Blood vessel wall thickness identification and recording method

Abstract

The invention is suitable for the technical field of blood vessel wall thickness identification, and provides a blood vessel wall thickness identification and recording method, which comprises the following steps: S1, injecting a contrast agent into the body of a patient through an intravenous injection; s2, placing the patient on a CT machine scanning bed; s3, starting a scanning module; s4, the image processing module receives scanning data transmitted by the scanning module; s5, the blood flow monitoring module receives scanning data; s6, an interactive characterization module receives the image model transmitted by the image processing module, analyzes and processes the image model, receives the blood flow data packet of the blood flow monitoring module, analyzes and processes the blood flow data packet, and packages the blood flow data packet into a total data set, and the interactive characterization module comprises an image characterization unit for characterizing an image; according to the method, the problem that the longitudinal and transverse thicknesses of the blood vessel wall cannot be accurately positioned in the prior art is solved, and the purpose of representing the longitudinal and transverse section graphs and section data of the blood vessel through the interactive representation module is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of blood vessel wall thickness identification, and more specifically, to a blood vessel wall thickness identification and recording method. Background Art

[0002] Blood vessel wall thickness testing is an important part of cardiovascular disease diagnosis and research. By measuring blood vessel wall thickness, important information about vascular health and cardiovascular disease risk can be obtained. Blood vessel wall thickness analysis can assess arteriosclerosis, cardiovascular disease risk, hypertension, diabetes, inflammation, treatment effects, and provide a basis for individualized treatment planning and early diagnosis. This information is of great significance for the prevention, diagnosis and treatment of cardiovascular disease.

[0003] At present, CT machines are usually used to image blood vessels and obtain blood vessel thickness. Usually, the thickness of the blood vessel wall is presented in data or graphical form, and the cross-section of the blood vessel wall is usually used to show the thickness difference of the blood vessel wall. However, this method of presenting cross-sectional images can only compare the transverse blood vessel wall thickness of the blood vessels, while the longitudinal blood vessel wall comparison is difficult to display intuitively. In particular, the site where the blood vessel wall thickness increases is usually the site where the disease occurs. This problem is easier to find through the longitudinal blood vessel wall thickness comparison. If there is only a transverse blood vessel wall thickness comparison, the doctor needs to call multiple different groups of transverse blood vessel walls to perform a longitudinal comparison, which is prone to comparison errors and omissions.

[0004] In the diagnosis of some diseases, the lateral and longitudinal comparisons of the blood vessel wall need to be combined to be more conducive to the treatment of the disease. To this end, a method for identifying and recording the thickness of the blood vessel wall is proposed to improve the existing problems. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a method for identifying and recording the thickness of a blood vessel wall, which can analyze the thickness of the blood vessel wall to achieve longitudinal and transverse comparisons at the same time, and can intuitively observe whether the blood vessel wall has uneven thickness from longitudinal and transverse angles.

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

[0007] S1. Inject contrast agent into the patient's body through the vein, wherein the contrast agent is selected at the detection site according to the patient's own conditions, so that the blood vessels are more clearly visible in the CT image;

[0008] S2. Place the patient on the CT scanning bed, ensure that the patient lies flat and is comfortable, is within the field of view of the scanner, and does not carry any metal objects or obstructions;

[0009] S3, start the scanning module, the scanning module rotates and scans; at the same time, the CT machine scanning bed cooperates with the scanning module to move horizontally, the CT machine scanning bed drives the patient's whole body to slowly pass through the scanning module, and the scanning module transmits the scanning data;

[0010] S4, the image processing module receives the scan data transmitted by the scanning module, performs model building and image enhancement processing on the scan data, and outputs the image processing to complete the image model;

[0011] S5, the blood flow monitoring module receives the scan data, analyzes the perfusion parameters of the blood vessels through the blood flow monitoring module, obtains multiple groups of blood flow parameters through the 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, analyzes and processes the image model, 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 the image, a representation data unit for representing the data, and a warning prompt unit for representing the warning prompt;

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

[0014] By adopting the above technical solution, the present invention receives the blood vessel wall image through the image processing module and performs edge enhancement processing through the combined effect of intravenous contrast agent and CT machine, so that the edge of the blood vessel wall is clearly visible, which is convenient for doctors to observe the condition of the blood vessel wall, and by making a horizontal and vertical abnormality judgment on the blood vessel wall at the same time, the thickness of the blood vessel wall is compared. At the same time, by observing the blood flow data such as the flow rate and the amount of intravenous contrast agent, the blood flow condition of the patient can be known.

