Artificial artery and vein arm vascular imaging methods and related devices

Abstract

This application relates to a method and related apparatus for vascular imaging of an artificial artery and vein arm. The method includes the following steps: acquiring superficial near-infrared images and deep near-infrared images; layering the deep near-infrared images based on the superficial near-infrared images to obtain venous and arterial image information; acquiring pulsed Doppler ultrasound images and obtaining the relative positional relationship between the venous and arterial vessels based on the pulsed Doppler ultrasound images; calculating theoretical arterial image information based on the relative positional relationship from the venous image information; matching the theoretical arterial image and the arterial image, and completing the arterial image based on the matching result. This application has the advantage of improving the imaging effect of vascular imaging instruments.

Description

Technical Field

[0001] This application relates to the field of medical imaging, and in particular to a method and device for imaging blood vessels in an artificial artery and vein arm. Background Art

[0002] A vascular imaging system is a medical device primarily used to display the vascular structures on or near the surface of a patient's skin in real time and with clear imaging technology. This device is extremely helpful to medical personnel, especially when procedures such as venipuncture, catheterization, and blood draws are required.

[0003] Angiography machines typically operate on the principle of near-infrared (NIR) light technology. In this technology, the machine emits near-infrared light of a specific wavelength, which is then captured by a high-resolution imaging system. Because hemoglobin has a strong absorption capacity for near-infrared light, the absorption and scattering characteristics of blood within blood vessels differ significantly from those of surrounding tissues, allowing blood vessels to be clearly displayed in the image.

[0004] An arteriovenous fistula (AVF) is an artificial connection established between an artery and a vein, typically used in hemodialysis patients. Clinically, doctors need to observe the location of the fistula, the course of the blood vessel, and blood flow. However, vascular imaging equipment is primarily used to visualize vascular structures on or near the skin surface. For deeper vessels, especially arteries, near-infrared light does not easily penetrate to their depth, resulting in shallow, fine, or discontinuous arterial images that are insufficient for clinical requirements. Summary of the Invention

[0005] To improve the imaging effect of vascular imaging instruments, this application provides a method and related device for imaging vascular vessels in an artificial artery and vein arm.

[0006] Firstly, this application provides a method for imaging blood vessels in an artificial artery and vein arm, which employs the following technical solution:

[0007] A method for imaging blood vessels in an artificial artery and vein arm includes the following steps:

[0008] The device acquires shallow near-infrared images and deep near-infrared images. The shallow near-infrared image includes venous vessel image information, while the deep near-infrared image includes both venous and arterial vessel image information. The shallow near-infrared image is generated by the device based on the reflected light from the target object using near-infrared light of a first intensity emitted by the device itself. The deep near-infrared image is generated by the device based on the reflected light from the target object using near-infrared light of a second intensity emitted by the device itself, where the second intensity is greater than the first intensity.

[0009] Based on shallow near-infrared images, deep near-infrared images are layered to obtain venous and arterial image information;

[0010] Acquire pulsed Doppler ultrasound images and obtain the relative positional relationship between veins and arteries based on the pulsed Doppler ultrasound images;

[0011] Theoretical arterial image information is calculated from venous image information based on relative positional relationships.

[0012] Match theoretical arterial images with actual arterial images, and complete the arterial images based on the matching results.

[0013] By employing the above technical solution, shallow near-infrared images, due to their low light intensity, primarily capture images of veins. However, with increased near-infrared light irradiation power, deep near-infrared images can contain clearer vein images and somewhat blurry and incomplete arterial images, necessitating processing of the arterial images. Pulse Doppler ultrasound can accurately acquire images of both veins and arteries; however, it has drawbacks. While offering a large imaging range, it requires the probe to maintain a specific angle with the blood flow direction, and its temporal resolution is relatively low, potentially failing to capture rapidly changing blood flow velocity and direction in real time. Therefore, it cannot compare to near-infrared images in this respect. This solution utilizes pulse Doppler ultrasound images acquired at appropriate locations to calculate the relative positions of veins and arteries. Then, using this relative positional relationship and definitive real-time vein image information, it calculates the real-time theoretical arterial image information. Because the acquisition position of near-infrared images is prone to significant variation—meaning they are not acquired from the same location as pulsed Doppler ultrasound images—the relative positional relationships may deviate due to this difference. Therefore, the theoretical arterial image will actually differ from the actual image. To address this, the theoretical and actual arterial images can be matched, and the matching portions in the arterial image can be supplemented to obtain a complete arterial image. Since the arterial and venous images are acquired at the same location and time, the difference is minimal, meeting clinical requirements. Similarly, this technique can simultaneously and clearly image arteriovenous fistulas located in both deep and superficial layers.

