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

Abstract

The present application relates to carotid artery stent simulation technical field, and relates to a kind of carotid artery stent simulation method and system based on virtual intervention algorithm, the method includes obtaining first image information, and first image information includes carotid artery image information;According to the stenosis degree of carotid artery and the composition index of each plaque area determined according to first image information;According to the stenosis degree of carotid artery and the composition index of each plaque area, construct virtual carotid artery three-dimensional model;Obtain the three-dimensional model of virtual stent of pre-set;The contact between the three-dimensional model of virtual stent of pre-set and virtual carotid artery three-dimensional model is analyzed using mechanical analysis method, and simulation result is obtained, and simulation result includes the shape after stent is unfolded in patient body, the dynamic constraint change between node is accurately captured in the present application and the mutual influence between the stress of blood vessel and stent expansion, greatly improve the precision of simulation, to improve the safety of operation.

Description

Technical Field

[0001] This invention relates to the field of carotid artery stent simulation technology, and more specifically, to a carotid artery stent simulation method and system based on a virtual interventional algorithm. Background Technology

[0002] In the treatment of carotid artery disease, especially during stent placement surgery, a core challenge is accurately predicting and achieving the ideal stent deployment state within the complex vascular environment of the patient. The stent deployment state directly affects the surgical outcome; failure to achieve precise apposition to the vessel wall can lead to thrombosis or restenosis. However, due to significant individual differences in the geometry, degree of stenosis, plaque composition, and mechanical properties of the carotid artery wall among each patient, coupled with the complex interactions between the aortic wall and potential dissecting membranes, constructing an accurate mechanical model applicable to different individuals and incorporating these differences is extremely challenging. While existing techniques can simulate the radial expansion of the stent, they struggle to accurately capture the dynamic constraints between nodes and the interaction between vascular stress and stent expansion, resulting in inaccurate simulation results. Summary of the Invention

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

[0004] To achieve the above objectives, the embodiments of this application provide the following technical solutions:

[0005] On one hand, embodiments of this application provide a carotid artery stent simulation method based on a virtual interventional algorithm, the method comprising:

[0006] Acquire first image information, which includes carotid artery image information;

[0007] The degree of stenosis of the carotid artery and the composition indicators of each plaque region are determined based on the first image information;

[0008] A virtual three-dimensional model of the carotid artery is constructed based on the degree of stenosis of the carotid artery and the compositional indicators of each plaque region.

[0009] Obtain a preset 3D model of the virtual support;

[0010] The contact between the preset virtual stent 3D model and the virtual carotid artery 3D model is analyzed using mechanical analysis methods to obtain simulation results, including the morphology of the stent after deployment in the patient's body.

[0011] Secondly, embodiments of this application provide a carotid artery stent simulation system based on a virtual interventional algorithm, the system comprising:

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

[0013] The first processing module is used to determine the degree of stenosis of the carotid artery and the composition indicators of each plaque region based on the first image information.

[0014] The second processing module is used to construct a virtual three-dimensional model of the carotid artery based on the degree of stenosis of the carotid artery and the composition indicators of each plaque region.

[0015] The second acquisition module is used to acquire a preset virtual support 3D model;

[0016] The simulation module is used to analyze the contact between the preset virtual stent 3D model and the virtual carotid artery 3D model using mechanical analysis methods, and obtain simulation results, including the morphology of the stent after it is deployed in the patient's body.

[0017] Thirdly, embodiments of this application provide a carotid artery stent simulation device based on a virtual interventional algorithm. The device includes a memory and a processor. The memory stores a computer program; the processor executes the computer program to implement the steps of the aforementioned carotid artery stent simulation method based on the virtual interventional algorithm.

[0018] Fourthly, embodiments of this application provide a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described carotid artery stent simulation method based on a virtual interventional algorithm.

[0019] The beneficial effects of this invention are as follows:

[0020] This invention utilizes first image information to determine the degree of stenosis of the carotid artery and the composition indicators of each plaque region. Based on the degree of stenosis and the composition indicators of each plaque region, a virtual three-dimensional model of the carotid artery is constructed. Then, a mechanical analysis method is used to analyze the contact between a preset virtual stent three-dimensional model and the virtual carotid artery three-dimensional model to obtain simulation results. This simulation simulates the radial expansion of the stent and the response of the vascular tissue. This simulation combines the nonlinear mechanical properties of the blood vessel and the physical properties of the stent. Based on the dynamic constraints between nodes, and considering the combined effects of vascular stress and stent expansion, the fit between the stent and the vessel wall is iteratively optimized, which greatly improves the accuracy of the simulation and thus improves the safety of the surgery.

