Non-invasive measurement method for measuring heart stent stress
By using virtual scene modeling and electromagnetic wave energy density mapping, the problem of accurately measuring the stress distribution of cardiac stents has been solved, enabling non-invasive, safe, and efficient stress monitoring, reducing measurement costs and patient trauma risks.
Patent Information
- Application Number
- CN202511475900.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-16
- Publication Date
- 2025-11-25
AI Technical Summary
Existing technologies cannot accurately measure the stress distribution of cardiac stents in power frequency electromagnetic environments, leading to an increased risk of fatigue crack propagation and fracture of stent materials. Furthermore, existing tools suffer from secondary surgical trauma and data distortion.
By collecting experimental datasets, establishing virtual scenarios and performing digital modeling, and combining contribution index and weight index to screen test points, non-invasive measurements are performed using electric field strength and magnetic flux density to establish the mapping relationship between electromagnetic wave energy density and stress.
It achieves non-invasive and safe measurement, reduces the risk of patient trauma, improves measurement accuracy and robustness in complex environments, and reduces computational costs.
Smart Images
Figure CN121009714A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of stress measurement, in particular to a non-invasive measurement method for measuring the stress of a heart stent. BACKGROUND
[0002] Patients implanted with heart stents have significant safety risks in power frequency electromagnetic environments: alternating magnetic fields induce eddy currents in the metal conductors of the stent, which interact with the external magnetic field to produce Lorentz forces, causing abnormal changes in the stress distribution of the stent. This persistent stress concentration not only accelerates the propagation of fatigue cracks in the stent material, significantly increasing the risk of stent fracture. When the stress exceeds the material yield limit for a long time, it may even cause plastic deformation of the stent, severely damaging its functional integrity.
[0003] At present, the existing technical system faces double limitations, which seriously restricts the research and application of reliable stress monitoring tools. On the one hand, there is a lack of in-vivo direct measurement means, and implantable sensors need to be removed by secondary surgery, which not only significantly increases the risk of patient trauma and the probability of immune rejection, but also disturbs the original electromagnetic field distribution due to phenomena such as eddy current effect, leading to distorted measurement data. On the other hand, virtual simulation lacks precision for subtle stress changes, although finite element models are widely used, they are difficult to accurately reproduce the coupling effects between complex electromagnetic fields and living tissues in real dynamic physiological environments, especially unable to accurately quantify the subtle but critical stress gradient changes caused by eddy current effects. It is these two major defects that lead to the lack of truly reliable in-vivo stress monitoring tools in the existing technical system, which seriously hinders the ability to conduct early and dynamic assessment and early warning of heart stent failure risks. SUMMARY
[0004] The purpose of the present application is to provide a non-invasive measurement method for measuring the stress of a heart stent to solve the problems raised in the background art.
[0005] To solve the above technical problems, the present application provides a non-invasive measurement method for measuring the stress of a heart stent, comprising: S100, collecting experimental data sets, scanning to obtain three-dimensional data and spatial positions of human bodies and power equipment in reality, generating a digital human body model containing a heart stent and a power equipment digital model, and establishing a virtual scene.
[0006] The experimental data set refers to the electric field intensity and magnetic flux density of each test point in the human body model and the stress distribution data of the heart stent obtained by adjusting the influence parameters and actually measuring in an experimental environment where a human body model containing a heart stent and power equipment are placed, thereby establishing a data record.
[0007] By adjusting parameters such as the power of electrical equipment and the relative position of the human body and the equipment, the stress distribution of the cardiac stent and the electric field strength and magnetic flux density at the test point were measured, and a parameter-response mapping relationship library was established.
[0008] Each data record stores different test results, including influencing parameters, electric field strength and magnetic flux density at each test point, and stress distribution data of the cardiac stent.
[0009] The influencing parameters include the operating power of the power equipment, human geometry, human conductivity, dielectric parameters, and the relative spatial position between the human model and the power equipment.
[0010] The virtual scene creation includes: S101, scanning the three-dimensional body posture data of the human body in a standing posture in reality, and the relative spatial position data of the power equipment.
[0011] S102. Generate a digital human body model based on computed tomography scan data, and implant a digital model of a coronary stent into the coronary artery of the digital human body model.
[0012] S103. Generate a digital model of the power equipment, and set the power equipment model and the human body model according to their actual relative positions based on the scanned spatial location data.
[0013] By replacing physical experiments with digital modeling, experimental costs are reduced and scene reproduction is achieved. A high-precision digital twin of the real environment is established, providing a foundation for subsequent virtual measurements.
[0014] S200. Set test points in the digital human body model within the virtual scene, calculate the contribution index of each test point based on the experimental dataset, and filter and label these test points according to the contribution index. Specifically, this includes: S201. Set n test surfaces in the digital human body model of the virtual scene, and set m test points on each test surface. The test points in each test surface are uniformly distributed in a matrix, and record the spatial coordinates of each test point.
