A method for identifying and recording blood vessel wall thickness

Abstract

This invention relates to the field of blood vessel wall thickness identification technology and provides a method for identifying and recording blood vessel wall thickness, comprising the following steps: S1, injecting a contrast agent intravenously into the patient; S2, placing the patient on a CT scanner bed; S3, starting the scanning module; S4, the image processing module receiving the scanning data transmitted by the scanning module; S5, the blood flow monitoring module receiving the scanning data; S6, the interactive representation module receiving the image model transmitted by the image processing module, analyzing and processing the image model, receiving the blood flow data packet from the blood flow monitoring module, analyzing and processing the blood flow data packet, and packaging it into a total data group. The interactive representation module includes an image representation unit that represents the image. This method solves the problem in the prior art that it is impossible to accurately compare and locate the longitudinal and transverse thickness of the blood vessel wall, and achieves the purpose of representing the longitudinal and transverse cross-sectional graphics and cross-sectional data of the blood vessel through the interactive representation module.

Description

Technical Field

[0001] This invention relates to the field of blood vessel wall thickness identification technology, and more specifically, to a method for identifying and recording blood vessel wall thickness. Background Technology

[0002] Vascular wall thickness measurement is a crucial step in the diagnosis and research of cardiovascular diseases. Measuring vascular wall thickness provides important information about vascular health and cardiovascular disease risk. Vascular wall thickness analysis can assess arteriosclerosis, cardiovascular disease risk, hypertension, diabetes, inflammation, and treatment effectiveness, and provides a basis for personalized treatment planning and early diagnosis. This information is of great significance for the prevention, diagnosis, and treatment of cardiovascular diseases.

[0003] Currently, CT scans are commonly used to image blood vessels and obtain their thickness. This thickness is typically presented as data or graphically, usually using cross-sectional images to show differences in vessel wall thickness. However, this method only allows for horizontal comparison of vessel wall thickness, making vertical comparison difficult to visualize. Especially since areas of increased vessel wall thickness are often sites of disease, vertical comparison makes these issues much easier to detect. If only horizontal comparisons are available, doctors need to use multiple sets of different horizontal vessel wall images for vertical comparison, which can easily lead to errors and omissions in the comparison.

[0004] In the diagnosis of some diseases, a combined assessment of transverse and longitudinal comparisons of blood vessel walls is necessary for more effective treatment. Therefore, this paper proposes a method for identifying and recording blood vessel wall thickness to address existing problems. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method for identifying and recording blood vessel wall thickness by analyzing the thickness of the blood vessel wall, achieving simultaneous longitudinal and transverse comparisons, and intuitively observing whether there is uneven thickness of the blood vessel wall from both longitudinal and transverse perspectives.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for identifying and recording blood vessel wall thickness, comprising the following steps:

[0007] S1. Inject a contrast agent into the patient's body via a vein. The contrast agent is selected at the detection site according to the patient's own condition to make the blood vessels more clearly visible in the CT image.

[0008] S2. Place the patient on the CT scanner bed, ensuring the patient is lying flat and comfortable and within the scanner's field of view, and that the patient is not carrying any metal objects or obstructing objects.

[0009] S3. Start the scanning module. The scanning module rotates and scans. At the same time, the CT scanner bed moves horizontally in coordination with the scanning module. The CT scanner bed slowly moves the patient's whole body through the scanning module, and the scanning module transmits the scan data.

[0010] S4. The image processing module receives the scanning data transmitted by the scanning module, performs model building and image enhancement processing on the scanning data, and outputs the image model after image processing is completed.

[0011] S5. The blood flow monitoring module receives scan data, analyzes the perfusion parameters of the blood vessels through the blood flow monitoring module, obtains multiple sets of blood flow parameters through analysis, and packages the blood flow parameters into blood flow data packets for transmission.

[0012] S6. The interactive representation module receives the image model transmitted by the image processing module and analyzes and processes the image model. It also receives the blood flow data packet from the blood flow monitoring module, analyzes and processes the blood flow data packet, and packages it into a total data group. The interactive representation module includes an image representation unit for representing images, a representation data unit for representing data, and a warning unit for representing prompts and alerts.

[0013] S7. The recording module encrypts and records the total data set output by the interactive representation unit.

