signals; Fourier-transforming each scale-transformed signal to obtain an axial frequency domain signal in logarithmic space, and denoising the axial frequency domain signal; dividing the N×k denoised axial frequency domain signals into k groups of single-scale signals based on K scales, and performing decorrelation calculations on the single-scale signals of each group to obtain a single-scale blood flow signal; acquiring a multi-scale blood flow signal based on the k groups of single-scale blood flow signals; and acquiring a blood flow image based on the multi-scale blood flow signals. This application improves the recognition accuracy of blood flow signals.

Description

[Technical Field]

[0001] The present application relates to the field of optical coherence tomography, and in particular to optical coherence tomography angiography methods, devices, electronic devices and storage media. [Background technology]

[0002] Optical coherence tomography (OCT) is a highly sensitive, high-resolution, high-speed, non-invasive imaging technique that is widely used in the diagnosis of ocular fundus diseases and is of great significance for the detection and treatment of ophthalmic diseases.

[0003] As a tomographic imaging method, OCT uses the coherence of light to scan and capture images of the fundus. One scan is called an A-scan, and a group of adjacent, consecutive scans is called a B-scan. A B-scan is the commonly seen OCT cross-section and is the most important imaging method of OCT in medical diagnosis.

[0004] In the field of OCT, OCTA (optical coherence tomography angiography) is a new technology that has advanced in recent years. Retinal OCTA maps can intuitively display the shape and distribution of retinal blood flow, making them important for diagnosing ocular fundus diseases. In a stationary eye, the only moving material at the fundus is blood cells flowing within the blood vessels. OCTA uses red blood cells flowing within the blood vessels as a contrast agent, measures the difference in OCT signals caused by the moving blood cells, and provides blood flow information. This allows for rapid and noninvasive reconstruction of the three-dimensional structure of the retinal and choroidal vessels, thereby enabling visualization of the vascular network. Angiography requires repeated imaging of the same location and calculating the difference between the B-scans to obtain the blood flow signal at the current location. Summary of the Invention [Problem to be solved by the invention]

[0005] However, because OCTA has a high axial resolution, it is susceptible to the effects of pulsation jitter noise, which can easily cause errors in the imaging results. Therefore, the axial resolution is generally lowered to reduce the effects of noise, but lowering the axial resolution can reduce the accuracy of blood flow signal recognition. [Means for solving the problem]

[0006] To improve the recognition accuracy of blood flow signals, the present application provides an optical coherence tomography angiography method, device, electronic device and storage medium.

[0007] According to a first aspect, the present application provides an optical coherence tomography angiography method comprising:

[0008] The optical coherence tomography angiography method is Acquiring time-domain signals of the target area, which are N interference spectrum signals obtained by repeating A-scans N times on the target area; a step of scaling each of the N interference spectrum signals based on k scales to obtain N×k scale-converted signals, wherein scaling any of the interference spectrum signals based on the k scales includes a step of decomposing any of the interference spectrum signals based on each of the K scales to obtain k scale-converted signals corresponding to any of the interference spectrum signals; Fourier transforming and logarithmizing each of the scaled signals to obtain axial frequency domain signals in logarithmic space, and denoising the axial frequency domain signals; dividing the N×k denoised axial frequency domain signals into k groups of single-scale signals based on the k scales, and performing decorrelation calculations on the single-scale signals of each group to obtain single-scale blood flow signals; obtaining a multi-scale blood flow signal based on the k groups of single-scale blood flow signals; and obtaining a blood flow image based on the multi-scale blood flow signal.

[0009] According to the above-described embodiment, the blood flow signal is a non-stationary signal having local fluctuations, and one interference spectrum signal is decomposed into multiple segments after one scale conversion. Therefore, when performing subsequent decorrelation calculation, it is possible to compare the axial frequency domain signals corresponding to the interference spectrum signals of each segment corresponding to the same target region at different times. By decomposing and comparing the segments, it is possible to more accurately capture the non-stationary state fluctuation signal, enabling accurate extraction of the blood flow signal.

[0010] Furthermore, a single interference spectrum signal can be transformed at multiple scales, with the number of decomposition segments corresponding to each scale being different. This means that the scales at which the segments are decomposed and compared are different, allowing for measurement of non-stationary signal fluctuations at different scales. Therefore, by simultaneously transforming a single interference spectrum signal and fusing multiple scales, the rate of misidentification at a single scale can be reduced, improving the extraction accuracy and processing efficiency of blood flow signals. Furthermore, because all points in a single interference spectrum signal are retained, more image details can be restored, improving the signal-to-noise ratio.

[0011] In a possible embodiment, the interference spectrum signal corresponds to a sampling time period, and the any one of the interference spectrum signals is decomposed based on each of the K scales, and k scale conversion signals corresponding to the any one of the interference spectrum signals are step-wise generated. The method includes an operation of decomposing any of the interference spectrum signals based on any of the k scales to obtain a scale-converted signal corresponding to any of the scales of the interference spectrum signals, the operation including: a step of identifying a decomposed signal corresponding to any one of the scales, the decomposed signal being a signal in the sampling time period T, the decomposed signal including at least one time group, the time group including adjacent first and second time periods, adjacent two of the first time periods, or adjacent two of the second time periods; multiplying any one of the interference spectrum signals corresponding to the first time period by a forward signal to obtain a first signal; multiplying any one of the interference spectrum signals corresponding to the second time period by a reverse direction signal to obtain a second signal; and obtaining a scale-converted signal in which any of the interference spectrum signals corresponds to any of the scales based on at least one of the first signals and at least one of the second signals.

[0012] According to the above embodiment, the sampling time period T is divided according to at least one time group corresponding to the scale, and the interference spectrum signal is divided into at least one different first signal and at least one different second signal according to the forward signal and the backward signal, thereby realizing segmentation of the interference spectrum signal and obtaining a scale-converted signal corresponding to the scale.

[0013] In a possible embodiment, the step of denoising the axial frequency domain signal in logarithmic space to obtain the denoised axial frequency domain signal comprises: identifying a splitting threshold; if the amplitude of the axial frequency domain signal is greater than or equal to the segmentation threshold, then the denoised axial frequency domain signal is equal to the axial frequency domain signal in logarithmic space; If the amplitude of the axial frequency domain signal is less than the segmentation threshold, the denoised axial frequency domain signal is equal to the segmentation threshold.

