Method for simulating unfolding of intravascular stent, electronic equipment and computer readable storage medium

Abstract

The invention discloses a method for simulating unfolding of an intravascular stent, electronic equipment and a computer readable storage medium, and the method comprises the steps: building a simplified compression model of the intravascular stent in a microcatheter based on the size of the microcatheter, the simplified compression model comprises node particles on a simplified spiral line of a weaving wire and ghost particles on a central axis section of the intravascular stent; acquiring a blood vessel model comprising the target blood vessel segment, wherein the blood vessel model comprises a blood vessel center line and a blood vessel wall; the simplified compression model is arranged along the center line of the blood vessel, the ghost particles are located on the center line of the blood vessel, and the node particles surround the center line of the blood vessel to form a spiral structure; and carrying out geometric shape constraint and physical elastic constraint on the intravascular stent, and simulating an expansion process of the intravascular stent in the target vascular section to obtain an expansion model of the intravascular stent. According to the method provided by the embodiment of the invention, a reliable stent unfolding form can be obtained.

Description

Technical Field

[0001] This application generally relates to the field of medical image processing technology. More specifically, this application relates to a method, electronic device, and computer-readable storage medium for simulating vascular stent deployment. Background Technology

[0002] Unruptured intracranial aneurysms (UIA) are a common cerebrovascular disease with an incidence of approximately 5%. Rupture of UIA can easily lead to fatal subarachnoid hemorrhage (SAH), severely threatening the patient's life. With the continuous development of materials science and interventional vascular technology, interventional vascular therapy has become one of the core methods for treating UIA. This treatment method uses a microcatheter to deliver vascular stents, coils, and other hyperelastic materials to the blood vessel supplying the aneurysm. The expanded stent provides support and blocks blood flow into the aneurysm cavity, thereby reducing the risk of rupture. During the interventional procedure, the final deployment morphology of the stent within the vessel directly determines the treatment outcome. This deployment process is influenced by multiple factors, including the geometry of the target vessel segment (such as vessel curvature and lumen diameter), the stent material properties (such as elastic modulus and braiding density), and the interaction between the vessel and the stent. Currently in clinical practice, doctors rely heavily on personal experience to predict the stent deployment status. In complex vascular environments (such as multi-branched, highly tortuous vessels), stents often fail to achieve the expected shape and coverage. The lack of precise stent deployment simulation tools before the procedure has become a key issue restricting the safety and effectiveness of interventional treatment.

[0003] In view of this, there is an urgent need to provide a method for simulating vascular stent deployment, so as to significantly improve computational efficiency while ensuring simulation accuracy, accurately reflect the deployment status of vascular stent in the target vascular segment, and provide scientific and reliable technical support for preoperative surgical planning of endovascular interventional therapy. Summary of the Invention

[0004] In order to at least address one or more of the technical problems mentioned above, this application proposes a method, electronic device, and computer-readable storage medium for simulating vascular stent deployment in several aspects.

[0005] In a first aspect, this application provides a method for simulating vascular stent deployment, comprising: establishing a simplified compression model of the vascular stent in the microcatheter based on the size of the microcatheter, wherein the vascular stent includes multiple interwoven braided filaments in a helical structure, the simplified compression model including node particles on the simplified helical lines of the braided filaments and ghost particles on the central axis segment of the vascular stent; obtaining a vascular model including a target vascular segment, the vascular model including a vascular centerline and a vascular wall; arranging the simplified compression model along the vascular centerline, wherein the ghost particles are located on the vascular centerline, and the node particles form a helical structure around the vascular centerline; and simulating the deployment process of the vascular stent in the target vascular segment by applying geometric and physical elastic constraints to the vascular stent to obtain a deployment model of the vascular stent.

[0006] In some embodiments, establishing a simplified compression model of the vascular stent in the microcatheter based on the microcatheter size includes: simplifying the braided filaments of the vascular stent into a helix; constructing a spatial helix model based on the rotation angle of each braided filament in the microcatheter, the inner radius of the microcatheter, and the pitch coefficient of each braided filament; creating node particles on the helix based on the spatial helix model; determining the central axis segment around which the helix is ​​surrounded based on the compression length of the vascular stent in the microcatheter; and creating ghost particles at equal intervals on the central axis segment.

[0007] In other embodiments, the spatial helix model includes: Where x(t), y(t), and z(t) represent the spatial coordinates of the helix, t represents the rotation angle of the braided filament in the microcatheter, r represents the inner radius of the microcatheter, and h represents the pitch coefficient of the braided filament.

[0008] In some other embodiments, the geometric constraints include node spacing constraints and helical curvature constraints, and the node spacing constraints include: the Euclidean distance between adjacent node particles is always equal to a preset fixed distance; the helical curvature constraints include: for any three consecutive node particles, the difference between the current state angle formed during the unfolding process and its initial angle in the microcatheter is less than a first threshold.

[0009] In some embodiments, the helical curvature constraint includes: ;in, p represents the curvature constraint value of any three consecutive nodal particles on the spiral. i-1 p i p i+1 This represents the spatial coordinates of any three consecutive nodal particles along the same spiral line. Represents nodal particle p iThe initial included angle, M1 represents the first threshold, M1=0.2.

[0010] In other embodiments, the physical elastic constraint includes an axial tensile elastic constraint and a bending constraint, wherein the axial tensile elastic constraint is used to limit the tensile deformation of the ghost particle along the centerline of the blood vessel, and the bending constraint is used to limit the bending deformation of the ghost particle along the centerline of the blood vessel.

[0011] In some other embodiments, the axial tensile elastic constraint includes: calculating the axial tensile elastic potential energy based on the spatial coordinates of the ghost particles during deployment; and constraining and predicting the movement positions of the ghost particles and the node particles during deployment based on the decreasing trend of the axial tensile elastic potential energy; wherein the formula for calculating the axial tensile elastic potential energy is: ;in, k represents the axial tensile elastic potential energy. s G represents the elastic modulus of the vascular stent. i G j Represents the spatial coordinates of adjacent ghost particles. This indicates the actual distance between adjacent ghost particles. This represents the natural distance between adjacent ghost particles in the absence of external force.