[0014] By employing the above technical solution, this invention utilizes the combined effects of intravenous contrast agent injection and CT scanning. The image processing module receives images of the blood vessel wall and performs edge enhancement processing, making the vessel wall edges clearly visible. This facilitates observation of the vessel wall condition by doctors. Furthermore, it simultaneously assesses transverse and longitudinal abnormalities of the vessel wall and compares vessel wall thickness. Additionally, by observing blood flow data such as the flow rate and volume of the intravenously injected contrast agent, the patient's blood flow status can be determined.

[0015] The present invention is further configured such that the image enhancement processing includes the following specific steps:

[0016] S41. Perform preliminary processing on the image. First, perform a noise reduction step on the image by filtering it with a Gaussian filter to remove noise from the image; correct geometric distortion in the image and normalize the image pixel values ​​so that all pixel values ​​are set to 255.

[0017] S42. Enhance contrast: By adjusting the image histogram to make the pixel distribution uniform, and by using linear and non-linear transformations to stretch the range of image pixel values, a good contrast can be obtained.

[0018] S43. Perform blood vessel wall edge detection;

[0019] The present invention is further configured such that: the blood vessel wall edge detection is processed by an optimized Canny edge detection algorithm, the steps of which include:

[0020] S431. Calculate the gradient magnitude and direction of the image using the Sobe l operator;

[0021] S432. Non-maximum suppression is performed in the gradient direction to refine the edges;

[0022] S433. Based on the required threshold for the image, set two sets of thresholds, namely high threshold and low threshold, and detect connection edges, including weak edges and strong edges, through the two thresholds;

[0023] S434. Perform edge connection, connect weak edges and strong edges into a whole, and strengthen weak edges and strong edges to the same edge strength.

[0024] The present invention is further configured such that the blood flow data packet includes:

[0025] Blood flow: The amount of blood flowing through a unit length of blood vessel per unit time;

[0026] Blood volume: The total blood volume is obtained by calculating the volume of blood flow within blood vessels;

[0027] Mean transit time: The average time it takes for contrast agent to pass through a unit length of blood vessel;

[0028] Peak time: The time it takes for the contrast agent to reach its peak concentration.

[0029] By adopting the above technical solution, and combining blood flow data and vessel wall thickness data, a comprehensive diagnosis can be made to determine whether the slowed blood flow is caused by abnormal vessel wall thickness, and also to determine whether there are cases where the blood flow velocity is abnormal even though the vessel wall thickness is not abnormal.

[0030] The present invention is further configured such that: the interactive representation module analyzes the image model, including: obtaining the thickness of the blood vessel wall, judging transverse anomalies of the blood vessel wall, and judging longitudinal anomalies of the blood vessel wall.

[0031] The present invention is further configured such that: the thickness of the blood vessel wall is obtained by calculating the difference between the inner and outer walls of the blood vessel wall; by calling the image model and establishing a polar coordinate system according to the transverse group of the blood vessel, the data of the inner and outer walls of the blood vessel wall are obtained through image analysis, and the coordinates of the data of the inner and outer walls of the blood vessel wall are aligned.

[0032] The present invention is further configured such that the determination of transverse abnormalities of the blood vessel wall includes the following steps: the pole of the polar coordinate system is set as the center of the blood vessel, and the coordinates are radiated outward from the center of the blood vessel in a 360° direction. Based on the position of the blood vessel wall, the inner wall value of the blood vessel wall is set as Ω1, and the outer wall value of the blood vessel wall is set as Ω2. The difference between the two points is the thickness of the blood vessel wall. The blood vessel is statistically analyzed and compared as a whole in 360°.

[0033] The present invention is further configured such that: the comparison step includes setting a horizontal threshold, comparing the wall thickness of the blood vessel group in the horizontal direction, and if there is a difference in the wall thickness of the blood vessel group that exceeds the threshold, it is displayed in the warning unit that represents the warning, including the specific data and image model of the blood vessel group in the horizontal direction.

[0034] The present invention is further configured such that the judgment of longitudinal abnormality of blood vessel wall includes the following steps: setting a longitudinal threshold; after the blood vessel wall thickness is obtained, the blood vessel wall thickness is plotted as a longitudinal plotting curve; the longitudinal plotting curve is analyzed to determine whether there is a longitudinal abnormality in the blood vessel wall; if the slope of the longitudinal plotting curve exceeds the longitudinal threshold, it is displayed in the warning unit that represents the warning, including the specific data and image model of this group of longitudinal blood vessels.

