A numerical simulation system for stent intervention smoothed particle dynamics based on intravascular images

Abstract

The application discloses a kind of numerical simulation systems of stent intervention smooth particle dynamics based on intravascular image;Including: vascular attribute collection module, stent establishment module, particle and assembly module, stent release setting module and release stent mechanics simulation module, particle and assembly module reads the three-dimensional vascular model of vascular attribute collection module, the three-dimensional stent model of stent establishment module and the specified lesion position of input, and constitutes the complete model to be particle;Particle is generated after particle by the particleization of stent model through the pre-processing of smooth particle method, and the complete model after particle;Stent release setting module sets given geometric constraint to stent;Release stent mechanics simulation module simulates using smooth particle method according to the boundary condition of force.The present application combines patient image data, realizes the stress distribution when stent expansion is predicted before operation, improves compliance and support force balance;Evaluate the risk of plaque rupture, avoid vascular injury.

Description

Technical Field

[0001] This invention belongs to the field of computer-aided simulation modeling technology, specifically a numerical simulation system for stent intervention smooth particle dynamics (SPH) based on intravascular images, which is used to process medical information technology and implantable intravascular prostheses. Background Technology

[0002] Percutaneous coronary intervention (PCI), which involves implanting a stent into a diseased blood vessel, is a common method for diagnosing and treating diseases such as those of the coronary arteries, peripheral arteries, and aorta. Among these, acute coronary syndrome (ACS) is a serious cardiovascular disease caused by myocardial ischemia or infarction due to thrombosis or insufficient blood flow, and is the leading cause of sudden death and paralysis.

[0003] In percutaneous coronary intervention (PCI), coronary angiography is the most commonly used imaging tool for diagnosing atherosclerosis and guiding stent implantation. However, its low resolution and two-dimensional projection imaging methods are insufficient to analyze the type and distribution of atherosclerotic plaques. Therefore, relying solely on coronary angiography often fails to provide the optimal clinical diagnosis and treatment plan for acute coronary syndrome (ACS). Intravascular imaging techniques such as intravascular ultrasound (IVUS) and optical coherence tomography (OCT) can more accurately observe and quantify atherosclerotic plaques and guide stent implantation.

[0004] In percutaneous transluminal coronary angioplasty (PTCA), the network of metallic supports implanted within the coronary arteries is called a coronary stent. During PCI, the stent is attached to a balloon catheter, which is then delivered to the lesion site. Pressure is applied to the balloon, causing it to inflate and expand, opening the stent and the occluded coronary artery. Finally, the balloon catheter is withdrawn, leaving the stent at the lesion site, effectively dilating the diseased coronary artery and restoring blood flow. However, excessive compression of the vessel by the stent can cause excessive stress on the vessel wall, leading to damage. The dilation of the vessel also alters the hemodynamic environment near the vessel wall, inducing thrombosis and intimal hyperplasia. These mechanical factors can all contribute to atherosclerotic plaque formation, resulting in in-vascular restenosis after stent implantation.

[0005] However, current biomechanical simulations of coronary artery physiology, such as that of blood vessels and stents, are mainly concentrated in academic research and rely on mature commercial software such as finite element method (FEM) software Comsol and Abaqus. Using commercial software for modeling requires data export, preprocessing, and transfer, which is time-consuming, expensive, and has many limitations. Users need to process the model step by step through an interactive interface, including mesh generation and parameter setting, resulting in low automation and requiring a high level of proficiency in the software. Furthermore, during simulations, simplified geometries are often used for blood vessel models, stent models, and lipid plaques, making it impossible to make patient-specific settings. At the same time, traditional finite element method (FEM) methods are computationally expensive and struggle to handle intricate and complex interbody structures, which limits the application of coronary artery physiology simulation in the clinical field.

