Carotid artery stent simulation method and system based on virtual intervention algorithm

Abstract

The invention relates to the technical field of carotid artery stent simulation, and relates to a carotid artery stent simulation method and system based on a virtual intervention algorithm, and the method comprises the steps: obtaining first image information which comprises carotid artery image information; determining the stenosis degree of the carotid artery and the component index of each plaque area according to the first image information; constructing a virtual carotid artery blood vessel three-dimensional model according to the stenosis degree of the carotid artery and the component index of each plaque region; obtaining a preset virtual support three-dimensional model; the contact between a preset virtual stent three-dimensional model and a virtual carotid artery blood vessel three-dimensional model is analyzed through a mechanical analysis method, a simulation result is obtained, and the simulation result comprises the unfolded form of the stent in the body of the patient; the dynamic constraint change between the nodes and the mutual influence between the blood vessel stress and the stent expansion can be accurately captured, and the simulation precision is greatly improved, so that the operation safety is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of carotid stent simulation, and in particular to a carotid stent simulation method and system based on a virtual intervention algorithm. Background Art

[0002] In the field of carotid artery disease treatment, especially during stent intervention surgery, the core challenge is how to accurately predict and achieve the ideal deployment state of the stent in the complex vascular environment of the patient's body. The deployment state of the stent directly affects the surgical effect. If it cannot be accurately attached to the wall, it may cause thrombosis or restenosis. However, due to the significant individual differences in the geometry, degree of stenosis, plaque composition and mechanical properties of the carotid artery of each patient, coupled with the complex interactions of structures such as the aortic wall and potential dissecting membranes, it is extremely challenging to construct an accurate mechanical model that is suitable for different individuals and includes these differences. Although existing technical means can simulate the radial expansion of the stent, it is difficult to accurately capture the dynamic constraint changes between nodes and the mutual influence between vascular forces and stent expansion, resulting in inaccurate simulation results. Summary of the invention

[0003] The purpose of the present invention is to provide a carotid stent simulation method and system based on a virtual intervention algorithm to improve the above-mentioned problems.

[0004] In order to achieve the above objectives, the present application provides the following technical solutions:

[0005] On the one hand, an embodiment of the present application provides a carotid stent simulation method based on a virtual intervention algorithm, the method comprising:

[0006] Acquiring first image information, wherein the first image information includes carotid artery image information;

[0007] determining the degree of stenosis of the carotid artery and the component index of each plaque area according to the first image information;

[0008] Constructing a virtual carotid artery 3D model according to the stenosis degree of the carotid artery and the component index of each plaque area;

[0009] Obtaining a preset virtual bracket three-dimensional model;

[0010] The contact between the preset virtual stent three-dimensional model and the virtual carotid artery three-dimensional model is analyzed by using a mechanical analysis method to obtain a simulation result, wherein the simulation result includes the shape of the stent after being deployed in the patient's body.

[0011] In a second aspect, an embodiment of the present application provides a carotid stent simulation system based on a virtual intervention algorithm, the system comprising:

[0012] A first acquisition module, used to acquire first image information, wherein the first image information includes carotid artery image information;

[0013] A first processing module, used for determining the stenosis degree of the carotid artery and the component index of each plaque area according to the first image information;

[0014] A second processing module is used to construct a virtual carotid artery vascular three-dimensional model according to the stenosis degree of the carotid artery and the component index of each plaque area;

[0015] A second acquisition module is used to acquire a preset virtual stent three-dimensional model;

[0016] The simulation module is used to analyze the contact between the preset virtual stent three-dimensional model and the virtual carotid artery three-dimensional model using a mechanical analysis method to obtain a simulation result, wherein the simulation result includes the shape of the stent after being deployed in the patient's body.

[0017] In a third aspect, an embodiment of the present application provides a carotid stent simulation device based on a virtual intervention algorithm, the device comprising a memory and a processor. The memory is used to store a computer program; the processor is used to implement the steps of the above-mentioned carotid stent simulation method based on a virtual intervention algorithm when executing the computer program.