[0035] By adopting the above technical solutions, the judgment of transverse and longitudinal abnormalities of the blood vessel wall can ensure a comprehensive comparison of the patient's blood vessels, without any omissions in the comparison, and prevent diagnostic errors caused by comparing only the transverse or longitudinal direction. Furthermore, by analyzing the information on transverse abnormalities of the blood vessel wall, the patient's transverse group data can also be obtained. Through the specific data of the transverse group, it is possible to analyze whether there is a problem of vascular stenosis in this transverse group (such as the overall thickness of the transverse group being too large or the thickness of a certain part affecting blood flow), so as to give the patient an early warning. In addition, by analyzing the longitudinal curve of the blood vessel wall, the patient's longitudinal blood vessel wall data can be obtained, thereby clearly understanding whether there are any abnormalities in the blood vessel wall.

[0036] The present invention is further configured such that: the image representation unit in the interactive representation module represents the image model transmitted by the image processing module, and the image processing model has an interactive function. When a doctor or patient who needs to view this image report moves the mouse to a certain point of the image processing model, the representation data unit pops up, representing the data at that point.

[0037] By adopting the above technical solution, the present invention can accurately display the patient's blood vessel wall information through the interactive representation module. The image representation unit, representation data unit, and early warning unit in the interactive representation module can accurately display the information needed by the doctor, and can intuitively observe the horizontal cross-section and vertical interface of different blood vessel wall parts. Furthermore, the early warning unit can prevent missed diagnoses caused by the doctor not observing the problem in the blood vessel.

[0038] In summary, this application includes at least one of the following beneficial technical effects of the method for identifying and recording blood vessel wall thickness:

[0039] 1. This invention utilizes the combined effects of intravenous contrast agent injection and CT scanning. An image processing module receives images of the blood vessel wall and performs edge enhancement processing, making the vessel wall edges clearly visible. This facilitates observation of the vessel wall by doctors. Furthermore, it simultaneously identifies abnormalities in the vessel wall both horizontally and vertically, and compares the vessel wall thickness. Additionally, by observing blood flow data such as the flow rate and volume of the intravenously injected contrast agent, the patient's blood flow status can be determined.

[0040] 2. The present invention can accurately display the patient's blood vessel wall information through the interactive representation module. The image representation unit, representation data unit and early warning unit in the interactive representation module can accurately display the information needed by the doctor. It can also intuitively observe the horizontal cross-section and vertical interface of different blood vessel wall parts. Furthermore, the early warning unit can prevent the doctor from missing the diagnosis due to not observing the problem in the blood vessel.

[0041] 3. The assessment of transverse and longitudinal abnormalities of the blood vessel wall ensures a comprehensive comparison of the patient's blood vessels, eliminating any omissions and preventing diagnostic errors caused by comparing only the transverse or longitudinal sections. Furthermore, by analyzing information on transverse abnormalities, the patient's transverse data can be obtained. Through specific data from the transverse group, it is possible to analyze whether there are vascular stenosis issues (such as an overall excessive thickness of the transverse group or abnormal thickness in a certain area affecting blood flow), providing early warning to the patient. In addition, by analyzing the longitudinal curves of the blood vessel wall, the patient's longitudinal blood vessel wall data can be obtained, thus clearly understanding whether there are any abnormalities in the blood vessel wall. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the overall structure of a method for identifying and recording blood vessel wall thickness according to the present invention.

[0043] Figure 2 This is a schematic diagram of the blood vessel wall in this invention.

[0044] Figure 3 This is a schematic diagram showing the orientation of the optimized Canny edge detection algorithm in this invention. Detailed Implementation

[0045] It should be noted that, unless otherwise specified, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0046] Please see Figures 1-3 The present invention provides the following technical solutions:

[0047] Example 1, see Figures 1-3 A method for identifying and recording blood vessel wall thickness, comprising the following steps:

[0048] S1. Inject contrast agent into the patient's body via intravenous injection. The contrast agent is selected at the detection site according to the patient's own condition to make the blood vessels more clearly visible in the CT image.

[0049] The basic principle of intravenous contrast imaging is to make blood vessels more clearly visible in images by injecting a contrast agent into the vein. The contrast agent is usually an iodine-containing compound that absorbs X-rays, thus creating a high-contrast vascular image in the image. In clinical practice, the amount of contrast agent administered should be selected based on the patient's height, weight, and physical condition to avoid causing harm to the patient.

[0050] S2. Place the patient on the CT scanner bed, ensuring the patient is lying flat and comfortable and within the scanner's field of view, and that the patient is not carrying any metal objects or obstructing objects.