[0006] Smoothed Particle Hydrodynamics (SPH) is a meshless, Lagrangian-based numerical simulation technique that discretizes a continuous medium into a series of particles representing material properties. By tracking the motion of these particles, it simulates large-scale, high-dimensional physical problems. This method overcomes the limitations of traditional commercial finite element software or other computational methods, demonstrating superior performance in handling large deformations, free surface flow, and complex boundary conditions. With advancements in computational technology and the deepening of theoretical research, there is a need for a system that combines SPH particle hydrodynamics with intravascular imaging to establish a computational model of the mechanical behavior of coronary stent intervention coupling systems, studying the coupling mechanism between stenotic vessels and stents. This system can also analyze the distribution of equivalent stress on the inner wall of stenotic vessels during coronary stent intervention, investigate the influence of stent connector geometry on vessel wall damage, and thus assist surgeons in optimizing surgical plans, predicting the mechanical behavior during stent implantation preoperatively, assessing plaque rupture and vessel damage risks, and providing postoperative recommendations for coronary stent structural design. Summary of the Invention

[0007] To address the problems existing in the background technology, this invention provides a numerical simulation system for stent intervention smooth particle dynamics based on intravascular images. By geometric and mechanical modeling of the interaction between vascular biological tissues (such as blood vessel walls and plaques) and medical devices (stents), and combining the advantages of the SPH method in handling large deformation and multibody system dynamics problems, the system numerically simulates the expansion and deformation mechanism of stents, as well as the deformation and mechanical behavior of plaques and blood vessels. The technical solution includes: a blood vessel attribute collection module, a stent gripping and establishment module, a particleization and assembly module, a stent release setting module, and a stent release mechanical simulation module. The blood vessel attribute collection module and the stent gripping and establishment module are connected to the particleization and assembly module, and the particleization and assembly module is sequentially connected to the stent release setting module and the stent release mechanical simulation module.

[0008] The vascular attribute collection module reads intravascular image information from the intravascular imaging system and constructs a three-dimensional vascular model based on the intravascular image information.

[0009] The pressure-grip stent creation module constructs a three-dimensional stent model based on the stent type and angle.

[0010] The particleization and assembly module reads the 3D vascular model from the vascular attribute collection module, the 3D stent model from the compressive stent creation module, and the input specified lesion location. It assembles the 3D stent model to the specified lesion location of the 3D vascular model to form a complete model to be particleized. Through smooth particle method preprocessing, the stent model is particleized to generate a particleized complete model.

[0011] The stent release setting module sets the given geometric constraints, force boundary conditions, total duration, and step size for the stent; the geometric constraints include: constraining circumferential displacement, constraining axial displacement, constraining radial displacement, constraining plaque, and constraining vascular axial displacement of the particled complete model, allowing the particled complete model to move freely in the radial direction.

[0012] Release Stent Mechanical Simulation Module: Reads the particle-generated complete model after being constrained by the stent release setting module, and uses the smooth particle method to simulate the stress-strain transient changes of the simulated object during the expansion and rebound of the stent.

[0013] The three-dimensional stent model created by the pressure-grip stent creation module also includes a balloon.

[0014] The boundary conditions for the force include either a given pressure load or a given radial displacement load.

[0015] In the stent release setting module, the blood vessel and the plaque are in bonded contact; the outer surface of the plaque is the contact surface, and the inner surface of the blood vessel is the target surface; the stent and the inner wall of the blood vessel are in frictional contact.

[0016] The stent release mechanical simulation module simulates the free expansion phase of the coronary stent based on the force boundary conditions input by the stent release setting module. The simulation results of the stent release mechanical simulation module can be exported to a database or other modules through an interface.

[0017] In the mechanical simulation module for stent release, the stent and calcified plaque tissue are treated using a linear elastic model, while the lipid plaque and vascular wall tissue are treated using a hyperelastic model.

[0018] The simulation results include: the stress on the plaque, the degree of plaque deformation, the stress on the stent, the length of the stent after release, the length of the stent before release, the diameter of each section before stent release, the diameter of each section after stent release, the degree of stent deformation, the stress on the blood vessel, and the degree of blood vessel deformation.

[0019] The stent release mechanical simulation module (500) and the stent implantation process risk assessment module (600) are both connected to the display and the database.