[0018] In a fourth aspect, an embodiment of the present application provides a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned carotid stent simulation method based on a virtual intervention algorithm are implemented.

[0019] The beneficial effects of the present invention are:

[0020] The present invention uses the first image information to determine the degree of stenosis of the carotid artery and the component index of each plaque area, constructs a virtual carotid artery vascular three-dimensional model according to the degree of stenosis of the carotid artery and the component index of each plaque area, and then uses a mechanical analysis method to analyze the contact between a preset virtual stent three-dimensional model and the virtual carotid artery vascular three-dimensional model to obtain a simulation result, simulating the radial expansion of the stent and the response of the vascular tissue. This simulation combines the nonlinear mechanical characteristics of the blood vessel and the physical properties of the stent, and is based on dynamic constraints between nodes. Taking into account the combined effects of vascular force and stent expansion, the stent and the vascular wall are iteratively optimized to fit each other, thereby greatly improving the accuracy of the simulation and thus improving the safety of the operation.

[0021] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 It is a schematic flow chart of a carotid stent simulation method based on a virtual intervention algorithm described in an embodiment of the present invention.

[0024] Figure 2 It is a schematic diagram of the structure of a carotid stent simulation system based on a virtual intervention algorithm described in an embodiment of the present invention.

[0025] Figure 3 It is a schematic diagram of the structure of a carotid stent simulation device based on a virtual intervention algorithm described in an embodiment of the present invention.

[0026] Labels in the figure: 800, carotid stent simulation device based on virtual intervention algorithm; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component; 901, first acquisition module; 902, first processing module; 903, second processing module; 904, second acquisition module; 905, simulation module. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0029] Embodiment 1:

[0030] This embodiment provides a carotid artery stent simulation method based on a virtual intervention algorithm. It can be understood that a scenario can be laid out in this embodiment, for example: a scenario for predicting the surgical success rate of installing a stent in a patient's carotid artery.

[0031] See also Figure 1 , the figure shows that the method includes step S1, step S2, step S3, step S4 and step S5, which specifically include:

[0032] Step S1, obtaining first image information, wherein the first image information includes carotid artery image information;

[0033] In this step, the first image information is a carotid artery CT or high-resolution MRI image.

[0034] Step S2, determining the degree of stenosis of the carotid artery and the component index of each plaque area according to the first image information;

[0035] The step S2 also includes step S21, step S22, step S23, step S24, step S25 and step 26, which specifically include:

[0036] Step S21, using an image segmentation algorithm to process the first image information to obtain second image information and third image information, wherein the second image information includes a carotid artery lumen image, and the third image information includes a plaque area image;

[0037] Step S22, determining the degree of stenosis of the carotid artery according to the second image information;

[0038] In this step, quantifying the degree of stenosis of the carotid artery according to the carotid artery lumen image is a technical solution well known to those skilled in the art, and thus will not be described in detail here.

[0039] Step S23, determining the grayscale values ​​of different tissues in the third image information to obtain grayscale information;

[0040] Step S24, determining the component information of the plaque according to the grayscale information;

[0041] In this step, different tissues have different grayscale values, so the tissue composition can be inferred based on the grayscale value.

[0042] Step S25, analyzing the signal intensity in the third image information to obtain percentage information corresponding to each component;

[0043] In this step, the CT value or MR signal intensity of the plaque area is quantitatively analyzed, and the percentage of each component (lipid, fiber, calcification, etc.) is calculated based on the CT value or signal intensity range of different components.

[0044] Step S26: obtaining a plaque component index according to the component information and the percentage information.

[0045] In this embodiment, the severity of carotid artery stenosis and the stability of plaques can be more accurately assessed by quantifying the degree of carotid artery stenosis and plaque composition indexes.

