Optimization method for magnetocaloric intravascular stent and magnetocaloric intravascular stent

Abstract

The method comprises the following steps: firstly, determining an initial topology structure, and setting the width of a strut wire, the height of a near-end support ring, the height of a far-end support ring and the number of V-shaped repeating units as design variables; corresponding performance indexes are set as stent inlet and outlet temperature difference, stent covering section blood vessel inner wall average temperature and average wall surface shear stress. 28 sets of design points are generated through Box-Behnken design, multi-physics field numerical simulation is carried out, and corresponding objective function response data are obtained. And constructing an agent model between the structural parameters and each objective function based on a response surface method, implementing multi-objective optimization by adopting a non-dominated sorting genetic algorithm to obtain a Pareto non-dominated solution set, and selecting an optimal design according to the priority. By means of the optimized stent, uniform distribution of the temperature of the inner wall of the blood vessel and hemodynamic safety are kept in the heating process, and the safety of the thermal therapy process is guaranteed by adopting a Curie point material.

Description

Technical Field

[0001] This invention relates to three major technical fields: implantable magnetothermal medical devices, numerical simulation of biothermal-fluid coupling, and multi-objective evolutionary algorithm optimization. In particular, it relates to an optimization method for magnetothermal vascular stents and a magnetothermal vascular stent. Background Technology

[0002] Vascular stents are inserted into the lesion segment of a blood vessel after balloon angioplasty to support the narrowed or blocked segment, reduce vascular remodeling and elastic recoil, and maintain blood flow within the vessel lumen. Clinical data shows that patients using bare-metal stents (BMS) have an in-stent restenosis (ISR) rate as high as 48.3% two years after surgery. While drug-eluting stents reduce the ISR rate to 10%, long-term anticoagulant medication is required to prevent in-stent thrombosis. In recent years, thermotherapy for the prevention of ISR has received widespread attention from researchers both domestically and internationally.

[0003] Researchers have found that thermotherapy (43–50°C) can effectively inhibit the proliferation of vascular smooth muscle cells without affecting the proliferation and growth of endothelial cells. Various stent heating methods have been proposed, such as heating the stent site using a special catheter via invasive surgery, heating the stent through radiofrequency power supply resonance, and generating heat by using an alternating magnetic field to induce eddy currents within the stent. However, when using these methods, it is difficult to accurately measure the temperature of the target area in the body, and the heat output is difficult to control. Improper use can easily cause irreversible damage to the blood vessel wall. Therefore, how to achieve autonomous temperature control of the stent has become a pressing problem to be solved in thermotherapy. Optimization of stent structural parameters is key to achieving a balance between thermotherapy, mechanics, and hemodynamics. Previous studies have only investigated the mechanical properties of stents (plastic deformation, stress concentration, etc.) through numerical simulation-driven structural parameter optimization (number of units, strut size, spatial arrangement, etc.). It should be noted that traditional stents, because they do not require active heat generation, primarily focus on mechanical support performance and hemodynamic characteristics in their optimization design. Given that magnetothermal stents need to have good temperature rise performance, this places higher demands on the stent's topological design. As a new generation of thermotherapy interventional devices, the thermal effect of magnetothermal stents can significantly change the temperature field distribution of blood vessel walls. Therefore, it is necessary to establish a coupled optimization model of structure-heat transfer performance-hemodynamics to ensure the uniformity of stent thermotherapy temperature on the one hand, and maintain hemodynamic safety on the other. Summary of the Invention

[0004] This invention discloses an optimized method for magnetic thermal vascular stents and a magnetic thermal vascular stent, in order to solve any of the above-mentioned and potential problems in the prior art.

[0005] To achieve the above objectives, the technical solution provided by the present invention is: an optimized method for a magnetic thermal vascular stent, which specifically includes the following steps:

[0006] S1) Parametric Modeling: Establish a geometric model of the vascular stent in 3D finite element software, and define the strut width th, proximal support ring height hs1, distal support ring height hs2, and number of V-shaped repeating elements N. s Set as a design variable;

[0007] S2) Experimental point design: The Box-Behnken design was used to combine the support wire width th, the near-end support ring height hs1, the far-end support ring height hs2, and the number of V-shaped repeating units Ns in three levels to generate 28 sets of design points.