[0021] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the carotid artery stent simulation method based on virtual interventional algorithm described in an embodiment of the present invention.

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

[0025] Figure 3 This is a schematic diagram of the carotid artery stent simulation device based on the virtual intervention algorithm described in this embodiment of the invention.

[0026] The diagram is labeled as follows: 800, Carotid artery 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 Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0028] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0029] Example 1:

[0030] This embodiment provides a carotid artery stent simulation method based on a virtual interventional algorithm. It can be understood that in this embodiment, a scenario can be set up, such as a scenario for predicting the success rate of carotid artery stent implantation surgery.

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

[0032] Step S1: Obtain first image information, which 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: Determine the degree of stenosis of the carotid artery and the composition indicators of each plaque region based on the first image information;

[0035] Step S2 further includes steps S21, S22, S23, S24, S25, and S26, which specifically include:

[0036] Step S21: Process the first image information using an image segmentation algorithm to obtain second image information and third image information. The second image information includes a carotid artery lumen image, and the third image information includes a plaque region image.

[0037] Step S22: Determine the degree of stenosis of the carotid artery based on the second image information;

[0038] In this step, quantifying the degree of carotid artery stenosis based on carotid artery lumen images is a well-known technique and will not be elaborated here.

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

[0040] Step S24: Determine the composition information of the patch based on the grayscale information;

[0041] In this step, different tissues have different gray values, so the tissue composition can be deduced from the gray values.

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

[0043] In this step, quantitative analysis of CT values ​​or MR signal intensity is performed on the plaque area. Based on the CT values ​​or signal intensity ranges of different components (lipids, fibers, calcifications, etc.), the percentage of each component is calculated.

[0044] Step S26: Obtain the plaque composition index based on the component information and the percentage information.

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

[0046] Step S3: Construct a virtual three-dimensional model of the carotid artery based on the degree of stenosis of the carotid artery and the composition indicators of each plaque region;

[0047] In this step, a three-dimensional model of a virtual carotid artery is generated using geometric modeling based on the quantified degree of carotid artery stenosis. Since different tissue components have different stress-strain relationships, uniaxial tensile tests are performed on each plaque region to collect measurement data. The measurement data is analyzed to obtain stress-strain curves, and the material property data is set based on the stress-strain curves. It should be noted that uniaxial tensile testing is a well-known experimental procedure in the field, so it will not be described in detail here.

[0048] Step S4: Obtain the preset virtual scaffold 3D model;

[0049] In this step, based on the preset bracket model and size, the bracket is parametrically modeled in a virtual environment to obtain the preset virtual bracket 3D model.

[0050] Step S5: Analyze the contact between the preset virtual stent 3D model and the virtual carotid artery 3D model using mechanical analysis methods to obtain simulation results, including the morphology of the stent after deployment in the patient's body.

[0051] In this step, a virtual stent 3D model is placed within a carotid artery 3D model at the location and angle specified by the surgeon. Mechanical analysis is used to simulate the stent's deployment within the carotid artery, revealing the stress distribution and deformation between the stent and the vessel wall. In a specific implementation, a carotid artery stenosis model with a diameter of 8mm and a length of 30mm is virtually implanted using a stent with a diameter of 7mm and a length of 40mm. Simulation results show that stress concentration at both ends of the stent may lead to excessive expansion of the vessel wall, while the stress distribution at the stenosis site is more uniform, effectively opening the stenosis. By comparing the virtual implantation of different stents, surgeons can select the optimal stent type and size and predict short-term and long-term postoperative outcomes, thereby improving the safety and effectiveness of the surgery.

