Hemodynamic index prediction method, device, equipment and medium

Abstract

The application discloses a hemodynamic index prediction method and device, equipment and medium, relates to the technical field of blood vessel fluid simulation, and comprises the following steps: carrying out grid division on a left ventricular three-dimensional model; setting boundary conditions and total viscosity at a current time step of blood flow; performing inner loop iterative updating on the velocity field of each grid unit based on the total viscosity at the current time step and by using the N-S equation and the incompressible condition, obtaining new total viscosity at the current time step based on the updated velocity field; re-performing inner loop iteration until a preset outer loop stop condition is met, and determining whether the current time step meets a preset stop condition; if the current time step does not meet the preset stop condition, the new total viscosity at the current time step is determined as the total viscosity of the next time step, then re-iteration is performed until the current time step meets the preset stop condition, and the hemodynamic index is obtained based on the new total viscosity at the current time step. The accuracy of predicting the hemodynamic index is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vascular fluid simulation, in particular to a hemodynamic index prediction method, device, equipment and medium. BACKGROUND

[0002] Hemodynamic indexes (such as blood flow velocity, pressure distribution, wall shear stress, etc.) are key basis for evaluating heart function and related diseases (such as coronary heart disease, cardiomyopathy), but it is difficult to achieve precise measurement of full spatial distribution through invasive examination (such as catheter measurement), and thus numerical simulation technology is needed to predict hemodynamic indexes to provide non-invasive and detailed reference for disease diagnosis and treatment plan formulation.

[0003] At present, the model in hemodynamic simulation lacks personalization, and the geometric structure constructed based on a general template deviates from the actual anatomical characteristics of a patient, affecting the simulation accuracy. When a left ventricle is simulated using a traditional computational fluid dynamics method, a more simple and general Newtonian fluid and laminar flow are often used to simulate blood and blood flow, and the flow detail information caused by the complex structure of the left ventricle is ignored, resulting in low accuracy of the predicted hemodynamic indexes.

[0004] In summary, how to improve the accuracy of predicting hemodynamic indexes is a problem to be solved in the field. SUMMARY

[0005] Therefore, the purpose of the present application is to provide a hemodynamic index prediction method, device, equipment and medium to improve the accuracy of predicting hemodynamic indexes. The specific scheme is as follows:

[0006] In a first aspect, the present application discloses a hemodynamic index prediction method, comprising:

[0007] constructing a left ventricle three-dimensional model according to image data of a target object, and performing mesh division on the left ventricle three-dimensional model to obtain a plurality of mesh units;

[0008] setting a boundary condition of blood flow and a total viscosity at a current time step; wherein the total viscosity is the sum of blood viscosity and turbulent viscosity;

[0009] performing inner loop iterative updating on the velocity field of each mesh unit based on the total viscosity at the current time step and using the N-S equation and the incompressible condition, updating the total viscosity at the current time step based on the obtained updated velocity field, to obtain a new total viscosity at the current time step;

[0010] jump back to the step of iteratively updating the velocity field of each of the grid cells based on the total viscosity at the current time step and using the N-S equation and the incompressible condition in an inner loop until a preset outer loop stop condition is met, and determining whether the current time step meets a preset stop condition;

[0011] If the current time step does not meet the preset stop condition, a new total viscosity at the current time step is determined as the total viscosity of a next time step, the next time step is taken as a new current time step, and then the step of iteratively updating the velocity field of each of the grid cells based on the total viscosity at the current time step and using the N-S equation and the incompressible condition in an inner loop is re-jumped;

[0012] If the current time step meets the preset stop condition, a hemodynamic index is obtained based on the new total viscosity at the current time step.

[0013] Optionally, the iteratively updating the velocity field of each of the grid cells based on the total viscosity at the current time step and using the N-S equation and the incompressible condition in an inner loop comprises:

[0014] setting an initial pressure field and an initial velocity field of each of the grid cells, and determining the initial pressure field and the initial velocity field as a current pressure field and a current velocity field;

[0015] discretizing the N-S equation on each of the grid cells in spatial dimensions and time dimensions based on the current velocity field to construct a linear equation group not including a pressure term, wherein a dependent variable of the linear equation group is a velocity of the grid cell;

[0016] inputting the total viscosity at the current time step into the linear equation group to obtain a target approximate velocity field;

[0017] constructing a pressure equation using the incompressible condition and the target approximate velocity field, and updating the current pressure field using the pressure equation to obtain an updated pressure field;

[0018] correcting the target approximate velocity field based on the updated pressure field to obtain an updated velocity field;

[0019] if a preset inner loop stop condition is not met, determining the updated pressure field and the updated velocity field as a new current pressure field and a new current velocity field respectively, and re-jumping to the step of discretizing the N-S equation on each of the grid cells in spatial dimensions and time dimensions based on the current velocity field;

[0020] if the preset inner loop stop condition is met, outputting the updated velocity field.

[0021] Optionally, the total viscosity at the current time step is set, comprising:

[0022] setting the blood viscosity at the current time step, setting the turbulent viscosity at the current time step, and determining the sum of the blood viscosity at the current time step and the turbulent viscosity at the current time step as the total viscosity at the current time step;

[0023] Correspondingly, the total viscosity at the current time step is updated based on the obtained updated velocity field to obtain a new total viscosity at the current time step, comprising:

[0024] the blood viscosity at the current time step and the turbulent viscosity at the current time step are updated based on the obtained updated velocity field to obtain a new blood viscosity at the current time step and a new turbulent viscosity at the current time step, respectively;

[0025] the sum of the new blood viscosity at the current time step and the new turbulent viscosity at the current time step is determined as the new total viscosity at the current time step.

[0026] Optionally, the blood viscosity at the current time step is updated based on the obtained updated velocity field to obtain a new blood viscosity at the current time step, comprising:

[0027] the shear rate of each grid cell is obtained based on the obtained updated velocity field;

[0028] the shear rate is input into a target rheological model to analyze the flow field shear characteristics of each grid cell, and the blood viscosity at the current time step is updated according to the flow field shear characteristics to obtain a new blood viscosity at the current time step; wherein the target rheological model is selected from Carreau-Yasuda model, Power-law model, Herschel Bulkley model, Carreau model and Bingham model based on a preset prediction requirement.

