Free flap riser anastomosis intelligent decision-making method and system based on hemodynamics

Abstract

The invention provides an intelligent decision-making method and system for free flap riser anastomosis based on hemodynamics. According to the method, firstly, the blood flow direction, the flow speed and the blood vessel wall tension parameter of a target blood vessel are collected, and a dynamic correlation model of intravascular fluid mechanics and external tissue deformation is established; a three-dimensional blood flow pressure gradient map is generated based on the model, then, a system automatically screens an anastomosis path meeting a blood flow resistance threshold value and the endothelial cell survival rate requirement, and the optimal anastomosis pose is determined by analyzing the blood vessel wall elasticity matching degree and the turbulence generation probability. Then, guiding the accurate adjustment of the clamping direction of the blood vessel and the suture needle pitch in the operation, ensuring that the pressure of the lumen of the anastomosed blood vessel is kept balanced when the anastomosed blood vessel is axially stretched, meanwhile, effectively inhibiting local vortex, and remarkably improving the long-term smoothness of the anastomosis of the blood vessel. According to the method, mechanical precise matching and blood flow stable control of free flap vascular anastomosis are achieved, and the postoperative risk is remarkably reduced.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent decision-making for free flap riser anastomosis, and in particular to a method and system for intelligent decision-making for free flap riser anastomosis based on hemodynamics. Background Art

[0002] In the field of free flap vascular anastomosis surgery, as microsurgery's requirements for surgical precision continue to increase, the vascular anastomosis process faces three key technical demands. First, it is necessary to achieve dynamic monitoring of the three-dimensional biomechanical properties of blood vessels, including the simultaneous acquisition of hemodynamic parameters and vascular wall mechanical properties. Secondly, it is required to establish an intelligent anastomosis plan decision system that can predict blood flow stability and tissue damage risks at different anastomosis angles based on fluid mechanics characteristics. Finally, it is necessary to convert the theoretical optimal solution into intraoperative operating parameters that can be accurately executed, including quantitative control of the vascular clamping position and suture spacing.

[0003] The current mainstream technology utilizes a computational fluid dynamics simulation system based on preoperative imaging. Using micro-CT or ultrasound imaging to obtain 3D vascular structural data, finite element models are then constructed to simulate the hemodynamic performance of different anastomotic strategies. This system outputs key parameters such as vessel wall stress distribution and blood pressure gradients, and presents recommended anastomotic strategies through a visual interface.

[0004] This approach has significant technical limitations. The simulation system relies on static preoperative imaging data and cannot correct for parameter changes caused by intraoperative vascular deformation. The computational model struggles to balance accuracy and efficiency. High-precision modeling significantly increases computational time, impacting the timeliness of intraoperative decision-making. The recommended solutions output by the system lack clear quantitative implementation standards, resulting in significant deviations between actual surgical procedures and theoretical plans. Summary of the Invention

[0005] The present application provides a hemodynamic-based intelligent decision-making method and system for free flap riser anastomosis, which is used to solve the problem of inaccurate free flap riser anastomosis in the prior art.

[0006] In a first aspect, the present application provides an intelligent decision-making method for free flap riser anastomosis based on hemodynamics, comprising:

[0007] Obtain the parameter combination of blood flow direction, flow velocity and vascular wall tension in the target blood vessel of the free flap, and simultaneously establish a correlation model between the intravascular fluid dynamics characteristics and external tissue deformation;

[0008] Based on the parameter combination and the correlation model, a visual topological map of the blood flow pressure gradient distribution in a three-dimensional space is generated in the vascular anastomosis area, wherein the visual topological map maps the evolution path of the stress concentration area of ​​the vascular wall under different anastomosis angles;

[0009] According to the evolution path of the visual topological map and in combination with the preset biomechanical constraint rules of vascular anastomosis, a set of anastomotic paths that meet the minimum blood flow resistance threshold and the vascular endothelial cell survival rate requirements are automatically screened;

[0010] In the anastomotic path set, an optimal anastomotic posture scheme that takes into account both mechanical stability and physiological compatibility is generated by iteratively comparing the elastic deformation matching degree of the blood vessel walls on both sides of the anastomotic point and the blood turbulence generation probability;

[0011] The optimal anastomotic posture scheme is used to adjust the blood vessel clamping orientation and suture needle spacing distribution, so that the anastomosed blood vessels maintain pressure balance inside and outside the lumen under axial tension, while suppressing the local eddy current effect caused by blood reflux.

[0012] Optionally, in the anastomotic path set, an optimal anastomotic posture scheme taking into account both mechanical stability and physiological compatibility is generated by iteratively comparing the elastic deformation matching degree of the vascular walls on both sides of the anastomotic point and the probability of blood turbulence generation, including:

[0013] Based on each path in the anastomotic path set, the elastic modulus and deformation response curves of the vascular walls on both sides of the anastomosis point are extracted respectively. The elastic deformation matching degree of the vascular walls on both sides is calculated by the deformation direction consistency coefficient and the strain energy density ratio.

[0014] A hemodynamic simulation model is constructed at the anastomosis point to simulate the flow trajectory at different flow rates, and the probability of blood turbulence generation is calculated based on the local shear force distribution and eddy strength between the blood flow and the blood vessel wall;

[0015] Establishing a joint evaluation function based on the elastic deformation matching degree and the blood turbulence generation probability, and adjusting the spatial coordinates and angular offset of the anastomosis point through multiple iterations to optimize the value of the joint evaluation function until it approaches a preset optimization range;

[0016] When the value of the joint evaluation function falls within the preset optimization interval, the spatial coordinates, angle offset and vascular wall deformation distribution of the current anastomosis point are recorded to generate the optimal anastomosis posture solution.

[0017] Optionally, based on the parameter combination and the correlation model, a visual topological map of the blood flow pressure gradient distribution in the three-dimensional space is generated in the vascular anastomosis area, including:

[0018] Based on the continuous change characteristics of blood flow direction and flow velocity in the parameter combination, the pressure difference of each spatial point in the vascular anastomosis area is calculated through the correlation model, and the pressure difference is converted into the weight value of the three-dimensional grid node;

[0019] constructing a layered pressure gradient field in the vascular anastomosis area according to the weight value, wherein each layer in the layered pressure gradient field corresponds to a pressure interval of the contact surface between the vascular wall and the blood flow, and the boundary line between the layers is defined as a pressure change line;

[0020] Using the pressure variation line as a reference, the axial projection of the vascular anastomosis region is rotated at preset angle intervals in the three-dimensional grid, the maximum deformation region of the vascular wall surface under the action of the pressure gradient field is calculated at each rotation angle, and the geometric center point of the maximum deformation region is marked as the stress concentration point;

[0021] The stress concentration points at different rotation angles are connected in time series to form an evolution path. The evolution path is constrained by the hierarchical distribution of the pressure gradient field, generating a visual topological map in three-dimensional space in which the pressure intensity is distinguished by color depth and the degree of stress concentration is represented by line segment density.

[0022] Optionally, based on the evolution path of the visualized topological map and in combination with preset biomechanical constraint rules for vascular anastomosis, a set of anastomotic paths that meet the minimum blood flow resistance threshold and the vascular endothelial cell survival rate requirements are automatically screened, including:

[0023] Extracting geometric features of the evolution path in the visualized topological map, and inputting the geometric features into a preset vascular anastomosis biomechanical constraint rule library, wherein the biomechanical constraint rule library includes blood flow resistance calculation rules and endothelial cell survival rate assessment rules;

[0024] According to the blood flow resistance calculation rule, the blood flow resistance value of each evolution path is calculated, and the evolution paths with blood flow resistance values ​​less than or equal to the minimum blood flow resistance threshold are screened out to generate candidate low resistance paths;

[0025] According to the endothelial cell survival rate evaluation rule, the retained evolution path is subjected to a vascular wall stress distribution analysis. If the area ratio of the region exceeding the endothelial cell tolerance threshold in the stress distribution is less than a preset survival rate critical value, a candidate survival path is generated;

[0026] The intersection of the candidate low-resistance path and the candidate survivable path is taken as a matching path set.

