Catheter positioning method and system for vascular intervention and storage medium

Abstract

The invention belongs to the technical field of vascular intervention, and particularly relates to a catheter positioning method and system for vascular intervention and a storage medium, and the method comprises the steps: obtaining a plurality of DSA images, shooting internal images of a blood vessel at preset time intervals, obtaining a corresponding first position during shooting, transmitting the DSA images, the internal images of the blood vessel and the first position to a processing module, the method comprises the following steps: establishing a three-dimensional model of a blood vessel, acquiring a three-dimensional coordinate point corresponding to a first position in the three-dimensional model of the blood vessel, simulating and acquiring a simulated internal image shot at the position of the three-dimensional coordinate point in the three-dimensional model, calculating the similarity between the blood vessel internal image and the simulated internal image, and acquiring the simulated internal image having the maximum similarity with the blood vessel internal image. Three-dimensional coordinate points corresponding to the simulation internal image are obtained, the center line of the current moving area is obtained, the catheter is made to move forwards along the center line, and the moving direction of the catheter is changed in advance if the catheter encounters a special area. The accuracy and success rate of the vascular interventional operation can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of vascular intervention, and in particular relates to a catheter positioning method, system and storage medium for vascular intervention. Background Art

[0002] In vascular interventional surgery, it is crucial to accurately locate the position of the catheter in the target blood vessel. A similar prior art Chinese patent application with publication number CN118476864A provides a method and system for automatically adjusting a three-dimensional medical image based on a guidewire perspective. The method obtains and processes angiographic images acquired by DSA to obtain a three-dimensional model of the blood vessel, matches and superimposes the three-dimensional model of the blood vessel with the DSA perspective image, and obtains a fused image; in the fused image, the guidewire head end is located using guidewire recognition technology, and it is determined whether the guidewire moves or rotates; if the guidewire moves, the image position is automatically adjusted according to the guidewire movement position; if the guidewire rotates, the image angle is automatically adjusted according to the guidewire rotation position; if the guidewire moves and rotates, the image position and angle are adjusted simultaneously according to the guidewire movement position and the guidewire rotation position. However, when acquiring angiographic images acquired by DSA, the accuracy of the verification image is not considered. If an inaccurate image is obtained, the subsequent generated three-dimensional model will also be inaccurate, affecting subsequent treatment. Similar prior art also has a Chinese patent application with publication number CN113855239A, which discloses a guidewire navigation system and method in vascular intervention surgery, the system comprising: a C-arm X-ray imaging device, used to generate angiography images of blood vessels in the patient's lesion site, and send the angiography images to an image processor; a first positioning sensor, arranged on the C-arm X-ray imaging device; a second positioning sensor, arranged at the end of the guidewire; a third positioning sensor, arranged on the flat-panel detector of the C-arm X-ray imaging device; a positioning instrument, the positioning instrument is used to obtain the first spatial coordinates of the first positioning sensor, the second spatial coordinates of the second positioning sensor, and the third spatial coordinates of the third positioning sensor, and send the first spatial coordinates, the second spatial coordinates and the third spatial coordinates to the image processor; an image processor, used to complete the navigation of the guidewire. However, this method does not consider the accuracy of the image during the transmission process when sending the angiography image to the image processor. Therefore, the present invention provides a catheter positioning method, system and storage medium for vascular intervention. Summary of the invention

[0003] The present invention collects and transmits DSA images, images inside blood vessels and corresponding two-dimensional positions, adds verification data at preset time intervals during transmission to ensure the accuracy of the images, establishes a three-dimensional model of the blood vessels, obtains three-dimensional coordinate points corresponding to the two-dimensional positions in the three-dimensional model, simulates and obtains simulated internal images taken at the positions of the three-dimensional coordinate points in the three-dimensional model, accurately reverses the three-dimensional position of the catheter tip based on the similarity between the simulated internal images and the corresponding real internal images of the blood vessels, obtains the center line of the current moving area during the movement of the catheter, and moves the catheter forward along the center line. If a special area is encountered, the moving direction of the catheter is changed in advance to prevent the catheter from mistakenly entering a non-target blood vessel or damaging the blood vessel wall.

[0004] In order to achieve the above-mentioned object of the invention, the present invention provides the following catheter positioning method for vascular intervention, which is implemented by performing the following steps: Step S1, obtaining a plurality of DSA images, setting a micro camera at the top of the catheter, after the catheter is inserted into the blood vessel, as the top of the catheter moves in the blood vessel, the micro camera takes pictures of the inside of the blood vessel at preset time intervals to obtain images inside the blood vessel, and records a first position of the top of the catheter in the blood vessel while taking the images inside the blood vessel, and transmits the DSA images, the images inside the blood vessel and the first position to a processing module; Step S2, verifying the DSA image, the internal image of the blood vessel and the first position that need to be verified. After successful verification, a three-dimensional model of the blood vessel is established based on the DSA image, the first position and the corresponding internal image of the blood vessel, a three-dimensional coordinate point corresponding to the first position is obtained in the three-dimensional model of the blood vessel, and a simulated internal image taken at the position of the three-dimensional coordinate point is simulated and obtained in the three-dimensional model; Step S3, calculating the similarity between the blood vessel internal image and the corresponding simulated internal image, obtaining the simulated internal image having the greatest similarity to the blood vessel internal image, and obtaining the three-dimensional coordinate point corresponding to the simulated internal image, wherein the three-dimensional coordinate point is the second position of the catheter tip in the blood vessel, and the second position is the three-dimensional position of the catheter tip in the three-dimensional model of the blood vessel; Step S4: in the process of moving the catheter, the center line of the current moving area is obtained, and the catheter is moved forward along the center line. Based on the internal image of the blood vessel and the DSA image, it is determined whether there is a special area in front of the moving direction of the catheter. If so, the moving direction of the catheter is changed.

