Microcirculation imaging method and device based on spectral complex value decorrelation OCT (Optical Coherence Tomography)
Through the spectral complex value decorrelation OCT method, using the difference in the degree of correlation between lymph and blood flow, efficient separation and imaging of lymph and blood vessels is achieved, and a high-resolution three-dimensional stereoscopic structure image of the microcirculation structure is generated, solving the problem of unclear imaging of microcirculation structure in the prior art and supporting disease diagnosis.
Patent Information
- Application Number
- CN202510197469.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-21
- Publication Date
- 2025-07-18
AI Technical Summary
The prior art cannot quickly and efficiently perform high-quality contrast on lymph and blood vessels alone, resulting in the imaging of the microcirculation structure that is not clear enough and cannot provide high-resolution three-dimensional structural information of the microcirculation structure.
Using a microcirculation imaging method based on segmented complex value decorrelation OCT, through multiple B-Scan scans, inverse Fourier transform and complex value decorrelation processing, the differences in the degree of correlation between lymph signal and blood flow are used to separate and highlight the images of lymph and blood vessels, combined with morphological filtering and image registration, high-resolution three-dimensional stereoscopic structure images are generated.
It realizes efficient separation and imaging of lymph and blood vessels, provides high-resolution three-dimensional stereoscopic structure images of microcirculation structures, and supports tracking and monitoring of microcirculation structure abnormalities and diagnosis of related diseases.
Smart Images
Figure CN120323920A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of optical imaging of microcirculation structures, and particularly to the field of microcirculation imaging methods and devices based on spectral complex-valued decorrelation OCT. Background Art
[0002] Microcirculation structures include blood vessels and lymphatic vessels with diameters less than 150 microns, and are direct participants in the material exchange of cells and tissues, playing an important role in the proper homeostasis regulation of the in-vivo biological system. Abnormal microcirculation structures are associated with various diseases, such as cancer, diabetes, neurological diseases, wounds, and inflammation. Understanding microcirculation structures or simultaneously angiographing microvessels and lymphatic vessels plays an important role in determining the causes of these diseases and developing potential treatment methods. In existing technologies, Photoplethysmography (PPG) only allows for point evaluation of microvessels, lacks the ability to measure lymph, and cannot provide three-dimensional stereoscopic structure information of microcirculation structures; Orthogonal Polarization Spectroscopy (OPS) can only image microvessels, is time-consuming, and can only provide semi-quantitative evaluation; Near-Infrared Spectroscopy (NIRS) can only detect microvessels and cannot provide depth information of microvessels; the results of Tissue Reflectance Spectrophotometry (TRS) are affected by melanin or cytochrome and cannot image lymphatic vessels.
[0003] Optical Coherence Tomography (OCT) has developed into a major optical imaging modality in biomedical optics and medicine. OCT performs high-resolution, cross-sectional, and three-dimensional volume imaging of the internal microstructure of biological tissues by measuring backscattered light. Due to its ability to perform real-time in-situ imaging of tissue pathology and having a resolution that is 1 to 2 orders of magnitude finer than traditional ultrasound, OCT has become a powerful imaging modality and is applied in multiple clinical specialties as well as basic science and biological research. OCT has previously been used for simultaneous imaging of microvessels and lymphatic vessels, such as an image intensity-based threshold segmentation method, but this method is greatly affected by noise; a lymphatic imaging method combined with deep learning, but requires a large number of high-quality pre-trained samples; an imaging method based on speckle decorrelation will mix lymph with microvessels and requires additional method algorithms for separation, and the mismatch between two B-Scan frames will have a greater impact on lymphatic imaging.
