Tracheal stent mechanical property optimization design method based on multi-scale finite element

Abstract

The invention relates to the technical field of medical instrument design, in particular to a tracheal stent mechanical property optimization design method based on multi-scale finite elements. According to the mechanical property optimization design method for the tracheal stent provided by the invention, microstructure parameter evolution and macroscopic physiological environment response are deeply integrated through a multi-scale finite element model, and a time-varying material attribute algorithm module is originally introduced to realize dynamic mechanical coupling simulation in a degradation process; in combination with an intelligent early-warning mechanism for dissection partition differentiation erosion boundaries and structural failure, collaborative optimization of mutual exclusion performance indexes such as flexible contact stress distribution of radial supporting force of the stent and long-term structural integrity is precisely coordinated, and a Pareto optimal solution is output on the premise of guaranteeing biocompatibility constraint; according to the method, core technical obstacles of micro effect distortion and dynamic performance prediction deficiency in traditional design are fundamentally solved, and a personalized stent design scheme with high reliability, low complication risk and long service cycle is provided for clinic.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical device design, and particularly relates to a tracheal stent mechanical property optimization design method based on multi-scale finite elements. BACKGROUND

[0002] Tracheal stenosis is a life-threatening condition that can cause difficulty breathing and even suffocation, and the causes of tracheal stenosis can be trauma, tumor, post-intubation injury, and congenital problems. At present, tracheal stent implantation is a common and effective treatment for tracheal stenosis. Through minimally invasive interventional treatment, a stent is placed at the stenosis to expand the trachea and maintain airway patency. The current types of stents include metal stents such as nickel-titanium memory alloy stents and stainless steel stents, which have the advantages of accurate positioning and good immediate effect, but the disadvantages are permanent implantation, easy displacement, granulation tissue proliferation, difficult removal, and possible fracture. Silicone stents are removable and less likely to stimulate granulation tissue, but have the disadvantages of poor self-expansion, easy displacement, and the need for rigid bronchoscopic placement. Biodegradable polymer stents have the advantages of being absorbed by the human body and not requiring secondary surgery for removal, but also have the technical difficulties of complex mechanical property control and the importance of mechanical behavior during the degradation process.

[0003] Existing tracheal stent designs are mostly based on experience or simple mechanical calculations, and it is difficult to accurately consider the complex mechanical properties of tracheal tissue and the mechanical behavior of the stent under different physiological conditions.

[0004] As a common mechanical analysis method, the finite element method has certain application in stent design, but the traditional finite element method has limitations in dealing with multi-scale problems. The tracheal stent structure contains different scales such as macroscopic overall morphology and microscopic grid structure, and the interaction process between tracheal tissue and the stent also involves multi-scale mechanical behavior. The existing technology is difficult to accurately capture the microscopic structure of the tracheal stent, such as wire diameter, weaving angle, node configuration, which has a key influence on the macroscopic mechanical behavior, resulting in significant prediction deviation of the stent self-locking mechanism, radial stiffness and collapse mode. At the same time, the existing method lacks accurate simulation capability for the comprehensive action of the dynamic contact between the stent and the irregular viscoelastic tracheal wall, the real physiological environment such as respiratory movement, cough, tissue remodeling, and the biological interface such as mucus and blood flow. It is difficult to accurately evaluate the local contact stress distribution and to meet the requirements of biocompatibility. In addition, the existing technology lacks an efficient and systematic design optimization process, relying on time-consuming and labor-intensive physical trial and error or isolated finite element analysis, which is difficult to balance and optimize the conflicting multi-objective performance, and has weak prediction ability for key time-varying problems such as microscopic stress concentration area fatigue failure under millions of cyclic loads, stress environment evolution caused by tissue ingrowth remodeling, and dynamic degradation of degradable stent strength and stiffness. It is difficult to reveal the internal microscopic origin mechanism of the macroscopic performance of the stent, which hinders the on-demand design innovation.