[0012] In some embodiments, the bending constraint includes: calculating the bending potential energy based on the actual included angle formed by the ghost particles during the unfolding process; and constraining and predicting the movement positions of the ghost particles and the node particles during the unfolding process based on the decreasing trend of the bending potential energy; wherein the formula for calculating the bending potential energy is: ;in, k represents bending potential energy. b This represents the flexural modulus of the vascular stent. This represents the actual included angle formed by three consecutive ghost particles. This represents the natural bending angle of three consecutive ghost particles under no external force.

[0013] In other embodiments, simulating the deployment process of the vascular stent in the target vascular segment by imposing geometric and physical elastic constraints on the vascular stent further includes: acquiring surface mesh data of the vascular wall; calculating the shortest distance between the node particle and the surface mesh in each iteration of the simulation of the vascular stent deployment; if the shortest distance is less than or equal to a preset safety distance, determining that the node particle has touched the vascular wall, and controlling the node particle to stop moving.

[0014] In a second aspect, this application provides an electronic device comprising: a processor configured to execute program instructions; and a memory configured to store the program instructions, which, when loaded and executed by the processor, cause the processor to perform a method according to any one of the first aspects of this application.

[0015] In a third aspect, this application provides a computer-readable storage medium storing program instructions that, when loaded and executed by a processor, cause the processor to perform the method according to any one of the first aspects of this application.

[0016] Using the method for simulating vascular stent deployment provided above, this embodiment of the application establishes a simplified compressed model based on the microcatheter size, including node particles on the simplified spiral line containing braided filaments and ghost particles on the stent's central axis segment. It then obtains a vascular model containing the target vascular segment's centerline and vessel wall, arranges the simplified compressed model along the vascular centerline, and simulates the stent deployment process using geometric and physical elastic constraints. This approach significantly improves computational efficiency and provides reliable simulations of stent deployment morphology while maintaining simulation accuracy. The simplified compressed model, through the combination of node particles and ghost particles, retains the key features of the stent structure while simplifying computation. The combination of geometric and physical elastic constraints compensates for the deficiency of purely geometric methods in neglecting the elastic properties of stent materials, thereby facilitating the accurate simulation and prediction of the vascular stent's deployment state within the target vascular segment. Attached Figure Description

[0017] The above and other objects, features, and advantages of exemplary embodiments of this application will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of this application are illustrated by way of example and not limitation, and the same or corresponding reference numerals denote the same or corresponding parts, wherein: Figure 1 A flowchart of a method for simulating vascular stent deployment according to some embodiments of this application is shown; Figure 2 The present application shows a schematic diagram of a simulated vascular stent deployment process according to some embodiments; Figure 3 A schematic block diagram of a system for simulating vascular stent deployment according to an embodiment of this application is shown. Detailed Implementation

[0018] The technical solutions of the embodiments of this application 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 this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] It should be understood that the terms "comprising" and "including" used in the specification and claims of this application indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0020] It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this specification and claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this specification and claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations.

[0021] As used in this specification and claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [described condition or event] is detected," or "in response to detection of [described condition or event]."

[0022] Figure 1 A flowchart illustrating a method for simulating vascular stent deployment according to some embodiments of this application is shown. Figure 2 A schematic diagram illustrating the simulated vascular stent deployment process of some embodiments of this application is shown below. Figure 1 and Figure 2 An illustrative example is provided.

[0023] like Figure 1As shown, method 100 includes: in step S110, based on the size of the microcatheter, a simplified compression model of the vascular stent in the microcatheter is established, wherein the vascular stent includes multiple interwoven braided filaments in a spiral structure, and the simplified compression model includes node particles on the simplified spiral lines of the braided filaments and ghost particles on the central axis segment of the vascular stent; in step S120, a vascular model including the target vascular segment is obtained, the vascular model including the vascular centerline and the vascular wall; in step S130, the simplified compression model is arranged along the vascular centerline, wherein the ghost particles are located on the vascular centerline, and the node particles form a spiral structure around the vascular centerline; in step S140, by applying geometric and physical elastic constraints to the vascular stent, the deployment process of the vascular stent in the target vascular segment is simulated to obtain a deployment model of the vascular stent.

[0024] A microcatheter is a minimally invasive medical device specifically designed for the precise delivery of therapeutic instruments within blood vessels. Microcatheters are characterized by their small size and flexibility, and their core function is to act as a channel to safely deliver highly elastic interventional devices such as stents and coils to the target vascular segment (e.g., the parent artery of an aneurysm). Specifically, the vascular stent is first compressed to a tiny diameter (matching the inner diameter of the microcatheter), inserted into the microcatheter, and then delivered to the target location. Upon arrival at the target location, a release mechanism allows the vascular stent to dislodge from the microcatheter and expand. The dimensions of the microcatheter can include its internal radius and length. Simply put, the size of the microcatheter directly determines the compressed state (or release state) of the vascular stent. For example… Figure 2 As shown in Figure (a), the vascular stent 220 is compressed within the microcatheter 210.

[0025] Vascular stents are primarily composed of multiple interwoven, helical braided filaments. These filaments are often made of highly elastic metallic materials such as cobalt-chromium alloys and platinum-chromium alloys. The resulting mesh structure provides sufficient support while adapting to the curvature of the blood vessel. Based on the stent's model and material, characteristic parameters can be obtained. For example, the Tubridge TB3025 stent has an unconstrained diameter (or natural diameter) of 3.5 mm and an unconstrained length (or natural length) of 25 mm under no external force, resulting in a shortening rate of approximately 25%. This means that the compression length of this type of stent in a microcatheter can be calculated based on the shortening rate. Furthermore, based on the stent's model and material, the number of braided filaments, the radius / diameter of a single braided filament, the pitch coefficient, and the elastic modulus (kJ) can also be determined. s ), flexural modulus (k) bThese features, such as the stent's natural deployment shape, support strength, and adaptability to blood vessels, collectively determine the stent's characteristics and are the fundamental input data for constructing the simplified compression model and deployment simulation in this application. Based on these features of the stent and the dimensions of the microcatheter, a compression model of the stent within the microcatheter can be obtained.

