Method for constructing three-dimensional digital model of medical catheter

Abstract

The invention relates to the technical field of three-dimensional digital model construction, in particular to a three-dimensional digital model construction method of a medical catheter. The method comprises the following steps that imaging processing is conducted on a medical catheter communication area through high-resolution CT scanning, and a CT slice image of the communication area is obtained; based on the slice image, the lumen form is analyzed, the pressure intensity density distribution difference is calculated, then viscosity boundary effect analysis is conducted, and lumen form data and viscosity boundary effect data of the catheter trend are obtained; then, performing diameter shrinkage dynamic analysis on the inner cavity of the cylindrical section based on the data, and quantifying a lag reaction to obtain dynamic shrinkage lag reaction data; and finally, constructing a three-dimensional digital model for catheter communication in combination with the dynamic contraction lag reaction data and the viscosity boundary effect data. According to the method, the three-dimensional digital model construction technology is optimized, so that the three-dimensional digital model construction technology is more perfect.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional digital model construction, and in particular to a three-dimensional digital model construction method for a medical catheter. Background Art

[0002] Three-dimensional digital modeling of medical catheters has become an important direction in modern medical research and clinical applications. Medical catheters, as a common medical device, are widely used in various medical operations, such as intravenous injection, drainage, catheterization, hemodialysis, etc., which can directly affect the treatment effect and the patient's recovery process. In the past, medical catheters were mostly designed and evaluated using two-dimensional images or a single fluid mechanics model. This method is prone to ignore the microscopic morphological characteristics inside the catheter and its influence on factors such as fluid flow and pressure distribution in the complex three-dimensional structure and dynamic change process, thereby affecting the accuracy and safety of treatment. With the continuous development of computer science and medical image processing technology, more and more studies have begun to explore the precise modeling and simulation of catheters based on three-dimensional digital models. This method can obtain detailed images of the catheter through a high-resolution CT scanner, and based on this, a more realistic and accurate three-dimensional digital model can be constructed. Through this model, researchers can not only clearly present the geometric morphology of the catheter, but also analyze and simulate complex factors such as fluid dynamics, pressure distribution, and interaction with blood vessels and tissues in the catheter lumen, providing more intuitive and reliable reference data for clinical treatment. However, a traditional method for constructing a three-dimensional digital model of a medical catheter has problems such as inaccurate analysis of the complex internal morphology of the catheter and large errors in constructing the three-dimensional digital model. Summary of the invention

[0003] Based on this, it is necessary to provide a method for constructing a three-dimensional digital model of a medical catheter to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for constructing a three-dimensional digital model of a medical catheter is provided, the method comprising the following steps:

[0005] Step S1: performing CT imaging processing on the medical catheter connection area by a high-resolution CT scanner to obtain a CT image of the catheter connection area; performing multi-plane image slicing processing on the CT image of the catheter connection area to obtain a CT slice image of the connection area;

[0006] Step S2: performing lumen morphology analysis based on the connected area CT slice image to obtain catheter direction lumen morphology data; performing pressure density distribution difference calculation based on the catheter direction lumen morphology data to obtain lumen morphology pressure density distribution difference; performing viscous boundary effect analysis on the lumen morphology pressure density distribution difference to obtain lumen morphology viscous boundary effect data;

[0007] Step S3: Based on the lumen morphology viscous boundary effect data, a dynamic analysis of the contraction of the inner cavity diameter of the cylindrical cross section is performed to obtain the dynamic contraction data of the inner cavity diameter of the cross section; the hysteresis reaction of the dynamic contraction data of the inner cavity diameter of the cross section is quantified to obtain the dynamic contraction hysteresis reaction data;

[0008] Step S4: constructing a three-dimensional digital model based on the dynamic contraction hysteresis response data and the lumen morphology viscosity boundary effect data to obtain a three-dimensional digital model of the catheter connection.

[0009] The present invention uses a high-resolution CT scanner to perform CT imaging processing on the connected area of ​​a medical catheter, and can obtain an accurate image of the internal structure of the catheter. Further, the connected area of ​​the catheter is sliced ​​with multiple planes to generate a CT slice image of the connected area. This process can provide high-precision three-dimensional structural information of the catheter, and help provide reliable basic data for subsequent analysis. Through this processing method, the internal morphology of the catheter can be clearly presented, laying the foundation for subsequent lumen morphology analysis and mechanical calculation. Based on the CT slice image of the connected area, the lumen morphology is analyzed to obtain the direction of the catheter and the morphological data of the lumen. These data reflect the geometric morphology of the catheter in space and provide a basis for further fluid dynamics analysis. On this basis, the distribution change of the pressure and density of the fluid in the lumen is obtained by calculating the pressure density distribution difference. Next, the viscous boundary effect of the lumen morphology is analyzed to study the interaction between the fluid on the lumen surface and the wall, which is of great significance to the flow characteristics of the fluid, pressure changes and the transmission efficiency of the lumen. Based on the viscous boundary effect data of the lumen morphology, a dynamic analysis of the contraction of the lumen diameter of the cylindrical section is further performed. This analysis can simulate the geometric changes of the lumen under different working conditions, especially the changes in diameter contraction caused by fluid dynamics. These dynamic data can reveal how the catheter is affected by fluid pressure and viscous forces during operation. By quantifying the hysteresis response of the cross-sectional lumen diameter contraction dynamic data, the response time and behavior characteristics of the lumen under different flow conditions can be evaluated, thereby providing support for catheter design and optimization. A three-dimensional digital model of the catheter is constructed through a comprehensive analysis of the dynamic contraction hysteresis response data and the lumen morphology viscous boundary effect data. The model can fully and realistically simulate the geometry, fluid behavior and dynamic response of the catheter with high accuracy and reliability. The three-dimensional digital model provides a scientific basis for further engineering design, performance prediction and optimization. It can also be used for virtual experiments and simulation analysis to help better understand the working state of the catheter under different conditions and guide improvements and innovations in practical applications. Therefore, the present invention optimizes a traditional method for constructing a three-dimensional digital model of a medical catheter, solves the problems of inaccurate analysis of the complex morphology inside the catheter and large errors in constructing the three-dimensional digital model in the traditional method for constructing a three-dimensional digital model of a medical catheter, improves the accuracy of the analysis of the complex morphology inside the catheter, and reduces the error in constructing the three-dimensional digital model.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: performing CT imaging processing on the medical catheter connection area by a high-resolution CT scanner to obtain a CT image of the catheter connection area;

[0012] Step S12: performing image smoothing filtering on the CT imaging of the connected area of ​​the catheter to obtain a CT filtered image of the connected area;

[0013] Step S13: performing artifact correction on the connected region CT filter image to obtain a connected region CT corrected image;

[0014] Step S14: performing multi-plane image slicing processing on the connected region CT corrected image to obtain a connected region CT slice image.

[0015] The present invention performs CT imaging processing on the medical catheter connection area through a high-resolution CT scanner to obtain a CT image of the catheter connection area. This step provides basic data for the entire analysis process. High-resolution CT imaging can accurately capture the detailed information of the catheter and its connection area, including key features such as the inner cavity morphology and the direction of the pipeline. High-quality CT images provide reliable initial data for subsequent image processing, morphological analysis and mechanical calculation, and are the basis for all subsequent analysis work. The CT image of the catheter connection area is smoothed and filtered to obtain a CT filtered image of the connection area. Image smoothing filtering can effectively remove noise and unnecessary details in CT imaging and improve the quality of the image. After smoothing filtering, the signal in the image will be clearer and more uniform, making the internal structure, morphology and boundary of the catheter more obvious, which is helpful for subsequent analysis and processing and avoiding errors caused by noise or detail interference. The CT filtered image of the connection area is subjected to artifact correction to obtain a CT corrected image of the connection area. Artifacts (such as metal artifacts, motion artifacts, etc.) will appear in CT imaging, and these artifacts will affect the accuracy of the image and the accuracy of subsequent analysis. Artifact correction technology can identify and correct these unrealistic image information and restore the actual morphology of the catheter area, thereby ensuring that the image is more accurate in reflecting the internal structure and lumen morphology of the catheter, and providing a clear image basis for subsequent detailed analysis. The connected area CT corrected image is sliced ​​in multiple planes to obtain a connected area CT slice image. This step generates multiple image views such as cross-sections, longitudinal sections, and oblique sections of the catheter area by slicing on different planes. This slicing process helps to observe the structural features inside the catheter in all directions and analyze its morphology, direction, and geometric changes in the lumen from different angles. Through multi-plane slicing, comprehensive spatial information can be obtained, providing sufficient data support for subsequent lumen morphology analysis.

[0016] Preferably, step S2 comprises the following steps:

[0017] Step S21: performing lumen morphology analysis based on the connected area CT slice image to obtain catheter direction lumen morphology data;

[0018] Step S22: calculating the direction curvature of the catheter direction lumen morphology data to obtain the direction curvature data of the lumen morphology;

[0019] Step S23: Calculating the pressure density distribution difference according to the lumen shape direction curvature data and the catheter direction lumen shape data to obtain the lumen shape pressure density distribution difference;

[0020] Step S24: Performing viscous boundary effect analysis on the pressure density distribution difference of the lumen morphology to obtain viscous boundary effect data of the lumen morphology.

[0021] The present invention performs lumen morphology analysis based on the connected area CT slice image to obtain the catheter direction and lumen morphology data. By analyzing the CT slice image, the geometric morphological features inside the catheter can be extracted, including the direction, curvature, inner diameter and other information of the pipeline. These morphological data provide a basis for further analyzing the fluid properties, mechanical behavior and response of the catheter under different working conditions. Accurate lumen morphological data is crucial for subsequent flow simulation, pressure distribution analysis and optimization design. The direction curvature calculation is performed on the catheter direction lumen morphology data to obtain the direction curvature data of the lumen morphology. Curvature calculation can reveal the degree of change of the direction of the catheter in space, especially when the catheter has a curved part, calculating the direction curvature helps to quantify the geometric characteristics of these bends. Through the direction curvature data, the influence of the degree of catheter bending on fluid flow, pressure distribution, etc. can be deeply analyzed, which helps to understand the flow characteristics of the fluid in the pipeline and the change of flow resistance. According to the lumen morphology direction curvature data and the catheter direction lumen morphology data, the pressure density distribution difference is calculated to obtain the lumen morphology pressure density distribution difference. This step simulates the flow state of the fluid in the catheter and its influence on the pressure and density distribution in the lumen by considering the curvature of the lumen morphology and the changes in the direction of the catheter. By calculating the difference in pressure and density distribution, it is possible to reveal the non-uniformity of the fluid flow in the lumen, and how structural features such as bending and stenosis affect the flow behavior and pressure changes of the fluid. This is of great significance for evaluating catheter performance and optimizing design schemes. The viscous boundary effect analysis of the pressure density distribution difference of the lumen morphology is performed to obtain the viscous boundary effect data of the lumen morphology. When the fluid flows in the catheter, the area close to the wall of the tube will be affected by the boundary effect, the fluid flow rate is low and the pressure distribution is uneven. By analyzing the viscous boundary effect, we can deeply understand the interaction between the fluid and the lumen wall, especially when the fluid viscosity is high, the influence of the boundary layer is crucial to the flow behavior. This analysis helps to evaluate the stability of the flow in the catheter, the fluid transmission efficiency and the flow resistance, thereby providing a basis for optimizing the pipeline design.

