Intelligent pressure-sensing cerebral vascular stent deformation control method and system

Abstract

The present application relates to the technical field of medical devices, and more particularly to a method and system for intelligent pressure sensing cerebral vascular stent deformation control, which obtains the intravascular pulsatile pressure signal and the current morphology of the stent, predicts the deformation trend using a biomechanical model, matches the prediction result with the vessel wall stress constraint, generates a drive signal sequence to differentially regulate each microstructure unit, and adaptively corrects the model parameters based on the contact stress feedback during the regulation process to achieve closed-loop control. The present application realizes precise and adaptive regulation of stent deformation, and improves the biomechanical adaptability of the stent and the blood vessel.

Description

Technical Field

[0001] This invention relates to the field of medical device technology, and in particular to a method and system for controlling the deformation of cerebral vascular stents with intelligent pressure sensing. Background Technology

[0002] In the field of interventional treatment for cerebrovascular diseases, stent implantation is a routine method for restoring vascular patency. Traditional cerebrovascular stents typically employ a passively expandable metal mesh structure, relying on their own radial support to maintain vascular patency after implantation. The design and implantation process of these stents mainly depends on preoperative imaging assessments and static vascular geometric parameters. The stent's morphology and mechanical properties remain essentially fixed after implantation, making it unable to respond to dynamic changes in the physiological environment after implantation.

[0003] In existing technologies, to cope with the periodic pulsating pressure of blood vessels, some improvements attempt to introduce a certain degree of flexibility or pre-set deformation capability into the stent material or structure. For example, using superelastic alloys or designing special hinge structures aims to enable the stent to better conform to the pulsation of blood vessels. However, these conventional approaches are essentially still passive adaptations, with their deformation patterns being preset and fixed, lacking the ability to actively sense and regulate based on real-time physiological signals.

[0004] This passive adaptation mode has significant limitations. The pulsating pressure within blood vessels is not constant; its amplitude, frequency, and waveform dynamically change depending on the patient's heart rate, blood pressure, vascular elasticity, and local lesions. Fixed stent deformation characteristics cannot precisely match this dynamically changing mechanical environment, leading to uneven local stress distribution. During peak pressure periods, insufficient deformation of the stent can generate excessive concentrated stress on the vessel wall, increasing the risk of intimal damage or even perforation; conversely, excessive deformation during trough pressure periods can result in insufficient support, affecting the stability and support of the blood vessel. Summary of the Invention

[0005] This invention provides a method and system for controlling the deformation of cerebral vascular stents with intelligent pressure sensing, which can solve the problems in the prior art.

[0006] A first aspect of the present invention provides a method for controlling the deformation of a cerebral vascular stent with intelligent pressure sensing, comprising: The pulsating pressure signal in the blood vessel and the current morphological parameters of the stent microstructure are obtained. Based on the time-domain and frequency-domain characteristics of the pulsating pressure signal, the deformation trend of the stent microstructure is predicted by a biomechanical response model to obtain the deformation prediction result. The deformation prediction results are matched with the preset vascular wall stress distribution constraints to determine the target control amount for each region of the stent microstructure. Based on the target control amount, a driving signal sequence for the stent microstructure unit is generated. The driving signal sequence maps the phase information of the pulsating pressure to the spatial distribution of the microstructure unit to achieve phase-differentiated driving of the microstructure unit in different regions. The driving signal sequence is used to apply periodic regulation to the stent microstructure unit, and the contact stress feedback data between the stent microstructure and the blood vessel wall is collected in real time during the regulation process. The contact stress feedback data is used to adaptively correct the material response parameters in the biomechanical response model to form a closed-loop regulation.

[0007] Based on the time-domain and frequency-domain characteristics of the pulsating pressure signal, the deformation trend of the scaffold microstructure is predicted using a biomechanical response model, and the deformation prediction results include: The pulsating pressure signal is decomposed in the time domain to extract the pressure peak sequence, pressure change rate sequence and pressure period parameter. The pulsating pressure signal is then transformed in the frequency domain to extract the dominant frequency component and the amplitude distribution of each frequency component. The pressure peak sequence and the dominant frequency component are input into the biomechanical response model. The biomechanical response model establishes the stress-strain response mapping of each unit of the scaffold microstructure under pulsating pressure through coupled calculation of material constitutive relations and geometric nonlinear constraints. Based on the stress-strain response mapping, and combined with the pressure change rate sequence, the deformation rate of each unit of the support microstructure is time-series extrapolated to generate deformation prediction results including deformation amplitude, deformation direction and deformation phase.

[0008] The pressure peak sequence and the dominant frequency component are input into a biomechanical response model. This biomechanical response model, through coupled calculations of material constitutive relations and geometric nonlinear constraints, establishes the stress-strain response mapping of each unit of the scaffold microstructure under pulsating pressure, including: The load boundary conditions applied to each unit of the support microstructure are determined based on the pressure peak sequence, and the time evolution law of the load boundary conditions is determined based on the dominant frequency component. Based on the material properties of the support microstructure, a material constitutive relation including the elastic modulus tensor, Poisson's ratio tensor and yield criterion is constructed, and a strain rate-related correction term is introduced into the material constitutive relation to characterize the time-varying characteristics of the material response under pulsating load. Based on the spatial geometric configuration of the scaffold microstructure, a geometric nonlinear constraint is established, which includes the large deformation assumption and contact nonlinear constraint. The geometric nonlinear constraint tracks the coordinate changes and morphological evolution of each unit of the scaffold microstructure during the deformation process by updating the Lagrange description method. The load boundary conditions, the material constitutive relation, and the geometric nonlinear constraints are coupled and solved to obtain the stress field distribution and strain field distribution of each unit of the support microstructure under pulsating pressure. Based on the stress field distribution and the strain field distribution, the stress-strain response mapping of each unit of the support microstructure is constructed.

[0009] The deformation prediction results are matched with preset vessel wall stress distribution constraints to determine the target control amount for each region of the stent microstructure. Based on the target control amount, a driving signal sequence for the stent microstructure unit is generated, including: The predicted deformation amplitude and direction of each region of the stent microstructure are extracted from the deformation prediction results. Based on the physiological and mechanical properties of the blood vessel wall, the preset blood vessel wall stress distribution constraints are determined. Substitute the predicted deformation amplitude and the predicted deformation direction into the stent-vascular wall contact mechanics model, calculate the expected stress distribution at each position of the vascular wall under the action of the predicted deformation result, and calculate the deviation between the expected stress distribution and the vascular wall stress distribution constraint to obtain the stress deviation amount of each region. Based on the stress deviation, the target control amount required to satisfy the stress distribution constraint of the blood vessel wall in each region of the stent microstructure is determined by the inverse solution method. The target control amount is mapped according to the spatial distribution relationship of each region of the stent microstructure to generate the driving amplitude and driving timing corresponding to each microstructure unit. Based on the driving amplitude and the driving timing, a driving signal sequence for the support microstructure unit is constructed.