[0051] S3. Start the scanning module. The scanning module rotates and scans. At the same time, the CT scanner bed moves horizontally in coordination with the scanning module. The CT scanner bed slowly moves the patient's whole body through the scanning module, and the scanning module transmits the scan data.

[0052] The scanning module typically consists of a rotating mechanism and an X-ray emitting mechanism. It should be understood that the scanning module in this embodiment uses existing technology. Scanning modules that can be directly or improved by those skilled in the art can be applied to the system of this embodiment. The specific structure of the transmission module is not absolutely limited here.

[0053] S4. The image processing module receives the scanning data transmitted by the scanning module, performs model building and image enhancement processing on the scanning data, and outputs the image model after image processing is completed.

[0054] S5. The blood flow monitoring module receives scan data, analyzes the perfusion parameters of the blood vessels through the blood flow monitoring module, obtains multiple sets of blood flow parameters through analysis, and packages the blood flow parameters into blood flow data packets for transmission.

[0055] S6. The interactive representation module receives the image model transmitted by the image processing module and analyzes and processes the image model. It also receives the blood flow data packet from the blood flow monitoring module, analyzes and processes the blood flow data packet, and packages it into a total data group. The interactive representation module includes an image representation unit for representing images, a representation data unit for representing data, and a warning unit for representing prompts and alerts.

[0056] S7. The recording module encrypts and records the total data set output by the interactive representation unit. Asymmetric encryption is used, which is performed by two sets of keys: a public key for encrypting the data and a private key for decrypting the data. The user terminal can only decrypt the data using the private key.

[0057] It should be noted that the image enhancement process in S4 includes the following specific steps:

[0058] S41. Perform preliminary image processing. First, perform a noise reduction step on the image by filtering it with a Gaussian filter to remove noise from the image. Correct geometric distortion in the image and normalize the image pixel values ​​to set them all to 255. The unique pixel values ​​ensure that the sharpness of each part of the image is consistent and there are no artifacts or differences caused by uneven sharpness.

[0059] S42. Enhance contrast: By adjusting the image histogram to make the pixel distribution uniform, and by using linear and non-linear transformations to stretch the range of image pixel values, a good contrast can be obtained.

[0060] S43. Perform blood vessel wall edge detection;

[0061] See Figure 3 Blood vessel wall edge detection is performed using an optimized Canny edge detection algorithm, and the steps include:

[0062] S431. Calculate the gradient magnitude and direction (NE, NW, SW, SE) of the image using the Sobe l operator;

[0063] S432. Non-maximum suppression is performed in the gradient direction to refine the edges;

[0064] S433. Based on the required threshold for the image, set two sets of thresholds, namely high threshold and low threshold, and detect connection edges, including weak edges and strong edges, through the two thresholds;

[0065] S434. Perform edge connection, connect weak edges and strong edges into a whole, and strengthen weak edges and strong edges to the same edge strength.

[0066] By detecting the edge of the blood vessel wall, it is possible to separate the blood vessel wall from other tissues and organs in the scanned data, selecting only the blood vessel wall for display. This avoids the problem of overlap between the blood vessel wall image and other tissue or organ images, which can lead to errors.

[0067] In this embodiment, the blood flow data packet includes:

[0068] Blood flow: The amount of blood flowing through a unit length of blood vessel per unit time;

[0069] Blood volume: The total blood volume is obtained by calculating the volume of blood flow within blood vessels;

[0070] Mean transit time: The average time it takes for contrast agent to pass through a unit length of blood vessel;

[0071] Peak time: The time it takes for the contrast agent to reach its peak concentration.

[0072] By combining blood flow data and vessel wall thickness data, a comprehensive diagnosis can be made to determine whether the slowed blood flow is caused by abnormal vessel wall thickness, or whether there are cases where the blood flow velocity is abnormal even though the vessel wall thickness is not abnormal. This helps doctors to assess the condition and thus improves medical efficiency.

[0073] The interactive representation module analyzes the image model, including obtaining the vessel wall thickness, identifying transverse anomalies, and identifying longitudinal anomalies.

[0074] The thickness of the blood vessel wall is obtained by calculating the difference between the inner and outer walls of the blood vessel wall. By calling the image model and establishing a polar coordinate system based on the transverse group of the blood vessel, the data of the inner and outer walls of the blood vessel wall are obtained through image analysis, and the coordinates of the inner and outer wall data of the blood vessel wall are aligned.