[0027] Optionally, the vessel clamping orientation and suture needle spacing distribution are adjusted through the optimal anastomotic posture scheme to maintain pressure balance inside and outside the lumen of the anastomosed vessel under axial tension, while suppressing the local eddy current effect caused by blood backflow, including:

[0028] Determine the initial clamping position of the vascular clamp according to the distribution state of vascular wall deformation recorded in the optimal anastomotic posture plan;

[0029] During the suturing process, the axial stretching degree of the blood vessel wall is detected based on the initial clamping position. If the axial stretching degree exceeds a preset threshold, the clamping pressure is reduced according to a gradient and the tilt angle of the blood vessel clamp is adjusted synchronously to maintain a constant pressure difference between the inside and outside of the lumen, and a clamping adjustment parameter is generated at the same time;

[0030] According to the relative position relationship between the blood flow direction and the suture thread in the clamping adjustment parameter, the dense area of ​​the suture stitch length is set on the upstream side of the blood flow, and the sparse area is set on the downstream side of the blood flow, and the stitch length ratio of the dense area to the sparse area is proportional to the blood flow velocity, so as to generate an anti-reflux stitch length distribution;

[0031] Based on the anti-reflux needle distance distribution, an auxiliary inhibition structure is added on the downstream side of the anastomosis. The auxiliary inhibition structure changes the local lumen shape by matching the blood flow direction with the lumen pressure distribution, so that the flow direction of the reflux blood forms an acute angle interference with the mainstream direction, thereby eliminating the local eddy current effect.

[0032] Optionally, a joint evaluation function is established according to the elastic deformation matching degree and the blood turbulence generation probability, and the spatial coordinates and angular offset of the anastomosis point are adjusted through multiple iterations to optimize the value of the joint evaluation function until it approaches a preset optimization interval, including:

[0033] Dynamically adjust the weight ratio of the elastic deformation matching degree and the blood turbulence generation probability according to the blood vessel diameter, superimpose the inverse of the elastic deformation matching degree and the blood turbulence generation probability according to the weight, and construct a joint evaluation function;

[0034] Taking the initial spatial coordinates and the angular offset as the starting point, the position of the anastomosis point is moved by an alternating stepping strategy with axial priority and radial priority, and the value of the joint evaluation function is recalculated after each movement;

[0035] When the value of the joint evaluation function is iterated three times in a row, the numerical gradient change of the three adjacent iteration results is calculated and compared with the preset convergence threshold. If the change is lower than the threshold, it is determined to have entered the optimization interval, otherwise the adaptive adjustment of the coordinate offset is continued.

[0036] Optionally, stress concentration points at different rotation angles are connected in time series to form an evolution path. The evolution path is constrained by the hierarchical distribution of the pressure gradient field, and a visual topological map is generated in three-dimensional space, in which the pressure intensity is distinguished by color depth and the stress concentration degree is represented by line segment density, including:

[0037] Based on the pressure gradient field hierarchy, the stress concentration points at adjacent rotation angles are connected according to the continuity of the vascular axial projection surface. If the pressure intensity between adjacent points crosses the hierarchy boundary, a virtual point smoothing path is inserted to generate an evolution path.

[0038] Based on the evolution path, the pressure intensity value of each point is converted into the corresponding color depth in the gradient color band, and the line segment density is scaled according to the deformation gradient value around the stress concentration point in the evolution path to generate a pressure and stress dual encoding path;

[0039] In the pressure and stress dual encoding path, when the evolution path segment crosses different pressure levels, the transparency gradually decays with the number of level transitions, and periodic flashing marks are superimposed on the segment at the level boundary to generate a visual topological map.

[0040] In a second aspect, the present application provides an intelligent decision-making system for free flap riser anastomosis based on hemodynamics, comprising:

[0041] An acquisition module is used to obtain the parameter combination of blood flow direction, flow velocity and vascular wall tension in the target blood vessel in the free flap, and simultaneously establish a correlation model between the intravascular fluid dynamics characteristics and external tissue deformation;

[0042] a mapping module for generating a visual topological map of the blood flow pressure gradient distribution in a three-dimensional space in the vascular anastomosis region based on the parameter combination and the association model, wherein the visual topological map maps the evolution path of the stress concentration region of the vascular wall at different anastomosis angles;

[0043] A screening module is used to automatically screen out anastomotic path sets that meet the minimum blood flow resistance threshold and the vascular endothelial cell survival rate requirements based on the evolution path of the visual topological map and the preset vascular anastomosis biomechanical constraint rules;

[0044] a generation module configured to generate an optimal anastomotic posture scheme that takes into account both mechanical stability and physiological compatibility by iteratively comparing the elastic deformation matching degree of the vascular walls on both sides of the anastomotic point and the blood turbulence generation probability in the anastomotic path set;

[0045] The suppression module is used to adjust the vascular clamping orientation and suture needle spacing distribution through the optimal anastomosis posture scheme, so that the anastomosed blood vessels maintain the pressure balance inside and outside the lumen under axial tension, while suppressing the local eddy current effect caused by blood reflux.

[0046] In a third aspect, an embodiment of the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an intelligent decision-making method for free flap standoff anastomosis based on hemodynamics as described in the first aspect above.

[0047] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements an intelligent decision-making method for free flap standoff anastomosis based on hemodynamics as described in the first aspect.

[0048] This application obtains a parameter combination of blood flow direction, flow velocity, and vascular wall tension in the target vessel of a free flap, and simultaneously establishes a correlation model between the intravascular fluid dynamics characteristics and external tissue deformation. Its technical effect is to achieve multi-dimensional dynamic coupling modeling of vascular biomechanical parameters, accurately quantify the interaction between fluid dynamics and tissue deformation, and provide cross-scale mechanical data support for anastomosis path planning. By generating a visual topological map of the blood flow pressure gradient distribution in three-dimensional space in the vascular anastomosis area based on the parameter combination and correlation model, its technical effect is to construct a spatiotemporal evolution map of the mechanical environment of the anastomosis area, intuitively map the dynamic migration law of the vascular wall stress concentration area, and assist in the prediction of vascular structural integrity during surgery. By combining the evolution path of the visual topological map with biomechanical constraint rules to select anastomosis path set that meets the minimum blood flow resistance threshold and endothelial survival rate, its technical effect is to establish a multi-objective optimization decision-making mechanism, simultaneously avoid the risk of hemodynamic instability and tissue ischemic damage, and improve the efficiency and safety of anastomosis plan screening. By iteratively comparing the matching degree of vascular wall elastic deformation and the probability of blood turbulence generation within the anastomotic path set, the optimal anastomotic posture solution is generated. The technical effect is to achieve the coordinated optimization of vascular mechanical properties and blood flow physiological properties, inhibiting anastomotic structural fatigue damage and turbulence-induced thrombosis. By adjusting the vascular clamping position and suture needle spacing to maintain luminal pressure balance and inhibit local eddy current effects, the technical effect is to achieve precise adaptation of vascular geometric reconstruction and fluid dynamics, reducing the incidence of postoperative anastomotic stenosis and vascular spasm.

[0049] Furthermore, by extracting the elastic modulus and deformation response curves of the vascular walls on both sides of the anastomosis point, the elastic deformation matching degree is calculated. The technical effect is to establish a cross-regional compatibility assessment model for the mechanical properties of the vascular wall, ensuring the coordination of anastomotic end face deformation. By constructing a hemodynamic simulation model at the anastomosis point to calculate the turbulence generation probability, the technical effect is to quantify the shear damage risk of blood flow disturbances to the vascular endothelium and achieve dynamic prediction of flow stability. By establishing a joint evaluation function for elastic deformation matching and turbulence generation probability for iterative optimization, the technical effect is to construct a dual-factor closed-loop optimization framework of mechanics and fluid mechanics, driving the anastomotic posture parameters to converge toward the preset biocompatibility threshold. By recording the optimized spatial coordinates and angular offsets, the optimal anastomotic posture solution is generated. The technical effect is to output precise surgical parameters that meet the dual constraints of mechanical strength and hemodynamics, ensuring the long-term structural stability and physiological functionality of the anastomosis.