[0026] Because the braided filament has a certain radius, traditional compression models require describing the three-dimensional shape of the filament and calculating each position on the filament during the simulation unfolding process (such as the finite element method). This is computationally intensive and time-consuming, making it difficult to implement in real-time clinical surgical scenarios. In the technical solution of this application, the braided filament is simplified into a helix, and further simplified into discrete node particles. The central axis segment is simplified into ghost particles used to monitor the bending deformation of vascular stents. In the simulation unfolding calculation, only the unfolding position of the helix needs to be calculated based on the node particles and ghost particles. This greatly simplifies the calculation process, reduces the computational load, and significantly reduces the computation time, enabling the method of this embodiment to be applied to scenarios requiring real-time calculation.

[0027] In some embodiments, the simplification of braided filaments into helices can be achieved using skeleton extraction techniques. Each braided filament corresponds to a simplified helix. Features such as the rotation angle of the braided filaments are preserved during the simplification process. Node particles on the helix can be obtained by discretizing the helix, for example, by selecting node particles randomly or at equal intervals along the helix. Alternatively, the intersections between helices can be used as node particles. The length of the vascular stent determines the length of its central axis segment, around which the braided filaments form a helical structure. In some embodiments, ghost particles can be created randomly or at equal intervals along the central axis segment.

[0028] In some embodiments, establishing a simplified compression model of the vascular stent in the microcatheter based on the microcatheter size includes: simplifying the braided filaments of the vascular stent into a helix; constructing a spatial helix model based on the rotation angle of each braided filament in the microcatheter, the internal radius of the microcatheter, and the pitch coefficient of each braided filament; creating node particles on the helix based on the spatial helix model; determining the central axis segment around which the helix surrounds based on the compression length of the vascular stent in the microcatheter; and creating ghost particles at equal intervals on the central axis segment.

[0029] In other embodiments, the spatial helix model includes: (Formula 1); Where x(t), y(t), and z(t) represent the spatial coordinates (Cartesian coordinates) of the helix, t represents the rotation angle of the braided filament in the microcatheter, r represents the inner radius of the microcatheter, and h represents the pitch coefficient of the braided filament.

[0030] Formula 1 defines the coordinate equations for a continuous spiral, reflecting the continuous helical trajectory of each braided filament within the microcatheter. Specifically, x(t) = r × cos(t) describes the position of the spiral along the X-axis; y(t) = r × sin(t) describes the position along the Y-axis, forming a circular motion perpendicular to the Z-axis (the axial direction of the stent), reflecting the circumferential shape of the spiral; z(t) = h × t describes the position of the spiral along the Z-axis (the axial direction of the stent), determined by the pitch coefficient h (which determines the axial advance rate) and the rotation angle t (which determines the rotation progress), reflecting the axial extension of the spiral. In short, these three equations together constitute a continuous spiral model of a single braided filament, with the value of t covering the complete rotation process of the braided filament, generating all continuous coordinate points on the spiral.

[0031] Within the continuous range of values ​​for t, several discrete rotation angle values ​​are selected (e.g., t = 0°, 30°, 60°…360°), and substituted into the formulas for x(t), y(t), and z(t). Each discrete t value will calculate a set of (x, y, z) coordinates, which are the coordinates of a node particle on a single braided filament. For example, assuming t takes 10 discrete values, the coordinates of 10 node particles on a single braided filament can be obtained. These node particles are arranged in the order of t to reconstruct the spiral structure of the braided filament. The number of node particles on a single braided filament can be created as needed.

[0032] Suppose P represents the coordinate matrix of the nodal particles on all the spiral lines of a vascular stent. Let P represent the coordinates of the k-th node particle on the g-th spiral, n represent the number of node particles on a single spiral, and M2 represent the number of spirals. Then P can be expressed as... ,in =1, ..., M2; k=1, ..., n; R represents the matrix dimension. It can be determined according to Formula 1 above. In other embodiments, M2 can be 24, 48, or 72, etc.

[0033] The compressed length of a vascular stent in a microcatheter refers to the actual axial length of the stent when it is compressed and housed within the microcatheter. This length needs to be calculated in conjunction with the unconstrained length and shortening rate of the stent. For example, the TB3025 stent has an unconstrained length of 25mm and a shortening rate of 25%. Since the shortening rate is the proportion by which the stent shortens from a compressed state to an unconstrained state, its compressed length within the microcatheter is approximately 25mm ÷ (1-25%) ≈ 33.3mm. The central axis segment is the virtual central path of the stent in the compressed state. Its length is equal to the compressed length mentioned above. It is the core reference around which all the simplified spirals of the braided wires are surrounded, ensuring that the overall distribution of the spirals conforms to the housed form of the vascular stent within the microcatheter.

[0034] Ghost particles are virtual monitoring points deployed along the central axis segment. Their core function is to capture the bending and stretching deformation of the central axis during stent deployment. In this embodiment, ghost particles can be created at equal intervals along the central axis segment, meaning that the axial spacing between ghost particles is uniform (e.g., for a 33.3mm central axis segment, if the interval is set to 0.5mm, approximately 67 ghost particles will be created sequentially from the start to the end of the axis). This arrangement ensures a uniform natural distance between adjacent ghost particles, simplifying the subsequent calculation logic of axial stretching elastic potential energy and uniformly covering the entire central axis segment, accurately monitoring deformation at different locations. For example, when calculating bending potential energy, three consecutive equidistant ghost particles can stably reflect the local bending angle of the axis, avoiding deformation monitoring deviations caused by uneven particle distribution, and ultimately providing reliable node data support for simulating the physical laws of the stent deployment process.

[0035] In some embodiments, the coordinates of the ghost particle can be recorded as: R represents the matrix dimension, and m represents the number of ghost particles.

[0036] In step S120, a vascular model including the target vessel segment is acquired. The vascular model includes the vessel centerline and vessel wall. The core of this step is to construct a personalized digital model of the vascular anatomy based on the patient's clinical images, providing a precise anatomical benchmark for subsequent stent deployment simulation.