[0029] Optionally, the turbulent viscosity at the current time step is updated based on the obtained updated velocity field to obtain a new turbulent viscosity at the current time step, comprising:

[0030] the transport equation of a target turbulence model is solved based on the obtained updated velocity field to obtain turbulent kinetic energy and specific dissipation rate; wherein the target turbulence model is selected from direct numerical simulation model, large eddy simulation model and Reynolds-averaged Navier-Stokes model according to a preset prediction requirement;

[0031] the turbulent viscosity at the current time step is updated using the turbulent kinetic energy and the specific dissipation rate to obtain a new turbulent viscosity at the current time step.

[0032] Optionally, the left ventricle three-dimensional model is meshed to obtain a plurality of mesh units, comprising:

[0033] The number of boundary layers, the stretching factor are set, and the left ventricle three-dimensional model is meshed by using a tetrahedral meshing algorithm and a boundary layer encryption algorithm to obtain a plurality of mesh units; wherein the mesh density at the boundary in the left ventricle three-dimensional model is greater than the mesh density at the non-boundary.

[0034] Optionally, the boundary conditions of blood flow include flow conditions, pressure boundary conditions, turbulence intensity, and boundary conditions of turbulence viscosity ratio; wherein the flow conditions are set based on the real-time flow state of blood entering the left ventricle in the image data of the target object, the pressure boundary conditions are set based on the blood pressure obtained by the pressure wire, the turbulence intensity is the ratio of the turbulence fluctuation velocity to the average velocity at the boundary in the left ventricle three-dimensional model, and the turbulence viscosity ratio is the ratio of the turbulence viscosity to the molecular motion viscosity.

[0035] In a second aspect, the present application discloses a blood flow dynamics index prediction device, comprising:

[0036] The meshing module is configured to construct a left ventricle three-dimensional model according to image data of a target object, and mesh the left ventricle three-dimensional model to obtain a plurality of mesh units;

[0037] The parameter setting module is configured to set the boundary conditions of blood flow and the total viscosity at the current time step; wherein the total viscosity is the sum of the blood viscosity and the turbulence viscosity;

[0038] The inner loop module is configured to update the velocity field of each mesh unit based on the total viscosity at the current time step and by using the N-S equation and the incompressible condition, update the total viscosity at the current time step based on the obtained updated velocity field, and obtain the new total viscosity at the current time step;

[0039] The outer loop module is configured to jump back to the step of updating the velocity field of each mesh unit based on the total viscosity at the current time step and by using the N-S equation and the incompressible condition until a preset outer loop stopping condition is met, and judge whether the current time step meets a preset stopping condition;

[0040] a time step updating module, configured to, if the preset stop condition is not met at the current time step, determine a new total viscosity at the current time step as a total viscosity of a next time step, and take the next time step as a new current time step, and then jump back to the step of iteratively updating the velocity field of each grid cell based on the total viscosity at the current time step and using the N-S equation and the incompressibility condition;

[0041] an index prediction module, configured to, if the preset stop condition is met at the current time step, obtain a hemodynamic index based on the new total viscosity at the current time step.

[0042] In a third aspect, the present application discloses an electronic device, comprising:

[0043] a memory configured to save a computer program;

[0044] a processor configured to execute the computer program to implement the steps of the hemodynamic index prediction method disclosed above.

[0045] In a fourth aspect, the present application discloses a computer readable storage medium configured to store a computer program; wherein the computer program is executed by a processor to implement the steps of the hemodynamic index prediction method disclosed above.

[0046] The application has the beneficial effects that: the application constructs a left ventricular three-dimensional model according to image data of a target object, and performs mesh partitioning on the left ventricular three-dimensional model to obtain a plurality of mesh units; a boundary condition of blood flow and a total viscosity at a current time step are set; wherein the total viscosity is a sum of blood viscosity and turbulent viscosity; an inner loop iterative update is performed on a velocity field of each mesh unit based on the total viscosity at the current time step and by using N-S equation and incompressible condition, to update the total viscosity at the current time step based on the obtained updated velocity field, to obtain a new total viscosity at the current time step; the step of performing the inner loop iterative update on the velocity field of each mesh unit based on the total viscosity at the current time step and by using N-S equation and incompressible condition is re-jumped to until a preset outer loop stop condition is met, and whether the current time step meets a preset stop condition is judged; if the current time step does not meet the preset stop condition, the new total viscosity at the current time step is determined as a total viscosity of a next time step, and the next time step is taken as a new current time step, and then the step of performing the inner loop iterative update on the velocity field of each mesh unit based on the total viscosity at the current time step and by using N-S equation and incompressible condition is re-jumped to; if the current time step meets the preset stop condition, a hemodynamic index is obtained based on the new total viscosity at the current time step. It can be seen that the application constructs a left ventricular three-dimensional model based on image data of a target object and performs mesh partitioning, sets a boundary condition of blood flow and a total viscosity at a current time step (i.e. a sum of blood viscosity and turbulent viscosity), considers the non-Newtonian characteristics and turbulent influence of blood, makes the simulation initial condition more consistent with physiological reality, performs an inner loop iterative update on a velocity field by using N-S equation and incompressible condition based on the total viscosity at the current time step, can ensure that the velocity field meets the basic conservation law (mass conservation and momentum conservation) of fluid flow, and improves the calculation accuracy of the velocity field, updates the total viscosity based on the updated velocity field after the inner loop, realizes dynamic coupling of the viscosity and the flow field, makes the total viscosity adapt to the current flow state in real time, and improves the dynamic accuracy of simulation, iterates repeatedly through the outer loop until the stop condition is met, can gradually optimize the total viscosity and the flow field, and guarantees the convergence and reliability of the simulation result at the current time step, through the progression of time steps and the stop condition judgment, the complete physiological process can be covered, the hemodynamic index is finally obtained based on the optimized total viscosity, and it can be ensured that the index can accurately reflect the real hemodynamic state of the left ventricle of the target object, and provide a reliable basis for related clinical analysis or research. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the accompanying drawings in the following description only only the embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of the provided drawings.

