A guide catheter multi-modal imaging fusion navigation system and guide catheter

Abstract

The application relates to the technical field of medical devices, and particularly discloses a guide catheter multi-modal imaging fusion navigation system and a guide catheter, which comprises the following modules: a catheter tip coarse positioning module, which acquires blood vessel shape distribution data of a target part of a patient, determines a real-time coarse position range of a catheter tip, and acquires an internal tissue image in front of the catheter tip; a retraction parameter determination module, which determines a wall diameter line retraction coefficient and a wall center retraction vector according to the front image; a catheter tip specific positioning module, which determines a real-time specific position in the coarse position range by using the above coefficient and vector; a navigation multiple determination module, which determines a navigation adjustment multiple in combination with the blood vessel shape data, the catheter tip specific position and the retraction coefficient; a navigation adjustment vector generation module, which generates a navigation direction adjustment vector based on a deviation vector and the wall center retraction vector; and a navigation adjustment execution module, which generates a navigation adjustment instruction according to the adjustment multiple and the adjustment vector; and the application realizes accurate control and navigation of the guide catheter.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical devices, in particular to a guide catheter multi-modal imaging fusion navigation system and a guide catheter. BACKGROUND

[0002] In the field of modern medicine, interventional therapy as an important treatment method is widely used in the treatment of cardiovascular diseases, neurological diseases and other diseases. The guide catheter plays a key role in interventional therapy, which needs to accurately reach the target site in the patient's body to realize subsequent diagnosis or treatment operation. Therefore, accurate navigation is crucial for the operation of the guide catheter, which is directly related to the success rate of treatment and the safety of patients. Traditional guide catheter navigation mainly relies on a single imaging mode, such as X-ray angiography, ultrasound imaging or magnetic resonance imaging. But each mode has its limitations. For example, X-ray angiography can clearly show the shape of blood vessels, but the display capability of surrounding tissues is limited; ultrasound imaging can provide real-time soft tissue information, but the image resolution is relatively low; magnetic resonance imaging can provide high-resolution anatomical structure images, but the imaging speed is slow and the equipment cost is high. Single imaging mode is difficult to provide comprehensive and real-time position information of the guide catheter in the body and detailed information of the surrounding tissues, which limits the accuracy and real-time of guide catheter navigation. With the continuous development of medical imaging technology, multi-modal imaging fusion technology has gradually become a research hotspot. By combining the advantages of multiple imaging modes, more accurate information can be obtained. The application of multi-modal imaging fusion technology to guide catheter navigation system is expected to break through the limitations of traditional single imaging mode and achieve more accurate and real-time navigation. This guide catheter multi-modal imaging fusion navigation system is of great significance to improve the accuracy of interventional therapy and reduce the risk of surgery, and has broad application prospects in the field of interventional therapy, which can promote the development of interventional therapy technology to a more precise and intelligent direction and bring better treatment effect to patients.

[0003] However, due to the uncertainty and complexity of blood vessel structure, the existing guide catheter navigation technology cannot accurately locate the real-time specific position of the catheter tip. In terms of navigation adjustment, it cannot determine the fine navigation adjustment degree and direction based on the blood vessel shape at the real-time position of the catheter tip, and finally cannot generate accurate navigation adjustment instructions, so that the guide catheter lacks accurate adjustment basis in the navigation process, affecting the accuracy and real-time of navigation, increasing the difficulty and risk of interventional therapy.

[0004] Therefore, the present application proposes a guide catheter multi-modal imaging fusion navigation system and a guide catheter. SUMMARY

[0005] The application provides a guide catheter multimodal imaging fusion navigation system and a guide catheter.

[0006] The application provides a guide catheter multimodal imaging fusion navigation system, comprising:

[0007] A catheter tip rough positioning module is used for obtaining the real-time rough position range of the catheter tip based on the target site blood vessel shape distribution data of a patient acquired in advance by using multiple imaging modes and the initial entering position of the guide catheter tip in the patient, and obtaining the internal tissue image of the front blood vessel of the catheter tip.

[0008] A retraction parameter determination module is used for determining the inner wall caliber line retraction coefficient and the inner wall center retraction vector based on the internal tissue image of the front blood vessel.

[0009] A catheter tip specific positioning module is used for specifically positioning the real-time specific position of the catheter tip in the real-time rough position range of the catheter tip based on the inner wall caliber line retraction coefficient and the inner wall center retraction vector, thereby improving the navigation accuracy.

[0010] A navigation multiple determination module is used for determining the navigation adjustment multiple based on the target site blood vessel shape distribution data and the real-time specific position of the specifically positioned catheter tip and the inner wall caliber line retraction coefficient, thereby enhancing the navigation adaptability.

[0011] A navigation adjustment vector generation module is used for determining the offset vector from the catheter tip position to the center of the minimum caliber circle line in the internal tissue image of the front blood vessel, and generating the navigation direction adjustment vector based on the offset vector and the inner wall center retraction vector.

[0012] A navigation adjustment execution module is used for generating the navigation adjustment instruction of the guide catheter based on the navigation adjustment multiple and the navigation direction adjustment vector.

[0013] Preferably, the catheter tip rough positioning module comprises:

[0014] A blood vessel multi-modal imaging sub-module is used to acquire the blood vessel morphology distribution data of a target site of a patient based on multiple imaging modes;

[0015] A catheter tip coarse positioning sub-module is used to coarsely position the real-time coarse position range of a catheter tip in the blood vessel distribution data of a target site based on the initial entry position of a guide catheter tip in a patient and the body length of the guide catheter, while acquiring the internal tissue image of the front blood vessel of the catheter tip.

[0016] Preferably, the retraction parameter determination module comprises:

[0017] An orifice circle line calibration sub-module is used to determine all the orifice circle lines of the internal wall of the blood vessel in the internal tissue image of the front blood vessel.

[0018] A retraction parameter determination sub-module is used to determine the internal wall orifice line retraction coefficient and the internal wall center retraction vector based on all the orifice circle lines of the internal wall of the blood vessel in the internal tissue image of the front blood vessel.

[0019] Preferably, the orifice circle line calibration sub-module comprises:

[0020] A light and shadow change significant region calibration unit is used to determine the light and shadow gradient of each pair of adjacent pixel points in the internal tissue image of the front blood vessel, and screen out a set of significant light and shadow gradients from all the light and shadow gradients of the adjacent pixel points in each frame of the internal tissue image of the blood vessel wall, and calibrate all the light and shadow change significant regions in the corresponding frame of the internal tissue image of the blood vessel wall based on the set of significant light and shadow gradients.

[0021] A diffusion analysis line calibration unit is used to take the center pixel of the light and shadow change significant region as a starting pixel point, and take the exhaustive line segment of the starting pixel point and each adjacent pixel point in the corresponding light and shadow change significant region as a single diffusion analysis line of the light and shadow change significant region.

[0022] A gradient establishment degree calculation unit is used to calculate the gradient establishment degree of each diffusion analysis line in the light and shadow change significant region based on the light and shadow gradient of all the adjacent pixel points in the light and shadow change significant region.

[0023] A total gradient establishment degree calculation unit is used to connect two diffusion analysis line segments that belong to each other on the extension line of all the diffusion analysis lines in all the diffusion analysis lines in all the light and shadow change significant regions in the internal tissue image of the front blood vessel, to obtain a plurality of exhaustive connected diffusion analysis line segments, and determine the total gradient establishment degree of each exhaustive connected diffusion analysis line segment based on the gradient establishment degree of all the diffusion analysis lines in each exhaustive connected diffusion analysis line segment.

[0024] An effective analysis line calibration unit is used to take all the exhaustive connected diffusion analysis line segments with a total gradient establishment degree not less than a threshold value as all the effective analysis line segments.

[0025] The caliber circle line calibration unit is configured to calibrate all the intravascular wall caliber circle lines in the front intravascular tissue image based on all the effective analysis line segments.