[0015] The present invention is further configured as follows: the image enhancement process comprises the following specific steps:

[0016] S41, performing preliminary processing on the image, firstly performing a denoising step on the image, filtering the image with a Gaussian filter to remove the noise in the image; correcting the geometric distortion in the image, normalizing the image pixel values, and setting the pixel values ​​to 255;

[0017] S42, enhancing contrast; adjusting the histogram of the image to make the pixel distribution uniform, stretching the image pixel value range through linear and nonlinear transformations, and obtaining a good contrast;

[0018] S43, performing blood vessel wall edge detection;

[0019] The present invention is further configured as follows: the blood vessel wall edge detection is processed by optimizing the Canny edge detection algorithm, the steps of which include:

[0020] S431, calculating the gradient magnitude and direction of the image, using the Sobel operator for calculation;

[0021] S432, performing non-maximum suppression in the gradient direction to refine the edge;

[0022] S433, according to the threshold value required by the image, two groups of threshold values ​​are set, namely a high threshold value and a low threshold value, and connected edges, including weak edges and strong edges, are detected through the two threshold values;

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

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

[0025] Blood flow: the amount of blood that flows through a blood vessel per unit length per unit time;

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

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

[0028] Peak time: The time when the contrast agent reaches its peak concentration.

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

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

[0031] The present invention is further configured as follows: the vascular wall thickness is obtained by calculating the difference between the inner wall and the outer wall of the vascular wall; by calling the image model and establishing a polar coordinate system based on the vascular transverse group, the vascular wall inner wall and vascular wall outer wall data are obtained through image analysis, and the vascular wall inner wall and outer wall data are aligned in coordinates.

[0032] The present invention is further configured as follows: the judgment of lateral abnormality of the blood vessel wall includes the following steps: the pole of the polar coordinate system is set to the center of the blood vessel, and diverges from the center of the blood vessel to the circumference in a 360° direction. According to the position of the blood vessel wall, the inner wall value of the blood vessel wall is set to Ω1, and the outer wall value of the blood vessel is set to Ω2. The difference between the two points is the thickness of the blood vessel wall, and the blood vessel 360 as a whole is statistically analyzed and compared.

[0033] The present invention is further configured as follows: the comparison step includes setting a transverse threshold, comparing the vascular wall thickness of the vascular transverse group, and if the vascular wall thickness difference within the vascular transverse group exceeds the threshold, it is displayed in a warning prompt unit representing the warning prompt, including the specific data and image model of this group of vascular transverse groups.

[0034] The present invention is further configured as follows: the judgment of longitudinal abnormality of the blood vessel wall includes the following steps: setting a longitudinal threshold value, after the blood vessel wall thickness is acquired, drawing the blood vessel wall thickness into a longitudinal drawing curve, analyzing whether there is a longitudinal abnormality in the blood vessel wall by analyzing the longitudinal drawing curve, and if there is a situation where the slope of the longitudinal drawing curve exceeds the longitudinal threshold value, it is displayed in an early warning prompt unit representing the early warning, including the specific data and image model of this group of blood vessel longitudinal groups.