[0005] If these multi-scale problems cannot be effectively handled, the mechanical properties of the stent cannot be accurately analyzed, the optimization design of the stent is difficult to achieve, and the designed stent may have problems such as insufficient support, poor flexibility, and poor compatibility with tracheal tissue, affecting the treatment effect and patient recovery. SUMMARY

[0006] The purpose of the present application is to provide a tracheal stent mechanical property optimization design method based on multi-scale finite elements.

[0007] Technical scheme

[0008] A tracheal stent mechanical property optimization design method based on multi-scale finite elements, comprising the following steps:

[0009] An integrated multi-scale finite element model containing a tracheal macroscopic geometric model and a stent microscopic geometric model is established, wherein the stent microscopic geometric model at least includes wire diameter parameters, weaving angle parameters and node configuration parameters representing the microstructure of the stent material;

[0010] A time-varying material property algorithm module is embedded in the stent microscopic geometric model, which dynamically corrects the material elastic modulus parameters and yield strength parameters according to the preset degradation kinetics equation and the body fluid erosion boundary condition;

[0011] define a mechanical connection relationship between the stent micro-geometry model and the trachea macro-geometry model in the integrated multi-scale finite element model;

[0012] perform stent implantation state simulation calculation based on the integrated multi-scale finite element model, the stent implantation state simulation calculation being required to be performed step by step within a preset time span, and the time-varying material property algorithm module being called to update material parameters before each step calculation, and the overall mechanical response at the macro scale and the local stress and strain distribution at the micro scale being obtained synchronously;

[0013] extract key performance indicators based on the overall mechanical response, the key performance indicators including mutually restrictive radial support force indicators and flexibility indicators, stent-trachea interface contact stress distribution indicators, and residual structural integrity indicators;

[0014] establish a multi-objective optimization problem taking the wire diameter parameter, the braiding angle parameter, and the node configuration parameter as design variables, the multi-objective optimization problem aiming to simultaneously optimize the radial support force indicators, the flexibility indicators, the stent-trachea interface contact stress distribution indicators, and the residual structural integrity indicators, and being limited by physiological load conditions and biocompatibility constraints;

[0015] iteratively solve the multi-objective optimization problem by using an intelligent optimization algorithm until a Pareto optimal stent micro-geometry parameter combination is output;

[0016] generate a final stent design scheme according to the Pareto optimal stent micro-geometry parameter combination.

[0017] In one example embodiment, the stent micro-geometry model adopts representative volume element modeling, the representative volume element modeling needs to satisfy periodic boundary conditions, and must explicitly simulate the contact and friction behavior between wire lines.

[0018] In one example embodiment, the trachea macro-geometry model is a bio-realistic model reconstructed based on medical images, and the bio-realistic model must include viscoelastic tracheal wall tissue properties.

[0019] In one example embodiment, the physiological load conditions need to superimpose respiratory cycle dynamic load and cough impact load, and the respiratory cycle dynamic load and the cough impact load are applied to the integrated multi-scale finite element model through a real-time boundary condition updating module.

[0020] In one example embodiment, the intelligent optimization algorithm is a multi-objective genetic algorithm or a multi-objective particle swarm optimization algorithm, and the multi-objective genetic algorithm or the multi-objective particle swarm optimization algorithm needs to automatically trigger the stent implantation state simulation calculation during operation.

[0021] In an example embodiment, the contact stress distribution index of the stent tracheal interface is obtained by maximum value statistics and non-uniformity quantification of the contact pressure cloud map, and the non-uniformity quantification adopts a stress distribution standard deviation algorithm.

[0022] In an example embodiment, the body fluid erosion boundary condition needs to be set according to the anatomical partition difference, wherein the mucosa layer area erosion rate factor γ is 1.5-2 times that of the cartilage ring area, and the erosion rate factor γ is calibrated by an in vitro tissue permeation experiment.

[0023] In an example embodiment, the remaining structural integrity index is calculated by the effective bearing cross-sectional area decay rate of a micro-representative element, and when the effective bearing cross-sectional area decay rate exceeds 40%, it is determined that the structure fails.