[0048] Figure 1 A hemodynamic index prediction method flow chart disclosed by the present application;

[0049] Figure 2 A specific model schematic diagram disclosed by the present application;

[0050] Figure 3 A specific model mesh partitioning schematic diagram disclosed by the present application;

[0051] Figure 4 A specific circulation iteration schematic diagram disclosed by the present application;

[0052] Figure 5 A specific hemodynamic analysis schematic diagram disclosed by the present application;

[0053] Figure 6 A hemodynamic index prediction device structure schematic diagram disclosed by the present application;

[0054] Figure 7 An electronic device structure diagram disclosed by the present application. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments only are some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0056] Hemodynamic indexes (such as blood flow velocity, pressure distribution, wall shear stress, etc.) are the key basis for evaluating heart function and related diseases (such as coronary heart disease, cardiomyopathy), but it is difficult to achieve precise measurement of full spatial distribution through invasive examination (such as catheter measurement) to obtain these indexes, and it is difficult to achieve precise measurement of full spatial distribution, so it is necessary to predict hemodynamic indexes through numerical simulation technology to provide non-invasive and detailed reference for disease diagnosis and treatment plan formulation.

[0057] Currently, the model in hemodynamic simulation lacks personalization, and the geometric structure constructed based on a general template deviates from the actual anatomical characteristics of the patient, affecting the accuracy of the simulation. Secondly, when using traditional computational fluid dynamics methods to simulate the left ventricle, more simple and general Newtonian fluid and laminar flow are often used to simulate blood and blood flow, ignoring the flow detail information caused by the complex structure of the left ventricle, resulting in low accuracy of the predicted hemodynamic indicators.

[0058] Therefore, the present application correspondingly provides a hemodynamic indicator prediction scheme to improve the accuracy of predicting hemodynamic indicators.

[0059] Referring to Figure 1 The embodiments of the present application disclose a hemodynamic indicator prediction method, which comprises:

[0060] Step S11: constructing a left ventricular three-dimensional model according to image data of a target object, and performing mesh division on the left ventricular three-dimensional model to obtain a plurality of mesh units.

[0061] The image data of the target object can be a CTA (Computed Tomography Angiography) image of the target object, and a left ventricular three-dimensional model of the target object is reconstructed based on the image data. For example Figure 2 As shown in a specific model diagram, the left ventricular three-dimensional model includes a total of 1 aortic inlet and 5 main outlets, which are: left subclavian artery, left common carotid artery, brachiocephalic trunk, right coronary artery and left main trunk. Among them, according to different cutting methods of the model, the 5 main outlets can continue to branch, for example, the left main trunk outlet has two small branches.

[0062] In this embodiment, the mesh division on the left ventricular three-dimensional model to obtain a plurality of mesh units comprises: setting the number of boundary layers, the stretch factor, and using a tetrahedral mesh division algorithm and a boundary layer encryption algorithm to divide the left ventricular three-dimensional model to obtain a plurality of mesh units; wherein the grid density at the boundary of the left ventricular three-dimensional model is greater than the grid density at the non-boundary.

[0063] Computational fluid dynamics is to convert continuous fluid mechanics problems into discrete problems that can be processed by computers, so it is necessary to divide the three-dimensional model into meshes, for example Figure 3 As shown in a specific model mesh division diagram, the number of boundary layers and the stretch factor are set, the three-dimensional left ventricular model is imported, and then a tetrahedral unstructured mesh generation algorithm and a boundary layer encryption algorithm are selected to divide the left ventricular three-dimensional model into meshes, as shown in Figure 3(a) The final algorithm shown can automatically generate the bulk region grid and the boundary layer grid in the fluid domain and at the wall boundary to complete the grid construction that meets the requirements of turbulent flow calculation. It should be noted that, as shown in Figure 3 (b) The turbulent flow model shown needs to use a dense boundary layer grid at the boundary to accurately capture the flow characteristics of the boundary layer.

[0064] Step S12: setting the boundary condition of blood flow and the total viscosity at the current time step; wherein the total viscosity is the sum of the blood viscosity and the turbulent viscosity.

[0065] In this embodiment, the boundary condition of blood flow includes the flow condition, the pressure boundary condition, the turbulent intensity, and the turbulent viscosity ratio boundary condition; wherein the flow condition is set based on the real-time flow state of blood entering the left ventricle in the image data of the target object, the pressure boundary condition is set based on the blood pressure obtained by the pressure wire, the turbulent intensity is the ratio of the turbulent fluctuation velocity to the average velocity at the boundary in the left ventricular three-dimensional model, and the turbulent viscosity ratio is the ratio of the turbulent viscosity to the molecular motion viscosity.

[0066] The boundary condition of blood flow includes two parts: (1) the basic flow condition and the pressure boundary condition; and (2) the turbulent intensity and the turbulent viscosity ratio boundary condition. The inflow blood flow and the blood pressure of the left ventricle are obtained using Doppler ultrasound image or CT perfusion imaging data and a pressure wire, and are respectively set as the aortic inlet flow and the outlet reference pressure, i.e. the flow condition is set based on the real-time flow state of blood entering the left ventricle in the image data of the target object, and the pressure boundary condition is set based on the blood pressure obtained by the pressure wire. The turbulent intensity is the ratio of the turbulent fluctuation velocity to the average velocity at the boundary in the left ventricular three-dimensional model, and the turbulent viscosity ratio is the ratio of the turbulent viscosity to the molecular motion viscosity. The specific turbulent intensity and turbulent viscosity ratio can be selected as commonly recommended values, for example, 5% and 10 respectively.

[0067] In this embodiment, the total viscosity at the current time step is set, including: setting the blood viscosity at the current time step, the turbulent viscosity at the current time step, and determining the sum of the blood viscosity at the current time step and the turbulent viscosity at the current time step as the total viscosity at the current time step.

[0068] It can be understood that the total viscosity at the initial time step is first set as the total viscosity at the current time step, and the total viscosity is the sum of the blood viscosity and the turbulent viscosity, so the blood viscosity at the initial time step and the turbulent viscosity at the initial time step should be set, and the blood viscosity at the initial time step and the turbulent viscosity at the initial time step are taken as the blood viscosity at the current time step and the turbulent viscosity at the current time step, and then the sum of the blood viscosity at the current time step and the turbulent viscosity is determined as the total viscosity at the current time step .