[0046] Step S3, constructing a virtual carotid artery 3D model according to the stenosis degree of the carotid artery and the component index of each plaque area;

[0047] In this step, a three-dimensional model of a virtual carotid artery is generated using a geometric modeling method based on the quantified degree of carotid artery stenosis. Since different tissue components have different corresponding stress-strain relationships, a uniaxial tensile test is performed based on the component indicators of each plaque area to collect measurement data, and the measurement data is analyzed to obtain a stress-strain curve, thereby setting the material property data based on the stress-strain curve. It should be noted that the uniaxial tensile test is an experimental protocol well known in the art, so it will not be described in detail here.

[0048] Step S4, obtaining a preset virtual bracket three-dimensional model;

[0049] In this step, according to the preset stent model and size, the stent is parametrically modeled in a virtual environment to obtain a preset virtual stent three-dimensional model.

[0050] Step S5: Analyze the contact between the preset virtual stent three-dimensional model and the virtual carotid artery three-dimensional model using a mechanical analysis method to obtain a simulation result, wherein the simulation result includes the shape of the stent after deployment in the patient's body.

[0051] In this step, the virtual stent 3D model is placed in the carotid artery 3D model at the position and angle specified by the operator. The deployment process of the stent in the carotid artery is simulated by mechanical analysis methods to obtain the stress distribution and deformation between the stent and the blood vessel wall. In a specific implementation scheme: a carotid artery stenosis model with a diameter of 8 mm and a length of 30 mm is implanted with a stent with a diameter of 7 mm and a length of 40 mm. The simulation results show that stress concentration at both ends of the stent may lead to excessive expansion of the blood vessel wall, while the stress distribution at the stenosis is relatively uniform, which can effectively open the stenosis. By comparing the virtual implantation of different stents, the operator can choose the optimal stent model and size, and predict the short-term and long-term effects after surgery, thereby improving the safety and effectiveness of the operation.

[0052] The step S5 also includes step S51, step S52, step S53, step S54, step S55 and step S56, which specifically include:

[0053] Step S51, importing the preset virtual stent three-dimensional model into the virtual carotid artery three-dimensional model to obtain a virtual stent-carotid artery three-dimensional model;

[0054] Step S52, meshing the virtual stent-carotid artery 3D model using a simplex deformable mesh technology to obtain a first 3D model;

[0055] In this step, the virtual stent-carotid artery 3D model is meshed using simple deformable mesh technology, which can simplify the geometric shape and transform the blood vessels and stents into a mesh structure composed of vertices and triangular faces, thereby achieving the ability to quickly simulate complex structural deformations, reducing simulation complexity, and greatly improving calculation speed.

[0056] Step S53, obtaining the nonlinear stress-strain relationship of the vascular tissue;

[0057] The step S53 also includes step S531, step S532, step S533 and step S534, which specifically include:

[0058] Step S531, obtaining the geometric shape of the virtual stent three-dimensional model and the material property data corresponding to the virtual carotid artery three-dimensional model;

[0059] In this step, the stent is a precision medical device whose geometry directly affects its expansion effect and long-term stability in the blood vessel. Different stent designs, such as open-loop or closed-loop structures, will lead to different mechanical properties. At the same time, the material properties corresponding to the virtual carotid artery 3D model, such as Young's modulus and Poisson's ratio, also determine the stiffness and flexibility of the stent.

[0060] Step S532, determining the radial expansion force of the virtual stent according to the geometric shape of the virtual stent three-dimensional model and the material property data corresponding to the virtual carotid artery three-dimensional model;

[0061] In this step, the radial expansion force of the virtual stent is its ability to resist the pressure of the blood vessel wall. According to the principle of material mechanics, the radial expansion force is determined by considering the stent as a thin-walled cylinder using Hooke's law.

[0062] Step S533, establishing a mechanical model of vascular tissue according to the radial expansion force of the virtual stent;

[0063] In this step, a mechanical model of vascular tissue is established through the theory of elastic mechanics, and vascular tissue is regarded as a hyperelastic material. The vascular wall is mainly composed of collagen fibers, elastin and smooth muscle cells. The combination of these components makes the blood vessels have a nonlinear stress-strain relationship. This means that blood vessels will show different stiffness at different strain levels. For example, under low strain, blood vessels mainly show the elasticity of elastin; under high strain, collagen fibers begin to play a role, which significantly increases the stiffness of blood vessels.