[0024] The tracheal stent mechanical property optimization design method provided by the application integrates microstructure parameter evolution and macro physiological environment response through a multi-scale finite element model, creatively introduces a time-varying material property algorithm module to realize dynamic mechanical coupling simulation of the degradation process, combines anatomical partition differential erosion boundary and structural failure intelligent early warning mechanism, accurately coordinates the synergistic optimization of mutually exclusive performance indicators such as stent radial support force flexibility contact stress distribution and long-term structural integrity, and outputs a Pareto optimal solution under the premise of ensuring biocompatibility constraints. The method fundamentally solves the core technical obstacles of micro-effect distortion and dynamic performance prediction missing in traditional design, provides a personalized stent design scheme with high reliability and low complication risk and long service life for clinical treatment, and promotes tracheal interventional therapy into a new paradigm of precise bionic design. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 The workflow diagram of the application;

[0026] Figure 2 The body fluid erosion boundary factor γ calibration flowchart in the embodiment of the application;

[0027] Figure 3 The structural failure threshold determination flowchart in the embodiment of the application. DETAILED DESCRIPTION

[0028] The specific embodiments of the application will be further described below with reference to the accompanying drawings. The same parts are denoted by the same reference numerals.

[0029] It should be noted that the words "front", "back", "left", "right", "up" and "down" used in the following description refer to the directions in the drawings, and the words "in" and "out" refer to the directions towards or away from the geometric center of a particular part.

[0030] In order to make the content of the present application easier to be clearly understood, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application.

[0031] The application discloses a tracheal stent mechanical property optimization design method based on a multi-scale finite element, comprising the following steps:

[0032] An integrated multi-scale finite element model containing a tracheal macroscopic geometric model and a stent microscopic geometric model is established, wherein the stent microscopic geometric model at least includes a wire diameter parameter, a weaving angle parameter and a node configuration parameter representing a stent material microstructure;

[0033] A time-varying material property algorithm module is embedded in the stent microscopic geometric model, and the time-varying material property algorithm module dynamically corrects material elastic modulus parameters and yield strength parameters according to a preset degradation kinetics equation and a body fluid erosion boundary condition;

[0034] A mechanical connection relationship between the stent microscopic geometric model and the tracheal macroscopic geometric model is defined in the integrated multi-scale finite element model;

[0035] A stent implantation state simulation calculation is performed based on the integrated multi-scale finite element model, and the stent implantation state simulation calculation needs to be performed step by step within a preset time span, and the time-varying material property algorithm module is called before each step calculation to update material parameters, and the overall mechanical response under a macroscopic scale and the local stress and strain distribution under a microscopic scale are synchronously obtained;

[0036] Key performance indicators are extracted based on the overall mechanical response, and the key performance indicators include a radial support force indicator and a flexibility indicator which are mutually restricted, a stent trachea interface contact stress distribution indicator and a residual structure integrity indicator;

[0037] A multi-objective optimization problem is established with the wire diameter parameter, the weaving angle parameter and the node configuration parameter as design variables, and the multi-objective optimization problem aims to simultaneously optimize the radial support force indicator, the flexibility indicator, the stent trachea interface contact stress distribution indicator and the residual structure integrity indicator, and is limited by physiological load conditions and biocompatibility constraints;

[0038] An intelligent optimization algorithm is used to iteratively solve the multi-objective optimization problem until a Pareto optimal stent microscopic geometric parameter combination is output;

[0039] A final stent design scheme is generated according to the Pareto optimal stent microscopic geometric parameter combination.

[0040] In one example embodiment, the stent micro-geometry model adopts representative volume element modeling, which needs to satisfy periodic boundary conditions and must explicitly simulate the contact friction behavior between the wires.

[0041] In one example embodiment, the trachea macro-geometry model is a biorealistic model reconstructed based on medical images, which must include viscoelastic tracheal wall tissue properties.

[0042] In one example embodiment, the physiological loading conditions need to superimpose respiratory cycle dynamic loads and cough impact loads, which are applied to the integrated multi-scale finite element model through real-time boundary condition updating modules.

[0043] In one example embodiment, the intelligent optimization algorithm is a multi-objective genetic algorithm or a multi-objective particle swarm optimization algorithm, which needs to automatically trigger the stent implantation state simulation calculation during operation.