[0020] The process for assessing the risk of warping at both ends of the stent includes:

[0021] Step 61: Obtain the remote segment L dist proximal segment L prox and the middle section L middle The boundary, simulating the interval between the proximal stent and the initial position of the plaque in the stent segment after stent release, is L. prox The interval between the distal end of the stent and the end position of the plaque in the stent segment is L. dist The interval from the start to the end of the plaque within the stent segment is L. middle This includes the following determinations:

[0022] If the marker indicating whether the distal edge of the stent is located within the plaque is marked as yes, then L dist Set to the first 20% of the bracket length; otherwise, set L. dist Defined as the region between the distal end of the stent and the distal section of the most distal plaque within the stent;

[0023] If the marker indicating whether the proximal edge of the stent is located within the plaque is "yes", then L prox Set to the last 20% of the bracket length; otherwise, set L. prox Defined as the region between the proximal end of the stent and the distal section of the nearest plaque within the stent;

[0024] L middle For L dist To L prox The area between;

[0025] Step 62: Calculate the average diameter of each segment:

[0026] Calculate L respectively middle L prox L dist Average support diameter D of the interval middle D forth and D back ;

[0027] Step 63, Assessing the risk of stent warping:

[0028] like Established,

[0029] This indicates a risk of warping at both ends of the stent implantation, where Inflation limit is the expansion threshold for judging whether warping occurs at both ends of the stent.

[0030] The process for assessing the risk of stent shortening is as follows:

[0031] like Established,

[0032] This indicates a risk of stent shortening, where L is the length of the stent after deployment. ori The length of the stent before release; the stent shortening is L. ori -L; Stent shortening rate (L) ori -L) / L ori *100%.

[0033] The process for determining the risk location is as follows: After the numerical simulation is completed, the location corresponding to the highest value in the stress field of the blood vessel wall is marked according to the stress calculation results. The marked location is the location where the blood vessel wall and its tissue are subjected to the greatest stress and the highest risk during the entire stent pre-simulation process.

[0034] The process for determining the risk of stent apposition is as follows: Information on the relationship between the generated stent edge and plaque is read; based on the stent morphology and vessel wall morphology, the risk of poor stent apposition is determined; after numerical simulation, the presence or absence of poor stent apposition is assessed based on the stent morphology and vessel wall morphology; the axial distance from the stent wire surface to the vessel lumen surface is calculated; if the axial distance from the stent wire surface to the lumen surface is less than the stent wire thickness, the stent wire is considered to have good apposition. The process for determining the degree of danger is as follows: the degree of danger at risk points is determined by combining the stress field and displacement field from the numerical calculation results; at risk points, there are clear stress magnitude and deformation values, which are marked and displayed.

[0035] When the stress value is greater than the stress threshold and the deformation is greater than the deformation threshold, it is judged as "high risk"; when the stress value is less than the stress threshold and the deformation is less than the deformation threshold, it is judged as "low risk"; when the stress value or deformation is within the corresponding threshold, it is judged as "medium risk".

[0036]

[0037] In the formula, Stress max and Strain max This represents the maximum stress and strain of the blood vessel wall in the calculation results; Stress lower and Stress upper These are the upper and lower limits of the stress risk threshold; that is, stress within this range poses a risk. lower and Strain upper These are the upper and lower limits of the strain risk threshold, meaning that there is risk within this range of strain.

[0038] The beneficial effects of this invention are as follows:

[0039] 1. It can optimize surgical plans and help doctors understand the biomechanical behavior during stent implantation; combine patient imaging data to achieve individualized surgical planning, predict stress distribution during stent expansion before surgery, and improve compliance and support balance; assess the risk of plaque rupture and avoid vascular damage; and can be used to improve stent structure after surgery and reduce postoperative complications.