[0005] As a preferred technical solution of the present invention, calculating the similarity between the internal image of the blood vessel and the corresponding simulated internal image comprises the following steps: Step S31, acquiring the blood vessel internal image and one of the simulated internal images, acquiring a plurality of first key areas of the blood vessel internal image, acquiring a plurality of second key areas of the simulated internal image, and pairing the first key areas with the second key areas based on position information to generate key area pairs; Step S32: for each of the first key areas, obtain a first value of each pixel in the first key area, then obtain a second value of a pixel adjacent to the pixel, and calculate a first difference and a second difference between the first value and a plurality of the second values; Step S33, obtaining a first average value of the plurality of first difference values, comparing the first average value with a preset first threshold value, if the first average value is greater than the first threshold value, setting the first characteristic value of the pixel point to a first preset value, otherwise setting the first characteristic value of the pixel point to a second preset value, and combining the first characteristic values ​​of each pixel in the first key area to generate a first characteristic combination of the first key area; Step S34, obtaining a second average value of the plurality of second difference values, comparing the second average value with a preset second threshold value, and if the first average value is greater than the second threshold value, setting the second characteristic value of the pixel point to a first preset value; otherwise, setting the second characteristic value of the pixel point to a second preset value, and combining the second characteristic values ​​of each pixel in the first key area to generate a second characteristic combination of the first key area; Step S35, repeating the steps S32 to S34, obtaining the first feature combination and the second feature combination of the second key area, and calculating the similarity of the key area pair based on the first feature combination and the second feature combination of the key area pair; Step S36: obtaining the similarities of all the key region pairs, and integrating all the similarities to obtain the overall similarity between the blood vessel internal image and the simulated internal image.

[0006] As a preferred technical solution of the present invention, determining the similarity of the key region pair based on the first feature combination and the second feature combination of the key region pair comprises the following steps: Step S351, obtaining a first number of the first preset values ​​included in the first feature combination, and also obtaining a second number of the first feature values ​​included in the first feature combination, dividing the first number by the second number to obtain a first ratio, and further calculating a second ratio of the first preset values ​​in the second feature combination; Step S352: if both the first ratio and the second ratio are greater than a preset third threshold, classify the key area pair consisting of the first key area and the corresponding second key area into the first category; otherwise, classify the key area pair into the second category; Step S353, obtaining two first feature combinations of the key area pair, initializing the first difference to zero, and comparing each of the first feature values ​​in the two first feature combinations in turn. If the two first feature values ​​are the same, the first difference remains unchanged; otherwise, the first difference is increased by one until all the first feature values ​​in the first feature combinations are compared to obtain a final first difference, and the first difference is divided by the second number to obtain a first similarity; Step S354, obtaining two second feature combinations of the key area pair, initializing the second difference to zero, and comparing each second feature value in the two second feature combinations in turn. If the two second feature values ​​are the same, the second difference remains unchanged; otherwise, the second difference is increased by one until all the second feature values ​​in the second feature combinations are compared, and a final second difference is obtained. The second difference is divided by the second number to obtain a second similarity; Step S355: Obtain an average value of the first similarity and the second similarity, and use the average value as the similarity of the key region pair.

[0007] As a preferred technical solution of the present invention, all the similarities are integrated, including the following steps: All the similarities are combined to calculate the overall similarity using a first formula, where the first formula is: , where S is the overall similarity, Xi is the similarity of the i-th key area pair, Wi is the weight value of the i-th key area pair, and N is the total number of key area pairs. If the key area pair belongs to the first category, the weight value of the key area pair is set to the first weight value. If the key area pair belongs to the second category, the weight value of the key area pair is set to the second weight value.

[0008] As a preferred technical solution of the present invention, transmitting the DSA image, the intravascular image and the first position to the processing module comprises the following steps: Step S11, obtaining the transmitted first DSA image, the first intravascular image and the first first position, extracting specific areas of the DSA image and the intravascular image, encoding the specific areas to obtain corresponding first coded data, then generating corresponding first hash data based on the first coded data, and obtaining a corresponding check code based on the first position; Step S12, sending the DSA image, the blood vessel internal area and the first hash data corresponding to each to the processing module, and sending the first position and the corresponding verification code to the processing module; Step S13, receiving the verification information of the processing module, if there is no error in the transmission process, not generating the hash data and the verification code for the predetermined number of the DSA images, the intravascular images and the first position to be transmitted next, and if there is an error in the transmission process, checking the transmission route and determining the cause of the error; Step S14, then repeating steps S11 to S12 for the DSA image, the intravascular image and the first position to be transmitted next, until all the DSA images and all the intravascular images are transmitted.

[0009] As a preferred technical solution of the present invention, verifying the acquired DSA image and the intravascular image comprises the following steps: Step S21, performing encoding processing on the DSA image and the intravascular image that have obtained corresponding hash data to obtain second encoded data, further generating corresponding second hash data based on the second encoded data, comparing the first hash data and the corresponding second hash data to determine whether they are the same, if they are the same, it means that there is no error in the transmission process of the DSA image and the intravascular image, and sending a verification success message to the acquisition module, otherwise sending a verification failure message to the acquisition module; Step S22: verify the corresponding first position based on the verification code to determine whether the first position is wrong. If wrong, correct the first position based on the verification code.

[0010] As a preferred technical solution of the present invention, judging whether there is a special area ahead of the moving direction of the catheter based on the internal image of the blood vessel and the DSA image includes the following steps: Step S41, collecting a number of the intravascular images acquired historically, specially marking special areas of the intravascular images as learning data, and generating a recognition model based on the learning data using a machine learning algorithm, wherein the recognition model includes a first unit and a second unit; Step S42, segmenting the blood vessel internal image to generate a plurality of sub-images, and using the first unit to identify the plurality of sub-images to obtain key information of each of the sub-images; Step S43: using the second unit to classify the key information, classifying different key information into different special categories, calculating recognition errors based on the special categories and the special marks, and adjusting parameters of the first unit and the second unit based on the recognition errors; Step S45, acquiring a number of new historically acquired intravascular images as new learning data, training the recognition model based on the learning data to obtain an updated recognition model, and acquiring a recognition error of the recognition model. If the recognition error is less than a preset error threshold, the training is stopped; otherwise, this step is repeated; Step S45: After the final recognition model is generated, the newly acquired internal image of the blood vessel is input into the recognition model to determine whether there is a special area in front.