[0015] S202. Analyze the influence parameters of each data record in the experimental dataset and compare them with the environment in the virtual scene. Calculate the difference index for each data record and mark data records with a difference index less than a threshold. Specifically, this includes: S2021. Obtaining data records. Influencing parameters, extract the operating power of power equipment Q sampling points were uniformly set on a cardiac stent within a human model, and the spatial distance between each sampling point and the power equipment was analyzed. .
[0016] S2022, Analyze the operating power of the digital model of power equipment in the virtual scene. Sampling points were mapped at corresponding locations on the digital model of the cardiac stent, and the spatial distance between each sampling point and the digital model of the power equipment was analyzed. .
[0017] S023, Substitute data into formula and record. The difference index is calculated for each data record. Similarly, the difference index is calculated for each data record individually.
[0018] Difference Index The calculation formula is: .
[0019] In the formula, and The first Spatial distance of each sampling point and The maximum and minimum values, It is a constant.
[0020] By calculating and filtering the difference index, only data records with a small SI are retained to ensure that the experimental data are highly relevant to the current scenario.
[0021] S203. Obtain the maximum stress value of the cardiac stent stress distribution in each labeled data record, as well as the electric field strength and magnetic flux density at each test point. Calculate the contribution index of each test point based on the fitted relationship between the maximum stress value and the electric field strength and magnetic flux density. Specifically, this includes: S2031. Count the number of labeled data records e, using the maximum stress value in each labeled data record as the dependent variable, and the electric field strength and magnetic flux density of all test points as independent variables, and package them into a sample.
[0022] S2032, Set intercept and regression coefficients And establish a linear regression model. Use the independent variables from these e samples as input values. The output value of each sample The difference between the dependent variable and the differential variable is used as the difference coefficient.
[0023] The expression is: .
[0024] S2033. Adjust the intercept and regression coefficients until the sum of the difference coefficients of all samples is minimized, and obtain the relationship after training is complete.
[0025] The relationship between electric field strength and magnetic flux density and the maximum stress of the support was established by linear regression.
[0026] S2034. Obtain the regression coefficient corresponding to each test point in the relational expression, and analyze the spatial distance between each test point in the virtual scene and the digital model of the power equipment. .
[0027] According to the formula: Calculate the contribution index of each test point .
[0028] In the formula, A constant greater than 1 The largest regression coefficient among all test points. The operating power of the digital model of the power equipment in the virtual scene. It is a natural constant. This is the attenuation coefficient.
[0029] By integrating statistical and electromagnetic theories, the contribution of test points is quantified.
[0030] Contribution Index This is a dimensionless numerical value used to quantify the contribution of this test point to the prediction of cardiac stent stress. The larger the value, the greater the contribution.
[0031] The square root of the operating power is used to reflect the fundamental relationship between electric field strength and power. Both electric and magnetic field strengths are related by... Proportional to power The larger the field strength, the stronger the field strength, and the more important the test point becomes.
[0032] The reciprocal of the spatial distance is used to represent the geometric diffusion and attenuation of the electromagnetic field. For point source radiation, in free space, the field strength decreases with distance; the smaller the distance, the stronger the field strength, and the higher the importance of the test point.
[0033] Exponential decay term Additional losses in the simulated environment. Attenuation coefficient. The non-ideal attenuation during propagation was captured. When When = 0, it represents free space; when When the value is greater than 0, the decay rate is faster, and the field strength and importance of the test point decrease.
[0034] Attenuation coefficient This represents the attenuation intensity of electromagnetic waves propagating in the environment. (Natural constant) Used for calculating exponential decay.
[0035] S204. Select and mark test points whose contribution index is greater than the threshold.
[0036] By intelligently optimizing the selection of test points, only test points that significantly contribute to the prediction of cardiac stent stress are retained, reducing redundant calculations and measurement costs.
[0037] S300. Set different equivalent points for each marked test point in the free space within the virtual scene, analyze the location of each equivalent point, calculate the weight index, and set the weight of the corresponding equivalent point according to the weight index. Specifically, this includes: S301. Mark the location of the marked test point in the virtual scene, establish h equivalent points for each marked test point in the free space, and generate the location of each equivalent point through a spatial coordinate transformation algorithm.
[0038] The spatial coordinate transformation algorithm satisfies: .
[0039] In the formula, For equivalent point space coordinates, Mark the spatial coordinates of the test point corresponding to the equivalent point. and This is the initial spacing; and Let be the random perturbation coefficient, and satisfy... .
[0040] It is the azimuth angle. The field strength gradient weighting factor. This is a nonlinear offset function based on the electric field intensity gradient.
[0041] random disturbance coefficient and This is a dimensionless value used to control the random offset amplitude of the equivalent point. When and The larger the value, the larger the offset range. The range is usually limited to [0.1, 0.5]. For highly uniform field regions, 0.1-0.2 is used, and for complex field regions, 0.3-0.5 is used.
[0042] Azimuth Using the spatial location of the marked test point as the center, the distribution angle of the equivalent points in the XY plane is usually uniformly distributed. To ensure isotropic coverage.
[0043] Field strength gradient weighting factor It is a dimensionless value used to control the intensity of the influence of the electric field gradient on the z-direction offset. A larger value significantly moves the equivalent point in the high gradient region further away, while a smaller value weakens the gradient effect.