[0010] Substituting the predicted deformation amplitude and the predicted deformation direction into the stent-vessel wall contact mechanics model, the expected stress distribution at various locations on the vessel wall under the action of the predicted deformation results is calculated as follows: The material mechanical parameters of the blood vessel wall are obtained. Based on the spatial position information of the stent microstructure and the geometric morphology data of the blood vessel wall, a stent-blood vessel wall contact mechanical model is established. The predicted deformation amplitude is converted into the normal displacement component and tangential displacement component of each unit of the stent microstructure at the contact interface. The predicted deformation direction is converted into the action angle of each unit of the stent microstructure relative to the contact interface of the blood vessel wall. Substituting the normal displacement component, the tangential displacement component, and the action angle into the stent-vascular wall contact mechanics model, the contact stress distribution of each unit of the stent microstructure applied to the vascular wall contact interface is obtained through stress transfer calculation at the contact interface. Based on the contact stress distribution and the material mechanical parameters, the expected stress distribution at each location of the blood vessel wall is determined by calculating the internal stress diffusion of the blood vessel wall.

[0011] The periodic regulation of the stent microstructure unit based on the driving signal sequence, and the real-time acquisition of contact stress feedback data between the stent microstructure and the vessel wall during the regulation process, include: Analyze the driving signal sequence and extract the driving amplitude, driving frequency and driving phase corresponding to each microstructure unit of the support; According to the driving amplitude, the driving frequency and the driving phase, the driving actuator applies a periodic adjustment to each stent microstructure unit. During the execution of the periodic adjustment, a stress sensing unit is arranged at the contact interface between the stent microstructure and the blood vessel wall. The stress sensing unit synchronously collects normal stress data and tangential stress data at the contact interface during each cycle of the periodic regulation, and associates and labels the normal stress data and tangential stress data with the spatial position of the corresponding microstructure unit according to the acquisition time, thereby generating contact stress feedback data.

[0012] A second aspect of the present invention provides an intelligent pressure-sensing cerebral vascular stent deformation control system, comprising: The prediction unit is used to acquire the pulsating pressure signal in the blood vessel and the current morphological parameters of the stent microstructure. Based on the time domain and frequency domain characteristics of the pulsating pressure signal, the deformation trend of the stent microstructure is predicted by a biomechanical response model to obtain the deformation prediction result. The control unit is used to match the deformation prediction results with the preset vascular wall stress distribution constraints, determine the target control amount for each region of the stent microstructure, and generate a driving signal sequence for the stent microstructure unit based on the target control amount. The driving signal sequence maps the phase information of the pulsating pressure to the spatial distribution of the microstructure unit to achieve phase-differentiated driving of the microstructure unit in different regions. The correction unit is used to apply periodic regulation to the stent microstructure unit based on the driving signal sequence, and to collect contact stress feedback data between the stent microstructure and the blood vessel wall in real time during the regulation process. The contact stress feedback data is used to adaptively correct the material response parameters in the biomechanical response model to form a closed-loop regulation.

[0013] A third aspect of the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0015] This method enables intelligent sensing and active control of the deformation process of cerebral vascular stents. By acquiring intravascular pulsating pressure signals in real time and combining them with stent microstructure morphological parameters, a biomechanical model is used to predict deformation trends, providing a reliable basis for subsequent precise control. Matching the prediction results with the stress distribution constraints of the vessel wall allows for accurate calculation of the target control amount required for each region of the stent. Based on the driving signal sequence generated by the target control amount, pressure phase information is cleverly mapped to the spatial distribution of microstructure units, realizing asynchronous and differentiated driving of microstructure units in different regions, thereby more precisely adapting to the complex mechanical environment of blood vessels.

[0016] This method constructs a complete closed-loop control system by applying periodic regulation and collecting contact stress feedback in real time. The material response parameters in the biomechanical model are dynamically corrected using real-time feedback data, significantly improving the accuracy and adaptability of model predictions. This adaptive mechanism ensures that the stent can continuously and stably respond to dynamic changes in the vascular environment during long-term implantation, effectively overcoming the regulation failure problem caused by material fatigue or environmental changes in traditional stents.

[0017] The closed-loop regulation mechanism significantly optimizes the mechanical interaction between the stent and the vessel wall. By actively regulating microstructural deformation, the stress distribution of the vessel wall can be maintained within a physiologically safe range, greatly reducing the risk of intimal hyperplasia or restenosis caused by stress concentration. The phase-differentiated driving strategy allows the stent to more gently conform to the vascular pulsation, reducing unnecessary mechanical friction and local damage, thereby improving the long-term prognosis and treatment safety of patients. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the intelligent pressure sensing method for controlling the deformation of a cerebral vascular stent according to an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0021] Figure 1 This is a flowchart illustrating the intelligent pressure-sensing cerebral vascular stent deformation control method according to an embodiment of the present invention. Figure 1 As shown, the method includes: A flowchart illustrating the intelligent pressure-sensing method for controlling the deformation of cerebral vascular stents is provided. The method acquires pulsating pressure signals within the blood vessels and the current morphological parameters of the stent microstructure. Based on the time-domain and frequency-domain characteristics of the pulsating pressure signals, the method predicts the deformation trend of the stent microstructure using a biomechanical response model, thereby obtaining the deformation prediction results. The deformation prediction results are matched with the preset vascular wall stress distribution constraints to determine the target control amount for each region of the stent microstructure. Based on the target control amount, a driving signal sequence for the stent microstructure unit is generated. The driving signal sequence maps the phase information of the pulsating pressure to the spatial distribution of the microstructure unit to achieve phase-differentiated driving of the microstructure unit in different regions. The driving signal sequence is used to apply periodic regulation to the stent microstructure unit, and the contact stress feedback data between the stent microstructure and the blood vessel wall is collected in real time during the regulation process. The contact stress feedback data is used to adaptively correct the material response parameters in the biomechanical response model to form a closed-loop regulation.

[0022] In one optional implementation, based on the time-domain and frequency-domain characteristics of the pulsating pressure signal, the deformation trend of the stent microstructure is predicted using a biomechanical response model, and the deformation prediction results include: The pulsating pressure signal is decomposed in the time domain to extract the pressure peak sequence, pressure change rate sequence and pressure period parameter. The pulsating pressure signal is then transformed in the frequency domain to extract the dominant frequency component and the amplitude distribution of each frequency component. The pressure peak sequence and the dominant frequency component are input into the biomechanical response model. The biomechanical response model establishes the stress-strain response mapping of each unit of the scaffold microstructure under pulsating pressure through coupled calculation of material constitutive relations and geometric nonlinear constraints. Based on the stress-strain response mapping, and combined with the pressure change rate sequence, the deformation rate of each unit of the support microstructure is time-series extrapolated to generate deformation prediction results including deformation amplitude, deformation direction and deformation phase.