[0064] Step S534: determining the nonlinear stress-strain relationship of the vascular tissue according to the mechanical model of the vascular tissue.

[0065] Step S54, determining radial constraints according to the nonlinear stress-strain relationship of the vascular tissue;

[0066] The step S54 also includes step S541, step S542 and step S543, which specifically include:

[0067] Step S541, converting the nonlinear stress-strain relationship of the vascular tissue into function information, wherein the function information includes a function related to strain energy density;

[0068] In this step, the strain energy density function describes the energy stored in the material during deformation. This conversion allows us to analyze the mechanical behavior of blood vessels from an energy perspective, and to calculate stress through the partial derivative of the strain energy density function, which is not only convenient for calculation and analysis, but also easier to fit with simulation data.

[0069] Step S542, obtaining carotid artery geometric parameters and hemodynamic parameters;

[0070] In this step, the geometric parameters of the carotid artery include the diameter, length, curvature and branch angle of the blood vessel. These parameters can be measured by medical imaging techniques such as CTA or MRI. Hemodynamic parameters include the flow rate, pressure and viscosity of the blood. These parameters directly affect the stress state of the blood vessel wall.

[0071] Step S543: determining the radial constraint of the blood vessel according to the carotid artery geometric parameters, the hemodynamic parameters and the function information.

[0072] In this step, by combining the strain energy density function, geometric shape and hemodynamic parameters, we can calculate the radial constraint force generated by the blood vessel wall to maintain its shape under given blood pressure and flow rate. The radial constraint force of the blood vessel can be regarded as a response of the blood vessel to its internal pressure. For the hyperelastic material model, this constraint force can be obtained by the partial derivative of the strain energy density function with respect to the radial strain.

[0073] Step S55, embedding the radial constraint into the first three-dimensional model to obtain a second three-dimensional model;

[0074] Step S56: Solve the deformation of the surface mesh where the support contacts the tube wall in the second three-dimensional model to obtain a simulation result.

[0075] The step S56 also includes step S561, step S562, step S563 and step S564, which specifically include:

[0076] Step S561, obtaining tangential constraints;

[0077] In this step, the first three-dimensional model has tangential constraints between nodes, specifically including: obtaining a node set constituting a grid model of the stent and the vascular wall; calculating the distance between adjacent nodes through the spatial position of each node in the node set; if the distance between the adjacent nodes is less than a first threshold, applying a repulsive force between the adjacent nodes; if the distance between the adjacent nodes is greater than a second threshold, applying an attractive force between the adjacent nodes; adding the repulsive force and the attractive force to the deformation potential energy function of the grid; iteratively adjusting the spatial position of each node in the node set according to the energy minimization principle. It should be noted that the first threshold is set for the purpose of applying a repulsive force to avoid node collision or penetration, and the second threshold is set for the purpose of applying an attractive force to maintain the continuity of the structure. The stent and the vascular wall need to maintain a certain shape and stability. Therefore, the first threshold and the second threshold are set according to the material properties of the stent and the mechanical properties of the vascular wall. In addition, according to the energy minimization principle, iteratively adjusting the spatial position of each node in the node set to obtain the final position of each node, and then using this position to adjust the shape of the stent and the vascular wall, thereby achieving the effect of improving the simulation accuracy.

[0078] Step S562: embedding the tangent constraint into the second three-dimensional model to obtain a third three-dimensional model;

[0079] In this step, the third 3D model includes tangential constraints and radial constraints.