[0004] In view of the problem that existing microvascular and lymphatic vessel detection methods cannot achieve efficient imaging simultaneously, how to use OCT technology to separately angiograph lymph and blood vessels, so as to obtain a high-resolution three-dimensional structure of the microcirculation structure, and provide an objective basis for the tracking and monitoring of abnormal microcirculation structures and the diagnosis of related diseases has become a key issue. Summary of the Invention
[0005] In view of the problems existing in the above-mentioned prior art, the present invention provides a microcirculation imaging method and device based on spectral complex-valued decorrelation OCT, aiming to solve the problem that existing methods cannot perform high-quality angiography on lymph and blood vessels separately quickly and efficiently. To achieve the above object, the present invention provides the following technical solutions:
[0006] First, a microcirculation imaging method based on spectral complex-valued decorrelation OCT is provided, which includes:
[0007] S1: Perform multiple B-Scan scans on the preset x lateral direction of the sample to be measured to obtain N groups of interference spectral data I n (k, x), where N is the number of repeated scans, I n is the nth frame of interference spectral data, k is the wave number, and x is the lateral scan coordinate;
[0008] S2: Extract data from each group of the interference spectral data I n (k, x) along the spectral k direction to obtain multiple groups of first interference spectral data I n (k 2i-1 , x) composed of the odd-numbered data of each interference spectral data, and multiple groups of second interference spectral data I n (k 2i , x) composed of the even-numbered data of each interference spectral data;
[0009] S31: Perform an inverse fast Fourier transform on each first interference spectral data I n (k 2i-1 , x) of different frames along the k direction to obtain N groups of first complex-valued signals
[0010] S32: Perform an inverse fast Fourier transform on each second interference spectral data I n (k 2i , x) of different frames along the k direction to obtain N groups of second complex-valued signals
[0011] S41: Calculate the complex-valued decorrelation between the first complex-valued signals and of adjacent two frames to obtain (N - 1) groups of first decorrelation data;
[0012] S42: Perform complex-valued decorrelation on the second complex-valued signals of two adjacent frames and to obtain (N - 1) groups of second decorrelated data;
[0013] S5: Perform image registration based on the first decorrelated data and the second decorrelated data, and average the complex-valued decorrelated signals of the registered images to obtain an image of the convex microcirculation structure.
[0014] The microcirculation imaging method based on spectral complex-valued decorrelation OCT provided by the present invention performs complex-valued decorrelation on the interference spectral data obtained by performing B-Scan scanning on the same position of a living biological tissue at adjacent times, and obtains a complex-valued decorrelated image containing information of random noise and sample signals. According to the characteristic that the lymph signal and the blood flow within adjacent times in OCT have a low degree of correlation, the complex-valued decorrelated image is inversely obtained, so that both lymph and blood vessels can be highlighted in the complex-valued decorrelated image, while other fixed structures have a high degree of correlation, resulting in weak signal intensity in the decorrelated image, thereby obtaining an image that can only highlight both lymph and blood vessels at the same time, that is, an image of the convex microcirculation structure.
[0015] Further, the calculation formula for one-time complex-valued decorrelation is as follows:
[0016]
[0017] where CC(z,x) is the complex-valued decorrelated signal, P and Q are the window sizes in the z and x directions for single calculation, is the conjugate complex number of S n and is the conjugate complex number of S n+1 .
[0018] Further, step S5 specifically includes:
[0019] S501: Reconstruct images for each first decorrelated data and the corresponding second decorrelated data to obtain 2(N - 1) frames of decorrelated images;
[0020] S502: Calculate the mean value of all pixel points of each frame of decorrelated image to obtain 2(N - 1) groups of mean values;
[0021] S503: Determine whether the difference between the mean value and a preset mean value exceeds a preset threshold. If so, perform image registration until the difference between the mean value of this group of decorrelated images and the preset mean value is less than the preset threshold;
[0022] S504: Average the complex-valued decorrelated signals of the same pixel points of the 2(N - 1) frames of registered decorrelated images to obtain an image that simultaneously highlights the microcirculation structure and background noise.
[0023] Further, after step S5, it further includes:
[0024] S601: Calculate the complex-valued decorrelation for the first complex-valued signal and the second complex-valued signal of each frame of registered interference spectral data, obtaining N sets of third decorrelation data; and the second complex-valued signal Calculate the complex-valued decorrelation to obtain N sets of third decorrelation data;
[0025] S602: Average the complex-valued decorrelation signals of the same pixel points of the N sets of third decorrelation data to obtain an image highlighting lymph and background noise;
[0026] S603: Perform a morphological filtering operation on the image highlighting lymph and background noise to obtain an image highlighting only lymph signals.
[0027] Further, it further includes:
[0028] S7: Subtract the image highlighting microcirculation structure from the image highlighting lymph and background noise to obtain an image highlighting only microvascular structure.
[0029] Further, it further includes:
[0030] S7: Obtain the images highlighting only lymph signals in different y directions for reconstruction to obtain a three-dimensional image highlighting only lymph structure, or obtain the images highlighting only microvascular signals in different y directions for reconstruction to obtain a three-dimensional image highlighting only microvascular structure, or obtain the images highlighting only lymph signals and only microvascular signals in different y directions for reconstruction to obtain a three-dimensional image highlighting both lymph structure and microvascular signals.