[0052] Step S5 further includes steps S51, S52, S53, S54, S55, and S56, which specifically include:

[0053] Step S51: Import the preset virtual stent 3D model into the virtual carotid artery 3D model to obtain the virtual stent-carotid artery 3D model;

[0054] Step S52: Use simple deformable mesh technology to mesh the virtual stent-carotid artery 3D model to obtain the first 3D model;

[0055] In this step, the simple deformable mesh technique is used to mesh the virtual stent-carotid artery 3D model, which simplifies the geometry and transforms the blood vessel and stent into a mesh structure composed of vertices and triangular facets. This enables the rapid simulation of complex structural deformations, reduces simulation complexity, and greatly improves computational speed.

[0056] Step S53: Obtain the nonlinear stress-strain relationship of vascular tissue;

[0057] Step S53 further includes steps S531, S532, S533, and S534, which specifically include:

[0058] Step S531: Obtain the geometric shape of the virtual stent 3D model and the material property data corresponding to the virtual carotid artery 3D model;

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

[0060] Step S532: Determine the radial expansion force of the virtual stent based on the geometry of the virtual stent 3D model and the material property data corresponding to the virtual carotid artery 3D model;

[0061] In this step, the radial expansion force of the virtual stent is its ability to resist pressure from the blood vessel wall. Based on the principles of mechanics of materials, the radial expansion force is determined by treating the stent as a thin-walled cylinder and using Hooke's law.

[0062] Step S533: Establish a mechanical model of vascular tissue based on the radial expansion force of the virtual stent;

[0063] In this step, a mechanical model of vascular tissue is established using the theory of elasticity, treating vascular tissue as a hyperelastic material. The blood vessel wall is mainly composed of collagen fibers, elastin, and smooth muscle cells. The combination of these components gives the blood vessel a nonlinear stress-strain relationship. This means that the blood vessel will exhibit different stiffnesses at different strain levels. For example, at low strain, the blood vessel mainly exhibits the elasticity of elastin; while at high strain, collagen fibers begin to play a role, significantly increasing the stiffness of the blood vessel.

[0064] Step S534: Determine the nonlinear stress-strain relationship of the vascular tissue based on the mechanical model of the vascular tissue.

[0065] Step S54: Determine the radial constraint based on the nonlinear stress-strain relationship of the vascular tissue;

[0066] Step S54 further includes steps S541, S542, and S543, which specifically include:

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

[0068] In this step, the strain energy density function describes the energy stored in the material during deformation. This transformation allows us to analyze the mechanical behavior of blood vessels from an energy perspective, and the stress can be calculated using the partial derivatives of the strain energy density function. This not only facilitates calculation and analysis but also makes it easier to fit simulation data.

[0069] Step S542: Obtain the geometric morphological parameters and hemodynamic parameters of the carotid artery;

[0070] In this step, the geometric parameters of the carotid artery include the vessel's diameter, length, tortuosity, and branching angles. These parameters can be measured using medical imaging techniques such as CTA or MRI. Hemodynamic parameters include blood flow velocity, pressure, and viscosity. These parameters directly affect the stress state of the vessel wall.

[0071] Step S543: Determine the radial constraint of the blood vessel based on the carotid artery geometric parameters, the hemodynamic parameters, and the function information.

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

[0073] Step S55: Embed the radial constraint into the first three-dimensional model to obtain the second three-dimensional model;

[0074] Step S56: Solve for the deformation of the curved mesh in contact between the support and the pipe wall in the second three-dimensional model to obtain the simulation results.

[0075] Step S56 further includes steps S561, S562, S563, and S564, which specifically include:

[0076] Step S561: Obtain tangential constraints;

[0077] In this step, the first 3D model has tangential constraints between nodes, specifically including: obtaining the node set of the mesh model constituting the stent and blood vessel wall; calculating the distance between adjacent nodes based on the spatial position of each node in the node set; if the distance between adjacent nodes is less than a first threshold, applying a repulsive force between the adjacent nodes; if the distance between adjacent nodes is greater than a second threshold, applying an attractive force between the adjacent nodes; adding the repulsive and attractive forces to the deformation potential energy function of the mesh; and iteratively adjusting the spatial position of each node in the node set according to the principle of energy minimization. It should be noted that the purpose of setting the first threshold is to apply a repulsive force to avoid node collisions or penetration, and the purpose of setting the second threshold is to apply an attractive force to maintain the continuity of the structure. The stent and blood vessel wall need to maintain a certain shape and stability. Therefore, the first and second thresholds are set according to the material properties of the stent and the mechanical properties of the blood vessel wall. In addition, according to the principle of energy minimization, the spatial position of each node in the node set is iteratively adjusted to obtain the final position of each node, and then this position is used to adjust the shape of the stent and blood vessel wall, thereby improving the simulation accuracy.