[0008] S3) Numerical simulation: Perform multiphysics simulation on each of the 28 design points obtained in S2) and extract the average wall temperature T. avg Blood inlet temperature T in Blood outlet temperature T out With mean wall shear stress (WSS) avg Find the objective function value and build an objective function dataset;

[0009] S4) Establishing a proxy model: Based on the objective function dataset in S3), a quadratic polynomial proxy model is constructed using the response surface methodology.

[0010] S5) Multi-objective optimization: Using the surrogate model established in S4), the non-dominated sorting genetic algorithm is called for iteration to obtain the Pareto front solution set of the objective problem;

[0011] S6) Optimized solution selection: From the Pareto solution set obtained in S5), according to T... avg ΔT, ΔT=|T out -T in |、WSS avg The optimal solution is selected based on the priority order, and a magnetic thermal vascular stent is manufactured according to the parameters of the optimal solution.

[0012] Furthermore, the design variable range in S1) is: th = 0.07mm-0.20mm, hs1 = 1.20mm-2.25mm, hs2 = 1.20mm-2.25mm, Ns = 6-13.

[0013] Furthermore, the multiphysics simulation conditions in S3) are as follows: the heating power drops to 0 W / m when the support temperature approaches 326.15 K. 3 The blood flow velocity boundary condition is taken from the actual coronary flow velocity curve, with a period of 1 second and a peak value of 28.70 cm / s.

[0014] Furthermore, the value of the objective function in S3) is obtained by performing COMSOL simulation on the 28 sets of design points designed in S2).

[0015] Furthermore, the quadratic polynomial proxy model in S4) is as follows:

[0016] y(hs1,hs2,th,Ns)=a0+a1hs1+a2hs2+a3th+a4Ns

[0017] +a5hs1hs2+a6hs1th+a7hs1Ns+a8hs2th+a9hs2Ns+a 10 Nsth

[0018] +a 11 hs1 2 +a 12 hs2 2 +a 13 th 2 +a 14 Ns 2

[0019] In the formula, a0 is a constant term, and a1~a 14 y is the regression coefficient, and y is the output variable.

[0020] Furthermore, in step S5), a non-dominated sorting genetic algorithm is called using MATLAB to input the obtained fitting equation into NSGA-II, setting the population size to 80, crossover probability to 0.80, mutation probability to 0.25, and maximum iterations to 200 generations, in order to minimize ΔT and maximize T. avg With WSS avg With the goal of obtaining the Pareto front solution set.

[0021] Furthermore, in step S6), candidate solutions satisfying Tavg > 43℃ and ΔT < 1℃ are screened from the Pareto solution set, and then WSS is selected from the candidate solutions. avg The largest design is considered the optimal solution.

[0022] Another objective of this invention is to provide a magnetothermal vascular stent, which is prepared by the above-described optimization method.

[0023] Furthermore, the magnetic thermal vascular stent is made of a nickel-copper alloy with a Curie point temperature of less than 53°C, and is formed by laser etching.

[0024] Furthermore, in the application of the magnetic thermal vascular stent in the prevention and treatment of coronary artery restenosis, the average temperature of the vascular inner wall during the hyperthermia process is higher than 43°C, the temperature difference ΔT between the stent inlet and outlet is reduced by 33.32%-54.72%, and the reduction in average wall shear stress is less than 9% compared with the initial design.

[0025] The beneficial effects of this invention are as follows: Due to the adoption of the above technical solution, the vascular stent of this invention, by adjusting the stent structural parameters, achieves an average vascular wall temperature higher than 43°C during thermotherapy, reduces the inlet-outlet temperature difference ΔT by 33.32%-54.72%, and lowers the average wall shear stress (WSS). avg The reduction was less than 9% compared to the initial design. Attached Figure Description

[0026] Figure 1 This is a flowchart of an optimized method for a magnetothermal vascular stent according to the present invention.

[0027] Figure 2 This is a parameterized support structure established in the embodiment, where the specific meanings of the parameters are marked;

[0028] Figure 3 This example shows a comparison between the predicted and actual values ​​of the objective function obtained by the surrogate model through response surface fitting in the embodiment.