[0069] Step S13: performing inner loop iteration update on the velocity field of each of the grid cells based on the total viscosity at the current time step and using the N-S equation and the incompressible condition, to update the total viscosity at the current time step based on the obtained updated velocity field, to obtain a new total viscosity at the current time step.

[0070] For example Figure 4 A specific loop iteration is shown in the figure. The complete computational fluid dynamics simulation selects the commonly used PIMPLE algorithm, which combines the advantages of PISO (Pressure-Implicit with Splitting of Operators) algorithm and SIMPLE (Semi-Implicit Method for Pressure-Linked Equations) algorithm, and can improve the solving efficiency and stability of the flow equation, especially in the case of large time step and complex flow. It should be noted that in the process of updating each time step, the outer loop iteration needs to be completed once for each time step update, and each outer loop iteration includes multiple inner loop iterations, that is, multiple inner loops are required for one outer loop, and multiple outer loops are required for the next time step update; wherein, the total viscosity is updated in each outer loop, and the velocity field and pressure field are updated in each inner loop.

[0071] In the embodiment, the inner loop iterative updating of the velocity field of each grid cell based on the total viscosity at the current time step and using the N-S equation and the incompressibility condition comprises: setting initial pressure fields and initial velocity fields of each grid cell, and determining the initial pressure fields and the initial velocity fields as current pressure fields and current velocity fields; discretizing the N-S equation in spatial dimensions and time dimensions on each grid cell based on the current velocity field to construct a linear equation set not including a pressure term; wherein the dependent variable of the linear equation set is the velocity of the grid cell; inputting the total viscosity at the current time step into the linear equation set to obtain a target approximate velocity field; constructing a pressure equation using the incompressibility condition and the target approximate velocity field, and updating the current pressure field using the pressure equation to obtain an updated pressure field; correcting the target approximate velocity field based on the updated pressure field to obtain an updated velocity field; if the preset inner loop stop condition is not met at present, determining the updated pressure field and the updated velocity field as new current pressure fields and new current velocity fields respectively, and rejumping to the step of discretizing the N-S equation in spatial dimensions and time dimensions on each grid cell based on the current velocity field; if the preset inner loop stop condition is met at present, outputting the updated velocity field.

[0072] Before all loops are performed, initialization needs to be performed, that is, setting initial pressure fields and initial velocity fields of each grid cell, and determining the initial pressure fields and the initial velocity fields as current pressure fields and current velocity fields; importing the divided grid, pre-processing the imported grid topology relationship to obtain a series of grid information for calculation, such as grid volume, body center, face center and the like; discretizing the N-S equation in spatial dimensions and time dimensions on each grid cell based on the current velocity field to construct a linear equation set not including a pressure term; wherein the dependent variable of the linear equation set is the velocity of the grid cell, that is, in an iteration step, the coefficient matrix A and the right constant vector b of the linear equation set are assembled according to the N-S equation. It needs to be noted that at this time, the pressure term does not need to be considered, and the total viscosity is used, and the specific linear equation set is:

[0073] ;

[0074] In the formula, is a velocity vector, t is time, is a convection term (used to describe the migration effect of the fluid), is the density of the fluid, is pressure, is a pressure gradient, is total viscosity, is a divergence operator.

[0075] Input the total viscosity at the current time step into the linear equation system to obtain the approximate velocity field of the target; utilize the incompressibility condition The approximate velocity field of the target is used to construct the pressure equation, and the pressure equation is used to update the current pressure field to obtain the updated pressure field. For example... Figure 4 As shown, the approximate velocity field of the target is corrected based on the updated pressure field to obtain the updated velocity field.

[0076] If the preset inner loop stopping condition is not met, the updated pressure field and the updated velocity field are determined as the new current pressure field and the new current velocity field, respectively, and the process jumps back to the step of discretizing the spatial and temporal dimensions of each grid cell of the NS equation based on the current velocity field; if the preset inner loop stopping condition is met, the updated velocity field is output.

[0077] In this embodiment, updating the total viscosity at the current time step based on the obtained updated velocity field to obtain a new total viscosity at the current time step includes: updating the blood viscosity and the turbulent viscosity at the current time step based on the obtained updated velocity field to obtain a new blood viscosity and a new turbulent viscosity at the current time step; and determining the sum of the new blood viscosity and the new turbulent viscosity at the current time step as the new total viscosity at the current time step.

[0078] Understandably, after obtaining the updated velocity field, the blood viscosity and turbulent viscosity at the current time step are updated based on the updated velocity field, thereby obtaining the new blood viscosity and the new turbulent viscosity at the current time step. Since the total viscosity is the sum of the blood viscosity and the turbulent viscosity, the sum of the new blood viscosity and the new turbulent viscosity at the current time step is determined as the new total viscosity at the current time step.

[0079] In a specific embodiment of blood viscosity update, the blood viscosity at the current time step is updated based on the obtained updated velocity field to obtain a new blood viscosity at the current time step. This includes: obtaining the shear rate of each grid cell based on the obtained updated velocity field; inputting the shear rate into a target rheological model to analyze the flow field shear characteristics of each grid cell, and updating the blood viscosity at the current time step according to the flow field shear characteristics to obtain a new blood viscosity at the current time step; wherein the target rheological model is selected from Carreau-Yasuda model, Power-law model, Herschel Bulkley model, Carreau model, and Bingham model based on preset prediction requirements.

[0080] In hemodynamics, blood is considered as a non-Newtonian fluid because its viscosity decreases with the increase of shear rate (i.e. velocity gradient). In order to accurately describe the viscosity change of blood during the flow process, the selection of non-Newtonian model is crucial, and the appropriate target rheological model needs to be selected from Carreau-Yasuda model, Power-law model, HerschelBulkley model, Carreau model and Bingham model according to the preset prediction requirements, and these rheological models are all functions of viscosity and shear rate. Based on the obtained updated velocity field, the shear rate of each grid cell is obtained, and the formula of the shear rate is as follows:

[0081] ;

[0082] In the formula, is the shear rate, is the component of the strain rate tensor D; wherein the strain force tensor D is used to describe the deformation rate of the fluid micro-cluster, and the formula for obtaining the strain force tensor D is as follows:

[0083] ;

[0084] In the formula, is the velocity vector, is the velocity gradient tensor, is the gradient operator, is the transpose of the velocity gradient tensor; that is, the updated velocity field obtains the strain force tensor, and then the shear rate of each grid cell is obtained based on the strain force tensor.