[0037] The target vessel segment is the location where the vascular stent needs to be deployed and function. For example, in some applications, the target vessel segment may include the carrier artery region of an unruptured intracranial aneurysm (UIA). The vascular model is a three-dimensional digital replica of the patient's actual vascular anatomy using computer technology. It must fully reflect the spatial morphology (such as curvature and lumen diameter) and structural boundaries (i.e., the vessel wall) of the target vessel segment, forming the basis for simulating the interaction between the vascular stent and the vessel. The vessel centerline is the core baseline of the vascular model, representing the central direction of the vessel lumen. In subsequent step S130, the simplified compression model of the vascular stent needs to be arranged along this vessel centerline to ensure that the initial position of the vascular stent before deployment conforms to the natural direction of the vessel. The vessel wall is the physical boundary of the vascular model, corresponding to the wall structure of the actual vessel, and serves as the barrier boundary during vascular stent deployment. In some embodiments, the simulated deployment process of the vascular stent requires collision detection based on the vessel wall to prevent the vascular stent from penetrating the vessel wall.

[0038] In some embodiments, the contrast of blood vessels can be enhanced by applying techniques such as Gaussian filtering for noise reduction and histogram equalization to medical images. Then, methods such as threshold segmentation are used to extract the vascular cavities (i.e., segmenting the vascular region from the non-vascular region), and a 3D vascular model can be generated through 3D reconstruction using algorithms such as Marching Cubes. This algorithm can efficiently convert voxel data into a surface mesh model, restoring the spatial morphology of the blood vessels. In other embodiments, by repairing and cropping the generated 3D vascular model, only the target vascular segment can be retained. In still other embodiments, by using techniques such as skeleton extraction to extract the curve generated by the center trajectory of the vascular cavity, representing the natural direction of the blood vessel, the centerline of the target vascular segment can be obtained.

[0039] In some embodiments, medical imaging may preferentially employ imaging techniques that can clearly present 3D vascular structures, such as 3D-DSA (digital subtraction angiography) or MRA-TOF (magnetic resonance time-of-flight). For example, taking a Siemens Artis Zee biplane digital subtraction angiography system for 3D-DSA acquisition, the contrast agent used is iohexol (370 mgI / mL), with the total dose controlled at 80-100 mL, injected via a forearm vein at a rate of 4.0 mL / s. Acquisition parameters include: tube voltage 70-80 kV, tube current 200-250 mA, rotation angle 200°, rotation speed 40° / s, and 200 frames acquired, ultimately achieving a voxel resolution of approximately 0.25 mm. 3 High-resolution images ensure that vascular details (such as microaneurysms and vascular stenosis) are clearly visible.

[0040] The vascular model constructed in step S120 has significant personalized and precise characteristics: on the one hand, it is generated based on the patient's own imaging data and can accurately reflect the anatomical differences of individual blood vessels (e.g., a patient's aneurysm-bearing artery has a curvature of 30° and a lumen diameter of 4mm, while another patient may have a curvature of 60° and a lumen diameter of 3mm); on the other hand, the direction of the vascular centerline and the spatial position of the vascular wall completely match the real anatomical structure, providing a reliable digital basis for the precise arrangement of stents along the centerline in subsequent step S130 and the stent deployment simulation in step S140.

[0041] In step S130, the simplified compression model is arranged along the centerline of the blood vessel, for example... Figure 2 As shown in Figure (b), this step is equivalent to arranging a simplified compression model of the vascular stent 220 along the centerline 232 of the blood vessel, so that the simplified compression model conforms to the natural direction of the target blood vessel segment, in order to simulate the initial state of the compressed vascular stent 220 in the target blood vessel segment 231.

[0042] Ghost particles are arranged along the centerline of the blood vessel, and based on the spatial coordinate relationship between ghost particles and node particles in the simplified compressed model, the node particles are arranged around the centerline of the blood vessel to form a spiral structure. In a specific embodiment, the Frenet-Serret framework can be used to obtain the tangent vector T (along the centerline, determining the axial direction of the vascular stent arrangement), normal vector N (perpendicular to the tangent, pointing to the inner side of the blood vessel wall), and binormal vector B (perpendicular to T and N) of each sampling point on the centerline of the blood vessel, forming a three-dimensional coordinate system for each sampling point on the centerline of the blood vessel. The Z-axis of the ghost particles is replaced with the tangent vector T of the centerline of the blood vessel (ensuring that the spiral extends along the direction of the blood vessel), the X-axis is replaced with the normal vector N, and the Y-axis is replaced with the binormal vector B (ensuring that the spiral rotates around the centerline of the blood vessel). Based on the spatial coordinate relationship between ghost particles and node particles, the node particles are arranged around the centerline of the blood vessel.

[0043] After completing step S130, method 100 can proceed to step S140. In step S140, by applying geometric and physical elastic constraints to the vascular stent, the deployment process of the vascular stent in the target vascular segment is simulated to obtain a deployment model of the vascular stent. The core of step S140 is to use a dual constraint mechanism to ensure that the deployment process of the simplified compression model of the vascular stent meets both structural integrity requirements and follows the laws of material physics, ultimately generating a stent deployment shape that conforms to the anatomical features of the patient's blood vessels, for example... Figure 2 As shown in Figure (c), the final deployment model of the vascular stent 220 in the target vascular segment 231 is obtained.

[0044] The geometric constraints described in this paper are constraint rules used to maintain the structural integrity of vascular stents during deployment, ensuring that the spatial shape of the stent braids does not undergo abnormal deformation (such as excessive stretching or twisting). The physical elastic constraints described in this paper are constraint rules that simulate the hyperelastic material properties of vascular stents, driving the stent to move towards the minimum potential energy state (i.e., the natural deployment state, or the state without external force) by calculating the elastic potential energy.