[0031] On the other hand, the present invention also provides a microcirculation imaging device based on spectral splitting complex-valued decorrelation OCT, which includes:
[0032] Spectral data acquisition unit: Used to perform multiple B-Scan scans on the preset x horizontal direction of the sample to be measured, obtaining N sets of interference spectral data I n (k, x), where N is the number of repeated scans, I n is the nth frame of interference spectral data, k is the wave number, and x is the horizontal scan coordinate;
[0033] Spectral data extraction unit: Used to extract data for each set of the interference spectral data I n (k, x) along the spectral k direction, obtaining multiple sets of first interference spectral data I n (k 2i-1 , x) composed of the single-numbered data of each interference spectral data, and multiple sets of second interference spectral data I n (k 2i , x) composed of the double-numbered data of each interference spectral data;
[0034] Inverse Fourier transform unit: used to perform a fast inverse Fourier transform on each first interference spectrum data I n (k 2i-1 , x) in the k direction to obtain N groups of first complex-valued signals Or, perform a fast inverse Fourier transform on each second interference spectrum data I n (k 2i , x) in the k direction to obtain N groups of second complex-valued signals
[0035] Complex-valued decorrelation calculation unit: used to calculate the complex-valued decorrelation between the first complex-valued signals of two adjacent frames and to obtain (N - 1) groups of first decorrelation data, or calculate the complex-valued decorrelation between the second complex-valued signals of two adjacent frames and to obtain (N - 1) groups of second decorrelation data;
[0036] Image calibration unit: used to perform image registration based on the first decorrelation data and the second decorrelation data, and average the complex-valued decorrelation signals of the registered images to obtain an image of the convex microcirculation structure.
[0037] Furthermore, the microcirculation imaging device based on spectral complex-valued decorrelation OCT further includes:
[0038] Morphological filtering subunit: used to perform morphological filtering operations on the image containing prominent lymph and background noise to obtain an image with only prominent lymph signals.
[0039] Furthermore, the microcirculation imaging device based on spectral complex-valued decorrelation OCT further includes:
[0040] Image subtraction unit: used to subtract the image of the convex microcirculation structure from the image containing prominent lymph and background noise to obtain an image with only the convex microvascular structure.
[0041] Furthermore, the microcirculation imaging device based on spectral complex-valued decorrelation OCT further includes:
[0042] Three-dimensional spatial image reconstruction unit: used to reconstruct the images with only prominent lymph signals in different y directions to obtain a three-dimensional image with only prominent lymph structures, or reconstruct the images with only convex microvascular signals in different y directions to obtain a three-dimensional image with only convex microvascular structures, or reconstruct the images with only prominent lymph signals and only convex microvascular signals in different y directions to obtain a three-dimensional image with both prominent lymph structures and convex microvascular signals.
[0043] In summary, the present invention has the following beneficial effects compared with the prior art:
[0044] 1. The microcirculation imaging method based on split-spectrum complex-valued decorrelation OCT provided by the present invention uses a split-spectrum reconstruction method to obtain an image highlighting lymphatics from a frame of B-Scan image data, and can quickly obtain a high-resolution three-dimensional stereoscopic structure image of lymphatic vessels.
[0045] 2. The microcirculation imaging method based on split-spectrum complex-valued decorrelation OCT provided by the present invention uses two frames of B-Scan image data adjacent in time to obtain an image highlighting both lymphatics and blood vessels. Combining with the previously obtained lymphatic image alone, it can quickly obtain a high-resolution three-dimensional stereoscopic structure image of microvessels.
[0046] 3. The microcirculation imaging method based on split-spectrum complex-valued decorrelation OCT provided by the present invention uses the previously obtained images highlighting lymphatics or blood vessels alone. Combining the two can simultaneously obtain high-resolution three-dimensional stereoscopic structure images of microvessels and lymphatic vessels, providing an objective basis for the imaging and diagnosis of microcirculation structures.