[0022] Preferably, step S23 includes the following steps:

[0023] Step S231: performing spatial segmentation processing on the catheter direction lumen morphology data to obtain lumen cross-section data of different regions;

[0024] Step S232: performing curvature distribution fitting on the lumen section data of different regions according to the lumen morphology and direction curvature data to obtain the local curvature distribution data of the lumen section;

[0025] Step S233: simulating the cross-sectional pressure distribution of the lumen cross-sectional data in different regions according to the local curvature distribution data of the lumen cross-sectional data to obtain cross-sectional pressure distribution data;

[0026] Step S234: performing axial non-steady-state fluctuation analysis on the cross-section pressure distribution data to obtain axial non-steady-state fluctuation data of pressure;

[0027] Step S235: Calculate the pressure density distribution difference based on the pressure axial non-steady-state fluctuation data and the cross-sectional pressure distribution data to obtain the lumen morphology pressure density distribution difference.

[0028] The present invention performs spatial segmentation processing on the catheter direction lumen morphology data to obtain lumen section data of different regions. Through the spatial segmentation technology, the entire lumen of the catheter can be divided into different regions according to its morphological characteristics (such as curvature, diameter, direction, etc.). This step helps to convert the complex three-dimensional catheter structure into a plurality of two-dimensional section data that are easy to analyze, thereby providing clear regional division for subsequent detailed analysis. This can accurately analyze the lumen changes in each region, especially in regions with obvious morphological differences, and can help identify key areas of fluid flow and improve analysis accuracy. According to the lumen morphology direction curvature data, the lumen section data of different regions are fitted with curvature distribution to obtain local curvature distribution data of the lumen section. This step quantifies the curvature degree and morphological changes of the lumen by performing curvature fitting on the lumen section of each region. The curvature distribution data can reveal the curvature changes of each region in the pipeline, especially for those regions with sharp bends or transitional changes, and the fitted curvature distribution data can accurately reflect the microscopic characteristics of the pipeline geometry. This helps to understand how different pipeline morphologies affect the flow characteristics of the fluid, and provides key data support for subsequent flow simulation and mechanical analysis. According to the local curvature distribution data of the lumen section, the section pressure distribution of the lumen section data in different regions is simulated to obtain the section pressure distribution data. By simulating the section pressure distribution of each region, the pressure change of the fluid in the lumen can be obtained, especially in the area with large curvature or the pipe bending and contraction, the pressure distribution will be significantly affected. This simulation helps to reveal the fluid dynamic characteristics in the pipeline, such as the local pressure change of the fluid, the flow velocity distribution, etc. The section pressure distribution data provides a specific basis for subsequent flow optimization, pressure control and pipeline design, ensuring the stability and efficiency of the fluid transportation process. The section pressure distribution data is analyzed by axial non-steady fluctuation to obtain the pressure axial non-steady fluctuation data. Axial non-steady fluctuation analysis can be used to study the dynamic behavior of the fluid inside the catheter, especially the non-steady effects caused by pressure fluctuations and flow velocity changes during the fluid flow process. Through this analysis, the fluctuation characteristics of pressure in time and space can be accurately captured, which is crucial for understanding the stability of the flow in the catheter, the propagation of shock waves, and the influence of pressure fluctuations on fluid transportation. The unsteady-state fluctuation data provide a scientific basis for further optimizing pipeline design and controlling fluid flow stability.

[0029] Preferably, step S234 includes the following steps:

[0030] Performing axial projection processing on the cross-section pressure distribution data to obtain axial projection pressure data;

[0031] Perform local fluctuation extraction based on the axial projection pressure data to obtain local pressure fluctuation data for each section;

[0032] Perform energy density spectrum analysis on the local pressure fluctuation data of each section to obtain pressure energy density fluctuation data;

[0033] Based on the pressure energy density fluctuation data, the axial non-steady-state fluctuation analysis is carried out to obtain the pressure axial non-steady-state fluctuation data.

[0034] The present invention performs axial projection processing on the cross-section pressure distribution data to obtain axial projection pressure data. By projecting the cross-section pressure distribution data in the axial direction, the data structure in the three-dimensional space can be effectively simplified, and the complex spatial pressure distribution information can be converted into one-dimensional axial pressure data. This processing helps to highlight the pressure change trend in the axial direction, which is convenient for further analysis of the dynamic characteristics of the fluid in the pipeline. The axial projection pressure data can provide intuitive and simplified input for subsequent fluctuation analysis and stability research, ensuring that the data processing in the analysis process is more efficient. Local fluctuation extraction is performed based on the axial projection pressure data to obtain local pressure fluctuation data of each section. Local fluctuation extraction can identify and extract high-frequency fluctuation components of pressure changing with time based on the axial projection pressure data. These fluctuation components are usually closely related to the dynamic effects of the fluid in the pipeline (such as flow velocity fluctuations, vortex formation, etc.). By extracting local fluctuation data, the non-steady-state behavior and its influence caused by the fluid inside the pipeline can be accurately identified, providing important information support for subsequent energy density spectrum analysis. Energy density spectrum analysis is performed on the local pressure fluctuation data of each section to obtain pressure energy density fluctuation data. Energy density spectrum analysis can quantify the energy distribution of pressure fluctuations in different frequency ranges and reveal the dynamic behavior of the fluid in the pipeline and its energy characteristics. Through this analysis, the main frequency components in the fluid system can be identified, especially those related to fluid vortex, vibration or other instability phenomena. The pressure energy density fluctuation data provides the necessary frequency and energy information for the subsequent axial unsteady fluctuation analysis, helping to analyze the stability and oscillation characteristics of the flow in the pipeline. Axial unsteady fluctuation analysis is performed based on the pressure energy density fluctuation data to obtain pressure axial unsteady fluctuation data. Axial unsteady fluctuation analysis combines the frequency components and energy information in the energy density spectrum analysis to further study whether the pressure fluctuation of the fluid in the pipeline has unsteady characteristics. Unsteady fluctuations usually reflect irregularities or sudden fluctuations in fluid flow. This analysis helps to evaluate the stability of fluid flow in the pipeline. Axial unsteady fluctuation data can reveal the time-varying characteristics of pressure fluctuations and the irregularity of flow, providing a key theoretical basis for pipeline design optimization, system stability assessment and flow control.

[0035] Preferably, step S24 includes the following steps:

[0036] Step S241: gridding the pressure density distribution difference of the lumen morphology to obtain gridded pressure density distribution data;

[0037] Step S242: Calculating the tangential stress distribution of the inner wall of the lumen based on the gridded data of the pressure density distribution to obtain the tangential stress distribution data of the inner wall of the lumen;

[0038] Step S243: sampling the tangential stress distribution data of the inner wall of the lumen at multiple points to obtain tangential stress sampling data;

[0039] Step S244: simulating flow characteristics according to the tangential stress sampling data to obtain lumen inner wall flow characteristics data;

[0040] Step S245: Perform viscous boundary effect analysis based on the lumen inner wall flow characteristic data to obtain lumen morphology viscous boundary effect data.

[0041] The present invention performs gridding processing on the pressure density distribution difference of the lumen morphology to obtain the pressure density distribution grid data. Through the gridding processing, the pressure density distribution difference data in the lumen can be converted into a structured grid form, which is convenient for subsequent analysis and calculation. After gridding, each small unit can accurately reflect the pressure distribution difference in the local area, ensuring the spatial resolution and accuracy of the data. This process is crucial for the accurate simulation of fluid dynamics, because it can provide reliable basic data for subsequent stress calculation, flow characteristic analysis, etc., and avoids the analysis error caused by uneven data. Based on the pressure density distribution grid data, the tangential stress distribution of the lumen inner wall is calculated to obtain the tangential stress distribution data of the lumen inner wall. Tangential stress is an important indicator to describe the friction between the fluid and the tube wall, which directly affects the resistance and stability of the fluid flow. By combining the pressure density distribution data with the geometric information of the lumen, the tangential stress distribution of the lumen inner wall can be calculated, thereby obtaining the detailed situation of the interaction between the fluid and the tube wall. This calculation provides specific stress field data for fluid dynamics simulation, which can effectively reveal the stress distribution characteristics of the inner wall of the pipeline, and thus provide a strong basis for pipeline design optimization. Multi-point sampling of the tangential stress distribution data of the inner wall of the lumen is performed to obtain tangential stress sampling data. Multi-point sampling technology can extract tangential stress data at multiple locations on the inner wall of the lumen, thereby obtaining a more comprehensive stress change situation. This process helps to capture the subtle differences in stress distribution in different areas of the inner wall of the lumen, especially in areas where stress is concentrated or fluctuates greatly during fluid flow. The data obtained by sampling can provide key local stress information for fluid dynamics simulation, thereby helping to analyze the changing trends of fluid flow and stress state, and can be used to optimize pipeline design. Flow characteristics simulation is performed based on the tangential stress sampling data to obtain flow characteristics data of the inner wall of the lumen. Based on the tangential stress sampling data, accurate flow characteristics simulation can be performed to analyze the dynamic characteristics of the fluid in the lumen, such as velocity field, eddy current, turbulence, etc. This simulation helps to reveal the instability, turbulence characteristics and flow resistance distribution of fluid flow in the pipeline. The lumen inner wall flow characteristic data obtained through simulation provides an important basis for further pipeline performance evaluation, flow optimization and fluid dynamics analysis, especially under complex pipeline morphology and flow conditions. Viscous boundary effect analysis is performed based on the lumen inner wall flow characteristic data to obtain lumen morphology viscous boundary effect data. Viscous boundary effect analysis can study the flow characteristics of the contact area between the fluid and the lumen inner wall, especially the viscous layer near the wall. By analyzing the lumen inner wall flow characteristic data, the boundary layer effect of the fluid under different lumen morphologies can be revealed, including the viscous friction and velocity gradient of the fluid. This analysis is crucial to understanding the dissipation mechanism of fluid flow in pipelines, which helps to optimize pipeline design, improve fluid delivery efficiency, and avoid energy loss or flow instability caused by excessive boundary effects.

[0042] Preferably, step S244 includes the following steps:

[0043] Performing spatial interpolation processing on the tangential stress sampling data to obtain tangential stress interpolation data;

[0044] The extrusion push rate response is performed based on the tangential stress interpolation data to obtain the extrusion push rate response data;

[0045] The flow characteristic data of the inner wall of the lumen is obtained by simulating the flow characteristic according to the extrusion push rate response data.

[0046] The present invention performs spatial interpolation processing on the tangential stress sampling data to obtain tangential stress interpolation data. The spatial interpolation processing generates an estimated value between the existing tangential stress sampling points, thereby forming a continuous and uniform stress distribution in the space of the lumen. This step effectively fills the gaps between the sampling points, making the stress data of the inner wall of the lumen smoother and more detailed, and avoiding the analysis error caused by insufficient local sampling points. The tangential stress interpolation data obtained by interpolation can more accurately reflect the friction distribution between the fluid and the tube wall, and provide high-resolution basic data for subsequent flow characteristic simulation. Based on the tangential stress interpolation data, an extrusion push rate response is performed to obtain an extrusion push rate response data. The extrusion push rate response refers to the speed reaction of the fluid pushing the tube wall under the action of tangential stress. By combining the tangential stress interpolation data with the dynamic characteristics of the fluid, the pushing effect of the fluid on the inner wall of the lumen can be simulated, and the rate response data under different conditions can be obtained. This process helps to identify the flow behavior of the fluid on the inner wall of the lumen and its interaction with the pipeline surface, reveals the relationship between the flow characteristics of the fluid and the friction of the tube wall, and thus provides key physical parameters for subsequent flow characteristic analysis. The flow characteristics are simulated according to the extrusion push rate response data to obtain the flow characteristics data of the inner wall of the lumen. The flow characteristics simulation can comprehensively analyze the flow state of the fluid on the inner wall of the lumen, including important parameters such as flow velocity, turbulence intensity, and flow resistance, by combining the extrusion push rate response data. This simulation helps reveal the laws of fluid flow and the instability of flow in the pipeline, and can reflect the response changes of the fluid under different flow conditions, thereby providing a scientific basis for pipeline design optimization, flow control, and efficiency improvement. Through the simulation results, designers can optimize the pipeline morphology, reduce flow losses, and improve fluid transmission performance.