[0026] Preferably, the caliber circle line calibration unit comprises:

[0027] The pixel exhaustive extension subunit is configured to calibrate all the passing pixels on the extension line of each effective analysis line segment in the front intravascular tissue image and connect them to obtain an exhaustive passing pixel line of each effective analysis line segment.

[0028] The pixel line ordering subunit is configured to randomly select one of the exhaustive passing pixel lines as a starting pixel line and sequentially calibrate the order of all the exhaustive passing pixel lines except the starting pixel line in the front intravascular tissue image in a clockwise direction to obtain a pixel line sequence.

[0029] The pixel interval taking subunit is configured to perform multiple sequential pixel sampling and fitting on all the exhaustive passing pixel lines in the pixel line sequence based on each interval pixel value in the step interval pixel value list to generate a plurality of pixel sampling value function lines of each interval pixel value.

[0030] The caliber circle line calibration subunit is configured to calculate the similarity of all the pixel sampling value function lines of each interval pixel value, connect and calibrate all the sampling pixels contained in each pixel sampling value function line corresponding to the interval pixel value with the maximum similarity in sequence in the front intravascular tissue image, and perform smooth fitting to obtain all the intravascular wall caliber circle lines.

[0031] Preferably, the retraction parameter determination sub-module comprises:

[0032] The intravascular wall caliber line retraction coefficient determination unit is configured to take the ratio of the minimum radius to the maximum radius among the radii of all the intravascular wall caliber circle lines in the front intravascular tissue image as the intravascular wall caliber line retraction coefficient.

[0033] The intravascular wall center retraction vector determination unit is configured to take the vector from the center coordinate of the intravascular wall caliber circle line with the maximum radius to the center coordinate of the intravascular wall caliber circle line with the minimum radius among all the intravascular wall caliber circle lines in the front intravascular tissue image as the intravascular wall center retraction vector.

[0034] Preferably, the catheter tip specific positioning module comprises:

[0035] The multi-dimensional morphological parameter link acquisition sub-module is configured to retrieve a multi-dimensional blood vessel morphological parameter link table based on the intravascular wall caliber line retraction coefficient and the intravascular wall center retraction vector to determine the multi-dimensional morphological parameters of the current front local blood vessel.

[0036] The first local blood vessel morphology intercepting sub-module is configured to determine local blood vessel morphology distribution data within a real-time rough position range of the catheter tip based on the blood vessel morphology distribution data of the target site of the patient;

[0037] The specific positioning sub-module is configured to match the current front local blood vessel multi-dimensional morphology parameter with the local blood vessel morphology distribution data within the real-time rough position range, and to specifically position the real-time specific position of the catheter tip in the real-time rough position range of the catheter tip.

[0038] Preferably, the navigation multiple determination module comprises:

[0039] The second local blood vessel morphology intercepting sub-module is configured to determine the current front local blood vessel morphology distribution data of the catheter tip based on the blood vessel morphology distribution data of the target site and the real-time specific position of the catheter tip.

[0040] The navigation multiple determination sub-module is configured to evaluate the current navigation adjustment difficulty based on the current front local blood vessel morphology distribution data of the catheter tip, and to determine the navigation adjustment multiple based on the current navigation adjustment difficulty and the inner wall caliber line retraction coefficient.

[0041] Preferably, the navigation adjustment vector generation module comprises:

[0042] The offset vector calibration sub-module is configured to determine an offset vector from the catheter tip position to the center of the minimum caliber circle line in the front blood vessel internal tissue image.

[0043] The direction adjustment vector calibration sub-module is configured to take the sum of the offset vector and the inner wall center retraction vector as the navigation direction adjustment vector.

[0044] The present application provides a guide catheter, comprising:

[0045] A flexible tubular structure and a catheter tip located at the front end of the flexible tubular structure,

[0046] The catheter tip is provided with a miniature imaging device for acquiring a front blood vessel internal tissue image of the catheter tip.

[0047] The wireless transmission module is configured to transmit the front blood vessel internal tissue image to the catheter tip rough positioning module in real time, and to receive the navigation adjustment instruction from the navigation adjustment execution module.

[0048] The navigation instruction execution module is configured to control the travel parameters of the catheter tip based on the navigation adjustment instruction.

[0049] The beneficial effects of this invention compared to existing technologies are as follows: The coarse positioning module for the catheter tip combines the vascular morphology distribution data of the target site obtained from multiple imaging modes with the initial entry position of the catheter tip to determine the real-time coarse position range and acquire images of the internal tissue of the blood vessel ahead, laying the foundation for subsequent operations. The indentation parameter determination module accurately determines the indentation coefficient of the inner wall diameter line and the indentation vector of the inner wall center based on this image, providing key quantitative information. The precise positioning module for the catheter tip uses the above parameters to accurately locate the real-time specific position of the catheter tip within the coarse position range, improving navigation accuracy. The navigation multiplier determination module combines multiple data to determine the navigation adjustment multiplier, enhancing navigation adaptability. The navigation adjustment vector generation module collaborates with the navigation adjustment execution module to generate navigation adjustment commands based on relevant vectors, achieving precise control of the guiding catheter, meeting clinical operation needs, and providing strong navigation support for interventional procedures and other surgeries.

[0050] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0051] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0052] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0053] Figure 1 This is a flowchart illustrating the execution logic of a multimodal imaging fusion navigation system for a guidance tube, as described in an embodiment of the present invention.

[0054] Figure 2 This is a flowchart illustrating the execution logic of the catheter tip coarse positioning module in an embodiment of the present invention.

[0055] Figure 3 This is a flowchart illustrating the execution logic of the indentation parameter determination module in this embodiment of the invention.

[0056] Figure 4 This is a flowchart illustrating the execution logic of the catheter tip positioning module in an embodiment of the present invention.

[0057] Figure 5 This is a flowchart illustrating the execution logic of the navigation multiple determination module in this embodiment of the invention.

[0058] Figure 6 This is a flowchart illustrating the execution logic of the navigation adjustment vector generation module in this embodiment of the invention. Detailed Implementation

[0059] The preferred embodiments of the present application are described below in conjunction with the accompanying drawings, it should be understood that the preferred embodiments described here are only used to illustrate and explain the present application, and are not used to limit the present application.

[0060] Embodiment 1:

[0061] The present application provides a guide catheter multi-modal imaging fusion navigation system, referring to Figure 1 , comprising:

[0062] The catheter tip coarse positioning module is used to obtain the real-time coarse position range of the catheter tip based on the blood vessel morphology distribution data of the target site of the patient obtained by the plurality of imaging modes and the initial entry position of the guide catheter tip in the patient's body, and simultaneously obtain the image of the internal tissue in front of the catheter tip;

[0063] The retraction parameter determination module is used to determine the inner wall caliber line retraction coefficient and the inner wall center retraction vector based on the internal tissue image in front of the blood vessel;

[0064] The catheter tip specific positioning module is used to specifically position the real-time specific position of the catheter tip in the real-time coarse position range of the catheter tip based on the inner wall caliber line retraction coefficient and the inner wall center retraction vector;

[0065] The navigation multiple determination module is used to determine the navigation adjustment multiple based on the blood vessel morphology distribution data of the target site, the real-time specific position of the specifically positioned catheter tip, and the inner wall caliber line retraction coefficient;

[0066] The navigation adjustment vector generation module is used to determine the offset vector from the catheter tip position to the center of the minimum caliber circle line in the internal tissue image in front of the blood vessel, and generate the navigation direction adjustment vector based on the offset vector and the inner wall center retraction vector;

[0067] The navigation adjustment execution module is used to generate the navigation adjustment instruction of the guide catheter based on the navigation adjustment multiple and the navigation direction adjustment vector.

[0068] In this embodiment, the plurality of imaging modes refers to the comprehensive use of different medical imaging technologies such as X-ray angiography, ultrasonic imaging, and magnetic resonance imaging. Each technology has its own advantages and disadvantages, and the combination of the two can obtain comprehensive and accurate information. For example, in cardiovascular imaging, X-ray angiography is used to display the shape of the blood vessel, ultrasonic imaging is used to observe the blood vessel wall and the surrounding soft tissue, and MRI is used to analyze the spatial relationship between the blood vessel and the surrounding structure.