[0075] The determination of transverse abnormalities in the blood vessel wall includes the following steps: The polar coordinate system's pole is set to the center of the blood vessel. From the center, the coordinates diverge in a 360° direction outwards. Based on the location of the blood vessel wall, the inner wall value is set as Ω1, and the outer wall value as Ω2. The difference between these two values ​​represents the blood vessel wall thickness. The entire blood vessel is statistically analyzed and compared across its 360° radius. The comparison step includes setting a transverse threshold and comparing the blood vessel wall thicknesses within a transverse group. If a difference in blood vessel wall thickness within the transverse group exceeds the threshold, it is displayed in the warning unit, including the specific data and image model of this transverse group.

[0076] The determination of longitudinal abnormalities in the blood vessel wall includes the following steps: setting a longitudinal threshold; after the blood vessel wall thickness is obtained, the blood vessel wall thickness is plotted as a longitudinal curve; the longitudinal curve is analyzed to determine whether there are longitudinal abnormalities in the blood vessel wall; if the slope of the longitudinal curve exceeds the longitudinal threshold, it is displayed in the warning unit that indicates the warning, including the specific data and image model of this group of blood vessels.

[0077] Specifically, in summary, the assessment of transverse and longitudinal abnormalities of the blood vessel wall ensures a comprehensive comparison of the patient's blood vessels, eliminating any omissions and preventing diagnostic errors caused by comparing only the transverse or longitudinal sections. Furthermore, by analyzing the information on transverse abnormalities, the patient's transverse group data can be obtained. Through the specific data of the transverse group, it is possible to analyze whether there is a problem of vascular stenosis in this transverse group (such as an excessive overall thickness of the transverse group or abnormal thickness in a certain area affecting blood flow), providing early warning to the patient. Moreover, by analyzing the longitudinal curves of the blood vessel wall, the patient's longitudinal blood vessel wall data can be obtained, thus clearly understanding whether there are any abnormalities in the blood vessel wall.

[0078] Example 2, please refer to Figures 1-3 This second embodiment is an improvement on the first embodiment. Although the first embodiment can identify and record the thickness of the blood vessel wall, in clinical practice, doctors need to visually observe the thickness of the blood vessel wall and make judgments based on the observed thickness. If the thickness of the blood vessel wall can be visually displayed, it can assist doctors in treatment. Therefore, it is necessary to set up an interactive representation module to represent the data required by doctors.

[0079] The interactive representation module receives the image model transmitted by the image processing module and analyzes and processes the image model. It also receives the blood flow data packet from the blood flow monitoring module, analyzes and processes the blood flow data packet, and packages it into a total data group. The interactive representation module includes an image representation unit for representing images, a representation data unit for representing data, and a warning unit for representing prompts and alerts.

[0080] In the interactive representation module, the image representation unit represents the image model transmitted by the image processing module. The image processing model has interactive functions. When the doctor or patient who needs to view this image report moves the mouse to a certain point of the image processing model, the representation data unit pops up, representing the data at that point.

[0081] The data displayed by the data representation unit includes the horizontal data of the blood vessel at that point in the model, the maximum value of the horizontal data of the blood vessel, and the minimum value of the horizontal data of the blood vessel; the image representation unit represents the overall model diagram of the patient's blood vessels, and when the mouse moves to a point, it can represent the horizontal cross-sectional diagram and the vertical interface diagram of the blood vessel at that point according to the operator's selection; if there are vertical or horizontal abnormalities in the thickness of the blood vessel wall, the warning prompt unit can send a warning prompt and the data that causes the warning.

[0082] Obviously, the embodiments described above are merely some, not all, embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort should fall within the scope of protection of the present invention.