[0011] The present invention also provides a catheter positioning system for vascular intervention, comprising the following modules: an acquisition module, for acquiring a plurality of DSA images, wherein a micro camera is arranged at the top of the catheter, and after the catheter is inserted into the blood vessel, as the top of the catheter moves in the blood vessel, the micro camera takes pictures of the inside of the blood vessel at preset time intervals to acquire an image inside the blood vessel, and while taking the image inside the blood vessel, a first position of the top of the catheter in the blood vessel is recorded, and the DSA image, the image inside the blood vessel and the first position are transmitted to the processing module; a processing module, configured to verify the DSA image, the internal image of the blood vessel and the first position to be verified, and after successful verification, establish a three-dimensional model of the blood vessel based on the DSA image, the first position and the corresponding internal image of the blood vessel, obtain a three-dimensional coordinate point corresponding to the first position in the three-dimensional model of the blood vessel, and simulate in the three-dimensional model to obtain a simulated internal image taken at the position of the three-dimensional coordinate point; a positioning module, used for calculating the similarity between the internal image of the blood vessel and the corresponding simulated internal image, obtaining the simulated internal image having the greatest similarity with the internal image of the blood vessel, and obtaining the three-dimensional coordinate point corresponding to the simulated internal image, wherein the three-dimensional coordinate point is the second position of the catheter tip in the blood vessel, and the second position is the three-dimensional position of the catheter tip in the three-dimensional model of the blood vessel; The control module is used to obtain the center line of the current moving area during the movement of the catheter, so that the catheter moves forward along the center line, and judge whether there is a special area in front of the moving direction of the catheter based on the internal image of the blood vessel and the DSA image. If so, change the moving direction of the catheter.

[0012] The present invention also provides a storage medium, wherein the storage medium stores program instructions, wherein when the program instructions are executed, the device where the storage medium is located is controlled to execute any one of the above-mentioned catheter positioning methods for vascular intervention.

[0013] Compared with the prior art, the beneficial effects of the present invention are at least as follows: In the present invention, by acquiring DSA images at different angles, a micro-shooting device is also arranged at the top of the catheter to acquire the internal image of the blood vessel, and the first position when the internal image of the blood vessel is captured is acquired at the same time, the DSA image, the internal image of the blood vessel and the first position are transmitted to the processing module, and verification data is added during the transmission process to ensure the correctness of the data transmission, and to ensure the accuracy of the subsequent establishment of the three-dimensional model, and a three-dimensional model of the blood vessel is established based on the DSA image and the internal image of the blood vessel, and a plurality of corresponding three-dimensional coordinate points in the three-dimensional model are acquired based on the first position, and simulated shooting is performed at the plurality of three-dimensional coordinate points in the three-dimensional model to acquire the corresponding simulated internal image, and the corresponding three-dimensional coordinate points are inferred from the simulated internal image with the greatest similarity to the real internal image of the blood vessel, so that the three-dimensional position of the top of the catheter can be acquired more accurately, thereby improving the accuracy and success rate of the interventional surgery, and in the process of moving the catheter, the catheter is moved along the center line of the blood vessel, and it is also determined whether there is a special area in front, and if so, the moving direction of the catheter is changed in advance to avoid the catheter from entering a non-target blood vessel by mistake or damaging the blood vessel wall. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flowchart of the steps of the catheter positioning method for vascular intervention of the present invention; Figure 2 It is a structural diagram of the catheter positioning system for vascular intervention of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0016] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0017] The present invention provides Figure 1 The catheter positioning method for vascular intervention is implemented by executing the following steps: Step S1, obtaining a plurality of DSA images, setting a micro camera at the top of the catheter, after the catheter is inserted into the blood vessel, as the top of the catheter moves in the blood vessel, the micro camera takes pictures of the inside of the blood vessel at preset time intervals to obtain the image inside the blood vessel, and records the first position of the top of the catheter in the blood vessel while taking the image inside the blood vessel, and transmits the DSA image, the image inside the blood vessel and the first position to the processing module.

[0018] Specifically, in order to obtain the overall image of the blood vessel, several DSA images are obtained by photographing the target object at different angles through an X-ray camera. The DSA image is a two-dimensional perspective image that can clearly display the blood vessel. Based on the DSA images taken at different angles, a three-dimensional image of the blood vessel can be obtained using three-dimensional reconstruction technology. The internal image of the blood vessel is obtained by photographing the inside of the blood vessel, and the first position of the catheter tip in the blood vessel is recorded at the same time. Since the DSA image is a two-dimensional image, the first position is the two-dimensional position of the catheter tip in the blood vessel. If the catheter is positioned and navigated based on the two-dimensional position only, the difficulty and risk of vascular intervention surgery will increase. Therefore, a three-dimensional model of the blood vessel is subsequently generated based on the DSA image and the internal image of the blood vessel. After the DSA image and the internal image of the blood vessel are obtained, they need to be processed. The DSA image and the internal image of the blood vessel and the corresponding first position need to be transmitted to the processing module. Since the DSA image and the internal image of the blood vessel are related to the life safety of the patient, in order to ensure that the data is not erroneous or lost during the transmission process, verification data such as hash data and checksums are added for synchronous transmission. The specific transmission method will be explained in detail later.

[0019] Step S2, verify the DSA image, the internal image of the blood vessel and the first position that need to be verified. After successful verification, establish a three-dimensional model of the blood vessel based on the DSA image, the first position and the corresponding internal image of the blood vessel, obtain the three-dimensional coordinate point corresponding to the first position in the three-dimensional model of the blood vessel, and simulate the internal image taken at the position of the three-dimensional coordinate point in the three-dimensional model.