[0069] In this embodiment, the blood vessel morphology distribution data of the target site of the patient is the distribution of the shape, direction, branch, etc. of the specific site blood vessel obtained by the plurality of imaging modes. For example, the brain blood vessel data includes its position, bending degree, caliber, and connection relationship, etc.

[0070] In this embodiment, the initial entry position of the guide catheter tip in the patient's body is the specific point where the catheter begins to enter the patient's body, for example, in a cardiac intervention procedure, it may enter from the femoral artery at the root of the thigh.

[0071] In this embodiment, the real-time rough position range of the catheter tip is the spatial range where the catheter tip is preliminarily determined based on the blood vessel morphology distribution data and the initial entry position, such as being presumed to be within a length of 1-3 centimeters of a certain blood vessel.

[0072] In this embodiment, the internal tissue image of the blood vessel in front of the catheter tip is obtained by the miniature imaging device of the catheter tip, which can present conditions such as lesions on the inner wall of the blood vessel, such as showing plaques or narrowing of the diameter of the inner wall of the blood vessel.

[0073] In this embodiment, the inner wall diameter line shrinkage coefficient is the ratio of the minimum and maximum radii of the inner wall diameter circle line, and the inner wall center shrinkage vector is the vector from the center of the maximum radius circle line to the center of the minimum radius circle line, for example, the maximum radius is 5 mm, the minimum radius is 3 mm, and the coefficient is 0.6; if the center of the maximum radius circle line is (10, 10) and the center of the minimum radius circle line is (8, 12), the vector is (-2, 2).

[0074] In this embodiment, the real-time specific position of the catheter tip is determined based on the rough position range, with the help of the above-mentioned coefficient and vector, such as being determined to be at a position 0.5 centimeters in front of the blood vessel bifurcation.

[0075] In this embodiment, the navigation adjustment multiple is determined comprehensively based on multiple aspects of information, which measures the degree or accuracy of catheter navigation adjustment, such as when the blood vessel is complex, the catheter is close to a narrow place, and the diameter changes greatly, the multiple may be relatively large.

[0076] In this embodiment, the center of the minimum diameter circle line is the center position of the circle line with the smallest radius of the inner wall diameter circle line of the blood vessel, which is similar to the center of the circle line with the smallest radius in the cross-sectional image of the blood vessel.

[0077] In this embodiment, the offset vector from the catheter tip position to the center of the minimum diameter circle line in the internal tissue image of the blood vessel in front is determined with the catheter tip as the starting point and the center of the minimum diameter circle line as the ending point, which reflects the position offset, such as the catheter tip (20, 20), the center of the minimum diameter circle line (25, 23), and the vector (5, 3).

[0078] In this embodiment, the navigation direction adjustment vector is obtained by adding the offset vector and the inner wall center shrinkage vector, which clearly indicates the adjustment direction of the catheter, such as the offset vector (3, 2), the shrinkage vector (1, -1), and the adjustment vector (4, 1).

[0079] In this embodiment, the navigation adjustment instruction of the guide catheter is generated based on the navigation adjustment multiple and the navigation adjustment vector, for example, the adjustment multiple is 2 and the adjustment vector is (4, 1), and the instruction can be to adjust the catheter position in the vector direction and at a 2-fold amplitude.

[0080] The above technology has the following beneficial effects: the catheter tip coarse positioning module combines the target site blood vessel shape distribution data obtained by multiple imaging modes and the catheter tip initial entry position to determine the real-time coarse position range and obtain the internal tissue image of the front blood vessel, thereby laying the foundation for subsequent operations. The retraction parameter determination module accurately determines the inner wall caliber line retraction coefficient and the inner wall center retraction vector based on the image, thereby providing key quantitative information. The catheter tip specific positioning module accurately positions the real-time specific position of the catheter tip within the coarse position range by means of the above-mentioned parameters, thereby improving the navigation accuracy. The navigation multiple determination module determines the navigation adjustment multiple in combination with multiple data, thereby enhancing the navigation adaptability. The navigation adjustment vector generation module cooperates with the navigation adjustment execution module to generate the navigation adjustment instruction based on the relevant vector, thereby realizing accurate control of the guide catheter and meeting the clinical operation requirements, thereby providing strong navigation support for surgeries such as interventional treatment.

[0081] Embodiment 2:

[0082] On the basis of embodiment 1, the catheter tip coarse positioning module refers to Figure 2 , and includes:

[0083] The blood vessel multi-modal contrast sub-module is configured to pre-acquire the target site blood vessel shape distribution data of a patient based on multiple imaging modes.

[0084] The catheter tip coarse positioning sub-module is configured to coarsely position the real-time coarse position range of the catheter tip in the target site blood vessel distribution data based on the initial entry position of the guide catheter tip in the patient's body and the guide catheter entry length, and simultaneously acquire the internal tissue image of the front blood vessel of the catheter tip.

[0085] In this embodiment, the guide catheter entry length refers to the length value of the guide catheter entering the patient's body from the guide catheter tip to a certain moment. It is an index for measuring the extension degree of the guide catheter in the patient's body, for example, during the interventional treatment process, the doctor can preliminarily judge the approximate position of the catheter in the body by measuring the guide catheter entry length.

[0086] In this embodiment, based on the initial entry position of the guide catheter tip in the patient's body and the guide catheter entry length, the real-time rough position range of the catheter tip in the target site blood vessel distribution data is roughly positioned, that is, the starting point of the guide catheter entering the patient's body is taken as the reference, combined with the length information of the guide catheter that has entered the body, in the "map" constructed by the target site blood vessel shape distribution data obtained through multiple imaging modes, the range where the guide catheter tip is at this moment is roughly determined. For example, it is known that the guide catheter starts to enter from the radial artery of the patient's wrist, the entry length is 20 cm, and at the same time, according to the previously obtained upper limb blood vessel shape distribution data, it is speculated that the guide catheter tip may be in a blood vessel range about 20 cm away from the starting point of the radial artery. This range is the real-time rough position range, although it is not the exact position, but it provides a starting search area for the subsequent more accurate positioning of the catheter tip.

[0087] The above-mentioned technology has the following beneficial effects: Embodiment 2 further refines and disassembles the catheter tip rough positioning module, and has many beneficial effects. On the one hand, the blood vessel multi-modal contrast sub-module pre-acquires the target site blood vessel shape distribution data based on multiple imaging modes. This multi-mode imaging method can present the blood vessel shape from different angles and with different characteristics, so that the obtained data is more comprehensive and accurate, providing rich and reliable basic information for subsequent catheter positioning, which helps to overcome the limitations of single imaging mode. On the other hand, the catheter tip rough positioning sub-module uses the initial entry position of the guide catheter tip in the patient's body and the guide catheter entry length as two key elements to roughly position the real-time rough position range of the catheter tip in the acquired target site blood vessel distribution data, and simultaneously acquire the internal tissue image of the front blood vessel. This not only provides an important initial range for further accurate positioning of the catheter tip, reduces the blindness of subsequent positioning search, and improves the positioning efficiency, but also enables the operator to understand the blood vessel condition in the forward direction of the catheter in advance, providing more targeted information support for subsequent operation of the catheter, and enhancing the safety and controllability of the guide catheter operation.

[0088] Embodiment 3:

[0089] On the basis of embodiment 1, the retraction parameter determination module refers to Figure 2 , comprising:

[0090] The caliber circle line calibration sub-module is configured to determine all blood vessel inner wall caliber circle lines in the internal tissue image of the front blood vessel.

[0091] The retraction parameter determination sub-module is configured to determine the inner wall caliber line retraction coefficient and the inner wall center retraction vector based on all blood vessel inner wall caliber circle lines in the internal tissue image of the front blood vessel.