[0047] Preferably, step S3 comprises the following steps:

[0048] Step S31: normalizing the lumen morphology viscosity boundary effect data to obtain morphology viscosity boundary normalized data;

[0049] Step S32: performing a dynamic analysis of the shrinkage of the inner cavity diameter of the cylindrical cross section based on the morphological viscosity boundary normalization data to obtain dynamic data of the shrinkage of the inner cavity diameter of the cross section;

[0050] Step S33: quantifying the delayed response of the cross-sectional inner cavity diameter contraction dynamic data to obtain dynamic contraction delayed response data.

[0051] The present invention performs normalization processing on lumen morphology viscous boundary effect data to obtain morphology viscous boundary normalized data. The purpose of normalization processing is to standardize viscous boundary effect data of different magnitudes and units so that these data can be compared and analyzed at the same scale. Through normalization, the deviation caused by differences in measurement range or calculation conditions can be eliminated, thereby ensuring the accuracy and consistency of subsequent analysis. This step can improve the comparability of data, especially when dealing with complex lumen morphology and large differences in fluid dynamic characteristics, the normalized data can provide a stable basis for further dynamic analysis. Based on the morphology viscous boundary normalized data, a dynamic analysis of the contraction of the inner cavity diameter of the cylindrical section is performed to obtain the dynamic data of the contraction of the inner cavity diameter of the cross section. The contraction of the inner cavity diameter of the cylindrical section is a key factor in studying the influence of the change of the inner wall morphology of the pipeline on the flow of fluid, especially the influence on the flow velocity, flow resistance and other aspects. Through dynamic analysis, it can be revealed how the change of lumen morphology affects the contraction process of the inner cavity diameter under different conditions. This process can capture the change of the inner cavity diameter caused by the viscous boundary effect during the flow of fluid, and further understand the influence of the pipeline morphology on the dynamic behavior of the fluid, providing a scientific basis for pipeline design optimization and flow characteristic adjustment. The dynamic contraction data of the cross-sectional inner cavity diameter are subjected to hysteresis quantification to obtain dynamic contraction hysteresis data. Hysteresis refers to the delayed effect of the change in the morphology of the inner wall of the lumen on the fluid flow, which is very important in the process of kinetic energy transfer and pipeline pressure change of the fluid. By quantifying the hysteresis, we can more accurately describe the effect of the contraction of the inner cavity diameter on the fluid flow and how the fluid responds to this morphological change. This analysis can reveal the long-term effect of lumen morphological changes on flow characteristics and fluid delivery efficiency, and provide a more detailed model for pipeline flow control, structural optimization and fluid dynamics analysis.

[0052] Preferably, step S32 includes the following steps:

[0053] Step S321: performing geometric feature discretization processing on the morphological viscosity boundary normalized data to obtain morphological viscosity geometric discrete data;

[0054] Step S322: Analyze the variation trend of the inner cavity diameter of the cylindrical section on the morphological viscosity geometric discrete data to obtain the variation trend of the inner cavity diameter of the cylindrical section;

[0055] Step S323: Based on the change trend of the inner cavity diameter of the cylindrical cross section, a dynamic analysis of the shrinkage of the inner cavity diameter of the cylindrical cross section is performed to obtain dynamic data of the shrinkage of the inner cavity diameter of the cross section.

[0056] The present invention performs geometric feature discretization processing on the morphological viscosity boundary normalized data to obtain morphological viscosity geometric discrete data. Discretization processing simplifies the morphological analysis process by converting continuous geometric data into a limited number of discrete points. This discretization not only helps to reduce the complexity of the data, but also enables subsequent analysis to focus on key geometric features, such as irregular shapes such as convex and concave, curved, etc. of the inner wall of the pipeline. By discretizing the data, the relationship between the lumen morphology and fluid dynamics can be captured and described more efficiently, which is of great significance for the analysis of complex pipeline geometry. The morphological viscosity geometric discrete data is analyzed for the trend of change of the inner cavity diameter of the cylindrical section to obtain the trend of change of the inner cavity diameter of the cylindrical section. This analysis step aims to reveal how the geometric features of the pipeline affect the change trend of the inner cavity diameter as the position changes. By analyzing the geometric discrete data, it can be identified whether the inner wall of the pipeline has irregular morphology, contraction or expansion, and how these changes affect the inner cavity diameter. The analysis provides the direct impact of the pipeline morphology on the fluid flow, provides accurate geometric parameters and change trends for subsequent dynamic analysis, and helps understand the stability and efficiency of the fluid flow in the pipeline. Based on the change trend of the inner diameter of the cylindrical section, the contraction dynamic analysis of the inner diameter of the cylindrical section is performed to obtain the dynamic data of the contraction of the inner diameter of the section. This step combines the previous diameter change trend to analyze the contraction dynamic behavior of the inner diameter of the pipeline under the action of external or fluid. By considering the mutual influence of fluid flow, pressure changes and pipeline morphology changes, the contraction process of the inner diameter under various working conditions can be simulated. This analysis result is crucial for optimizing pipeline design because it helps designers foresee the contraction phenomenon of the pipeline during actual operation, and then take measures to avoid problems such as increased flow resistance or pipeline damage.

[0057] Preferably, step S4 comprises the following steps:

[0058] Step S41: marking the contraction difference points of the dynamic contraction hysteresis reaction data to obtain the contraction difference marking points of the inner cavity diameter;

[0059] Step S42: performing boundary layer effect evaluation on the lumen morphology viscosity boundary effect data to obtain lumen morphology boundary layer effect data;

[0060] Step S43: constructing a three-dimensional digital model based on the inner cavity diameter shrinkage difference marking points and the lumen morphology boundary layer effect data to obtain a three-dimensional digital model of the catheter connection.

[0061] The present invention marks the contraction difference points of the dynamic contraction hysteresis reaction data to obtain the contraction difference marking points of the inner cavity diameter. This step is intended to identify the key difference points in the change of the inner cavity diameter of the pipeline, that is, the change of the contraction degree of the inner cavity diameter in different regions, by analyzing the contraction hysteresis reaction data. The marking of these difference points helps to gain a deeper understanding of the response time and influence of each part of the pipeline, especially the influence of different morphological regions on fluid flow and pipeline structure. By clarifying these difference points, targeted data support can be provided for subsequent optimization design, and at the same time, it helps to identify existing weaknesses or risk areas to ensure the reliability and safety of the pipeline in actual operation. The boundary layer effect is evaluated for the lumen morphology viscous boundary effect data to obtain the lumen morphology boundary layer effect data. The boundary layer effect refers to the viscous effect between the fluid and the pipe wall, which leads to the formation of a velocity gradient and affects the flow behavior of the fluid. By evaluating the boundary layer effect, the flow characteristics of the inner cavity of the pipeline can be better understood, especially the behavior of the fluid at the viscous boundary. The evaluation results can provide a deep insight into fluid dynamics and reveal the influence of changes in lumen morphology and velocity distribution on flow efficiency and pipeline stability. This data is crucial for predicting fluid mechanics properties such as flow resistance and pressure drop, thereby supporting more efficient pipeline design and operation optimization. A three-dimensional digital model is constructed based on the lumen diameter shrinkage difference marking points and lumen morphology boundary layer effect data to obtain a three-dimensional digital model of the catheter connection. By combining the aforementioned shrinkage difference points and boundary layer effect data, the construction of a three-dimensional digital model can more accurately simulate the flow environment and morphological changes inside the pipeline. This model can not only display the geometric shape of the pipeline lumen, but also reflect the influence of the lumen morphology on the fluid flow, providing intuitive visualization support for fluid dynamics analysis. Through this three-dimensional digital model, more accurate fluid flow simulation, pressure testing and optimization design can be performed. It provides an efficient analysis tool for decision-making during pipeline design and operation, ensuring that the pipeline system can achieve optimal operating performance under various conditions.

[0062] The beneficial effect of the present invention is that by performing CT imaging processing on the connected area of ​​the medical catheter through a high-resolution CT scanner, an accurate image of the internal structure of the catheter can be obtained. The connected area of ​​the catheter is further subjected to multi-plane image slicing processing to generate a CT slice image of the connected area. This process can provide high-precision three-dimensional structural information of the catheter, helping to provide reliable basic data for subsequent analysis. Through this processing method, the internal morphology of the catheter can be clearly presented, laying the foundation for subsequent lumen morphology analysis and mechanical calculation. Based on the CT slice image of the connected area, the lumen morphology is analyzed to obtain the direction of the catheter and the morphological data of the lumen. These data reflect the geometric morphology of the catheter in space and provide a basis for further fluid dynamics analysis. On this basis, the distribution change of the pressure and density of the fluid in the lumen is obtained by calculating the pressure density distribution difference. Next, the viscous boundary effect of the lumen morphology is analyzed to study the interaction between the fluid on the lumen surface and the wall, which is of great significance to the flow characteristics of the fluid, pressure changes and the transmission efficiency of the lumen. Based on the viscous boundary effect data of the lumen morphology, a dynamic analysis of the contraction of the lumen diameter of the cylindrical section is further performed. This analysis can simulate the geometric changes of the lumen under different working conditions, especially the changes in diameter contraction caused by fluid dynamics. These dynamic data can reveal how the catheter is affected by fluid pressure and viscous forces during operation. By quantifying the hysteresis response of the cross-sectional lumen diameter contraction dynamic data, the response time and behavior characteristics of the lumen under different flow conditions can be evaluated, thereby providing support for catheter design and optimization. A three-dimensional digital model of the catheter is constructed through a comprehensive analysis of the dynamic contraction hysteresis response data and the lumen morphology viscous boundary effect data. The model can fully and realistically simulate the geometry, fluid behavior and dynamic response of the catheter with high accuracy and reliability. The three-dimensional digital model provides a scientific basis for further engineering design, performance prediction and optimization. It can also be used for virtual experiments and simulation analysis to help better understand the working state of the catheter under different conditions and guide improvements and innovations in practical applications. Therefore, the present invention optimizes a traditional method for constructing a three-dimensional digital model of a medical catheter, solves the problems of inaccurate analysis of the complex morphology inside the catheter and large errors in constructing the three-dimensional digital model in the traditional method for constructing a three-dimensional digital model of a medical catheter, improves the accuracy of the analysis of the complex morphology inside the catheter, and reduces the error in constructing the three-dimensional digital model. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A schematic diagram of the steps of a method for constructing a three-dimensional digital model of a medical catheter;

[0064] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0065] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0066] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0067] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0068] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0069] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0070] To achieve this, please refer to Figures 1 to 3 A method for constructing a three-dimensional digital model of a medical catheter, the method comprising the following steps:

[0071] Step S1: performing CT imaging processing on the medical catheter connection area by a high-resolution CT scanner to obtain a CT image of the catheter connection area; performing multi-plane image slicing processing on the CT image of the catheter connection area to obtain a CT slice image of the connection area;

[0072] Step S2: performing lumen morphology analysis based on the connected area CT slice image to obtain catheter direction lumen morphology data; performing pressure density distribution difference calculation based on the catheter direction lumen morphology data to obtain lumen morphology pressure density distribution difference; performing viscous boundary effect analysis on the lumen morphology pressure density distribution difference to obtain lumen morphology viscous boundary effect data;

[0073] Step S3: Based on the lumen morphology viscous boundary effect data, a dynamic analysis of the contraction of the inner cavity diameter of the cylindrical cross section is performed to obtain the dynamic contraction data of the inner cavity diameter of the cross section; the hysteresis reaction of the dynamic contraction data of the inner cavity diameter of the cross section is quantified to obtain the dynamic contraction hysteresis reaction data;

[0074] Step S4: constructing a three-dimensional digital model based on the dynamic contraction hysteresis response data and the lumen morphology viscosity boundary effect data to obtain a three-dimensional digital model of the catheter connection.