[0047] For better understanding and implementation, the present invention will be described in detail below with reference to the accompanying drawings. Description of the Drawings
[0048] Figure 1 It is a schematic diagram of data distribution after a single B-Scan of the biological sample provided by the present invention;
[0049] Figure 2 It is a schematic diagram of the initial B-Scan cross-section in an embodiment;
[0050] Figure 3 It is a structural block diagram of the microcirculation imaging device based on split-spectrum complex-valued decorrelation OCT provided by the present invention;
[0051] Figure 4 For Figure 3 It is the execution flowchart of the microcirculation imaging device of split-spectrum complex-valued decorrelation OCT shown;
[0052] Figure 5 For Figure 2 The images obtained by performing inverse Fourier transform on the two frames of interference spectrum data after split-spectrum of the B-Scan cross-section data shown, where (a) is the cross-section image corresponding to the first complex-valued signal and (b) is the cross-section image corresponding to the second complex-valued signal ;
[0053] Figure 6 For Figure 2 The image obtained after complex-valued decorrelation processing of the cross-section shown, highlighting the microcirculation structure and background noise;
[0054] Figure 7 for Figure 2 The scan section shown highlights only the lymph and background noise;
[0055] Figure 8 for Figure 2 The scan sections shown highlight images of only microvascular structures;
[0056] Figure 9 This is a plane projection image of the sample containing only the three-dimensional microvascular structure;
[0057] Figure 10 This is a planar projection of the sample containing only three-dimensional lymph;
[0058] Figure 11 This is a planar projection of the sample that contains both lymphatic and three-dimensional microvascular structures. DETAILED DESCRIPTION
[0059] The correlation coefficient is used to express the similarity between two sets of data, and its range is from 0 to 1. The closer the correlation coefficient is to 1, the more similar the two sets of data are. The decorrelation coefficient is 1 minus the correlation coefficient, which indicates how dissimilar the two sets of data are.
[0060] After research, it is found that the Oct (optical coherence tomography) signal can be regarded as the signal of the sample itself plus a random noise. This random signal is caused by factors such as the detector itself and the instability of the light source. The intensity of the noise signal is very small and completely dissimilar. Therefore, the decorrelation degree of the noise at different positions in an image is close to 1. The position of the sample signal can be determined according to the size of the decorrelation coefficient.
[0061] When performing OCT imaging of the microcirculatory system, the signal at the lymphatic position of the microcirculatory structure is relatively weak, close to random noise, and its decorrelation coefficient is very high. In the same OCT spectral data, the signal of the microvessel is strong and its decorrelation coefficient is low. Therefore, it is impossible to label the microvessels and lymph at the same time with a single scan image. However, at different times at the same position, the blood flow in the microvessels changes all the time, so the complex-valued decorrelation coefficient of the blood area at the same position at different times is also high.
[0062] Based on the above principle, the present invention firstly Figure 1 The B-Scan scanning method shown in the figure is used to obtain the original data of living biological tissues, and the sample surface is established as follows Figure 1 Each B-Scan scan simultaneously acquires interference data of multiple points in the same y-axis direction, and obtains the interference pattern of a cross section of the sample and the intensity information I(k,x) of each point of the interference pattern, where k is the wave number of light and its value is It is related to the wavelength of the light wave used for OCT imaging. At this time, each frame of the image obtained by B-Scan scanning can be expressed as Figure 1 The interference spectral data I shown, which is composed of multiple I(k,x) arranged in an orderly manner n (k,x). During one imaging process, an initial B-Scan cross-sectional image is as shown in Figure 2 .
[0063] Please refer to Figure 3 . Figure 3 As shown in, it is a structural block diagram of a microcirculation imaging device based on spectral splitting complex-valued decorrelation OCT provided by the present invention, which includes: a spectral data acquisition unit 1, a spectral data extraction unit 2, an inverse Fourier transform unit 3, a complex-valued decorrelation calculation unit 4, and an image calibration unit 5. Please refer to Figure 4 . Figure 4 As shown in, it is a flowchart of a microcirculation imaging method based on spectral splitting complex-valued decorrelation OCT of the present invention. Combined with Figure 4 , it illustrates the functions of each component of the microcirculation imaging device based on spectral splitting complex-valued decorrelation OCT. Specifically
[0064] The spectral data acquisition unit 1 is used to execute step S1: perform multiple B-Scan scans on the preset x lateral direction of the sample to be measured, and obtain N groups of interference spectral data I n (k,x), where N is the number of repeated scans, I n is the nth frame of interference spectral data, k is the wave number, and x is the lateral scan coordinate
[0065] What the present invention solves is the biological imaging of living biological tissues. In order to prevent the living body from shaking during the imaging process, resulting in misalignment of B-Scan data and motion artifacts in the final decorrelated image, repeated scanning is performed in the data acquisition stage for subsequent image registration. Therefore, N frames of interference spectral images are obtained, and each frame is the interference spectral data scanned at different times. In the embodiment of the present invention, the repeated scanning is performed four times, so N is taken as 4
[0066] The spectral data extraction unit 2 is used to execute step S2: perform data extraction on each group of the interference spectral data I n (k,x) along the spectral k direction, and obtain multiple groups of first interference spectral data I n (k 2i-1 ,x) composed of the single-numbered data of each interference spectral data, and multiple groups of second interference spectral data I n (k 2i ,x) composed of the double-numbered data of each interference spectral data
[0067] Extract the data with k = 2i and k = 2i - 1 from each set of interference spectral data, and then perform data reconstruction to obtain the first interference spectral data I n (k 2i-1 , x), and the second interference spectral data I n (k 2i , x).