[0014] Optionally, the step of layering deep near-infrared images based on shallow near-infrared images to obtain venous and arterial image information includes:

[0015] Image alignment between shallow near-infrared images and deep near-infrared images;

[0016] Image difference is performed on deep near-infrared images using shallow near-infrared images;

[0017] Thresholding and noise reduction are performed on the differential deep near-infrared image;

[0018] The processed differential images are analyzed to obtain venous and arterial image information.

[0019] By employing the above technical solution, the shallow near-infrared image is spatially aligned with the deep near-infrared image. If they are not aligned, methods such as feature point matching (e.g., SIFT, SURF) and affine transformation are used to align them. The aligned shallow near-infrared image is then subtracted pixel-by-pixel from the deep near-infrared image, resulting in a difference image containing content absent in the shallow near-infrared image from the deep near-infrared image. Thresholding is applied to the difference image to more easily distinguish content absent in the shallow near-infrared image from the deep near-infrared image; the appropriate threshold is selected and adjusted according to the actual image conditions and requirements. Since the difference image may contain noise, denoising methods such as median filtering and Gaussian filtering can be used to process the difference image to obtain clearer results. The processed difference image is then analyzed to extract content absent in the shallow near-infrared image from the deep near-infrared image. This can be achieved through morphological operations, contour detection, and region growing.

[0020] Optionally, the step of analyzing the processed differential images to obtain venous and arterial image information includes:

[0021] The processed differential image is used as the first image, and the result of differentiating and processing the first image with a deep near-infrared image is used as the second image. The blood vessel centerline is extracted from the first image and the second image.

[0022] Detect the branch points of blood vessels along the center line of the blood vessels in the first and second images;

[0023] The blood vessel centerlines in the first and second images are segmented.

[0024] Vascular feature extraction is performed to obtain vascular feature information of the first image and the second image, which are used as arterial vascular image information and venous vascular image information, respectively. The vascular feature information includes length information, curvature information and width information.

[0025] By employing the above technical solution, the centerline of the blood vessel image is extracted, and branch points on the centerline are detected. Morphological operations such as endpoint detection and branch point detection can be used to find these points. Based on the branch points, the blood vessel centerline is divided into several segments, each representing the blood vessel path between two branch points. For each blood vessel path segment, its features, such as length, curvature, and width, are extracted. These features can be used to describe the relative positional relationships between blood vessels. Based on the extracted features, a feature matrix is ​​generated, where each row of the matrix represents a feature vector of a blood vessel path. This feature matrix can represent the relative positional relationships between blood vessels.

[0026] Optionally, the step of acquiring pulsed Doppler ultrasound images and obtaining the relative positional relationship between veins and arteries based on the pulsed Doppler ultrasound images includes:

[0027] Acquire pulsed Doppler ultrasound images and mark the venous, arterial, and stoma portions on the pulsed Doppler ultrasound images;

[0028] The centerline of blood vessels was extracted based on pulsed Doppler ultrasound images and correlated with veins, arteries, and stoma tubes.

[0029] A relative position feature matrix is ​​generated based on the centerline of each blood vessel.

[0030] Optionally, the pulsed Doppler ultrasound probe is positioned from the proximal end of the target to the distal end, or from the distal end of the target to the proximal end.