[0085] The shear rate is input into the target rheological model, so that the target rheological model analyzes the flow field shear characteristics of each grid cell based on the shear rate, and updates the blood viscosity at the current time step according to the flow field shear characteristics, to obtain the new blood viscosity at the current time step, and the specific formula is as follows:

[0086] ;

[0087] In the formula, is the empirical parameter obtained based on experimental fitting, which is respectively 0.644, 0.392, , and 0.110 s.

[0088] In a specific embodiment of updating the turbulent viscosity, the turbulent viscosity at the current time step is updated based on the obtained updated velocity field to obtain a new turbulent viscosity at the current time step, comprising: solving a transport equation of a target turbulence model based on the obtained updated velocity field to obtain turbulent kinetic energy and specific dissipation rate; wherein the target turbulence model is selected from a direct numerical simulation model, a large eddy simulation model and a Reynolds-averaged Navier-Stokes model according to a preset prediction requirement; and updating the turbulent viscosity at the current time step by using the turbulent kinetic energy and the specific dissipation rate to obtain the new turbulent viscosity at the current time step.

[0089] In computational fluid dynamics (CFD), turbulence models are used to simulate the complex behavior of turbulent flows. The choice and use of turbulence models have a great influence on the accuracy of simulation results. According to the complexity and accuracy of modeling, turbulence models can be mainly divided into three categories from high to low: direct numerical simulation (DNS) model, large eddy simulation (LES) model and Reynolds-averaged Navier-Stokes (RANS) model. According to the preset prediction requirement, the target turbulence model is selected from the direct numerical simulation model, the large eddy simulation model and the Reynolds-averaged Navier-Stokes model. Among them, the DNS model can resolve all turbulent scales, the LES model simulates the time and spatial variation of large-scale vortices, and the RANS model is simplified by considering the time average of turbulent flow. Considering the timeliness of simulation in the medical field, DNS model and LES model usually require more grid number and higher calculation time, therefore, the most commonly used two-equation model in RANS model, such as model, model and model are used in this embodiment. Among them, model combines the advantages of model and model, by using a blending function, the model is used near the wall region, and the model is used far from the wall region to improve the applicability of the model in the entire flow field. This hybrid method makes model have good performance in both wall surface and free flow region.

[0090] Solving a transport equation of a target turbulence model based on the obtained updated velocity field to obtain turbulent kinetic energy k and specific dissipation rate , the specific formula is as follows:

[0091] ;

[0092] ;

[0093] wherein k is a turbulent kinetic energy, is a rate of change of the turbulent kinetic energy with time, is a component of the velocity vector in the direction, is a spatial gradient of the turbulent kinetic energy in the direction, is a turbulent kinetic energy generation term, is a constant, specifically 0.09, is a blood viscosity, is a specific dissipation rate, is a diffusion coefficient of the K-equation, is a spatial coordinate component, is a rate of change of the specific dissipation rate with time, is a component of the specific dissipation rate in the direction, a is a constant, and S is a strain rate, is a constant, is a diffusion coefficient of the equation, is a first blending function for switching between the model and the model, is a constant, is a turbulent viscosity.

[0094] wherein the process of obtaining the turbulent kinetic energy k and the specific dissipation rate uses the following formulas:

[0095] ;

[0096] ;

[0097] ;

[0098] ;

[0099] ;

[0100] ;

[0101] ;

[0102] ;

[0103] wherein is a second blending function for switching between the model and the a second blending function of the model, is a hyperbolic tangent function, is a cross-diffusion term of turbulent kinetic energy k and specific dissipation rate is a constant, specifically is a constant, specifically is a constant, specifically , .

[0104] It should be noted that the above formula needs to use the near-wall distance y when calculating F1 and F2, which represents the shortest distance from each point to the left ventricular wall and the peripheral blood vessel wall. With reference to the breadth-first search method, the calculation of the near-wall distance y in the embodiment includes the following steps:

[0105] 1) Calculate the near-wall distance y of the grid T1 adjacent to the left ventricular wall and the peripheral blood vessel wall.

[0106] 2) Store the corresponding near-wall grid TW1 in the grid T1.

[0107] 3) Traverse the grid T2 adjacent to the grid T1, and calculate the near-wall distance y of T2 according to the wall grid stored in the grid T1.

[0108] 4) Store the corresponding near-wall grid TW2 in the grid T2.

[0109] 5) Iterate steps 3) and 4), and take the grid T2 as the new grid T1.

[0110] 6) Repeat steps 3) to 5), and compare the current near-wall distance y of the grid, if it is smaller than the current near-wall distance y, replace y and the corresponding near-wall grid.

[0111] 7) Obtain the near-wall distance y of the whole model.

[0112] Next, the turbulent kinetic energy and the specific dissipation rate are used to update the turbulent viscosity at the current time step to obtain the new turbulent viscosity at the current time step, and the calculation formula of the turbulent viscosity is:

[0113] .

[0114] Step S14: Re-jump to the step of iteratively updating the velocity field of each grid cell based on the total viscosity at the current time step and using the N-S equation and the incompressible condition in an inner loop until a preset outer loop stop condition is met, and determining whether the current time step meets a preset stop condition.

[0115] As Figure 4As shown, the outer loop number at the current time step reaches the preset outer loop stop condition, and then it can be determined whether the current time step meets the preset stop condition, i.e., whether the current time step is the preset target time step. If the current time step does not meet the preset stop condition, it means that the next time step iteration is needed. If the current time step meets the preset stop condition, new iteration of the time step is not needed.

[0116] Step S15: If the current time step does not meet the preset stop condition, the new total viscosity at the current time step is determined as the total viscosity of the next time step, and the next time step is taken as the new current time step, and then the step of iteratively updating the velocity field of each grid cell based on the total viscosity at the current time step and using the N-S equation and the incompressibility condition is re-executed.