[0078] Step S562: Embed the tangential constraint into the second three-dimensional model to obtain the third three-dimensional model;

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

[0080] Step S563: Perform numerical iterative calculations based on 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 3D model is obtained; the tangential distance is calculated based on the spatial coordinate information; the tangential force between nodes is constructed based on the tangential distance and a preset elastic coefficient; the radial expansion direction of the virtual stent is determined based on the spatial coordinate information; the radial expansion displacement of the stent nodes is calculated based on the radial expansion direction of the virtual stent; the radial resultant force on each node is calculated based on the radial expansion displacement and the radial constraint force of the blood vessel; and the contact state is obtained through iterative calculation based on the radial resultant force on each node and the tangential force between the nodes. It can be understood that by combining the radial resultant force and tangential force on each node, the final force on each node is obtained. After clarifying the expansion behavior of the stent, combined with the resistance of the blood vessel, a resultant force in an equilibrium state can be obtained, thereby driving the adjustment of the entire mesh model and achieving a balance between force and reaction force. The advantage of this is that it balances the interaction between internal and external forces, thus simulating a real biomechanical process and obtaining a more accurate force state. This is crucial for simulating the real behavior of the stent in the body and provides necessary data for iterative calculation. The radial resultant force is incorporated into the energy equation of a simple deformable mesh algorithm for iterative calculation. This step uses the calculated radial resultant force to update the energy equation, and then solves the equation to find the new node positions that minimize energy. This process is repeated until convergence. The stress state of each node affects its displacement, which in turn affects the stress on other nodes. This is a dynamic equilibrium process. When the network converges, the model tends to stabilize, and the final shape is the shape of the stent deployed within the blood vessel. This invention makes the simulation adaptive because each calculation provides feedback and adjusts the model. The simulation process is dynamic rather than static, and can realistically reflect the complex process of the interaction between the stent and the blood vessel. The calculation results are more accurate and closer to the actual morphology and stress distribution after stent implantation.

[0082] Step S564: Obtain simulation results based on the contact state.

[0083] It is understandable that the simulation results are based on the contact state calculated iteratively to predict the shape of the virtual stent after it is deployed in the blood vessel.

[0084] Example 2:

[0085] like Figure 2As shown, this embodiment provides a carotid artery stent simulation system based on a virtual interventional 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, specifically including:

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

[0087] The first processing module is used to determine the degree of stenosis of the carotid artery and the composition indicators of each plaque region based on the first image information.

[0088] The second processing module is used to construct a virtual three-dimensional model of the carotid artery based on the degree of stenosis of the carotid artery and the composition indicators of each plaque region.

[0089] The second acquisition module is used to acquire a preset virtual support 3D model;

[0090] The simulation module is used to analyze the contact between the preset virtual stent 3D model and the virtual carotid artery 3D model using mechanical analysis methods, and obtain simulation results, including the morphology of the stent after it is deployed in the patient's body.

[0091] In one specific embodiment of this 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, specifically including:

[0092] The first processing unit is used to process the first image information 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 region image.

[0093] The second processing unit is used to determine the degree of stenosis of the carotid artery based on the second image information;

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

[0095] The fourth processing unit is used to determine the composition information of the patch based on the grayscale information;

[0096] The fifth processing unit is used to analyze the signal intensity in the third image information to obtain the percentage information corresponding to each component;

[0097] The sixth processing unit is used to obtain plaque composition indicators based on the component information and the percentage information.

[0098] In one specific embodiment of this 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, specifically comprising:

[0099] The seventh processing unit is used to import the preset virtual stent 3D model into the virtual carotid artery 3D model to obtain a virtual stent-carotid artery 3D model.