[0023] After acquiring the pulsatile pressure signal within the blood vessel, the signal is first processed by time-domain decomposition. The pulsatile pressure signal carries pressure fluctuation information at different stages of the cardiac cycle, exhibiting a periodic peak-valley alternation characteristic in the time domain. By setting a sliding time window, the pressure signal is sampled in segments. Within each cardiac cycle, the pressure maximum point corresponding to the systolic phase and the pressure minimum point corresponding to the diastolic phase are identified. The pressure maximum points within multiple consecutively acquired cycles are recorded sequentially to form a pressure peak sequence. subscript Indicates the first One cardiac cycle. Simultaneously, the pressure difference between adjacent sampling points is compared to the time interval to obtain a pressure change rate sequence. This sequence reflects the rate of pressure rise or fall, characterizing the dynamic load characteristics of blood flow impacting the stent. Furthermore, by detecting the time interval between adjacent peaks in the pressure signal, pressure cycle parameters are extracted. This parameter represents the reciprocal of the heart rate, providing a periodic reference for subsequent frequency domain analysis.

[0024] After completing the time-domain decomposition, a Fast Fourier Transform (FFT) is applied to the same segment of the pulsating pressure signal to transform the time-domain signal into the frequency-domain space for analysis. The frequency component with the most concentrated energy in the spectrum is identified; this frequency component typically corresponds to the fundamental frequency of the heart rate and is labeled as the dominant frequency component. Besides the dominant frequency, the spectrum also contains multiple harmonic components, which are generated by the elastic vibration of the blood vessel wall and the turbulent effect of blood flow. The amplitude corresponding to each discrete frequency point in the spectrum is extracted to obtain the amplitude distribution vector. subscript Indicates the first Each frequency component has an amplitude distribution that reflects the degree of contribution of different frequency components to the stress on the stent. High-frequency components induce local resonance in the stent's microstructure, while low-frequency components dominate the overall deformation trend.

[0025] The extracted pressure peak sequence and dominant frequency components were used as input parameters and fed into a pre-constructed biomechanical response model. This model integrates the constitutive equations of the scaffold material, which describe the intrinsic relationship between stress and strain under external forces. For commonly used nickel-titanium alloy scaffold materials, the constitutive relationship exhibits nonlinear characteristics; when the strain is small, the stress and strain are approximately linearly related. Once the strain exceeds a certain threshold, the material enters a plastic deformation stage, and the stress growth rate slows down. The model uses a piecewise function to express this nonlinear constitutive relationship, employing the elastic modulus parameter in the elastic stage. Describing stiffness, a hardening coefficient parameter is introduced during the plastic stage. Describe the strain-enhancing effect of materials.

[0026] In addition to the material constitutive relation, the model also considers the geometric nonlinear constraints of the support microstructure. The support microstructure consists of multiple periodically arranged rhomboid elements. The supports of each element undergo bending and torsion under stress; this large deformation behavior cannot be described by linear geometric assumptions. A geometric nonlinearity correction factor is introduced. This factor is dynamically adjusted based on the slenderness ratio and bending angle of the support column. When the angle of deviation of the support column from the initial configuration increases, The value of increases accordingly, resulting in a decrease in the calculated equivalent stiffness, which more realistically reflects the attenuation of bearing capacity under large deformation conditions. The model uses an iterative solution algorithm to synchronously update the material constitutive state and geometric configuration in each iteration until the stress field and displacement field satisfy the equilibrium equation, thereby completing the coupled calculation of the material constitutive relation and geometric nonlinear constraints.

[0027] The output of the coupled calculation is the stress-strain response mapping relationship of each element of the support microstructure under a given pressure. This mapping relationship is expressed in the form of a partitioning function. Due to the non-uniformity of pressure distribution in space, different sectors are divided along the circumference of the support, resulting in differences in the local pressure experienced by each sector. The local pressure value of each element within each sector is then calculated. As the independent variable, the strain tensor of the element is obtained through mapping. The strain tensor includes strain components along the axial, radial, and circumferential directions of the support. Simultaneously, the corresponding stress tensor is calculated from the strain tensor based on the constitutive relation. The stress tensor describes the stress state in various directions within an element. By traversing all microstructure elements, a complete stress-strain response mapping table is constructed, which provides the mechanical basis for subsequent deformation rate derivation.

[0028] After obtaining the stress-strain response mapping, a pressure change rate sequence is introduced to extrapolate the temporal evolution of deformation. The deformation rate depends not only on the current stress level but also on the rate of stress change. The pressure change rate... Equivalent mass parameters of the unit Damping coefficient By combining these factors, a dynamic equation for the deformation rate is established. ,in Represents the displacement of the element. This represents the driving force of pressure. The equation is solved by time-stepping using numerical integration. In each time step, the driving force is obtained by looking up the stress-strain mapping table based on the current pressure value. The velocity and displacement are then updated, and the trajectory of deformation of each element over time is gradually deduced. During the time-series deduction, the multidimensional characteristics of deformation need to be recorded simultaneously.

[0029] In one optional implementation, the pressure peak sequence and the dominant frequency component are input into a biomechanical response model. This biomechanical response model establishes the stress-strain response mapping of each unit of the scaffold microstructure under pulsating pressure through coupled calculations of material constitutive relations and geometric nonlinear constraints. The load boundary conditions applied to each unit of the support microstructure are determined based on the pressure peak sequence, and the time evolution law of the load boundary conditions is determined based on the dominant frequency component. Based on the material properties of the support microstructure, a material constitutive relation including the elastic modulus tensor, Poisson's ratio tensor and yield criterion is constructed, and a strain rate-related correction term is introduced into the material constitutive relation to characterize the time-varying characteristics of the material response under pulsating load. Based on the spatial geometric configuration of the scaffold microstructure, a geometric nonlinear constraint is established, which includes the large deformation assumption and contact nonlinear constraint. The geometric nonlinear constraint tracks the coordinate changes and morphological evolution of each unit of the scaffold microstructure during the deformation process by updating the Lagrange description method. The load boundary conditions, the material constitutive relation, and the geometric nonlinear constraints are coupled and solved to obtain the stress field distribution and strain field distribution of each unit of the support microstructure under pulsating pressure. Based on the stress field distribution and the strain field distribution, the stress-strain response mapping of each unit of the support microstructure is constructed.