[0044] In one example embodiment, the stent-trachea interface contact stress distribution index is obtained by maximum value statistics and non-uniformity quantification of the contact pressure cloud map, and the non-uniformity quantification adopts a stress distribution standard deviation algorithm.

[0045] In one example embodiment, the body fluid erosion boundary condition needs to be set differently according to the anatomical partition of the trachea, wherein the mucosal layer area erosion rate factor γ calibration step is:

[0046] Step one: preparation of ex vivo tracheal samples Use a freezing microtome to longitudinally section fresh human tracheal tissue, and prepare mucosal layer samples and cartilage ring samples with a thickness of 0.5±0.05 mm. The mucosal layer sample needs to completely retain the epithelial layer and lamina propria structure, and the cartilage ring sample completely removes the muscular layer tissue. All sample surfaces are uniformly sprayed with a fluorescent nanoprobe marker with a particle size of 50 nanometers.

[0047] Step two: dynamic diffusion coefficient measurement Load the prepared sample into a custom double-layer microfluidic chip, continuously perfuse the upper cavity with simulated body fluid solution containing 0.1% FITC-dextran marker, and collect the diffusion permeate in the lower cavity. Use a laser confocal microscope to scan the X-Z profile at a frequency of 1 frame per second to capture real-time fluorescence intensity distribution data. The fluorescence penetration depth is fitted by a non-steady-state diffusion equation, which is expressed as a function of fluorescence intensity distribution I and time variable t.

[0048] wherein wherein, : the maximum fluorescence intensity value on the surface of the sample; : penetration depth (unit: pm), representing the surface of the fabric;

[0049] : Diffusion time (unit: s); : Effective diffusion coefficient to be solved (unit: mm² / s); : Integral intermediate variable; integral term .

[0050] Step three: Calibration of mucin catalytic effect

[0051] Micro-injection of 0.5 units / ml concentration of mucinase solution in a specific area of the mucosal layer sample, and continuous monitoring of the diffusion coefficient dynamic changes of the enzyme treatment area and non-treatment area. Calculate the diffusion enhancement ratio of the enzyme catalytic area as the catalytic factor, the formula is:

[0052]

[0053] , the superscript represents the enzyme treatment area, is the control area.

[0054] Step four: Calculation of adsorption free energy difference

[0055] Construct the molecular dynamics simulation system of mucin molecules and polymer materials, and divide the reaction coordinate window along the molecular adsorption direction by umbrella sampling technology. Harmonic constraint potential is applied in each window, and configuration distribution probability is generated to generate potential mean force curve. Extract the minimum potential energy value of the mucosal contact area and the cartilage contact area, and introduce the gas constant and absolute temperature parameters for standardization processing in the calculation process.

[0056] Obtain the potential mean force curve by umbrella sampling , calculate the free energy difference of the minimum potential energy position :

[0057] In the formula unit is , is the molecular center of mass distance (unit: nm).

[0058] Step five: Comprehensive calculation of erosion factor

[0059] Integrate the three types of parameters obtained in the first three steps: the effective diffusion coefficient ratio of the mucosal layer and the cartilage ring output by the dynamic diffusion experiment, the catalytic enhancement factor output by the enzyme treatment experiment, and the exponential adsorption free energy difference output by the molecular simulation. Multiply the three parameters to obtain the final erosion rate factor value:

[0060]

[0061] γ is the erosion rate factor; is the diffusion coefficient ratio, is the enzyme catalytic factor, is the energy correction term; ; is the adsorption free energy difference, where .

[0062] : gas constant = 8.314 J·mol⁻¹·K⁻¹

[0063] : absolute temperature = 310 K (37°C)

[0064] When the calculation result is in the interval of 1.5 to 2.0, it is determined that the factor is verified by calibration.