[0040] 2. The dual-modal imaging input module can construct a realistic geometric model of the blood vessel wall based on intravascular imaging technology: The OCT-IVUS dual-modal imaging system can accurately identify the intima and outer model information of blood vessels; at the same time, it can identify calcified plaques and lipid plaques, and supports researchers to manually modify and add plaque morphology information; this patent makes full use of the advantages of dual-modal imaging technology to accurately identify the biological tissue structure of the target blood vessel.

[0041] 3. The stent module constructs realistic 3D stent geometric models or uses parametric modeling to construct simplified stent models: Based on the structural characteristics of vascular stents, various vascular stent structures, including coiled, mesh, tubular, and annular stents, can be constructed. Due to the periodicity of stent structures, the smallest structural units can be arrayed to construct realistic 3D stents of different lengths, or simplified stent models can be quickly constructed through parametric modeling.

[0042] 4. The particle generation and assembly module generates multi-resolution isotropic body-fitted particle distributions, including complex geometries such as vessel walls, plaques, and stents: The SPH method does not require mesh creation and is very suitable for complex geometries such as stents and blood vessels. At the same time, it is also faster in processing multibody dynamics and is suitable for clinical applications. Based on the geometric model information, particle distributions in different computational domains are created for subsequent numerical simulations.

[0043] 5. Module for SPH Numerical Simulation: This module provides two methods to simulate the free expansion phase of coronary stents: given pressure load and given radial displacement load. Using the SPH method, it performs mechanical simulations on patient-specific vessel and stent models, including stent expansion and rebound processes, accurately solving for the stress state during the co-expansion of the stent and vessel. In particular, it can automatically model and simulate plaque structures and stents of different sizes, types, and materials, based on imaging information.

[0044] 6. Use a separate module to analyze the simulation results and determine the risks that may arise during the stent implantation process; in particular, it can quantitatively analyze the warping at both ends, providing a reference for users and reducing the error of experience-based judgment and the judgment time. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of an embodiment of a numerical simulation system for smooth particle dynamics of stent intervention based on intravascular images according to the present invention.

[0046] Figure 2 This is a schematic diagram showing the connection between the stent release mechanical simulation module and the stent implantation process risk assessment module in an embodiment of the present invention.

[0047] Figure 3 This is a flowchart illustrating an embodiment of the present invention.

[0048] Figure 4 This is a cross-sectional schematic diagram of a case of poor wall adhesion in an embodiment of the present invention.

[0049] Figure 5 This is a side view of the area for determining the risk of warping after stent release in an embodiment of the present invention (the scale is not the actual scale).

[0050] Among them, 100-Vascular property collection module, 200-Stent establishment module, 300-Particleization and assembly module, 400-Stent release setting module, 500-Stent release mechanical simulation module, and 600-Stent implantation process risk assessment module. Detailed Implementation

[0051] The present invention will be further described in detail below with reference to the accompanying drawings.

[0052] like Figures 1-3The embodiment of the present invention shown includes: a vascular property collection module 100, a compressible stent establishment module 200, a particleization and assembly module 300, a stent release setting module 400, a stent release mechanical simulation module 500, and a stent implantation process risk assessment module 600. The vascular property collection module 100 and the compressible stent establishment module 200 are connected to the particleization and assembly module 300, and the particleization and assembly module 300 is sequentially connected to the stent release setting module 400, the stent release mechanical simulation module 500, and the stent implantation process risk assessment module 600.

[0053] The vascular attribute collection module 100 reads the intravascular image information transmitted by the intravascular imaging system and constructs a three-dimensional vascular model based on the intravascular image information. The intravascular image information includes optical coherence tomography (OCT) and / or intravascular ultrasound (IVUS) image information. The intravascular imaging system can automatically identify and segment calcified plaques and lipid plaques in the intravascular image information.

[0054] The pressure-grip stent creation module 200: constructs a 3D stent model based on the balloon type (optional), stent type (including physical properties (material) and dimensions), and angle; the stent type uses the system default value or is input by the user according to the stent used into the pressure-grip stent creation module 200, which then retrieves the model from the database.

[0055] In this embodiment, the supports of various types and angles are constructed using CAD modeling software and stored in a database for later direct retrieval.