[0030] After acquiring the intravascular pulsating pressure signal, it needs to be converted into a mechanical input capable of driving the deformation of the stent microstructure. The pressure peak sequence reflects the instantaneous extreme state of intravascular pressure, and these peak points correspond to the moments when the stent microstructure experiences the maximum load. By identifying the systolic and diastolic pressure peaks within each cardiac cycle, the amplitude of the external force experienced by each stent unit at different times can be determined. The dominant frequency component describes the periodicity of pressure changes, typically based on the fundamental frequency and its harmonics corresponding to the heart rate. Combining the dominant frequency component with the pressure peak sequence allows for the reconstruction of a complete time-evolutionary load curve. For each discrete unit in the stent microstructure, based on its spatial location within the blood vessel, the pressure peak at that location is taken as the load amplitude for that unit, while the application time and duration of the load are determined based on the dominant frequency component. This approach ensures that the load boundary conditions encompass both the spatial distribution characteristics of the pressure and retain its dynamic information of temporal evolution.

[0031] Scaffold microstructures are typically made of shape memory alloys or polymer composites, and their mechanical behavior needs to be described through material constitutive relations. The elastic modulus tensor, in the form of a fourth-order tensor, characterizes the stiffness properties of a material in different directions. For isotropic materials, this tensor can be simplified to two independent parameters: Young's modulus and shear modulus. The Poisson's ratio tensor reflects the ratio of lateral contraction to longitudinal elongation of a material under uniaxial tension; for vascular stent materials, the Poisson's ratio is typically between 0.3 and 0.4. The yield criterion is used to determine whether a material has entered the plastic deformation stage, and Von's ratio is often used. The Mises criterion, or Tresca criterion, determines the current state of a material by comparing the equivalent stress with its yield strength. Due to the pulsating nature of intravascular pressure, the load on the stent microstructure exhibits a significant strain rate effect. Introducing a strain rate-related correction term increases the effective elastic modulus of the material with increasing strain rate; this correction term is typically described using the Cowper-Symonds or Johnson-Cook models. The corrected constitutive relation more accurately reflects the mechanical response characteristics of the stent under rapid loading conditions.

[0032] When stent microstructures operate within blood vessels, they undergo significant deformation, rendering the traditional small deformation assumption inapplicable. The large deformation assumption necessitates considering the difference between the current and initial configurations during analysis, employing an updated Lagrangian description method to track the motion trajectory of each element. This method uses the deformed current configuration as a reference configuration, updating node coordinates and element shapes in each incremental step to accurately capture the cumulative deformation effect of the stent under pulsating loads. The interaction between the stent and the vessel wall manifests as a contact problem, requiring handling through contact nonlinear constraints. The outer surface of the stent is defined as the principal contact surface, and the inner wall of the vessel as the secondary contact surface. Penetration constraints on the contact interface are enforced using the penalty function method or the Lagrangian multiplier method. When stent node intrusion into the vessel wall is detected, a restoring force proportional to the intrusion depth is applied to that node to prevent further penetration. The contact state continuously changes with stent deformation, requiring a re-search for contact pairs and updating of contact forces in each computational step. Geometric nonlinear constraints also include the hinged or fixed relationships between members in the stent microstructure; these connections determine the transmission paths of forces and moments within the structure.

[0033] The coupled solution process employs an incremental iterative strategy, dividing the entire pulsation cycle into several time steps. Within each time step, the external force vector applied to each element at the current moment is first determined based on the load boundary conditions. Then, the internal stress of the element is calculated based on the material constitutive relation, and the element stiffness matrix is ​​obtained through geometric nonlinear constraints. Due to the existence of geometric nonlinearity, the stiffness matrix changes with the deformation state and needs to be continuously updated during the iteration process. The Luwton-Raphson method is used for iterative solution. In each iteration, the residual vector, i.e., the difference between the external force vector and the internal force vector, is calculated. When the residual norm is less than a preset convergence threshold, the solution for the current time step is considered complete. The obtained displacement field reflects the spatial position changes of each element in the support microstructure. The strain field distribution can be calculated through the displacement gradient. The strain field includes normal strain components and shear strain components, which describe the tension, compression, and shape distortion of the material in different directions, respectively. Substituting the strain field into the material constitutive relation and combining it with the strain rate correction term, the stress field distribution can be calculated. The stress field also includes normal stress components and shear stress components, and its spatial distribution reflects the stress state inside the support.

[0034] When establishing the stress-strain response mapping, the stress state and strain state of each element are treated as corresponding data points. Due to the periodicity of pulsating pressure, the mechanical response of the stent microstructure exhibits a loading-unloading cyclic characteristic. Within a complete cycle, the stress-strain curve forms a closed loop, and the envelope area of ​​the loop reflects the energy dissipation characteristics of the material. For elements located at different spatial positions, the shape of their stress-strain response mapping varies due to differences in the amplitude and phase of the load they bear. Elements near the bifurcation or bend of the blood vessel typically experience higher stress levels, resulting in a steeper slope in their response curves. By analyzing the response mappings of all elements, stress concentration regions in the stent microstructure can be identified, providing a basis for subsequent deformation prediction and control. The response mapping is also used to assess whether the material has entered a nonlinear deformation stage. When the stress-strain curve exhibits obvious nonlinear characteristics, it indicates that the material is close to the yield state, requiring timely adjustment of the driving signal to avoid permanent deformation of the stent. This mapping relationship, as the core output of the biomechanical response model, directly correlates the pulsating pressure input with the stent deformation behavior, laying a theoretical foundation for achieving precise deformation control.

[0035] In one optional implementation, the deformation prediction results are matched with preset vascular wall stress distribution constraints to determine the target control amount for each region of the stent microstructure. Generating a driving signal sequence for the stent microstructure unit based on the target control amount includes: The predicted deformation amplitude and direction of each region of the stent microstructure are extracted from the deformation prediction results. Based on the physiological and mechanical properties of the blood vessel wall, the preset blood vessel wall stress distribution constraints are determined. Substitute the predicted deformation amplitude and the predicted deformation direction into the stent-vascular wall contact mechanics model, calculate the expected stress distribution at each position of the vascular wall under the action of the predicted deformation result, and calculate the deviation between the expected stress distribution and the vascular wall stress distribution constraint to obtain the stress deviation amount of each region. Based on the stress deviation, the target control amount required to satisfy the stress distribution constraint of the blood vessel wall in each region of the stent microstructure is determined by the inverse solution method. The target control amount is mapped according to the spatial distribution relationship of each region of the stent microstructure to generate the driving amplitude and driving timing corresponding to each microstructure unit. Based on the driving amplitude and the driving timing, a driving signal sequence for the support microstructure unit is constructed.