[0075] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of a process flow of a method for constructing a three-dimensional digital model of a medical catheter according to the present invention. In this example, the method for constructing a three-dimensional digital model of a medical catheter includes the following steps:

[0076] Step S1: performing CT imaging processing on the medical catheter connection area by a high-resolution CT scanner to obtain a CT image of the catheter connection area; performing multi-plane image slicing processing on the CT image of the catheter connection area to obtain a CT slice image of the connection area;

[0077] In an embodiment of the present invention, a high-resolution CT scanner is used to perform CT imaging of the medical catheter connection area. During CT scanning, appropriate scanning parameters, such as scanning resolution and scanning range, are first selected to ensure high-quality image acquisition of the catheter connection area. During CT imaging, the area where the catheter is located should be completely covered within the scanning range, and the accuracy of the scanning angle and position should be ensured. The two-dimensional CT image generated by the scanner can clearly display the three-dimensional structure of the catheter. Next, the obtained CT imaging image of the catheter connection area is subjected to multi-plane image slicing processing. This processing is performed by cutting the original three-dimensional CT image into multiple two-dimensional plane images for subsequent analysis. A slice thickness of 0.5 mm to 1 mm is usually used, and by adjusting the slice interval and position, it is ensured that the cross-sectional images of the catheter at various different positions can be accurately obtained. These images reflect the morphological characteristics of the catheter at different positions, and due to the high resolution of the CT image, each slice can finely display the detailed structure of the catheter.

[0078] Step S2: performing lumen morphology analysis based on the connected area CT slice image to obtain catheter direction lumen morphology data; performing pressure density distribution difference calculation based on the catheter direction lumen morphology data to obtain lumen morphology pressure density distribution difference; performing viscous boundary effect analysis on the lumen morphology pressure density distribution difference to obtain lumen morphology viscous boundary effect data;

[0079] In an embodiment of the present invention, the CT slice image is subjected to lumen morphology analysis. The analysis is based on image segmentation technology to distinguish the lumen of the catheter from the surrounding tissue. Commonly used image segmentation algorithms include threshold segmentation, region growing method and edge detection method. Specifically, using the threshold segmentation algorithm, the lumen area can be extracted from the background tissue according to the gray value. Then, in combination with the image processing tool, the direction of the catheter and the morphological data of the lumen are analyzed by extracting the lumen contour data. The focus of this process is to determine the geometric characteristics of the catheter, such as the diameter, curvature, wall thickness and other parameters of the lumen. Based on the obtained lumen morphological data, the pressure density distribution difference is calculated next. This step uses the basic principles of fluid mechanics, especially the Navier-Stokes equations, combined with the geometric shape of the catheter to simulate the flow of fluid in the catheter. By analyzing the pressure distribution data at different positions and comparing the pressure differences of each cross section, the pressure density distribution difference of the lumen is calculated. This process often discretizes the flow field inside the catheter through numerical simulation methods, such as finite difference method or finite element method. Finally, the viscous boundary effect analysis of the pressure density distribution difference of the lumen shape is carried out. Through the boundary layer theory, the velocity gradient and pressure change caused by the viscosity effect of the fluid near the catheter wall are analyzed, and then the viscous boundary effect data of the lumen is calculated. This process can reveal the fluid friction on the inner wall of the catheter and the interaction between the fluid and the wall.

[0080] Step S3: Based on the lumen morphology viscous boundary effect data, a dynamic analysis of the contraction of the inner cavity diameter of the cylindrical cross section is performed to obtain the dynamic contraction data of the inner cavity diameter of the cross section; the hysteresis reaction of the dynamic contraction data of the inner cavity diameter of the cross section is quantified to obtain the dynamic contraction hysteresis reaction data;

[0081] In the embodiment of the present invention, the lumen morphology viscous boundary effect data is subjected to dynamic analysis of the contraction of the inner cavity diameter of the cylindrical section. This analysis is based on the non-steady-state response model in material mechanics and fluid dynamics, and focuses on analyzing the contraction phenomenon of the inner cavity diameter caused by fluid mechanics. First, the morphological data of the lumen is discretized to calculate the initial diameter of the inner cavity of each slice. Then, based on variables such as the flow rate, viscosity, and temperature of the fluid, a dynamic simulation is performed to predict the contraction change of the inner cavity of the catheter cross section under different external pressures or internal flow rates. This analysis can reveal the dynamic change trend of the lumen under the action of fluid dynamics. The delayed reaction quantification of the dynamic data of the contraction of the inner cavity diameter of the cross section first needs to consider that when the fluid flows in the lumen, due to the inertial effect and the viscous effect, the diameter of the lumen does not change immediately, but there is a certain lag time. Therefore, the delayed reaction in the contraction process is quantitatively analyzed by a time series analysis method (such as an autoregressive moving average ARMA model), and then the dynamic contraction delayed reaction data at each time point are obtained. The hysteresis reaction quantification can provide accurate time and space information for the construction of subsequent three-dimensional digital models.

[0082] Step S4: constructing a three-dimensional digital model based on the dynamic contraction hysteresis response data and the lumen morphology viscosity boundary effect data to obtain a three-dimensional digital model of the catheter connection.

[0083] In an embodiment of the present invention, a three-dimensional digital model is constructed using dynamic contraction hysteresis reaction data and lumen morphology viscous boundary effect data. First, the dynamic contraction hysteresis reaction data is spatially interpolated to fill in the missing data between different time points. An interpolation algorithm (such as cubic interpolation or spline interpolation) is used to convert the contraction dynamic data at each moment into continuous three-dimensional spatial data. In this process, the continuity and smoothness of the model in space are ensured to avoid discontinuity or abnormality of the data. Next, based on the lumen morphology and viscous boundary effect data of the catheter, combined with the time series of dynamic contraction, a three-dimensional digital model is constructed. Modeling tools in computer-aided design (CAD) software, such as Bezier curves or B-spline curves, are used to accurately depict the three-dimensional morphology of the catheter at different time points. By reconstructing the morphological data at each moment, a three-dimensional digital model of the catheter is finally obtained. Finally, the model is optimized and refined to ensure the accuracy and computational efficiency of the model. The model is meshed using mesh refinement techniques (such as tetrahedral mesh or hexahedral mesh), and further verified through computational fluid dynamics (CFD) analysis to ensure the reliability and usability of the model. Through the above steps, the construction of a three-dimensional digital model of the medical catheter is completed, which can provide important support for subsequent clinical analysis, surgical planning and personalized treatment plans.

[0084] Preferably, step S1 comprises the following steps:

[0085] Step S11: performing CT imaging processing on the medical catheter connection area by a high-resolution CT scanner to obtain a CT image of the catheter connection area;

[0086] Step S12: performing image smoothing filtering on the CT imaging of the connected area of ​​the catheter to obtain a CT filtered image of the connected area;

[0087] Step S13: performing artifact correction on the connected region CT filter image to obtain a connected region CT corrected image;

[0088] Step S14: performing multi-plane image slicing processing on the connected region CT corrected image to obtain a connected region CT slice image.

[0089] In the embodiment of the present invention, the medical catheter connection area needs to be firstly subjected to CT imaging. To ensure the image quality, a high-resolution CT scanner is selected, which can provide a slice thickness of 0.5 mm to 1 mm, so as to ensure the acquisition of fine anatomical structure information. During the scanning process, the catheter connection area should be located in the center of the scanning volume to avoid distortion or information loss caused by scanning angle deviation. During CT imaging, appropriate scanning parameters, such as exposure time, scanning voltage and current, need to be set to improve the contrast and resolution of the imaging. After the scanning is completed, the obtained CT image presents the cross-sectional information of the catheter, and each layer of the image shows the tissue density distribution of the cross section. Different tissues, such as the catheter wall and the surrounding tissue, are distinguished in the image by different gray values. This information provides basic data for subsequent lumen morphology analysis and three-dimensional modeling. The CT imaging of the catheter connection area is subjected to image smoothing filtering to reduce noise and improve the image quality. The Gaussian filtering algorithm is used for smoothing. Specifically, the image is convolved with a Gaussian kernel function, and a Gaussian kernel function with a standard deviation of 1-2 pixels is selected, which is suitable for removing noise from most medical images. Gaussian filtering can smooth random noise in images and eliminate problems such as unclear edges and noise interference. During the implementation process, the noise level in the image is first calculated based on the grayscale distribution of the CT image, and a suitable filter window is selected for local processing. Within the neighborhood of each pixel, a Gaussian filter is used for weighted averaging, so that the pixel value is smoothly combined with the pixels in its neighborhood to reduce the impact of noise. After filtering, the details of the CT filtered image obtained are clearer and the noise is reduced, which is helpful for subsequent image analysis and processing. Artifact correction is performed on the connected area CT filtered image. Artifact refers to a false image generated during the CT scanning process due to hardware limitations, patient movement, etc. The presence of artifacts will affect the accuracy of the image, especially in the contour and detail display of the catheter wall. To remove artifacts, model-based artifact correction algorithms are used, such as the gradient vector flow method (GVF) or the iterative back projection algorithm. These methods can identify and correct artifacts by comparing the brightness changes and edge features in CT images. During the implementation process, the catheter outline is first extracted from the CT image using an edge detection algorithm (such as the Sobel operator or the Canny operator). Then, the grayscale value of the artifact area is gradually corrected based on the extracted edge information and the brightness distribution of the image using an iterative method or a convolution method. After correction, the catheter morphology of the image is more accurate, the interference of the artifact on subsequent processing is removed, and a more accurate image basis is provided for subsequent slicing processing. The connected area CT correction image is subjected to multi-plane image slicing processing. Through this step, the three-dimensional CT image is converted into multiple two-dimensional slice images for subsequent morphological analysis.The slicing method usually uses MPR (Multi-Planar Reconstruction) technology to cut the original three-dimensional image along different plane directions. Common cutting planes include cross-section (axial), sagittal (sagittal) and coronal (coronal). The thickness of the slice is generally set to 0.5 mm to 1 mm to facilitate the acquisition of detailed information about the catheter lumen. Each layer of slice image represents the cross-sectional structure of the catheter at that location, and because the slice thickness is relatively thin, the continuous morphology of the catheter in the entire area can be reconstructed by combining the upper and lower slices. In the specific implementation, the direction and position of the slice need to be accurately selected according to the actual direction of the catheter. For example, if the catheter is arranged along the direction of a major blood vessel in the body, the slice should be adjusted according to the longitudinal direction of the blood vessel. Through MPR technology, slice images in multiple directions can be obtained at the same time, making the three-dimensional structure of the catheter clearer, thereby providing detailed data support for subsequent lumen morphology analysis and digital modeling. After slicing, the obtained CT slice images will be saved as a two-dimensional image sequence. The pixel size and resolution of each slice will directly affect the accuracy of subsequent analysis. Therefore, special attention should be paid to the detail of the slices and the data saving format in this step to ensure that subsequent processing can proceed smoothly.