[0100] The eighth processing unit is used to perform mesh generation on the virtual stent-carotid artery 3D model using simple deformable mesh technology to obtain the first 3D model;

[0101] The first acquisition unit is used to acquire the nonlinear stress-strain relationship of vascular tissue;

[0102] The ninth processing unit is used to determine the radial constraint based on the nonlinear stress-strain relationship of the vascular tissue;

[0103] The tenth processing unit is used to embed the radial constraint into the first three-dimensional model to obtain the second three-dimensional model;

[0104] The eleventh processing unit is used to solve the deformation of the curved mesh in the second three-dimensional model where the support contacts the pipe wall, and obtain the simulation results.

[0105] In one specific embodiment of this disclosure, the first acquisition unit further includes a second acquisition unit, a twelfth processing unit, a thirteenth processing unit, and a fourteenth processing unit, specifically including:

[0106] The second acquisition unit is used to acquire the geometry of the virtual stent 3D model and the material property data corresponding to the virtual carotid artery 3D model.

[0107] The twelfth processing unit is used to determine the radial expansion force of the virtual stent based on the geometry of the virtual stent 3D model and the material property data corresponding to the virtual carotid artery 3D model.

[0108] The thirteenth processing unit is used to establish a mechanical model of vascular tissue based on the radial expansion force of the virtual stent.

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

[0110] In one specific embodiment of this disclosure, the ninth processing unit further includes a fifteenth processing unit, a third acquisition unit, and a sixteenth processing unit, specifically comprising:

[0111] The fifteenth processing unit is used to convert the nonlinear stress-strain relationship of the vascular tissue into function information, the function information including a function of strain energy density;

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

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

[0114] In one specific embodiment of this disclosure, the eleventh processing unit further includes a fourth acquisition unit, a seventeenth processing unit, an eighteenth processing unit, and a nineteenth processing unit, specifically comprising:

[0115] The fourth acquisition unit is used to acquire tangential constraints;

[0116] The seventeenth processing unit is used to embed the tangential constraint into the second three-dimensional model to obtain the third three-dimensional model;

[0117] The eighteenth processing unit is used to perform numerical iterative calculations based on the third three-dimensional model to obtain the contact state between the stent and the blood vessel wall;

[0118] The nineteenth processing unit is used to obtain simulation results based on the contact state.

[0119] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0120] Example 3:

[0121] Corresponding to the above method embodiments, this embodiment also provides a carotid stent simulation device based on a virtual intervention algorithm. 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 be referred to each other.

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

[0123] The processor 801 controls the overall operation of the carotid artery stent simulation device 800 based on the virtual interventional algorithm to complete all or part of the steps in the aforementioned carotid artery stent simulation method based on the virtual interventional algorithm. The memory 802 stores various types of data to support the operation of the carotid artery stent simulation device 800. This data may include, for example, instructions for any application or method operating on the carotid artery stent simulation device 800, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using 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 storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, 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 signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the carotid artery stent simulation device 800 based on the virtual intervention algorithm and other devices. Wireless communication includes, for example, Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, and an NFC module.

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

[0125] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the carotid artery stent simulation method based on the virtual interventional algorithm described above. For example, the computer-readable storage medium may be the memory 802 including the program instructions described above, which may be executed by the processor 801 of the carotid artery stent simulation device 800 based on the virtual interventional algorithm to complete the carotid artery stent simulation method based on the virtual interventional algorithm described above.

[0126] Example 4:

[0127] Corresponding to the above method embodiments, this embodiment also provides a readable storage medium. The readable storage medium described below can be referred to in conjunction with the carotid artery stent simulation method based on virtual intervention algorithm described above.

[0128] A readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the carotid artery stent simulation method based on the virtual interventional algorithm described in the above method embodiments.

[0129] Specifically, the readable storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other readable storage medium capable of storing program code.