[0092] In this embodiment, the vessel inner wall caliber circle line refers to a circle line in the front vessel inner tissue image obtained by the catheter tip for indicating the caliber of the vessel inner wall. These circle lines outline the inner wall boundary of the vessel at this cross-sectional position, and can intuitively reflect the thickness changes of the vessel at different positions. For example, in a vessel inner tissue image, a plurality of circle lines similar to circles or ellipses are identified by a specific algorithm, which are the vessel inner wall caliber circle lines. The size and shape changes of these circle lines reflect the changes of the vessel inner wall caliber, which helps doctors understand the morphological structure of the vessel, such as judging whether the vessel has stenosis or dilation, etc., and provides key basis for subsequent determination of the inner wall caliber line recession coefficient, the inner wall center recession vector, and the positioning and navigation adjustment of the catheter tip.

[0093] The beneficial effects of the above technology are that, in embodiment 3, the recession parameter determination module is optimized based on embodiment 1. The caliber circle line calibration sub-module accurately determines all vessel inner wall caliber circle lines in the front vessel inner tissue image, providing accurate data basis for subsequent parameter determination, and truly reflecting the morphological profile of the vessel inner wall. Based on these circle lines, the recession parameter determination sub-module scientifically determines the inner wall caliber line recession coefficient and the inner wall center recession vector, which are two key parameters for the specific positioning of the catheter tip, improve the positioning accuracy and navigation accuracy of the system, help doctors accurately control the catheter, reduce the damage to the vessel wall, improve the treatment effect, and optimize the performance of the guided catheter multi-modal imaging fusion navigation system as a whole, providing reliable technical support for clinical operation, and enhancing the safety and effectiveness of medical operation.

[0094] Embodiment 4:

[0095] Based on embodiment 3, the caliber circle line calibration sub-module comprises:

[0096] The light and shadow change significant region calibration unit is configured to determine the light and shadow transition amount of each pair of adjacent pixel points in the front vessel inner tissue image, and screen out a set of significant light and shadow transition amounts from all light and shadow transition amounts of adjacent pixel points in each frame of vessel inner tissue image, and calibrate all light and shadow change significant regions in the corresponding frame of vessel inner tissue image based on the set of significant light and shadow transition amounts.

[0097] The diffusion analysis line calibration unit is configured to take the center pixel of the light and shadow change significant region as a starting pixel point, and take the exhaustive line segment between the starting pixel point and each adjacent pixel point in the corresponding light and shadow change significant region as a single diffusion analysis line of the light and shadow change significant region.

[0098] The transition establishment degree calculation unit is configured to calculate the transition establishment degree of each diffusion analysis line in the light and shadow change significant region based on the light and shadow transition amount of all adjacent pixel points in the light and shadow change significant region.

[0099] The total gradual change validity calculation unit is configured to connect two diffusion analysis line segments on each other's extension line in all diffusion analysis lines in all light and shadow change significant regions in the front blood vessel internal tissue image, to obtain a plurality of exhaustive connection diffusion analysis line segments, and to determine a total gradual change validity of each exhaustive connection diffusion analysis line segment based on the gradual change validities of all diffusion analysis lines in each exhaustive connection diffusion analysis line segment.

[0100] The effective analysis line calibration unit is configured to regard all the exhaustive connection diffusion analysis line segments with a total gradual change validity not less than a threshold as all the effective analysis line segments.

[0101] The caliber circle line calibration unit is configured to calibrate all the blood vessel internal wall caliber circle lines in the front blood vessel internal tissue image based on all the effective analysis line segments.

[0102] In this embodiment, the light and shadow gradual change of each pair of adjacent pixel points refers to the change in light and shadow properties such as brightness or color between the two adjacent pixel points in the blood vessel internal wall tissue image. For example, if the brightness value of a pixel point is 100 and the brightness value of its adjacent pixel point is 120, the light and shadow gradual change of the pair of adjacent pixel points can be 20 (the specific calculation method depends on the light and shadow model adopted). By calculating the light and shadow gradual change of each pair of adjacent pixel points, the local light and shadow change in the image can be captured.

[0103] In this embodiment, the set of significant light and shadow gradual changes is selected from all the light and shadow gradual changes of the pairs of adjacent pixel points in each frame of blood vessel internal wall tissue image, because not all light and shadow gradual changes are important for identifying the blood vessel internal wall caliber circle line. The significant light and shadow gradual change usually refers to those relatively large light and shadow changes that can highlight the key features such as the boundary of the blood vessel internal wall. For example, a standard is set to select the light and shadow gradual changes of those adjacent pixel points with a light and shadow gradual change greater than a certain value (such as 50) to form a set of significant light and shadow gradual changes, and the elements in this set are more likely to be related to the boundary or important structure of the blood vessel internal wall.

[0104] In this embodiment, the blood vessel internal wall tissue image is obtained by a blood vessel internal wall tissue image acquisition unit. The blood vessel internal wall tissue image acquisition unit is configured to acquire the blood vessel internal wall tissue image by using a blood vessel internal wall tissue imaging device.

[0105] In this embodiment, the exhaustive line segment between the starting pixel point and each adjacent pixel point in the corresponding light and shadow change significant region is taken as the center pixel of the light and shadow change significant region as the starting pixel point, then the starting pixel point is connected with each adjacent pixel point in the region to form a line segment, and the part of the extension line of the line segment in the light and shadow change significant region is taken. For example, in a light and shadow change significant region, the center pixel is A, which is connected with adjacent pixels B, C, D, etc. The part of the line in the region is the exhaustive line segment, which is used for further analysis of the light and shadow change trend in the region.

[0106] In this embodiment, the gradient establishment degree of each diffusion analysis line in the light and shadow change significant region is calculated based on the light and shadow gradient variables of all adjacent pixel points in the light and shadow change significant region. The gradient establishment degree of each diffusion analysis line (i.e. the exhaustive line segment formed by the starting pixel point and the adjacent pixel point mentioned above) is calculated by comprehensively calculating the light and shadow gradient variables of all adjacent pixel points involved in the diffusion analysis line, to obtain a numerical value to represent the rationality degree of the light and shadow gradient on the diffusion analysis line. Specifically, it includes:

[0107] Among all the light and shadow gradient variables of the adjacent pixel points in the light and shadow change significant region, a plurality of pairs of sequentially adjacent pixel points with continuous positions and regularly changing light and shadow gradient variables are selected to form a single light and shadow gradient line.

[0108] Based on all the light and shadow gradient lines in the light and shadow change significant region, the gradient establishment degree of each diffusion analysis line in the light and shadow change significant region is calculated:

[0109]

[0110] In the formula, δ is the gradient establishment degree of a single diffusion analysis line, n is the total number of all light and shadow gradient lines in the light and shadow change significant region parallel to the diffusion analysis line, N i is the total number of pixels in the i-th light and shadow gradient line in the light and shadow change significant region parallel to the diffusion analysis line, N0 is a preset pixel total threshold, which is equal to the total number of pixels contained in the longest line segment in the light and shadow change significant region, m is the total number of adjacent light and shadow gradient variables in all light and shadow gradient lines in the light and shadow change significant region parallel to the diffusion analysis line, ΔS j is the difference between the j-th pair of adjacent light and shadow gradient variables in all light and shadow gradient lines in the light and shadow change significant region parallel to the diffusion analysis line.

[0111] In this embodiment, the exhaustively connected diffusion analysis line segment is obtained by connecting all pairs of diffusion analysis line segments that belong to each other on the extension lines of all diffusion analysis lines in the significant light and shadow change region in the internal tissue image of the front blood vessel. For example, there are line segments L1 and L2 in different significant light and shadow change regions, and if L2 is exactly on the extension line of L1, then connecting them forms an exhaustively connected diffusion analysis line segment. These line segments integrate the light and shadow change information of multiple regions, which helps to more comprehensively determine the vessel wall caliber circle line.

[0112] In this embodiment, the total gradualness establishment degree of each exhaustively connected diffusion analysis line segment is determined based on the gradualness establishment degrees of all diffusion analysis lines in each exhaustively connected diffusion analysis line segment. The total gradualness establishment degree is a value representing the overall light and shadow gradualness rationality of the exhaustively connected diffusion analysis line segment, which is obtained by aggregating the gradualness establishment degrees of all diffusion analysis lines contained in each exhaustively connected diffusion analysis line segment. For example, the gradualness establishment degrees can be added or other appropriate statistical methods can be used to obtain the total gradualness establishment degree, which is used to evaluate the effectiveness of the line segment for depicting the vessel wall caliber circle line.