[0090] Preferably, step S2 comprises the following steps:

[0091] Step S21: performing lumen morphology analysis based on the connected area CT slice image to obtain catheter direction lumen morphology data;

[0092] Step S22: calculating the direction curvature of the catheter direction lumen morphology data to obtain the direction curvature data of the lumen morphology;

[0093] Step S23: Calculating the pressure density distribution difference according to the lumen shape direction curvature data and the catheter direction lumen shape data to obtain the lumen shape pressure density distribution difference;

[0094] Step S24: Performing viscous boundary effect analysis on the pressure density distribution difference of the lumen morphology to obtain viscous boundary effect data of the lumen morphology.

[0095] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0096] Step S21: performing lumen morphology analysis based on the connected area CT slice image to obtain catheter direction lumen morphology data;

[0097] In the embodiment of the present invention, the lumen morphology of the connected area CT slice image is first analyzed to obtain the geometric data of the catheter. In order to accurately segment the catheter lumen and the surrounding tissue, it is necessary to use image segmentation technology. In this step, an image segmentation method based on edge detection, such as Canny edge detection or Sobel operator, is selected, combined with a threshold segmentation algorithm, and the contour of the lumen is identified by extracting the grayscale change characteristics of the image. In particular, when the catheter wall and the surrounding tissue have similar grayscale values, it is difficult to rely solely on threshold segmentation. Therefore, morphological operations such as expansion and corrosion operations are further applied to optimize edge extraction and reduce missed or false detections. Once the contour of the lumen is clearly extracted, the direction of the catheter is further analyzed to determine the axis of the lumen. In this process, the center line in each slice in the image can be calculated and the direction data of the catheter can be constructed using the center line method. The data reflects the three-dimensional spatial layout of the catheter in the entire connected area. By performing morphological analysis on the lumen contours in all slices, the lumen shape (such as diameter, morphological symmetry, curvature, etc.) of each layer is obtained, and combined with the position information of each layer of slices, the complete catheter geometric data is obtained. These data include the radial size, length and relative position of the catheter at different locations, laying the foundation for subsequent mechanical analysis and three-dimensional model construction.

[0098] Step S22: calculating the direction curvature of the catheter direction lumen morphology data to obtain the direction curvature data of the lumen morphology;

[0099] In an embodiment of the present invention, the direction curvature of the catheter direction lumen morphology data is first calculated to obtain the direction curvature data of the lumen morphology. The direction curvature is a key parameter describing the curvature degree of the catheter axis, reflecting the rate of change of the catheter direction. When calculating, it is first necessary to construct the spatial curve of the catheter based on the catheter direction data extracted in step S21. The curve can be smoothed by interpolating the coordinates of the center line of each layer of the catheter section to obtain a continuous catheter axis. Next, a curve fitting method is applied, and quadratic curve fitting or B-spline curve fitting is used to represent the smooth trajectory of the catheter axis. Based on the smooth curve obtained by fitting, its first-order derivative (tangent vector) and second-order derivative (normal vector) are calculated. The direction curvature is the modulus of the second-order derivative of the curve, that is, a measure of the curvature degree of the curve. The specific calculation method is to calculate the rate of change of the tangent vector between two adjacent points by the difference method to obtain the curvature data. These curvature data can characterize the smoothness and curvature of the catheter direction, and provide necessary geometric information for the subsequent calculation of the pressure density distribution difference.

[0100] Step S23: Calculating the pressure density distribution difference according to the lumen shape direction curvature data and the catheter direction lumen shape data to obtain the lumen shape pressure density distribution difference;

[0101] In an embodiment of the present invention, the pressure density distribution difference is calculated according to the lumen morphology data and the curvature data of the catheter. First, the fluid flow in the catheter is described by the Navier-Stokes equation, a basic equation in fluid mechanics. The equation can take into account factors such as the viscosity, inertia and pressure gradient of the fluid, thereby obtaining the velocity field and pressure field of the fluid in the catheter. In order to calculate the pressure density distribution difference, first, on each cross section of the catheter, the local pressure distribution is calculated based on the dynamic simulation of the fluid. The pressure density distribution difference refers to the difference between the pressure distributions at different parts of the catheter. By calculating the mean and standard deviation of the pressure on different cross sections of the catheter, the pressure density distribution characteristics of each cross section can be obtained. This step also needs to be combined with the curvature data of the catheter, taking into account the fluid dynamics differences between the curved area and the straight area of ​​the catheter. In the specific implementation, the fluid in the catheter is gridded by numerical simulation, and the pressure is discretized by the finite element method (FEM) or the finite difference method (FDM), and then the pressure distribution difference at different positions is calculated. This data can characterize the pressure fluctuations of the fluid within the catheter and the fluid dynamic effects caused by changes in the catheter geometry.

[0102] Step S24: Performing viscous boundary effect analysis on the pressure density distribution difference of the lumen morphology to obtain viscous boundary effect data of the lumen morphology.

[0103] In the embodiment of the present invention, in step S24, the viscous boundary effect analysis of the pressure density distribution difference of the lumen morphology is performed. First, the viscous boundary effect refers to the change in the velocity of the fluid near the tube wall due to the friction of the tube wall and the viscosity of the fluid during the flow of the fluid in the catheter, resulting in a boundary layer effect. This effect causes the flow velocity of the fluid to gradually decrease in the area close to the tube wall until the surface velocity in contact with the tube wall is zero. When performing the viscous boundary effect analysis, it is first necessary to calculate the tangential stress distribution of the inner wall of the lumen based on the data of the pressure density distribution difference. This process is based on the viscosity term in the Navier-Stokes equation to calculate the tangential stress of the fluid on the inner wall of the catheter. The distribution of tangential stress can be calculated by analytical solution or numerical solution, and it is particularly necessary to pay attention to the difference in stress distribution between the curved part and the straight part of the catheter. Then, the tangential stress is further sampled at multiple points to analyze the flow characteristics at different positions. The location of the sampling point should be reasonably selected according to the characteristics of the catheter morphology, such as the curved part, the straight part and the connection part. Finally, by analyzing the tangential stress data and combining it with the simulation of flow characteristics, the viscous boundary effect data of the lumen morphology was obtained. This data can reveal the friction, pressure distribution and flow characteristics between the fluid and the inner wall of the catheter under different conditions, providing a basis for the subsequent construction of a three-dimensional digital model to ensure that the model can accurately reflect the dynamic changes of the fluid in the catheter.

[0104] Preferably, step S23 includes the following steps:

[0105] Step S231: performing spatial segmentation processing on the catheter direction lumen morphology data to obtain lumen cross-section data of different regions;

[0106] Step S232: performing curvature distribution fitting on the lumen section data of different regions according to the lumen morphology and direction curvature data to obtain the local curvature distribution data of the lumen section;

[0107] Step S233: simulating the cross-sectional pressure distribution of the lumen cross-sectional data in different regions according to the local curvature distribution data of the lumen cross-sectional data to obtain cross-sectional pressure distribution data;

[0108] Step S234: performing axial non-steady-state fluctuation analysis on the cross-section pressure distribution data to obtain axial non-steady-state fluctuation data of pressure;

[0109] Step S235: Calculate the pressure density distribution difference based on the pressure axial non-steady-state fluctuation data and the cross-sectional pressure distribution data to obtain the lumen morphology pressure density distribution difference.

[0110] In an embodiment of the present invention, the catheter direction lumen morphology data needs to be spatially segmented to obtain lumen profile data of different regions. The purpose of spatial segmentation is to classify different parts of the catheter, especially for areas with complex lumen morphology, such as fine analysis of bends, branches or connection parts. First, different parts of the catheter are identified by a spatial segmentation method based on image gradient. Threshold-based segmentation techniques, such as Otsu algorithm or local adaptive threshold segmentation, rely on the intensity difference between the catheter and surrounding tissues in the CT image to separate each part of the catheter. When performing spatial segmentation, it is necessary to consider the geometric characteristics of the catheter, especially the curvature and morphological changes of the catheter. To this end, the definition of the segmentation boundary can be guided by analyzing the curvature data of the catheter direction. For example, in the straight part of the catheter, a simple threshold segmentation can be used to extract the lumen contour; while at the bend or branch of the catheter, the segmentation area needs to be dynamically adjusted according to the curvature change. The result of spatial segmentation is to divide the catheter into multiple regions, each of which represents different profile data of the lumen, providing a basis for subsequent curvature fitting and pressure distribution simulation. According to the curvature data of the lumen morphology, the curvature distribution of the lumen section data in different regions is fitted to obtain the local curvature distribution data of the lumen section. The core of this step is to model the curvature changes in different regions through the fitting algorithm in order to capture the local changes in the lumen morphology. To achieve this goal, the lumen section data of each segmented area is first combined with its corresponding curvature data to determine the curvature of the lumen. The least squares method is used to fit the section curvature of the lumen to obtain the local curvature data of each segmented area. In the straight part of the catheter, the local curvature is close to zero; while in the curved part, the local curvature value is large. In order to capture the changes in lumen morphology more accurately, spline curves (such as B-spline curves or cubic spline curves) are used to fit the curvature distribution of each segmented area. Spline curve fitting can smoothly process irregular data changes and ensure the continuity and smoothness of the fitting results. After obtaining the local curvature distribution, the bending changes in different areas of the catheter can be clearly represented, and detailed geometric features can be provided for subsequent pressure distribution simulation. Based on the local curvature distribution data of the lumen section, the section pressure distribution of the lumen section data in different regions is simulated to obtain the section pressure distribution data. First, according to the lumen morphology and fluid mechanics principles, the Navier-Stokes equation in fluid dynamics is used to model the fluid flow. In particular, considering the fluid flow characteristics of the curved part and the straight part of the lumen, the pipeline flow equation (such as the Hagen-Posset flow equation) is used for simplified calculation. For each section, the velocity distribution of the fluid flow is first determined according to the local curvature data of the catheter. Assuming that the fluid in the catheter is a stable incompressible fluid, the velocity distribution model is used to calculate the pressure at different positions on the section. The velocity distribution is affected by the local curvature. The greater the curvature, the greater the gradient of the velocity, which in turn affects the pressure distribution in the section.By solving the Navier-Stokes equations through numerical solutions (such as the finite difference method or the finite element method), the pressure distribution in the duct section is obtained. In each section, the pressure distribution will fluctuate to varying degrees due to changes in local shape. During the calculation process, considering the viscous boundary effect, the pressure distribution will show a large change near the pipe wall, and gradually stabilize with the distance away from the pipe wall. Finally, the cross-section pressure distribution data obtained through simulation will provide data support for subsequent fluctuation analysis and pressure density distribution difference calculation. The cross-section pressure distribution data is subjected to axial non-steady fluctuation analysis to obtain pressure axial non-steady fluctuation data. The purpose of non-steady fluctuation analysis is to study the pressure fluctuation characteristics that change with time in fluid flow, especially the fluctuation differences between different sections. First, the cross-section pressure data needs to be converted into pressure fluctuation data that change with time through time series analysis methods. Frequency domain analysis methods such as Fourier transform (FFT) are used to analyze the frequency characteristics of pressure fluctuations. By analyzing the pressure data in the time domain, the pressure fluctuation is decomposed into components of different frequencies, and the pressure fluctuation characteristics in the frequency domain are further obtained by the spectrum analysis method. This analysis can reveal the periodic changes of pressure fluctuations and the amplitude of instantaneous fluctuations, providing important information for further dynamic analysis. In addition, the axial unsteady fluctuations, that is, the pressure fluctuations along the axis of the catheter, need to be considered in the analysis. Different parts of the catheter will produce different pressure fluctuation patterns due to different geometric shapes (such as bends, branches, etc.) and flow states. These fluctuation patterns can be extracted by segmented analysis of each section of the catheter, and further used for subsequent pressure density distribution difference calculations. According to the pressure axial unsteady fluctuation data and the section pressure distribution data, the pressure density distribution difference is calculated to obtain the lumen morphology pressure density distribution difference. First of all, the pressure density distribution difference is an important indicator to describe the inhomogeneity of the fluid flow state in the lumen. By comparing the section pressure distribution and the axial unsteady fluctuation data, the pressure differences in different regions and at different time points are calculated. In order to calculate the pressure density distribution difference, the numerical integration method is used to calculate the difference between the local pressure distribution and the axial fluctuation for each section. By comparing the pressure fluctuations between different sections and combining the inertial and viscous properties of the fluid, the pressure density distribution difference in different areas of the catheter can be accurately calculated. This calculation not only involves the static pressure distribution, but also considers the pressure difference of the fluid during dynamic changes. Ultimately, the obtained lumen morphology pressure density distribution difference data can provide detailed fluid mechanics information for the subsequent three-dimensional digital model construction, ensuring that the model can accurately reflect the pressure changes and flow characteristics of the fluid.