[0044] Intelligent random distribution is achieved by using random perturbation coefficients and azimuth angles to improve spatial coverage uniformity. This technology is used to drive adaptive obstacle avoidance and circumvent high-field interference regions. It deeply integrates random sampling with gradient response to provide robustness for weight allocation in complex electromagnetic environments.
[0045] S302. Analyze the Euclidean distance between each equivalent point and the digital model of the power equipment. The COMSOL electromagnetic field simulation model was used for simulation testing. After the test was completed, the estimated electric field strength and estimated magnetic flux density at each equivalent point were obtained.
[0046] Test points are pre-defined fixed locations representing sampling positions of the target field distribution. Equivalent points are dynamically generated proxy points around the test points; their positions are determined by the transformation algorithm, and their number is adaptively set according to the scene type.
[0047] In a uniform field region, the field distribution is relatively flat, and a single equivalent point can characterize the test point's properties. Typically, there is only one equivalent point under a test point.
[0048] In regions with moderate gradient fields, there are usually two or more equivalent points below the test point because the direction of field strength change needs to be covered.
[0049] To cope with drastic field changes in high gradient or boundary regions, multi-directional sampling is used to improve robustness. Typically, there are four or more equivalent points under the test point.
[0050] S303. Calculate the weight index of each equivalent point based on the estimated electric field strength and the estimated magnetic flux density, input the weight index into the normalization activation function to generate weight values, and set weights for each equivalent point.
[0051] Weighting index The calculation formula is: .
[0052] In the formula, and Let be a dimensionless, adjustable coupling coefficient, and satisfy . . The magnitude of the rate of change of electric field intensity at the equivalent point. This is the field strength variance threshold.
[0053] This represents the magnetic field divergence value at the location of the equivalent point. For boundary recognition threshold, This is the error function.
[0054] Used to control the strength of the distance term's influence on the weights. A larger value significantly increases the weight of nearby equivalent points, while a smaller value weakens the distance sensitivity.
[0055] Used to control the contribution of boundary effects to the weights. A larger value indicates a dominant boundary effect, while a smaller value weakens the boundary effect. The specific value should be chosen based on the scenario, for example, a metal boundary. Free space .
[0056] The field strength variance threshold is used to determine the critical value for field strength stability. A large value indicates tolerance for drastic field changes, while a small value indicates a preference for uniform regions.
[0057] The magnetic field divergence value reflects the degree of magnetic flux leakage. It is calculated using Maxwell's equations and is zero in an ideal case, but non-zero at the boundary.
[0058] The boundary identification threshold is used to determine the critical value of boundary magnetic anomalies. A large value will only affect strong boundaries, while a small value will sensitively identify weak boundaries.
[0059] The error function is used to normalize the magnetic field divergence to a smooth function of [0,1]. It grows approximately linearly when the input value is less than 1, and saturates to 1 when it is greater than 2.
[0060] To address the issue of "limited space for non-invasive measurements," the electromagnetic state of test points within the body is indirectly characterized by equivalent points in free space, and a weighting mechanism is used to improve the representativeness of the measurements.
[0061] S400: Measure the electric field strength and magnetic flux density at each equivalent point, and calculate the electric field strength and magnetic flux density at the corresponding marked test points using weights. Calculate the electromagnetic wave energy density of the human cardiac stent, and measure the stress distribution on the cardiac stent using the virtual displacement method.
[0062] The formula for electromagnetic wave energy density is: .
[0063] The virtual displacement method satisfies the following formula: .
[0064] In the formula, For electric field strength, , is the magnetic flux density The dielectric constant of the medium. Permeability, The stress on the cardiac stent, For electromagnetic wave energy density, The displacement is in the generalized coordinate system.
[0065] In practice, a low-frequency electromagnetic field radiation analyzer is used to measure and record the electric field intensity and magnetic flux density at each equivalent point. A weighted algorithm is then used to combine the electric field intensity and magnetic flux density at each equivalent point to calculate and output the electric field intensity and magnetic flux density at the corresponding marked test point.
[0066] The electromagnetic wave energy density of the human heart stent is calculated by measuring the electric field strength and magnetic flux density, and the stress distribution on the heart stent is measured by measuring the virtual displacement method.
[0067] By mapping the easily measurable electric field strength and magnetic flux density in vitro to the stress of the in vivo scaffold, secondary harm to patients caused by implanted sensors can be avoided.
[0068] Achieving non-invasive stress measurement of cardiac stents, and calculating stress distribution by combining electric field strength and magnetic flux density inversion with physical principles.
[0069] Compared with the prior art, the beneficial effects achieved by the present invention are: non-invasive and safe measurement: completely eliminates the risk of secondary trauma and field distribution interference of implanted sensors, and realizes non-contact monitoring of cardiac stent stress through the measurement and weighted inversion of the electric field strength and magnetic flux density at the equivalent point outside the body.
[0070] Intelligent optimization of test points: Breaking through the traditional redundant test point layout mode, based on the contribution index, spatial attenuation, power influence and electromagnetic field inversion correlation, key test points are dynamically selected, significantly reducing the calculation and measurement costs.