[0130] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0131] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A carotid artery stent simulation method based on a virtual interventional algorithm, characterized in that, include: Acquire first image information, which includes carotid artery image information; The degree of stenosis of the carotid artery and the composition indicators of each plaque region are determined based on the first image information; A virtual three-dimensional model of the carotid artery is constructed based on the degree of stenosis of the carotid artery and the compositional indicators of each plaque region. Obtain a preset 3D model of the virtual support; The contact between the preset virtual stent 3D model and the virtual carotid artery 3D model was analyzed using mechanical analysis methods to obtain simulation results. These simulation results include the morphology of the stent after deployment within the patient's body, including: The preset virtual stent 3D model is imported into the virtual carotid artery 3D model to obtain the virtual stent-carotid artery 3D model. The virtual stent-carotid artery 3D model was meshed using simple deformable mesh technology to obtain the first 3D model; Obtain the nonlinear stress-strain relationship of vascular tissue; The radial constraint is determined based on the nonlinear stress-strain relationship of the vascular tissue. The radial constraint is embedded into the first three-dimensional model to obtain the second three-dimensional model; The deformation of the curved mesh in contact with the pipe wall in the second three-dimensional model is solved to obtain simulation results, including: Obtaining tangential constraints includes: Obtain the node set of the mesh model constituting the stent and blood vessel wall; calculate the distance between adjacent nodes based on the spatial position of each node in the node set; if the distance between adjacent nodes is less than a first threshold, apply a repulsive force between the adjacent nodes; if the distance between adjacent nodes is greater than a second threshold, apply an attractive force between the adjacent nodes; add the repulsive and attractive forces to the deformation potential energy function of the mesh; iteratively adjust the spatial position of each node in the node set according to the principle of energy minimization. The tangential constraint is embedded into the second three-dimensional model to obtain the third three-dimensional model; The contact state between the stent and the blood vessel wall is obtained by numerical iterative calculation based on the third three-dimensional model, including: Obtain the spatial coordinate information corresponding to each node in the third 3D model; calculate the tangential distance based on the spatial coordinate information; construct the tangential force between nodes based on the tangential distance and a preset elastic coefficient; determine the radial expansion direction of the virtual stent based on the spatial coordinate information; calculate the radial expansion displacement of the stent nodes based on the radial expansion direction of the virtual stent; calculate the radial resultant force on each node based on the radial expansion displacement and the radial constraint force of the blood vessel; perform iterative calculation based on the radial resultant force on each node and the tangential force between the nodes to obtain the contact state; The simulation results are obtained based on the contact state.

2. The carotid artery stent simulation method based on the virtual interventional algorithm according to claim 1, characterized in that, The degree of carotid artery stenosis and the compositional indicators of each plaque region are determined based on the first image information, including: The first image information is processed using an image segmentation algorithm to obtain second image information and third image information. The second image information includes a carotid artery lumen image, and the third image information includes a plaque region image. The degree of carotid artery stenosis is determined based on the second image information; The grayscale values ​​of different tissues in the third image information are determined to obtain grayscale information; The composition information of the patches is determined based on the grayscale information; The signal intensity in the third image information is analyzed to obtain the percentage information corresponding to each component; The plaque composition index is obtained based on the component information and the percentage information.

3. The carotid artery stent simulation method based on the virtual interventional algorithm according to claim 1, characterized in that, Obtaining the nonlinear stress-strain relationship of vascular tissue, including: Obtain the geometry of the virtual stent 3D model and the material property data corresponding to the virtual carotid artery 3D model; The radial expansion force of the virtual stent is determined based on the geometry of the virtual stent 3D model and the material property data corresponding to the virtual carotid artery 3D model. A mechanical model of vascular tissue is established based on the radial expansion force of the virtual stent; The nonlinear stress-strain relationship of the vascular tissue was determined based on the mechanical model of the vascular tissue.

4. The carotid artery stent simulation method based on the virtual interventional algorithm according to claim 1, characterized in that, Determining radial constraints based on the nonlinear stress-strain relationship of the vascular tissue includes: The nonlinear stress-strain relationship of the vascular tissue is transformed into a function information, the function information including a function of strain energy density; Obtain carotid artery geometry and hemodynamic parameters; The radial constraint of the blood vessel is determined based on the carotid artery geometry parameters, the hemodynamic parameters, and the function information.