[0031] By employing the above technical solution, the pulsed Doppler ultrasound device emits short-duration pulsed ultrasound waves through a single probe. These ultrasound waves propagate through different tissues and are eventually reflected back by red blood cells in the bloodstream. According to the Doppler effect, when ultrasound waves are reflected back from a moving object (such as red blood cells in the bloodstream), their frequency changes. This frequency change is proportional to the object's velocity and direction. The pulsed Doppler ultrasound device calculates blood flow velocity and direction by measuring the frequency change of the echo signal. When the probe is completely aligned with the direction of blood flow (i.e., an angle of 0 degrees), the Doppler frequency shift reaches its maximum, and the measured blood flow velocity will be higher than the actual value. Conversely, if the probe is facing away from the direction of blood flow (i.e., an angle of 180 degrees), the Doppler frequency shift reaches its minimum, and the measured blood flow velocity will be lower than the actual value. Therefore, by setting the pulsed Doppler ultrasound probe from the proximal end to the distal end of the target, or from the distal end to the proximal end of the target, veins, arteries, and stomas can be distinguished, and their relative positional feature matrix can be obtained.

[0032] Optionally, the step of matching the theoretical arterial image and the arterial image, and completing the arterial image based on the matching result, includes:

[0033] Feature extraction and matching are performed on theoretical arterial images and arterial images to generate a set of matching point pairs;

[0034] Matching point pairs are filtered based on the random sampling consensus algorithm;

[0035] Based on the filtered matching point pairs, the transformation matrix between the theoretical arterial image and the arterial image is calculated.

[0036] The theoretical arterial image is transformed based on the transformation matrix to align with the arterial image, and then region matching is performed between the transformed theoretical arterial image and the arterial image.

[0037] Regions in arterial images with a matching degree greater than a matching threshold are selected and optimized for completion.

[0038] By employing the above technical solution, features are extracted from vascular centerline images. These features can be the vessel's geometry (e.g., length, curvature), topological structure (e.g., bifurcation points, connectivity), or spatial location information. Feature matching algorithms (e.g., SIFT, SURF) are used to match features between the two images. This generates a set of matching point pairs, which can be used to calculate the degree of matching. Since feature matching may produce some erroneous matching point pairs, these pairs need to be filtered. This can be achieved using the Random Sample Consensus (RANSAC) algorithm or other robust methods. Based on the filtered matching point pairs, the degree of matching between the two vascular centerline images is calculated. This can be achieved by calculating the average distance between matching point pairs, the proportion of matching point pairs to the total number of points, and the spatial distribution of matching point pairs. The degree of matching can be represented by a numerical value; the higher the value, the better the matching degree. Based on the numerical value of the degree of matching, the similarity between the two vascular centerline images is evaluated. A threshold can be set; when the degree of matching is higher than the threshold, the two images are considered to have a high degree of matching. Regions with a matching degree greater than the matching threshold indicate regions where the aberrations between near-infrared images and Doppler ultrasound images are relatively minor. Optimizing and completing these regions will yield good arterial images.

[0039] Secondly, the artificial arteriovenous arm vascular imaging device provided in this application adopts the following technical solution:

[0040] An artificial arteriovenous arm vascular imaging device, comprising:

[0041] The near-infrared image acquisition module is used to acquire shallow near-infrared images and deep near-infrared images. The shallow near-infrared images contain venous blood vessel image information, and the deep near-infrared images contain venous blood vessel image information and arterial blood vessel image information.

[0042] The image layering module is used to layer deep near-infrared images based on shallow near-infrared images to obtain venous and arterial image information.

[0043] The image acquisition and calculation module is used to acquire pulsed Doppler ultrasound images and obtain the relative positional relationship between veins and arteries based on the pulsed Doppler ultrasound images.

[0044] The theoretical image generation module is used to calculate theoretical arterial image information from venous image information based on relative positional relationships;

[0045] The matching and completion module is used to match theoretical arterial images with arterial images and to complete the arterial images based on the matching results.

[0046] Thirdly, the computer device provided in this application adopts the following technical solution:

[0047] A computer device comprising:

[0048] one or more processors;

[0049] Memory;

[0050] One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to: perform the above-described artificial arteriovenous arm vascular imaging method.

[0051] Fourthly, the computer-readable storage medium provided in this application adopts the following technical solution:

[0052] A computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described above.

[0053] The storage medium stores at least one instruction, at least one program, code set, or instruction set, which is loaded and executed by the processor to implement the artificial arteriovenous arm vascular imaging method described above. Attached Figure Description

[0054] Figure 1 A flowchart illustrating an artificial arteriovenous arm vascular imaging method according to an embodiment of the present invention is shown.