[0036] After obtaining the deformation prediction results of the stent microstructure, these results need to be matched with the physiological and mechanical constraints of the blood vessel wall to determine the precise control amount of the stent microstructure in each region. The deformation prediction results contain the spatial response information of each region of the stent under pulsating pressure. This information is stored in the form of a vector field, with each spatial location point corresponding to a deformation vector. The predicted deformation amplitude and predicted deformation direction of each region are extracted from this vector field. The predicted deformation amplitude represents the distance by which the stent structure in that region deviates from its initial position, while the predicted deformation direction describes the motion trend of that region using a unit vector in three-dimensional space. During the extraction process, the overall stent structure is divided into several spatial grids along the circumferential and axial directions. Each grid corresponds to a local region. The grid size is determined based on the characteristic size of the stent microstructure unit, typically 1.5 to 2 times the span of the microstructure unit, ensuring that each grid contains a sufficient number of microstructure units for subsequent control.

[0037] The determination of stress distribution constraints on blood vessel walls is based on the physiological and mechanical properties of the blood vessel walls. Under physiological conditions, the circumferential stress on a normal blood vessel wall ranges from 8 to 15 kPa, the axial stress ranges from 3 to 6 kPa, and the shear stress does not exceed 2 kPa. Cerebral blood vessel walls are more fragile than those in other parts of the body; excessive stress can lead to endothelial cell damage or even rupture. Therefore, the preset stress distribution constraints on blood vessel walls must be formulated according to the actual condition of the patient's blood vessels. For areas with existing lesions in the blood vessel wall, the upper limit of stress constraints should be reduced to 60% to 70% of the normal value. For blood vessel bifurcation sites, due to the complex hemodynamic characteristics, stress distribution constraints must consider local turbulence effects, allowing for a slight increase in short-term stress peaks, but the duration should be controlled within 10% of the pulsation cycle.

[0038] The extracted predicted deformation amplitude and direction were substituted into the stent-vascular wall contact mechanics model for calculation. This contact mechanics model is based on elastic contact theory, considering the elastic modulus of the stent material, the viscoelastic properties of the vascular wall, and the friction coefficient between them. The model treats the stent-vascular wall contact as distributed point contact, with each microstructural unit's contact point with the vascular wall as an independent computational unit. When the stent deforms under the trend described by the deformation prediction results, the contact pressure at each contact point is calculated according to Hertzian contact theory. The magnitude of the contact pressure is proportional to the radial displacement of the stent at the contact point, and the proportionality coefficient is jointly determined by the elastic modulus of the stent material and the effective elastic modulus of the vascular wall. By traversing the contact pressure at all contact points, a predicted stress distribution map of each location on the vascular wall is constructed. This stress distribution map is represented in the form of a three-dimensional tensor field, containing circumferential stress components, axial stress components, and shear stress components.

[0039] The deviation between the expected stress distribution and the constraint of the vessel wall stress distribution is calculated using a point-by-point comparison method. The stress component at each point in the expected stress distribution map is compared with the upper limit of the corresponding stress constraint. When the circumferential stress at a certain point exceeds the upper limit of the constraint, the circumferential stress deviation at that point is the actual expected stress minus the upper limit of the constraint. The deviations of other stress components are calculated in the same way. For locations where the expected stress is lower than the lower limit of the constraint, the deviation is also calculated, but with a negative sign, indicating insufficient support of the stent for the vessel wall at that location. After the deviation calculation is completed, the stress deviation at each location of the vessel wall is mapped back to each region of the stent microstructure. The mapping relationship is established through the correspondence of contact points; that is, the stress deviation at a certain location of the vessel wall is borne by the stent microstructure unit in contact with that location. Since a stent microstructure region contains multiple contact points, the total stress deviation of that region is the vector sum of the stress deviations of all contact points.

[0040] Based on the stress deviation in each region, a reverse engineering method is used to determine the target control amount. The core idea of ​​the reverse engineering method is to adjust the morphology of the stent microstructure so that the resulting stress distribution can offset the stress deviation. For regions with positive stress deviation, it indicates that the pressure on the vessel wall is too high, and the radial expansion of the stent in this region needs to be reduced. The target control amount is negative, and the specific value is obtained through the inverse calculation of the contact mechanics model. For regions with negative stress deviation, the radial expansion of the stent in this region needs to be increased, and the target control amount is positive. During the reverse engineering process, the control capability boundary of the stent microstructure unit must be considered. The maximum radial control range of a single microstructure unit is usually 5% to 8% of its characteristic size. Exceeding this range will lead to material fatigue or structural failure. When the calculated target control amount exceeds the control capability boundary, a saturation limiting process is used to restrict the target control amount within the allowable range, and compensation is made by increasing the control amount in adjacent regions.

[0041] After the target control amount is determined, the driving signal is mapped according to the spatial distribution relationship of each region of the stent microstructure. The spatial distribution relationship is described by the three-dimensional geometric model of the stent, which records the spatial coordinates of each microstructure unit, its connection relationship with adjacent units, and the driving response characteristics of the unit. During the driving signal mapping process, the target control amount of each region is first converted into the driving force that the corresponding microstructure unit in the region needs to apply. The conversion relationship is determined based on the mechanical response curve of the microstructure unit. Then, according to the phase information of the pulsating pressure, the driving timing is assigned to microstructure units in different spatial locations. Microstructure units located in the upstream region of the blood vessel receive the pressure wave peak earlier, and their driving signal phase is ahead. Microstructure units located in the downstream region have a lagging driving phase. The phase difference is related to the propagation speed of the pressure wave in the blood vessel, which is usually 5 to 8 meters per second. By establishing the mapping relationship between spatial location and phase, the deformation control of each region of the stent is synchronized with the propagation of the pressure wave, realizing phase-differentiated driving.

[0042] The driving amplitude is determined based on the target control amount of each microstructure unit and the unit's response sensitivity. The response sensitivity is defined as the radial displacement generated by the microstructure unit under a unit driving force. Different structural forms of microstructure units have different response sensitivities, with rhomboid structural units having higher sensitivity and ring structural units having lower sensitivity.

[0043] In one optional implementation, the predicted deformation amplitude and the predicted deformation direction are substituted into the stent-vessel wall contact mechanics model to calculate the expected stress distribution at various locations of the vessel wall under the effect of the deformation prediction results, including: The material mechanical parameters of the blood vessel wall are obtained. Based on the spatial position information of the stent microstructure and the geometric morphology data of the blood vessel wall, a stent-blood vessel wall contact mechanical model is established. The predicted deformation amplitude is converted into the normal displacement component and tangential displacement component of each unit of the stent microstructure at the contact interface. The predicted deformation direction is converted into the action angle of each unit of the stent microstructure relative to the contact interface of the blood vessel wall. Substituting the normal displacement component, the tangential displacement component, and the action angle into the stent-vascular wall contact mechanics model, the contact stress distribution of each unit of the stent microstructure applied to the vascular wall contact interface is obtained through stress transfer calculation at the contact interface. Based on the contact stress distribution and the material mechanical parameters, the expected stress distribution at each location of the blood vessel wall is determined by calculating the internal stress diffusion of the blood vessel wall.