[0050] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0052] Figure 1 A flow chart of an intelligent decision-making method for free flap riser anastomosis based on hemodynamics provided by the present application is shown;

[0053] Figure 2 A scenario diagram of a hemodynamic-based intelligent decision-making system for free flap riser anastomosis provided by the present application is shown;

[0054] Figure 3 A schematic diagram of the structure of an intelligent decision-making system for free flap stand tube anastomosis based on hemodynamics provided by the present application is shown;

[0055] Figure 4 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0056] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0057] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0058] In the field of free flap vascular anastomosis surgery, the existing technology uses a static fluid dynamics simulation system based on preoperative images, which has three core defects: first, the finite element model relies on the three-dimensional structural data of the blood vessels collected before the operation, and cannot capture the blood flow parameter offset caused by dynamic deformation such as vascular traction and torsion during the operation, resulting in a mismatch between the simulation results and the actual mechanical environment during the operation; second, high-precision fluid dynamics calculations require several hours, which is in sharp conflict with the decision-making needs of microsurgery, forcing clinicians to adopt simplified models at the expense of prediction accuracy; third, the recommended anastomosis plan output by the system only provides macro-perspective suggestions, and lacks quantitative standards for key operating parameters such as vascular clamping orientation and suture needle distance, resulting in a high execution error rate between the theoretical plan and the operator's operation.

[0059] To address the above issues, this application proposes an intelligent anastomosis decision-making method based on hemodynamic feedback. Its innovation lies in constructing a full-process closed-loop system of "dynamic perception-intelligent deduction-precise execution." By acquiring blood flow direction, flow velocity, and vascular wall tension parameters during surgery, a dynamic coupling model of the internal and external mechanical characteristics of the blood vessels is established, breaking through the spatiotemporal lag of traditional static modeling. Based on a parallel computing architecture, a three-dimensional blood flow pressure gradient topology map is generated to achieve second-level dynamic visualization of anastomotic angles and stress concentration areas. Through dual-factor iterative optimization of elastic deformation matching and turbulence generation probability, a position scheme for suture needle spacing and vascular axial clamping angle quantized to 0.1mm is output. This solution reduces modeling time from hours to minutes, simultaneously improving prediction accuracy and decision-making timeliness, and reducing the execution error rate between theoretical solutions and intraoperative operations. It fundamentally solves the three major technical bottlenecks of static modeling inaccuracy, low computational efficiency, and ambiguous operational standards, significantly reducing the risk of postoperative vascular occlusion and anastomotic rupture.

[0060] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0061] Figure 1 The present invention provides a flowchart of an intelligent decision-making method for free flap stand tube anastomosis based on hemodynamics, as shown in FIG. Figure 1 As shown, the method includes:

[0062] 101. Obtain the parameter combination of blood flow direction, flow velocity and vascular wall tension in the target blood vessel of the free flap, and simultaneously establish a correlation model between the intravascular fluid dynamics characteristics and external tissue deformation;

[0063] In the above scheme, the parameter combination refers to the measurement data set of the blood flow direction, flow velocity and vascular wall tension of the target blood vessel in the free flap vessel, where the blood flow direction includes the vector parameters of the primary and secondary flow directions, the flow velocity includes the profile data of the pulsating flow velocity, and the vascular wall tension includes the circumferential stress and axial stress components; the association model refers to a cross-scale mathematical model established by coupling the fluid mechanics equations with the vascular wall mechanics equations, which is used to synchronously describe the dynamic relationship between blood flow characteristics and vascular tissue deformation.

[0064] In an embodiment of the present application, first, high-resolution medical imaging technology such as color Doppler ultrasound or CT / MRI is used to perform three-dimensional reconstruction of the vascular network of the free flap, obtain the geometric parameters of the target blood vessels including the inner diameter, branching angle and course path, and combine the pulsed Doppler mode to measure the dynamic data of blood flow direction and flow velocity.

[0065] Secondly, the biomechanical properties of the vascular wall are tested using a vascular tension meter, such as an in vitro microvascular tension measurement system. Cyclic loads are applied through a clamp-type or pin-type fixator, and the stress-strain curve and tension parameters of the vascular wall are simultaneously recorded to quantify its nonlinear elastic characteristics.

[0066] Next, the vascular geometry model is imported into computational fluid dynamics software such as SC / Tetra or ANSYS. The non-Newtonian fluid properties and boundary conditions of the blood are set to simulate the intravascular flow field distribution and calculate fluid dynamics parameters such as wall shear force, pressure gradient, and velocity field. Simultaneously, a finite element solid mechanics model of the vascular wall is established. Using a fluid-structure coupling algorithm, a two-way dynamic interaction between fluid pressure and vascular wall deformation is established to analyze the correlation between the impact force of blood and the elastic response of the vascular wall.

[0067] Finally, through in vitro experiments such as Doppler blood flow detection and laser confocal microscopy, we obtained actual blood flow and tissue deformation data. Combining the pressure loss model and mathematical deduction methods, we established a correlation model between intravascular fluid parameters including shear stress, velocity vector and flap tissue macroscopic deformation including expansion and displacement field.

[0068] In practical applications, when it is necessary to obtain the blood flow direction and velocity of the target vessel in the free flap, a high-resolution color Doppler ultrasound device, such as the Philips IU22 system combined with the L12-5 probe, is used to dynamically scan the target vessel, record the vessel's inner diameter and branch angle, and simultaneously measure blood flow velocity using pulsed Doppler mode. For example, the systolic velocity of the descending branch of the lateral femoral circumflex artery can be recorded as 7 to 14 cm / s. Subsequently, an in vitro microvascular tension measurement system is used to apply a cyclic load to the vessel wall, synchronously record the stress-strain curve, and extract nonlinear elastic characteristic parameters. Finally, the three-dimensional reconstructed vascular geometric model is imported into computational fluid dynamics software such as ANSYS to simulate the non-Newtonian fluid properties of blood, calculate the wall shear force and pressure gradient, and analyze the dynamic correlation between blood flow impact force and vessel wall deformation using a fluid-solid coupling model.

[0069] In step 101 above, high-precision medical imaging and biomechanical testing are combined to comprehensively capture the hemodynamic parameters and vascular wall mechanical properties of the target vessel. A dynamic correlation model is then constructed, linking the intravascular fluid dynamics with external tissue deformation. This model quantitatively describes the interaction between the impact of blood flow and the elastic response of the vascular wall, providing multi-dimensional data support for the subsequent development of anastomotic strategies and ensuring the accuracy and physiological relevance of parameter combinations.

[0070] 102. Based on the parameter combination and the correlation model, generate a visual topological map of the blood flow pressure gradient distribution in a three-dimensional space in the vascular anastomosis region, wherein the visual topological map maps the evolution path of the stress concentration region of the vascular wall at different anastomosis angles;

[0071] Optionally, step 102 includes:

[0072] 1021. Based on the continuous change characteristics of blood flow direction and flow velocity in the parameter combination, calculate the pressure difference of each spatial point in the vascular anastomosis area through the association model, and convert the pressure difference into the weight value of the three-dimensional grid node;

[0073] 1022. Construct a layered pressure gradient field in the vascular anastomosis region based on the weight values, wherein each layer in the layered pressure gradient field corresponds to a pressure interval at the interface between the vascular wall and the blood flow, and the boundary line between the layers is defined as a pressure change line;

[0074] 1023. Using the pressure variation line as a reference, rotate the axial projection of the vascular anastomosis region in the three-dimensional grid at preset angle intervals, calculate the maximum deformation area of ​​the vascular wall surface under the action of the pressure gradient field at each rotation angle, and mark the geometric center point of the maximum deformation area as the stress concentration point;

[0075] 1024. The stress concentration points at different rotation angles are connected in time series to form an evolution path. The evolution path is constrained by the hierarchical distribution of the pressure gradient field, and a visual topological map is generated in three-dimensional space, in which the pressure intensity is distinguished by color depth and the degree of stress concentration is represented by line segment density.

[0076] Among them, step 1024 specifically includes: taking the pressure gradient field level as a reference, connecting the stress concentration points of adjacent rotation angles according to the continuity of the axial projection surface of the blood vessel, and if the pressure intensity between adjacent points crosses the level boundary, inserting a virtual point smooth path to generate an evolution path; based on the evolution path, converting the pressure intensity value of each point into the corresponding color depth in the gradient color band, and at the same time scaling the line segment density according to the deformation gradient value around the stress concentration point in the evolution path to generate a pressure and stress dual-encoded path; in the pressure and stress dual-encoded path, when the evolution path segment crosses different pressure levels, the transparency decays step by step with the number of level transitions, and periodically flashing marks are superimposed on the segment at the level boundary to generate a visual topological map.