[0111] Preferably, step S234 includes the following steps:

[0112] Performing axial projection processing on the cross-section pressure distribution data to obtain axial projection pressure data;

[0113] Perform local fluctuation extraction based on the axial projection pressure data to obtain local pressure fluctuation data for each section;

[0114] Perform energy density spectrum analysis on the local pressure fluctuation data of each section to obtain pressure energy density fluctuation data;

[0115] Based on the pressure energy density fluctuation data, the axial non-steady-state fluctuation analysis is carried out to obtain the pressure axial non-steady-state fluctuation data.

[0116] In an embodiment of the present invention, the cross-sectional pressure distribution data is subjected to axial projection processing to obtain axial projection pressure data. The purpose of this operation is to convert the cross-sectional pressure distribution from a two-dimensional coordinate system into an axial pressure projection so as to analyze the pressure change characteristics along the axial direction in the catheter. The specific implementation method is: first select the projection direction along the axis of the catheter. The axis of the catheter usually has a certain curvature, so the projection processing will be performed along the center line of the catheter. When the projection method is used, the pressure value of each pixel point in each section is first calculated, and then these pressure values ​​are weighted averaged along the axial direction by integration to obtain a new pressure distribution along the axial direction. The main purpose of this step is to simplify the attention to local details in subsequent analysis through the axial average of pressure, and highlight the overall impact of axial flow on pressure. After obtaining the axial projection pressure data, step S234 then performs local fluctuation extraction to obtain local pressure fluctuation data for each section. Local fluctuations refer to small fluctuations in the pressure distribution caused by flow inhomogeneity. These fluctuations reflect the details of the flow state inside the catheter, especially in areas with large pressure changes, where local fluctuations are particularly significant. These local fluctuations can be extracted by performing time series analysis on the pressure data of each section along the axial projection. The specific operation method is: apply local frequency domain analysis methods such as short-time Fourier transform (STFT) or wavelet transform to the pressure projection data on each section to convert the time domain data into frequency domain data. Through this frequency domain analysis, the pressure fluctuation data in different frequency bands are extracted, especially the low-frequency and high-frequency fluctuation modes. The local fluctuation data of each section represents the pressure fluctuation amplitude and frequency characteristics caused by factors such as flow velocity changes and irregular lumen morphology in the region. After completing the local fluctuation extraction, the next step is to perform energy density spectrum analysis on the local pressure fluctuation data of each section to obtain the pressure energy density fluctuation data. The purpose of energy density spectrum analysis is to understand the contribution of each frequency component to the total fluctuation by decomposing the frequency components of the fluctuation signal into energy densities of different frequency bands. In specific implementation, firstly, the fast Fourier transform (FFT) is applied to the extracted local fluctuation data of each section to convert the pressure fluctuation data in the time domain into frequency domain data. Through the frequency domain data, the energy density of each frequency band is calculated, that is, the power spectrum density of the fluctuation signal in this frequency band. The results of the energy density spectrum analysis can show the frequency distribution characteristics of the pressure fluctuations and reveal the high-frequency or low-frequency energy concentration areas. For the flow inside the conduit, this analysis helps to identify the turbulent areas or the main frequencies of the pulsating pressure fluctuations generated in the fluid flow, so as to further infer the instability of the flow and the local flow characteristics. After obtaining the pressure energy density fluctuation data, the axial plane unsteady fluctuation analysis is finally performed to obtain the pressure axial plane unsteady fluctuation data. The purpose of this step is to study the dynamic characteristics of the pressure fluctuations in the conduit, especially the unsteady fluctuations in the axial direction of the pipe. This type of fluctuation usually involves the time variation of the pressure and the propagation characteristics of the fluctuation.By analyzing the energy density fluctuation data, the calculation of the axial non-steady-state fluctuation can identify the time evolution pattern of the pressure fluctuation and extract the propagation speed and amplitude of the pressure fluctuation in the axial direction. This analysis process is based on a time-varying flow model, combined with the lumen morphology data and the pressure distribution data, and uses numerical analysis methods (such as the finite difference method and the finite element method) to solve the changes in the pressure fluctuation in the axial direction of the catheter. Through the analysis of axial non-steady-state fluctuations, the pressure fluctuations caused by instability in the flow process can be captured, and the propagation characteristics of the fluctuations can be further clarified. Finally, the pressure axial non-steady-state fluctuation data provides dynamic and real flow data for the subsequent construction of the catheter three-dimensional model.

[0117] Preferably, step S24 comprises the following steps:

[0118] Step S241: gridding the pressure density distribution difference of the lumen morphology to obtain gridded pressure density distribution data;

[0119] Step S242: Calculating the tangential stress distribution of the inner wall of the lumen based on the gridded data of the pressure density distribution to obtain the tangential stress distribution data of the inner wall of the lumen;

[0120] Step S243: sampling the tangential stress distribution data of the inner wall of the lumen at multiple points to obtain tangential stress sampling data;

[0121] Step S244: simulating flow characteristics according to the tangential stress sampling data to obtain lumen inner wall flow characteristics data;

[0122] Step S245: Perform viscous boundary effect analysis based on the lumen inner wall flow characteristic data to obtain lumen morphology viscous boundary effect data.

[0123] In the embodiment of the present invention, it is first necessary to perform gridding on the pressure density distribution difference of the lumen morphology, so as to obtain gridded data of the pressure density distribution. The purpose of the gridding process is to convert the pressure density distribution difference from a continuous data form into a discrete grid structure, which is convenient for subsequent calculation and analysis. The specific implementation process is to first discretize the three-dimensional space of the lumen and divide it into regular grid units. Each grid unit corresponds to a small area in the lumen, and the pressure density data is mapped to these grid units. The common method of gridding is to use a hexahedral grid or a tetrahedral grid, and select a suitable grid division method according to the geometric shape of the catheter. In each grid unit, the pressure density value in the grid is obtained by an interpolation algorithm (such as cubic spline interpolation), so as to ensure that the pressure data is smoothly processed during the grid division process and avoid calculation errors caused by irregular grids. The purpose of gridding is to convert the original continuous pressure density distribution into a discrete data form, so that subsequent tangential stress calculation and other analyses can be effectively processed in numerical calculation. The tangential stress distribution of the inner wall of the lumen is calculated based on the gridded data of pressure density distribution to obtain the tangential stress distribution data of the inner wall of the lumen. The tangential stress is usually caused by the friction between the fluid and the tube wall, and directly affects the resistance of the fluid flow and the stress of the inner wall of the lumen. When calculating the tangential stress, firstly, according to the stress equation in fluid mechanics, combined with the pressure density distribution data, the tangential stress of the inner wall of the lumen is calculated using the flow state of the fluid. The specific method is to calculate the flow state in the lumen according to the Navier-Stokes equation, including the velocity gradient, velocity distribution and pressure distribution, and then calculate the tangential stress through the stress-strain relationship (such as the friction factor model). In mathematical implementation, the finite difference method or the finite element method is used to discretize the gridded data and calculate the tangential stress value of each grid unit. Due to the irregular geometric shape of the lumen, the influence of factors such as boundary layer effect and fluid viscosity needs to be considered in the calculation process. Finally, the obtained tangential stress distribution data reflects the stress distribution characteristics of different regions in the lumen and provides basic data for subsequent flow characteristics simulation. Multi-point sampling is performed on the tangential stress distribution data of the inner wall of the lumen to obtain tangential stress sampling data. The sampling operation is to extract representative local data from the tangential stress distribution to facilitate further flow characteristic simulation. Specifically, it is first necessary to select several key positions or areas in the lumen. These positions are usually in areas where the lumen geometry changes greatly or in areas with complex flow conditions. According to these selected areas, the data values ​​of multiple sampling points are extracted from the tangential stress distribution data by uniform sampling or key sampling based on flow characteristics. The tangential stress value of each sampling point represents the fluid friction stress characteristics of the point. The selection of sampling points should take into account the changes in the lumen morphology and the influence of the flow state. Generally, the number of sampling points should be sufficient to cover all areas with important flow characteristics in the lumen to ensure the accuracy of the simulation results.The sampling result is a discrete tangential stress data set, which provides a detailed description of the stress state of the inner wall of the lumen. The flow characteristics are simulated based on the tangential stress sampling data to obtain the flow characteristics data of the inner wall of the lumen. The purpose of flow characteristics simulation is to predict the motion behavior of the fluid in the lumen and its interaction with the inner wall of the lumen. This process usually involves a computational fluid dynamics (CFD) model, which calculates the velocity, pressure, shear stress and other characteristics of the fluid flow through numerical simulation. In specific implementation, the geometric model of the lumen is first constructed, and the sampled tangential stress data is used as the boundary condition. The motion equation of the fluid is discretely solved using numerical solution methods, such as the finite volume method (FVM) or the finite element method (FEM). Through these solution methods, data such as the velocity distribution, pressure distribution and stress response of the fluid in the lumen can be obtained. In particular, the flow state in the lumen includes different types such as laminar flow and turbulent flow. The dynamic behavior of the flow in these different states can be obtained through simulation. In addition, turbulence models (such as the k-ε model) and boundary layer effects can be introduced in the simulation process to further optimize the flow characteristics analysis. Finally, the flow characteristic data provides detailed flow information for the subsequent viscous boundary effect analysis. Viscous boundary effect analysis is performed based on the flow characteristic data of the lumen inner wall to obtain the viscous boundary effect data of the lumen morphology. The main purpose of the viscous boundary effect analysis is to study the interaction between the fluid and the lumen inner wall, especially the influence of the viscous effect of the fluid on the flow characteristics. According to the flow characteristic data, the boundary layer area in the lumen is first determined. These areas are the areas where the fluid contacts the tube wall and usually have large velocity gradients and tangential stresses. Through further analysis of the flow characteristic data, the tangential stress and normal stress distribution of the lumen inner wall and the flow characteristics of the fluid in the boundary layer can be calculated. The corresponding flow model (such as the viscous mechanics model) is further applied to calculate the shear stress, friction, etc. of the fluid in the boundary layer, and the viscous boundary effect data of the lumen morphology are obtained by solving the boundary layer equation. These data reflect the influence of the viscous effect on factors such as flow instability and pressure loss, and provide the necessary physical parameters and flow constraints for the subsequent accurate construction of the three-dimensional digital model.