Claims

1. A system for identifying and recording blood vessel wall thickness, characterized in that, The system used to identify and record blood vessel wall thickness includes the following steps: S1. Inject a contrast agent into the patient's body via a vein. The contrast agent is selected at the detection site according to the patient's own condition to make the blood vessels more clearly visible in the CT image. S2. Place the patient on the CT scanner bed, ensuring the patient is lying flat and comfortable and within the CT scanner's field of view, and that the patient is not carrying any metal objects or obstructing objects. S3. Start the scanning module. The scanning module rotates and scans. At the same time, the CT scanner bed moves horizontally in coordination with the scanning module. The CT scanner bed slowly moves the patient's whole body through the scanning module, and the scanning module transmits the scan data. S4. The image processing module receives the scanning data transmitted by the scanning module, performs model building and image enhancement processing on the scanning data, and outputs the image model after image processing. S5. The blood flow monitoring module receives scan data, analyzes the perfusion parameters of the blood vessels, obtains multiple sets of blood flow parameters, and packages the blood flow parameters into a blood flow data packet for transmission. S6. The interactive representation module receives the image model transmitted by the image processing module and analyzes and processes the image model. It also receives the blood flow data packet from the blood flow monitoring module, analyzes and processes the blood flow data packet, and packages it into a total data group. The interactive representation module includes an image representation unit for representing images, a representation data unit for representing data, and a warning unit for representing prompts and alerts. S7. The recording module encrypts and records the total data set output by the interactive representation module. and The interactive representation module analyzes the image model, including obtaining the blood vessel wall thickness, judging transverse anomalies of the blood vessel wall, and judging longitudinal anomalies of the blood vessel wall. The determination of longitudinal anomalies in the blood vessel wall includes the following steps: setting a longitudinal threshold; after the blood vessel wall thickness is obtained, the blood vessel wall thickness is plotted as a longitudinal curve; the longitudinal curve is analyzed to determine whether there are longitudinal anomalies in the blood vessel wall; if the slope of the longitudinal curve exceeds the longitudinal threshold, it is displayed in the warning unit that indicates the warning, including the specific data and image model of this group of longitudinal blood vessels.

2. The blood vessel wall thickness identification and recording system according to claim 1, characterized in that, The image enhancement process includes the following specific steps: S41. Perform preliminary processing on the image. First, perform a noise reduction step on the image by filtering it with a Gaussian filter to remove noise from the image; correct geometric distortion in the image and normalize the image pixel values ​​so that all pixel values ​​are set to 255. S42. Enhance contrast: By adjusting the image histogram to make the pixel distribution uniform, and by using linear and non-linear transformations to stretch the range of image pixel values, a good contrast can be obtained. S43. Perform blood vessel wall edge detection.

3. The blood vessel wall thickness identification and recording system according to claim 2, characterized in that, The blood vessel wall edge detection is performed using an optimized Canny edge detection algorithm, and the steps include: S431. Calculate the gradient magnitude and direction of the image using the Sobel operator; S432. Non-maximum suppression is performed in the gradient direction to refine the edges; S433. Based on the required threshold for the image, set two sets of thresholds, namely high threshold and low threshold, and detect connection edges, including weak edges and strong edges, through the two thresholds; S434. Perform edge connection, connecting weak edges and strong edges into a whole, and strengthening weak edges and strong edges to the same edge strength.

4. The blood vessel wall thickness identification and recording system according to claim 1, characterized in that: The blood flow data packet includes: Blood flow: The amount of blood flowing through a unit length of blood vessel per unit time; Blood volume: The total blood volume is obtained by calculating the volume of blood flow within blood vessels; Mean transit time: The average time it takes for contrast agent to pass through a unit length of blood vessel; Peak time: The time it takes for the contrast agent to reach its peak concentration.

5. The blood vessel wall thickness identification and recording system according to claim 1, characterized in that: The thickness of the blood vessel wall is obtained by calculating the difference between the inner and outer walls of the blood vessel wall. By calling the image model and establishing a polar coordinate system based on the transverse group of the blood vessel, the data of the inner and outer walls of the blood vessel wall are obtained through image analysis, and the coordinates of the inner and outer wall data of the blood vessel wall are aligned.

6. The blood vessel wall thickness identification and recording system according to claim 5, characterized in that, The determination of transverse abnormalities in the blood vessel wall includes the following steps: the pole of the polar coordinate system is set as the center of the blood vessel, and the coordinates are radiated outward from the center of the blood vessel in a 360° direction. Based on the position of the blood vessel wall, the inner wall value of the blood vessel wall is set as Ω1, and the outer wall value of the blood vessel wall is set as Ω2. The difference between the two points is the thickness of the blood vessel wall. The entire 360° of the blood vessel is statistically analyzed and compared.

7. The blood vessel wall thickness identification and recording system according to claim 6, characterized in that: The comparison step includes setting a horizontal threshold, comparing the wall thickness of the blood vessel group, and if the difference in wall thickness within the blood vessel group exceeds the threshold, it is displayed in the warning unit that represents the warning, including the specific data and image model of the blood vessel group.

8. The blood vessel wall thickness identification and recording system according to claim 1, characterized in that: The image representation unit in the interactive representation module represents the image model transmitted by the image processing module. The image processing module has an interactive function. When a doctor or patient who needs to view this image report moves the mouse to a certain point in the image processing model, the representation data unit pops up, representing the data at that point.

Images