[0071] Adaptive to complex environments: By adapting to random perturbations and electric field gradient responses, it adaptively avoids dynamic regions with drastic field strength changes and significant interference characteristics, thus improving robustness in complex boundary scenarios. Measurement noise is suppressed by adjusting the weights of field strength stability and boundary magnetic leakage.
[0072] Individualized model accuracy assurance: Based on real experimental data, a virtual scene is constructed. By matching the coupling relationship between power and position parameters through the difference index, the electromagnetic characteristic variation after stent implantation is accurately characterized, thereby improving the accuracy of field strength prediction.
[0073] Improved efficiency and universality: A unified weighting index quantifies the electromagnetic propagation characteristics of free space and metallic boundaries. The virtual displacement method directly maps electromagnetic energy density to stress distribution, avoiding repetitive simulation experiments. Attached Figure Description
[0074] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.
[0075] Figure 1 This is a flowchart illustrating a non-invasive measurement method for measuring cardiac stent stress according to the present invention.
[0076] Figure 2 This is a schematic diagram of a standing digital human body model.
[0077] Figure 3 This is a schematic diagram of a digital model of myocardial tissue and internal coronary arteries.
[0078] Figure 4 This is a schematic diagram of a digital model of a coronary artery with an implanted stent.
[0079] Figure 5 This is a schematic diagram of a digital model of a biodegradable magnesium alloy scaffold.
[0080] Figure 6 This is a multi-view schematic diagram of a digital model of a biodegradable magnesium alloy scaffold.
[0081] Figure 7 This is a schematic diagram of the distribution of test points in a digital human body model.
[0082] Figure 8 This is a schematic diagram of the distribution of test points in myocardial tissue.
[0083] Figure 9 This is a schematic diagram of a virtual scene. Detailed Implementation
[0084] 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.
[0085] Please see Figure 1 The present invention provides a non-invasive measurement method for measuring the stress of a cardiac stent, comprising: S100, collecting experimental datasets, scanning to obtain three-dimensional data and spatial positions of the human body and electrical equipment in reality, generating a digital human body model containing the cardiac stent and a digital model of the electrical equipment, and establishing a virtual scene.
[0086] Experimental datasets refer to the data obtained by adjusting the influencing parameters and actually measuring the electric field strength and magnetic flux density at each test point in the human body model, as well as the stress distribution data of the heart stent, in an experimental environment with a human body model containing a heart stent and electrical equipment, thereby establishing data records.
[0087] By adjusting parameters such as the power of electrical equipment and the relative position of the human body and the equipment, the stress distribution of the cardiac stent and the electric field strength and magnetic flux density at the test point were measured, and a parameter-response mapping relationship library was established.
[0088] Each data record stores different test results, including influencing parameters, electric field strength and magnetic flux density at each test point, and stress distribution data of the cardiac stent.
[0089] The influencing parameters include the operating power of the power equipment, human geometry, human conductivity, dielectric parameters, and the relative spatial position between the human model and the power equipment.
[0090] The virtual scene creation includes: S101, scanning the three-dimensional body posture data of the human body in a standing posture in reality, and the relative spatial position data of the power equipment.
[0091] S102. Generate a digital human body model based on computed tomography scan data, and implant a digital model of a coronary stent into the coronary artery of the digital human body model.
[0092] S103. Generate a digital model of the power equipment, and set the power equipment model and the human body model according to their actual relative positions based on the scanned spatial location data.
[0093] By replacing physical experiments with digital modeling, experimental costs are reduced and scene reproduction is achieved. A high-precision digital twin of the real environment is established, providing a foundation for subsequent virtual measurements.
[0094] S200. Set test points in the digital human body model within the virtual scene, calculate the contribution index of each test point based on the experimental dataset, and filter and label these test points according to the contribution index. Specifically, this includes: S201. Set n test surfaces in the digital human body model of the virtual scene, and set m test points on each test surface. The test points in each test surface are uniformly distributed in a matrix, and record the spatial coordinates of each test point.
[0095] S202. Analyze the influence parameters of each data record in the experimental dataset and compare them with the environment in the virtual scene. Calculate the difference index for each data record and mark data records with a difference index less than a threshold. Specifically, this includes: S2021. Obtaining data records. Influencing parameters, extract the operating power of power equipment Q sampling points were uniformly set on a cardiac stent within a human model, and the spatial distance between each sampling point and the power equipment was analyzed. .
[0096] S2022, Analyze the operating power of the digital model of power equipment in the virtual scene. Sampling points were mapped at corresponding locations on the digital model of the cardiac stent, and the spatial distance between each sampling point and the digital model of the power equipment was analyzed. .
[0097] S023, Substitute data into formula and record. The difference index is calculated for each data record. Similarly, the difference index is calculated for each data record individually. The calculation formula is: .
[0098] In the formula, and The first Spatial distance of each sampling point and The maximum and minimum values, It is a constant.
[0099] By calculating and filtering the difference index, only data records with a small SI are retained to ensure that the experimental data are highly relevant to the current scenario.