[0045] In step S140, using the particle positions arranged in step S130 as the initial state, the position changes of the node particles and ghost particles in each step are simulated through iterative calculation. In each iteration, the geometric constraint deviation of all node particles and the physical elastic potential energy of ghost particles are calculated first. Then, the particles are moved in the direction where the constraint is satisfied and the potential energy is minimized through a position dynamics algorithm (directly adjusting the particle positions to meet the constraints). When each particle stops moving, the unfolding process terminates. At this time, based on the final node particle coordinates, the spiral of each braided filament is fitted, and a complete three-dimensional unfolding model of the stent is generated based on the diameter of the braided filament. This unfolding model can clearly present information such as the coverage range of the vascular stent in the target vascular segment and the degree of fit with the vascular wall (e.g., whether it fits tightly at bends). In some embodiments, the conditions for each particle to stop moving may include reaching the upper limit of the number of iterations or the movement distance of all particles in the current round being less than the convergence threshold.

[0046] In other embodiments, after adjusting the position of each particle in each round, collision detection can be performed on the vascular stent. Specifically, step S140 may further include: acquiring surface mesh data of the vascular wall; calculating the shortest distance between the node particle and the surface mesh in each iteration of the simulated vascular stent deployment calculation; if the shortest distance is less than or equal to a preset safety distance, it is determined that the node particle has touched the vascular wall, and the node particle is controlled to stop moving.

[0047] In some embodiments, the representation of the vessel wall in the vascular model can be a surface mesh. Specifically, the vessel wall surface mesh data is a discretized representation of the anatomical morphology of the vessel wall, composed of a large number of interconnected mesh units (usually triangles or quadrilaterals). Each mesh unit contains vertices (spatial coordinates), edges (connecting vertices), and faces (forming the vessel wall surface), forming a three-dimensional mesh structure consistent with the morphology of the real vessel wall, which can accurately reflect details such as the curvature and convexity of the lumen. The surface mesh data of the vessel wall is obtained through the vascular model in step S120.

[0048] The core execution step of collision detection is "calculating the shortest distance between node particles and surface mesh in each iteration of the simulated vascular stent deployment calculation." Its purpose is to determine in real time whether the node particles of the vascular stent are about to contact the vessel wall, providing data for subsequent movement cessation. Here, the shortest distance is the minimum spatial distance from a single node particle to all mesh elements on the vessel wall surface. Invalid distances of node particles within the vessel lumen must be excluded (i.e., only the distance of the node particle in the direction of movement towards the vessel wall is calculated).

[0049] The preset safety distance mentioned above is a critical threshold set based on clinical practice and computational accuracy, and it needs to match the spatial sampling accuracy of the image voxels. For example, assuming the voxel side length is 0.25mm, the maximum error in vessel wall reconstruction is ±0.125mm (half the side length). The preset safety distance can be set to 0.1~0.15mm, which avoids misjudging contact due to computational errors and prevents node particles from getting too close to the vessel wall, leading to virtual penetration. The logic for determining contact with the vessel wall is as follows: when a node particle moves towards the vessel wall and the shortest distance to the vessel wall is less than or equal to the preset safety distance, it is considered that the node particle has contacted the vessel wall (simulating the physical contact between the real vascular stent and the vessel wall after expansion). At this time, controlling the node particle to stop moving freezes the spatial coordinates of the node particle. In subsequent iterations, its position will not be adjusted. Only node particles that have not been touched are allowed to continue to move according to constraints, ensuring that the vascular stent always adheres to the vessel wall and does not penetrate it after deployment.

[0050] Specifically, the collision detection implementation described above needs to be combined with the constraint calculation process: After the distance calculation is completed in each iteration, the shortest distance corresponding to each node particle is compared with the preset safety distance (e.g., 0.12mm). If the shortest distance between a node particle and the surface mesh is less than or equal to the preset safety distance (e.g., the shortest distance is 0.09mm, which is less than 0.12mm), then the node particle is marked as touching. In subsequent geometric constraint and physical elastic constraint calculations, the position update amount of the node particle is forcibly set to 0. If the shortest distance is greater than the preset safety distance (e.g., 0.12mm), then the constraint adjustment is performed normally. For example, a node particle of the TB3025 stent at the bend of the blood vessel is 0.13mm away from the blood vessel wall (greater than 0.12mm) in the 120th iteration and continues to move; after the 121st iteration, the distance becomes 0.10mm (less than 0.12mm), and it is determined to be touching and stops moving to prevent the node particle from penetrating the blood vessel wall and ensure that the stent's unfolded shape at the bend fits tightly with the blood vessel wall.

[0051] The unfolding process ends when the shortest distance of all node particles is less than or equal to a preset safety distance, or when the number of node particles whose shortest distance is less than or equal to the preset safety distance exceeds a preset proportion, or when the shortest distance of node particles corresponding to the target area of ​​the target vascular segment (such as the vascular area excluding the aneurysm cavity) is less than or equal to the preset safety distance, and the unfolded model of the vascular stent at this time is output. In some embodiments, when the unfolding process ends, an unfolding model is obtained based on the final position of each node particle and ghost particle. This unfolding model includes at least a helical equation fitted by the node particles. Based on the diameter of the braided filament and the helical equation, the helical line is restored to a braided filament, and a three-dimensional unfolded model of the vascular stent is output.

[0052] The above combination Figure 1 and Figure 2 The method for simulating vascular stent deployment according to the embodiments of this application has been described by way of example. In order to better understand the constraints of the embodiments of this application, further explanation will be given below.

[0053] In some embodiments, the geometric constraints include node spacing constraints and helical curvature constraints, and the node spacing constraints include: the Euclidean distance between adjacent node particles is always equal to a preset fixed distance; the helical curvature constraints include: for any three consecutive node particles, the difference between the current state angle formed during the unfolding process and its initial angle in the microcatheter is less than a first threshold.

[0054] In some embodiments, the node spacing constraint can be expressed by the following formula: (Formula 2); in, This indicates node spacing constraints. Represents the coordinates of any two adjacent nodes on the same spiral line. d represents the Euclidean distance between nodes. ij This represents a preset fixed distance between nodes. This preset fixed distance can be determined based on the natural distance of node particles under no external force.

[0055] The node spacing constraint means that the distance between adjacent node particles remains unchanged when the vascular stent expands and deploys. That is, in step S140, during the simulated vascular stent deployment process, the distance between node particles is always constrained to remain unchanged to ensure the stability of the geometric structure of the vascular stent.