[0065] In an embodiment,

[0066] The residual structural integrity index is calculated by the effective bearing cross-sectional area decay rate of the micro-representative element, and a corresponding structural failure judgment threshold is set. The cross-sectional area decay rate failure threshold determination step is as follows:

[0067] Step one: in-situ corrosion mechanics coupling test

[0068] Place the degradable polymer microfilament sample with a diameter of 100 μm in the cavity of the environmental scanning electron microscope, maintain a constant temperature of 37°C and a physiological simulation environment of 85% relative humidity. Continuously apply axial tensile strain to the sample, record the effective bearing width converted from the distance between the gold nano-marked points every hour, and simultaneously measure the maximum corrosion pit depth. According to the geometric relationship, the real-time cross-sectional area decay rate is calculated, and the formula is as follows:

[0069] wherein, is the dimensionless decay rate, represents the corrosion time (unit: hour).

[0070] Step two: identification of the mechanical instability critical point

[0071] Real-time analysis of the second derivative of the cross-sectional area decay rate and the corrosion pit expansion rate . When the cross-sectional area decay acceleration breaks through the threshold value, and the corrosion pit depth expansion rate reaches the double conditions, it is determined that the mechanical instability state is entered. Record the corresponding cross-sectional area decay rate critical value at this moment.

[0072] ​​​​​​Step three: fracture mechanics model verification

[0073] The stress intensity factor correction expression under the corrosion environment is established:

[0074] wherein is a geometric factor, is the applied stress (unit: MPa), is a corrosion topography coefficient;

[0075] The cross-section attenuation rate is introduced as a correction term for equivalent crack propagation. The double logarithmic relationship curve between crack propagation rate and stress intensity factor is fitted through in-situ test data, and the characteristic point of step mutation of Paris law index is identified. When the change rate of material stress sensitivity index exceeds 5.0, that is, , it is determined that the critical state of instability is reached, and the theoretical cross-section attenuation rate threshold is output.

[0076] Step four: clinical sample failure verification

[0077] Collect the fractured stent samples taken from the clinic, and reconstruct the three-dimensional geometric model using micro-computed tomography technology. Measure the minimum bearing cross-section size along the fracture surface, and calculate the actual cross-section loss ratio.

[0078] When more than 90% of the failure samples have a measured cross-section attenuation rate greater than 40%, it is confirmed that the threshold has clinical effectiveness. Finally, through the triangular mutual verification of the experimental critical value, the theoretical critical value and the clinical verification value, the cross-section attenuation rate is established as the structural failure criterion.

[0079] The tracheal stent mechanical property optimization design method based on multi-scale finite element of the embodiment couples micro-degradation dynamics and macro-biomechanics response, integrates the micro-geometric characteristics such as stent wire diameter parameters, braiding angle parameters and node configuration parameters with the viscoelastic characteristics of the patient-specific tracheal model and the respiratory dynamic load, and synchronously outputs the multi-objective Pareto optimal solution of the radial support force index, the flexibility index, the contact stress distribution index and the structural residual integrity index. The method successfully quantifies the erosion rate factor γ of the mucosal layer region 1.5-2 times the cartilage ring region in the degradable stent design and establishes 40% cross-section attenuation rate as the structural failure warning threshold. The in-vitro permeation experiment and clinical sample verification have confirmed that the method can significantly reduce the risk of stent displacement, reduce the probability of granulation tissue proliferation to a very low level, and ensure the stability of the radial support force of the stent to obtain a qualitative leap. Under the premise of ensuring the airway patency rate, the effective service period of the stent is greatly extended, the design iteration time is further shortened and the sample manufacturing cost is reduced by combining with the intelligent optimization algorithm, which provides a solution with excellent safety and durability for the treatment of tracheal stenosis.

[0080] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not to limit the present application; although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or equivalent replacements can be made to some technical features without departing from the spirit of the technical solutions of the present application, and all of them should be covered in the technical solution range of the present application claimed by the present application.