[0056] The particleization and assembly module 300 reads the 3D vascular model from the vascular attribute collection module 100, the 3D stent model from the compressible stent creation module 200, and the input specified lesion location. It assembles the 3D stent model to the specified lesion location of the 3D vascular model to form a complete model to be particleized. Through smooth particle preprocessing, the stent model is particleized to generate a complete particleized model. A multi-resolution isotropic body particle distribution is generated. The multi-resolution means that the particle size is larger in simple regions, which can reduce the number of particles and the amount of computation; smaller particles are generated in fine structures to simulate the details of the object. Isotropic body means that the particles are distributed inside the simulated object. The complete particleized model uses the surface of the blood vessel, stent, and plaque as the boundary to reflect the geometry of the research object.

[0057] Stent Release Setting Module 400: Sets the given geometric constraints, force boundary conditions (given pressure load or given radial displacement load), total duration, and step size for the stent; geometric constraints include: constrained circumferential displacement, constrained axial displacement, constrained radial displacement, constrained plaque, and constrained vascular axial displacement of the particled complete model, allowing the particled complete model to move freely in the radial direction; bonded contact is used between the blood vessel and the plaque; the outer surface of the plaque is the contact surface, and the inner surface of the blood vessel is the target surface; frictional contact is used between the stent and the inner wall of the blood vessel;

[0058] The stent release mechanical simulation module 500 (numerical simulation based on SPH) reads the particle-based complete model after being constrained by the stent release setting module 400, and uses the Smooth Particle Method (SPH) to simulate the stress-strain transient changes of the simulated object during stent expansion and rebound. Specifically, a linear elastic model is used for the stent and calcified plaque tissue, while a hyperelastic model is used for the lipid plaque and vascular wall tissue, generating information on the relationship between the stent edge and the plaque. In this embodiment, generating the information on the relationship between the stent edge and the plaque includes: marking the distal cross-section of the most distal plaque within the stent, marking the proximal cross-section of the nearest plaque within the stent, marking whether the distal edge of the stent is located within the plaque (yes or no), and marking whether the proximal edge of the stent is located within the plaque (yes or no).

[0059] The stent release mechanical simulation module 500 provides two methods to simulate the free expansion phase of the coronary stent: a given pressure load or a given radial displacement load (based on the force boundary conditions input by the stent release setting module 400); the simulation results of the stent release mechanical simulation module 500 can be exported to a database or other modules via an interface.

[0060] The simulation results include: the stress on the plaque, the degree of plaque deformation, the stress on the stent, the length of the stent after deployment, the length of the stent before deployment, the diameter of each section before stent deployment, the diameter of each section after stent deployment, the degree of stent deformation, the stress on the blood vessel, and the degree of blood vessel deformation.

[0061] In this embodiment, the stent release mechanical simulation module 500 is connected to the display, the database, and the stent implantation process risk assessment module 600; when the operator releases the stent mechanical simulation module 500 on the display, the expansion process is simulated dynamically, including the intuitive deformation and stress state of the stent and the blood vessel wall.

[0062] Stent implantation process risk assessment module 600: It is used to determine the stress on the blood vessel and the stent in the simulation results obtained by the stent release mechanical simulation module 500, and to determine the risk location, degree of danger, risk of stent apposition to the wall, risk of stent warping at both ends and risk of stent shortening during the stent implantation process;

[0063] in:

[0064] The process for determining the risk location is as follows: After the numerical simulation is completed, the location corresponding to the highest value in the stress field of the blood vessel wall is marked according to the stress calculation results. The marked location is the location where the blood vessel wall and its tissue are subjected to the greatest stress and the highest risk during the entire stent pre-simulation process.

[0065] The process for determining the degree of danger is as follows: the degree of danger of a risk point is determined by combining the stress field and displacement field results from numerical calculations. The location of the risk point has a clear stress value and deformation value, which are marked and displayed. When the stress value is greater than the stress threshold and the deformation value is greater than the deformation threshold, it is determined to be "high risk"; when the stress value is less than the stress threshold and the deformation value is less than the deformation threshold, it is determined to be "low risk"; when the stress value or deformation is within the corresponding threshold, it is determined to be "medium risk".