[0113] In this embodiment, the threshold value is a pre-set numerical standard used to determine whether the exhaustively connected diffusion analysis line segment is an effective analysis line segment. When the total gradualness establishment degree of the exhaustively connected diffusion analysis line segment is not less than the threshold value, it is considered that the line segment is valuable for determining the vessel wall caliber circle line and can be used as an effective analysis line segment. For example, if the threshold value is set to 0.8, and the total gradualness establishment degree of a certain exhaustively connected diffusion analysis line segment is 0.88, which is greater than the threshold value 0.8, then the line segment is identified as an effective analysis line segment, and the subsequent vessel wall caliber circle line is determined based on these effective analysis line segments.

[0114] The beneficial effects of the above technology are: embodiment 4 further refines the caliber circle line calibration sub-module based on embodiment 3. The light and shadow change significant region calibration unit determines the adjacent pixel point light and shadow gradual change value, filters out the significant light and shadow gradual change value set, and then calibrates all the light and shadow change significant regions, effectively focuses on the key feature region of the blood vessel inner wall, and lays a foundation for subsequent accurate analysis. The diffusion analysis line calibration unit takes the center pixel of the significant region as the starting point to construct a single diffusion analysis line, providing a specific path for the gradual change degree analysis. The gradual change degree calculation unit calculates the gradual change degree of each diffusion analysis line based on the light and shadow gradual change value of the adjacent pixels, and quantifies the light and shadow change characteristics. The total gradual change degree calculation unit connects the diffusion analysis line segments on the extension line and determines the total gradual change degree, further integrating information from the overall level. The effective analysis line calibration unit identifies the line segment with a total gradual change degree not less than the threshold value as the effective analysis line segment, realizing the selection of the key line segment. Finally, the caliber circle line calibration unit accurately calibrates the caliber circle line of the blood vessel inner wall according to the effective analysis line segment. Through this series of fine steps, the accuracy and reliability of the caliber circle line calibration of the blood vessel inner wall are greatly improved, providing more accurate data support for subsequent determination of the retraction parameter, which helps to improve the accuracy of the blood vessel shape analysis of the entire guide catheter multi-modal imaging fusion navigation system, thereby enhancing the navigation accuracy and clinical application value of the system.

[0115] Embodiment 5:

[0116] Based on embodiment 4, the caliber circle line calibration unit comprises:

[0117] The pixel exhaustive extension sub-unit is used for calibrating all the way pixels on the extension line of each effective analysis line segment in the front blood vessel internal tissue image and connecting them to obtain the exhaustive way pixel line of each effective analysis line segment;

[0118] The pixel line sorting sub-unit is used for randomly selecting an exhaustive way pixel line as the starting pixel line, and sequentially calibrating the order of all the exhaustive way pixel lines except the starting pixel line in the front blood vessel internal tissue image in the clockwise direction to obtain the pixel line sequence;

[0119] The pixel interval taking sub-unit is used for performing multiple sequential pixel sampling and fitting on all the exhaustive way pixel lines in the pixel line sequence based on each interval pixel value in the step interval pixel value list to generate a plurality of pixel sampling value function lines of each interval pixel value;

[0120] The caliber circle line calibration sub-unit is used for calculating the similarity of all the pixel sampling value function lines of each interval pixel value, connecting and calibrating all the sampling pixels contained in each pixel sampling value function line corresponding to the interval pixel value corresponding to the maximum similarity in the front blood vessel internal tissue image in sequence and smoothing fitting to obtain all the blood vessel inner wall caliber circle lines.

[0121] In this embodiment, the exhaustive path pixel line of an effective analysis line segment refers to a line formed by connecting all the pixel points on the extension line of each effective analysis line segment in the image of the inner tissue of the blood vessel in front. For example, if there is an effective analysis line segment, and its extension line passes through a series of pixel points, connecting these pixel points in turn will obtain the exhaustive path pixel line of the effective analysis line segment, which reflects the complete extension path of the effective analysis line segment in the image, and provides more detailed pixel information for subsequent determination of the lumen diameter circle line of the inner wall of the blood vessel.

[0122] In this embodiment, the step interval pixel value list is a list containing a series of interval pixel values, which change in a step manner and are used for sequential pixel sampling of the pixel line sequence. For example, the list can contain interval pixel values such as [1, 2, 3, 4, …, 15], each value representing the interval distance between adjacent sampling pixel points in the sampling process.

[0123] In this embodiment, the interval pixel value is a specific value in the step interval pixel value list, which determines the difference between the sorting values of adjacent sampling pixel points in the respective exhaustive path pixel line when sampling the exhaustive path pixel line. For example, if the interval pixel value is 3, then every 3 pixel points are sampled when sampling the exhaustive path pixel line.

[0124] In this embodiment, based on each interval pixel value in the step interval pixel value list, the exhaustive path pixel lines in the pixel line sequence are sampled and fitted multiple times to generate multiple pixel sampling value function lines for each interval pixel value. The specific process is as follows: for each interval pixel value in the step interval pixel value list, the pixel points are sequentially selected and sampled from the starting pixel of each exhaustive path pixel line in the pixel line sequence according to the interval value, and then the selected pixel points are fitted. For example, when the interval pixel value is 4, the first pixel point in the first exhaustive path pixel line, the fifth pixel point in the second exhaustive path pixel line, and the ninth pixel point in the third exhaustive path pixel line are sequentially selected to obtain a series of sampling pixel points, which are fitted into a function line by mathematical methods (such as curve fitting); then, the second pixel point in the first exhaustive path pixel line, the sixth pixel point in the second exhaustive path pixel line, and the tenth pixel point in the third exhaustive path pixel line are sequentially selected to obtain a series of sampling pixel points, which are fitted into a function line by mathematical methods (such as curve fitting). Therefore, multiple pixel sampling value function lines are generated, and each interval pixel value corresponds to a set of such function lines.

[0125] In this embodiment, the pixel sampling value function line is a function curve obtained by sampling and fitting the pixel line of the exhaustive approach at a certain interval pixel value. It describes the distribution of the sampling pixel points in the form of a function, which approximately represents the characteristics of the corresponding exhaustive approach pixel line at this sampling interval. For example, the pixel sampling value function line obtained by fitting may be in the form of y = ax + bx + c, where x represents the position or order value of the pixel point on the function line, and y represents the corresponding pixel value related information for subsequent analysis and comparison. 2

[0126] In this embodiment, the similarity of all pixel sampling value function lines of each interval pixel value is calculated to find the sampling interval and the corresponding pixel sampling value function line that best represent the characteristics of the vessel wall diameter circle line. By a specific similarity calculation method (such as calculating the distance between two curves, correlation coefficient, etc.), the multiple pixel sampling value function lines corresponding to each interval pixel value are compared with each other or as a whole to obtain their similarity. For example, the Euclidean distance is used to calculate the similarity between two pixel sampling value function lines, and the smaller the distance, the higher the similarity. By comparing the similarity of the pixel sampling value function lines at different interval pixel values, the case with the highest similarity is found, and the corresponding interval pixel value and pixel sampling value function line will be used for the final calibration of the vessel wall diameter circle line, because they can most accurately depict the profile of the vessel wall.

[0127] The beneficial effects of the above techniques are that embodiment 5 optimizes the diameter circle line calibration unit based on embodiment 4. The pixel exhaustive extension subunit calibrates all approach pixel points on the effective analysis line segment extension line and connects them into an exhaustive approach pixel line, fully exploiting the information. The pixel line ordering subunit forms a pixel line sequence by randomly selecting a starting pixel line and ordering the remaining pixel lines in a clockwise direction, laying the foundation for standardized operation. The pixel interval sampling subunit generates multiple pixel sampling value function lines by sampling and fitting multiple times according to the step interval pixel value list, approximating the profile of the vessel wall from multiple dimensions. The diameter circle line calibration subunit calculates the similarity of the function lines, selects the pixel sampling value function line corresponding to the interval pixel value with the highest similarity, connects the sampling pixel points, and smoothly fits the vessel wall diameter circle line to accurately exclude interference. These optimizations comprehensively improve the calibration accuracy of the vessel wall diameter circle line by the multi-modal imaging fusion navigation system of the guide catheter, provide reliable data support for subsequent links, and enhance the effectiveness and reliability of the system in clinical applications.