[0055] Figure 2 A flowchart illustrating sub-step S2 in one embodiment of the present invention is shown.

[0056] Figure 3 A flowchart illustrating sub-step S24 in one embodiment of the present invention is shown.

[0057] Figure 4 A flowchart illustrating sub-step S3 in one embodiment of the present invention is shown.

[0058] Figure 5 A flowchart illustrating sub-step S5 in one embodiment of the present invention is shown.

[0059] Figure 6 A schematic diagram of a computer device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0060] The present application will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the application and are not intended to limit the scope of the application.

[0061] In the following description, numerous specific details are set forth for purposes of explanation in order to provide a thorough understanding of the inventive concept. As part of this specification, some of the accompanying drawings of this disclosure are block diagrams illustrating structures and devices to avoid complicating the disclosed principles. For clarity, not all features of the actual embodiment need to be described. Furthermore, the language used in this disclosure has been primarily chosen for readability and instructional purposes and may not have been chosen to define or limit the subject matter of the invention, thus requiring the necessary claims to determine such inventive subject matter. References to “an embodiment” or “an embodiment” in this disclosure mean that a particular feature, structure, or characteristic described in connection with that embodiment is included in at least one embodiment, and multiple references to “an embodiment” or “an embodiment” should not be construed as necessarily referring to the same embodiment.

[0062] Unless explicitly defined, the terms “a,” “an,” and “the” are not intended to refer to a singular entity, but rather to include a general category whose specific examples can be used for illustration. Therefore, the use of the terms “a” or “an” can mean any number of at least one, including “a,” “one or more,” “at least one,” and “one or more.” The term “or” means any of the options and any combination of the options, including all options unless explicitly indicated that the options are mutually exclusive. The phrase “at least one of” when combined with a list of items refers to a single item in the list or any combination of items in the list. The phrase does not require all items listed unless explicitly defined as such.

[0063] This application discloses a method for imaging blood vessels in an artificial artery and vein arm. (Refer to...) Figure 1 The artificial arteriovenous arm vascular imaging method includes

[0064] S1. Acquire shallow near-infrared images and deep near-infrared images, wherein the shallow near-infrared image includes venous vessel image information, and the deep near-infrared image includes both venous and arterial vessel image information. The shallow near-infrared image is an image generated by the device based on the reflected light from the target object using near-infrared light of a first intensity emitted by itself; the deep near-infrared image is an image generated by the device based on the reflected light from the target object using near-infrared light of a second intensity emitted by itself, where the second intensity is greater than the first intensity.

[0065] Near-infrared vascular imaging system (NIRVIS) is a medical imaging technique that visualizes vascular structures beneath the skin using a near-infrared light source. Its basic principle utilizes the penetrating power of near-infrared light in tissues and the absorption characteristics of near-infrared light by hemoglobin in the blood. Near-infrared light has a wavelength in the range of approximately 700-900 nanometers, allowing it to penetrate the skin and other biological tissues. Within this wavelength range, hemoglobin in the blood has a high absorption rate for near-infrared light, while the skin, muscles, and other tissues have relatively low absorption. Therefore, when near-infrared light shines on the skin surface, it can penetrate the skin and be absorbed by the hemoglobin within the blood vessels. The unabsorbed light is scattered back to the skin surface and captured by the imager's detector.

[0066] Therefore, due to the low light intensity, shallow near-infrared images primarily contain only images of veins. However, with increased near-infrared light irradiation power, deep near-infrared images can contain clearer images of veins, as well as somewhat blurry and incomplete images of arteries, thus requiring processing of the arterial images.

[0067] It is important to note that in different embodiments, a projection device can be used to process the near-infrared image, and then the processed image can be projected onto the target object to achieve visualization of blood vessels. Alternatively, a display screen can be used to overlay the identified blood vessel image onto a natural light photograph to achieve visualization of blood vessels. Of course, other methods can also be used, as long as the image can be easily obtained and visualized.

[0068] S2. Based on the shallow near-infrared image, the deep near-infrared image is layered to obtain venous and arterial image information.

[0069] Since deep near-infrared images can contain clearer images of veins and relatively blurry and incomplete images of arteries, they can be processed using differential methods to obtain vein and artery image information.