[0029] Figure 4 In the example, T avg With WSS avg Response surface contour plot;

[0030] Figure 5 In the example, T in With T out Response surface contour plot;

[0031] Figure 6 The optimized support structure obtained in the embodiment;

[0032] Figure 7 This is the initial design support structure used in the embodiment;

[0033] Figure 8 The temperature cloud map and WSS cloud map of the two designs obtained by numerical simulation of the initial design and the optimized design in the example are shown.

[0034] Figure 9 The coronary velocity curve used in the example shows points a, b, c, and d, which correspond to the peak ΔT and WSS, respectively. avg Peak time;

[0035] Figure 10 The figures show the objective function curves for each of the two structures within a single pulsation cycle in the embodiment.

[0036] Figure 11 This represents the objective function value at the maximum time ΔT in the example.

[0037] Figure 12The objective function value at the minimum time ΔT in the example;

[0038] Figure 13 WSS in the example avg Maximum objective function value at time step;

[0039] Figure 14 WSS in the example avg Minimum objective function value at time step;

[0040] Figure 15 The images shown are of the support structure location and thermal images taken during the experiment.

[0041] Figure 16 In the example, when Q = 63.6 ml / min and H = 18 kA / m, T in With T out Curve showing change over time;

[0042] Figure 17 In the example, when Q = 84.8 ml / min and H = 18 kA / m, T in With T out Curve showing change over time;

[0043] Figure 18 In the example, when Q = 127.2 ml / min and H = 18 kA / m, T in With T out Curve showing change over time;

[0044] Figure 19 This is a convergence image of the genetic iteration process in the embodiment;

[0045] Figure 20 In the example, the final generation Pareto front ΔT-T avg Projection on the surface;

[0046] Figure 21 This example uses the final generation Pareto frontier WSS. avg -T avg Projection on the surface;

[0047] Figure 22 The final generation Pareto front ΔT-WSS in Example 4 avg Projection on the surface; Detailed Implementation

[0048] The technical solution of the present invention will be further illustrated below through specific embodiments. Those skilled in the art should understand that the embodiments described are merely illustrative of the present invention and should not be construed as limiting the invention.

[0049] Unless otherwise specified, the materials and raw materials used in the following examples can be obtained from other commercial sources.

[0050] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0051] like Figure 1 As shown, the present invention provides an optimized method for a magnetic thermal vascular stent, which specifically includes the following steps:

[0052] S1) Parametric Modeling: Establish a geometric model of the vascular stent in 3D finite element software, and define the strut width th, proximal support ring height hs1, distal support ring height hs2, and number of V-shaped repeating elements N. s Set as a design variable;

[0053] S2) Experimental point design: The Box-Behnken design was used to combine the support wire width th, the near-end support ring height hs1, the far-end support ring height hs2, and the number of V-shaped repeating units Ns in three levels to generate 28 sets of design points.

[0054] S3) Numerical simulation: Perform multiphysics simulation on each of the 28 design points obtained in S2) and extract the average wall temperature T. avg Blood inlet temperature T in Blood outlet temperature T out With mean wall shear stress (WSS) avg Find the objective function value and build an objective function dataset;

[0055] S4) Establishing a proxy model: Based on the objective function dataset in S3), a quadratic polynomial proxy model is constructed using the response surface methodology.

[0056] S5) Multi-objective optimization: Using the surrogate model established in S4), the non-dominated sorting genetic algorithm is called for iteration to obtain the Pareto front solution set of the objective problem;

[0057] S6) Optimized solution selection: From the Pareto solution set obtained in S5), according to T... avg ΔT (ΔT = |T) out -T in |), WSS avg The optimal solution is selected based on the priority order, and a magnetic thermal vascular stent is manufactured according to the parameters of the optimal solution.

[0058] Furthermore, the design variable range in S1) is: th = 0.07mm-0.20mm, hs1 = 1.20mm-2.25mm, hs2 = 1.20mm-2.25mm, Ns = 6-13.

[0059] Furthermore, the multiphysics simulation condition in S3) is: the heating power of the support drops to 0 W / m when the support temperature approaches 326.15 K. 3 The blood flow velocity boundary condition is taken from the actual coronary flow velocity curve, with a period of 1 second and a peak value of 28.70 cm / s.

[0060] Furthermore, the value of the objective function in S3) is obtained by performing COMSOL simulation on the 28 sets of design points designed in S2).