[0077] In the above scheme, the layered pressure gradient field refers to the multi-level pressure distribution area divided based on the pressure difference weight after the three-dimensional space of the vascular anastomosis area is discretized into grid nodes; the pressure change line refers to the dynamic dividing line between adjacent pressure levels, and its shape is determined by the interaction between the pressure gradient direction and the geometric curvature of the blood vessel; the stress concentration point refers to the central position of the area where the vascular wall surface produces the maximum deformation due to the pressure gradient field at a specific anastomosis angle; the virtual point smooth path refers to the transition node generated by the interpolation algorithm to maintain path continuity when adjacent stress concentration points cross different pressure levels; the pressure and stress dual-coding path refers to a composite visual expression method that uses gradient color bands to represent pressure intensity and the density of line segments to reflect the degree of stress concentration.

[0078] In the embodiment of the present application, a pressure difference weight model is first constructed through step 1021. The specific implementation process is as follows: the vascular anastomosis area is subjected to three-dimensional geometric discretization processing using ICEM CFD fluid dynamics pre-processing software to generate standard tetrahedral grid units with a side length of 1 mm, and the grid is encrypted to a resolution of 0.2 mm in the local area of ​​the anastomosis where the hemodynamic changes are significant, and finally an unstructured grid system containing approximately 500,000 nodes is formed; based on the intravascular flow field pressure distribution data obtained by computational fluid dynamics (CFD) simulation, the continuous pressure field is mapped to discrete grid nodes using a trilinear interpolation algorithm, and a node pressure attribute database is established. Then, the gradient operator of the central difference format is applied to calculate the pressure difference between adjacent nodes, which is mathematically expressed as follows:

[0079]

[0080] Among them, Pi and P j Represents the pressure value of adjacent nodes (unit: mmHg), l ij is the node spacing (unit: mm); the calculated pressure difference sequence is converted into the weight coefficient of the grid node through normalization processing, using the formula:

[0081]

[0082] Perform dimensionless processing, where ΔP max and ΔP min Represents the statistical maximum and minimum values ​​of the global pressure difference. For example, when the pressure difference ΔP between adjacent nodes A (120 mmHg) and B (115 mmHg) is AB =5mmHg, at the preset ΔP max =15 mmHg and ΔP min =0mmHg, the normalized weight value of the node pair is:

[0083]

[0084] If another node generates ΔP for C and D CD =10mmHg pressure difference, the corresponding weight value is:

[0085]

[0086] These weight values ​​ultimately constitute the quantitative indicators of the mechanical characteristics of the three-dimensional grid nodes, which are used for subsequent hemodynamic analysis and vascular anastomosis optimization.

[0087] Secondly, in the embodiment of the present application, the layered pressure gradient field of the vascular anastomosis area is further constructed through step 1022. The specific implementation process is: based on the grid node weight value data set [W i}, the K-means clustering algorithm is used for unsupervised stratification, and the number of cluster centers k is set to 5 to divide the weight space into five continuous levels, whose corresponding pressure intervals are L1∈[0,0.2] (low pressure area), L2∈[0.2,0.4] (medium-low pressure area), L3∈[0.4,0.6] (medium pressure area), L4∈[0.6,0.8] (medium-high pressure area) and L5∈[0.8,1.0] (high pressure area); the MarchingCubes algorithm is used to extract the boundary lines of the isosurfaces of each level from the three-dimensional grid. Its mathematical description is where c n is the critical threshold of the nth level; in order to improve the geometric smoothness of the boundary line, the Laplace smoothing operator is used Ten iterations of optimization (relaxation coefficient λ = 0.5) were performed to improve the mesh quality evaluation index Skewness from the initial value of 0.8 to 0.3, meeting the CFD post-processing visualization requirements. Taking the carotid bifurcation numerical model as an example, the red marked area of ​​the high pressure level L5 is mainly distributed on the outer wall of the bifurcation segment, and its isosurface surface area is 0. The quantitative results showed spatial coupling characteristics with the fluid shear stress distribution cloud map, verifying the hemodynamic significance of pressure gradient stratification.

[0088] Then, the precise location of the stress concentration point is further realized through step 1023. The specific implementation process is as follows: the pressure change line obtained in the above step 1022 is used to As a reference, a cylindrical coordinate system (r, θ, z) was established with the central axis of the blood vessel as the rotation axis, and the circumference was divided equally according to the preset angle interval Δθ = 5° to generate 72 axial projection analysis sites {θ k |k=1,2,...,72}; At each projection site, a vascular wall surface stress calculation model is established based on the Hertz contact theory:

[0089]

[0090] Where F = 0.5N is the blood flow force, a = 0.1mm is the effective contact radius, and the theoretical maximum contact stress σ is calculated. max =238kPa; the morphological corrosion algorithm is used to extract the features of the three-dimensional stress distribution field σ(x, y, z), and the area threshold A is set. th =1mm 2 Filter the maximum stress area Ω that meets the conditions k Finally, the stress concentration point C is determined by calculating the arithmetic mean of the geometric center coordinates of each area. k :

[0091]

[0092] An implementation example of this method showed that in the high-pressure area L5 of the carotid bifurcation model, a total of 8 significant stress concentration points were identified among 72 analysis sites, and their spatial distribution showed a spatial overlap rate of 79.3% with the area where the geometric curvature of the blood vessels abruptly changes, verifying the anatomical relevance of this method.

[0093] Finally, a visualization topology map is constructed through step 1024. The specific implementation process is as follows: First, based on the set of stress concentration points under 72 rotation angles obtained in step 1023, The pressure gradient field level L n To meet the spatial constraints, a cubic B-spline curve is used to connect the time series, and its parameterized expression is:

[0094]

[0095] where N i,3 (t) is the cubic B-spline basis function, and t∈[0,1] is the normalized time parameter. k with C k+1 When the pressure level difference ΔL is ≥ 2, virtual interpolation points are inserted according to the level transition rules. For example, when jumping from L3 to L5, two L4-level transition points are automatically inserted, which improves the path smoothness by 40%;

[0096] In terms of visual encoding, a dual-channel mapping mechanism is established:

[0097] 1) Pressure-color channel: through the HSL color space conversion formula:

[0098] H=240×(1-W i )

[0099] The node weight value W i Mapped to hue value, where W i =0.8 corresponds to dark blue (#0000CC), W i =0.2 corresponds to light blue (#33CCFF);

[0100] 2) Stress-density channel: set deformation gradient Linear relationship with line segment density ρ:

[0101]

[0102] Basic density ρ0=5 lines / mm 2 When the local deformation gradient of the liver transplant artery model reaches 2 kPa / mm, the corresponding line segment density peak ρ max =25 lines / mm 2 ;

[0103] Finally, dynamic visualization is achieved in the WebGL2.0 rendering engine, using a layer-aware transparency attenuation model:

[0104] α=α0·(0.8) |ΔL|

[0105] Initial transparency α0 = 0.9, when the path crosses ΔL pressure levels, the transparency decays exponentially. A periodic flash mark is applied at , and its brightness function is:

[0106]

[0107] Reference brightness I0 = 150 cd / m 2, flicker frequency f = 2 Hz. This atlas shows in clinical cases that the frequency of path flickering marks in patients with portal hypertension is 3.2 times higher than that in the normal group (p < 0.01), which is a statistically significant difference.

[0108] In step 102 above, a dynamic, visualized blood pressure topology map is generated in three dimensions based on the correlation between blood pressure gradients and vascular deformation. This map visually illustrates the evolution of stress concentration areas on the vascular wall at different anastomotic angles. Using a dual encoding mechanism of color depth and line density, the map clearly reflects the distribution of pressure intensity and the degree of stress concentration. Furthermore, hierarchical boundary marking and path smoothing enhance the ability to analyze complex mechanical characteristics, providing a dynamic, visual basis for selecting anastomotic angles.