[0020] Specifically, since verification data is added to some DSA images, internal blood vessel images and the first position during the transmission process, the verification data refers to the first hash data, the second hash data and the check code mentioned later, so the DSA images, internal blood vessel images and the first position with added verification data are verified. After the verification is successful, a three-dimensional model of the blood vessel is established based on the acquired DSA images, the first position and the internal blood vessel images. The three-dimensional model can help us to have a more comprehensive understanding of the overall situation and characteristics of the blood vessels, and obtain the three-dimensional coordinate point corresponding to the first position in the three-dimensional model. Since the first position is a two-dimensional coordinate, it will correspond to multiple three-dimensional coordinate points after being mapped to the three-dimensional model. In order to more accurately locate the catheter position, the simulated internal image taken at the three-dimensional coordinate point position is simulated in the three-dimensional model.

[0021] If verification fails, check the transmission line and find out the cause of the data transmission error.

[0022] Step S3, calculate the similarity between the internal image of the blood vessel and the corresponding simulated internal image, obtain the simulated internal image with the greatest similarity to the internal image of the blood vessel, obtain the three-dimensional coordinate point corresponding to the simulated internal image, the three-dimensional coordinate point is the second position of the catheter tip in the blood vessel, and the second position is the three-dimensional position of the catheter tip in the three-dimensional model of the blood vessel.

[0023] Specifically, by calculating the similarity between the internal image of the blood vessel and the corresponding simulated internal image, the specific method for calculating the similarity will be explained in detail later, and obtaining the simulated internal image with the greatest similarity to the internal image of the blood vessel. The three-dimensional coordinate point corresponding to the simulated internal image is the second position of the catheter tip in the real blood vessel. Since the three-dimensional coordinate point is obtained from the three-dimensional model, the second position is the three-dimensional position in the real blood vessel. The above method can more accurately determine the exact position of the catheter tip in the blood vessel, thereby improving the accuracy and success rate of the operation.

[0024] Step S4: During the process of moving the catheter, the center line of the current moving area is obtained, and the catheter is moved forward along the center line. Based on the internal image of the blood vessel and the DSA image, it is determined whether there is a special area in front of the moving direction of the catheter. If so, the moving direction of the catheter is changed.

[0025] Specifically, in order to prevent the catheter from accidentally entering a non-target blood vessel or damaging the blood vessel wall and to reduce surgical risks and complications, the center line of the current moving area is obtained during the movement of the catheter. The moving area refers to the front and rear areas of the catheter tip in the blood vessel. The center line of the moving area is obtained and the catheter is controlled to move forward along the center line. During the movement, it is also determined based on the internal image of the blood vessel and the DSA image whether there is a special area in front of the catheter movement direction. Special areas include narrow areas, curved areas, and bifurcation areas. If so, the movement direction of the catheter needs to be changed in advance to prevent the blood vessel from accidentally entering a non-target blood vessel area or damaging the blood vessel wall.

[0026] The present invention acquires DSA images at different angles, and also arranges a micro-shooting device at the top end of the catheter to acquire the internal image of the blood vessel, and at the same time acquires the first position when the internal image of the blood vessel is captured, and transmits the DSA image, the internal image of the blood vessel and the first position to a processing module, and adds verification data during the transmission process to ensure the correctness of the data transmission, and ensures the accuracy of the subsequent establishment of the three-dimensional model, and establishes a three-dimensional model of the blood vessel based on the DSA image and the internal image of the blood vessel, and acquires a plurality of corresponding three-dimensional coordinate points in the three-dimensional model based on the first position, and simulates and shoots at the plurality of three-dimensional coordinate points in the three-dimensional model to acquire the corresponding simulated internal image, and acquires the simulated internal image with the greatest similarity to the real internal image of the blood vessel and inverts the corresponding three-dimensional coordinate points, so as to more accurately acquire the three-dimensional position of the top end of the catheter, thereby improving the accuracy and success rate of the interventional surgery, and in the process of moving the catheter, the catheter is moved along the center line of the blood vessel, and it is also determined whether there is a special area in front, and if there is, the moving direction of the catheter is changed in advance to avoid the catheter from mistakenly entering a non-target blood vessel or damaging the blood vessel wall.

[0027] Further, calculating the similarity between the blood vessel internal image and the corresponding simulated internal image includes the following steps: Step S31, obtaining an internal image of a blood vessel and a simulated internal image, obtaining multiple first key areas of the internal image of the blood vessel, obtaining multiple second key areas of the simulated internal image, and pairing the first key areas with the second key areas based on the position information to generate key area pairs.

[0028] Specifically, in order to accurately calculate the similarity between the simulated internal image and the internal image of the blood vessel, the key areas of the two images are first obtained respectively. For example, in the internal area of ​​the blood vessel, the central area of ​​the blood vessel and the blood vessel wall area generally have different characteristics, so the key area may include the central area of ​​the blood vessel and the blood vessel wall area. In order to divide the internal image more accurately, the internal image can also be divided into multiple key areas according to the spatial position. The key areas of the internal image of the blood vessel are collectively referred to as the first key areas, and the key areas of the simulated internal image are collectively referred to as the second key areas.

[0029] Step S32: for each first key area, obtain the first value of each pixel in the first key area, then obtain the second value of the pixel adjacent to the pixel, and calculate the first difference and the second difference between the first value and the plurality of second values.

[0030] Specifically, in order to calculate the similarity more accurately, the first value of each pixel in the first key area is obtained, and the first value can be the brightness value of the pixel. The second value of the pixel adjacent to the current pixel is also obtained, and the second value refers to the brightness value of the adjacent pixel. The first difference is the difference between the first value and the second value, and the second difference is the absolute value of the difference between the first value and the second value. The difference represents the difference in brightness level between the pixel and the adjacent pixel. The absolute value of the difference means that the positive or negative brightness difference is not considered, and only the size of the difference is concerned. By counting the first difference and the second difference, it is provided to observe and analyze the image from different perspectives. Combining the two can more comprehensively understand the characteristics of the image and improve the accuracy of subsequent accuracy calculations.