[0128] Embodiment 6:

[0129] On the basis of embodiment 3, the retraction parameter determination sub-module includes:

[0130] ​a lumen wall diameter line indentation coefficient determination unit configured to determine a lumen wall diameter line indentation coefficient as a ratio of a minimum radius to a maximum radius among radii of all lumen wall diameter circle lines in the internal tissue image of the front blood vessel;

[0131] a lumen wall center indentation vector determination unit configured to determine a lumen wall center indentation vector as a vector from a center coordinate of a lumen wall diameter circle line with the maximum radius to a center coordinate of a lumen wall diameter circle line with the minimum radius among all lumen wall diameter circle lines in the internal tissue image of the front blood vessel.

[0132] The above technology has the beneficial effects that: the embodiment 6 designs the indentation parameter determination submodule based on the embodiment 3, the lumen wall diameter line indentation coefficient determination unit determines a lumen wall diameter line indentation coefficient as a ratio of a minimum radius to a maximum radius among radii of all lumen wall diameter circle lines in the internal tissue image of the front blood vessel, and this simple definition can quickly reflect the degree of change of the lumen wall diameter and provide an important quantitative index for catheter positioning and navigation. The lumen wall center indentation vector determination unit determines a lumen wall center indentation vector as a vector from a center coordinate of a lumen wall diameter circle line with the maximum radius to a center coordinate of a lumen wall diameter circle line with the minimum radius among all lumen wall diameter circle lines in the internal tissue image of the front blood vessel, which accurately describes the position offset of the lumen wall center. These two units optimize the indentation parameter determination logic, improve the accuracy and efficiency of parameter determination, make the guided catheter multi-modal imaging fusion navigation system process catheter positioning and navigation adjustment based on more reasonable and scientific parameters, improve the overall performance and reliability of the system, and provide strong support for clinical interventional therapy.

[0133] Embodiment 7:

[0134] Based on the embodiment 1, the catheter tip specific positioning module refers to Figure 4 , and includes:

[0135] a multi-dimensional morphological parameter linkage acquisition submodule configured to determine a current front local blood vessel multi-dimensional morphological parameter by searching a multi-dimensional blood vessel morphological parameter linkage table based on the lumen wall diameter line indentation coefficient and the lumen wall center indentation vector;

[0136] a first local blood vessel morphological interception submodule configured to determine local blood vessel morphological distribution data in a real-time rough position range of the catheter tip based on blood vessel morphological distribution data of a target site of the patient;

[0137] a specific positioning submodule configured to match the current front local blood vessel multi-dimensional morphological parameter with the local blood vessel morphological distribution data in the real-time rough position range, and specifically position a real-time specific position of the catheter tip in the real-time rough position range of the catheter tip.

[0138] In this embodiment, the multi-dimensional blood vessel morphology parameter linkage table is a pre-established database or data table that stores the inner wall caliber line indentation coefficient and inner wall center indentation vector and various parameters related to different blood vessel morphologies and their mutual relationships. These parameters can cover multiple dimensions of information such as the diameter, curvature, branch angle, spatial position, etc. of the blood vessel. For example, the table can record the common value range of the diameter, curvature, branch angle, spatial position, etc. of a certain type of blood vessel under a certain inner wall caliber line indentation coefficient and inner wall center indentation vector. This linkage table provides an important reference for determining the specific position of the catheter tip. By querying the table, detailed parameters related to the current blood vessel morphology can be quickly obtained.

[0139] In this embodiment, the current front local blood vessel multi-dimensional morphology parameters are retrieved from the multi-dimensional blood vessel morphology parameter linkage table based on the inner wall caliber line indentation coefficient and the inner wall center deviation vector, and reflect a set of parameters that reflect the morphological characteristics of the local blood vessel in front of the catheter tip in multiple dimensions. For example, after the inner wall caliber line indentation coefficient and the inner wall center deviation vector are determined, the diameter, curvature, and connection angle with the surrounding blood vessel branches of the current front local blood vessel are obtained by querying the multi-dimensional blood vessel morphology parameter linkage table. These parameters comprehensively describe the multi-dimensional morphology of the local blood vessel, which helps to further accurately position the catheter tip.

[0140] In this embodiment, the current front local blood vessel multi-dimensional morphology parameters are matched with the local blood vessel morphology distribution data within the real-time rough position range, and the real-time specific position of the catheter tip is specifically positioned within the real-time rough position range of the catheter tip. It is to compare and analyze the current front local blood vessel multi-dimensional morphology parameters obtained from the multi-dimensional blood vessel morphology parameter linkage table with the local blood vessel morphology distribution data within the real-time rough position range of the catheter tip determined according to the blood vessel morphology distribution data of the patient's target site. For example, compare the diameter, curvature, branch position, etc. of the blood vessels of the two, and find the most suitable part. Once a region with high matching degree is found, the real-time specific position of the catheter tip can be determined within this real-time rough position range, thereby achieving accurate navigation and positioning of the catheter tip and providing accurate position information for subsequent interventional treatment.

[0141] The beneficial effects of the above technology are: embodiment 7 is based on embodiment 1, the specific positioning module of the catheter tip is improved. The multi-dimensional morphological parameter link acquisition submodule retrieves the multi-dimensional blood vessel morphological parameter link table by means of the inner wall caliber line shrinkage coefficient and the inner wall center shrinkage vector to determine the multi-dimensional morphological parameter of the current front local blood vessel, realizes the conversion from the key parameter to the specific blood vessel morphological information, and provides the specific morphological basis for the catheter positioning. The first local blood vessel morphological intercepting submodule accurately determines the local blood vessel morphological distribution data in the real-time rough position range of the catheter tip according to the blood vessel morphological distribution data of the target site of the patient, narrows the positioning range, and makes the subsequent operation more directional. The specific positioning submodule matches the above two data, thereby specifically positioning the real-time specific position of the catheter tip in the real-time rough position range, and significantly improves the accuracy and efficiency of the catheter tip positioning. Through this series of closely related steps, the module optimizes the catheter tip positioning process, enhances the positioning accuracy of the multi-modal imaging fusion navigation system in the actual application of the guide catheter, helps the doctor to control the catheter more accurately, reduces the blood vessel wall damage caused by positioning deviation, improves the treatment effect, and provides more reliable technical support for clinical interventional therapy.

[0142] Embodiment 8:

[0143] On the basis of embodiment 1, the navigation multiple determination module refers to Figure 5 , comprising:

[0144] The second local blood vessel morphological intercepting submodule is used for determining the current front local blood vessel morphological distribution data of the catheter tip based on the blood vessel morphological distribution data of the target site and the real-time specific position of the specifically positioned catheter tip.

[0145] The navigation multiple determination submodule is used for evaluating the current navigation adjustment difficulty based on the current front local blood vessel morphological distribution data of the catheter tip, and determining the navigation adjustment multiple based on the current navigation adjustment difficulty and the inner wall caliber line shrinkage coefficient.

[0146] In this embodiment, based on the blood vessel morphological distribution data of the target site and the real-time specific position of the specifically positioned catheter tip, the current front local blood vessel morphological distribution data of the catheter tip is determined: this means that the morphological distribution data of the region immediately in front of the catheter tip is circumscribed by using the morphological distribution data of the whole blood vessel of the target site of the patient and combining the just accurately determined real-time specific position information of the catheter tip. For example, it is known that the morphological distribution data of the whole heart coronary artery is determined after the guide catheter tip is determined at a certain position of the coronary artery, and the specific morphology of a small section of the coronary artery in front of the guide catheter tip is extracted, such as the bending direction of the blood vessel, the change of the pipe diameter and other data. This part of data is the current front local blood vessel morphological distribution data of the catheter tip, which focuses on the blood vessel condition of the region where the catheter is about to travel, and prepares for the subsequent navigation adjustment.