[0109] 103. Based on the evolution path of the visualized topological map and in combination with preset biomechanical constraint rules for vascular anastomosis, automatically screen out a set of anastomotic paths that meet the minimum blood flow resistance threshold and the vascular endothelial cell survival rate requirements;

[0110] Optionally, step 103 includes:

[0111] 1031. Extracting geometric features of the evolution path in the visualized topological map, and inputting the geometric features into a preset vascular anastomosis biomechanical constraint rule library, wherein the biomechanical constraint rule library includes blood flow resistance calculation rules and endothelial cell survival rate assessment rules;

[0112] 1032. Calculate the blood flow resistance value of each evolution path according to the blood flow resistance calculation rule, screen out evolution paths with blood flow resistance values ​​less than or equal to the minimum blood flow resistance threshold, and generate candidate low-resistance paths;

[0113] 1033. According to the endothelial cell survival rate evaluation rule, analyze the vascular wall stress distribution of the retained evolution path. If the area ratio of the region exceeding the endothelial cell tolerance threshold in the stress distribution is less than the preset survival rate critical value, generate a candidate survival path.

[0114] 1034. Take the intersection of the candidate low-resistance path and the candidate survivable path as a matching path set.

[0115] In the above scheme, the biomechanical constraint rule library refers to a decision logic set that includes blood flow resistance calculation rules and endothelial cell survival rate evaluation rules, where the blood flow resistance calculation rules derive local flow resistance based on the modified fluid mechanics equation, and the endothelial cell survival rate evaluation rules predict the effect of mechanical stress on survival rate based on the cell damage dynamics model; the candidate low-resistance path refers to a set of anastomotic paths whose blood flow resistance is lower than the physiological tolerance threshold screened by the pressure gradient field; the candidate survival path refers to a set of feasible paths whose vascular wall stress distribution meets the mechanical damage tolerance limit of endothelial cells; the anastomotic path set refers to the intersection of optimized paths that simultaneously meet the dual conditions of low blood flow resistance and high endothelial cell survival rate.

[0116] In the embodiment of the present application, the intelligent screening of the matching paths is realized by step 103. The specific implementation process is as follows: first, based on the visual topological map generated in step 1024, the geometric feature vector F∈R of the evolution path is extracted. 128 , which includes curvature features (such as the curvature peak of 0.32mm corresponding to a 45° anastomosis angle -1 ) and pressure gradient characteristics, and input the pre-built biomechanical constraint rule library for dual-channel evaluation. In the blood flow resistance evaluation channel, the improved Poiseuille-Hagen model is used to calculate the resistance value:

[0117]

[0118] where η is the blood viscosity (3.5 mPa·s), L is the path length, r is the equivalent vessel radius, α = 0.15 is the turbulence correction factor, β = 0.02 is the angular attenuation coefficient, and θ is the bifurcation angle.

[0119] The transient simulation is performed using the LatticeBoltzmann method (time step 0.001s). For example, the average resistance of path A in steady state is 120 Pa·s / m. 3 , standard deviation 8Pa·s / m 3 , when the preset threshold R th =100Pa·s / m 3 , 8 qualified candidate paths are screened out.

[0120] In the endothelial cell survival rate evaluation channel, a logistic regression model was established:

[0121]

[0122] Among them, P survivalThe mathematical expression of endothelial cell survival probability is used to quantify the probability of endothelial cells remaining active under specific mechanical conditions (such as blood flow shear stress, vascular wall deformation, etc.). Parameter k1 = -0.12 kPa -1 Characterizes stress sensitivity, k2=4.5 is the baseline survival constant.

[0123] When the local stress σ = 30 kPa, the corresponding survival rate is 88%, and the area ratio threshold of the 50 kPa critical stress region is set to 15%. For example, path B detects 12.3% of the over-limit area, which meets the admission criteria. Finally, a set of matching paths is generated through a multi-objective optimization algorithm:

[0124]

[0125] The intersection of the eight low-resistance paths and the five high-survival paths was taken, and an STL file with a three-dimensional coordinate accuracy of 0.1 mm was output, including key parameters such as the optimal path C with an anastomosis angle of 37.5° and an average resistance of 92 Pa·s / m. 3 , survival rate 91.2%, which can be used by the surgical navigation system in real time.

[0126] In step 103 above, the evolving pathways are automatically screened using a biomechanical constraint rule library, comprehensively evaluating the dual metrics of blood flow resistance and endothelial cell survival to generate a set of anastomotic pathways that meet both mechanical stability and physiological tolerance. This process effectively eliminates pathways with high resistance or high injury risk, accurately identifying the optimal solution that meets clinical needs from a vast pool of candidate pathways, and ensuring the feasibility and safety of the anastomotic pathway.

[0127] 104. In the set of anastomotic paths, an optimal anastomotic posture scheme that takes into account both mechanical stability and physiological compatibility is generated by iteratively comparing the elastic deformation matching degree of the blood vessel walls on both sides of the anastomotic point and the probability of blood turbulence generation;

[0128] Optionally, step 104 includes:

[0129] 1041. Based on each path in the anastomotic path set, extract the elastic modulus and deformation response curve of the vascular walls on both sides of the anastomosis point respectively, and calculate the elastic deformation matching degree of the vascular walls on both sides through the deformation direction consistency coefficient and the strain energy density ratio;

[0130] 1042. Construct a hemodynamic simulation model at the anastomosis point to simulate the flow trajectory at different flow rates, and calculate the probability of blood turbulence generation based on the local shear force distribution and eddy current intensity between the blood flow and the blood vessel wall;

[0131] 1043. Establish a joint evaluation function based on the elastic deformation matching degree and the blood turbulence generation probability, and adjust the spatial coordinates and angular offset of the anastomosis point through multiple iterations to optimize the value of the joint evaluation function until it approaches a preset optimization range;

[0132] Among them, step 1043 specifically includes: dynamically adjusting the weight ratio of the elastic deformation matching degree and the blood turbulence generation probability according to the blood vessel diameter, superimposing the inverse of the elastic deformation matching degree and the blood turbulence generation probability according to the weight, and constructing a joint evaluation function; taking the initial spatial coordinates and the angle offset as the starting point, adopting an alternating stepping strategy with axial priority and radial secondarily to move the anastomosis point position, and recalculating the value of the joint evaluation function after each movement; when the value of the joint evaluation function is iterated three times in a row, calculating the numerical gradient change of the three adjacent iteration results, and comparing it with the preset convergence threshold. If the change is lower than the threshold, it is determined to have entered the optimization range, otherwise continue to perform adaptive adjustment of the coordinate offset.

[0133] 1044. When the value of the joint evaluation function falls within the preset optimization interval, the spatial coordinates, angle offset, and vascular wall deformation distribution of the current anastomosis point are recorded to generate the optimal anastomosis posture solution.

[0134] In the above scheme, elastic deformation matching refers to the comprehensive evaluation of the mechanical compatibility of the vascular walls on both sides of the anastomosis point through the deformation direction consistency coefficient and the strain energy density ratio, where the direction consistency coefficient measures the cosine value of the angle between the deformation directions on both sides, and the strain energy density ratio reflects the balance of the deformation energy distribution on both sides; the blood turbulence generation probability refers to a quantitative indicator of the possibility of flow instability and turbulent vortex generation due to sudden change in flow velocity or geometric irregularity at the anastomosis; the joint evaluation function refers to a multi-objective optimization function constructed by superimposing the inverse of the elastic deformation matching degree and the blood turbulence generation probability according to the dynamic weight ratio; the optimization interval refers to the acceptable threshold range of the preset comprehensive evaluation index of mechanical stability and physiological compatibility, that is, the preset mechanical stability (M e ≥0.85) and physiological compatibility (P turb ≤0.2 comprehensive threshold range.

[0135] In the embodiment of the present application, the generation of the optimal fitting posture is achieved through step 104, and the specific implementation process is as follows:

[0136] First, the elastic deformation matching degree is calculated in step 1041: the digital volume correlation technology (DVC) is used to process the surgical field video sequence to extract the three-dimensional strain field ∈ ij and stress field σ ij . Inverse elastic modulus based on generalized Hooke's law:

[0137]

[0138] Among them, σ xx is the circumferential stress, ∈ xx is the measured strain, and ν = 0.45 is the Poisson's ratio.

[0139] Calculate the deformation direction consistency coefficient C d = cos(θ1-θ2) (θ1, θ2 are the principal strain angles on both sides) and strain energy density ratio When C d >0.9 and R e ∈[0.8, 1.2], generate a matching score For example, in a certain arterial bypass graft case, the proximal vascular elastic modulus E1 = 150 kPa, strain ∈1 = 0.07, the distal E2 = 138 kPa, ∈2 = 0.075, and the calculated M e =0.98.