[0068] The inverse Fourier transform unit 3 is used to perform step S31 and step S32, where step S31: perform a fast inverse Fourier transform on each first interference spectral data I n (k 2i-1 , x) along the k direction to obtain N sets of first complex-valued signals
[0069]
[0070] And step S32: perform a fast inverse Fourier transform on each second interference spectral data I n (k 2i , x) along the k direction to obtain N sets of second complex-valued signals
[0071] Figure 2 The image after B-Scan cross-sectional spectral division shown is as Figure 5 shown, where (a) is the cross-sectional image corresponding to , and (b) is the cross-sectional image corresponding to . For each frame of interference spectral data, an OCT complex-valued signal distributed with depth z is obtained by performing an inverse Fourier transform along the k direction.
[0072] The complex-valued decorrelation calculation unit 4 is used to perform step S41 and step S42, where step S41: calculate the complex-valued decorrelation between the first complex-valued signals and of two adjacent frames to obtain 2(N - 1) sets of first decorrelation data.
[0073] And step S42: calculate the complex-valued decorrelation between the second complex-valued signals and of two adjacent frames to obtain 2(N - 1) sets of second decorrelation data.
[0074] Specifically, the calculation formula for complex-valued decorrelation is as follows:[[]]
[0075]
[0076] where CC(z, x) is the complex-valued decorrelation signal, P and Q are the window sizes in the z and x directions for a single calculation, is the conjugate complex number of S n is Sn+1 The conjugate complex number. When calculating, a sliding window calculation method is adopted. P and Q are the window sizes in the z and x directions for a single calculation, usually taking P = Q = 2. When calculating and for the complex-valued decorrelation between them, S in the formula n corresponds to S n+1 corresponds to Calculate and for the complex-valued decorrelation between them, S in the formula n corresponds to S n+1 corresponds to And so on.
[0077] The image calibration unit 5 is used to execute step S5: perform image registration based on the first decorrelation data and the second decorrelation data, and average the complex-valued decorrelation signals of the registered images to obtain an image of the convex microcirculation structure.
[0078] Specifically, step S5 includes:
[0079] S501: Perform graphic reconstruction on each first decorrelation data and its corresponding second decorrelation data to obtain 2(N - 1) frames of decorrelated images.
[0080] S502: Calculate the mean value of all pixel points of each frame of decorrelated image to obtain 2(N - 1) groups of mean values.
[0081] Average all the obtained complex-valued decorrelation results. The calculation formula is:
[0082]
[0083] N z *N x represents the data volume size in CC j (z, x), and CC j (z, x) represents the pixel value corresponding to the serial number (z, x) of each frame of decorrelated image. In the present invention, the maximum value of j is 6.
[0084] S503: According to whether the difference between the mean value and a preset mean value exceeds a preset threshold, if so, perform image registration until the difference between the mean value of this group of decorrelated images and a preset mean value is less than the preset threshold.
[0085] At the same cross-sectional position are similar. When a certain mean value is significantly greater than other mean values, it is determined that the image is mismatched. Then, for the two frames of images S n and S n+1Perform image registration until the judgment criterion is met; after meeting the judgment criterion, re-calculate the complex value de-correlation for the two registered frames; the complex value de-correlation results that do not meet the judgment criterion will be directly discarded. The judgment criterion of the present invention is:
[0086]
[0087] That is, image misregistration. Image registration is a method that uses a certain method and is based on a certain evaluation criterion to optimally map one or more pictures (locally) to the target picture. Usually, it maps the coordinates of one picture (source image, Moving Image) to another image (target image, Fixed Image) to obtain the registered image pair (MovedImage), that is, by comparison, the transformation coefficient is obtained, and the coordinates of the offset image are transformed so that it matches the target image after transformation.
[0088] S504: Average the complex value de-correlation signals of the same pixel points of the 2(N - 1) de-correlated images after registration to obtain an image that simultaneously highlights the microcirculation structure and background noise.
[0089] The final complex value de-correlated image is the average of the complex value de-correlation results that meet the preset mean value, and its calculation method is:
[0090]
[0091] That is, the pixel value corresponding to the (z, x) position in the final image of the convex microcirculation structure is the mean value of the corresponding (z, x) positions in all CC j (z, x). The obtained image A of the convex microcirculation structure and background noise is as Figure 6 shown.