[0066]

[0067] In the above formula, Stress max and Strain max This represents the maximum stress and strain of the blood vessel wall in the calculation results;

[0068] Stress lower and Stress upper These are the upper and lower limits of the stress risk threshold; that is, within this range, the vascular condition is at risk. lower and Strain upper These are the upper and lower limits of the strain risk threshold, meaning that within this range, the vascular condition is at risk.

[0069] The process for assessing the risk of stent shortening is as follows:

[0070] like Established

[0071] If the stent shortening rate exceeds the specified abbreviation limit, it is considered to have a risk of stent shortening, where L is the length of the stent after deployment. ori The length of the stent before release (obtained from the particleization and assembly module 300); the stent shortening is L. ori -L; Stent shortening rate (L) ori -L) / L ori *100%.

[0072] The main application areas of this invention are endovascular interventional diagnosis and treatment, including intelligent diagnosis and treatment of coronary artery, peripheral artery, and aortic plaques; although the background technology of this invention uses the coronary artery as an example, the content of this invention is also applicable to interventional applications of peripheral arteries, aorta, etc.

[0073] The process for assessing the risk of stent apposition is as follows: Information on the relationship between the generated stent edge and plaque is read; based on the stent morphology and vessel wall morphology, the risk of poor stent apposition is assessed; after numerical simulation, the presence or absence of poor stent apposition is determined based on the stent morphology and vessel wall morphology; the axial distance from the stent wire surface to the vessel lumen surface is calculated; if the axial distance from the stent wire surface to the lumen surface is less than the stent wire thickness, the stent wire is considered to have good apposition.

[0074] Because the SPH method discretizes the research object into particles in three-dimensional space, a neighborhood search can be performed on the particles on the outer surface of the stent. The neighborhood radius R is the stent wire thickness. If there are no vessel wall particles in the neighborhood, the location is marked as poor stent apposition. Based on the identification of poor apposition, the curvature and area of ​​the poor apposition can be marked in the calculation results, such as... Figure 4 As shown.

[0075] The process for determining the risk of stent warping at both ends is as follows: Based on the diameters of each section before and after stent release, the proximal and distal diameters of the stent and the diameter of the stent body are obtained. When the difference between the proximal and distal diameters and the stent body diameter exceeds a threshold, the system determines that there is a risk of stent warping at both ends. This is because when the stent system is filled to the maximum pressure, warping occurs at the proximal and distal ends of the stent, exhibiting the characteristic of bulging at both ends and narrowing in the middle (dog bone effect). After the numerical calculation is completed, the diameter along the stent under the maximum filling state is calculated, thereby determining the risk of stent warping at both ends by observing the deformation state of the stent during filling.

[0076] In actual implantation, the "dog bone effect" of coronary stents occurs due to the uneven distribution of balloon pressure under varying resistance. Early causes were primarily due to design flaws in the stent / balloon itself, while modern causes are more often attributed to severe vascular calcification. Therefore, the process of identifying and assessing plaque near the stent edge includes:

[0077] Step 61: Obtain the remote segment L dist proximal segment L prox and the middle section L middle The boundary includes the following criteria:

[0078] If the marker indicating whether the distal edge of the stent is located within the plaque is marked as yes, then L dist Set to the first 20% of the bracket length; otherwise, set L.dist Defined as the region between the distal end of the stent and the distal section of the most distal plaque within the stent;

[0079] If the marker indicating whether the proximal edge of the stent is located within the plaque is "yes", then L prox Set to the last 20% of the bracket length; otherwise, set L. prox Defined as the region between the proximal end of the stent and the distal section of the nearest plaque within the stent;

[0080] L middle For L dist To L prox The area between;

[0081] The results obtained are as follows Figure 5 As shown, the interval between the proximal end of the stent and the starting position of the plaque in the stent segment after stent release is L. prox The interval between the distal end of the stent and the end position of the plaque in the stent segment is L. dist The interval from the start to the end of the plaque within the stent segment is L. middle .