[0061] Furthermore, the quadratic polynomial proxy model in S4) is as follows:

[0062] y(hs1,hs2,th,Ns)=a0+a1hs1+a2hs2+a3th+a4Ns

[0063] +a5hs1hs2+a6hs1th+a7hs1Ns+a8hs2th+a9hs2Ns+a 10 Nsth

[0064] +a 11 hs1 2 +a 12 hs2 2 +a 13 th 2 +a 14 Ns 2

[0065] In the formula, a0 is a constant term, and a1~a 14 y is the regression coefficient, and y is the output variable.

[0066] Furthermore, in step S5), a non-dominated sorting genetic algorithm is called using MATLAB. The obtained fitting equation is written as shown in NSGA-II, with the population size set to 80, crossover probability to 0.80, mutation probability to 0.25, and maximum iterations to 200 generations, in order to minimize ΔT and maximize T. avg With WSS avg With the goal of obtaining the Pareto front solution set.

[0067] Furthermore, in step S6), candidate solutions satisfying Tavg > 43℃ and ΔT < 1℃ are screened from the Pareto solution set, and then WSS is selected from the candidate solutions. avg The largest design is considered the optimal solution.

[0068] A magnetothermal vascular stent is prepared by optimizing the above-mentioned method.

[0069] The magnetic thermal vascular stent is made of a nickel-copper alloy with a Curie point temperature of less than 53°C, and is formed by laser etching.

[0070] In the application of the magnetic thermal vascular stent in the prevention and treatment of coronary artery restenosis, the average temperature of the vascular inner wall during the hyperthermia process is higher than 43°C, the temperature difference ΔT between the stent inlet and outlet is reduced by 33.32%-54.72%, and the reduction in average wall shear stress is less than 9% compared with the initial design.

[0071] Example:

[0072] An optimized method for a magnetothermal vascular stent, the optimized method specifically comprising:

[0073] First, the initial topology of the stent is defined, and the ring height hs, ring density Ns, and strut width th are selected as design variables. The corresponding performance indicators (objective functions) are set as the stent inlet / outlet temperature difference ΔT and the average temperature T of the vessel wall in the stent-covered segment. avg and mean wall shear stress (WSS) avg Subsequently, a Box-Behnken design was used to generate 28 parameter combinations, and multiphysics numerical simulations were performed on each scaffold to obtain the corresponding objective function response data. A surrogate model between the structural parameters and each objective function was constructed based on the response surface methodology and incorporated into a non-dominated sorting genetic algorithm for multi-objective optimization. The optimization results yielded a Pareto non-dominated solution set, from which the optimal design was selected based on priority. The specific steps included are as follows:

[0074] (1). Parametric modeling and design variable definition

[0075] A parametric magnetothermal vascular stent model was created using COMSOL Multiphysics 6.1 software. Figure 1 To specify the meaning of the geometric parameters, four sets of key parameters are defined, including: support ring height (hs1, hs2), with a value range of 1.2mm-2.25mm; support wire width (th), with a value range of 0.07mm-0.2mm; and the number of V-shaped repeating units (Ns), with a value range of 6-13 (rounded to the nearest integer).

[0076] (2). Determine the objective function for the prevention and treatment of ISR with thermotherapy.

[0077] Previous studies have shown that endothelial cells are highly sensitive to smooth muscle cells (WSS), and WSS levels below 0.5 Pa may promote atherosclerosis. Furthermore, heat treatment at 43°C for 2 hours effectively inhibits smooth muscle cell proliferation with minimal impact on endothelial cells. Therefore, in its optimization objectives, this invention aims to minimize the area of ​​vascular region with WSS < 0.5 Pa while maintaining a uniform temperature distribution within the vascular wall, i.e., reducing the temperature difference between the vascular wall and the stent, and the temperature difference within the stent implantation area, while ensuring that the average temperature of the vascular wall is above 43°C. To this end, the following relevant quantitative indicators are defined:

[0078] Average wall temperature T avg (T avg =∫ Γ TdA / A), mean temperature at the blood inlet (T) in Average temperature T at the blood outlet out Inlet and outlet temperature difference ΔT (ΔT=|Tout-Tin|) Average wall shear force WSS avg (WSS avg =∫ Γ WSSdA / A), expressed as follows:

[0079] Maximize T avg (hs1,hs2,Ns,th)

[0080] Maximize WSS avg (hs1,hs2,Ns,th)

[0081] MinimizeΔT(hs1,hs2,Ns,th)

[0082] (3) Experimental site design

[0083] th, hs1, hs2, and Ns were used as variable parameters, and the Box-Behnken Design (BBD) method was employed for experimental design. First, three levels (low, medium, and high) were set for each parameter. Then, an experimental design matrix was generated. This matrix did not include all possible parameter combinations, but rather reduced the number of experiments by selecting appropriate experimental points, while ensuring the ability to assess the linear and quadratic effects of each factor. The range of selected parameters is shown in Table 2.

[0084]

[0085] Table 2

[0086] (4). Multiphysics simulation

[0087] For each parameter combination in step (3), a circumferentially symmetrical geometric structure is established, and a thermal-fluid coupling numerical simulation is performed. The simulation establishes blood as a temperature-dependent non-Newtonian fluid. Heat transfer and fluid flow in the blood are described by the continuity equation, the Navier-Stokes equation, and the energy equation. The heat conduction process of the surrounding tissue and the stent is described by the Pennes biothermal equation and the thermal conductivity differential equation. To describe the Curie point characteristics of the stent, the stent's heating power is also set as a function of temperature, which is 3.62 × 10⁻⁶ K near the set Curie temperature of 319.15 K. 8 W / m 3 The response gradually decreases. The simulation results record the objective function determined in step (2). The constituent response datasets are listed in Table 3.

[0088] (5). Establishing the proxy model:

[0089] Using the least squares regression method, T0 was constructed based on 28 sets of simulation data. avg T in T out WSS avg The proxy model has the following general expression:

[0090] y(hs1,hs2,th,Ns)=a0+a1hs1+a2hs2+a3th+a4Ns

[0091] +a5hs1hs2+a6hs1th+a7hs1Ns+a8hs2th+a9hs2Ns+a 10 Nsth

[0092] +a 11 hs1 2 +a 12 hs2 2 +a 13 th 2 +a 14 Ns 2

[0093] Where a0 represents a constant term, a1-a 14 The coefficients are represented in Table 1, and y represents the output variable, i.e., T. avg WSS avg ,T in ,T out .

[0094]

[0095] Table 1

[0096] (6) Multi-objective optimization:

[0097] Based on the surrogate model established in step (5), the non-dominated sorting genetic algorithm (NSGA-II) is run using MATLAB to search for the non-dominated (Pareto) solution to the optimization problem determined in step (2). The code is set with the following conditions: maximum number of generations is 200, population size is 80, crossover probability is 0.8, and mutation probability is 0.25.

[0098] (7) Optimal design selection

[0099] Because the objective functions are mutually constrained, this method follows T... avg →ΔT→WSS avg Filter by priority: first remove T. avg For combinations with temperatures <43℃, select WSS from the subset where ΔT <1℃. avg The largest solution is defined as the optimal design.

[0100] This embodiment illustrates the establishment of the proxy model in Example 1, after completing 28 sets of numerical calculations and obtaining T. avg WSS avg T in T out After obtaining the four sets of response values, import them into Design-Expert 13.0 and match them with the original BBD sampling matrix, with the encoding levels (-1 / 0 / +1) corresponding to the actual intervals 0.07–0.20mm (th), 1.20–2.25mm (hs1), 1.20–2.25mm (hs2), and 6–13 (Ns). Then, go to Analysis, select the Linear Regression model, and enable NoTransform.

[0101] This embodiment performed an analysis of variance on the established proxy model (Table 3).

[0102]

[0103]

[0104] Table 3

[0105] The results showed that each response variable model was statistically significant, with p-values ​​for all regression models less than 0.0001 (F-values ​​ranging from 83.69 to 702.41), indicating a significant nonlinear relationship between the input parameters and the output response. Model accuracy analysis showed that the standard deviations were all less than 1, and the coefficients of determination (R²) were also high. 2 ) and adjusted coefficient of determination (Adj.R) 2 The prediction determination coefficients were 0.9670-0.9950 and 0.9554-0.9936, respectively, with a difference of less than 0.032. The prediction determination coefficients were similar to those of the Adj.R.2 The differences were controlled within 0.2 (maximum deviation 0.0783), indicating that the model has good robustness in predicting unknown samples. Furthermore, the signal-to-noise ratio (Adeq.Precision) of all models was significantly higher than the critical value of 4 (35.94-94.44), while the coefficient of variation (CV%) ranged from 0.257% to 1.280%, indicating that the experimental design had sufficient sensitivity and the data dispersion was within an acceptable range.