[0147] In this embodiment, the current navigation adjustment difficulty is assessed based on the local vascular morphology distribution data in front of the catheter tip: the ease or difficulty of guiding the catheter forward is determined according to the morphological distribution characteristics of the local blood vessels in front of the catheter tip. If the local blood vessel morphology in front is complex, such as abrupt changes in vessel diameter, multiple branches with tricky branch angles, or extremely tortuous vessel course, then the navigation adjustment difficulty is high; conversely, if the blood vessel is relatively straight and the diameter changes smoothly, the navigation adjustment difficulty is relatively low. For example, when a severe narrowing suddenly occurs in the blood vessel in front of the catheter tip, and there are multiple small branches around it, it is difficult to accurately control the catheter through this area, and the navigation adjustment difficulty is assessed as high, specifically including:

[0148]

[0149] In the formula, Given the current navigation adjustment difficulty, α represents the weight of the change angle in the current forward extension direction of the patient's blood vessel, and θ represents the change angle in the current forward extension direction of the patient's blood vessel. max For 360 degrees, n b n represents the number of anterior vascular branches in the patient. b·max The maximum number of vascular branches is the preset value, β is the weight of the patient's anterior vascular curvature, and c is the patient's anterior vascular curvature. max This is the preset maximum curvature value.

[0150] In this embodiment, the navigation adjustment factor is determined based on the current navigation adjustment difficulty and the inner wall diameter indentation coefficient: a factor for adjusting the guidance catheter's navigation is determined by comprehensively considering both factors. For example, it can be determined based on a preset list of navigation difficulty and inner wall diameter indentation coefficient - navigation adjustment factor. If the navigation adjustment difficulty is high, and the inner wall diameter indentation coefficient indicates significant changes in vessel morphology, then the navigation adjustment factor will be relatively large, meaning a greater degree of navigation adjustment is required; conversely, if the navigation adjustment difficulty is low and the vessel morphology changes are relatively stable (inner wall diameter indentation coefficient close to 1), the navigation adjustment factor will be small. This navigation adjustment factor is used to subsequently generate navigation adjustment commands to precisely control the guidance catheter's movement to adapt to different vessel conditions.

[0151] The beneficial effects of the above technology are: Embodiment 8 optimizes the navigation multiple determination module based on Embodiment 1: The second local blood vessel morphology extraction sub-module can accurately determine the current local blood vessel morphology distribution data in front of the catheter tip based on the target site blood vessel morphology distribution data and the real-time specific position of the catheter tip. This process closely combines the actual position of the catheter tip with the overall blood vessel morphology, providing detailed and targeted data basis for subsequent navigation multiple determination, which helps to better grasp the blood vessel condition in front of the catheter. The navigation multiple determination sub-module evaluates the current navigation adjustment difficulty with the help of the current local blood vessel morphology distribution data in front of the catheter tip, and determines the navigation adjustment multiple in combination with the inner wall caliber line recession coefficient. This comprehensive consideration of the complexity of blood vessel morphology and the relative change of blood vessel inner wall caliber makes the determination of navigation adjustment multiple more scientific and reasonable. By accurately evaluating the navigation difficulty, the system can flexibly adjust the navigation multiple according to the actual situation, avoiding excessive or insufficient catheter operation due to improper navigation multiple, thereby improving the accuracy and stability of navigation. Overall, the optimized navigation multiple determination module of Embodiment 8 improves the adaptability and accuracy of the multi-modal imaging fusion navigation system for guiding catheter in the navigation process. It can better assist doctors to adjust catheter operation according to the actual condition of blood vessels, reduce the risk of surgery, improve the success rate and safety of clinical interventional treatment, and bring better treatment effect to patients.

[0152] Embodiment 9:

[0153] On the basis of Embodiment 1, the navigation adjustment vector generation module refers to Figure 6 , comprising:

[0154] The offset vector calibration sub-module is used to determine the offset vector from the catheter tip position to the center of the minimum caliber circle line in the front blood vessel internal tissue image.

[0155] The direction adjustment vector calibration sub-module is used to take the sum of the offset vector and the inner wall center recession vector as the navigation direction adjustment vector.

[0156] The beneficial effects of the above technology are: Embodiment 9 innovatively designs the navigation adjustment vector generation module based on Embodiment 1: the offset vector calibration submodule accurately determines the offset vector from the catheter tip position to the center of the minimum diameter circle in the front vascular internal tissue image. This vector intuitively reflects the positional deviation relationship between the catheter tip and the center of the minimum diameter of the blood vessel, providing clear spatial pointing information for catheter navigation adjustment, helping the operator to clearly understand the relative offset between the current position and the target position of the catheter, making the navigation adjustment more targeted. And the direction adjustment vector calibration submodule ingeniously adds the offset vector and the inner wall center recession vector to obtain the navigation direction adjustment vector. This combination not only considers the offset between the catheter tip and the center of the minimum diameter circle, but also integrates the overall trend of the center position of the inner wall of the blood vessel, so that the generated navigation direction adjustment vector more comprehensively and accurately reflects the actual direction that needs to be adjusted in the blood vessel. Overall, the navigation adjustment vector generation module of Embodiment 9 greatly optimizes the navigation adjustment mechanism of the multi-modal imaging fusion navigation system of the guide catheter by scientifically and reasonably determining the navigation direction adjustment vector. It enables the system to more accurately provide guidance for the operator in terms of navigation adjustment direction, effectively improves the accuracy and flexibility of catheter navigation in the blood vessel, helps to reduce operation errors, and improves the safety and effectiveness of catheter operation in the clinical interventional treatment process, providing more reliable technical support for patient treatment.

[0157] The present application provides a guide catheter, comprising:

[0158] a flexible tubular structure and a catheter tip located at the front end of the flexible tubular structure,

[0159] The catheter tip is provided with a miniature imaging device for acquiring a front vascular internal tissue image of the catheter tip;

[0160] a wireless transmission module for transmitting the front vascular internal tissue image to the catheter tip coarse positioning module in real time, and receiving navigation adjustment instructions from the navigation adjustment execution module;

[0161] a navigation instruction execution module for controlling the travel parameters of the catheter tip based on the navigation adjustment instructions.

[0162] The guide catheter provided by the application has many beneficial effects. The miniature imaging device at the catheter tip can obtain the image of the internal tissue of the front blood vessel, provide the real-time and intuitive blood vessel condition for operation, facilitate path planning, and ensure safe and accurate operation. The wireless transmission module realizes real-time transmission of the image to the coarse positioning module and receiving of the navigation adjustment instruction, ensures efficient information circulation between the modules, and improves system response and operation efficiency. The navigation instruction execution module accurately controls the catheter tip travel parameters according to the instruction, and helps the doctor to accurately control the catheter to reach the target position. This integrated design integrates all modules into the guide catheter, reduces system complexity, enhances adaptability in complex and narrow blood vessel space, ultimately improves treatment effect, reduces patient pain and complications, shortens operation time, and provides a powerful tool for clinical blood vessel interventional treatment.

[0163] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application also intends to include these modifications and variations.

Claims

1. A guided catheter multimodality imaging fusion navigation system, characterized in that, The method comprises the following steps: A catheter tip rough positioning module is used to pre-acquire the blood vessel shape distribution data of a target site of a patient based on multiple imaging modes, and to obtain the real-time rough position range of the catheter tip in the patient's body in combination with the initial entry position of the guide catheter tip in the patient's body, while obtaining the internal tissue image of the front blood vessel of the catheter tip; A retraction parameter determination module is used to determine the inner wall caliber line retraction coefficient and the inner wall center retraction vector based on the internal tissue image of the front blood vessel; A catheter tip specific positioning module is used to specifically position the real-time specific position of the catheter tip in the real-time rough position range of the catheter tip based on the inner wall caliber line retraction coefficient and the inner wall center retraction vector; A navigation multiple determination module is used to determine the navigation adjustment multiple based on the blood vessel shape distribution data of the target site, the real-time specific position of the specifically positioned catheter tip, and the inner wall caliber line retraction coefficient; A navigation adjustment vector generation module is used to determine the offset vector from the catheter tip position to the center of the minimum caliber circle line in the internal tissue image of the front blood vessel, and to generate the navigation direction adjustment vector based on the offset vector and the inner wall center retraction vector; A navigation adjustment execution module is used to generate the navigation adjustment instruction of the guide catheter based on the navigation adjustment multiple and the navigation direction adjustment vector.