[0014] According to the above-described embodiment, the axial frequency domain signal is divided with noise reduction based on the division threshold, thereby improving the S / N ratio.

[0015] In a possible embodiment, the step of identifying the splitting threshold comprises: extracting noise signals corresponding to all of the axial frequency domain signals based on a clustering algorithm; and determining a division threshold based on the noise signal.

[0016] According to the above-described embodiment, the noise signal is extracted by unsupervised learning, the division threshold is identified, and the threshold is automatically determined based on the axial frequency domain signal, thereby improving the accuracy of determining the division threshold and improving the S / N ratio.

[0017] In a possible embodiment, the step of performing decorrelation calculations on the single-scale signals of each group to obtain a single-scale blood flow signal comprises: determining first, second and third order statistics of the single scale signals for each of the groups; and performing decorrelation calculations based on the first, second and third order statistics to determine the single-scale blood flow signal.

[0018] According to the above-described embodiment, by utilizing higher-order statistics, more useful information can be extracted from signals at a single scale, and decorrelation calculations can be performed, thereby improving the accuracy of extraction of signals at a single blood flow scale.

[0019] In a possible embodiment, each of said scaled signals is Fourier transformed and logarithmized to obtain an axial frequency domain signal in logarithmic space, followed by: performing an alignment operation on all of the axial frequency domain signals based on a phase correlation algorithm; The noise-removed axial frequency domain signal is a signal that has been noise-removed based on the axial frequency domain signal after an alignment operation.

[0020] According to the above-described embodiment, the alignment operation achieves the effect of reducing jitter interference to the axial frequency domain signal and improving the S / N ratio.

[0021] In a possible embodiment, the method further comprises the step of dispersion compensating each of the interference spectrum signals of the time-domain signal before scaling each of the interference spectrum signals of the N interference spectrum signals based on k scales.

[0022] According to a second aspect, the present application provides an optical coherence tomography angiography apparatus comprising:

[0023] Optical coherence tomography angiography equipment an acquisition module for acquiring time-domain signals of the target area, the time-domain signals being N interference spectrum signals obtained by repeating A-scans N times on the target area; a scale conversion module for scaling each of the N interference spectrum signals based on k scales to obtain N×k scale-converted signals, wherein scaling any of the interference spectrum signals based on the k scales includes decomposing any of the interference spectrum signals based on each of the K scales to obtain the k scale-converted signals corresponding to any of the interference spectrum signals; a frequency domain transform module for Fourier transforming each of the scaled signals and taking the logarithm thereof to obtain an axial frequency domain signal in logarithmic space, and for denoising the axial frequency domain signal; a decorrelation module for dividing the N×k denoised axial frequency domain signals into k groups of single-scale signals based on the k scales, and performing decorrelation calculations on the single-scale signals of each group to obtain a single-scale blood flow signal; a fusion module for obtaining a multi-scale blood flow signal based on the k groups of single-scale blood flow signals; and an imaging module for obtaining a blood flow image based on the multi-scale blood flow signal.

[0024] According to the above-described embodiment, the blood flow signal is a non-stationary signal having local fluctuations, and one interference spectrum signal is decomposed into multiple segments after one scale conversion. When subsequent decorrelation calculation is performed, the axial frequency domain signals corresponding to the interference spectrum signals of each segment corresponding to the same target region at different times can be compared. By decomposing and comparing the segments, the non-stationary fluctuation signal can be captured more accurately, and accurate blood flow signal extraction becomes possible.

[0025] In addition, a single interference spectrum signal can be transformed at multiple scales, with the number of decomposition segments corresponding to each scale being different. This means that the scales at which the segments are decomposed and compared are different, allowing for measurement of non-stationary signal fluctuations at different scales. Therefore, by simultaneously transforming the scale of a single interference spectrum signal and fusing multiple scales, the rate of misidentification at a single scale can be reduced, improving the extraction accuracy and processing efficiency of blood flow signals. Furthermore, because all points in a single interference spectrum signal are retained, more image details can be restored, improving the signal-to-noise ratio.

[0026] In a possible embodiment, the scale conversion module decomposes any of the interference spectrum signals based on any of the scales to obtain the scale-converted signals, specifically: The interference spectrum signal corresponds to a sampling time period T; Identifying a decomposed signal corresponding to any of the scales, the decomposed signal being a signal in the sampling time period T, the decomposed signal including at least one time group, the time group including adjacent first and second time periods, two adjacent first time periods, or two adjacent second time periods; multiplying the interference spectrum signal corresponding to the first time period by a forward signal to obtain a first signal; multiplying the interference spectrum signal corresponding to the second time period by a reverse direction signal to obtain a second signal; A scale conversion unit is used to obtain the scale-converted signal based on at least one of the first signals and at least one of the second signals.

[0027] In a possible embodiment, the device further comprises a denoising module for denoising the axial frequency domain signal in logarithmic space, and obtaining the denoised axial frequency domain signal comprises, in particular: used to identify a splitting threshold, if the amplitude of the axial frequency domain signal is greater than or equal to the segmentation threshold, then the denoised axial frequency domain signal is equal to the axial frequency domain signal in logarithmic space; If the amplitude of the axial frequency domain signal is less than the segmentation threshold, the denoised axial frequency domain signal is equal to the segmentation threshold.

[0028] In a possible embodiment, the denoising module, when determining the dividing threshold, specifically: extracting noise signals corresponding to all of the axial frequency domain signals based on a clustering algorithm; and determining a division threshold based on the noise signal.

[0029] In a possible embodiment, when the decorrelation module performs decorrelation calculations on the single-scale signals of each group to obtain a single-scale blood flow signal, specifically: determining first, second and third order statistics of the single-scale signals for each of the groups; and determining the single-scale blood flow signal by performing decorrelation calculations based on the first-order statistics, second-order statistics, and third-order statistics.

[0030] In a possible embodiment, the apparatus further comprises an alignment module, which specifically comprises, after obtaining the axial frequency domain signal in logarithmic space: is used to perform an alignment operation on all said axial frequency domain signals based on a phase correlation algorithm; The denoised axial frequency domain signal is a signal that has been denoised based on the axial frequency domain signal after an alignment operation.

[0031] In a possible embodiment, the device further comprises a dispersion compensation module, which specifically comprises: The time-domain signal is used to dispersion-compensate each of the interference spectrum signals among the N interference spectrum signals before scaling each of the interference spectrum signals among the N interference spectrum signals based on k scales.