[0082] Step 62: Calculate the average diameter of each segment:

[0083] Calculate L respectively middle L prox L dist Average support diameter D of the interval middle D forth and D back ;

[0084] Step 63, Assessing the risk of stent warping:

[0085] like Established,

[0086] This indicates a potential risk of warping at both ends of the stent implantation. The "Inflation limit" in the formula is the expansion threshold for judging warping at both ends of the stent, typically taken as 10%. If the stent shows no warping at all, then D... middle D forth and D back Equal; the larger this value is, the larger the support L prox L dist The greater the warping between the two intervals.

Claims

1. A numerical simulation system for smooth particle dynamics in stent intervention based on intravascular images, characterized in that, include: The system includes a vascular property collection module (100), a compressible stent establishment module (200), a particleization and assembly module (300), a stent release setting module (400), and a stent release mechanical simulation module (500). The vascular property collection module (100) and the compressible stent establishment module (200) are connected to the particleization and assembly module (300). The particleization and assembly module (300) is sequentially connected to the stent release setting module (400), the stent release mechanical simulation module (500), and the stent implantation process risk assessment module (600). The release stent mechanical simulation module (500) reads the particle-generated complete model after being constrained by the stent release setting module (400), and uses the smooth particle method to simulate according to the boundary conditions of the force, and obtains the simulation results of the stress-strain transient changes of the simulated object during the expansion and rebound of the stent. The stent implantation process risk assessment module (600) is used to obtain the stress conditions of the blood vessel and the stent in the simulation results through the stent release mechanical simulation module (500), and to determine the risk location, degree of danger, risk of stent apposition to the wall, risk of stent warping at both ends and risk of stent shortening during the stent implantation process; The process for determining the risk location is as follows: After the numerical simulation is completed, the location corresponding to the highest value in the stress field of the blood vessel wall is marked according to the stress calculation results. The marked location is the location where the blood vessel wall and its tissue are subjected to the greatest stress and the highest risk during the entire stent pre-simulation process. The process for determining the risk of stent apposition is as follows: Information on the relationship between the generated stent edge and plaque is read; based on the stent morphology and vessel wall morphology, the risk of poor stent apposition is determined; after numerical simulation, the presence of poor stent apposition is determined based on the stent morphology and vessel wall morphology; the axial distance from the stent wire surface to the vessel lumen surface is calculated; if the axial distance from the stent wire surface to the lumen surface is less than the stent wire thickness, the stent wire is considered to have good apposition. The process for determining the degree of danger is as follows: the degree of danger at risk points is determined by combining the stress field and displacement field from the numerical calculation results; at risk points, there are clear stress magnitude and deformation values, which are marked and displayed. When the stress value is greater than the stress threshold and the deformation is greater than the deformation threshold, it is judged as "high risk"; when the stress value is less than the stress threshold and the deformation is less than the deformation threshold, it is judged as "low risk"; when the stress value or deformation is within the corresponding threshold, it is judged as "medium risk". In the formula, Stress max and Strain max This represents the maximum stress and strain of the blood vessel wall in the calculation results; Stress lower and Stress upper These are the upper and lower limits of the stress risk threshold; that is, stress within this range poses a risk. lower and Strain upper These are the upper and lower limits of the strain risk threshold, meaning that there is risk within this range of strain.

2. The numerical simulation system for smooth particle dynamics of stent intervention based on intravascular images according to claim 1, characterized in that, The vascular attribute collection module (100) reads the intravascular image information transmitted by the intravascular imaging system and constructs a three-dimensional vascular model based on the intravascular image information; The pressure-grip stent creation module (200) constructs a three-dimensional stent model based on the stent type and angle; the three-dimensional stent model includes a balloon.