[0092] The microcirculation imaging method based on spectral complex value de-correlation OCT provided by the present invention performs complex value de-correlation on the interference spectral data obtained by B-Scan scanning of the same position of a living biological tissue at adjacent times, and obtains a complex value de-correlated image containing information of random noise and sample signals. According to the characteristic that the lymph signal in OCT has a low correlation with blood flow within adjacent times, the complex value de-correlated image is inversely obtained, so that both lymph and blood vessels can be highlighted in the complex value de-correlated image, while other fixed structures have a high correlation degree, resulting in weak signal intensity in the de-correlated image, thereby obtaining an image that can only highlight lymph and blood vessels simultaneously, that is, an image of the convex microcirculation structure.
[0093] In addition, sometimes during the diagnosis of related diseases, it is necessary to separately analyze the microvessels or lymph of the microcirculation structure. Therefore, after step S5, the present invention further includes:
[0094] S601: Calculate the complex-valued decorrelation for the first complex-valued signal and the second complex-valued signal of each frame of registered interferometric spectral data, obtaining N groups of third decorrelation data. and the second complex-valued signal Input the corresponding first complex-valued signal
[0095] and the second complex-valued signal into the complex-valued decorrelation calculation unit, and the corresponding N groups of third decorrelation data can be obtained. For the two complex-valued signals split by the spectral reconstruction technology, the signals except for the noise are very similar. Based on this characteristic, calculate the complex-valued decorrelation signal for the two complex-valued signals of the same frame. At this time, the correlation coefficient of the blood vessels between the two frames of complex-valued signals is approximately 1, and the complex-valued decorrelation signal is approximately 0. At this time, for each group of the third decorrelation data, only the complex-valued decorrelation signal of the lymph and random noise signals is approximately 1.
[0096] S602: Average the complex-valued decorrelation signals of the same pixel point of the N groups of third decorrelation data to obtain an image highlighting lymph and background noise.
[0097] The complex-valued decorrelation image obtained by performing complex-valued decorrelation according to the two spectral signals of the same frame is used as the image B highlighting lymph, which only retains lymph and background noise, and the result image is as Figure 7 shown.
[0098] S603: Perform morphological filtering operations on the image highlighting lymph and background noise to obtain an image highlighting only lymph signals.
[0099] In the cross-sectional view, the morphology of lymphatic vessels is similar to an oval or a circle, while the background does not have a fixed morphology. Based on the morphological characteristics of lymphatic vessels, morphological filtering is performed, and most of the background noise can be discarded. Then, image masking operations can be performed to remove most of the background noise signals.
[0100] Further, S7: Subtract the image highlighting lymph and background noise from the image highlighting microcirculation structure to obtain an image highlighting only microvascular structure.
[0101] Subtract the weighted complex-valued decorrelation image CC A from the complex-valued decorrelation image CC B to obtain the vascular image C of the living biological tissue alone, that is, CC C = CC A - m·CC B , where m = 2 in this example. The image retaining only vascular information is as Figure 8 shown.
[0102] Characterize the separate lymph image and vascular image in different ways and present them in the same picture, and the simultaneous imaging of blood vessels and lymph can be achieved.
[0103] In addition, by repeating the above operations S1 to S7 at different positions in the y direction, images that only highlight lymphatic signals and images that only highlight microvascular signals in different y directions can be obtained.
[0104] Furthermore, reconstruct the images that only highlight lymphatic signals in different y directions to obtain a three-dimensional image that only highlights lymphatic structures, or reconstruct the images that only highlight microvascular signals in different y directions to obtain a three-dimensional image that only highlights microvascular structures, or reconstruct the images that only highlight lymphatic signals and only highlight microvascular signals in different y directions to obtain a three-dimensional image that simultaneously highlights lymphatic structures and microvascular signals.
[0105] In the present invention, the projection diagram of the three-dimensional image that only highlights microvascular structures generated is as Figure 9 shown, and the projection diagram of the three-dimensional image that only highlights lymphatic structures is as Figure 10 shown, and the projection diagram of the three-dimensional image that simultaneously highlights lymphatic structures and microvascular signals is as Figure 11 shown.
[0106] Through the above processing, a three-dimensional lymphatic image or a three-dimensional vascular image of a living biological tissue that only highlights lymphatic structures, or a three-dimensional image in which blood vessels and lymph coexist can be obtained, thereby achieving the rapid and efficient provision of three-dimensional lymphatic and microvascular structure information.