[0032] According to a third aspect, the present application provides an electronic device.

[0033] Electronic devices include: one or more processors; a memory; one or more computer programs stored in memory and configured for execution by one or more processors; The one or more computer programs are arranged to perform the optical coherence tomography angiography method described above.

[0034] According to a fourth aspect, the present application provides a computer-readable storage medium comprising:

[0035] The computer-readable storage medium stores a computer program that, when loaded by a processor, executes the optical coherence tomography angiography method described above.

[0036] In summary, the present application includes at least the following beneficial technical effects:

[0037] A single interference spectrum signal is decomposed into multiple segments after a single scale transformation. Subsequent decorrelation calculations allow comparison of the axial frequency domain signals corresponding to the interference spectrum signals of each segment corresponding to the same target region at different times. By decomposing and comparing segments, the non-steady-state fluctuation signals can be more accurately captured, enabling accurate blood flow signal extraction. Furthermore, because all points in a single interference spectrum signal are retained, more image details can be restored, improving the signal-to-noise ratio. At the same time, a single interference spectrum signal can be transformed at multiple scales, with different numbers of decomposed segments corresponding to each scale. Therefore, by simultaneously scaling a single interference spectrum signal and fusing multiple scales, the error rate at a single scale can be reduced, improving the accuracy and processing efficiency of blood flow signal extraction. [Brief explanation of the drawings]

[0038] [Figure 1] FIG. 1 is a schematic flow chart of an optical coherence tomography angiography method according to an embodiment of the present application. [Figure 2] FIG. 2 is a schematic diagram of multi-scale transformation of a time domain signal. [Figure 3] FIG. 3 shows (a) a vitreous blood flow image, (b) a deep image, and (c) a superficial blood flow image according to an example of the present application. [Figure 4] FIG. 4 is a schematic flowchart of steps S021 to S023 in the embodiment of the present application. [Figure 5] FIG. 5 is a schematic flowchart of steps S031 to S033 in the embodiment of the present application. [Figure 6] FIG. 6 is a schematic diagram of decorrelation calculation based on a single scale signal in an embodiment of the present application. [Figure 7] FIG. 7 is a schematic diagram of an optical coherence tomography angiography apparatus according to an embodiment of the present application. [Figure 8] FIG. 8 is a schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE INVENTION

[0039] The present application will be described in further detail below with reference to FIGS.

[0040] After reading this specification, a person skilled in the art may, if necessary, make amendments to the embodiments of this application without making any creative contribution, but as long as they fall within the scope of the claims of this application, they will be protected by patent law.

[0041] In order to clarify the objectives, technical means and advantages of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is clear that the described embodiments are only a part of the embodiments of the present application, and are not all of the embodiments. All other embodiments that can be obtained by those skilled in the art based on the embodiments of the present application without requiring creative ingenuity are included in the protection scope of the present application.

[0042] Furthermore, in this specification, the term "and / or" simply describes a relationship between related objects and indicates that three types of relationships may exist; for example, A and / or B can indicate that only A exists, that both A and B exist, or that only B exists. Furthermore, in this specification, unless otherwise specified, " / " generally indicates that related objects are in an "or" relationship.

[0043] In order to facilitate understanding of the technical solution proposed in this application, some elements introduced in the description of this application will be first described. It should be understood that the following description is merely for facilitating understanding of these elements from the perspective of understanding the contents of the embodiments of this application, and does not necessarily cover all possible cases.

[0044] (1) A-scan: For the depth reflection contour reflected from the sample in the depth direction, one A-scan is performed to obtain one interference spectrum signal, which contains all the image information of one axial depth of the sample, and then Fourier transform is performed to obtain an image of one axial depth of one sample.

[0045] (2) B-scan: A B-scan contains multiple adjacent, consecutive, and differently positioned interference spectrum signals (A-scan signals) (e.g., N1 interference spectrum signals). Each interference spectrum signal contains X points, and these spectra constitute an N1 × X data packet. Specifically, time-domain optical coherence tomography (OCT) technology requires depth scanning, which is disadvantageous for high-speed, real-time OCT imaging. Frequency-domain optical coherence tomography (OCT) systems can obtain all information (A-scan signals) in the depth direction (Z direction) by Fourier transforming the interference spectrum of the sample arm and reference arm. This eliminates the need for mechanical depth scanning (A-scan) and only requires lateral scanning (X and Y directions). A single X direction scan forms a B-scan image, and a Y direction scan forms a three-dimensional OCT signal.

[0046] (3) The axial resolution of an OCTA system refers to the minimum distance at which depth tomography of a sample can be resolved in the axial direction. The number of points contained in each interference spectrum signal is determined by the axial resolution of the OCTA system. The lateral and axial resolutions of an OCTA image are not correlated. The OCTA system can provide an axial resolution of approximately 1-15 μm, independent of the lateral resolution. The axial resolution mainly depends on the coherence length of the light source.

[0047] (4) S / N ratio is an index for evaluating an image by calculating the ratio of signal to noise in the image, and is a global evaluation standard. For OCTA images, the higher the S / N ratio, the less speckle noise there is in the image, the better the image noise reduction effect, and the better the image quality.

[0048] An embodiment of the present application provides an optical coherence tomography angiography method performed by electronic equipment. Referring to Figure 1, the method includes the following steps:

[0049] In step S101, a time domain signal of a target region is obtained.

[0050] Here, the time-domain signal is N interference spectrum signals obtained by repeating A-scans N times on the target region. The formula for the time-domain signal is I(i, x, t), where i = 1, 2, ... N. X is the scan position (i.e., the target region), t is the scan time point in the current A-scan, and the time corresponding to each A-scan is the sampling time period T. In general, the scan rate of A-scans is set to 7000 times per second, 10000 times per second, ...

[0051] The interference spectrum signal acquired by a single A-scan can then be Fourier transformed to obtain depth information for the sample. The more data points in a single interference spectrum signal, the more points in the Fourier transformed image there will be, and the higher the image resolution. In angiography, a high-resolution image can more accurately identify blood flow signals in different tissues and at different levels.

[0052] The duration of one A-scan (sampling time period T) is an artificially set time. When setting the duration of one A-scan, factors such as the power of the incident light, the reflectivity of the sample, the measurement depth, and the speed at which depth information is acquired must be taken into consideration. For example, in a weakly scattering medium, the minimum detectable optical signal is determined by the sensitivity of the OCT system, while in a strongly scattering medium, the maximum imaging depth is determined by the sensitivity of the OCT system. Extending the signal acquisition time of one A-scan and slowing down the speed at which depth information is acquired can improve the sensitivity of OCT.