[0070] Specifically, refer to Figure 2 In one embodiment, S2 includes the following sub-steps S21-S24.

[0071] S21. Align the shallow near-infrared image and the deep near-infrared image.

[0072] The shallow near-infrared image is spatially aligned with the deep near-infrared image. If they are not aligned, try aligning them using methods such as feature point matching (e.g., SIFT, SURF) and affine transformation.

[0073] S22. Perform image difference analysis on deep near-infrared images using shallow near-infrared images.

[0074] The aligned shallow near-infrared image is subtracted pixel by pixel from the deep near-infrared image, which produces a difference image containing content in the deep near-infrared image that is not present in the shallow near-infrared image.

[0075] S23. Perform thresholding and noise reduction on the differential deep near-infrared image.

[0076] Thresholding is applied to the difference images to more easily distinguish content in the deep near-infrared images that is not present in the shallow near-infrared images. The appropriate threshold is selected and adjusted based on the actual image conditions and requirements. Since the difference images may contain noise, denoising methods such as median filtering and Gaussian filtering can be used to process the difference images to obtain clearer results.

[0077] S24. Analyze the processed differential images to obtain venous and arterial image information.

[0078] The processed difference images are analyzed to extract content from the deep near-infrared images that is not present in the shallow near-infrared images. This can be achieved through morphological operations, contour detection, region growing, and other methods.

[0079] Specifically, refer to Figure 3 In one embodiment, S24 may include the following steps:

[0080] S241. Using the processed differential image as the first image, and the result of differentiating and processing the first image with a deep near-infrared image as the second image, extract the blood vessel centerline from the first image and the second image.

[0081] Extract the centerline of the blood vessel image and detect the branch points on the centerline. Morphological operations such as endpoint detection and branch point detection can be used to find these points.

[0082] S242. Detect the branch points of blood vessels along the center line of blood vessels in the first and second images.

[0083] Based on the branching points, the vascular centerline is divided into several segments, each representing the vascular path between two branching points.

[0084] S243. Perform vascular segmentation on the vascular centerline of the first and second images.

[0085] For each vascular path, its features are extracted, such as length, curvature, and width. These features can be used to describe the relative positional relationships between blood vessels.

[0086] S244. Perform vascular feature extraction to obtain vascular feature information of the first image and the second image, which are used as arterial vascular image information and venous vascular image information, respectively. The vascular feature information includes length information, curvature information, and width information.

[0087] Based on the extracted features, a feature matrix is ​​generated, where each row of the matrix represents a feature vector of a blood vessel path. This feature matrix can be used to represent the relative positional relationships between blood vessels.

[0088] S3. Acquire pulsed Doppler ultrasound images and determine the relative positions of veins and arteries based on these images. The pulsed Doppler ultrasound probe is positioned from the proximal end of the target towards the distal end, or from the distal end of the target towards the proximal end.

[0089] Pulse Doppler ultrasound emits short pulses of ultrasound waves through a probe. These waves propagate through different tissues and are eventually reflected back by red blood cells in the bloodstream. According to the Doppler effect, the frequency of an ultrasound wave changes when reflected from a moving object (such as a red blood cell in the bloodstream). This frequency change is proportional to the object's velocity and direction. Pulse Doppler ultrasound calculates blood flow velocity and direction by measuring the frequency change of the echo signal. When the probe is directly facing the direction of blood flow (i.e., at a 0-degree angle), the Doppler frequency shift reaches its maximum, and the measured blood flow velocity will be higher than the actual value. Conversely, if the probe is facing away from the direction of blood flow (i.e., at a 180-degree angle), the Doppler frequency shift reaches its minimum, and the measured blood flow velocity will be lower than the actual value. Therefore, by positioning the pulse Doppler ultrasound probe from the proximal end to the distal end of the target, or from the distal end to the proximal end of the target, veins, arteries, and stomas can be distinguished, and their relative positional feature matrix can be obtained.

[0090] It should be noted that the pulsed Doppler ultrasound device and the near-infrared photovascular imaging device can be integrated, completely separate, or separate but connected by a cable. Different embodiments may have different forms, as long as the imaging process of the pulsed Doppler ultrasound device and the near-infrared photovascular imaging device does not interfere with each other.

