Flow Allocation Acquisition Method, Device and Medium for Hemodynamic Analysis

Abstract

The present application relates to a method, device and medium for obtaining flow distribution for hemodynamic analysis. The method includes: obtaining a diseased artery model, the inlet flow of the diseased artery, and the circuit models of each downstream branch; the parameter values of the components of the circuit model are obtained through the flow distribution of each downstream branch of the healthy artery; based on the inlet flow, computational fluid dynamics simulation is performed using the diseased artery model and the circuit model to predict the first predicted value of the inlet blood pressure of the diseased artery and the first predicted value of the flow distribution of each downstream branch of the diseased artery; if the error between the first predicted value of the inlet blood pressure and the measured inlet blood pressure of the diseased artery is within a preset range, then the first predicted value of the flow distribution is determined as the target flow distribution. Using this method can improve the accuracy of the prediction results of multi-scale simulation.

Description

Technical Field

[0001] The present application relates to the technical field of fluid simulation, and particularly to a method, device, and medium for obtaining flow distribution for hemodynamic analysis. Background Art

[0002] In hemodynamic research, computational fluid dynamics simulation can be used to simulate the flow of blood in blood vessels (such as arteries), thereby providing information on hemodynamic characteristics such as blood flow velocity, pressure distribution, and vortex formation.

[0003] In the traditional technology, when predicting the hemodynamic characteristics of an artery through computational fluid dynamics (CFD) simulation of the artery, it is necessary to obtain the flow distribution of each downstream branch of the artery, but there are great difficulties in obtaining this flow distribution. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, and medium for obtaining flow distribution for hemodynamic analysis that can improve the accuracy of the prediction results of multi-scale simulation for the above technical problems.

[0005] In a first aspect, the present application provides a method for obtaining flow distribution for hemodynamic analysis, including:

[0006] Obtaining a diseased artery model, the inlet flow of the diseased artery, and the circuit models of each downstream branch; the parameter values of the components of the circuit model are obtained through the flow distribution of each downstream branch of a healthy artery;

[0007] Based on the inlet flow, performing computational fluid dynamics simulation using the diseased artery model and the circuit model to predict a first inlet blood pressure prediction value of the diseased artery and a first flow distribution prediction value of each downstream branch of the diseased artery;

[0008] If the error between the first inlet blood pressure prediction value and the measured inlet blood pressure of the diseased artery is within a preset range, then determine the first flow distribution prediction value as the target flow distribution.

[0009] In one embodiment, the obtaining of the diseased artery model includes:

[0010] Constructing a three-dimensional model of the diseased artery according to the computed tomography angiography image of the diseased artery to obtain the diseased artery model.

[0011] In one embodiment, the obtaining of the circuit models of each downstream branch includes:

[0012] Repairing the diseased artery model to obtain a healthy artery model;

[0013] Determine the outlet boundary conditions, and based on the inlet flow rate, perform computational fluid dynamics simulation using a healthy artery model and the outlet boundary conditions to predict the second inlet blood pressure prediction value of the healthy artery model, as well as the outlet blood pressure prediction values and the second flow distribution prediction values of each downstream branch of the healthy artery;

[0014] Set the guessed value of the inlet blood pressure of the healthy artery model;

[0015] Based on the second inlet blood pressure prediction value, the outlet blood pressure prediction value, the second flow distribution prediction value, and the guessed value of the inlet blood pressure, obtain the parameter values of the components in the representation circuit corresponding to each downstream branch of the diseased artery; the representation circuit is used to represent the functional state of the downstream branch;

[0016] Obtain a circuit model based on the parameter values of each component in the representation circuit.

[0017] In one embodiment, the determining the outlet boundary conditions includes:

[0018] Obtain the flow distribution ratios of each downstream branch of the healthy artery, and determine the flow distribution ratios as the outlet boundary conditions.

[0019] In one embodiment, the circuit model is a single-element Windkessel circuit model, the component is a distal resistor, and the parameter value is the distal impedance;

[0020] The performing computational fluid dynamics simulation based on the inlet flow rate using the diseased artery model and the circuit model to predict the first inlet blood pressure prediction value of the diseased artery and the first flow distribution prediction values of each downstream branch of the diseased artery includes:

[0021] Based on the inlet flow rate, perform steady-state computational fluid dynamics simulation using the diseased artery model and the single-element Windkessel circuit model to predict the first inlet blood pressure prediction value of the diseased artery model and the first flow distribution prediction values of each downstream branch.