[0100] S203. Obtain the maximum stress value of the cardiac stent stress distribution in each labeled data record, as well as the electric field strength and magnetic flux density at each test point. Calculate the contribution index of each test point based on the fitting formula between the maximum stress value and the electric field strength and magnetic flux density. Specifically, this includes: S2031. Count the number of labeled data records e, using the maximum stress value in each labeled data record as the dependent variable, and the electric field strength and magnetic flux density of all test points as independent variables, and package them into a sample.
[0101] S2032, Set intercept and regression coefficients And establish a linear regression model. Use the independent variables from these e samples as input values. The output value of each sample The difference between the dependent variable and the differential variable is used as the difference coefficient.
[0102] The expression is: .
[0103] S2033. Adjust the intercept and regression coefficients until the sum of the difference coefficients of all samples is minimized, and obtain the relationship after training is complete.
[0104] The relationship between electric field strength and magnetic flux density (independent variables) and maximum stress of the support (dependent variable) was established by linear regression.
[0105] S2034. Obtain the regression coefficient corresponding to each test point in the relational expression, and analyze the spatial distance between each test point in the virtual scene and the digital model of the power equipment. .
[0106] According to the formula: Calculate the contribution index of each test point .
[0107] In the formula, A constant greater than 1 The largest regression coefficient among all test points. The operating power of the digital model of the power equipment in the virtual scene. It is a natural constant. This is the attenuation coefficient.
[0108] By integrating statistics (regression model) and electromagnetic theory (field strength attenuation model), the contribution of test points is quantified.
[0109] Contribution Index This is a dimensionless numerical value used to quantify the contribution of this test point to the prediction of cardiac stent stress. The larger the value, the greater the contribution.
[0110] The square root of the operating power is used to reflect the fundamental relationship between electric field strength and power. Both electric and magnetic field strengths are related by... Proportional to power The larger the field strength, the stronger the field strength, and the more important the test point becomes.
[0111] The reciprocal of the spatial distance is used to represent the geometric diffusion attenuation of the electromagnetic field (i.e., the inverse square law). For point source radiation, in free space, the field strength decreases with distance; the smaller the distance, the stronger the field strength, and the higher the importance of the test point.
[0112] Exponential decay term Additional losses in the simulated environment. Attenuation coefficient. It captured non-ideal attenuation during propagation (such as atmospheric absorption, terrain, or building obstruction). When When = 0, it represents free space (no additional decay); when When the value is greater than 0, the decay rate is faster, and the field strength and importance of the test point decrease.
[0113] Attenuation coefficient This represents the attenuation intensity of electromagnetic waves propagating in the environment. (Natural constant) (Approximately equal to 2.71828), used for exponential decay calculations.
[0114] S204. Select and mark test points whose contribution index is greater than the threshold.
[0115] By intelligently optimizing the selection of test points, only test points that significantly contribute to the prediction of cardiac stent stress are retained, reducing redundant calculations and measurement costs.
[0116] S300. Set different equivalent points for each marked test point in the free space within the virtual scene, analyze the location of each equivalent point, calculate the weight index, and set the weight of the corresponding equivalent point according to the weight index. Specifically, this includes: S301. Mark the location of the marked test point in the virtual scene, establish h equivalent points for each marked test point in the free space, and generate the location of each equivalent point through a spatial coordinate transformation algorithm.
[0117] The spatial coordinate transformation algorithm satisfies: .
[0118] In the formula, For equivalent point space coordinates, Mark the spatial coordinates of the test point corresponding to the equivalent point. and This is the initial spacing; and Let be the random perturbation coefficient, and satisfy... .
[0119] It is the azimuth angle. The field strength gradient weighting factor. This is a nonlinear offset function based on the electric field intensity gradient.
[0120] random disturbance coefficient and This is a dimensionless value used to control the random offset amplitude of the equivalent point. When and The larger the value, the larger the offset range. The range is usually limited to [0.1, 0.5]. For highly uniform field regions, 0.1-0.2 is used, and for complex field regions, 0.3-0.5 is used.
[0121] Azimuth Using the spatial location of the marked test point as the center, the distribution angle of the equivalent points in the XY plane is usually uniformly distributed. To ensure isotropic coverage.
[0122] Field strength gradient weighting factor It is a dimensionless value used to control the intensity of the influence of the electric field gradient on the z-direction offset. A larger value significantly moves the equivalent point in the high gradient region further away, while a smaller value weakens the gradient effect.
[0123] Intelligent random distribution is achieved by using random perturbation coefficients and azimuth angles to improve spatial coverage uniformity. This technology is used to drive adaptive obstacle avoidance and circumvent high-field interference regions. It deeply integrates random sampling with gradient response to provide robustness for weight allocation in complex electromagnetic environments.
[0124] S302. Analyze the Euclidean distance between each equivalent point and the digital model of the power equipment. The COMSOL electromagnetic field simulation model was used for simulation testing. After the test was completed, the estimated electric field strength and estimated magnetic flux density at each equivalent point were obtained.