[0107] In summary, compared with the prior art, the beneficial effects of the present invention include the following points:
[0108] 1. The microcirculation imaging method based on spectral decomposition complex-valued decorrelation OCT provided by the present invention uses a spectral reconstruction method to obtain an image that highlights lymphatics from a frame of B-Scan image data, and can quickly obtain a high-resolution three-dimensional stereoscopic structure image of lymphatic vessels.
[0109] 2. The microcirculation imaging method based on spectral decomposition complex-valued decorrelation OCT provided by the present invention uses two adjacent frames of B-Scan image data in time to obtain an image that simultaneously highlights lymphatics and blood vessels, and combines with the previously obtained lymphatic image alone to quickly obtain a high-resolution three-dimensional stereoscopic structure image of microvessels.
[0110] 3. The microcirculation imaging method based on spectral decomposition complex-valued decorrelation OCT provided by the present invention uses the previously obtained images that only highlight lymphatics or blood vessels alone, and the combination of the two can simultaneously obtain a high-resolution three-dimensional stereoscopic structure image of microvessels and lymphatic vessels, providing an objective basis for the imaging and diagnosis of microcirculation structures.
[0111] Based on the same inventive concept described above, the present invention also provides an electronic device, which can be a server, a desktop computing device, or a mobile computing device (such as a laptop computing device, a handheld computing device, a tablet computer, a netbook, etc.) and other terminal devices. The device includes one or more processors and a memory, where the processor is used to execute a program to implement the above-mentioned microcirculation imaging method based on spectral complex value decorrelation OCT; the memory is used to store a computer program executable by the processor.
[0112] Based on the same inventive concept, the present invention also provides a computer-readable storage medium, corresponding to the foregoing embodiments of the microcirculation imaging method based on spectral complex value decorrelation OCT. The computer-readable storage medium stores a computer program thereon, and when the program is executed by a processor, it implements the steps recorded in any of the above embodiments.
[0113] The present invention may be in the form of a computer program product implemented on one or more storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing program codes therein. Computer-usable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information may be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include but are not limited to: phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information accessible by a computing device.
[0114] The above-described embodiments merely represent several implementation manners of the present invention, and their descriptions are relatively specific and detailed, but should not be construed as limiting the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the inventive concept of the present invention, several modifications and improvements can still be made, and the present invention also intends to include these modifications and variations.
Claims
1. A microcirculation imaging method based on spectral complex value decorrelation OCT, characterized in that Including: S1: Perform multiple B-Scan scans on the preset x horizontal direction of the sample to be measured, and obtain N sets of interference spectral data I n (k, x), where N is the number of repeated scans, and I n is the nth frame of interference spectral data, k is the wave number, and x is the horizontal scan coordinate; S2: Extract data from each set of the interference spectrum data I n (k,x) along the spectral k direction to obtain multiple sets of first interference spectrum data I n (k 2i-1 ,x) composed of the odd-numbered data of each interference spectrum data, and multiple sets of second interference spectrum data I n (k 2i ,x); S31: Perform an inverse fast Fourier transform on each first interference spectrum data I n (k 2i-1 , x) along the k direction to obtain N groups of first complex-valued signals S32: For each second interference spectrum data I of different frames n (k 2i , x), perform an inverse fast Fourier transform along the k direction to obtain N groups of second complex-valued signals S41: Perform complex-valued decorrelation on the first complex-valued signals of two adjacent frames and to obtain (N - 1) groups of first decorrelated data; S42: Perform complex-valued decorrelation on the second complex-valued signals of two adjacent frames and to obtain (N - 1) groups of second decorrelated data S5: Perform image registration based on the first decorrelated data and the second decorrelated data, and average the complex-valued decorrelated signals of the registered images to obtain an image of the convex microcirculation structure.
2. The microcirculation imaging method based on spectral complex value decorrelation OCT according to claim 1, wherein The calculation formula for one-time complex-valued decorrelation is as follows: Among them, CC(z, x) is a complex-valued decorrelated signal, and P and Q are the window sizes in the z and x directions for single calculation. is S n the conjugate complex number of is S n+1 the conjugate complex number of.
3. The microcirculation imaging method based on split-spectrum complex-valued decorrelation OCT according to claim 2, wherein Step S5 specifically includes: S501: Reconstruct images for each first decorrelated data and the corresponding second decorrelated data to obtain 2(N - 1) frames of decorrelated images; S502: Calculate the mean value of all pixel points of each frame of decorrelated image to obtain 2(N - 1) groups of mean values; S503: Determine whether the difference between the mean value and a preset mean value exceeds a preset threshold. If so, perform image registration until the difference between the mean value of this group of decorrelated images and the preset mean value is less than the preset threshold; S504: Average the complex-valued decorrelated signals of the same pixel points of the 2(N - 1) frames of decorrelated images after registration to obtain an image that simultaneously shows the convex microcirculation structure and background noise.