[0053] When analyzing stationary signals, the most common and most important analytical method is the Fourier transform. However, since the Fourier analysis uses a global transform (corresponding to one sampling time period T), it is not possible to express the time-frequency locality of the signal, which is the most fundamental property of a non-stationary signal. The blood flow signal considered in the examples of this application is a non-stationary signal.

[0054] To extract blood flow signal fluctuations, we proposed an improved algorithm for split-spectrum amplitude-decorrelation angiography (SSADA-OCT). SSADA divides the entire OCT spectrum into several narrow bands, and for each band, calculates and averages the correlation between different B-scans. A specific step during data processing is to separate the spectrum using bandpass filters at different positions (e.g., four equally spaced bandpass filters), a step called spectral splitting. Then, based on M independent decorrelation images obtained between each pair of B-scans, the resulting image has low axial resolution but the same lateral resolution.

[0055] However, although the above spectral division can obtain more accurate fluctuations of the blood flow signal and improve the S / N ratio, some data points of the interference spectrum signal are discarded, which reduces the axial resolution of the reconstructed image.

[0056] In order to maintain high axial resolution and accurately extract the changed blood flow signal, an embodiment of the present application retains all data points corresponding to one A-scan, decomposes the signal corresponding to one A-scan into unit signals of multiple segments (i.e., one scale conversion) based on the nature of local changes in the blood flow signal, and analyzes changes in the blood flow signal within the unit signals of each segment to accurately extract the blood flow signal.

[0057] In step S102, each of the N interference spectrum signals is scale-converted based on the k scales to obtain N×k scale-converted signals.

[0058] Here, scaling any of the interference spectrum signals based on k scales includes decomposing any of the interference spectrum signals based on each scale to obtain k scale-converted signals corresponding to the any of the interference spectrum signals.

[0059] When scaling the interference spectrum signal in one step, the interference spectrum signal can be decomposed into multiple segment unit signals and the axial frequency domain signals corresponding to the unit signals of each segment can be compared. Decomposing and comparing segments allows for more accurate capture of non-stationary fluctuation signals within a single interference spectrum signal, enabling accurate extraction of blood flow signals.

[0060] The first-order interference spectrum signal is transformed at multiple scales, and the number of decomposition segments corresponding to each scale is different, i.e., the scale at which the segments are decomposed and compared varies, so that non-stationary fluctuating signals can be measured at different scales.

[0061] Furthermore, there may be overlap between frequency bands when dividing the spectrum, and repeated calculations may result in a decrease in calculation efficiency. Compared with spectrum division, the embodiment of the present application not only can retain all points of one interference spectrum signal, but also can avoid repeated calculations of data.

[0062] In step S103, each scaled signal is Fourier transformed and logarithmized to obtain an axial frequency domain signal in logarithmic space.

[0063] In step S104, based on the K scales, the N×k denoised axial frequency domain signals are divided into k groups of single-scale signals, and a decorrelation calculation is performed on the single-scale signals of each group to obtain single-scale blood flow signals.

[0064] In the embodiment of the present application, the principle of denoising the axial frequency domain signal is as follows: The quantitative effect of the decorrelation algorithm on motion contrast is highly dependent on the noise level of the original A-scan signal. As the signal strength attenuates (e.g., in deep tissue regions), random noise gradually becomes the dominant component, thereby increasing the decorrelation value and resulting in decorrelation artifacts. For example, motion contrast generated based on decorrelation calculations typically cannot distinguish between random noise and decorrelation caused by red blood cell movement, so regions with a weak signal-to-noise ratio are likely to be misclassified as blood flow signal regions. This severely impacts the contrast of the blood flow image. Meanwhile, the denoising process can reduce or eliminate errors caused by the random noise signal in the subsequent decorrelation calculation of the denoised axial frequency domain signal.

[0065] In step S105, a multi-scale blood flow signal is obtained based on the k groups of single-scale blood flow signals.

[0066] Here, one B-scan signal includes multiple A-scan signals at different positions. After the step processing of steps S101 to S104, the A-scan signal at any position is a multi-scale blood flow signal corresponding to any position. By analyzing the blood flow signals at the scales at each position of one B-scan signal, blood vessel signals for the entire structural cross section can be obtained.

[0067] In the embodiment of the present application, the extraction accuracy and processing efficiency of blood flow signals can be improved by simultaneously performing scale conversion on a single initially acquired interference spectrum signal and fusing it at multiple scales.

[0068] In step S106, a blood flow image is obtained based on the multi-scale blood flow signals.

[0069] Specifically, visualization software is used to convert the multi-scale blood flow signals into a vascular image. Figure 3 shows (a) a vitreous blood flow image, (b) a deep image, and (c) a superficial blood flow image corresponding to the fused multi-scale blood flow signals. Fusion of multiple scales reduces the false positive rate at a single scale, improves the signal-to-noise ratio, and enables accurate recognition of blood flow signals corresponding to different tissues and levels.

[0070] In a possible embodiment of the present application, referring to FIG. 2 and FIG. 4, in step S102, decomposing any interference spectrum signal based on any scale to obtain a scale-converted signal includes the following steps:

[0071] In step S021, a decomposition signal corresponding to any scale is identified.

[0072] Here, each scale corresponds to one preset decomposition signal, the format of which is shown below. [1,1,-1,-1] [1,1,-1,-1] [1,-1,-1,1] [1,-1,1,~1] ...

[0073] Here, the decomposed signal is a signal in a sampling time period T, and the decomposed signal includes at least one time group, and the time group includes adjacent first and second time periods, two adjacent first time periods, or two adjacent second time periods.

[0074] Taking [1,1,-1,~1] as an example, the decomposed signal corresponds to two time groups, a first time group (T1,T1) and a second time group (T2,T2), where the first time group is located before the second time group in the sampling time period T.

[0075] Taking [1,-1,-1,1] as an example, the decomposed signal corresponds to two time groups, a first time group (T1,T2) and a second time group (T2,T1), where the first time group is located before the second time group in the sampling time period T.