[0031] Step S33, obtain the first average value of multiple first difference values, compare the first average value with the preset first threshold value, if the first average value is greater than the first threshold value, set the first eigenvalue of the pixel point to the first preset value, otherwise set the first eigenvalue of the pixel point to the second preset value, and combine the first eigenvalues ​​of each pixel in the first key area to generate the first feature combination of the first key area.

[0032] Specifically, a first average value of the first difference is calculated, and the first average value is used as the average difference between the representative pixel and multiple adjacent pixels. A first threshold is preset, and the first threshold is used to determine whether the brightness difference is significant. If the difference is large, the first eigenvalue of the pixel is set to a first preset value, such as 1; otherwise, the first eigenvalue of the pixel is set to a second preset value, such as 0; the first eigenvalues ​​of all pixels in the first key area are combined to generate a first feature combination. For example, the first feature combination of a first key area with 9 pixels may be (1, 0, 1, 0, 1, 0, 1, 0).

[0033] Step S34, obtain the second average value of multiple second difference values, compare the second average value with the preset second threshold value, if the first average value is greater than the second threshold value, set the second eigenvalue of the pixel point to the first preset value, otherwise set the second eigenvalue of the pixel point to the second preset value, and combine the second eigenvalues ​​of each pixel in the first key area to generate the second feature combination of the first key area.

[0034] Specifically, the second feature combination of the first key area is obtained by the above method, and the second feature combination may be (1,1,1,0,1,0,1,1,1).

[0035] Step S35, repeating steps S32 to S34, obtaining the first feature combination and the second feature combination of the second key area, and calculating the similarity of the key area pair based on the first feature combination and the second feature combination of the key area pair.

[0036] Specifically, repeat the above steps S32 to S34 to obtain the first feature combination and the second feature combination corresponding to the second key area, and calculate the similarity of the key area pair based on the first feature combination and the second feature combination of the key area pair. The specific method for calculating the similarity will be explained in detail later.

[0037] Step S36: Obtain the similarities of all key region pairs, integrate all similarities, and obtain the overall similarity between the blood vessel internal image and the simulated internal image.

[0038] Specifically, since a set of intravascular images and simulated intravascular images will have multiple sets of key area pairs, after obtaining the similarity of the key area pairs, it is necessary to synthesize the similarity of the key area pairs to obtain the overall similarity between the intravascular images and the simulated intravascular images. The specific method of synthesizing the similarities to obtain the overall similarity will be explained in detail later.

[0039] Further, determining the similarity of the key region pair based on the first feature combination and the second feature combination of the key region pair includes the following steps: Step S351, obtain a first number of first preset values ​​included in the first feature combination, also obtain a second number of first feature values ​​included in the first feature combination, divide the first number by the second number to obtain a first ratio, and a second ratio of the first preset value in the second feature combination.

[0040] Specifically, the first number represents the number of pixels with large differences in brightness values ​​in the first key area, and the second number represents the number of all pixels in the first key area. The above method is used to calculate the first ratio of the number of pixels with large differences in brightness values ​​to the total number of pixels. The above method is also used to calculate the second ratio of the pixels with large absolute values ​​of brightness value differences to the total number of pixels, which provides a data basis for the subsequent calculation of similarity.

[0041] Step S352: if both the first ratio and the second ratio are greater than a preset third threshold, the key area pair consisting of the first key area and the corresponding second key area is classified into the first category; otherwise, the key area pair is classified into the second category.

[0042] Specifically, if the first ratio and the second ratio are both greater than a preset third threshold value, the third threshold value is a standard value set based on historical experimental data, indicating that both the brightness value difference and the absolute value of the brightness value difference exceed the standard value, and the corresponding key area pairs are classified into the first category, otherwise they are classified into the second category. The first category represents that the corresponding key area pairs play a greater role in determining whether two images are similar, and the second category represents that the key area pairs do not play as great a role as the first category in determining whether two images are similar.

[0043] Step S353, obtain two first feature combinations of the key area pair, initialize the first difference to zero, and compare each first eigenvalue in the two first feature combinations in turn. If the two first eigenvalues ​​are the same, the first difference remains unchanged. Otherwise, add one to the first difference until all the first eigenvalues ​​in the first feature combination are compared to obtain the final first difference, and divide the first difference by the second number to obtain the first similarity.

[0044] Specifically, the first difference between the two first feature combinations of the key area pair is obtained by the above method. The smaller the first difference, the higher their similarity. The first difference is divided by the total number of pixels to obtain the first similarity calculated based on the first feature combination. The first similarity is calculated based on the first feature combination. The first feature combination is calculated based on the brightness value difference, so the first similarity is the similarity calculated considering the brightness value difference.

[0045] Step S354, obtain two second feature combinations of the key area pair, initialize the second difference to zero, and compare each second eigenvalue in the two second feature combinations in turn. If the two second eigenvalues ​​are the same, the second difference remains unchanged. Otherwise, add one to the second difference until all the second eigenvalues ​​in the second feature combination are compared to obtain the final second difference, and divide the second difference by the second number to obtain the second similarity.

[0046] Specifically, the second difference between the two second feature combinations corresponding to the key area pair is obtained by the above method. The larger the second difference, the smaller the similarity between the two. The second difference is divided by the total number of pixels to obtain the second similarity calculated based on the first feature combination. The second similarity is calculated based on the second feature combination. The second feature combination is calculated based on the absolute value of the brightness value difference, so the second similarity is the similarity calculated based on the absolute value of the brightness value difference.

[0047] Step S355: Calculate the average of the first similarity and the second similarity, and use the average as the similarity of the key region pair.

[0048] Specifically, an average value of the first similarity and the second similarity is obtained, and the average value is used as the similarity of the key area pair. The above method averages the similarities calculated from two different angles to obtain the final similarity. By comprehensively considering the similarities calculated from two angles, the calculated similarity can be made more accurate.