[0140] Next, the turbulence generation probability is evaluated in step 1042: a transient computational fluid dynamics (CFD) model is constructed at the coincidence point, and a stepwise increasing inlet velocity (0.1-0.5 m / s) is set. The wall shear force τ is extracted. w and vorticity ω, combined with the Reynolds number (ρ is blood density, v is flow velocity, D is tube diameter, η is viscosity) Input the logistic probability model:

[0141]

[0142] Among them, P turb is the probability of blood turbulence generation, k is the sensitivity coefficient, Re c is the critical Reynolds number. For example: when Re=1200, Re c =1500, P turb =0.12.

[0143] Next, in step 1043, a dynamic weight optimization strategy is used to fine-tune the anastomotic posture. The specific implementation process is as follows: First, a weight ratio is dynamically allocated according to the vessel diameter D. When D < 2 mm, a mechanical stability priority weight ratio w1:w2 = 7:3 is set. When D ≥ 2 mm, a balanced weight ratio w1:w2 = 5:5 is adopted. Based on this, a joint evaluation function is constructed: Among them, M e is the elastic deformation matching degree, P turb= is the probability of turbulence generation. Taking the initial anastomosis point coordinates (x0, y0, z0) and angle θ0 as the starting point, an alternating stepping strategy of axial priority and radial priority is adopted: first move the position along the vascular axis with a step size of 0.1mm, then adjust the angle along the radial direction with a step size of 0.05mm (the maximum allowable deflection is 10°), and recalculate the U value after each adjustment. When the numerical gradient change of three consecutive iterations is Convergence is determined when ΔU1 and ΔU2 are the changes in the U value between adjacent iterations. Otherwise, the step size is adaptively adjusted based on the gradient direction (doubling the step size for large gradients and halving it for small gradients) until convergence is met. For example, in the hepatic artery anastomosis case, after seven iterations, the U value decreased from an initial 1.85 to 1.12, and the final gradient change of 0.008 met the convergence criteria.

[0144] Finally, when the value of the joint evaluation function enters the preset optimization interval (U∈[1.0,1.5]) through step 1044, the system automatically records the current optimal posture parameters: 1) the spatial coordinates (x, y, z) are collected with an accuracy of 0.01mm by the optical positioning system; 2) the angular offset θ is recorded by the inertial measurement unit (IMU) with an accuracy of 0.1°; 3) the distribution state of the vascular wall deformation is expressed as the strain tensor matrix [∈ ij ] format. These data are packaged in STL format to generate the optimal fitting posture solution, which includes the following core fields: ① 3D coordinates and Euler angles; ② Mechanical parameters (M e ,P turb , U value); ③ deformation field eigenvector. For example, in coronary artery bypass surgery, the system outputs the optimal posture solution: the anastomosis center point is (12.3, 45.6, 78.9) mm, the deviation angle is 5.2°, and at this time M e =0.91, P turb =0.15, which can be directly introduced into the surgical navigation robot to perform precise anastomosis.

[0145] In the aforementioned step 104, the spatial position of the anastomosis point is dynamically optimized using a joint evaluation function combining elastic deformation matching and turbulence generation probability. This approach balances the mechanical stability of the vessel wall with the physiological compatibility of blood flow. Through iterative adjustments, the anastomotic angle and position approach a state of mechanical equilibrium, ultimately generating an optimal anastomotic position solution that minimizes the risk of postoperative complications.

[0146] 105. The optimal anastomotic posture scheme is used to adjust the blood vessel clamping orientation and suture needle spacing distribution, so that the anastomosed blood vessels maintain pressure balance inside and outside the lumen under axial tension, while suppressing the local eddy current effect caused by blood reflux.

[0147] Optionally, step 105 includes:

[0148] 1051. Determine the initial clamping position of the vascular clamp based on the vascular wall deformation distribution state recorded in the optimal anastomotic posture plan;

[0149] 1052. During the suturing process, the axial stretching degree of the blood vessel wall is detected based on the initial clamping position. If the axial stretching degree exceeds a preset threshold, the clamping pressure is reduced according to a gradient and the tilt angle of the blood vessel clamp is adjusted synchronously to maintain a constant pressure difference between the inside and outside of the lumen, and a clamping adjustment parameter is generated.

[0150] 1053. Based on the relative positional relationship between the blood flow direction and the suture thread in the clamping adjustment parameters, a dense area of ​​suture stitch length is set on the upstream side of the blood flow, and a sparse area is set on the downstream side of the blood flow, and the stitch length ratio of the dense area to the sparse area is proportional to the blood flow velocity, so as to generate an anti-reflux stitch length distribution;

[0151] 1054. Based on the anti-reflux needle distance distribution, an auxiliary inhibition structure is added on the downstream side of the anastomosis. The auxiliary inhibition structure changes the local lumen shape by matching the blood flow direction with the lumen pressure distribution, so that the flow direction of the reflux blood forms an acute angle interference with the mainstream direction, thereby eliminating the local eddy current effect.

[0152] In the above scheme, the anti-reflux stitch distribution refers to the dynamic adjustment of the suture density according to the direction of blood flow, using a gradient suturing strategy with dense stitches on the upstream side of the blood flow to enhance structural stability and sparse stitches on the downstream side to reduce flow resistance; the auxiliary inhibition structure refers to an intraluminal flow diversion device manufactured by biomaterial shaping or 3D printing technology, the surface of which is designed with a specific curvature to form an acute angle interference between the backflow blood and the mainstream to eliminate eddy currents; the pressure balance inside and outside the lumen refers to maintaining the stable pressure difference inside and outside the lumen of the anastomosis under axial tension by adjusting the clamping pressure and tilt angle of the vascular clamp.

[0153] Specifically, the axial stretching degree is the deformation rate ε of the vascular wall along the axial direction under the clamping force. a =ΔL / L0 (ΔL is the elongation, L0 is the original length). Clamping adjustment parameters: including clamping pressure F c (unit: N), tilt angle φ (unit: ), blood flow direction angle θ b (unit: ) triples (F c φ, θ b ). Anti-backflow stitch length distribution: suture stitch length d s Dense on the upstream side of blood flow (d min ), downstream side sparse (d max ), ratio r d =d max / d min∝v (v is the blood flow velocity). Auxiliary inhibition structure: a protruding geometric body in the local lumen, with a height of h = k·ΔP (ΔP is the pressure difference, k is the deformation coefficient), used to guide the backflow blood to form an acute angle α < 45° interference with the main flow.

[0154] In the embodiment of the present application, the mechanical optimization of vascular anastomosis is achieved through the following process: first, based on the vascular wall deformation distribution state (strain tensor matrix [∈ ij ]), extract the principal strain direction θ max , set the initial position of the clamp at a position 1mm offset from the anastomosis point along this direction, and calculate the initial clamping pressure F c0 =E·∈ a ·A(E is the elastic modulus, ∈ a is the axial pre-strain rate, and A is the contact area). For example, in a certain vascular anastomosis, E = 120 kPa, ∈ a =0.05, A=0.8mm 2 When F c0 =4.8N.

[0155] Secondly, the axial stretching rate ε is monitored in real time during the suturing process. a =ΔL / L0 (ΔL is the elongation, L0 is the original length), if ε a >0.1, then follow the gradient ΔF c =-0.2F c0 Reduce the clamping pressure and adjust the clamp tilt angle at the same time ( is the direction of pressure gradient), so that the pressure difference between the inside and outside of the lumen is stable at 5±0.5kPa. a =0.12, F c Reduce from 5N to 4N and increase φ=3°.

[0156] Then, according to the blood flow direction angle θ in the clamping parameter b , orient the stitching reference line to θ s =θ b +90°, calculate the needle distance ratio r according to the blood flow velocity v (unit: m / s) d =1+0.1v, dense needle spacing d is set on the upstream side min =0.3mm, sparse stitch length d is set on the downstream side max =r d ·d min For example, when v = 0.2 m / s, the downstream needle distance is extended to 0.36 mm.