2. The guide catheter multimodality imaging fusion navigation system of claim 1, wherein, The catheter tip rough positioning module comprises: A blood vessel multi-modal contrast sub-module is used to pre-acquire the blood vessel shape distribution data of a target site of a patient based on multiple imaging modes; A catheter tip rough positioning sub-module is used to coarsely position the real-time rough position range of the catheter tip in the target site blood vessel distribution data based on the initial entry position of the guide catheter tip in the patient's body and the length of the guide catheter entering the body, while obtaining the internal tissue image of the front blood vessel of the catheter tip.

3. The guide catheter multimodality imaging fusion navigation system of claim 1, wherein, The retraction parameter determination module comprises: A caliber circle line calibration sub-module is used to determine all the blood vessel inner wall caliber circle lines in the internal tissue image of the front blood vessel; A retraction parameter determination sub-module is used to determine the inner wall caliber line retraction coefficient and the inner wall center retraction vector based on all the blood vessel inner wall caliber circle lines in the internal tissue image of the front blood vessel.

4. The guide catheter multimodality imaging fusion navigation system of claim 3, wherein, The caliber circle line calibration sub-module comprises: A light and shadow change significant region calibration unit is used to determine the light and shadow gradient of each pair of adjacent pixel points in the internal tissue image of the front blood vessel, and to screen out a set of significant light and shadow gradients from all the light and shadow gradients of the adjacent pixel points in each frame of the internal tissue image of the blood vessel inner wall, and to calibrate all the light and shadow change significant regions in the corresponding frame of the internal tissue image of the blood vessel inner wall based on the set of significant light and shadow gradients; A diffusion analysis line calibration unit is used to take the center pixel of the light and shadow change significant region as the starting pixel point, and to take the exhaustive line segment of the starting pixel point and each adjacent pixel point in the corresponding light and shadow change significant region as a single diffusion analysis line of the light and shadow change significant region; A gradient establishment degree calculation unit is used to calculate the gradient establishment degree of each diffusion analysis line in the light and shadow change significant region based on the light and shadow gradient of all the adjacent pixel points in the light and shadow change significant region. The total gradual change validity calculation unit is configured to connect two-to-two diffusion analysis line segments on the extension lines of each of all diffusion analysis lines in all light shadow change significant regions in the front blood vessel internal tissue image, to obtain a plurality of exhaustive connection diffusion analysis line segments, and to determine a total gradual change validity of each of the exhaustive connection diffusion analysis line segments based on the gradual change validities of all diffusion analysis lines in each of the exhaustive connection diffusion analysis line segments; The effective analysis line calibration unit is configured to take all the exhaustive connection diffusion analysis line segments with the total gradual change validity not less than the threshold as all effective analysis line segments. The caliber circle line calibration unit is configured to calibrate all the blood vessel inner wall caliber circle lines in the front blood vessel internal tissue image based on all the effective analysis line segments.

5. The guide catheter multimodality imaging fusion navigation system of claim 4, wherein, The caliber circle line calibration unit comprises: The pixel exhaustive extension subunit is configured to calibrate all the passing-through pixel points on the extension line of each of the effective analysis line segments in the front blood vessel internal tissue image and to connect the passing-through pixel points to obtain an exhaustive passing-through pixel line of each of the effective analysis line segments. The pixel line ordering subunit is configured to randomly select one of the exhaustive passing-through pixel lines as a starting pixel line, to sequentially calibrate the order of all the exhaustive passing-through pixel lines except the starting pixel line in the front blood vessel internal tissue image in a clockwise direction, and to obtain a pixel line sequence. The pixel interval taking subunit is configured to perform multiple sequential pixel sampling and fitting on all the exhaustive passing-through pixel lines in the pixel line sequence based on each interval pixel value in the step interval pixel value list to generate a plurality of pixel sampling value function lines of each interval pixel value. The caliber circle line calibration subunit is configured to calculate the similarity of all the pixel sampling value function lines of each interval pixel value, to connect and calibrate all the sampling pixel points included in each pixel sampling value function line corresponding to the interval pixel value corresponding to the maximum similarity in sequence in the front blood vessel internal tissue image, and to perform smooth fitting to obtain all the blood vessel inner wall caliber circle lines.

6. The guide catheter multimodality imaging fusion navigation system of claim 3, wherein, The retraction parameter determination sub-module comprises: The inner wall caliber line retraction coefficient determination unit is configured to take the ratio of the minimum radius to the maximum radius in the radii of all the blood vessel inner wall caliber circle lines in the front blood vessel internal tissue image as an inner wall caliber line retraction coefficient. The inner wall center retraction vector determination unit is configured to take the vector from the center coordinate of the blood vessel inner wall caliber circle line with the maximum radius to the center coordinate of the blood vessel inner wall caliber circle line with the minimum radius in all the blood vessel inner wall caliber circle lines in the front blood vessel internal tissue image as an inner wall center retraction vector.

7. The guide catheter multimodality imaging fusion navigation system of claim 1, wherein, The catheter tip specific positioning module comprises: The multi-dimensional morphological parameter linkage acquisition sub-module is configured to retrieve a multi-dimensional blood vessel morphological parameter linkage table based on the inner wall caliber line retraction coefficient and the inner wall center retraction vector to determine the current front local blood vessel multi-dimensional morphological parameter. The first local blood vessel morphological interception sub-module is configured to determine local blood vessel morphological distribution data in the real-time rough position range of the catheter tip based on the target site blood vessel morphological distribution data of the patient. The specific positioning sub-module is configured to match the current front local blood vessel multi-dimensional morphological parameter with the local blood vessel morphological distribution data in the real-time rough position range of the catheter tip, and to specifically position the real-time specific position of the catheter tip in the real-time rough position range of the catheter tip.

8. The guide catheter multimodality imaging fusion navigation system of claim 1, wherein, The navigation multiple determination module comprises: The second local blood vessel shape intercepting sub-module is configured to determine the current front local blood vessel shape distribution data of the catheter tip based on the blood vessel shape distribution data of the target site and the real-time specific position of the specific positioning catheter tip. The navigation multiple determination sub-module is configured to evaluate the current navigation adjustment difficulty based on the current front local blood vessel shape distribution data of the catheter tip, and determine the navigation adjustment multiple based on the current navigation adjustment difficulty and the inner wall caliber line recession coefficient.

9. The guide catheter multimodality imaging fusion navigation system of claim 1, wherein, The navigation adjustment vector generation module comprises: The offset vector calibration sub-module is configured to determine the offset vector from the catheter tip position to the center of the minimum caliber circle line in the front blood vessel internal tissue image. The direction adjustment vector calibration sub-module is configured to take the sum of the offset vector and the inner wall center recession vector as the navigation direction adjustment vector.

10. A guide catheter, comprising: The navigation adjustment vector generation module comprises: The flexible tubular structure and the catheter tip at the front end of the flexible tubular structure, The catheter tip is provided with a miniature imaging device for acquiring a front blood vessel internal tissue image of the catheter tip; The wireless transmission module is configured to transmit the front blood vessel internal tissue image to the catheter tip coarse positioning module in real time, and receive the navigation adjustment instruction from the navigation adjustment execution module in the guide catheter multi-modal imaging fusion navigation system according to any one of claims 1 to 9; The navigation instruction execution module is configured to control the travel parameters of the catheter tip based on the navigation adjustment instruction.

Images