[0049] Furthermore, all similarities are integrated, including the following steps: Use the first formula to comprehensively calculate the overall similarity of all similarities. The first formula is: , where S is the overall similarity, Xi is the similarity of the i-th key area pair, Wi is the weight value of the i-th key area pair, and N is the total number of key area pairs. If the key area pair belongs to the first category, the weight value of the key area pair is set to the first weight value. If the key area pair belongs to the second category, the weight value of the key area pair is set to the second weight value.

[0050] Specifically, since the internal image of the blood vessel and the simulated internal image are composed of multiple pairs of key area pairs, and the key area pairs have been classified before, the first category represents the corresponding key area pairs that play a greater role in judging whether the two images are similar, so the weight value of the key similarity pairs belonging to the first category is set to the first weight value, such as 0.6. The second category represents the key area pairs that play a relatively small role in judging whether the two images are similar, so the weight value of the key similarity pairs belonging to the second category is set to the second weight value, such as 0.4, and the weighted average of all key similarity pairs is performed to obtain the overall similarity.

[0051] Further, transmitting the DSA image, the intravascular image and the first position to the processing module comprises the following steps: Step S11, obtaining the transmitted first DSA image, the first intravascular image, and the first first position, extracting specific areas of the DSA image and the intravascular image, encoding the specific areas to obtain corresponding first coded data, then generating corresponding first hash data based on the first coded data, and obtaining a corresponding check code based on the first position; Step S12, sending the DSA image, the internal area of ​​the blood vessel and the first hash data corresponding to each to the processing module, and sending the first position and the corresponding verification code to the processing module; Step S13, receiving the verification information of the processing module, if there is no error in the transmission process, for the predetermined number of DSA images, the intravascular images and the first position to be transmitted next, no hash data and check code are generated, if there is an error in the transmission process, checking the transmission route and determining the cause of the error; Step S14, then repeating steps S11 to S12 for the next DSA image, the intravascular image and the first position to be transmitted, until all DSA images and all intravascular images are transmitted.

[0052] Specifically, since frequent verification of the transmitted images will increase the transmission time, but in order to ensure the correctness of the images during the transmission process, it is considered to add verification data to the above data to be sent next time after sending a predetermined number of DSA images, internal blood vessel images and the first position. The verification data includes the first hash data, the second hash data and the check code. If there is no error in the verification, verification data is added to the newly sent image again after sending a predetermined number of image data. The above method can not only ensure the accuracy of the image data during the transmission process, but also prevent the transmission time from being delayed too long.

[0053] Furthermore, the acquired DSA images and the intravascular images are verified, including the following steps: Step S21, performing encoding processing on the DSA image and the intravascular image corresponding to the hash data to obtain second encoded data, further generating corresponding second hash data based on the second encoded data, comparing the first hash data and the corresponding second hash data to determine whether they are the same, if they are the same, it means that there is no error in the transmission process of the DSA image and the intravascular image, and sending a verification success message to the acquisition module, otherwise sending a verification failure message to the acquisition module; Step S22: verify the corresponding first position based on the check code to determine whether the first position is wrong. If wrong, correct the first position based on the check code.

[0054] Specifically, since corresponding verification data is added when certain images are sent, and the verification data includes the first hash data, the second hash data and the check code, the image data needs to be verified at the receiving side, that is, the processing module. Since the DSA image and the intravascular image are image data, it takes a lot of time to correct them using correction data. Therefore, in the event of a verification failure, it is necessary to check and correct the transmission circuit and then retransmit it. The position information is relatively small, and in the event of an error, a check code can be used for correction. The check code can use a Hamming code or other check code with a correction function. The above method can quickly and accurately verify the intravascular image and the DSA image.

[0055] Further, judging whether there is a special area ahead of the moving direction of the catheter based on the internal image of the blood vessel includes the following steps: Step S41, collecting a number of historically acquired intravascular images, specially marking special areas of the intravascular images as learning data, and generating a recognition model based on the learning data using a machine learning algorithm, wherein the recognition model includes a first unit and a second unit; Step S42, segmenting the internal image of the blood vessel to generate a plurality of sub-images, and using the first unit to identify the plurality of sub-images to obtain key information of each sub-image; Step S43, using the second unit to classify the key information, classifying different key information into different special categories, calculating the recognition error based on the special categories and special marks, and adjusting the parameters of the first unit and the second unit based on the recognition error; Step S45, obtaining a number of new historically obtained blood vessel internal images as new learning data, training the recognition model based on the learning data to obtain an updated recognition model, obtaining the recognition error of the recognition model, and stopping the training if the recognition error is less than a preset error threshold, otherwise, repeating this step; Step S45: After the final recognition model is generated, the newly acquired internal image of the blood vessel is input into the recognition model to determine whether there is a special area in front.

[0056] Specifically, the first unit is a feature extraction unit, and the second unit is a classification unit. The above method can quickly train a model that can effectively identify special areas of blood vessels. The newly acquired internal image of the blood vessel is input into the recognition model, and the recognition model identifies whether there are special areas in the internal image of the blood vessel.

[0057] According to another aspect of the embodiment of the present invention, Figure 2 As shown, a catheter positioning system for vascular intervention is also provided, including an acquisition module, a processing module, a positioning module and a control module, which are used to implement the catheter positioning method for vascular intervention as described above. The specific functions of each module are as follows: An acquisition module is used to acquire a plurality of DSA images. The DSA image is a two-dimensional image and can clearly display the blood vessel. A micro camera is arranged at the top of the catheter. After the catheter is inserted into the blood vessel, as the top of the catheter moves in the blood vessel, the micro camera takes pictures of the inside of the blood vessel at preset time intervals to acquire the image inside the blood vessel. While taking the image inside the blood vessel, the first position of the top of the catheter in the blood vessel is recorded, and the DSA image, the image inside the blood vessel and the first position are transmitted to the processing module. A processing module is used to verify the DSA image, the internal image of the blood vessel and the first position that need to be verified. After successful verification, a three-dimensional model of the blood vessel is established based on the acquired DSA image, the first position and the corresponding internal image of the blood vessel, a three-dimensional coordinate point corresponding to the first position is acquired in the three-dimensional model of the blood vessel, and a simulated internal image taken at the position of the three-dimensional coordinate point is simulated in the three-dimensional model; A positioning module is used to calculate the similarity between the internal image of the blood vessel and the corresponding simulated internal image, obtain the simulated internal image with the greatest similarity to the internal image of the blood vessel, and obtain the three-dimensional coordinate point corresponding to the simulated internal image, the three-dimensional coordinate point is the second position of the catheter tip in the blood vessel, and the second position is the three-dimensional position of the catheter tip in the three-dimensional model of the blood vessel; The control module is used to obtain the center line of the current moving area during the movement of the catheter, so that the catheter moves forward along the center line, and judge whether there is a special area in front of the moving direction of the catheter based on the internal image of the blood vessel and the DSA image. If so, the moving direction of the catheter is changed.