[0056] In other embodiments, the helical curvature constraint includes: (Formula 3); in, p represents the curvature constraint value of any three consecutive nodal particles on the spiral. i-1 p i p i+1 This represents the spatial coordinates of any three consecutive nodal particles along the same spiral line. Represents nodal particle p i The initial included angle, M1 represents the first threshold. In some embodiments, M1 = 0.2. After obtaining a simplified compression model of the vascular stent in the microcatheter, the initial included angle can be determined based on the spatial coordinates of three consecutive node particles (as determined based on Equation 1).

[0057] Represents nodal particle p i-1 p i p i+1 The cosine value of the angle formed in the current state, where, p i-1 Point to p i The vector, p i Point to p i+1 The vector, then This represents the dot product operation of two vectors. It represents the product of the magnitudes of two vectors.

[0058] The helical curvature constraint limits the difference between the actual and initial angles of any three consecutive nodes on the helical line (e.g., a cosine difference of less than 0.2 corresponds to an angle difference of approximately 11.5°). This constrains the range of change in the helical curvature during stent deployment, ensuring the bending of each node particle. Consequently, during the simulated deployment in step S140, the helical line maintains a smooth and continuous curve shape, thus preserving the helical structure and preventing sharp angles or irregular twists. This ensures the integrity of the stent structure (preventing "virtual breakage" or morphological distortion of the braided filaments due to excessive local bending, conforming to the hyperelastic material properties of real stents). This helps reduce the deviation between the simulated stent deployment shape and the actual clinical deployment shape, laying a structural foundation for the effective execution of subsequent physical elastic constraints and ultimately improving the accuracy of the stent deployment model.

[0059] In other embodiments, the physical elastic constraints include axial tensile elastic constraints and bending constraints. The axial tensile elastic constraints are used to limit the tensile deformation of the ghost particles along the blood vessel centerline, and the bending constraints are used to limit the bending deformation of the ghost particles along the blood vessel centerline.

[0060] Physical elastic constraints are used to ensure that the stent deployment process conforms to the physical laws of materials, avoiding excessive deformation or abnormal morphology. The core function of axial tensile elastic constraints is to limit the tensile deformation of ghost particles along the axial direction of the blood vessel centerline, maintaining the stability of the stent's axial length. Axial tensile deformation refers to the elongation of the stent along the blood vessel centerline during deployment. Ghost particles, acting as virtual monitoring points along the stent's central axis, directly reflect the degree of axial tension through changes in their spacing. This constraint is based on the elastic properties of the stent material: ghost particles have a fixed natural spacing in the absence of external force. During deployment, if the spacing between adjacent ghost particles increases due to factors such as blood vessel stretching or stent expansion, the constraint simulates the material's elastic restoring force, driving the particles to move in the direction of decreasing spacing, thus preventing excessive axial stretching of the stent. For example, the TB3025 stent has a compressed length of about 33.3 mm in the microcatheter and a natural length of 25 mm in the unconstrained state. When unfolded, the axial tensile elastic constraint limits the excessive expansion of the ghost particle spacing, ensuring that the final axial length of the stent is as close as possible to the natural length. This prevents the coverage area from exceeding the target area due to excessive stretching, and also prevents axial relaxation due to insufficient constraint. This aligns with the physical property of stent materials such as cobalt-chromium alloys that can recover their natural shape after stretching.

[0061] The core function of bending constraint is to limit the bending deformation of ghost particles along the vessel centerline, ensuring that the stent's bending shape conforms to the material's natural bending characteristics and avoiding excessive bending. Bending deformation refers to the phenomenon where the angle formed by consecutive particles deviates from the natural angle when the central axis of the stent, composed of ghost particles, bends along the vessel's direction. This constraint controls the degree of this deviation by simulating the stent material's bending resistance. In implementation, it relies on the distribution of ghost particles along the vessel centerline and real-time monitoring of the bending angle formed by three consecutive ghost particles. If the particle angle at the vessel bend is too small (i.e., excessive bending), the constraint simulates the material's bending resistance, adjusting the particle positions to increase the angle, making the bending degree of the central axis conform to the stent material's natural bending limit. If the angle is too large (i.e., insufficient bending), it drives particle fine-tuning to adapt to the vessel's natural direction. For example, when the stent is deployed in a highly curved arterial segment carrying an aneurysm, the central axis formed by ghost particles may have an excessively small angle due to the vessel's curvature. The bending constraint will intervene in time to adjust this, preventing stress concentration and morphological distortion of the braided wires due to excessive bending, while ensuring the stent smoothly conforms to the vessel's curved contour and does not detach from the vessel wall due to excessive bending resistance.

[0062] In some other embodiments, the axial tensile elastic constraint includes: calculating the axial tensile elastic potential energy based on the spatial coordinates of the ghost particles during the unfolding process; and constraining and predicting the movement positions of the ghost particles and node particles during the unfolding process based on the decreasing trend of the axial tensile elastic potential energy; wherein, the formula for calculating the axial tensile elastic potential energy is: (Formula 4); in, k represents the axial tensile elastic potential energy. s G represents the elastic modulus of a vascular stent. i G j Represents the spatial coordinates of adjacent ghost particles. This represents the actual distance between adjacent ghost particles. This represents the natural distance between adjacent ghost particles in the absence of external forces.

[0063] In other embodiments, the constraint conditions for the axial tensile elastic constraint include: (Formula 5); in, G represents the axial tensile elastic constraint value. i G j Represents the spatial coordinates of adjacent ghost particles. This represents the actual distance between adjacent ghost particles. This represents the natural distance between adjacent ghost particles in the absence of external forces.

[0064] By calculating the axial tensile elastic potential energy in each round of calculation and adjusting the particle position in the direction of the constraint in Formula 5 (i.e., the actual distance between adjacent ghost particles approaches the natural distance, which is also the direction of the minimum axial tensile elastic potential energy), the aforementioned role and effect of axial tensile elastic constraint can be achieved.