[0125] Test points are pre-defined fixed locations (such as grid intersections) that represent the sampling locations of the target field distribution. Equivalent points are dynamically generated proxy points around the test points; their positions are determined by the transformation algorithm, and their number is adaptively set according to the scene type.
[0126] In a uniform field region, the field distribution is relatively flat, and a single equivalent point can characterize the test point's properties. Typically, there is only one equivalent point under a test point.
[0127] In medium gradient field regions, since the direction of field strength change needs to be covered (such as antenna sidelobe regions), there are usually two or more equivalent points under the test point.
[0128] To cope with drastic field changes (metal edges, dielectric interfaces) in high gradient or boundary regions, multi-directional sampling is used to improve robustness. Typically, there are four or more equivalent points under the test point.
[0129] S303. Calculate the weight index of each equivalent point based on the estimated electric field strength and the estimated magnetic flux density, input the weight index into the normalization activation function to generate weight values, and set weights for each equivalent point.
[0130] Weighting index The calculation formula is: .
[0131] In the formula, and Let be a dimensionless, adjustable coupling coefficient, and satisfy . . The magnitude of the rate of change of electric field intensity at the equivalent point. This is the field strength variance threshold.
[0132] This represents the magnetic field divergence value at the location of the equivalent point. For boundary recognition threshold, This is the error function.
[0133] Used to control the strength of the distance term's influence on the weights. A larger value significantly increases the weight of nearby equivalent points, while a smaller value weakens the distance sensitivity.
[0134] Used to control the contribution of boundary effects to the weights. A larger value indicates a dominant boundary effect, while a smaller value weakens the boundary effect. The specific value should be chosen based on the scenario, for example, a metal boundary. Free space .
[0135] The field strength variance threshold is used to determine the critical value for field strength stability. A large value indicates tolerance for drastic field changes, while a small value indicates a preference for uniform regions.
[0136] The magnetic field divergence value reflects the degree of magnetic flux leakage. It is calculated using Maxwell's equations and is zero in an ideal case, but non-zero at the boundary.
[0137] The boundary identification threshold is used to determine the critical value of boundary magnetic anomalies. A large value will only affect strong boundaries, while a small value will sensitively identify weak boundaries.
[0138] The error function is used to normalize the magnetic field divergence to a smooth function of [0,1]. It grows approximately linearly when the input value is less than 1, and saturates to 1 when it is greater than 2.
[0139] To address the issue of "limited space for non-invasive measurements," the electromagnetic state of test points within the body is indirectly characterized by equivalent points in free space, and a weighting mechanism is used to improve the representativeness of the measurements.
[0140] S400: Measure the electric field strength and magnetic flux density at each equivalent point, and calculate the electric field strength and magnetic flux density at the corresponding marked test points using weights. Calculate the electromagnetic wave energy density of the human cardiac stent, and measure the stress distribution on the cardiac stent using the virtual displacement method.
[0141] The formula for electromagnetic wave energy density is: .
[0142] The virtual displacement method satisfies the following formula: .
[0143] In the formula, For electric field strength, , is the magnetic flux density The dielectric constant of the medium. Permeability, The stress on the cardiac stent, For electromagnetic wave energy density, The displacement is in the generalized coordinate system.
[0144] In practice, a low-frequency electromagnetic field radiation analyzer is used to measure and record the electric field intensity and magnetic flux density at each equivalent point. A weighted algorithm is then used to combine the electric field intensity and magnetic flux density at each equivalent point to calculate and output the electric field intensity and magnetic flux density at the corresponding marked test point.
[0145] The electromagnetic wave energy density of the human heart stent is calculated by measuring the electric field strength and magnetic flux density, and the stress distribution on the heart stent is measured by measuring the virtual displacement method.
[0146] By mapping the easily measurable electric field strength and magnetic flux density in vitro to the stress of the in vivo scaffold, secondary harm to patients caused by implanted sensors can be avoided.
[0147] Achieving non-invasive stress measurement of cardiac stents, and calculating stress distribution by combining electric field strength and magnetic flux density inversion with physical principles.
[0148] Example 1: Please refer to Figure 2 The digital human model maintains a standardized standing posture, with feet shoulder-width apart and arms hanging naturally. The internal cardiac muscle tissue contains a cardiac stent. The model is 1750mm tall, with a shoulder width of 525mm. The cardiac muscle tissue is 117mm wide, 133mm high, and has a chest thickness of 272mm.
[0149] Please see Figure 3 The coronary stent is implanted into the coronary artery within the myocardium as needed, and the relative position of the stent within the blood vessel is recorded. The coronary artery... Figure 4 For example, the yellow part is the heart stent. The coronary artery is composed of fat, blood vessel wall and blood. The fat is 1 mm thick, the blood vessel wall is 0.27 mm thick, and the blood vessel diameter is 3.53 mm.
[0150] Cardiac stent selection Figure 5 Taking biodegradable magnesium alloy scaffolds as an example, including but not limited to Figure 6 The stent shape is shown in the front and top views. The cardiac stent is 19.7 mm long, 3 mm in diameter, and 0.1 mm thick. The entire heart model and the implanted stent are securely and stably fixed in the chest cavity.