[0076] Furthermore, in order to easily express the k decomposed signals, the k decomposed signals corresponding to the k scales are arranged in rows to form a non-zero orthogonal signal S. The non-zero orthogonal signal S can be expressed by the following equation:

number

[0077] In step S022, the interference spectrum signal corresponding to the first time period is multiplied by the forward signal to obtain a first signal, and the interference spectrum signal corresponding to the second time period is multiplied by the backward signal to obtain a second signal.

[0078] Here, the first signal and the second signal are different. Referring to FIG. 2, the decomposition signal illustrated in the embodiment of the present application is a square wave signal, but the first signal and the second signal in the embodiment of the present application may also include other types of signals, for example, a smooth transition between the first signal and the second signal.

[0079] In the above example, 1 is the forward signal and -1 is the reverse signal, T1 corresponds to the forward signal and T2 corresponds to the reverse signal.

[0080] In step S023, a scale-converted signal is obtained based on at least one first signal and at least one second signal.

[0081] The resulting scale-converted signal is given by the following equation (2).

number

[0082] Furthermore, each of the N interference spectrum signals is scale-converted based on the K scales to obtain N×k scale-converted signals.

number

[0083] In the embodiments of the present application, different scales and corresponding decomposition signals can be set according to different application requirements. After scale conversion, each interference spectrum signal corresponds to k scale-converted signals, and N interference spectrum signals can obtain a total of N×k scale-converted signals.

[0084] In a possible embodiment of the present application, in step S101, after obtaining the time-domain signal of the target region, before performing scale conversion on each of the N interference spectrum signals according to the k scales, the method further includes: dispersion compensating each of the interference spectrum signals among the time-domain signals.

[0085] The axial resolution of an OCT system is inversely proportional to the half-width of the interferometry signal. In the actual optical path of the system, differences in the length of dispersive material between the reference and sample arms can cause dispersion mismatch, which can introduce phase changes into the interferometry signal and broaden the spectrum of the interferometry signal, resulting in reduced axial resolution and a reduced signal-to-noise ratio in the OCTA image.

[0086] Known dispersion mismatches originate from system components such as the collimator, scanning objective lens, optical fiber, and measured sample. At different sample depths, the detection light passes through different lengths of dispersive medium, resulting in different amounts of dispersion mismatch, which results in different axial resolutions depending on the sample depth. Therefore, compensating for dispersion with depth change is particularly important to ensure that the axial resolution at different depths within the imaging depth range approaches the theoretical axial resolution.

[0087] Currently, dispersion compensation methods for OCTA systems can be divided into physical compensation and numerical compensation. Physical compensation methods compensate for the dispersion mismatch between the two arms by adding a hardware device to the reference arm. However, physical compensation methods require adding or modifying hardware devices in the optical path, which increases system complexity, introduces other noise, and increases costs. On the other hand, numerical compensation methods only require subsequent processing to eliminate dispersion mismatch on the data collected by the OCTA. One embodiment of this application employs numerical compensation to compensate for the dispersion of a time-domain signal.

[0088] Specifically, there are mainly the following types of numerical compensation methods:

[0089] The first is the iterative compensation method, which is a method of repeatedly searching for dispersion compensation coefficients that provide the optimal compensation effect, using the image quality (e.g., sharpness, Rayleigh entropy, etc.) of the airspace being A-scanned as an evaluation function. MarKs et al. performed dispersion compensation using an iterative method based on the golden section method to improve the resolution of the OCTA system.

[0090] The second method is the deconvolution method, which calculates the convolution kernel of the dispersion parameters that change with depth in the sample depending on the structure and material of the sample, and removes the dispersion mismatch through the deconvolution operation.

[0091] The third is the short-time Fourier transform method, that is, dividing the spectrum by a window function and calculating the dispersion phase to be compensated by light of different frequencies.

[0092] The fourth is the airspace extraction method, which involves setting a window function according to a predetermined threshold for the spectrum of the interference spectrum signal, extracting Gaussian peak signals at different depths of the sample, obtaining the corresponding spectrum by a fitting method, and then removing the second- and third-order dispersion terms to correct the dispersion mismatch at different depths.

[0093] The fifth is the dispersion coefficient fitting method, that is, a method in which the dispersion coefficients at different depths of the sample are pre-calculated using an iterative method to obtain the material information of the sample and correct the weak signal at the deep position of the sample.

[0094] In the embodiment of the present application, after performing dispersion compensation on the time-domain signal in step S101 to obtain an updated time-domain signal, in step S102, based on the k scales, each interference spectrum signal of the dispersion-compensated time-domain signal is scaled by k scales to obtain N×k scaled signals.

[0095] OCTA data acquisition requires sequential B-scans acquired at the same location at regular time intervals, and then reconstructing microvascular images by comparing uncorrelated signals between B-scans. During data acquisition, data discontinuities inevitably occur due to sample movement, environmental noise, system vibration, and other factors, resulting in distortions and corruption of the vascular image, resulting in motion artifacts. Artifacts caused by sample movement significantly degrade OCTA image quality and affect quantitative analysis of tissue microvascularity.

[0096] There are various causes of sample motion, including breathing, heartbeat, and unintentional shaking of the measurement site. Even small movements, such as micrometer-scale skin movements caused by heartbeat, can increase background noise and ultimately affect the effectiveness of angiography. The longer the data acquisition interval, the more severe the motion artifacts become. Therefore, motion artifact removal is a key element in OCTA vessel extraction algorithms. The main method for artifact removal is image alignment.

[0097] Specifically, image alignment, including techniques such as conversion, interpolation, and shifting, is performed to correct distortions and cuts in images affected by motion and obtain images free of motion artifacts.

[0098] In the embodiment of the present application, the main factor that reduces the S / N ratio is translation. Therefore, in a possible embodiment of the present application, step S103 further includes: Fourier transforming and logarithmizing each scaled signal to obtain axial frequency domain signals in logarithmic space, and then performing an alignment operation on all axial frequency domain signals based on a phase correlation algorithm. Here, the denoised axial frequency domain signals are signals denoised based on the axial frequency domain signals after the alignment operation.

[0099] Phase correlation algorithms can handle situations where the images are translated. The Fourier-Mellin (FMT) algorithm combined with the log-polar coordinate model is an extension of the Fourier phase correlation algorithm, and FMT can directly handle rotated and scaled images. Such frequency-domain based methods use all of the content information of the images for alignment.