[0065] In some embodiments, the bending constraint includes: calculating the bending potential energy based on the actual included angle formed by the ghost particles during the unfolding process; and constraining the predicted movement positions of the ghost particles and node particles during the unfolding process based on the decreasing trend of the bending potential energy; wherein the formula for calculating the bending potential energy is: (Formula 6); in, k represents bending potential energy. b This represents the flexural modulus of a vascular stent. This represents the actual angle formed by three consecutive ghost particles. This represents the natural bending angle of three consecutive ghost particles under no external force. In some embodiments, =0.

[0066] In other embodiments, It can be calculated based on the following formula: (Public Notice 7); in, Represents three consecutive ghost particles (G i-1 Gi G i+1 The actual included angle formed by ) G represents i-1 Pointing to G i The vector, G represents i Pointing to G i+1 The vector, then This represents the dot product operation of two vectors. It represents the product of the magnitudes of two vectors.

[0067] In some other embodiments, the constraint conditions for the bending constraint include: (Announcement No. 8) in, Indicates the bending constraint value. This represents the actual angle formed by three consecutive ghost particles. This represents the natural bending angle of three consecutive ghost particles under no external force.

[0068] By calculating the bending potential energy in each round of calculation and adjusting the particle position in the direction of the constraint in Formula 8 (i.e., the actual angle formed by three consecutive ghost particles approaches the natural bending angle, which is also the direction of minimum bending potential energy), the aforementioned bending constraint effect can be achieved.

[0069] In summary, the method according to the embodiments of this application, through a simplified compression model combining node particles and ghost particles, significantly reduces the computational dimension while retaining the core structural features of the spiral braided vascular stent, effectively improving simulation efficiency; relying on the arrangement along the centerline of the blood vessel, it ensures that the initial position of the simulated vascular stent deployment conforms to the natural direction of the blood vessel, avoiding the impact of initial posture deviation on the simulation results; through the synergistic effect of geometric shape constraints and physical elastic constraints, it compensates for the deficiency of pure geometric simulation in ignoring the elastic properties of the stent material, and can accurately reproduce the real deployment state of the vascular stent in the target blood vessel segment while ensuring simulation accuracy, providing scientific and reliable technical support for preoperative surgical planning of vascular interventional therapy, helping doctors predict the stent coverage and blood vessel fit, and improving the safety and effectiveness of treatment.

[0070] This application also provides an electronic device, including: a processor configured to execute program instructions; and a memory configured to store the program instructions, which, when loaded and executed by the processor, cause the processor to perform the functions described above according to this application. Figures 1-2 Any of the methods described herein. The following will combine... Figure 3 The system shown is described.

[0071] Figure 3A schematic block diagram of a system for simulating vascular stent deployment according to an embodiment of this application is shown. The system 300 may include an electronic device 301 according to an embodiment of this application, as well as its peripheral devices and an external network, wherein the electronic device 301 is used to simulate vascular stent deployment to achieve the aforementioned combination. Figures 1-2 The technical solutions of any of the embodiments described in this application.

[0072] like Figure 3 As shown, the electronic device 301 may include a CPU 3011, which may be a general-purpose CPU, a dedicated CPU, or other information processing and program execution unit. Furthermore, the electronic device 301 may also include a mass storage device 3012 and a read-only memory (ROM) 3013. The mass storage device 3012 can be configured to store various types of data, including medical images, vascular models, simplified compressed models, vascular stent models, characteristic parameters, and other data, as well as various programs required to run methods for simulating vascular stent deployment. The ROM 3013 can be configured to store data required for the power-on self-test of the electronic device 301, the initialization of various functional modules in the system, the drivers for the system's basic input / output, and the data required to boot the operating system.

[0073] Furthermore, electronic device 301 also includes other hardware platforms or components, such as the TPU 3014, GPU 3015, FPGA 3016, and MLU 3017 shown. It is understood that although various hardware platforms or components are shown in electronic device 301, they are merely exemplary and not limiting, and those skilled in the art can add or remove corresponding hardware as needed. For example, electronic device 301 may include only a CPU as a known hardware platform and another hardware platform as the test hardware platform of this application.

[0074] The electronic device 301 of this application also includes a communication interface 3018, through which it can connect to a local area network / wireless local area network (LAN / WLAN) 305, and further connect to a local server 306 or the Internet 307 via the LAN / WLAN. Alternatively or additionally, the electronic device 301 of this application can also directly connect to the Internet or a cellular network via the communication interface 3018 based on wireless communication technology, such as third-generation ("3G"), fourth-generation ("4G"), or fifth-generation ("5G") wireless communication technology. In some application scenarios, the electronic device 301 of this application can also access an external network server 308 and a possible database 309 as needed to obtain data such as various known vascular stent models, material types, and characteristic parameters, and can remotely store various parameters or intermediate data.

[0075] Peripherals of electronic device 301 may include a display device 302, an input device 303, and a data transmission interface 304. In one embodiment, the display device 302 may include, for example, one or more speakers and / or one or more visual displays, configured to provide voice prompts and / or display images and videos of the simulation process or unfolding results of the device. The input device 303 may include, for example, a keyboard, mouse, microphone, or other input buttons or controls, configured to receive input of medical images or vascular models or user commands. The data transmission interface 304 may include, for example, a serial interface, parallel interface, or Universal Serial Bus interface (“USB”), Small Computer System Interface (“SCSI”), Serial ATA, FireWire (“FireWire”), PCI Express, and High Definition Multimedia Interface (“HDMI”), configured for data transmission and interaction with other devices or systems. According to the present application, the data transmission interface 304 can receive medical images, etc., and transmit various types of data and results to electronic device 301.

[0076] The CPU 3011, mass storage 3012, read-only memory ROM 3013, TPU 3014, GPU 3015, FPGA 3016, MLU 3017, and communication interface 3018 of the electronic device 301 of this application can be interconnected via bus 3019, and can interact with peripheral devices through this bus. In one embodiment, the CPU 3011 can control other hardware components and peripheral devices in the electronic device 301 through bus 3019.