[0117] It can be understood that if the current time step does not meet the preset stop condition, the new total viscosity at the current time step is determined as the total viscosity of the next time step, and the next time step is taken as the new current time step, and then the step of iteratively updating the velocity field of each grid cell based on the total viscosity at the current time step and using the N-S equation and the incompressibility condition is re-executed. For example, the current time step is time step 70, and the preset stop condition is that the current time step is equal to 80. Therefore, the current time step does not meet the preset stop condition, and the new total viscosity at the current time step needs to be determined as the total viscosity of the next time step, so that the iteration loop of time step 71 is performed until the time step is 80, and the iteration loop is stopped.

[0118] Step S16: If the current time step meets the preset stop condition, the hemodynamic index is obtained based on the new total viscosity at the current time step.

[0119] If the current time step meets the preset stop condition, the hemodynamic index is obtained based on the new total viscosity at the current time step. If the current time step meets the preset stop condition, the hemodynamic index is obtained based on the final output total viscosity. The hemodynamic index includes wall shear stress (WSS), endothelial cell activation potential (ECAP), vorticity (Vorticity), etc. These indexes can quantitatively describe the flow characteristics of blood in the blood vessel, the influence of blood on the blood vessel wall, the degree of blood flow disorder, etc., and assist doctors in assessing the risk of cardiovascular diseases and helping doctors to perform corresponding personalized case analysis and surgical planning.

[0120] The application has the advantages that: the application constructs a left ventricular three-dimensional model according to image data of a target object, and performs mesh division on the left ventricular three-dimensional model to obtain a plurality of mesh units; a boundary condition of blood flow and a total viscosity at a current time step are set; the total viscosity is a sum of blood viscosity and turbulent viscosity; a velocity field of each mesh unit is iteratively updated based on the total viscosity at the current time step and by using the N-S equation and an incompressible condition, the total viscosity at the current time step is updated based on the updated velocity field, to obtain new total viscosity at the current time step; the step of iteratively updating the velocity field of each mesh unit based on the total viscosity at the current time step and by using the N-S equation and the incompressible condition is restarted until a preset outer loop stop condition is met, and whether the current time step meets a preset stop condition is judged; if the current time step does not meet the preset stop condition, the new total viscosity at the current time step is determined as total viscosity of a next time step, the next time step is taken as a new current time step, and then the step of iteratively updating the velocity field of each mesh unit based on the total viscosity at the current time step and by using the N-S equation and the incompressible condition is restarted; if the current time step meets the preset stop condition, a hemodynamic index is obtained based on the new total viscosity at the current time step. Therefore, the application constructs a left ventricular three-dimensional model based on image data of a target object and performs mesh division, sets a boundary condition of blood flow and total viscosity at a current time step (i.e. a sum of blood viscosity and turbulent viscosity), considers the non-Newtonian characteristics of blood and the influence of turbulent flow, so that the initial simulation condition is more consistent with physiological reality; the velocity field is iteratively updated based on the total viscosity at the current time step and by using the N-S equation and the incompressible condition, so that the velocity field can meet the basic conservation law of fluid flow (mass conservation and momentum conservation), and the calculation accuracy of the velocity field is improved; the total viscosity is updated based on the updated velocity field after the inner loop, the dynamic coupling between viscosity and flow field is realized, the total viscosity can be adapted to the current flow state in real time, and the dynamic accuracy of simulation is improved; the total viscosity and flow field are gradually optimized through repeated iteration until the stop condition is met, so that the convergence and reliability of the simulation result at the current time step are ensured; through the progress of time steps and the stop condition judgment, the complete physiological process can be covered, the hemodynamic index is finally obtained based on the optimized total viscosity, and it can be ensured that the index can accurately reflect the real hemodynamic state of the left ventricle of the target object, and provide a reliable basis for related clinical analysis or research.

[0121] In the following Figure 5A specific hemodynamic analysis schematic diagram is shown as an example for corresponding description of the present application. 1) A left ventricular three-dimensional model is constructed according to image data of a target object, and the left ventricular three-dimensional model is meshed to obtain a plurality of mesh units. 2) Boundary conditions of blood flow are set, i.e. basic flow conditions, pressure boundary conditions, turbulence intensity, and boundary conditions of turbulence viscosity ratio. 3) A target rheological model is selected from Carreau-Yasuda model, Power-law model, Herschel Bulkley model, Carreau model, and Bingham model according to a preset prediction requirement. 4) A target turbulence model is selected from direct numerical simulation model, large eddy simulation model, and Reynolds-averaged Navier-Stokes model according to the preset prediction requirement. 5) Fluid dynamics simulation is calculated based on the PIMPLE algorithm. In each time step update, a plurality of outer loops are included, the total viscosity is updated in each outer loop, and the total viscosity is the sum of blood viscosity and turbulence viscosity. In each outer loop, a plurality of inner loops are included, the velocity field is updated in each inner loop, and the total viscosity is updated using the finally updated velocity field after the plurality of inner loops, thereby completing one outer loop, then performing the next outer loop, until the outer loop is completed, indicating that the next time step update and cycle are required, until the time step reaches the preset target time step, and the final total viscosity is output. 6) Hemodynamic indexes are obtained based on the finally output total viscosity to complete hemodynamic analysis.

[0122] Reference is made to Figure 6 As shown, the embodiment of the present application discloses a hemodynamic index prediction device, which comprises:

[0123] The meshing module 11 is configured to construct a left ventricular three-dimensional model according to image data of a target object, and mesh the left ventricular three-dimensional model to obtain a plurality of mesh units.

[0124] The parameter setting module 12 is configured to set boundary conditions of blood flow and total viscosity at a current time step; wherein the total viscosity is the sum of blood viscosity and turbulence viscosity.

[0125] The inner loop module 13 is configured to perform inner loop iterative update on the velocity field of each mesh unit based on the total viscosity at the current time step and using the N-S equation and the incompressible condition, to update the total viscosity at the current time step based on the obtained updated velocity field, to obtain new total viscosity at the current time step.

[0126] The outer loop module 14 is configured to jump back to the step of performing inner loop iterative update on the velocity field of each mesh unit based on the total viscosity at the current time step and using the N-S equation and the incompressible condition, until a preset outer loop stopping condition is met, and whether the current time step meets a preset stopping condition is judged.