[0044] When obtaining the material mechanical parameters of the blood vessel wall, the main parameters include the elastic modulus, Poisson's ratio, density, and viscoelastic coefficient. The elastic modulus of the blood vessel wall reflects its stiffness characteristics under stress and is usually obtained through ultrasonic elastography or biomechanical testing devices. Its value generally ranges from 0.5 to 5.0 MPa. The elastic properties of the blood vessel wall vary among different patients due to differences in age, lesion severity, and degree of tissue fibrosis. Poisson's ratio describes the ratio of transverse to longitudinal strain under uniaxial stress. As a biological soft tissue, the blood vessel wall has a Poisson's ratio close to 0.5, exhibiting approximately incompressible characteristics. The density parameter is used for calculating the mass effect in subsequent dynamic analysis; the density of the blood vessel wall is typically taken as 1.05 to 1.10 g / cm³. The viscoelastic coefficient reflects the energy dissipation characteristics of the blood vessel wall under periodic pulsating loads. This parameter is extracted through dynamic mechanical analysis and is crucial for describing the stress relaxation behavior of the blood vessel wall under pulsating pressure cycles.

[0045] When establishing a stent-vascular wall contact mechanics model, the spatial location information of the stent microstructure must first be obtained, including the three-dimensional coordinates of each microstructure unit within the vascular lumen, the connection relationships between units, and the tilt angle of each unit relative to the vascular axis. The geometric morphology data of the vascular wall is obtained through medical image reconstruction, encompassing the axial diameter variation curve, vascular wall thickness distribution, and curvature characteristics at vascular branches. The contact mechanics model employs a contact algorithm based on the penalty function method, defining contact pairs between the stent microstructure surface and the inner surface of the vascular wall. Contact is considered to have occurred when the distance between the two contact surfaces is less than a set contact detection threshold. The contact stiffness coefficient is determined based on the combined elastic modulus of the stent material and the vascular wall material, through... To make an estimate, among which This indicates the elastic modulus of the support material. This represents the elastic modulus of the blood vessel wall.

[0046] In converting the predicted deformation amplitude into normal and tangential displacement components of each element of the stent microstructure at the contact interface, it is necessary to first determine the local coordinate system of the contact interface. A right-handed coordinate system is established at each contact point, with the contact surface normal as the Z-axis and the vessel axis as the X-axis. The deformation displacement vector of the stent microstructure element is decomposed in this coordinate system. Normal displacement component This represents the displacement of the stent microstructure unit perpendicular to the vessel wall surface. This component directly affects the radial compression of the vessel wall and is obtained by dot product of the predicted deformation amplitude and the normal unit vector at the contact point. The tangential displacement component includes a tangential component along the vessel axis. Along with the tangential component along the circumference of the blood vessel, these two components describe the relative slippage of the stent microstructure units on the surface of the blood vessel wall, and are calculated by the dot product of the predicted deformation displacement vector and the corresponding tangential unit vector, respectively.

[0047] When the predicted deformation direction is converted into the interaction angle of each unit of the stent microstructure relative to the contact interface with the blood vessel wall, the interaction angle is... Defined as the angle between the deformation displacement vector and the normal to the contact surface. This angle is calculated using the inverse cosine function, i.e. ,in The magnitude represents the deformation displacement vector. The angle of application directly determines the distribution ratio of normal stress and shear stress in the contact stress. When the angle of application is close to zero, the deformation mainly generates normal compressive stress. When the angle of application is large, significant tangential shear stress will be generated. This shear stress induces damage to the intima of the blood vessel wall.

[0048] After substituting the normal displacement components, tangential displacement components, and the angle of application into the stent-vessel wall contact mechanics model, the contact stress distribution is obtained through stress transfer calculations at the contact interface. The normal component of the contact stress... It is positively correlated with the normal displacement component, through Calculations are performed. The tangential component of the contact stress relates to frictional behavior and is described using the Coulomb friction model. When the tangential displacement does not reach the slip threshold, the tangential contact stress... pass Calculation, where The tangential contact stiffness coefficient is... This is the composite value of the tangential displacement components. When the tangential contact stress exceeds the maximum static friction force, the contact interface enters a slip state, at which point the tangential stress... ,in The coefficient of kinetic friction is usually taken to be between 0.1 and 0.3.

[0049] When calculating the internal stress diffusion of the blood vessel wall based on contact stress distribution and material mechanical parameters, a finite element method based on stress equilibrium is employed. The blood vessel wall is discretized into multiple layers of mesh elements, with the thickness of each layer proportionally determined to the actual thickness of the blood vessel wall. Contact stress is applied as a boundary load to the nodes on the inner surface of the blood vessel wall. The displacement response of each node is obtained by assembling and solving the element stiffness matrix. The stress at each location on the blood vessel wall is calculated using the strain-displacement relationship and constitutive equations. For linear elastic materials, the stress... pass Determined, where is the elastic stiffness tensor. These are the components of the strain tensor. During stress diffusion, the differences in material parameters among the various layers of the vessel wall are considered. Different elastic moduli and Poisson's ratios are used for the intima, media, and adventitia to ensure the accuracy of stress distribution calculations. The final expected stress distribution includes the Von Mises equivalent stress, maximum principal stress, and shear stress at various locations on the vessel wall. These stress indicators are used for subsequent matching with preset vessel wall stress distribution constraints to determine the target control parameters for each region of the stent microstructure.

[0050] In one optional implementation, the periodic regulation of the stent microstructure unit based on the driving signal sequence, and the real-time acquisition of contact stress feedback data between the stent microstructure and the vessel wall during the regulation process, include: Analyze the driving signal sequence and extract the driving amplitude, driving frequency and driving phase corresponding to each microstructure unit of the support; According to the driving amplitude, the driving frequency and the driving phase, the driving actuator applies a periodic adjustment to each stent microstructure unit. During the execution of the periodic adjustment, a stress sensing unit is arranged at the contact interface between the stent microstructure and the blood vessel wall. The stress sensing unit synchronously collects normal stress data and tangential stress data at the contact interface during each cycle of the periodic regulation, and associates and labels the normal stress data and tangential stress data with the spatial position of the corresponding microstructure unit according to the acquisition time, thereby generating contact stress feedback data.