[0100] Specifically, the steps of the phase correlation algorithm for performing the translation process are as follows:

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[0101] Based on the above-mentioned phase correlation algorithm, in step S103, all of the axial frequency domain signals in the logarithmic space are aligned based on the above-mentioned phase correlation algorithm to obtain all of the aligned axial frequency domain signals, and then noise-removed all of the aligned axial frequency domain signals to obtain noise-removed axial frequency domain signals.

[0102] In a possible embodiment of the present application, referring to FIG. 5, after step S103, denoising the axial frequency domain signal in logarithmic space to obtain a denoised axial frequency domain signal includes the following steps:

[0103] In step S031, a division threshold is specified.

[0104] The noise reduction method for decorrelation artifacts is as follows: When statistically analyzing the changes in A-scan signals, an intensity threshold is set to isolate the influence of noise signals on OCTA, and an intensity mask is generated to remove all signals with low S / N ratios, thereby reducing or eliminating errors caused by the random noise signals in the subsequent decorrelation calculation. In the present embodiment, the segmentation threshold is an intensity mask, and the segmentation threshold is typically set based on artificial experience. To further improve accuracy, in the present embodiment, determining the segmentation threshold includes extracting noise signals corresponding to all axial frequency domain signals based on a clustering algorithm, and determining the segmentation threshold based on the noise signals.

[0105] The clustering method finds clusters in the signal and finds and removes outliers in the signal to achieve the purpose of removing noise signals. In the embodiment of the present application, the signals that fall outside the set of clusters are noise signals.

[0106] The K-means algorithm is a traditional distance-based clustering algorithm that uses K as an input parameter and randomly selects K center points to ultimately divide n objects into K clusters, where members of the same cluster have high similarity and members of different clusters have high dissimilarity. Specifically, the clustering centers in the K-means clustering algorithm are identified by calculating the average values ​​of the attributes of all signal objects within a cluster, and the distribution of noise signal classes can be obtained by performing K-means clustering analysis of image signal intensities.

[0107] Specifically, the flow of extracting noise signals based on the K-means clustering algorithm includes step S11 (not shown), step S12 (not shown), step S13 (not shown), step S14 (not shown), and step S15 (not shown).

[0108] In step S11, K clustering centers are selected as current clustering centers using a farthest-first strategy (K is a natural number).

[0109] In step S12, all axial frequency domain signals are clustered according to the current clustering center, and each axial frequency domain signal is clustered into a cluster represented by the nearest clustering center.

[0110] In step S12, the mean value of each current cluster is calculated as the new clustering center.

[0111] In step S13, it is determined whether the center of the new clustering is the same as the center of the previous clustering. If they are the same, step S14 is executed; if they are not the same, the new clustering center is set as the current clustering center and steps S12 to S13 are repeated.

[0112] In step S14, the distance between any two clustering centers among all the new clustering centers is calculated.

[0113] In step S15, it is determined whether the distance between the centers of any two clusterings is greater than a set reference threshold. If so, the part of the cluster where the distance between the centers of any two clusterings is greater than the set reference threshold is selected, and the axial frequency domain signal corresponding to the selected cluster is treated as a noise signal. If not, information characterizing the absence of a noise signal is output.

[0114] After obtaining the noise signal, the noise signal is subjected to distribution statistical processing to obtain the mean and variance of the noise signal, and a dividing threshold is determined based on the mean and variance. A specific example of a method for selecting an optimal threshold is the maximum inter-class variance method. Since the method for determining a threshold based on the mean and variance is well known to those skilled in the art, a description thereof will not be repeated herein.

[0115] After the splitting threshold is identified, the axial frequency domain signal can be split based on the signal strength corresponding to the splitting threshold to separate valid targets from noise and improve the signal-to-noise ratio of the image.

[0116] In step S032, if the amplitude of the axial frequency domain signal is greater than or equal to the segmentation threshold, the denoised axial frequency domain signal is equal to the axial frequency domain signal in logarithmic space.

[0117] In step S033, if the amplitude of the axial frequency domain signal is less than the division threshold, the denoised axial frequency domain signal is equal to the division threshold.

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[0118] After obtaining the noise-removed axial frequency domain signal, the blood flow signal is extracted by differential analysis of adjacent B-scan images based on the principle of decorrelation algorithm, utilizing the change in signal correlation due to blood flow.

[0119] In a possible embodiment of the present application, referring to FIG. 6, in step S105, performing decorrelation calculations on the single-scale signals of each group to obtain single-scale blood flow signals includes determining first-order statistics, second-order statistics, and third-order statistics of the single-scale signals of each group, and performing decorrelation calculations based on the first-order statistics, second-order statistics, and third-order statistics to determine single-scale blood flow signals.

number

[0120] From a statistical point of view, the statistical characteristics of a normally distributed random variable (vector) can be completely described by first-order and second-order statistics. For example, for a Gaussian-distributed random vector, knowing its mathematical expectation and covariance matrix allows us to know its joint probability density function. For a Gaussian random process, knowing its mean value and autocorrelation function (or autocovariance function) allows us to know its probability structure, i.e., its overall statistical characteristics.

[0121] However, if a random variable or process does not follow a Gaussian distribution, its statistical characteristics cannot be fully expressed by first-order and second-order statistics. In other words, first-order and second-order statistics do not contain all the information, and higher-order statistics also contain a lot of useful information.

[0122] The main motivations for using higher-order statistics in signal processing include: 1) suppressing the effects of additive colored noise in unknown power spectra, 2) identifying non-minimum phase systems or reconstructing non-minimum phase signals, 3) extracting various information through Gaussian deviations, and 4) detecting and representing nonlinearities in signals and identifying nonlinear systems.

[0123] Therefore, higher-order statistics signal processing is a method for extracting useful information from the higher-order statistics of non-Gaussian signals, especially information that cannot be extracted from first-order or second-order statistics. From this perspective, higher-order statistics is an important method that complements random signal processing methods based on correlation functions and power spectra, and can provide a means for solving many signal processing problems that cannot be solved by second-order statistics.

[0124] Performing decorrelation calculations based on first, second and third order statistics to determine a single scale blood flow signal includes the following equation:

number

[0125] In a possible embodiment of the present application, in step S106, obtaining a multi-scale blood flow signal based on k groups of single-scale blood flow signals includes superimposing k groups of single-scale blood flow signals to obtain a multi-scale blood flow signal.

number

[0126] While the above-described embodiments describe the optical coherence tomography angiography method in terms of a method flow, the following embodiments describe the optical coherence tomography angiography apparatus 100 in terms of virtual modules or virtual units. Specifically, the following embodiments are described in detail.