[0106] This embodiment employs the optimization process described in the previous example to optimize the structure of a mesh scaffold. The genetic iterative optimization process gradually stabilized after 80 iterations and converged in the 130th generation. In the final generation of the population, WSS... avg T avg The ΔT values ​​reached 2.179 Pa, 45.146 °C, and 0.0003 °C, respectively. Figure 19 In the final generation of the Pareto front, as Tavg increases, ΔT shows a trend of first increasing and then decreasing. Figure 20 This is because at lower temperatures (T... avg At temperatures below 40℃, convective heat transfer between the stent and blood flow removes most of the heat. At this temperature, the dominant factors in temperature distribution are biological metabolic heat production and convective heat transfer between the blood and stent. However, in higher temperature regions (T...), the heat loss is primarily due to these factors. avg (>44℃), at this temperature, the stent temperature dominates the temperature field around the stent, weakening the influence of blood flow convection and heat exchange, thus achieving a smaller ΔT value. WSS avg and T avg Negative correlation ( Figure 22 This is due to T avg In higher-valued samples, both Ns and th values ​​increased, enhancing the stent's impact on blood flow and leading to a decrease in WSS. Furthermore, WSS... avg The relationship with ΔT is similar to that between ΔT and T. avg The relationship, that is, with WSS avg As ΔT increases, it also shows a trend of first increasing and then decreasing.

[0107] Due to WSS and T avg They exhibit a negative correlation, therefore the optimized result yields a set of Pareto solutions, in which T... avg The first screening criterion was >43℃. Then, solutions with a WSS reduction of less than 10% were selected, and the design structure with the smallest ΔT was chosen from the remaining solutions. The final structure was determined as follows: th = 0.199mm, hs1 = 2.25mm, hs2 = 1.76mm, Ns = 9.

[0108] The surrogate model analysis obtained in this embodiment is used to obtain the target function's average wall temperature (T).avg ), average temperature at the blood inlet (T) in ), average temperature at the blood outlet (T) out Wall mean shear force (WSS) avg The relationship between the structural parameters (hs1, hs2, Ns, th) and the support structure. Figure 4 , 5 The image shows the response surface contour plot obtained based on the surrogate model. The plot indicates that th and Ns play a dominant role in temperature field regulation, with a significant interaction effect (Ns>10 and th>0.15 mm). Inlet and outlet temperatures are dominated by th, and this effect is more pronounced when Ns is large. WSS avg WSS is mainly regulated by hs and Ns. Appropriately increasing hs during the optimization process can increase WSS. avg .

[0109] The obtained optimized support structure ( Figure 6 Analysis revealed that the optimized design, achieved by increasing th, Ns, and adjusting hs, maintained a uniform vascular wall temperature above 43°C, thereby inhibiting excessive smooth muscle cell proliferation and preventing the enlargement of low WSS regions, thus reducing the risk of thrombosis and intimal hyperplasia. This strategy maintained a ΔT reduction of 33.32%-54.72% and a WSS... avg The reduction is less than 9% compared to the initial design. Figure 8 , Figure 10-14 );

[0110] The optimized support structure found in this embodiment was fabricated using Monel 400 material (Curie point temperature 53℃). Two support designs were developed: an initial design and an optimized design. The optimization effect was verified through this thermal experiment. The infrared thermal image of the experiment is shown below. Figure 15 As shown, the inlet and outlet temperatures for the three flow rates are as follows: Figure 16-18 As shown, the optimized stent exhibits better magnetocaloric properties, and the uniformity of stent temperature distribution is significantly improved. In the three flow rates used in the experiment, the temperature difference between the outer walls of the blood vessels before and after the stent was reduced by 40%-62.07%, and the outlet temperature was significantly improved (0.5℃-1.1℃, decreasing with increasing flow rate).