[0051] The drive signal sequence carries all the instructions for precise control of the stent's microstructural units. To achieve differentiated control of each microstructural unit, a comprehensive analysis of the drive signal sequence is necessary. The drive signal sequence employs a multi-channel encoding method, with each microstructural unit corresponding to an independent control channel. The analysis process begins with the data frame structure of the signal sequence. Each data frame contains the spatial identifier code of the microstructural unit, drive parameter fields, and timing synchronization markers. The drive amplitude parameter defines the control force applied to the microstructural unit, and its value range is calibrated based on the material elastic modulus and expected deformation of the microstructural unit. The drive frequency parameter is related to the dominant frequency component of the pulsating pressure, typically between 0.8 and 2.5 Hz, ensuring that the control action maintains an appropriate frequency match with blood flow pulsation. The drive phase parameter reflects the temporal differences in control actions between different microstructural units. The phase value is expressed in radians, ranging from 0 to 2π, and the phase distribution enables a wave-like control mode of the stent along the circumferential and axial directions. The parsing module adopts a parallel processing architecture, simultaneously decoding multiple data frames. The extracted driving amplitude, driving frequency, and driving phase are grouped and stored according to the microstructure unit number to form a structured driving parameter matrix.

[0052] The actuator is responsible for converting the analyzed drive parameters into actual physical control. This mechanism comprises a distributed array of drive units, each mechanically coupled to one or more stent microstructure units. The drive units employ shape memory alloy microactuators, achieving micrometer-level displacement output through phase transitions triggered by current pulses. Based on the drive amplitude parameters, the controller calculates the current intensity applied to the actuator. The current intensity and target displacement exhibit an approximately linear relationship, but the nonlinear response characteristics of the material must be considered. The drive frequency parameter determines the repetition frequency of the current pulses. The controller generates a pulse width modulation signal with a specific duty cycle, achieving frequency control by adjusting the pulse on / off time. The drive phase parameter is implemented through a timing controller, which uses the peak moment of the pulsating pressure as the zero-phase reference point and sets corresponding trigger delays based on the phase offset of each microstructure unit. The periodic control effect manifests as periodic micro-displacements of the stent microstructure units. These micro-displacements guide the stent as a whole to produce dynamic deformation coordinated with blood flow pulsations, reducing the rigid contact stress between the stent and the vessel wall. During the execution of the regulatory effect, the driving actuators remain in a continuous working state, dynamically adjusting the driving parameters of each unit according to the real-time updated driving signal sequence, so as to ensure that the regulatory strategy can adapt to changes in the intravascular environment.

[0053] To acquire contact mechanics information between the stent microstructure and the vessel wall, a stress sensing unit array is deployed at the contact interface. The stress sensing units employ flexible piezoresistive sensors, with a polymer film substrate approximately 50 micrometers thick and a sensitive layer composed of nano-carbon material deposited on its surface. The arrangement of the sensing units fully considers the geometric characteristics of the stent, with multiple sensing nodes evenly distributed along the circumference of the stent, each node covering a contact area of ​​approximately 2 to 3 square millimeters. A biocompatible adhesive is used to fix the sensing units to the outer surface of the stent, ensuring a tight fit between the sensor and the stent-vessel wall contact interface while maintaining the sensor's flexibility to avoid additional interference with the contact mechanics. The signal output of the sensing units is led out through micro-wires with a helical winding layout, providing sufficient deformation margin to prevent signal wire breakage during radial expansion or contraction of the stent.

[0054] The stress sensing unit can simultaneously detect both normal and tangential stress components. Normal stress reflects the radial support force of the stent on the vessel wall, and its value is related to the degree of stent expansion and the elastic recoil force of the vessel wall. The sensor acquires normal stress data by measuring the pressure change perpendicular to the contact surface. The resistance of the sensitive layer decreases with increasing compressive stress, and the resistance change is converted into a voltage signal via a Wheatstone bridge circuit. Tangential stress originates from blood flow shearing and the relative fretting between the stent and the vessel wall; excessive tangential stress can lead to endothelial damage. The sensor employs a biaxial strain gauge structure, acquiring tangential stress data by measuring the strain of the sensitive layer in the tangential direction. The resistance change caused by strain is also converted into a standard voltage signal through a signal conditioning circuit.

[0055] The data acquisition process and periodic control are strictly synchronized in time. The acquisition controller uses the period of the drive signal as the sampling time window, performing multiple high-frequency samples within each control cycle. The sampling frequency is set to 20 to 50 times the control frequency to ensure the capture of transient changes in contact stress. Normal stress data and tangential stress data are acquired synchronously at the same time. The acquired raw voltage signals are converted into digital quantities by an analog-to-digital converter and then enter the data processing unit. The data processing unit timestamps each set of sampled data with microsecond-level accuracy, recording the precise moment of data acquisition. Simultaneously, based on the spatial number of the sensing unit, the stress data is spatially associated with the corresponding support microstructure unit. The association and annotation process establishes a multi-dimensional data structure containing time, space, and stress type dimensions, which can fully describe the evolution of the mechanical state of the contact interface during the control process. The generated contact stress feedback data is output in the form of data packets. Each data packet contains the acquisition time, microstructure unit identifier, normal stress value, tangential stress value, and data quality flag, providing a reliable feedback information basis for subsequent adaptive correction.

[0056] A second aspect of the present invention provides an intelligent pressure-sensing cerebral vascular stent deformation control system, comprising: The prediction unit is used to acquire the pulsating pressure signal in the blood vessel and the current morphological parameters of the stent microstructure. Based on the time domain and frequency domain characteristics of the pulsating pressure signal, the deformation trend of the stent microstructure is predicted by a biomechanical response model to obtain the deformation prediction result. The control unit is used to match the deformation prediction results with the preset vascular wall stress distribution constraints, determine the target control amount for each region of the stent microstructure, and generate a driving signal sequence for the stent microstructure unit based on the target control amount. The driving signal sequence maps the phase information of the pulsating pressure to the spatial distribution of the microstructure unit to achieve phase-differentiated driving of the microstructure unit in different regions. The correction unit is used to apply periodic regulation to the stent microstructure unit based on the driving signal sequence, and to collect contact stress feedback data between the stent microstructure and the blood vessel wall in real time during the regulation process. The contact stress feedback data is used to adaptively correct the material response parameters in the biomechanical response model to form a closed-loop regulation.