[0127] a time step updating module 15, configured to, if the preset stop condition is not met at the current time step, determine a new total viscosity at the current time step as a total viscosity at a next time step, and take the next time step as a new current time step, and then jump back to the step of iteratively updating the velocity field of each grid cell based on the total viscosity at the current time step and using the N-S equation and the incompressibility condition;

[0128] an index predicting module 16, configured to, if the preset stop condition is met at the current time step, obtain a hemodynamic index based on the new total viscosity at the current time step.

[0129] Further, the embodiment of the present application also provides an electronic device. Figure 7 FIG. 1 is a structural diagram of an electronic device 20 according to an exemplary embodiment, and the content in the figure should not be considered as any limitation on the use range of the present application.

[0130] Figure 7 FIG. 1 is a structural diagram of an electronic device 20 according to an exemplary embodiment, and the content in the figure should not be considered as any limitation on the use range of the present application. The electronic device provided by the embodiment of the present application can specifically include at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25 and a communication bus 26. The memory 22 is configured to store a computer program, and the processor 21 is configured to load and execute the computer program to implement the related steps in the hemodynamic index predicting method performed by the electronic device according to any of the preceding embodiments.

[0131] In the embodiment, the power supply 23 is configured to provide working voltage for each hardware device on the electronic device; the communication interface 24 can create a data transmission channel between the electronic device and external devices, and the communication protocol followed by the communication interface 24 can be any communication protocol applicable to the technical solution of the present application, which is not limited specifically herein; the input / output interface 25 is configured to obtain external input data or output data to the outside, and the specific interface type can be selected according to the specific application needs, which is not limited specifically herein.

[0132] The processor 21 can include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 can be implemented in at least one of a hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), a PLA (Programmable Logic Array). The processor 21 can also include a main processor and a coprocessor, the main processor being a processor for processing data in a wake-up state, also known as a CPU (Central Processing Unit), and the coprocessor being a low-power processor for processing data in a standby state. In some embodiments, the processor 21 can be integrated with a GPU (Graphics Processing Unit) that is responsible for rendering and drawing the content required to be displayed by the display screen. In some embodiments, the processor 21 can also include an AI (Artificial Intelligence) processor for processing machine learning-related computing operations.

[0133] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a magnetic disk or an optical disk, etc., and the resources stored thereon include an operating system 221, a computer program 222 and data 223, etc., and the storage mode can be temporary storage or permanent storage.

[0134] The operating system 221 is used to manage and control each hardware device on the electronic device and the computer program 222 to realize the operation and processing of the processor 21 on the mass data 223 in the memory 22, and can be Windows, Unix, Linux, etc. In addition to the computer program capable of completing the blood flow dynamics index prediction method disclosed in any of the preceding embodiments executed by the electronic device, the computer program 222 can further include a computer program capable of completing other specific work. The data 223 can include data received by the electronic device from an external device, as well as data collected by the self input / output interface 25, etc.

[0135] Further, the present application also discloses a computer readable storage medium for storing a computer program; wherein the computer program is executed by the processor to realize the blood flow dynamics index prediction method disclosed in the preceding embodiments. The specific steps of the method can refer to the corresponding contents disclosed in the preceding embodiments, and will not be repeated here.

[0136] The various embodiments described in the specification are progressive in nature, and each embodiment highlights the differences from other embodiments. The same or similar parts among the various embodiments can be mutually referred to. For the apparatus disclosed by the embodiments, since it corresponds to the method disclosed by the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part.

[0137] Those skilled in the art will further appreciate that the units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various examples have been described herein in terms of their functionality, which has been described generally and symbolically in flow charts. There has been described, without loss of generality, various examples in terms of their steps and specific functionality. It will be recognized that different implementations of the examples described herein can execute at different times and at different structures of hardware and software, and that not all of the described acts are necessary to implement the examples described in this specification. Embodiments of the present application can be realized in adaptation to each particular application in terms of a corresponding amount and structure of hardware and software. These descriptions in no way limit the scope of the application. Those skilled in the art will also recognize that the examples described herein could be implemented in a suitably programmed general purpose computer. Such programs can be stored on any form of non-transitory computer readable medium, including a storage device like a floppy disk, hard disk, optical disk, ROM, etc., programmed memory such as ROM, RAM, etc., or transmitted over a transmission medium such as a carrier wave or electronic signal.

[0138] Finally, it should be noted that the terms "first", "second", and the like, herein do not denote any order, quantity, combination, or importance, but are used to distinguish one entity or action from another, and cannot imply or suggest any such actual relationship or sequence between the entities or actions. Moreover, the terms "include", "contain", or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article, or device. Without more limitations, the element defined by the phrase "including a" does not exclude the presence of additional identical elements in the process, method, article, or device including the element.

[0139] The above describes in detail a hemodynamic index prediction method, device, equipment and medium provided by the application. The principles and implementation manners of the application are described by using specific examples. The above description of the examples is only used to help understand the method of the application and the core idea thereof. Meanwhile, for those skilled in the art, the specific implementation manners and application ranges can be changed according to the idea of the application. In conclusion, the content of the specification should not be understood as a limitation on the application.

Claims

1. A hemodynamic indicator prediction method, characterized by, The method comprises the following steps: constructing a left ventricular three-dimensional model according to image data of a target object, and performing mesh partitioning on the left ventricular three-dimensional model to obtain a plurality of mesh units; setting a blood flow boundary condition and a total viscosity at a current time step; wherein the total viscosity is the sum of blood viscosity and turbulent viscosity; wherein the blood flow boundary condition comprises a flow condition, a pressure boundary condition, and a boundary condition of turbulent intensity and turbulent viscosity ratio; performing inner loop iterative updating on a velocity field of each mesh unit based on the total viscosity at the current time step and using the N-S equation and the incompressible condition, updating the total viscosity at the current time step based on the obtained updated velocity field, and obtaining a new total viscosity at the current time step; reverting to the step of performing inner loop iterative updating on the velocity field of each mesh unit based on the total viscosity at the current time step and using the N-S equation and the incompressible condition until a preset outer loop stop condition is met, and determining whether the current time step meets a preset stop condition; if the current time step does not meet the preset stop condition, determining the new total viscosity at the current time step as the total viscosity of a next time step, taking the next time step as a new current time step, and then reverting to the step of performing inner loop iterative updating on the velocity field of each mesh unit based on the total viscosity at the current time step and using the N-S equation and the incompressible condition; if the current time step meets the preset stop condition, obtaining a hemodynamic index based on the new total viscosity at the current time step.