[0124] Preferably, step S244 includes the following steps:

[0125] Performing spatial interpolation processing on the tangential stress sampling data to obtain tangential stress interpolation data;

[0126] The extrusion push rate response is performed based on the tangential stress interpolation data to obtain the extrusion push rate response data;

[0127] The flow characteristic data of the inner wall of the lumen is obtained by simulating the flow characteristic according to the extrusion push rate response data.

[0128] In the embodiment of the present invention, the tangential stress sampling data is subjected to spatial interpolation processing so as to describe the change of tangential stress in the lumen in more detail. The process mainly converts the discrete data obtained by sampling into continuous stress distribution data. Common interpolation methods include linear interpolation, quadratic interpolation and cubic interpolation. The specific selection of which interpolation method depends on the lumen morphology and flow characteristics. For more complex lumen structures, it is recommended to use the cubic interpolation method, which can effectively capture nonlinear changes. The specific steps of the interpolation operation are: first, according to the position of the tangential stress sampling point in space, the interpolation point is established, and according to the data value of the sampling point, the tangential stress value of each interpolation point is calculated by the interpolation algorithm (such as cubic interpolation method). The interpolated tangential stress interpolation data can provide a smoother stress distribution in the entire lumen range, eliminate the data discontinuity caused by the discrete sampling points, and make the flow characteristic simulation more accurate. The extrusion push rate response analysis is performed based on the obtained tangential stress interpolation data. The purpose of this process is to study the influence of tangential stress on the flow of fluid in the lumen during the fluid flow process, especially the extrusion response of the fluid on the inner wall of the lumen. The squeeze push rate response analysis usually uses models related to fluid mechanics, and mainly focuses on the flow response of the inner wall of the lumen after being stressed. The specific analysis method is to perform numerical calculations based on the spatial distribution data of the tangential stress, combined with the rheological properties of the fluid, using the stress-strain relationship and the flow equation (such as the Navier-Stokes equation). During the calculation process, the local velocity distribution of the fluid on the inner wall of the lumen is first determined by the relationship between stress and velocity, and then the fluid push rate is calculated based on the flow velocity and the geometric shape of the lumen. The squeeze push rate response is analyzed by simulating how the fluid responds to the force on the inner wall of the lumen under different flow conditions. The greater the squeeze response, the higher the resistance to fluid flow, which is crucial for the subsequent flow characteristic simulation. The flow characteristic simulation is performed based on the squeeze push rate response data to further obtain the flow characteristic data of the inner wall of the lumen. The core purpose of the flow characteristic simulation is to analyze the specific flow mode (such as laminar flow, turbulent flow) of the fluid in the lumen and the key parameters of the flow, such as velocity distribution, shear stress, pressure distribution, etc. When simulating flow characteristics, first construct a geometric model of the lumen, and discretize the model to form a computational grid. Next, based on the extrusion push rate response data and other physical parameters (such as fluid viscosity, density, etc.), use the numerical solution methods of fluid dynamics (such as finite volume method, finite element method) to perform flow simulation. The numerical solution method iteratively solves the Navier-Stokes equations to obtain the velocity field, pressure field, and stress field of the fluid in different regions. These simulation results can reveal the fluid state and force conditions in the lumen. For example, the velocity distribution shows large changes in the narrow part of the lumen, but is more uniform in the area with a spacious lumen.The result of the flow characteristic simulation is the flow characteristic data of the inner wall of the lumen, which reflects the specific behavior of the fluid in the lumen, such as whether the flow is laminar or turbulent, whether the flow velocity meets the design requirements, whether the stress of the inner wall of the lumen will cause local flow instability, etc. This data provides a theoretical basis for the subsequent lumen design and optimization, especially in the design of medical catheters, these flow characteristic data can help optimize the structural design of the catheter and ensure its stability and efficiency in practical applications.

[0129] Preferably, step S3 comprises the following steps:

[0130] Step S31: normalizing the lumen morphology viscosity boundary effect data to obtain morphology viscosity boundary normalized data;

[0131] Step S32: performing a dynamic analysis of the shrinkage of the inner cavity diameter of the cylindrical cross section based on the morphological viscosity boundary normalization data to obtain dynamic data of the shrinkage of the inner cavity diameter of the cross section;

[0132] Step S33: quantifying the delayed response of the cross-sectional inner cavity diameter contraction dynamic data to obtain dynamic contraction delayed response data.

[0133] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0134] Step S31: normalizing the lumen morphology viscosity boundary effect data to obtain morphology viscosity boundary normalized data;

[0135] In an embodiment of the present invention, the viscosity boundary effect data of the lumen morphology is first normalized. The purpose of the normalization process is to scale the viscosity boundary effect data to a standard range, usually 0 to 1, so as to facilitate comparison and integration between different data sources during further analysis. This process can eliminate the deviation caused by the difference in data magnitude, and facilitate subsequent calculations and model building. The specific operation of normalization is to first calculate the maximum and minimum values ​​of the data. Then, by subtracting the minimum value from the original data value and dividing it by the difference between the maximum and minimum values, all data are converted to values ​​between 0 and 1. The data processed by this method can ensure that the viscosity boundary effects of different areas in the lumen are compared and analyzed under the same standard scale, thereby avoiding the influence of numerical value differences on subsequent analysis.

[0136] Step S32: performing a dynamic analysis of the shrinkage of the inner cavity diameter of the cylindrical cross section based on the morphological viscosity boundary normalization data to obtain dynamic data of the shrinkage of the inner cavity diameter of the cross section;

[0137] In the embodiment of the present invention, based on the morphological viscosity boundary normalization data obtained in step S31, the contraction dynamic analysis of the inner cavity diameter of the cylindrical section is performed. The purpose of this analysis is to study the change of the inner cavity diameter of the lumen under the action of external fluid pressure or other forces, especially the dynamic response of this change in time. Since the lumen is usually cylindrical, the focus of the analysis is to study the deformation process of the lumen cross section, especially the dynamic change of the inner cavity diameter under different flow conditions. This analysis is carried out by constructing a dynamic model based on fluid mechanics. First, using the normalized viscosity boundary effect data, combined with the flow characteristics of the fluid in the lumen, an interaction model between the lumen wall and the fluid is established. The model usually includes the influence of factors such as pressure, velocity and stress on the change of the shape of the lumen wall. Use an appropriate numerical method (such as finite difference method or finite element method) to discretize the model, so as to obtain dynamic data of the change of the inner cavity diameter of the lumen over time. By analyzing the change trend of the inner cavity diameter, the deformation characteristics of the lumen in practical applications, especially under different fluid flow conditions, can be further understood. This data is of great significance for evaluating the morphological changes that occur in the catheter during use.

[0138] Step S33: quantifying the delayed response of the cross-sectional inner cavity diameter contraction dynamic data to obtain dynamic contraction delayed response data.

[0139] In an embodiment of the present invention, the dynamic data of the contraction of the inner cavity diameter of the cross section is subjected to hysteresis reaction quantification to reveal the hysteresis effect of the lumen under the action of dynamic load. The hysteresis effect generally refers to the phenomenon that the response of the system does not occur immediately after being subjected to an external force, but there is a certain time delay or lag. In fluid mechanics, the hysteresis effect is mainly reflected in the time delay between the fluid pressure and the change of the lumen morphology. The specific operation steps of the hysteresis reaction quantification include: first, according to the dynamic data of the contraction of the lumen diameter of the lumen, the relationship between the lumen diameter and the fluid pressure is analyzed to determine the hysteresis time of the change of the lumen diameter of the lumen. Specifically, the lumen diameter and fluid pressure data are first extracted, and the time lag between the two is quantified by correlation analysis. For example, the cross-correlation analysis method can be used to calculate the time delay between the change of the lumen diameter and the change of the pressure. The cross-correlation function can help find the time offset of the maximum correlation between the two, so as to accurately estimate the hysteresis effect. Once the hysteresis time is obtained, the intensity and duration of the hysteresis effect can be further quantified by combining it with the dynamic data of the contraction of the lumen diameter. These hysteresis data are important for understanding the changes in lumen morphology during long-term use, especially when subjected to cyclic external pressure. The quantification of hysteresis not only helps optimize the design of catheters, but also provides a basis for predicting their performance in different clinical applications.

[0140] Preferably, step S32 includes the following steps:

[0141] Step S321: performing geometric feature discretization processing on the morphological viscosity boundary normalized data to obtain morphological viscosity geometric discrete data;

[0142] Step S322: Analyze the variation trend of the inner cavity diameter of the cylindrical section on the morphological viscosity geometric discrete data to obtain the variation trend of the inner cavity diameter of the cylindrical section;

[0143] Step S323: Based on the change trend of the inner cavity diameter of the cylindrical cross section, a dynamic analysis of the shrinkage of the inner cavity diameter of the cylindrical cross section is performed to obtain dynamic data of the shrinkage of the inner cavity diameter of the cross section.

[0144] In the embodiment of the present invention, the geometric feature discretization processing is first performed on the morphological viscosity boundary normalized data. Discretization processing is to convert continuous geometric morphological data into a discrete point set, so as to facilitate numerical calculation and subsequent analysis. The specific operation is to segment the morphological data of each lumen section according to the set resolution and divide it into several small cells. Each cell corresponds to a geometric point, which represents the morphological characteristics of a small area on the lumen section. During discretization, a suitable discretization accuracy is selected to ensure the accuracy of the data and the calculation efficiency. Usually, the discretization method used is a grid-based discretization, and the lumen section is divided by a uniform or non-uniform grid. The density and resolution of the grid will affect the accuracy of the final calculation. Therefore, when performing discretization, the balance between the grid size and the complexity of the lumen morphology must be considered. After the discretization processing, the obtained morphological viscosity geometric discrete data will provide basic data support for the subsequent analysis of the change trend of the inner cavity diameter of the cylindrical section. Based on the morphological viscosity geometric discrete data obtained in step S321, the change trend analysis of the inner cavity diameter of the cylindrical section is performed. The purpose of this analysis is to study the changing trend of the lumen diameter at different positions of the lumen through the discretized data, especially the pattern of its change over time or fluid conditions. First, according to the discretized data, the lumen diameter value of each cross-sectional position is calculated. Then, these lumen diameter values ​​are trend fitted by regression analysis or curve fitting method (such as least squares method or polynomial fitting). Through this method, the changing trend of the lumen diameter of the lumen at different cross-sections can be obtained, revealing the morphological change law of the lumen under different flow conditions. During the analysis process, it is necessary to perform statistical analysis on the diameter changes at different positions to confirm whether there is uniform or uneven contraction behavior. Further, based on these analysis results, it can be judged whether the lumen will have local contraction or expansion. Through trend analysis, the law of the change of lumen diameter over time, flow velocity, pressure and other factors is obtained, which provides necessary reference data for subsequent dynamic analysis. Based on the changing trend of the lumen diameter of the cylindrical section obtained in step S322, the contraction dynamic analysis of the lumen diameter of the cylindrical section is performed. This analysis aims to study the dynamic behavior of the lumen diameter over time under certain external forces or fluid dynamics, especially the response characteristics of the lumen contraction after being stressed. The specific operation steps are: first, combine the external pressure of the lumen, fluid flow rate and flow conditions to establish a morphological change model of the lumen after being stressed. A fluid dynamics model can be used, especially considering the dynamic influence of the fluid in the lumen, to obtain dynamic data of the lumen diameter over time through mathematical modeling and numerical simulation. In the model, the change of the lumen diameter is usually caused by the internal and external pressure difference, fluid friction and viscosity effect. By comprehensively considering these factors, the change of the lumen diameter under dynamic conditions can be simulated. The numerical methods used for contraction dynamic analysis can include finite element analysis (FEA) or computational fluid dynamics (CFD) simulation.These methods can accurately capture the deformation characteristics of the lumen, especially the contraction behavior when subjected to pulsating fluid pressure. Through dynamic analysis, dynamic data such as the contraction rate, maximum contraction degree and rebound characteristics of the lumen diameter are obtained. These data can provide important numerical basis for further analysis of the stability, pressure resistance and long-term performance of the lumen.