[0058] According to another aspect of an embodiment of the present invention, a storage medium is provided, wherein the storage medium stores program instructions, wherein when the program instructions are executed, the device where the storage medium is located is controlled to execute any one of the above-mentioned catheter positioning methods for vascular intervention.

[0059] In summary, the catheter positioning method for vascular intervention of the present invention includes acquiring a plurality of DSA images, taking an internal image of the blood vessel at a preset time interval and acquiring the corresponding first position when taking the image, transmitting the DSA image, the internal image of the blood vessel and the first position to a processing module, establishing a three-dimensional model of the blood vessel, acquiring a three-dimensional coordinate point corresponding to the first position in the three-dimensional model of the blood vessel, simulating and acquiring a simulated internal image taken at the position of the three-dimensional coordinate point in the three-dimensional model, calculating the similarity between the internal image of the blood vessel and the corresponding simulated internal image, acquiring a simulated internal image with the greatest similarity to the internal image of the blood vessel, acquiring the three-dimensional coordinate point corresponding to the simulated internal image, acquiring the center line of the current moving area, moving the catheter forward along the center line, and changing the moving direction of the catheter in advance if encountering a special area. The present invention can improve the accuracy and success rate of vascular intervention surgery.

[0060] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0061] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0062] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0063] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0064] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A catheter positioning method for vascular intervention, characterized in that: The method comprises the following steps: Step S1, obtaining a plurality of DSA images, setting a micro camera at the top of the catheter, after the catheter is inserted into the blood vessel, as the top of the catheter moves in the blood vessel, the micro camera takes pictures of the inside of the blood vessel at preset time intervals to obtain images inside the blood vessel, and records a first position of the top of the catheter in the blood vessel while taking the images inside the blood vessel, and transmits the DSA images, the images inside the blood vessel and the first position to a processing module; Step S2, verifying the DSA image, the internal image of the blood vessel and the first position that need to be verified. After successful verification, a three-dimensional model of the blood vessel is established based on the DSA image, the first position and the corresponding internal image of the blood vessel, a three-dimensional coordinate point corresponding to the first position is obtained in the three-dimensional model of the blood vessel, and a simulated internal image taken at the position of the three-dimensional coordinate point is simulated and obtained in the three-dimensional model; Step S3, calculating the similarity between the blood vessel internal image and the corresponding simulated internal image, obtaining the simulated internal image having the greatest similarity to the blood vessel internal image, and obtaining the three-dimensional coordinate point corresponding to the simulated internal image, wherein the three-dimensional coordinate point is the second position of the catheter tip in the blood vessel, and the second position is the three-dimensional position of the catheter tip in the three-dimensional model of the blood vessel; Step S4: in the process of moving the catheter, the center line of the current moving area is obtained, and the catheter is moved forward along the center line. Based on the internal image of the blood vessel and the DSA image, it is determined whether there is a special area in front of the moving direction of the catheter. If so, the moving direction of the catheter is changed.

2. The method according to claim 1, characterized in that Calculating the similarity between the blood vessel internal image and the corresponding simulated internal image comprises the following steps: Step S31, acquiring the blood vessel internal image and one of the simulated internal images, acquiring a plurality of first key areas of the blood vessel internal image, acquiring a plurality of second key areas of the simulated internal image, and pairing the first key areas with the second key areas based on position information to generate key area pairs; Step S32: for each of the first key areas, obtain a first value of each pixel in the first key area, then obtain a second value of a pixel adjacent to the pixel, and calculate a first difference and a second difference between the first value and a plurality of the second values; Step S33, obtaining a first average value of the plurality of first difference values, comparing the first average value with a preset first threshold value, if the first average value is greater than the first threshold value, setting the first characteristic value of the pixel point to a first preset value, otherwise setting the first characteristic value of the pixel point to a second preset value, and combining the first characteristic values ​​of each pixel in the first key area to generate a first characteristic combination of the first key area; Step S34, obtaining a second average value of the plurality of second difference values, comparing the second average value with a preset second threshold value, and if the first average value is greater than the second threshold value, setting the second characteristic value of the pixel point to a first preset value; otherwise, setting the second characteristic value of the pixel point to a second preset value, and combining the second characteristic values ​​of each pixel in the first key area to generate a second characteristic combination of the first key area; Step S35, repeating the steps S32 to S34, obtaining the first feature combination and the second feature combination of the second key area, and calculating the similarity of the key area pair based on the first feature combination and the second feature combination of the key area pair; Step S36: obtaining the similarities of all the key region pairs, and integrating all the similarities to obtain the overall similarity between the blood vessel internal image and the simulated internal image.