[0035] By adopting the above technical scheme, the judgment of transverse abnormality and longitudinal abnormality of the vascular wall can ensure the all-round comparison of the patient's blood vessels, without any omissions in the comparison, and prevent diagnostic errors caused by only comparing the transverse or longitudinal direction, and by analyzing the information of the judgment of transverse abnormality of the vascular wall, the transverse group data of the patient's blood vessels can also be obtained. Through the specific data of the transverse group, it can be analyzed whether there is a problem of vascular stenosis in this transverse group (such as the overall thickness of the transverse group is too large or the thickness of a certain place is abnormal and the blood flow is imaged), so as to inform the patient of the warning in advance, and by analyzing the longitudinal curve of the vascular wall, the longitudinal vascular wall data of the patient can be obtained, so as to clearly understand whether there is an abnormality in the vascular wall.

[0036] The present invention is further configured as follows: 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 in the image processing model, a representation data unit pops up to represent the data at this point.

[0037] By adopting the above technical scheme, the present invention can accurately display the patient's vascular wall information through the interactive characterization module, and can accurately display the information required by the doctor through the image characterization unit, the characterization data unit, and the early warning prompt unit in the interactive characterization module, and can intuitively observe the transverse cross-section and longitudinal interface of different vascular wall parts, and the early warning prompt unit can avoid missed diagnosis caused by the doctor's failure to observe problems with the blood vessels here.

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

[0039] 1. The present invention uses the combined effect of intravenous contrast agent and CT machine to receive the blood vessel wall image through the image processing module and perform edge enhancement processing to make the edge of the blood vessel wall clearly visible, which is convenient for doctors to observe the condition of the blood vessel wall, and to judge the abnormality of the blood vessel wall in the horizontal and vertical directions at the same time, and to compare the thickness of the blood vessel wall. At the same time, by observing the blood flow data such as the flow rate and the amount of intravenous contrast agent, the blood flow condition of the patient can be known.

[0040] 2. The present invention can accurately display the patient's vascular wall information through the interactive representation module. The image representation unit, representation data unit, and early warning prompt unit in the interactive representation module can accurately display the information required by the doctor, and can intuitively observe the transverse cross-section and longitudinal interface of different vascular wall parts. The early warning prompt unit can avoid missed diagnosis caused by the doctor's failure to observe problems with the blood vessels.

[0041] 3. The judgment of transverse abnormality and longitudinal abnormality of the vascular wall can ensure the all-round comparison of the patient's blood vessels, without any omissions in the comparison, and prevent diagnostic errors caused by only comparing the transverse or longitudinal direction. By analyzing the information of the judgment of transverse abnormality of the vascular wall, the transverse group data of the patient's blood vessels can also be obtained. Through the specific data of the transverse group, it can be analyzed whether there is a problem of vascular stenosis in this transverse group (such as the overall thickness of the transverse group is too large or the thickness of a certain part is abnormal and the blood flow is imaged), and the patient is informed of the warning in advance. By analyzing the longitudinal curve of the vascular wall, the patient's longitudinal vascular wall data can be obtained, so as to clearly understand whether there is an abnormality in the vascular wall. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0043] Figure 2 Schematic diagram of the blood vessel wall in the present invention.

[0044] Figure 3 It is a schematic diagram of the orientation of optimizing the Canny edge detection algorithm in the present invention. DETAILED DESCRIPTION

[0045] It should be noted that, unless otherwise specified, all technical and scientific terms used in this application have the same meanings as commonly understood by ordinary technicians in the technical field to which this application belongs.

[0046] See also Figure 1-Figure 3 , the present invention provides the following technical solutions:

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

[0048] S1. Inject contrast agent into the patient's body through the vein. The contrast agent is selected at the detection site according to the patient's own conditions 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 visible in images by intravenous contrast agents. Contrast agents are usually compounds containing iodine that can absorb X-rays, thereby forming high-contrast vascular images in images. In clinical practice, the amount of intravenous contrast agents should be selected according to the patient's height, weight, and physical condition to avoid causing damage to the patient's body.