[0151] Please refer to Figure 7 and Figure 8 Taking the digital human body model and digital heart model of this size as examples, the specific locations of the magnetic field and electric field test points in the human body and heart are marked. The human body tissue is divided into 6 layers and the heart tissue into 4 layers. Several points are selected as test points in each layer in a matrix form. The test points are required to be evenly distributed.
[0152] Record the spatial location of each test point in the human body and heart, mark it in the actual test environment, and use it as an equivalent point.
[0153] Experiments were conducted in an environment with a background magnetic field of less than 50 nT. Environmental parameters were monitored in real time using a temperature and humidity recorder, with the temperature strictly controlled within 22 ± 1℃ and the humidity maintained within 50 ± 5% RH. Standardized environmental control effectively eliminated interference from external factors on the measurement results, ensuring the reliability of the data.
[0154] The radiation source was a roadside three-phase power transformer with a capacity of 200kVA and an operating frequency of 50Hz. The stability of the radiation was verified by measuring the magnetic field at the center point every five minutes using a FieldMan electromagnetic radiation analyzer from Narda.
[0155] Please see Figure 9 Measurements were taken at equivalent points below the radiation source. Figure 9 This is a virtual scenario diagram illustrating the testing process of this application example. The example uses a Narda FieldMan multi-functional electromagnetic radiation analyzer for measurement. The testing device includes a probe, an insulating extension rod, and the analyzer body. The analyzer body is connected to a computer via a cable, and all test data is saved to a storage device or computer.
[0156] The testing equipment must be securely installed, and the equipment itself should not significantly interfere with the local electromagnetic environment of the heart or human body. Combine the selected digital probe with the analyzer body, securely connect it to the host via cable, and switch the mode to magnetic field after powering on.
[0157] Secure the probe to the bracket, ensuring the probe sensor is aligned with the equivalent point. Move the probe close to each equivalent point sequentially; the isotropic probe will automatically synthesize triaxial data and display the total field strength. Once the values stabilize, record the electric field strength and magnetic flux density at the equivalent point as the data for the corresponding test point. Repeat the measurement multiple times under the same conditions to evaluate the system's stability and reduce random errors. After measurement, transmit the data to the data storage computer via cable.
[0158] This application indirectly obtains the stress generated by the stent by measuring and calculating the electromagnetic wave energy density generated on the stent. This measurement method can obtain relatively accurate stress values without damaging the human body or the stent, so that the stent material and stent geometry can be adjusted and improved based on the stress measurement results to increase its stability.
[0159] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.
[0160] In the testing method of this invention, the digital human body model serves only as a reference for calculating the spatial position of the test point, providing a coordinate positioning reference for the actual electromagnetic field measurement. This digital human body model does not participate in the actual electromagnetic field measurement; the actual measurement is the equivalent point calculated from the test point in free space.
[0161] In the description of this application, all relevant dimensions of the human body, heart, and stent are used only for the purpose of describing this application and are not intended to indicate or imply that the human body, heart, and stent must have fixed dimensions, and therefore should not be construed as a limitation of this application.
[0162] The human body and heart mentioned in this application are only used as examples of digital models to design test points. The purpose is to clearly describe the measurement process and details, and it is not required to use real human bodies and hearts for measurement. Therefore, it should not be construed as a limitation on this application.
[0163] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A non-invasive method for measuring cardiac stent stress, characterized in that: The method includes: S100: Collect experimental datasets, scan and obtain three-dimensional data and spatial location of real human bodies and power equipment, generate digital human body models and power equipment digital models including cardiac stents, and establish virtual scenes. S200. Set test points in the digital human body model in the virtual scene, calculate the contribution index of each test point in combination with the experimental dataset, and filter and mark these test points according to the contribution index. S300. Set different equivalent points for each marked test point in the free space of the virtual scene, analyze the location of each equivalent point and calculate the weight index, and set the weight of the corresponding equivalent point according to the weight index. S400: Measure the electric field strength and magnetic flux density at each equivalent point, and calculate the electric field strength and magnetic flux density at the corresponding marked test points based on the weights; calculate the electromagnetic wave energy density of the human heart stent, and measure the stress distribution on the heart stent using the virtual displacement method.
2. The non-invasive measurement method for measuring cardiac stent stress according to claim 1, characterized in that: In S100, the experimental dataset refers to the data on electric field strength and magnetic flux density at each test point within the human body model, as well as the stress distribution data of the heart stent, obtained by adjusting the influencing parameters and actually measuring them in an experimental environment with a human body model containing a heart stent and electrical equipment, thereby establishing a data record. Each data record stores different test results, specifically including influencing parameters, electric field strength and magnetic flux density at each test point, and stress distribution data of the cardiac stent; influencing parameters include the operating power of the power equipment, human geometry, human conductivity, dielectric parameters, and the relative spatial position between the human model and the power equipment; Virtual scene creation includes: S101. Scan the three-dimensional body posture data of the human body in a standing posture in reality, as well as the relative spatial position data of the power equipment; S102. Generate a digital human body model based on computed tomography scan data, and implant a digital model of a coronary stent into the coronary artery of the digital human body model. S103. Generate a digital model of the power equipment, and set the power equipment model and the human body model according to their actual relative positions based on the scanned spatial location data.