[0111] The embodiments described above provide a detailed explanation of the technical solution of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, additions, or similar substitutions made within the scope of the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An optimized method for a magnetothermal vascular stent, characterized in that, Includes the following steps: S1) Parametric Modeling: Establish a geometric model of the vascular stent in 3D finite element software, and define the strut width th, proximal support ring height hs1, distal support ring height hs2, and number of V-shaped repeating elements N. s Set as a design variable; S2) Experimental point design: The Box-Behnken design was used to combine the support wire width th, the near-end support ring height hs1, the far-end support ring height hs2, and the number of V-shaped repeating units Ns in three levels to generate 28 sets of design points. S3) Numerical simulation: Perform multiphysics simulation on each of the 28 design points obtained in S2) and extract the average wall temperature T. avg Blood inlet temperature T in Blood outlet temperature T out With mean wall shear stress (WSS) avg Find the objective function value and build an objective function dataset; S4) Establishing a proxy model: Based on the objective function dataset in S3), a quadratic polynomial proxy model is constructed using the response surface methodology. S5) Multi-objective optimization: Using the surrogate model established in S4), the non-dominated sorting genetic algorithm is called for iteration to obtain the Pareto front solution set of the objective problem; S6) Optimized solution selection: From the Pareto solution set obtained in S5), according to T... avg ΔT, ΔT=|T out -T in |、WSS avg The optimal solution is selected based on the priority order, and a magnetic thermal vascular stent is manufactured according to the parameters of the optimal solution.

2. The optimization method according to claim 1, characterized in that, The design variable range in S1) is: th = 0.07mm-0.20mm, hs1 = 1.20mm-2.25mm, hs2 = 1.20mm-2.25mm, Ns = 6-13.

3. The optimization method according to claim 1, characterized in that, The conditions for the multiphysics simulation in S3) are: the heating power drops to 0 W / m when the support temperature is close to 326.15 K. 3 The blood flow velocity boundary condition is taken from the actual coronary flow velocity curve, with a period of 1 second and a peak value of 28.70 cm / s.

4. The optimization method according to claim 1, characterized in that, The value of the objective function in S3) is obtained by performing COMSOL simulation on the 28 sets of design points designed in S2).

5. The optimization method according to claim 1, characterized in that, The quadratic polynomial proxy model in S4 is as follows: y(hs1,hs2,th,Ns)=a0+a1hs1+a2hs2+a3h+a4Ns+a5hs1hs2+a6hs1th+a7hs1Ns+a8hs2th+a9hs2Ns+a 10 Nsth…+a 11 hs1 2 +a 12 hs2 2 +a 13 th 2 +a 14 Ns 2 In the formula, a0 is a constant term, and a1~a 14 y is the regression coefficient and y is the output variable.

6. The optimization method according to claim 1, characterized in that, S5) uses MATLAB to call the non-dominated sorting genetic algorithm, and the obtained fitting equation is written as in NSGA-II, setting the population size to 80, crossover probability to 0.80, mutation probability to 0.25, and maximum iterations to 200 generations, in order to minimize ΔT and maximize T. avg With WSS avg With the goal of obtaining the Pareto front solution set.

7. The optimization method according to claim 1, characterized in that, In S6), the Pareto solution set is selected to satisfy T. avg Candidate solutions with temperatures >43℃ and ΔT <1℃ are then selected, and WSS is chosen from the candidate solutions. avg The largest design is considered the optimal solution.

8. A magnetothermal vascular stent, characterized in that, The magnetothermal vascular stent was prepared by optimizing the method described in any one of claims 1-7.

9. The magnetothermal vascular stent according to claim 8, characterized in that, The magnetic thermal vascular stent is made of a nickel-copper alloy with a Curie point temperature of less than 53°C, and is formed by laser etching.

10. The magnetothermal vascular stent according to claim 9, characterized in that, In the application of the magnetic thermal vascular stent in the prevention and treatment of coronary artery restenosis, the average temperature of the vascular inner wall during the hyperthermia process is higher than 43°C, the temperature difference ΔT between the stent inlet and outlet is reduced by 33.32%-54.72%, and the reduction in average wall shear stress is less than 9% compared with the initial design.

Images