[0091] Specifically, refer to Figure 4 In one embodiment, S3 may include the following steps:

[0092] S31. Acquire pulsed Doppler ultrasound images and mark the venous portion, arterial portion, and stoma portion on the pulsed Doppler ultrasound images.

[0093] It's important to note that because pulsed Doppler ultrasound images are relatively localized and the patient is constantly moving during the procedure, a comprehensive scan of the arm can be performed to create a vascular model. Based on this model, the veins, arteries, and stoma segments can then be marked. However, this modeling process isn't always necessary; it only needs to be done during the initialization phase. In other words, the model can be obtained through data import without requiring the patient's intervention. The data can be obtained through professional pulsed Doppler ultrasound imaging or through other methods.

[0094] S32. Extract the centerline of blood vessels based on pulsed Doppler ultrasound images and correlate it with veins, arteries and stoma tubes.

[0095] Specifically, the pulsed Doppler ultrasound image can be preprocessed first, such as through smoothing filtering and noise reduction, to improve image quality. Then, the preprocessed vascular image is binarized, setting vascular regions to 1 (white) and non-vascular regions to 0 (black). A distance transform is then applied to the binarized image. The purpose of the distance transform is to set the value of each pixel in the binary image to the distance to the nearest background (non-vascular region) pixel. This results in higher pixel values ​​in the vascular centerline region. Local maxima are then found; these local maxima points represent the vascular centerline, a process known as skeletonization, which can be achieved through non-maximum suppression or other methods. Finally, any potential skeleton fractures are repaired, for example, through morphological operations (such as dilation, erosion, opening, and closing operations).

[0096] S33. Generate a relative position feature matrix based on the centerline of each blood vessel.

[0097] Branching points along the central axis of a blood vessel can be detected using morphological operations such as endpoint detection and branch point detection. Based on these branching points, the central axis is divided into segments. Each segment represents the vascular path between two branching points. For each vascular path segment, features such as length, curvature, and width are extracted. These features can be used to describe the relative positional relationships between blood vessels. Based on the extracted features, a feature matrix is ​​generated, where each row of the matrix represents a feature vector of a blood vessel path. This feature matrix represents the relative positional relationships between blood vessels.

[0098] S4. Based on the relative positional relationship, calculate the theoretical arterial image information from the venous image information.

[0099] To generate a predicted arterial centerline from a vein image using the vein centerline and relative position feature matrix, the following method can be used:

[0100] A relative position feature matrix is ​​used to estimate the location of the predicted arterial centerline. This matrix contains the relative positional relationship between the venous centerline and the predicted arterial centerline in the deep near-infrared vascular image. Applying this matrix around the venous centerline in the deep near-infrared vascular image predicts the approximate location of the predicted arterial centerline. For example, if the relative position feature matrix indicates that the predicted arterial centerline is 10 pixels to the left of the venous centerline, then the predicted arterial centerline can be located 10 pixels to the left of the venous centerline in the venous image.

[0101] S5. Match the theoretical arterial image with the actual arterial image, and complete the arterial image based on the matching results.

[0102] S51. Perform feature extraction and matching on theoretical arterial images and arterial images to generate a set of matching point pairs.

[0103] Features are extracted from the blood vessel centerline image. These features can be the vessel's geometry (e.g., length, curvature), topological structure (e.g., bifurcation points, connectivity), or spatial location information. Feature matching algorithms (e.g., SIFT, SURF) are then used to match features between the two images. This generates a set of matching point pairs, which can be used to calculate the degree of matching.

[0104] S52. Filter matching point pairs based on the random sampling consensus algorithm.

[0105] Since feature matching may produce some erroneous matching pairs, these pairs need to be filtered. This can be achieved using the Random Sampling Consensus (RANSAC) algorithm or other robust methods.

[0106] S53. Based on the filtered matching point pairs, calculate the transformation matrix between the theoretical arterial image and the arterial image.

[0107] Based on the selected matching point pairs, the degree of matching between two blood vessel centerline images is calculated. This can be achieved by calculating the average distance between matching point pairs, the proportion of matching point pairs to the total number of points, and the spatial distribution of matching point pairs.