3. The non-invasive measurement method for measuring cardiac stent stress according to claim 2, characterized in that: S200 includes: S201. Set n test surfaces in the digital human body model of the virtual scene, and set m test points on each test surface; the test points in each test surface are evenly distributed in a matrix, and record the spatial coordinates of each test point; S202. Analyze the influence parameters of each data record in the experimental dataset and compare them with the environment in the virtual scene; calculate the difference index of each data record and mark the data records with a difference index less than the threshold. S203. Obtain the maximum stress value of the cardiac stent stress distribution in each marked data record, as well as the electric field strength and magnetic flux density at each test point; calculate the contribution index of each test point based on the fitting relationship between the maximum stress value and the electric field strength and magnetic flux density. S204. Select and mark test points whose contribution index is greater than the threshold.
4. The non-invasive measurement method for measuring cardiac stent stress according to claim 3, characterized in that: S202 includes: S2021, Obtain Data Records Influencing parameters, extract the operating power of power equipment Q sampling points were uniformly set on the cardiac stent within the human model, and the spatial distance between each sampling point and the power equipment was analyzed. ; S2022, Analyze the operating power of the digital model of power equipment in the virtual scene. Sampling points were mapped at corresponding locations on the digital model of the cardiac stent, and the spatial distance between each sampling point and the digital model of the power equipment was analyzed. ; S023, Substitute data into formula and record. The difference index is calculated; similarly, the difference index for each data record is calculated separately; difference index The calculation formula is: ； In the formula, and The first Spatial distance of each sampling point and The maximum and minimum values, It is a constant.
5. A non-invasive method for measuring cardiac stent stress according to claim 3, characterized in that: S203 includes: S2031, The number of statistically marked data records e, the maximum stress value in each marked data record is used as the dependent variable, and the electric field strength and magnetic flux density of all test points are used as independent variables, and packaged into a sample; S2032, Set intercept and regression coefficients A linear regression model is then established, and the independent variables from these e samples are used as input values. The output value of each sample The difference between the dependent variable and the differential variable is used as the difference coefficient; the expression is as follows: ； S2033. Adjust the intercept and regression coefficients until the sum of the difference coefficients of all samples is minimized, and obtain the relationship after training is completed. S2034. Obtain the regression coefficient corresponding to each test point in the relational expression, and analyze the spatial distance between each test point in the virtual scene and the digital model of the power equipment. Substitute into the formula to calculate the contribution index of each test point. : ； In the formula, A constant greater than 1 The largest regression coefficient among all test points. The operating power of the digital model of the power equipment in the virtual scene. It is a natural constant. This is the attenuation coefficient.
6. The non-invasive measurement method for measuring cardiac stent stress according to claim 3, characterized in that: The S300 includes: S301. Mark the position of the test points in the virtual scene. Establish h equivalent points for each test point in free space and generate the position of the equivalent points through a spatial coordinate transformation algorithm. S302. Analyze the Euclidean distance between the equivalent point and the digital model of the power equipment. The COMSOL electromagnetic field simulation model was used for simulation testing. After the test was completed, the estimated electric field strength and estimated magnetic flux density at the equivalent point were obtained. S303. Calculate the weight index of the equivalent point based on the estimated electric field strength and the estimated magnetic flux density, input the weight index into the normalization activation function to generate weight values, and set weights for each equivalent point.
7. A non-invasive method for measuring cardiac stent stress according to claim 6, characterized in that: In S301, the spatial coordinate transformation algorithm satisfies: ； In the formula, For equivalent point space coordinates, Mark the spatial coordinates of the test point corresponding to the equivalent point; and This is the initial spacing; and For random disturbance coefficients; It is the azimuth angle. The field strength gradient weighting factor. This is a nonlinear offset function based on the electric field intensity gradient.
8. A non-invasive method for measuring cardiac stent stress according to claim 6, characterized in that: In S303, the weight index The calculation formula is: ； In the formula, and Let be a dimensionless, adjustable coupling coefficient, and satisfy . ; The magnitude of the rate of change of electric field intensity at the equivalent point. The threshold value for field strength variance; This represents the magnetic field divergence value at the location of the equivalent point. For boundary recognition threshold, This is the error function.
9. A non-invasive method for measuring cardiac stent stress according to claim 6, characterized in that: In S400, a low-frequency electromagnetic field radiation analyzer is used to measure and record the electric field intensity and magnetic flux density at each equivalent point. A weighted algorithm is used to combine the electric field intensity and magnetic flux density at each equivalent point to calculate and output the electric field intensity and magnetic flux density at the corresponding marked test points. The electromagnetic wave energy density of the human heart stent is calculated, and the stress distribution on the heart stent is measured using the virtual displacement method. The virtual displacement method satisfies the following formula: ； In the formula, The stress on the cardiac stent, Electromagnetic energy for power equipment The displacement is in the generalized coordinate system.