[0050] S2. Place the patient on the CT scanning bed, ensure that the patient lies flat and is comfortable, is within the field of view of the scanner, and does not carry any metal objects or obstructions;

[0051] S3, start the scanning module, the scanning module rotates and scans; at the same time, the CT machine scanning bed cooperates with the scanning module to move horizontally, the CT machine scanning bed drives the patient's whole body to slowly pass through the scanning module, and the scanning module transmits the scanning data;

[0052] The scanning module is usually composed of a rotating mechanism and an X-ray emitting mechanism. It should be understood that the scanning module in this embodiment adopts the existing technology. The scanning modules that can be directly or improved by technical personnel in this field can be applied to the system of this embodiment. No absolute limitation is made on the specific structure of the transmission module here.

[0053] S4, the image processing module receives the scan data transmitted by the scanning module, performs model building and image enhancement processing on the scan data, and outputs the image processing to complete the image model;

[0054] S5, the blood flow monitoring module receives the scan data, analyzes the perfusion parameters of the blood vessels through the blood flow monitoring module, obtains multiple groups of blood flow parameters through the 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, analyzes and processes the image model, 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 the image, a representation data unit for representing the data, and a warning prompt unit for representing the warning prompt;

[0056] S7. The recording module encrypts and records the total data group output by the interactive representation unit; wherein asymmetric encryption is adopted, and encryption is performed through two sets of keys, a set of public keys is used to encrypt the data, and a set of private keys is used to decrypt the data, wherein the user end can only decrypt through the private key.

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

[0058] S41. Perform preliminary processing on the image. First, perform a denoising step on the image, filter it with a Gaussian filter, and remove the noise in the image; correct the geometric distortion in the image, normalize the image pixel values, and set the pixel values ​​to 255; the pixel values ​​are uniquely determined to ensure that the clarity of each part of the image is consistent, and there are no artifacts or differences caused by uneven clarity.

[0059] S42, enhancing contrast; adjusting the histogram of the image to make the pixel distribution uniform, stretching the image pixel value range through linear and nonlinear transformations, and obtaining a good contrast;

[0060] S43, performing blood vessel wall edge detection;

[0061] See also Figure 3 ,The vascular wall edge detection is processed by optimizing the Canny edge detection algorithm, and its steps include,

[0062] S431, calculating the gradient magnitude and direction (NE, NW, SW, SE) of the image, using the Sobel operator for calculation;

[0063] S432, performing non-maximum suppression in the gradient direction to refine the edge;

[0064] S433, according to the threshold value required by the image, two groups of threshold values ​​are set, namely a high threshold value and a low threshold value, and connected edges, including weak edges and strong edges, are detected through the two threshold values;

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

[0066] Through the detection of the edge of the blood vessel wall, the blood vessel wall can be separated from other tissues and organs in the scan data, and only the blood vessel wall can be selected for display, avoiding the overlap of other tissues and organs with the blood vessel wall image.

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

[0068] Blood flow: the amount of blood that flows through a blood vessel per unit length per unit time;

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

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

[0071] Peak time: The time when the contrast agent reaches its peak concentration.

[0072] By combining blood flow data and blood vessel wall thickness data, a comprehensive diagnosis can be made to determine whether the slow blood flow is caused by abnormal blood vessel wall thickness. It can also be determined whether there is a situation where the blood vessel wall thickness is not abnormal but the blood flow is abnormal, which makes it easier for doctors to judge the condition and thus improve medical efficiency.

[0073] The interactive representation module analyzes the image model, including obtaining the thickness of the vascular wall, judging the lateral abnormality of the vascular wall, and judging the longitudinal abnormality of the vascular wall;

[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 vessels, the inner and outer wall data 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 judgment of lateral abnormality of the blood vessel wall includes the following steps: the pole of the polar coordinate system is set to the center of the blood vessel, and the 360° direction is diverged from the center of the blood vessel to the circumference. According to the position of the blood vessel wall, the inner wall value of the blood vessel wall is set to Ω1, and the outer wall value of the blood vessel wall is set to Ω2. The difference between the two points is the thickness of the blood vessel wall, and the blood vessel 360 is statistically compared as a whole. Among them, the comparison step includes setting a lateral threshold, comparing the thickness of the blood vessel wall of the lateral group of the blood vessel, and if the difference in the thickness of the blood vessel wall in the lateral group of the blood vessel exceeds the threshold, it is displayed in the warning prompt unit representing the warning prompt, including the specific data and image model of this group of lateral groups of blood vessels.