4. The microcirculation imaging method based on split-spectrum complex-valued decorrelation OCT according to claim 3, wherein After step S5, it further includes: S601: Calculate the complex-valued decorrelation for the first complex-valued signal and the second complex-valued signal of each frame of registered interferometric spectral data to obtain N sets of third decorrelation data; and the second complex-valued signal calculate the complex-valued decorrelation to obtain N sets of third decorrelation data; S602: Average the complex-valued decorrelated signals of the same pixel points of N groups of third decorrelated data to obtain an image that shows lymph nodes and background noise; S603: Perform a morphological filtering operation on the image that shows lymph nodes and background noise to obtain an image that only shows lymph node signals.
5. The microcirculation imaging method based on split-spectrum complex-valued decorrelation OCT according to claim 4, wherein It also includes: S7: Subtract the image of the convex microcirculation structure from the image that shows lymph nodes and background noise to obtain an image that only shows the convex microvascular structure.
6. The microcirculation imaging method based on split-spectrum complex-valued decorrelation OCT according to claim 5, wherein It also includes: S7: Reconstruct the images that only show lymph node signals in different y directions to obtain a three-dimensional image that only shows the lymph node structure, or reconstruct the images that only show convex microvascular signals in different y directions to obtain a three-dimensional image that only shows the convex microvascular structure, or reconstruct the images that only show lymph node signals and the images that only show convex microvascular signals in different y directions to obtain a three-dimensional image that simultaneously shows the lymph node structure and convex microvascular signals.
7. A microcirculation imaging device based on spectral complex value decorrelation OCT, characterized in that, Including: Spectral data acquisition unit: It is used to perform multiple B-Scan scans on the preset x horizontal direction of the sample to be measured, and obtain N groups of interference spectral data I n (k, x), where N is the number of repeated scans, and I n is the nth frame of interference spectral data, k is the wave number, and x is the horizontal scan coordinate; Spectral data extraction unit: used to perform data extraction on each group of the interference spectral data I n (k,x) along the spectral k direction to obtain multiple groups of first interference spectral data I composed of the odd-numbered data of each interference spectral data n (k 2i-1 ,x), and multiple groups of second interference spectral data I composed of the even-numbered data of each interference spectral data n (k 2i ,x); Inverse Fourier transform unit: used to perform an inverse fast Fourier transform on each first interference spectrum data I n (k 2i-1 , x) in the k direction to obtain N sets of first complex-valued signals Or, perform an inverse fast Fourier transform on each second interference spectrum data I n (k 2i , x) in the k direction to obtain N sets of second complex-valued signals Complex value decorrelation calculation unit: used to perform complex value decorrelation calculation on the first complex value signal of two adjacent frames and to obtain (N - 1) groups of first decorrelated data, or to perform complex value decorrelation calculation on the second complex value signal of two adjacent frames and to obtain (N - 1) groups of second decorrelated data; Image calibration unit: Used to perform image registration based on the first decorrelated data and the second decorrelated data, and average the complex-valued decorrelated signals of the registered images to obtain an image of the convex microcirculation structure.
8. The microcirculation imaging device based on spectral complex value decorrelation OCT according to claim 7, characterized in that, It also includes: Morphological filtering sub-unit: Used to perform a morphological filtering operation on the image that shows lymph nodes and background noise to obtain an image that only shows lymph node signals.
9. The microcirculation imaging device based on spectral complex value decorrelation OCT according to claim 8, wherein It also includes: Image subtraction unit: Used to subtract the image of the convex microcirculation structure from the image that shows lymph nodes and background noise to obtain an image that only shows the convex microvascular structure.
10. The microcirculation imaging device based on spectral complex value decorrelation OCT according to claim 9, characterized in that, It also includes: Three-dimensional space image reconstruction unit: Used to reconstruct the images that only show lymph node signals in different y directions to obtain a three-dimensional image that only shows the lymph node structure, or reconstruct the images that only show convex microvascular signals in different y directions to obtain a three-dimensional image that only shows the convex microvascular structure, or reconstruct the images that only show lymph node signals and the images that only show convex microvascular signals in different y directions to obtain a three-dimensional image that simultaneously shows the lymph node structure and convex microvascular signals.