[0145] Preferably, step S4 comprises the following steps:

[0146] Step S41: marking the contraction difference points of the dynamic contraction hysteresis reaction data to obtain the contraction difference marking points of the inner cavity diameter;

[0147] Step S42: performing boundary layer effect evaluation on the lumen morphology viscosity boundary effect data to obtain lumen morphology boundary layer effect data;

[0148] Step S43: constructing a three-dimensional digital model based on the inner cavity diameter shrinkage difference marking points and the lumen morphology boundary layer effect data to obtain a three-dimensional digital model of the catheter connection.

[0149] In the embodiment of the present invention, it is first necessary to identify the difference points of the inner cavity diameter on each cross section of the lumen based on the dynamic contraction hysteresis reaction data. These difference points are usually manifested as obvious diameter contraction or expansion of the lumen in certain parts, marking the key change position of the lumen morphology under the dynamic state. First, the dynamic data of lumen diameter contraction is analyzed in time series to identify the maximum contraction point and rebound point of the lumen diameter change in the dynamic process. By performing differential analysis on the diameter change of each cross section, the threshold method or the rate of change method is used to identify the areas with significant contraction or expansion. These areas are marked as "contraction difference points", that is, the key response points of the lumen under dynamic conditions. The key technology of this step is to accurately extract points with significant physical significance from the global dynamic data by setting a suitable judgment threshold. These marking points not only help to determine the area where the lumen morphology changes, but also provide accurate geometric information for the subsequent construction of a three-dimensional digital model. By evaluating the boundary layer effect of the lumen morphology viscous boundary effect data, the purpose is to analyze the influence of the viscosity effect of the fluid near the lumen surface on the overall flow and morphological changes. The viscous boundary layer effect refers to the gradual decrease of the flow velocity of the fluid in the lumen due to the viscosity of the fluid when it contacts the wall of the tube, resulting in changes in local pressure and flow velocity, thereby affecting the morphological changes of the lumen. The evaluation process starts from the fluid mechanics characteristics of the lumen wall. First, the velocity distribution and pressure distribution of the fluid in the lumen need to be obtained. Through the numerical analysis of the viscous boundary effect data, the shear stress distribution on the lumen surface and its vicinity can be obtained. This analysis usually uses the boundary layer theory in fluid mechanics to quantify the intensity and range of the boundary layer effect by calculating the viscous force and velocity gradient of the fluid near the lumen surface. During the evaluation process, it is necessary to comprehensively consider the effects of factors such as fluid viscosity, flow velocity, and temperature on the boundary layer effect, and use numerical methods such as CFD simulation to calculate the specific effects of these effects on the lumen morphology. These evaluation results can help understand the stability and pressure resistance of the lumen morphology and its response to flow changes, and provide more detailed boundary layer information for the final three-dimensional digital model. It is a key step to finally build a three-dimensional digital model. According to the lumen diameter contraction difference marking points obtained in step S41 and the lumen morphology boundary layer effect data evaluated in step S42, combined with the geometric morphology of the catheter, a complete three-dimensional digital catheter model is constructed. First, the key cross-sectional positions of the lumen are determined using the difference marking points, and based on the lumen diameter data at these positions, an interpolation method (such as cubic spline interpolation or B-spline interpolation) is used to smooth and expand the cross-sectional data to generate the geometric contour of the entire catheter connection area. By performing three-dimensional reconstruction on the different cross-sectional data of the lumen, a three-dimensional geometric model of the lumen is obtained. Next, according to the boundary layer effect data in step S42, the geometric features of the lumen inner wall in the model are further refined, especially the fluid mechanics properties near the lumen surface.This process requires proper treatment of the lumen surface so that it can accurately reflect the viscosity of the fluid near the inner surface of the lumen. At this time, the data of the boundary layer effect will refine the lumen surface grid to ensure that the interaction between the fluid and the tube wall can be accurately reproduced in the model. Finally, by integrating the data in steps S41 and S42, a complete three-dimensional digital model is generated using computer-aided design (CAD) software or modeling tools. The model not only contains the geometric information of the lumen, but also reflects the boundary effect, viscosity influence and contraction behavior of the fluid in the lumen. This three-dimensional digital model will serve as the basis for subsequent analysis, simulation, optimization and practical application.

[0150] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0151] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for constructing a three-dimensional digital model of a medical catheter, characterized in that: The following steps are involved: Step S1: performing CT imaging processing on the medical catheter connection area by a high-resolution CT scanner to obtain a CT image of the catheter connection area; performing multi-plane image slicing processing on the CT image of the catheter connection area to obtain a CT slice image of the connection area; Step S2: performing lumen morphology analysis based on the connected area CT slice image to obtain catheter direction lumen morphology data; performing pressure density distribution difference calculation based on the catheter direction lumen morphology data to obtain lumen morphology pressure density distribution difference; performing viscous boundary effect analysis on the lumen morphology pressure density distribution difference to obtain lumen morphology viscous boundary effect data; Step S3: Based on the lumen morphology viscous boundary effect data, a dynamic analysis of the contraction of the inner cavity diameter of the cylindrical cross section is performed to obtain the dynamic contraction data of the inner cavity diameter of the cross section; the hysteresis reaction of the dynamic contraction data of the inner cavity diameter of the cross section is quantified to obtain the dynamic contraction hysteresis reaction data; Step S4: constructing a three-dimensional digital model based on the dynamic contraction hysteresis response data and the lumen morphology viscosity boundary effect data to obtain a three-dimensional digital model of the catheter connection.

2. The method for constructing a three-dimensional digital model of a medical catheter according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: performing CT imaging processing on the medical catheter connection area by a high-resolution CT scanner to obtain a CT image of the catheter connection area; Step S12: performing image smoothing filtering on the CT imaging of the connected area of ​​the catheter to obtain a CT filtered image of the connected area; Step S13: performing artifact correction on the connected region CT filter image to obtain a connected region CT corrected image; Step S14: performing multi-plane image slicing processing on the connected region CT corrected image to obtain a connected region CT slice image.

3. The method for constructing a three-dimensional digital model of a medical catheter according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing lumen morphology analysis based on the connected area CT slice image to obtain catheter direction lumen morphology data; Step S22: calculating the direction curvature of the catheter direction lumen morphology data to obtain the direction curvature data of the lumen morphology; Step S23: Calculating the pressure density distribution difference according to the lumen shape direction curvature data and the catheter direction lumen shape data to obtain the lumen shape pressure density distribution difference; Step S24: Performing viscous boundary effect analysis on the pressure density distribution difference of the lumen morphology to obtain viscous boundary effect data of the lumen morphology.

4. The method for constructing a three-dimensional digital model of a medical catheter according to claim 3, characterized in that: Step S23 includes the following steps: Step S231: performing spatial segmentation processing on the catheter direction lumen morphology data to obtain lumen cross-section data of different regions; Step S232: performing curvature distribution fitting on the lumen section data of different regions according to the lumen morphology and direction curvature data to obtain the local curvature distribution data of the lumen section; Step S233: simulating the cross-sectional pressure distribution of the lumen cross-sectional data in different regions according to the local curvature distribution data of the lumen cross-sectional data to obtain cross-sectional pressure distribution data; Step S234: performing axial non-steady-state fluctuation analysis on the cross-section pressure distribution data to obtain axial non-steady-state fluctuation data of pressure; Step S235: Calculate the pressure density distribution difference based on the pressure axial non-steady-state fluctuation data and the cross-sectional pressure distribution data to obtain the lumen morphology pressure density distribution difference.

5. The method for constructing a three-dimensional digital model of a medical catheter according to claim 4, characterized in that: Step S234 includes the following steps: Performing axial projection processing on the cross-section pressure distribution data to obtain axial projection pressure data; Perform local fluctuation extraction based on the axial projection pressure data to obtain local pressure fluctuation data for each section; Perform energy density spectrum analysis on the local pressure fluctuation data of each section to obtain pressure energy density fluctuation data; Based on the pressure energy density fluctuation data, the axial non-steady-state fluctuation analysis is carried out to obtain the pressure axial non-steady-state fluctuation data.

6. The method for constructing a three-dimensional digital model of a medical catheter according to claim 3, characterized in that: Step S24 includes the following steps: Step S241: gridding the pressure density distribution difference of the lumen morphology to obtain gridded pressure density distribution data; Step S242: Calculating the tangential stress distribution of the inner wall of the lumen based on the gridded data of the pressure density distribution to obtain the tangential stress distribution data of the inner wall of the lumen; Step S243: sampling the tangential stress distribution data of the inner wall of the lumen at multiple points to obtain tangential stress sampling data; Step S244: simulating flow characteristics according to the tangential stress sampling data to obtain lumen inner wall flow characteristics data; Step S245: Perform viscous boundary effect analysis based on the lumen inner wall flow characteristic data to obtain lumen morphology viscous boundary effect data.

7. The method for constructing a three-dimensional digital model of a medical catheter according to claim 6, characterized in that: Step S244 includes the following steps: Performing spatial interpolation processing on the tangential stress sampling data to obtain tangential stress interpolation data; The extrusion push rate response is performed based on the tangential stress interpolation data to obtain the extrusion push rate response data; The flow characteristic data of the inner wall of the lumen is obtained by simulating the flow characteristic according to the extrusion push rate response data.

8. The method for constructing a three-dimensional digital model of a medical catheter according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: normalizing the lumen morphology viscosity boundary effect data to obtain morphology viscosity boundary normalized data; Step S32: performing a dynamic analysis of the shrinkage of the inner cavity diameter of the cylindrical cross section based on the morphological viscosity boundary normalization data to obtain dynamic data of the shrinkage of the inner cavity diameter of the cross section; Step S33: quantifying the delayed response of the cross-sectional inner cavity diameter contraction dynamic data to obtain dynamic contraction delayed response data.

9. The method for constructing a three-dimensional digital model of a medical catheter according to claim 8, characterized in that: Step S32 includes the following steps: Step S321: performing geometric feature discretization processing on the morphological viscosity boundary normalized data to obtain morphological viscosity geometric discrete data; Step S322: Analyze the variation trend of the inner cavity diameter of the cylindrical section on the morphological viscosity geometric discrete data to obtain the variation trend of the inner cavity diameter of the cylindrical section; Step S323: Based on the change trend of the inner cavity diameter of the cylindrical cross section, a dynamic analysis of the shrinkage of the inner cavity diameter of the cylindrical cross section is performed to obtain dynamic data of the shrinkage of the inner cavity diameter of the cross section.

10. The method for constructing a three-dimensional digital model of a medical catheter according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: marking the contraction difference points of the dynamic contraction hysteresis reaction data to obtain the contraction difference marking points of the inner cavity diameter; Step S42: performing boundary layer effect evaluation on the lumen morphology viscosity boundary effect data to obtain lumen morphology boundary layer effect data; Step S43: constructing a three-dimensional digital model based on the inner cavity diameter shrinkage difference marking points and the lumen morphology boundary layer effect data to obtain a three-dimensional digital model of the catheter connection.

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