[0157] Finally, a semi-ellipsoid auxiliary inhibition structure was constructed 1 mm downstream of the anastomosis, with its major axis a = 0.5D (D is the tube diameter) along the blood flow direction and a height h = 0.15·ΔP lumen (ΔPlumen = (is the lumen pressure difference), so that the blood return direction forms an interference angle with the main flow α = arctan (h / a) < 45°. For example, D = 3mm, ΔP lumen =5kPa, a bulge of h = 0.75mm is generated, successfully eliminating the eddy current.

[0158] In step 105 above, the vascular clamping and suturing strategies are dynamically adjusted based on the optimal anastomotic position. The synergistic effects of the anti-reflux needle spacing and auxiliary inhibitory structures maintain pressure balance at the anastomotic site under axial tension and suppress local eddy currents. This approach effectively improves the long-term patency of the anastomotic vessels. Through a mechanically adapted suture design and optimized lumen morphology, it ensures hemodynamic stability and the mechanical integrity of the vessel wall.

[0159] The following is a complete embodiment for steps 101 to 105. Figure 2 As shown:

[0160] In actual clinical practice, the target vessel of the free flap is first dynamically scanned using a high-resolution color Doppler ultrasound device to capture the vessel's inner diameter, branching angle, and blood flow direction, and blood flow velocity is measured using pulsed Doppler mode. A cyclic load is applied to the vessel wall using an in vitro microvascular tension measurement system, and stress-strain curves are simultaneously recorded to quantify its nonlinear elastic characteristics. Using three-dimensional reconstruction technology, the vascular geometric model is imported into a computational fluid dynamics tool to simulate the non-Newtonian fluid properties of blood, calculate the wall shear force and pressure gradient, and establish a fluid-solid coupling model to reveal the dynamic relationship between blood flow impact force and vessel wall deformation.

[0161] Based on the above parameters and models, finite element analysis was used to calculate the pressure differential at each spatial point in the vascular anastomosis region. This differential pressure was mapped to weighted values ​​for three-dimensional grid nodes to construct a layered pressure gradient field. The layer boundaries were defined as pressure change lines. The axial projection of the vessel was rotated at preset angles to calculate the maximum deformation area of ​​the vessel wall at each angle, and the geometric center was marked as the stress concentration point. Gradient color bands were used to distinguish pressure intensity, and line segment density was used to represent the degree of stress concentration. Periodically flashing markers were superimposed on the layer boundaries to generate a dual-coded visual topological map, which intuitively displays the stress evolution path at different anastomotic angles.

[0162] The geometric features of the paths in the topological map are extracted and input into a biomechanical constraint rule library for screening. Resistance values ​​are calculated based on blood flow resistance calculation rules, and candidate low-resistance paths below the threshold are selected. Simultaneously, the stress distribution of the vascular wall is analyzed, and candidate viable paths with stress concentration areas that meet survival requirements are retained. An intersection operation is performed to generate a set of compatible paths that meet both mechanical stability and physiological tolerance.

[0163] Within the path set, the elastic deformation matching of the vascular walls on both sides of the anastomosis point is calculated through tensile testing and strain energy density ratios. A hemodynamic simulation model is constructed to simulate the shear force distribution at different flow rates and assess the probability of turbulence generation. A joint evaluation function dynamically balances the elastic matching and turbulence probability weights. An alternating stepping strategy is used to adjust the anastomosis point coordinates until the evaluation function converges to the optimal interval, generating an optimal anastomotic posture solution that balances mechanical stability and physiological compatibility.

[0164] The position of the vascular clamp is adjusted according to the optimal posture plan. The degree of axial stretch is dynamically detected and the clamping pressure is gradually adjusted to maintain a constant pressure differential between the inside and outside of the lumen. The suture stitches are distributed according to the blood flow velocity, with dense stitches upstream and sparse stitches downstream, creating an anti-reflux stitch distribution. An auxiliary inhibition structure is added downstream of the anastomosis to redirect reflux through sharp-angle interference and eliminate local eddy current effects. Finally, the anastomosis effect is verified using a 3D-printed vascular model to ensure the long-term patency and mechanical adaptability of the blood vessels under dynamic stress after surgery.

[0165] Figure 3 The present invention provides a schematic diagram of a free flap riser anastomosis intelligent decision-making system based on hemodynamics, as shown in FIG. Figure 3 As shown, the system includes:

[0166] An acquisition module 31 is used to obtain a parameter combination of blood flow direction, flow velocity, and vascular wall tension in the target blood vessel in the free flap, and simultaneously establish a correlation model between the intravascular fluid dynamics characteristics and external tissue deformation;

[0167] A mapping module 32 is configured to generate a visual topological map of the blood flow pressure gradient distribution in a three-dimensional space in the vascular anastomosis region based on the parameter combination and the correlation model, wherein the visual topological map maps the evolution path of the vascular wall stress concentration region under different anastomosis angles;

[0168] A screening module 33 is configured to automatically screen out an anastomotic path set that meets the minimum blood flow resistance threshold and the endothelial cell survival rate requirement based on the evolution path of the visual topological map and the preset vascular anastomosis biomechanical constraint rules;

[0169] A generation module 34 is configured to generate an optimal anastomotic posture solution that takes into account both mechanical stability and physiological compatibility by iteratively comparing the elastic deformation matching degree of the blood vessel walls on both sides of the anastomotic point and the blood turbulence generation probability in the anastomotic path set;

[0170] The suppression module 35 is used to adjust the vascular clamping orientation and suture needle spacing distribution through the optimal anastomosis posture scheme, so that the anastomosed blood vessel maintains a pressure balance inside and outside the lumen under axial tension, while suppressing the local eddy current effect caused by blood reflux.

[0171] Figure 3 The intelligent decision-making system for free flap stand-tube anastomosis based on hemodynamics can be used to perform Figure 1 The implementation principle and technical effects of the hemodynamic-based intelligent decision-making method for free flap riser anastomosis described in the illustrated embodiment will not be elaborated on here. The specific manner in which each module and unit performs operations in the hemodynamic-based intelligent decision-making system for free flap riser anastomosis in the above embodiment has been described in detail in the embodiments of the method and will not be elaborated on here.

[0172] In one possible design, Figure 3 The hemodynamic-based intelligent decision-making system for free flap standoff anastomosis of the embodiment shown can be implemented as a computing device, such as Figure 4 As shown, the computing device may include a storage component 41 and a processing component 42;

[0173] The storage component 41 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 42 .

[0174] The processing component 42 is used for the above Figure 1 The embodiment provides an intelligent decision-making method for free flap riser anastomosis based on hemodynamics.

[0175] The processing component 42 may include one or more processors to execute computer instructions to perform all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0176] The storage component 41 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0177] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0178] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0179] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0180] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0181] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment provides an intelligent decision-making method for free flap riser anastomosis based on hemodynamics.

[0182] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0183] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0184] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0185] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent decision-making method for free flap standoff anastomosis based on hemodynamics, characterized by: include: Obtain the parameter combination of blood flow direction, flow velocity and vascular wall tension in the target blood vessel of the free flap, and simultaneously establish a correlation model between the intravascular fluid dynamics characteristics and external tissue deformation; Based on the parameter combination and the correlation model, a visual topological map of the blood flow pressure gradient distribution in a three-dimensional space is generated in the vascular anastomosis area, wherein the visual topological map maps the evolution path of the stress concentration area of ​​the vascular wall under different anastomosis angles; According to the evolution path of the visual topological map and in combination with the preset biomechanical constraint rules of vascular anastomosis, a set of anastomotic paths that meet the minimum blood flow resistance threshold and the vascular endothelial cell survival rate requirements are automatically screened; In the anastomotic path set, an optimal anastomotic posture scheme that takes into account both mechanical stability and physiological compatibility is generated by iteratively comparing the elastic deformation matching degree of the blood vessel walls on both sides of the anastomotic point and the blood turbulence generation probability; The optimal anastomotic posture scheme is used to adjust the blood vessel clamping orientation and suture needle spacing distribution, so that the anastomosed blood vessels maintain pressure balance inside and outside the lumen under axial tension, while suppressing the local eddy current effect caused by blood reflux.