[0080] Step S563, performing numerical iterative calculation according to the third three-dimensional model to obtain the contact state between the stent and the blood vessel wall;

[0081] In this step, the spatial coordinate information corresponding to each node in the third three-dimensional model is obtained; the tangential distance is calculated according to the spatial coordinate information; the tangential force between nodes is constructed according to the tangential distance and the preset elastic coefficient; the radial expansion direction of the virtual stent is determined according to the spatial coordinate information; the radial expansion displacement of the stent node is calculated according to the radial expansion direction of the virtual stent; the radial resultant force on each node is calculated according to the radial expansion displacement and the radial constraint force of the blood vessel; the contact state is obtained by iteratively calculating the radial resultant force on each node and the tangential force between the nodes. It can be understood that the final force on each node is obtained by combining the radial resultant force and the tangential force on each node. After the expansion behavior of the stent is clarified, it is combined with the resistance of the blood vessel to obtain a resultant force in a balanced state, thereby promoting the adjustment of the entire grid model and realizing a balance between force and reaction force. The advantage of this is that the balance takes into account the interaction between internal and external forces, so that the real biomechanical process can be simulated and a more accurate force state can be obtained, which is crucial for simulating the real behavior of the stent in the body and provides necessary data for iterative calculation. Iterative calculation is performed by adding the radial resultant force to the energy equation of the simple deformable grid algorithm. This step uses the calculated radial resultant force to update the energy equation, and then solves this equation to find the new node position that minimizes the energy, and repeats this process until convergence. The stress state of each node will affect its displacement, and then affect the stress of other nodes. This is a dynamic balance process. When the network converges, the model will tend to be stable, and the final shape is the shape of the stent deployed in the blood vessel. The present invention makes the simulation adaptive because each calculation is feeding back and adjusting the model. The simulation process is dynamic, not static, and can truly reflect the complex process of interaction between the stent and the blood vessel. The result of this calculation is more accurate and closer to the actual shape and stress distribution of the stent after implantation.

[0082] Step S564: obtain a simulation result according to the contact state.

[0083] It can be understood that the shape of the virtual stent after being deployed in the blood vessel is predicted based on the iteratively calculated contact state, that is, the simulation result.

[0084] Embodiment 2:

[0085] like Figure 2As shown, this embodiment provides a carotid stent simulation system based on a virtual intervention algorithm, the system includes a first acquisition module 901, a first processing module 902, a second processing module 903, a second acquisition module 904 and a simulation module 905, which specifically include:

[0086] A first acquisition module, used to acquire first image information, wherein the first image information includes carotid artery image information;

[0087] A first processing module, used for determining the stenosis degree of the carotid artery and the component index of each plaque area according to the first image information;

[0088] A second processing module is used to construct a virtual carotid artery vascular three-dimensional model according to the stenosis degree of the carotid artery and the component index of each plaque area;

[0089] A second acquisition module is used to acquire a preset virtual stent three-dimensional model;

[0090] The simulation module is used to analyze the contact between the preset virtual stent three-dimensional model and the virtual carotid artery three-dimensional model using a mechanical analysis method to obtain a simulation result, wherein the simulation result includes the shape of the stent after being deployed in the patient's body.

[0091] In a specific embodiment of the present disclosure, the first processing module further includes a first processing unit, a second processing unit, a third processing unit, a fourth processing unit, a fifth processing unit and a sixth processing unit, which specifically include:

[0092] A first processing unit is used to process the first image information by using an image segmentation algorithm to obtain second image information and third image information, wherein the second image information includes a carotid artery lumen image, and the third image information includes a plaque area image;

[0093] a second processing unit, configured to determine the degree of stenosis of the carotid artery according to the second image information;

[0094] A third processing unit, used to determine the grayscale values ​​of different tissues in the third image information to obtain grayscale information;

[0095] A fourth processing unit, configured to determine component information of the plaque according to the grayscale information;

[0096] a fifth processing unit, configured to analyze the signal strength in the third image information to obtain percentage information corresponding to each component;

[0097] The sixth processing unit is used to obtain a plaque component index according to the component information and the percentage information.