[0077] In operation, the processor CPU 3011 of the electronic device 301 of this application can receive medical images, vascular models, or vascular stent parameters through the input device 303 or the data transmission interface 304, and retrieve computer program instructions or code stored in the memory 3012 to simulate the deployment of the vascular stent in the received vascular model, so as to obtain the deployment state of the vascular stent in the target vascular segment. After the CPU 3011 determines the deployment model result by executing the program instructions, it can display the result on the display device 302 or output the result through voice prompts. In addition, the electronic device 301 can also upload the stent deployment result to a network, such as a remote database 309, through the communication interface 3018.

[0078] It should also be understood that any module, unit, component, server, computer, terminal, or device of the executable instructions in this application may include or otherwise access computer-readable media, such as storage media, computer storage media, or data storage devices (removable) and / or non-removable) such as disks, optical discs, or magnetic tapes. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data.

[0079] Based on the foregoing, this application also provides a computer-readable storage medium storing computer-readable instructions thereon, which, when executed by one or more processors, implement the above-described combination of instructions. Figures 1-2 The method described in any of the embodiments.

[0080] Computer-readable storage media can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented using computer-readable / executable instructions that can be stored or otherwise maintained by such a computer-readable medium.

[0081] While numerous embodiments of this application have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will arise for those skilled in the art without departing from the spirit and intent of this application. It should be understood that various alternatives to the embodiments of this application described herein may be employed in the practice of this application. The appended claims are intended to define the scope of protection of this application and therefore cover equivalents or alternatives within the scope of these claims.

Claims

1. A method for simulating deployment of a vascular stent, comprising: establishing a simplified compressed model of a vascular stent in a microcatheter based on dimensions of the microcatheter, wherein the vascular stent comprises a plurality of braided wires interwoven in a helical structure, and the simplified compressed model comprises node particles on simplified helical lines of the braided wires and ghost particles on a center axis segment of the vascular stent; obtaining a vascular model comprising a target vascular segment, the vascular model comprising a vascular centerline and a vascular wall; arranging the simplified compressed model along the vascular centerline, wherein the ghost particles are located on the vascular centerline and the node particles form a helical structure around the vascular centerline; simulating a deployment process of the vascular stent in the target vascular segment by performing geometric and physical elastic constraints on the vascular stent to obtain a deployed model of the vascular stent. 2.The method of claim 1, wherein establishing a simplified compressed model of a vascular stent in a microcatheter based on dimensions of the microcatheter comprises: simplifying the braided wires of the vascular stent into helical lines; constructing a spatial helical line model based on a rotation angle of each braided wire in the microcatheter, an inner radius of the microcatheter, and a pitch coefficient of each braided wire; creating node particles on the helical lines based on the spatial helical line model; determining a center axis segment around which the helical lines are arranged based on a compressed length of the vascular stent in the microcatheter; creating ghost particles equidistantly on the center axis segment. 3.The method of claim 2, wherein the spatial helical line model comprises: ； wherein x(t), y(t), z(t) represent spatial coordinates of the helical line, t represents a rotation angle of the braided wire in the microcatheter, r represents an inner radius of the microcatheter, and h represents a pitch coefficient of the braided wire. 4.The method of claim 1, wherein the geometric constraints comprise an inter-node distance constraint and a helical line curvature constraint, and The inter-node distance constraint includes: a Euclidean distance between adjacent node particles is always equal to a preset fixed distance; the helical line curvature constraint comprises that, for any three consecutive node particles, a difference between a current state included angle formed by the three node particles in a deployment process and an initial included angle of the three node particles in the microcatheter is less than a first threshold. 5.The method of claim 4, wherein the helical line curvature constraint comprises: ； wherein, represents the curvature constraint value of any three consecutive node particles on a helix, p i-1 , p i , p i+1 represents the spatial coordinates of any three consecutive node particles on the same helix, represents the initial included angle of the node particle p i , and M1 represents a first threshold value, M1 = 0.

2. 6.The method of any one of claims 1-5, wherein the physical elastic constraints comprise an axial tensile elastic constraint for limiting tensile deformation of the ghost particles along the vascular centerline and a bending constraint for limiting bending deformation of the ghost particles along the vascular centerline. 7.The method of claim 6, wherein the axial tensile elastic constraint comprises: calculating an axial tensile elastic potential energy based on spatial coordinates of the ghost particles in a deployment process; constraining predicted moving positions of the ghost particles and the node particles in the deployment process based on a tendency of the axial tensile elastic potential energy to decrease; wherein a calculation formula of the axial tensile elastic potential energy is: ； wherein represents the axial tensile elastic potential energy, k s represents the elastic modulus of the vascular stent, G i , G j represents the spatial coordinates of adjacent ghost particles, represents the actual distance of adjacent ghost particles, represents the natural distance of adjacent ghost particles in the absence of external forces.

8. The method of claim 6, wherein the bending constraint comprises: calculating a bending potential energy based on an actual included angle formed by the ghost particle during the deployment process; constraining the predicted moving positions of the ghost particle and the node particles during the deployment process based on a tendency of the bending potential energy to decrease; wherein the bending potential energy is calculated according to the following formula: ； wherein, represents a bending potential, k b represents a bending modulus of the vascular stent, represents an actual included angle of three consecutive said ghost particles, represents a natural bending angle of three consecutive said ghost particles in the absence of external force.

9. The method of claim 1, wherein the simulating the deployment process of the vascular stent in the target vascular segment by performing the geometric and physical elastic constraints on the vascular stent further comprises: obtaining surface mesh data of the vessel wall; calculating a shortest distance between the node particle and the surface mesh in each iteration calculation of the simulation of the deployment of the vascular stent; if the shortest distance is less than or equal to a preset safety distance, determining that the node particle touches the vessel wall, and controlling the node particle to stop moving.

10. An electronic device, comprising: comprising: a processor configured to execute program instructions; and a memory configured to store the program instructions, which, when loaded and executed by the processor, cause the processor to perform the method according to any one of claims 1-9.

11. A computer readable storage medium having stored therein program instructions, wherein the program instructions are executable by a computer for causing the computer to perform the method of any one of claims 1-10. which, when loaded and executed by the processor, cause the processor to perform the method according to any one of claims 1-9.

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