[0076] The judgment of longitudinal abnormality of 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 into a longitudinal drawing curve, and by analyzing the longitudinal drawing curve, it is analyzed whether there is a longitudinal abnormality in the blood vessel wall. If the slope of the longitudinal drawing curve exceeds the longitudinal threshold, it is displayed in the early warning prompt unit representing the early warning, including the specific data and image model of this group of blood vessel longitudinal group.

[0077] Specifically, in summary, the judgment of transverse abnormality of the vascular wall and the judgment of longitudinal abnormality of the vascular wall can ensure the all-round comparison of the patient's blood vessels, without any omissions in the comparison, and prevent diagnostic errors caused by only comparing the transverse or longitudinal direction, and by analyzing the information of the judgment of transverse abnormality of the vascular wall, the transverse group data of the patient's blood vessels can also be obtained. Through the specific data of the transverse group, it can be analyzed whether there is a problem of vascular stenosis in this transverse group (such as the overall thickness of the transverse group is too large or the thickness of a certain location is abnormal and the blood flow is imaged), and the patient is informed of the warning in advance, and by analyzing the longitudinal curve of the vascular wall, the patient's longitudinal vascular wall data can be obtained, so as to clearly understand whether there is an abnormality in the vascular wall.

[0078] For example 2, please refer to Figure 1-Figure 3 The second embodiment makes the following improvements on the basis of the first embodiment. Although the first embodiment can realize the identification and recording of the vascular wall thickness, in clinical practice, doctors need to visually observe the thickness of the vascular wall and make judgments based on the observed vascular wall thickness. If the vascular wall thickness can be displayed visually, 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, analyzes and processes the image model, 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 the image, a representation data unit for representing the data, and an early warning prompt unit for representing the prompt warning;

[0080] The image representation unit in the interactive representation module represents the image model transmitted by the image processing module. The image processing model has an interactive function. When the 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 to represent the data at this point.

[0081] Among them, the data that pops up from the characterization data unit includes the transverse data of the blood vessels at that point in the model, the maximum value of the transverse data of the blood vessels, and the minimum value of the transverse data of the blood vessels; the image characterization unit that characterizes the image characterizes the overall model diagram of the patient's blood vessels, and when the mouse moves to a point, it can characterize the transverse cross-sectional diagram and longitudinal interface diagram of the blood vessels at that point according to the operator's selection; if there are longitudinal and transverse abnormalities in the thickness of the blood vessel wall, the early warning prompt unit can send an early warning prompt and the data that causes the warning.

[0082] Obviously, the above-described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

Claims

1. A method for identifying and recording blood vessel wall thickness, characterized in that: The following steps are involved: S1. Inject contrast agent into the patient's body through the vein, wherein the contrast agent is selected at the detection site according to the patient's own conditions, so that the blood vessels are more clearly visible in the CT image; S2. Place the patient on the CT scanning bed, ensure that the patient lies flat and is comfortable, is within the field of view of the scanner, and does not carry any metal objects or obstructions; S3, start the scanning module, the scanning module rotates and scans; at the same time, the CT machine scanning bed cooperates with the scanning module to move horizontally, the CT machine scanning bed drives the patient's whole body to slowly pass through the scanning module, and the scanning module transmits the scanning data; S4, the image processing module receives the scan data transmitted by the scanning module, performs model building and image enhancement processing on the scan data, and outputs the image processing to complete the image model; S5, the blood flow monitoring module receives the scan data, analyzes the perfusion parameters of the blood vessels through the blood flow monitoring module, obtains multiple groups of blood flow parameters through the analysis, and packages the blood flow parameters into blood flow data packets for transmission; S6, the interactive representation module receives the image model transmitted by the image processing module, analyzes and processes the image model, 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 the image, a representation data unit for representing the data, and a warning prompt unit for representing the warning prompt; S7. The recording module encrypts and records the total data group output by the interactive representation unit.