3. The numerical simulation system for smooth particle dynamics of stent intervention based on intravascular images according to claim 1, characterized in that, The stent release setting module (400) sets the given geometric constraints, force boundary conditions, total duration and step size for the stent; the geometric constraints include: constrained circumferential displacement, constrained axial displacement, constrained radial displacement, constrained plaque and constrained vascular axial displacement of the particled complete model, allowing the particled complete model to move freely in the radial direction; In the stent release setting module (400), the blood vessel and the plaque are in bonded contact; the outer surface of the plaque is the contact surface, and the inner surface of the blood vessel is the target surface; the stent and the inner wall of the blood vessel are in frictional contact. The boundary conditions for the force include either a given pressure load or a given radial displacement load.

4. The numerical simulation system for smooth particle dynamics of stent intervention based on intravascular images according to claim 1, characterized in that, The particleization and assembly module (300) reads the three-dimensional vascular model from the vascular attribute collection module (100), the three-dimensional stent model from the gripping stent creation module (200), and the input specified lesion location, and assembles the three-dimensional stent model to the specified lesion location of the three-dimensional vascular model to form a complete model to be particleized; through smooth particle method preprocessing, the stent model is particleized to generate a complete model after particleization.

5. The numerical simulation system for smooth particle dynamics of stent intervention based on intravascular images according to claim 1 or 3, characterized in that, The stent release mechanical simulation module (500) simulates the free expansion stage of the coronary stent based on the force boundary conditions input by the stent release setting module (400). The simulation results of the stent release mechanical simulation module (500) are exported to the database or other modules through the interface.

6. The numerical simulation system for smooth particle dynamics of stent intervention based on intravascular images according to claim 5, characterized in that, In the stent release mechanical simulation module (500), the stent and calcified plaque tissue are treated with a linear elastic model, and the lipid plaque and vascular wall tissue are treated with a hyperelastic model. The simulation results include: the stress on the plaque, the degree of plaque deformation, the stress on the stent, the length of the stent after release, the length of the stent before release, the diameter of each section before stent release, the diameter of each section after stent release, the degree of stent deformation, the stress on the blood vessel, and the degree of blood vessel deformation.

7. The numerical simulation system for smooth particle dynamics of stent intervention based on intravascular images according to claim 1, characterized in that, The stent release mechanical simulation module (500) and the stent implantation process risk assessment module (600) are both connected to the display and the database.

8. The numerical simulation system for smooth particle dynamics of stent intervention based on intravascular images according to claim 1, characterized in that, The process for assessing the risk of warping at both ends of the stent includes: Step 61: Obtain the remote segment L dist proximal segment L prox and the middle section L middle The boundary, simulating the interval between the proximal stent and the initial position of the plaque in the stent segment after stent release, is L. prox The interval between the distal end of the stent and the end position of the plaque in the stent segment is L. dist The interval from the start to the end of the plaque within the stent segment is L. middle This includes the following determinations: If the marker indicating whether the distal edge of the stent is located within the plaque is marked as yes, then L dist Set to the first 20% of the bracket length; otherwise, set L. dist Defined as the region between the distal end of the stent and the distal section of the most distal plaque within the stent; If the marker indicating whether the proximal edge of the stent is located within the plaque is "yes", then L prox Set to the last 20% of the bracket length; otherwise, set L. prox Defined as the region between the proximal end of the stent and the distal section of the nearest plaque within the stent; L middle For L dist To L prox The area between; Step 62: Calculate the average diameter of each segment: Calculate L respectively middle L prox L dist Average support diameter D of the interval middle D forth and D back ; Step 63, Assessing the risk of stent warping: young Established, This indicates a risk of warping at both ends of the stent implantation, where Inflation limit is the expansion threshold for judging whether warping occurs at both ends of the stent.

9. The numerical simulation system for smooth particle dynamics of stent intervention based on intravascular images according to claim 8, characterized in that, The process for assessing the risk of stent shortening is as follows: young Established, This indicates a risk of stent shortening, where L is the length of the stent after deployment. ori This refers to the length of the stent before it is released. The stent shortening is L ori -L; Stent shortening rate (L) ori -L) / L ori *100%.

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