2. The hemodynamic indicator prediction method according to claim 1, characterized in that, The step of performing inner loop iterative updating on the velocity field of each mesh unit based on the total viscosity at the current time step and using the N-S equation and the incompressible condition comprises: setting an initial pressure field and an initial velocity field of each mesh unit, and determining the initial pressure field and the initial velocity field as a current pressure field and a current velocity field; discretizing the N-S equation in spatial dimensions and time dimensions on each mesh unit based on the current velocity field, to construct a linear equation group not including a pressure term; wherein the dependent variable of the linear equation group is the velocity of the mesh unit; inputting the total viscosity at the current time step into the linear equation group to obtain a target approximate velocity field; constructing a pressure equation using the incompressible condition and the target approximate velocity field, and updating the current pressure field using the pressure equation to obtain an updated pressure field; correcting the target approximate velocity field based on the updated pressure field to obtain an updated velocity field; if the preset inner loop stop condition is not met, determining the updated pressure field and the updated velocity field as a new current pressure field and a new current velocity field respectively, and reverting to the step of discretizing the N-S equation in spatial dimensions and time dimensions on each mesh unit based on the current velocity field; if the preset inner loop stop condition is met, outputting the updated velocity field.

3. The hemodynamic indicator prediction method according to claim 1, characterized in that, setting the total viscosity at the current time step comprises: set a blood viscosity at the current time step, set a turbulent viscosity at the current time step, and determine a sum of the blood viscosity at the current time step and the turbulent viscosity at the current time step as a total viscosity at the current time step; Correspondingly, the total viscosity at the current time step is updated based on the obtained updated velocity field to obtain a new total viscosity at the current time step, including: The blood viscosity at the current time step and the turbulent viscosity at the current time step are updated based on the obtained updated velocity field to obtain a new blood viscosity at the current time step and a new turbulent viscosity at the current time step, respectively. A sum of the new blood viscosity at the current time step and the new turbulent viscosity at the current time step is determined as a new total viscosity at the current time step.

4. The hemodynamic indicator prediction method according to claim 3, characterized in that, The blood viscosity at the current time step is updated based on the obtained updated velocity field to obtain a new blood viscosity at the current time step, including: The shear rate of each grid cell is obtained based on the obtained updated velocity field; The shear rate is input into a target rheological model to analyze the flow field shear characteristics of each grid cell, and the blood viscosity at the current time step is updated according to the flow field shear characteristics to obtain a new blood viscosity at the current time step; wherein the target rheological model is selected from Carreau-Yasuda model, Power-law model, Herschel Bulkley model, Carreau model and Bingham model based on a preset prediction requirement.

5. The hemodynamic indicator prediction method according to claim 3, characterized in that, The turbulent viscosity at the current time step is updated based on the obtained updated velocity field to obtain a new turbulent viscosity at the current time step, including: The transport equation of a target turbulence model is solved based on the obtained updated velocity field to obtain turbulent kinetic energy and specific dissipation rate; wherein the target turbulence model is selected from direct numerical simulation model, large eddy simulation model and Reynolds-averaged Navier-Stokes model according to a preset prediction requirement; The turbulent viscosity at the current time step is updated using the turbulent kinetic energy and the specific dissipation rate to obtain a new turbulent viscosity at the current time step.

6. The hemodynamic indicator prediction method of claim 1, wherein, The left ventricular three-dimensional model is meshed to obtain a plurality of grid cells, including: The number of boundary layers, the stretch factor, and the tetrahedral meshing algorithm and boundary layer encryption algorithm are used to mesh the left ventricular three-dimensional model to obtain a plurality of grid cells; wherein the grid density at the boundary of the left ventricular three-dimensional model is greater than the grid density at the non-boundary.

7. The hemodynamic indicator prediction method according to any one of claims 1 to 6, characterized in that, The boundary conditions of the blood flow include flow conditions, pressure boundary conditions, turbulent intensity, and turbulent viscosity ratio boundary conditions; wherein the flow conditions are set based on the real-time flow state of the blood entering the left ventricle in the image data of the target object, the pressure boundary conditions are set based on the blood pressure obtained by the pressure guide wire, the turbulent intensity is the ratio of the turbulent fluctuation velocity to the average velocity at the boundary of the left ventricular three-dimensional model, and the turbulent viscosity ratio is the ratio of the turbulent viscosity to the molecular motion viscosity.

8. A hemodynamic indicator prediction apparatus, characterized by, including: a mesh division module configured to construct a left ventricle three-dimensional model according to image data of a target object, and to divide the left ventricle three-dimensional model into a plurality of mesh units; a parameter setting module configured to set a boundary condition of blood flow and a total viscosity at a current time step; wherein the total viscosity is a sum of blood viscosity and turbulent viscosity; and wherein the boundary condition of blood flow includes a flow condition, a pressure boundary condition, and a boundary condition of turbulent intensity and turbulent viscosity ratio; an inner loop module configured to iteratively update a velocity field of each mesh unit based on the total viscosity at the current time step and by using N-S equation and incompressible condition, to update the total viscosity at the current time step based on the obtained updated velocity field, and to obtain a new total viscosity at the current time step; an outer loop module configured to jump back to the step of iteratively updating the velocity field of each mesh unit based on the total viscosity at the current time step and by using N-S equation and incompressible condition until a preset outer loop stop condition is met, and to determine whether the current time step meets a preset stop condition; a time step updating module configured to, if the current time step does not meet the preset stop condition, determine the new total viscosity at the current time step as a total viscosity at a next time step, to set the next time step as a new current time step, and to jump back to the step of iteratively updating the velocity field of each mesh unit based on the total viscosity at the current time step and by using N-S equation and incompressible condition; an index predicting module configured to, if the current time step meets the preset stop condition, obtain a hemodynamic index based on the new total viscosity at the current time step.

9. An electronic device, comprising: comprising: a memory configured to save a computer program; a processor configured to execute the computer program to implement steps of the hemodynamic index predicting method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, for storing a computer program; wherein the computer program is executed by a processor to implement steps of the hemodynamic index predicting method according to any one of claims 1 to 7.

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