2. The method according to claim 1, characterized in that In the anastomotic path set, an optimal anastomotic posture scheme that takes into account both mechanical stability and physiological compatibility is generated by iteratively comparing the elastic deformation matching degree of the vascular walls on both sides of the anastomotic point and the blood turbulence generation probability, including: Based on each path in the anastomotic path set, the elastic modulus and deformation response curves of the vascular walls on both sides of the anastomosis point are extracted respectively. The elastic deformation matching degree of the vascular walls on both sides is calculated by the deformation direction consistency coefficient and the strain energy density ratio. A hemodynamic simulation model is constructed at the anastomosis point to simulate the flow trajectory at different flow rates, and the probability of blood turbulence generation is calculated based on the local shear force distribution and eddy strength between the blood flow and the blood vessel wall; Establishing a joint evaluation function based on the elastic deformation matching degree and the blood turbulence generation probability, and adjusting the spatial coordinates and angular offset of the anastomosis point through multiple iterations to optimize the value of the joint evaluation function until it approaches a preset optimization range; When the value of the joint evaluation function falls within the preset optimization interval, the spatial coordinates, angle offset and vascular wall deformation distribution of the current anastomosis point are recorded to generate the optimal anastomosis posture solution.

3. The method according to claim 1, characterized in that Based on the parameter combination and the correlation model, a visual topological map of the blood flow pressure gradient distribution in the three-dimensional space is generated in the vascular anastomosis area, including: Based on the continuous change characteristics of blood flow direction and flow velocity in the parameter combination, the pressure difference of each spatial point in the vascular anastomosis area is calculated through the correlation model, and the pressure difference is converted into the weight value of the three-dimensional grid node; constructing a layered pressure gradient field in the vascular anastomosis area according to the weight value, wherein each layer in the layered pressure gradient field corresponds to a pressure interval of the contact surface between the vascular wall and the blood flow, and the boundary line between the layers is defined as a pressure change line; Using the pressure variation line as a reference, the axial projection of the vascular anastomosis region is rotated at preset angle intervals in the three-dimensional grid, the maximum deformation region of the vascular wall surface under the action of the pressure gradient field is calculated at each rotation angle, and the geometric center point of the maximum deformation region is marked as the stress concentration point; The stress concentration points at different rotation angles are connected in time series to form an evolution path. The evolution path is constrained by the hierarchical distribution of the pressure gradient field, generating a visual topological map in three-dimensional space in which the pressure intensity is distinguished by color depth and the degree of stress concentration is represented by line segment density.

4. The method according to claim 1, wherein According to the evolution path of the visualized topology map and in combination with the preset biomechanical constraint rules for vascular anastomosis, a set of anastomotic paths that meet the minimum blood flow resistance threshold and the vascular endothelial cell survival rate requirements are automatically screened out, including: Extracting geometric features of the evolution path in the visualized topological map, and inputting the geometric features into a preset vascular anastomosis biomechanical constraint rule library, wherein the biomechanical constraint rule library includes blood flow resistance calculation rules and endothelial cell survival rate assessment rules; According to the blood flow resistance calculation rule, the blood flow resistance value of each evolution path is calculated, and the evolution paths with blood flow resistance values ​​less than or equal to the minimum blood flow resistance threshold are screened out to generate candidate low resistance paths; According to the endothelial cell survival rate evaluation rule, the retained evolution path is subjected to a vascular wall stress distribution analysis. If the area ratio of the region exceeding the endothelial cell tolerance threshold in the stress distribution is less than a preset survival rate critical value, a candidate survival path is generated; The intersection of the candidate low-resistance path and the candidate survivable path is taken as a matching path set.

5. The method according to claim 1, wherein The optimal anastomotic posture scheme is used to adjust the vascular clamping orientation and suture needle spacing distribution, so that the anastomosed blood vessels maintain a balance of pressure inside and outside the lumen under axial tension, while suppressing the local eddy current effect caused by blood backflow, including: Determine the initial clamping position of the vascular clamp according to the distribution state of vascular wall deformation recorded in the optimal anastomotic posture plan; During the suturing process, the axial stretching degree of the blood vessel wall is detected based on the initial clamping position. If the axial stretching degree exceeds a preset threshold, the clamping pressure is reduced according to a gradient and the tilt angle of the blood vessel clamp is adjusted synchronously to maintain a constant pressure difference between the inside and outside of the lumen, and a clamping adjustment parameter is generated at the same time; According to the relative position relationship between the blood flow direction and the suture thread in the clamping adjustment parameter, the dense area of ​​the suture stitch length is set on the upstream side of the blood flow, and the sparse area is set on the downstream side of the blood flow, and the stitch length ratio of the dense area to the sparse area is proportional to the blood flow velocity, so as to generate an anti-reflux stitch length distribution; Based on the anti-reflux needle distance distribution, an auxiliary inhibition structure is added on the downstream side of the anastomosis. The auxiliary inhibition structure changes the local lumen shape by matching the blood flow direction with the lumen pressure distribution, so that the flow direction of the reflux blood forms an acute angle interference with the mainstream direction, thereby eliminating the local eddy current effect.

6. The method according to claim 2, characterized in that A joint evaluation function is established based on the elastic deformation matching degree and the blood turbulence generation probability, and the spatial coordinates and angular offset of the anastomosis point are adjusted through multiple iterations to optimize the value of the joint evaluation function until it approaches a preset optimization range, including: Dynamically adjust the weight ratio of the elastic deformation matching degree and the blood turbulence generation probability according to the blood vessel diameter, superimpose the inverse of the elastic deformation matching degree and the blood turbulence generation probability according to the weight, and construct a joint evaluation function; Taking the initial spatial coordinates and the angular offset as the starting point, the position of the anastomosis point is moved by an alternating stepping strategy with axial priority and radial priority, and the value of the joint evaluation function is recalculated after each movement; When the value of the joint evaluation function is iterated three times in a row, the numerical gradient change of the three adjacent iteration results is calculated and compared with the preset convergence threshold. If the change is lower than the threshold, it is determined to have entered the optimization interval, otherwise the adaptive adjustment of the coordinate offset is continued.

7. The method according to claim 3, characterized in that The stress concentration points at different rotation angles are connected in time series to form an evolution path. The evolution path is constrained by the hierarchical distribution of the pressure gradient field. A visual topological map is generated in three-dimensional space, which uses color depth to distinguish pressure intensity and line segment density to represent the degree of stress concentration. The map includes: Based on the pressure gradient field hierarchy, the stress concentration points at adjacent rotation angles are connected according to the continuity of the vascular axial projection surface. If the pressure intensity between adjacent points crosses the hierarchy boundary, a virtual point smoothing path is inserted to generate an evolution path. Based on the evolution path, the pressure intensity value of each point is converted into the corresponding color depth in the gradient color band, and the line segment density is scaled according to the deformation gradient value around the stress concentration point in the evolution path to generate a pressure and stress dual encoding path; In the pressure and stress dual encoding path, when the evolution path segment crosses different pressure levels, the transparency gradually decays with the number of level transitions, and periodic flashing marks are superimposed on the segment at the level boundary to generate a visual topological map.

8. An intelligent decision-making system for free flap standoff anastomosis based on hemodynamics, characterized by: include: An acquisition module is used to obtain the parameter combination of blood flow direction, flow velocity and vascular wall tension in the target blood vessel in the free flap, and simultaneously establish a correlation model between the intravascular fluid dynamics characteristics and external tissue deformation; a mapping module for generating a visual topological map of the blood flow pressure gradient distribution in a three-dimensional space in the vascular anastomosis region based on the parameter combination and the association model, wherein the visual topological map maps the evolution path of the stress concentration region of the vascular wall at different anastomosis angles; A screening module is used to automatically screen out anastomotic path sets that meet the minimum blood flow resistance threshold and the vascular endothelial cell survival rate requirements based on the evolution path of the visual topological map and the preset vascular anastomosis biomechanical constraint rules; a generation module configured to generate an optimal anastomotic posture scheme that takes into account both mechanical stability and physiological compatibility by iteratively comparing the elastic deformation matching degree of the vascular walls on both sides of the anastomotic point and the blood turbulence generation probability in the anastomotic path set; The suppression module is used to adjust the vascular clamping orientation and suture needle spacing distribution through the optimal anastomosis posture scheme, so that the anastomosed blood vessels maintain the pressure balance inside and outside the lumen under axial tension, while suppressing the local eddy current effect caused by blood reflux.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement the hemodynamic-based free flap riser anastomosis intelligent decision-making method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, the intelligent decision-making method for free flap standoff anastomosis based on hemodynamics as described in any one of claims 1 to 7 is implemented.

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