[0108] S54. Transform the theoretical arterial image based on the transformation matrix to align it with the arterial image, and then perform region matching on the transformed theoretical arterial image and the arterial image.

[0109] The degree of matching can be represented by a numerical value; the higher the value, the better the matching. Based on the numerical value of the matching degree, the similarity between two images of the vessel centerline is evaluated. A threshold can be set; when the matching degree is higher than the threshold, the two images are considered to have a high degree of matching.

[0110] S55. Extract regions in the arterial image with a matching degree greater than the matching threshold and perform optimization and completion.

[0111] Regions with a matching degree greater than the matching threshold indicate regions where the aberrations between near-infrared images and Doppler ultrasound images are relatively minor. Optimizing and completing these regions will yield good arterial images.

[0112] To calculate the matching degree between two vascular centerline images, the degree of matching can be determined based on the selected matching point pairs. This can be achieved by calculating the average distance between matching point pairs, the proportion of matching point pairs to the total number of points, and the spatial distribution of matching point pairs. The matching degree can be represented by a numerical value; the higher the value, the better the matching degree. The similarity between the two vascular centerline images is evaluated based on the numerical matching degree. A threshold can be set; when the matching degree is higher than the threshold, the two images are considered to have a high degree of matching.

[0113] S6. Treat the differentiated venous image, the optimized and completed arterial image, and the stoma tube as different layers and output them outwards as needed.

[0114] It should be noted that since the stoma tube is located between the deep and shallow layers, it can be obtained through differential and completion methods. In some embodiments, the stoma tube image may be identified as a blood vessel. In other embodiments, the stoma tube image can be obtained through Doppler ultrasound images. The principle is similar to that described above, and will not be repeated here.

[0115] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0116] In one embodiment, an artificial arteriovenous arm vascular imaging device is provided, which corresponds one-to-one with the artificial arteriovenous arm vascular imaging method described in the above embodiments. The functional modules of this artificial arteriovenous arm vascular imaging device are detailed below:

[0117] The near-infrared image acquisition module is used to acquire shallow near-infrared images and deep near-infrared images. The shallow near-infrared images contain venous blood vessel image information, and the deep near-infrared images contain venous blood vessel image information and arterial blood vessel image information.

[0118] The image layering module is used to layer deep near-infrared images based on shallow near-infrared images to obtain venous and arterial image information.

[0119] The image acquisition and calculation module is used to acquire pulsed Doppler ultrasound images and obtain the relative positional relationship between veins and arteries based on the pulsed Doppler ultrasound images.

[0120] The theoretical image generation module is used to calculate theoretical arterial image information from venous image information based on relative positional relationships;

[0121] The matching and completion module is used to match theoretical arterial images with arterial images and to complete the arterial images based on the matching results.

[0122] Specific limitations regarding the artificial arteriovenous arm vascular imaging device can be found in the limitations of the artificial arteriovenous arm vascular imaging method described above, and will not be repeated here. Each module in the aforementioned artificial arteriovenous arm vascular imaging device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0123] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database contains data related to an artificial arteriovenous arm vascular imaging method. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an artificial arteriovenous arm vascular imaging method.

[0124] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the artificial arteriovenous arm vascular imaging method of the above embodiment, for example... Figure 1 S1-S6 are shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit of the artificial arteriovenous arm vascular imaging device in the above embodiments. To avoid repetition, these will not be described again here.

[0125] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When executed by a processor, the computer program implements the artificial arteriovenous arm vascular imaging method of the above embodiment, for example... Figure 1 S1-S6 are shown. Alternatively, when the computer program is executed by the processor, it implements the functions of each module / unit in the artificial arteriovenous arm vascular imaging device in the above device embodiment. To avoid repetition, it will not be described again here.

[0126] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments of this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0127] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0128] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for imaging blood vessels in an artificial artery and vein in the arm, characterized in that, The following steps are involved: Acquire superficial near-infrared images and deep near-infrared images, wherein the superficial near-infrared images contain venous vessel image information, and the deep near-infrared images contain venous vessel image information and arterial vessel image information; Based on shallow near-infrared images, deep near-infrared images are layered to obtain venous and arterial image information; Acquire pulsed Doppler ultrasound images and obtain the relative positional relationship between veins and arteries based on the pulsed Doppler ultrasound images; Theoretical arterial image information is calculated from venous image information based on relative positional relationships. Match theoretical arterial images with actual arterial images, and complete the arterial images based on the matching results.