5. A carotid artery stent simulation system based on a virtual interventional algorithm, characterized in that, include: A first acquisition module is used to acquire first image information, the first image information including carotid artery image information; The first processing module is used to determine the degree of stenosis of the carotid artery and the composition indicators of each plaque region based on the first image information. The second processing module is used to construct a virtual three-dimensional model of the carotid artery based on the degree of stenosis of the carotid artery and the composition indicators of each plaque region. The second acquisition module is used to acquire a preset virtual support 3D model; The simulation module is used to analyze the contact between the preset virtual stent 3D model and the virtual carotid artery 3D model using mechanical analysis methods, and obtain simulation results, including the morphology of the stent after it is deployed in the patient's body. The simulation module includes: The seventh processing unit is used to import the preset virtual stent 3D model into the virtual carotid artery 3D model to obtain a virtual stent-carotid artery 3D model. The eighth processing unit is used to perform mesh generation on the virtual stent-carotid artery 3D model using simple deformable mesh technology to obtain the first 3D model; The first acquisition unit is used to acquire the nonlinear stress-strain relationship of vascular tissue; The ninth processing unit is used to determine the radial constraint based on the nonlinear stress-strain relationship of the vascular tissue; The tenth processing unit is used to embed the radial constraint into the first three-dimensional model to obtain the second three-dimensional model; The eleventh processing unit is used to solve the deformation of the curved mesh in the second three-dimensional model where the support contacts the pipe wall, and obtain the simulation results; The eleventh processing unit includes: The fourth acquisition unit, used to acquire tangential constraints, includes: Obtain the node set of the mesh model constituting the stent and blood vessel wall; calculate the distance between adjacent nodes based on the spatial position of each node in the node set; if the distance between adjacent nodes is less than a first threshold, apply a repulsive force between the adjacent nodes; if the distance between adjacent nodes is greater than a second threshold, apply an attractive force between the adjacent nodes; add the repulsive and attractive forces to the deformation potential energy function of the mesh; iteratively adjust the spatial position of each node in the node set according to the principle of energy minimization. The seventeenth processing unit is used to embed the tangential constraint into the second three-dimensional model to obtain the third three-dimensional model; The eighteenth processing unit is used to perform numerical iterative calculations based on the third three-dimensional model to obtain the contact state between the stent and the blood vessel wall, including: Obtain the spatial coordinate information corresponding to each node in the third 3D model; calculate the tangential distance based on the spatial coordinate information; construct the tangential force between nodes based on the tangential distance and a preset elastic coefficient; determine the radial expansion direction of the virtual stent based on the spatial coordinate information; calculate the radial expansion displacement of the stent nodes based on the radial expansion direction of the virtual stent; calculate the radial resultant force on each node based on the radial expansion displacement and the radial constraint force of the blood vessel; perform iterative calculation based on the radial resultant force on each node and the tangential force between the nodes to obtain the contact state; The nineteenth processing unit is used to obtain simulation results based on the contact state.

6. The carotid artery stent simulation system based on the virtual interventional algorithm according to claim 5, characterized in that, The first processing module includes: The first processing unit is configured to process the first image information 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 region image. The second processing unit is used to determine the degree of stenosis of the carotid artery based on the second image information; The third processing unit is used to determine the grayscale values ​​of different tissues in the third image information to obtain grayscale information; The fourth processing unit is used to determine the composition information of the patch based on the grayscale information; The fifth processing unit is used to analyze the signal intensity in the third image information to obtain the percentage information corresponding to each component; The sixth processing unit is used to obtain plaque composition indicators based on the component information and the percentage information.

7. The carotid artery stent simulation system based on the virtual interventional algorithm according to claim 5, characterized in that, The first acquisition unit includes: The second acquisition unit is used to acquire the geometry of the virtual stent 3D model and the material property data corresponding to the virtual carotid artery 3D model. The twelfth processing unit is used to determine the radial expansion force of the virtual stent based on the geometry of the virtual stent 3D model and the material property data corresponding to the virtual carotid artery 3D model. The thirteenth processing unit is used to establish a mechanical model of vascular tissue based on the radial expansion force of the virtual stent. The fourteenth processing unit is used to determine the nonlinear stress-strain relationship of the vascular tissue based on the mechanical model of the vascular tissue.

8. The carotid artery stent simulation system based on the virtual interventional algorithm according to claim 5, characterized in that, The ninth processing unit includes: The fifteenth processing unit is used to convert the nonlinear stress-strain relationship of the vascular tissue into function information, the function information including a function of strain energy density; The third acquisition unit is used to acquire carotid artery geometric parameters and hemodynamic parameters; The sixteenth processing unit is used to determine the radial constraint of the blood vessel based on the carotid artery geometric parameters, the hemodynamic parameters, and the function information.

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