[0127] An embodiment of the present application provides an optical coherence tomography angiography apparatus 100. Referring to FIG. 7, the optical coherence tomography angiography apparatus 100 includes: an acquisition module 1001 for acquiring time domain signals of a target area, which are N interference spectrum signals obtained by repeating A-scans N times on the target area; a scale conversion module 1002 for scaling each of the N interference spectrum signals based on K scales to obtain N×k scale-converted signals, wherein scaling any of the interference spectrum signals based on the k scales includes decomposing any of the interference spectrum signals based on each scale to obtain k scale-converted signals corresponding to any of the interference spectrum signals; a frequency domain transform module 1003 for Fourier transforming and logarithmizing each scaled signal to obtain an axial frequency domain signal in logarithmic space; a decorrelation module 1004 for dividing the N×k denoised axial frequency domain signals into k groups of single-scale signals based on the K scales, and performing decorrelation calculations on the single-scale signals of each group to obtain single-scale blood flow signals; a fusion module 1005 for obtaining a multi-scale blood flow signal based on the k groups of single-scale blood flow signals; and an imaging module 1006 for obtaining a blood flow image based on the multi-scale blood flow signal.

[0128] According to the above-described embodiment, the blood flow signal is a non-stationary signal having local fluctuations, and one interference spectrum signal is decomposed into multiple segments after one scale conversion. When subsequent decorrelation calculation is performed, the axial frequency domain signals corresponding to the interference spectrum signals of each segment corresponding to the same target region at different times can be compared. By decomposing and comparing the segments, the non-stationary fluctuation signal can be captured more accurately, and accurate blood flow signal extraction becomes possible.

[0129] In addition, a single interference spectrum signal can be transformed at multiple scales, with the number of decomposition segments corresponding to each scale being different. This means that the scales at which the segments are decomposed and compared are different, allowing for measurement of non-stationary signal fluctuations at different scales. Therefore, by simultaneously transforming the scale of a single interference spectrum signal and fusing multiple scales, the rate of misidentification at a single scale can be reduced, improving the extraction accuracy and processing efficiency of blood flow signals. Furthermore, because all points in a single interference spectrum signal are retained, more image details can be restored, improving the signal-to-noise ratio.

[0130] In a possible embodiment, when the interference spectrum signal corresponds to a sampling time period T, the scale conversion module 1002 decomposes any interference spectrum signal according to any scale to obtain a scale-converted signal, specifically: Identifying a decomposed signal corresponding to any scale, the decomposed signal being a signal in a sampling time period T, the decomposed signal including at least one time group, the time group including adjacent first and second time periods, two adjacent first time periods, or two adjacent second time periods; multiplying the interference spectrum signal corresponding to the first time period by the forward signal to obtain a first signal; multiplying the interference spectrum signal corresponding to the second time period by the reverse direction signal to obtain a second signal; and deriving a scale-converted signal based on at least one first signal and at least one second signal.

[0131] In a possible embodiment, the apparatus further comprises a denoising module for denoising the axial frequency domain signal in logarithmic space, and obtaining the denoised axial frequency domain signal comprises, in particular: used to identify a splitting threshold, If the amplitude of the axial frequency domain signal is greater than or equal to the segmentation threshold, the denoised axial frequency domain signal is equal to the axial frequency domain signal in logarithmic space. If the amplitude of the axial frequency domain signal is less than the segmentation threshold, the denoised axial frequency domain signal is equal to the segmentation threshold.

[0132] In a possible embodiment, the denoising module determines the splitting threshold by: Extracting noise signals corresponding to all axial frequency domain signals based on a clustering algorithm; and determining a division threshold based on the noise signal. In a possible embodiment, the decorrelation module 1004 performs decorrelation calculations on the single-scale signals of each group to obtain a single-scale blood flow signal, specifically: determining first, second and third order statistics of the single scale signals of each group; A decorrelation calculation is performed based on first, second and third order statistics to determine a single scale blood flow signal.

[0133] In a possible embodiment, the apparatus further comprises an alignment module, which, after Fourier transforming and logarithmizing each scaled signal to obtain an axial frequency domain signal in logarithmic space, specifically: Based on a phase correlation algorithm, all axial frequency domain signals are used for alignment manipulation.

[0134] Here, the noise-removed axial frequency domain signal is a signal that has been noise-removed based on the axial frequency domain signal after the alignment operation. In a possible embodiment, the device further comprises a dispersion compensation module, which in particular comprises: Before each of the N interference spectrum signals is scale-converted based on the K scales, each of the N interference spectrum signals is used to dispersion-compensate each of the interference spectrum signals among the time-domain signals.

[0135] The optical coherence tomography angiography apparatus according to the embodiments of the present application is applied to the above method embodiments, which will not be described again here.

[0136] An embodiment of the present application provides an electronic device. As shown in FIG. 8, the electronic device 1000 shown in FIG. 8 includes a processor 1001 and a memory 1003. Here, the processor 1001 and the memory 1003 are connected via, for example, a bus 1002. Optionally, the electronic device 1000 may further include a transceiver 1004. Note that in actual applications, the number of transceivers 1004 is not limited to one, and the configuration of the electronic device 1000 does not limit the embodiment of the present application.

[0137] The processor 1001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. It may embody or implement various exemplary logic boxes, modules, and circuits described in the disclosure of this application. The processor 1001 may also be a combination that implements computing functions, including a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0138] The bus 1002 may include a path for transferring information between the above-mentioned components. The bus 1002 may be, for example, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 1002 may be divided into an address bus, a data bus, a control bus, and the like. Note that, for convenience of illustration, only one thick line is shown in FIG. 8, but this does not mean that there is only one bus or only one type of bus.

[0139] Memory 1003 may be, but is not limited to, ROM (Read Only Memory) or other types of static storage capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage capable of storing information and instructions, EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disk storage devices (including compressed disks, laser disks, compact disks, digital versatile disks, Blu-ray disks, etc.), magnetic disk storage media or other magnetic storage devices, or other medium usable to carry or store desired program code in the form of instructions or data structures and accessible by a computer.

[0140] The memory 1003 is used to store and execute the application code of the technical solution of the present application, and the execution is controlled by the processor 1001. The processor 1001 executes the application code stored in the memory 1003 to implement the above-mentioned method embodiments.