[0057] A third aspect of the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0058] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0059] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling cerebral vascular stent deformation based on intelligent pressure sensing, characterized in that, include: The pulsating pressure signal within the blood vessel and the current morphological parameters of the stent microstructure are acquired. Based on the time-domain and frequency-domain characteristics of the pulsating pressure signal, the deformation trend of the stent microstructure is predicted using a biomechanical response model, yielding deformation prediction results, including: The pulsating pressure signal is decomposed in the time domain to extract the pressure peak sequence, pressure change rate sequence and pressure period parameter. The pulsating pressure signal is then transformed in the frequency domain to extract the dominant frequency component and the amplitude distribution of each frequency component. The pressure peak sequence and the dominant frequency component are input into the biomechanical response model. The biomechanical response model establishes the stress-strain response mapping of each unit of the scaffold microstructure under pulsating pressure through coupled calculation of material constitutive relations and geometric nonlinear constraints. Based on the stress-strain response mapping, and combined with the pressure change rate sequence, the deformation rate of each unit of the support microstructure is time-series extrapolated to generate deformation prediction results including deformation amplitude, deformation direction and deformation phase. The deformation prediction results are matched with the preset vascular wall stress distribution constraints to determine the target control amount for each region of the stent microstructure. Based on the target control amount, a driving signal sequence for the stent microstructure unit is generated. The driving signal sequence maps the phase information of the pulsating pressure to the spatial distribution of the microstructure unit to achieve phase-differentiated driving of the microstructure unit in different regions. The driving signal sequence is used to apply periodic regulation to the stent microstructure unit, and the contact stress feedback data between the stent microstructure and the blood vessel wall is collected in real time during the regulation process. The contact stress feedback data is used to adaptively correct the material response parameters in the biomechanical response model to form a closed-loop regulation.

2. The method according to claim 1, characterized in that, The pressure peak sequence and the dominant frequency component are input into a biomechanical response model. This biomechanical response model, through coupled calculations of material constitutive relations and geometric nonlinear constraints, establishes the stress-strain response mapping of each unit of the scaffold microstructure under pulsating pressure, including: The load boundary conditions applied to each unit of the support microstructure are determined based on the pressure peak sequence, and the time evolution law of the load boundary conditions is determined based on the dominant frequency component. Based on the material properties of the support microstructure, a material constitutive relation including the elastic modulus tensor, Poisson's ratio tensor and yield criterion is constructed, and a strain rate-related correction term is introduced into the material constitutive relation to characterize the time-varying characteristics of the material response under pulsating load. Based on the spatial geometric configuration of the scaffold microstructure, a geometric nonlinear constraint is established, which includes the large deformation assumption and contact nonlinear constraint. The geometric nonlinear constraint tracks the coordinate changes and morphological evolution of each unit of the scaffold microstructure during the deformation process by updating the Lagrange description method. The load boundary conditions, the material constitutive relation, and the geometric nonlinear constraints are coupled and solved to obtain the stress field distribution and strain field distribution of each unit of the support microstructure under pulsating pressure. Based on the stress field distribution and the strain field distribution, the stress-strain response mapping of each unit of the support microstructure is constructed.

3. The method according to claim 1, characterized in that, The deformation prediction results are matched with preset vessel wall stress distribution constraints to determine the target control amount for each region of the stent microstructure. Based on the target control amount, a driving signal sequence for the stent microstructure unit is generated, including: The predicted deformation amplitude and direction of each region of the stent microstructure are extracted from the deformation prediction results. Based on the physiological and mechanical properties of the blood vessel wall, the preset blood vessel wall stress distribution constraints are determined. Substitute the predicted deformation amplitude and the predicted deformation direction into the stent-vascular wall contact mechanics model, calculate the expected stress distribution at each position of the vascular wall under the action of the predicted deformation result, and calculate the deviation between the expected stress distribution and the vascular wall stress distribution constraint to obtain the stress deviation amount of each region. Based on the stress deviation, the target control amount required to satisfy the stress distribution constraint of the blood vessel wall in each region of the stent microstructure is determined by the inverse solution method. The target control amount is mapped according to the spatial distribution relationship of each region of the stent microstructure to generate the driving amplitude and driving timing corresponding to each microstructure unit. Based on the driving amplitude and the driving timing, a driving signal sequence for the support microstructure unit is constructed.

4. The method according to claim 3, characterized in that, Substituting the predicted deformation amplitude and the predicted deformation direction into the stent-vessel wall contact mechanics model, the expected stress distribution at various locations on the vessel wall under the action of the predicted deformation results is calculated as follows: The material mechanical parameters of the blood vessel wall are obtained. Based on the spatial position information of the stent microstructure and the geometric morphology data of the blood vessel wall, a stent-blood vessel wall contact mechanical model is established. The predicted deformation amplitude is converted into the normal displacement component and tangential displacement component of each unit of the stent microstructure at the contact interface. The predicted deformation direction is converted into the action angle of each unit of the stent microstructure relative to the contact interface of the blood vessel wall. Substituting the normal displacement component, the tangential displacement component, and the action angle into the stent-vascular wall contact mechanics model, the contact stress distribution of each unit of the stent microstructure applied to the vascular wall contact interface is obtained through stress transfer calculation at the contact interface. Based on the contact stress distribution and the material mechanical parameters, the expected stress distribution at each location of the blood vessel wall is determined by calculating the internal stress diffusion of the blood vessel wall.

5. The method according to claim 1, characterized in that, The periodic regulation of the stent microstructure unit based on the driving signal sequence, and the real-time acquisition of contact stress feedback data between the stent microstructure and the vessel wall during the regulation process, include: Analyze the driving signal sequence and extract the driving amplitude, driving frequency and driving phase corresponding to each microstructure unit of the support; According to the driving amplitude, the driving frequency and the driving phase, the driving actuator applies a periodic adjustment to each stent microstructure unit. During the execution of the periodic adjustment, a stress sensing unit is arranged at the contact interface between the stent microstructure and the blood vessel wall. The stress sensing unit synchronously collects normal stress data and tangential stress data at the contact interface during each cycle of the periodic regulation, and associates and labels the normal stress data and tangential stress data with the spatial position of the corresponding microstructure unit according to the acquisition time, thereby generating contact stress feedback data.

6. An intelligent pressure-sensing cerebral vascular stent deformation control system, used to implement the method as described in any one of claims 1-5, characterized in that, include: The prediction unit is used to acquire the pulsating pressure signal in the blood vessel and the current morphological parameters of the stent microstructure. Based on the time domain and frequency domain characteristics of the pulsating pressure signal, the deformation trend of the stent microstructure is predicted by a biomechanical response model to obtain the deformation prediction result. The control unit is used to match the deformation prediction results with the preset vascular wall stress distribution constraints, determine the target control amount for each region of the stent microstructure, and generate a driving signal sequence for the stent microstructure unit based on the target control amount. The driving signal sequence maps the phase information of the pulsating pressure to the spatial distribution of the microstructure unit to achieve phase-differentiated driving of the microstructure unit in different regions. The correction unit is used to apply periodic regulation to the stent microstructure unit based on the driving signal sequence, and to collect contact stress feedback data between the stent microstructure and the blood vessel wall in real time during the regulation process. The contact stress feedback data is used to adaptively correct the material response parameters in the biomechanical response model to form a closed-loop regulation.

7. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 5.

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