2. A method for identifying and recording blood vessel wall thickness according to claim 1, characterized in that: The image enhancement process includes the following specific steps: S41, performing preliminary processing on the image, firstly performing a denoising step on the image, filtering the image with a Gaussian filter to remove the noise in the image; correcting the geometric distortion in the image, normalizing the image pixel values, and setting the pixel values ​​to 255; S42, enhancing contrast; adjusting the histogram of the image to make the pixel distribution uniform, stretching the image pixel value range through linear and nonlinear transformations, and obtaining a good contrast; S43. Perform blood vessel wall edge detection.

3. A method for identifying and recording blood vessel wall thickness according to claim 2, characterized in that: The blood vessel wall edge detection is processed by optimizing the Canny edge detection algorithm, and the steps include: S431, calculating the gradient magnitude and direction of the image, using the Sobel operator for calculation; S432, performing non-maximum suppression in the gradient direction to refine the edge; S433, according to the threshold value required by the image, two groups of threshold values ​​are set, namely a high threshold value and a low threshold value, and connected edges, including weak edges and strong edges, are detected through the two threshold values; S434, perform edge connection, connect the weak edge and the strong edge into a whole, and strengthen the weak edge and the strong edge to the same edge strength.

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

5. A method for identifying and recording blood vessel wall thickness according to claim 1, characterized in that: The interactive representation module analyzes the image model. Including obtaining the thickness of the blood vessel wall, judging the transverse abnormality of the blood vessel wall, and judging the longitudinal abnormality of the blood vessel wall.

6. A method for identifying and recording blood vessel wall thickness according to claim 5, characterized in that: The blood vessel wall thickness is obtained by calculating the difference between the inner wall and the outer wall of the blood vessel wall; by calling the image model and establishing a polar coordinate system according to the blood vessel transverse group, the inner wall and the outer wall data of the blood vessel wall are obtained through image analysis, and the coordinates of the inner wall and the outer wall data of the blood vessel wall are aligned.

7. A method for identifying and recording blood vessel wall thickness according to claim 6, characterized in that: The lateral abnormality judgment of the blood vessel wall includes the following steps: the pole of the polar coordinate system is set to the center of the blood vessel, and diverges from the center of the blood vessel to the circumference in a 360° direction. According to the position of the blood vessel wall, the inner wall value of the blood vessel wall is set to Ω1, and the outer wall value of the blood vessel is set to Ω2. The difference between the two points is the blood vessel wall thickness, and the blood vessel 360 as a whole is statistically compared.

8. A method for identifying and recording blood vessel wall thickness according to claim 7, characterized in that: The comparison step includes setting a transverse threshold, comparing the vascular wall thickness of the vascular transverse group, and if the vascular wall thickness difference within the vascular transverse group exceeds the threshold, it is displayed in a warning prompt unit representing the warning, including the specific data and image model of this group of vascular transverse groups.

9. A method for identifying and recording blood vessel wall thickness according to claim 5, characterized in that: The determination of longitudinal abnormality of the blood vessel wall comprises the following steps: setting a longitudinal threshold value, after the blood vessel wall thickness is acquired, drawing the blood vessel wall thickness into a longitudinal drawing curve, analyzing whether there is a longitudinal abnormality in the blood vessel wall by analyzing the longitudinal drawing curve, and if there is a situation where the slope of the longitudinal drawing curve exceeds the longitudinal threshold value, it is displayed in an early warning prompt unit representing the early warning, including the specific data and image model of this group of blood vessel longitudinal groups.

10. A method for identifying and recording blood vessel wall thickness 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 model 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, a representation data unit pops up to represent the data at this point.

Images