[0098] In a specific implementation of the present disclosure, the simulation module includes a seventh processing unit, an eighth processing unit, a first acquisition unit, a ninth processing unit, a tenth processing unit and an eleventh processing unit, which specifically include:

[0099] a seventh processing unit, configured to import the preset virtual stent three-dimensional model into the virtual carotid artery three-dimensional model to obtain a virtual stent-carotid artery three-dimensional model;

[0100] an eighth processing unit, configured to mesh the virtual stent-carotid artery vascular three-dimensional model using a simple deformable mesh technology to obtain a first three-dimensional model;

[0101] A first acquisition unit, used for acquiring a nonlinear stress-strain relationship of vascular tissue;

[0102] a ninth processing unit, configured to determine radial constraints according to a nonlinear stress-strain relationship of the vascular tissue;

[0103] A tenth processing unit, configured to embed the radial constraint into the first three-dimensional model to obtain a second three-dimensional model;

[0104] The eleventh processing unit is used to solve the deformation of the surface mesh where the support contacts the tube wall in the second three-dimensional model to obtain a simulation result.

[0105] In a specific implementation of the present disclosure, the first acquisition unit further includes a second acquisition unit, a twelfth processing unit, a thirteenth processing unit, and a fourteenth processing unit, which specifically include:

[0106] A second acquisition unit is used to acquire the geometric shape of the virtual stent three-dimensional model and the material property data corresponding to the virtual carotid artery three-dimensional model;

[0107] a twelfth processing unit, configured to determine a radial expansion force of the virtual stent according to the geometric shape of the virtual stent three-dimensional model and material property data corresponding to the virtual carotid artery three-dimensional model;

[0108] A thirteenth processing unit, configured to establish a mechanical model of the vascular tissue according to the radial expansion force of the virtual stent;

[0109] A fourteenth processing unit is used to determine the nonlinear stress-strain relationship of the vascular tissue according to the mechanical model of the vascular tissue.

[0110] In a specific implementation of the present disclosure, the ninth processing unit also includes a fifteenth processing unit, a third acquisition unit, and a sixteenth processing unit, which specifically include:

[0111] A fifteenth processing unit, configured to convert the nonlinear stress-strain relationship of the vascular tissue into function information, wherein the function information includes a function related to strain energy density;

[0112] A third acquisition unit is used to acquire carotid artery geometric parameters and hemodynamic parameters;

[0113] A sixteenth processing unit is used to determine the radial constraint of the blood vessel according to the carotid artery geometric parameters, the hemodynamic parameters and the function information.

[0114] In a specific implementation of the present disclosure, the eleventh processing unit further includes a fourth acquisition unit, a seventeenth processing unit, an eighteenth processing unit, and a nineteenth processing unit, which specifically include:

[0115] A fourth acquisition unit, used for acquiring a tangential constraint;

[0116] A seventeenth processing unit, configured to embed the tangent constraint into the second three-dimensional model to obtain a third three-dimensional model;

[0117] An eighteenth processing unit, configured to perform numerical iterative calculation according to the third three-dimensional model to obtain a contact state between the stent and the blood vessel wall;

[0118] A nineteenth processing unit is used to obtain a simulation result according to the contact state.

[0119] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0120] Embodiment 3:

[0121] Corresponding to the above method embodiment, a carotid stent simulation device based on a virtual intervention algorithm is also provided in this embodiment. The carotid stent simulation device based on a virtual intervention algorithm described below and the carotid stent simulation method based on a virtual intervention algorithm described above can refer to each other.

[0122] Figure 3 FIG. 8 is a block diagram of a carotid stent simulation device 800 based on a virtual intervention algorithm according to an exemplary embodiment. Figure 3 As shown, the virtual intervention algorithm-based carotid artery stent simulation device 800 may include: a processor 801 and a memory 802. The virtual intervention algorithm-based carotid artery stent simulation device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0123] The processor 801 is used to control the overall operation of the virtual intervention algorithm-based carotid stent simulation device 800 to complete all or part of the steps in the above-mentioned virtual intervention algorithm-based carotid stent simulation method. The memory 802 is used to store various types of data to support the operation of the virtual intervention algorithm-based carotid stent simulation device 800, which may include, for example, instructions for any application or method used to operate on the virtual intervention algorithm-based carotid stent simulation device 800, as well as application-related data, such as contact data, messages sent and received, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 802 or sent via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be a keyboard, a mouse, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the carotid stent simulation device 800 based on the virtual intervention algorithm and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include: Wi-Fi module, Bluetooth module, NFC module.