[0141] Here, the electronic devices include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals such as in-vehicle navigation terminals, and fixed terminals such as digital TVs and desktop computers. They may also be servers, etc. Note that the electronic devices shown in FIG. 8 are merely examples and do not limit the functions or scope of use of the embodiments of the present disclosure.

[0142] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed on a computer, the computer can execute the corresponding content of the above-described method embodiment. Compared with the prior art, in this embodiment, when a user reserves a certain service type in a certain time slot, instead of directly determining whether the registration time slots corresponding to other service types are already occupied, the system determines whether the registration time slots associated with the actual occupied time slots for each of the other service types are already occupied based on the actual occupied time slots and the corresponding time slot occupancy thresholds. That is, if it is determined based on the actual occupied time slots that the registration time slots of the other service types associated therewith are not occupied, it is determined that the time slots can still be reserved by the user, thereby reducing fragmented time and wasting resources. Furthermore, the waiting time for medical personnel and patients is also reduced, improving consultation efficiency.

[0143] Note that although the steps in the flowcharts in the accompanying drawings are shown in order as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated otherwise in this specification, there is no strict order restriction on the execution of these steps, and they may be executed in other orders. Furthermore, at least some of the steps in the flowcharts in the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages may not necessarily be completed at the same time but may be executed at different times. The execution order is not necessarily sequential, and steps may be executed in order or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0144] The above description is only a part of the embodiments of the present application, and those skilled in the art can make various improvements and modifications without departing from the principles of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.

Claims

1. Acquiring time-domain signals of the target area, which are N interference spectrum signals obtained by repeating A-scans N times on the target area; a step of scaling each of the N interference spectrum signals based on k scales to obtain N×k scaled signals, wherein scaling any of the interference spectrum signals based on the k scales includes decomposing any of the interference spectrum signals based on each of the K scales to obtain k scaled signals corresponding to any of the interference spectrum signals, wherein one interference spectrum signal is decomposed into a plurality of segments after one scale conversion, and each of the obtained scaled signals retains all of the data points; Fourier transforming and logarithmizing each of the scaled signals to obtain axial frequency domain signals in logarithmic space, and denoising the axial frequency domain signals; dividing the N×k denoised axial frequency domain signals into k groups of single-scale signals based on the k scales, and performing decorrelation calculations on the single-scale signals of each group to obtain single-scale blood flow signals; obtaining a multi-scale blood flow signal based on the k groups of single-scale blood flow signals; obtaining a blood flow image based on the multi-scale blood flow signal; 1. An optical coherence tomography angiography method comprising:

2. The interference spectrum signal corresponds to a sampling time period T, and the step of decomposing any of the interference spectrum signals based on each of the K scales to obtain k scale-converted signals corresponding to any of the interference spectrum signals includes: The method includes an operation of decomposing any of the interference spectrum signals based on any of the k scales to obtain a scale-converted signal corresponding to any of the scales of the interference spectrum signals, the operation including: a step of identifying a decomposed signal corresponding to any one of the scales, the decomposed signal being a signal in the sampling time period T, the decomposed signal including at least one time group, the time group including adjacent first and second time periods, two adjacent first time periods, or two adjacent second time periods; multiplying any one of the interference spectrum signals corresponding to the first time period by a forward signal to obtain a first signal; multiplying any one of the interference spectrum signals corresponding to the second time period by a reverse direction signal to obtain a second signal; and obtaining a scale-converted signal in which any of the interference spectrum signals corresponds to any of the scales based on at least one of the first signals and at least one of the second signals.

2. The method of claim 1 .

3. Denoising the axial frequency domain signal in logarithmic space to obtain the denoised axial frequency domain signal comprises: identifying a splitting threshold; if the amplitude of the axial frequency domain signal is greater than or equal to the segmentation threshold, then the denoised axial frequency domain signal is equal to the axial frequency domain signal in logarithmic space; If the amplitude of the axial frequency domain signal is less than the segmentation threshold, the denoised axial frequency domain signal is equal to the segmentation threshold.

2. The method of claim 1 .

4. The step of identifying the division threshold includes: extracting noise signals corresponding to all of the axial frequency domain signals based on a clustering algorithm; and determining a division threshold based on the noise signal.

4. The method of claim 3.

5. The step of performing decorrelation calculations on the single-scale signals of each group to obtain a single-scale blood flow signal includes: determining first, second and third order statistics of the single scale signals for each of the groups; and performing decorrelation calculations based on the first, second and third order statistics to determine the single-scale blood flow signal.

2. The method of claim 1 .

6. Each of the scaled signals is Fourier transformed and logarithmized to obtain an axial frequency domain signal in logarithmic space, followed by: further comprising aligning all of the axial frequency domain signals based on a phase correlation algorithm.

2. The method of claim 1 .

7. and further comprising the step of dispersion compensating each of the interference spectrum signals of the time domain signal before scaling each of the interference spectrum signals of the N interference spectrum signals based on k scales.

2. The method of claim 1 .

8. an acquisition module for acquiring time-domain signals of the target area, the time-domain signals being N interference spectrum signals obtained by repeating A-scans N times on the target area; a scale conversion module for scaling each of the N interference spectrum signals based on k scales to obtain N×k scaled signals, wherein scaling any of the interference spectrum signals based on the k scales includes decomposing any of the interference spectrum signals based on each of the K scales to obtain k scaled signals corresponding to any of the interference spectrum signals, wherein one interference spectrum signal is decomposed into a plurality of segments after one scale conversion, and each of the obtained scaled signals retains all of the data points; a frequency domain transform module for Fourier transforming each of the scaled signals and taking the logarithm thereof to obtain an axial frequency domain signal in logarithmic space, and for denoising the axial frequency domain signal; a decorrelation module for dividing the N×k denoised axial frequency domain signals into k groups of single-scale signals based on the k scales, and performing decorrelation calculations on the single-scale signals of each group to obtain single-scale blood flow signals; a fusion module for obtaining a multi-scale blood flow signal based on the k groups of single-scale blood flow signals; an imaging module for obtaining a blood flow image based on the multi-scale blood flow signal; An optical coherence tomography angiography device characterized by:

9. one or more processors; a memory; one or more computer programs stored in memory and configured for execution by one or more processors; The one or more computer programs are used to execute the optical coherence tomography angiography method according to any one of claims 1 to 7. An electronic device characterized by:

10. A computer program is stored that is loaded by a processor and executes the optical coherence tomography angiography method according to any one of claims 1 to 7. A computer-readable storage medium comprising:

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