3. The method according to claim 2, characterized in that Determining the similarity of the key area pair based on the first feature combination and the second feature combination of the key area pair includes the following steps: Step S351, obtaining a first number of the first preset values ​​included in the first feature combination, and also obtaining a second number of the first feature values ​​included in the first feature combination, dividing the first number by the second number to obtain a first ratio, and further calculating a second ratio of the first preset values ​​in the second feature combination; Step S352: if both the first ratio and the second ratio are greater than a preset third threshold, classify the key area pair consisting of the first key area and the corresponding second key area into the first category; otherwise, classify the key area pair into the second category; Step S353, obtaining two first feature combinations of the key area pair, initializing the first difference to zero, and comparing each of the first feature values ​​in the two first feature combinations in turn. If the two first feature values ​​are the same, the first difference remains unchanged; otherwise, the first difference is increased by one until all the first feature values ​​in the first feature combinations are compared to obtain a final first difference, and the first difference is divided by the second number to obtain a first similarity; Step S354, obtaining two second feature combinations of the key area pair, initializing the second difference to zero, and comparing each second feature value in the two second feature combinations in turn. If the two second feature values ​​are the same, the second difference remains unchanged; otherwise, the second difference is increased by one until all the second feature values ​​in the second feature combinations are compared, and a final second difference is obtained. The second difference is divided by the second number to obtain a second similarity; Step S355: Obtain an average value of the first similarity and the second similarity, and use the average value as the similarity of the key region pair.

4. The method according to claim 2, characterized in that: All the similarities are combined, including the following steps: All the similarities are combined to calculate the overall similarity using a first formula, where the first formula is: , where S is the overall similarity, Xi is the similarity of the i-th key area pair, Wi is the weight value of the i-th key area pair, and N is the total number of key area pairs. If the key area pair belongs to the first category, the weight value of the key area pair is set to the first weight value. If the key area pair belongs to the second category, the weight value of the key area pair is set to the second weight value.

5. The method according to claim 1, characterized in that Transmitting the DSA image, the intravascular image and the first position to the processing module comprises the following steps: Step S11, obtaining the transmitted first DSA image, the first intravascular image and the first first position, extracting specific areas of the DSA image and the intravascular image, encoding the specific areas to obtain corresponding first coded data, then generating corresponding first hash data based on the first coded data, and obtaining a corresponding check code based on the first position; Step S12, sending the DSA image, the blood vessel internal area and the first hash data corresponding to each to the processing module, and sending the first position and the corresponding verification code to the processing module; Step S13, receiving the verification information of the processing module, if there is no error in the transmission process, not generating the hash data and the verification code for the predetermined number of the DSA images, the intravascular images and the first position to be transmitted next, and if there is an error in the transmission process, checking the transmission route and determining the cause of the error; Step S14, then repeating steps S11 to S12 for the DSA image, the intravascular image and the first position to be transmitted next, until all the DSA images and all the intravascular images are transmitted.

6. The method according to claim 1, characterized in that Verifying the acquired DSA image and the intravascular image includes the following steps: Step S21, performing encoding processing on the DSA image and the intravascular image that have obtained corresponding hash data to obtain second encoded data, further generating corresponding second hash data based on the second encoded data, comparing the first hash data and the corresponding second hash data to determine whether they are the same, if they are the same, it means that there is no error in the transmission process of the DSA image and the intravascular image, and sending a verification success message to the acquisition module, otherwise sending a verification failure message to the acquisition module; Step S22: verify the corresponding first position based on the verification code to determine whether the first position is wrong. If wrong, correct the first position based on the verification code.

7. The method according to claim 1, characterized in that Judging whether there is a special area ahead of the moving direction of the catheter based on the blood vessel internal image and the DSA image includes the following steps: Step S41, collecting a number of the intravascular images acquired historically, specially marking special areas of the intravascular images as learning data, and generating a recognition model based on the learning data using a machine learning algorithm, wherein the recognition model includes a first unit and a second unit; Step S42, segmenting the blood vessel internal image to generate a plurality of sub-images, and using the first unit to identify the plurality of sub-images to obtain key information of each of the sub-images; Step S43: using the second unit to classify the key information, classifying different key information into different special categories, calculating recognition errors based on the special categories and the special marks, and adjusting parameters of the first unit and the second unit based on the recognition errors; Step S45, acquiring a number of new historically acquired intravascular images as new learning data, training the recognition model based on the learning data to obtain an updated recognition model, and acquiring a recognition error of the recognition model. If the recognition error is less than a preset error threshold, the training is stopped; otherwise, this step is repeated; Step S45: After the final recognition model is generated, the newly acquired internal image of the blood vessel is input into the recognition model to determine whether there is a special area in front.

8. A catheter positioning system for vascular intervention, used to implement the catheter positioning method for vascular intervention as claimed in any one of claims 1 to 7, characterized in that: Includes the following modules: an acquisition module, for acquiring a plurality of DSA images, wherein a micro camera is arranged at the top of the catheter, and after the catheter is inserted into the blood vessel, as the top of the catheter moves in the blood vessel, the micro camera takes pictures of the inside of the blood vessel at preset time intervals to acquire an image inside the blood vessel, and while taking the image inside the blood vessel, a first position of the top of the catheter in the blood vessel is recorded, and the DSA image, the image inside the blood vessel and the first position are transmitted to the processing module; a processing module, configured to verify the DSA image, the internal image of the blood vessel and the first position to be verified, and after successful verification, establish a three-dimensional model of the blood vessel based on the DSA image, the first position and the corresponding internal image of the blood vessel, obtain a three-dimensional coordinate point corresponding to the first position in the three-dimensional model of the blood vessel, and simulate in the three-dimensional model to obtain a simulated internal image taken at the position of the three-dimensional coordinate point; a positioning module, used for calculating the similarity between the internal image of the blood vessel and the corresponding simulated internal image, obtaining the simulated internal image having the greatest similarity with the internal image of the blood vessel, and obtaining the three-dimensional coordinate point corresponding to the simulated internal image, wherein the three-dimensional coordinate point is the second position of the catheter tip in the blood vessel, and the second position is the three-dimensional position of the catheter tip in the three-dimensional model of the blood vessel; The control module is used to obtain the center line of the current moving area during the movement of the catheter, so that the catheter moves forward along the center line, and judge whether there is a special area in front of the moving direction of the catheter based on the internal image of the blood vessel and the DSA image. If so, change the moving direction of the catheter.

9. A storage medium, characterized in that: The storage medium stores program instructions, wherein when the program instructions are executed, the device where the storage medium is located is controlled to execute the catheter positioning method for vascular intervention according to any one of claims 1 to 7.

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