[0124] In an exemplary embodiment, the carotid stent simulation device 800 based on a virtual intervention algorithm can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned carotid stent simulation method based on a virtual intervention algorithm.

[0125] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned carotid stent simulation method based on a virtual intervention algorithm are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions can be executed by the processor 801 of the carotid stent simulation device 800 based on the virtual intervention algorithm to complete the above-mentioned carotid stent simulation method based on the virtual intervention algorithm.

[0126] Embodiment 4:

[0127] Corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment. The readable storage medium described below and the carotid stent simulation method based on a virtual intervention algorithm described above can refer to each other.

[0128] A readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the carotid stent simulation method based on a virtual intervention algorithm of the above method embodiment.

[0129] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other readable storage medium that can store program codes.

[0130] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0131] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A carotid stent simulation method based on a virtual intervention algorithm, characterized in that: include: Acquiring first image information, wherein the first image information includes carotid artery image information; determining the degree of stenosis of the carotid artery and the component index of each plaque area according to the first image information; Constructing a virtual carotid artery 3D model according to the stenosis degree of the carotid artery and the component index of each plaque area; Obtaining a preset virtual bracket three-dimensional model; The contact between the preset virtual stent three-dimensional model and the virtual carotid artery three-dimensional model is analyzed by using a mechanical analysis method to obtain a simulation result, wherein the simulation result includes the shape of the stent after being deployed in the patient's body.

2. The carotid stent simulation method of the virtual intervention algorithm according to claim 1, characterized in that: Determining the degree of stenosis of the carotid artery and the component index of each plaque area according to the first image information includes: Processing the first image information by using an image segmentation algorithm to obtain second image information and third image information, wherein the second image information includes a carotid artery lumen image, and the third image information includes a plaque area image; determining the degree of stenosis of the carotid artery according to the second image information; Determining the grayscale values ​​of different tissues in the third image information to obtain grayscale information; Determining component information of the plaque according to the grayscale information; Analyze the signal intensity in the third image information to obtain percentage information corresponding to each component; A plaque component index is obtained according to the component information and the percentage information.

3. The carotid stent simulation method of the virtual intervention algorithm according to claim 1, characterized in that: The contact between the preset virtual stent three-dimensional model and the virtual carotid artery three-dimensional model is analyzed by using a mechanical analysis method, including: Importing the preset virtual stent three-dimensional model into the virtual carotid artery three-dimensional model to obtain a virtual stent-carotid artery three-dimensional model; Meshing the virtual stent-carotid artery vascular three-dimensional model using a simple deformable mesh technique to obtain a first three-dimensional model; Obtaining nonlinear stress-strain relationships in vascular tissue; determining a radial constraint based on a nonlinear stress-strain relationship of the vascular tissue; embedding the radial constraint into the first three-dimensional model to obtain a second three-dimensional model; The deformation of the surface mesh where the support and the tube wall are in contact in the second three-dimensional model is solved to obtain a simulation result.

4. The carotid stent simulation method of the virtual intervention algorithm according to claim 3, characterized in that: Obtain nonlinear stress-strain relationships for vascular tissue, including: Acquire the geometric shape of the virtual stent three-dimensional model and the material property data corresponding to the virtual carotid artery three-dimensional model; Determining the radial expansion force of the virtual stent according to the geometric shape of the virtual stent three-dimensional model and the material property data corresponding to the virtual carotid artery three-dimensional model; Establishing a mechanical model of vascular tissue according to the radial expansion force of the virtual stent; The nonlinear stress-strain relationship of the vascular tissue is determined according to the mechanical model of the vascular tissue.