2. The method for imaging blood vessels in an artificial artery and vein arm according to claim 1, characterized in that, The shallow near-infrared image is an image generated by the device based on the reflected light from the target object using near-infrared light of a first intensity emitted by itself; the deep near-infrared image is an image generated by the device based on the reflected light from the target object using near-infrared light of a second intensity emitted by itself, where the second intensity is greater than the first intensity.

3. The method for imaging blood vessels in an artificial artery and vein arm according to claim 2, characterized in that, The step of layering deep near-infrared images based on shallow near-infrared images to obtain venous and arterial image information includes: Image alignment between shallow near-infrared images and deep near-infrared images; Image difference is performed on deep near-infrared images using shallow near-infrared images; Thresholding and noise reduction are performed on the differential deep near-infrared image; The processed differential images are analyzed to obtain venous and arterial image information.

4. The method for imaging blood vessels in an artificial artery and vein arm according to claim 3, characterized in that, The step of analyzing the processed differential images to obtain venous and arterial image information includes: The processed differential image is used as the first image, and the result of differentiating and processing the first image with a deep near-infrared image is used as the second image. The blood vessel centerline is extracted from the first image and the second image. Detect the branch points of blood vessels along the center line of the blood vessels in the first and second images; The blood vessel centerlines in the first and second images are segmented. Vascular feature extraction is performed to obtain vascular feature information of the first image and the second image, which are used as arterial vascular image information and venous vascular image information, respectively. The vascular feature information includes length information, curvature information and width information.

5. The method for imaging blood vessels in an artificial artery and vein arm according to claim 4, characterized in that, The steps of acquiring pulsed Doppler ultrasound images and obtaining the relative positional relationship between veins and arteries based on the pulsed Doppler ultrasound images include: Acquire pulsed Doppler ultrasound images and mark the venous, arterial, and stoma portions on the pulsed Doppler ultrasound images; The centerline of blood vessels was extracted based on pulsed Doppler ultrasound images and correlated with veins, arteries, and stoma tubes. A relative position feature matrix is ​​generated based on the centerline of each blood vessel.

6. The method for imaging blood vessels in an artificial artery and vein arm according to claim 5, characterized in that, The step of matching theoretical arterial images and arterial images, and completing the arterial images based on the matching results, includes: Feature extraction and matching are performed on theoretical arterial images and arterial images to generate a set of matching point pairs; Matching point pairs are filtered based on the random sampling consensus algorithm; Based on the filtered matching point pairs, the transformation matrix between the theoretical arterial image and the arterial image is calculated. The theoretical arterial image is transformed based on the transformation matrix to align with the arterial image, and then region matching is performed between the transformed theoretical arterial image and the arterial image. Regions in arterial images with a matching degree greater than a matching threshold are selected and optimized for completion.

7. The method for imaging blood vessels in an artificial artery and vein arm according to claim 6, characterized in that, The pulsed Doppler ultrasound probe is positioned from the proximal end of the target toward the distal end, or from the distal end of the target toward the proximal end.

8. A device for imaging blood vessels in an artificial artery and vein arm, characterized in that, include: The near-infrared image acquisition module is used to acquire shallow near-infrared images and deep near-infrared images. The shallow near-infrared images contain venous blood vessel image information, and the deep near-infrared images contain venous blood vessel image information and arterial blood vessel image information. The image layering module is used to layer deep near-infrared images based on shallow near-infrared images to obtain venous and arterial image information. The image acquisition and calculation module is used to acquire pulsed Doppler ultrasound images and obtain the relative positional relationship between veins and arteries based on the pulsed Doppler ultrasound images. The theoretical image generation module is used to calculate theoretical arterial image information from venous image information based on relative positional relationships; The matching and completion module is used to match theoretical arterial images with arterial images and to complete the arterial images based on the matching results.

9. A computer device, characterized in that, It includes: one or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to: perform the artificial arteriovenous arm vascular imaging method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement: the artificial arteriovenous arm vascular imaging method as described in any one of claims 1 to 7.

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