Claims

1. A tracheal stent mechanical performance optimization design method based on multi-scale finite element analysis, characterized in that: The following steps are involved: Establishing an integrated multi-scale finite element model including a tracheal macro-geometric model and a stent micro-geometric model, wherein the stent micro-geometric model includes at least wire diameter parameters, braiding angle parameters, and node configuration parameters that characterize the stent material's microstructure; A time-varying material property algorithm module is embedded in the stent micro-geometric model, and the time-varying material property algorithm module dynamically modifies the material elastic modulus parameter and yield strength parameter according to a preset degradation kinetic equation and body fluid erosion boundary conditions; defining a mechanical connection relationship between the stent micro-geometric model and the trachea macro-geometric model in the integrated multi-scale finite element model; Performing a stent implantation state simulation calculation based on the integrated multi-scale finite element model, wherein the stent implantation state simulation calculation needs to be performed in steps within a preset time span, and before each step of the calculation, the time-varying material property algorithm module is called to update material parameters, thereby simultaneously obtaining the overall mechanical response at the macroscale and the local stress and strain distribution at the microscale; Extracting key performance indicators based on the overall mechanical response, the key performance indicators include mutually constrained radial support force indicators and flexibility indicators, stent-trachea interface contact stress distribution indicators and residual structural integrity indicators; Establishing a multi-objective optimization problem with the wire diameter parameter, the braiding angle parameter, and the node configuration parameter as design variables, wherein the multi-objective optimization problem aims to simultaneously optimize the radial support force index, the flexibility index, the stent-trachea interface contact stress distribution index, and the residual structural integrity index, and is subject to physiological load conditions and biocompatibility constraints; Iteratively solving the multi-objective optimization problem using an intelligent optimization algorithm until a Pareto optimal combination of stent micro-geometric parameters is output; A final stent design solution is generated based on the Pareto optimal stent micro-geometric parameter combination.

2. The method for optimizing the mechanical properties of a tracheal stent based on multi-scale finite elements according to claim 1, characterized in that: The micro-geometric model of the stent is modeled using representative voxels, which must satisfy periodic boundary conditions and must explicitly simulate the contact friction behavior between the wires.

3. The method for optimizing the mechanical properties of a tracheal stent based on multi-scale finite elements according to claim 1, characterized in that: The tracheal macro-geometric model is a bio-simulation model reconstructed based on medical images, and the bio-simulation model must include viscoelastic tracheal wall tissue properties.

4. The method for optimizing the mechanical properties of a tracheal stent based on multi-scale finite elements according to claim 1, wherein: The physiological load condition needs to be superimposed with the respiratory cycle dynamic load and the cough impact load, and the respiratory cycle dynamic load and the cough impact load are applied to the integrated multi-scale finite element model through a real-time boundary condition updating module.

5. The method for optimizing the mechanical properties of a tracheal stent based on multi-scale finite element analysis according to claim 1, wherein: The intelligent optimization algorithm is a multi-objective genetic algorithm or a multi-objective particle swarm optimization algorithm. During the operation of the multi-objective genetic algorithm or the multi-objective particle swarm optimization algorithm, the stent implantation state simulation calculation needs to be automatically triggered.

6. The method for optimizing the mechanical properties of a tracheal stent based on multi-scale finite elements according to claim 1, characterized in that: The stent-trachea interface contact stress distribution index is obtained by performing maximum value statistics and non-uniformity quantification on the contact pressure cloud map, and the non-uniformity quantification adopts a stress distribution standard deviation algorithm.

7. The method for optimizing the mechanical properties of a tracheal stent based on multi-scale finite element analysis according to claim 1, wherein: The body fluid erosion boundary conditions need to be differentiated according to the anatomical divisions of the trachea, and different erosion rate factors are set for different anatomical divisions. The erosion rate factors are calibrated through in vitro tissue penetration experiments.

8. The method for optimizing the mechanical properties of a tracheal stent based on multi-scale finite elements according to claim 1, wherein: The remaining structural integrity index is calculated by the effective load-bearing cross-sectional area attenuation rate of the microscopic representative element, and a corresponding structural failure judgment threshold is set. When the effective load-bearing cross-sectional area attenuation rate exceeds the threshold, it is judged as structural failure.

9. A device for optimizing the mechanical properties of a tracheal stent using multi-scale finite element method, comprising a processor programmed to execute the method according to any one of claims 1 to 8. 10 . A non-transitory computer-readable storage medium having instructions stored thereon, wherein the instructions are executable by a processor to cause the processor to perform the method according to claim 1 .

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