5. The carotid stent simulation method of the virtual intervention algorithm according to claim 3, characterized in that: Determining radial constraints according to the nonlinear stress-strain relationship of the vascular tissue includes: Converting the nonlinear stress-strain relationship of the vascular tissue into function information, wherein the function information includes a function related to strain energy density; Obtain carotid artery geometric parameters and hemodynamic parameters; The radial constraint of the blood vessel is determined according to the carotid artery geometric parameters, the hemodynamic parameters and the function information.

6. A carotid stent simulation system based on virtual intervention algorithm, characterized in that: include: A first acquisition module, used to acquire first image information, wherein the first image information includes carotid artery image information; A first processing module, used for determining the stenosis degree of the carotid artery and the component index of each plaque area according to the first image information; A second processing module is used to construct a virtual carotid artery vascular three-dimensional model according to the stenosis degree of the carotid artery and the component index of each plaque area; A second acquisition module is used to acquire a preset virtual stent three-dimensional model; The simulation module is used to analyze the contact between the preset virtual stent three-dimensional model and the virtual carotid artery three-dimensional model using a mechanical analysis method to obtain a simulation result, wherein the simulation result includes the shape of the stent after being deployed in the patient's body.

7. The carotid stent simulation system of the virtual intervention algorithm according to claim 6, characterized in that: The first processing module comprises: A first processing unit is used to process the first image information by using an image segmentation algorithm to obtain second image information and third image information, wherein the second image information includes a carotid artery lumen image, and the third image information includes a plaque area image; a second processing unit, configured to determine the degree of stenosis of the carotid artery according to the second image information; A third processing unit, used to determine the grayscale values ​​of different tissues in the third image information to obtain grayscale information; A fourth processing unit, configured to determine component information of the plaque according to the grayscale information; a fifth processing unit, configured to analyze the signal strength in the third image information to obtain percentage information corresponding to each component; The sixth processing unit is used to obtain a plaque component index according to the component information and the percentage information.

8. The carotid stent simulation system of the virtual intervention algorithm according to claim 6, characterized in that: The simulation module comprises: a seventh processing unit, configured to import the preset virtual stent three-dimensional model into the virtual carotid artery three-dimensional model to obtain a virtual stent-carotid artery three-dimensional model; an eighth processing unit, configured to mesh the virtual stent-carotid artery vascular three-dimensional model using a simple deformable mesh technology to obtain a first three-dimensional model; A first acquisition unit, used for acquiring a nonlinear stress-strain relationship of vascular tissue; a ninth processing unit, configured to determine a radial constraint according to a nonlinear stress-strain relationship of the vascular tissue; A tenth processing unit, configured to embed the radial constraint into the first three-dimensional model to obtain a second three-dimensional model; The eleventh processing unit is used to solve the deformation of the surface mesh where the support contacts the tube wall in the second three-dimensional model to obtain a simulation result.

9. The carotid stent simulation system of the virtual intervention algorithm according to claim 8, characterized in that: The first acquisition unit includes: A second acquisition unit is used to acquire the geometric shape of the virtual stent three-dimensional model and the material property data corresponding to the virtual carotid artery three-dimensional model; a twelfth processing unit, configured to determine a radial expansion force of the virtual stent according to the geometric shape of the virtual stent three-dimensional model and material property data corresponding to the virtual carotid artery three-dimensional model; A thirteenth processing unit, configured to establish a mechanical model of the vascular tissue according to the radial expansion force of the virtual stent; A fourteenth processing unit is used to determine the nonlinear stress-strain relationship of the vascular tissue according to the mechanical model of the vascular tissue.

10. The carotid stent simulation system of the virtual intervention algorithm according to claim 8, characterized in that: The ninth processing unit comprises: A fifteenth processing unit, configured to convert the nonlinear stress-strain relationship of the vascular tissue into function information, wherein the function information includes a function related to strain energy density; A third acquisition unit is used to acquire carotid artery geometric parameters and hemodynamic parameters; A sixteenth processing unit is used to determine the radial constraint of the blood vessel according to the carotid artery geometric parameters, the hemodynamic parameters and the function information.

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