[0022] In one embodiment, the obtaining the parameter values of the components in the representation circuit corresponding to each downstream branch of the diseased artery based on the second inlet blood pressure prediction value, the outlet blood pressure prediction value, the second flow distribution prediction value, and the guessed value of the inlet blood pressure includes:

[0023] Obtain the pressure drops of each downstream branch of the healthy artery based on the second inlet blood pressure prediction value and the outlet blood pressure prediction value;

[0024] Each of the pressure drops is respectively determined as the pressure drop of each downstream branch of the diseased artery, and an estimated value of the outlet blood pressure of each downstream branch of the diseased artery is obtained based on the estimated value of the inlet blood pressure and the pressure drop.

[0025] Based on the estimated value of the outlet blood pressure and the second predicted value of the flow rate distribution, parameter values of components in the representation circuit corresponding to each downstream branch of the diseased artery are obtained.

[0026] In one embodiment, after predicting the first estimated value of the inlet blood pressure of the diseased artery and the first predicted value of the flow rate distribution of each downstream branch of the diseased artery by performing computational fluid dynamics simulation using the diseased artery model and the circuit model based on the inlet flow rate, the method further includes:

[0027] If the error between the first estimated value of the inlet blood pressure and the measured inlet blood pressure of the diseased artery is outside a preset range, return to the step of setting the estimated value of the inlet blood pressure of the healthy artery model until the error between the first estimated value of the inlet blood pressure and the measured inlet blood pressure of the diseased artery is within the preset range.

[0028] In one embodiment, the way to determine the error is:

[0029] Obtain the inlet blood pressure of the diseased artery;

[0030] Determine the absolute value of the difference between the inlet blood pressure of the diseased artery and the first estimated value of the inlet blood pressure;

[0031] Obtain the error based on the absolute value and the inlet blood pressure of the diseased artery.

[0032] In a second aspect, the present application further provides a device for obtaining flow rate distribution for hemodynamic analysis, including:

[0033] An acquisition module, configured to acquire a diseased artery model, the inlet flow rate of the diseased artery, and circuit models of each downstream branch; the parameter values of the components of the circuit model are obtained through the flow rate distribution of each downstream branch of the healthy artery;

[0034] A simulation module, configured to perform computational fluid dynamics simulation using the diseased artery model and the circuit models based on the inlet flow rate, and predict the first estimated value of the inlet blood pressure of the diseased artery and the first predicted value of the flow rate distribution of each downstream branch of the diseased artery;

[0035] A determination module, configured to, if the error between the first estimated value of the inlet blood pressure and the measured inlet blood pressure of the diseased artery is within a preset range, determine the first predicted value of the flow rate distribution as the target flow rate distribution.

[0036] In a third aspect, the present application further provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0037] Obtain a diseased artery model, the inlet flow rate of the diseased artery, and the circuit models of each downstream branch; the parameter values of the components of the circuit model are obtained through the flow rate distribution of each downstream branch of the healthy artery;

[0038] Based on the inlet flow rate, perform computational fluid dynamics simulation using the diseased artery model and the circuit model to predict the first inlet blood pressure prediction value of the diseased artery and the first flow rate distribution prediction values of each downstream branch of the diseased artery;

[0039] If the error between the first inlet blood pressure prediction value and the actually measured inlet blood pressure of the diseased artery is within a preset range, then determine the first flow rate distribution prediction value as the target flow rate distribution.

[0040] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0041] Obtain a diseased artery model, the inlet flow rate of the diseased artery, and the circuit models of each downstream branch; the parameter values of the components of the circuit model are obtained through the flow rate distribution of each downstream branch of the healthy artery;

[0042] Based on the inlet flow rate, perform computational fluid dynamics simulation using the diseased artery model and the circuit model to predict the first inlet blood pressure prediction value of the diseased artery and the first flow rate distribution prediction values of each downstream branch of the diseased artery;

[0043] If the error between the first inlet blood pressure prediction value and the actually measured inlet blood pressure of the diseased artery is within a preset range, then determine the first flow rate distribution prediction value as the target flow rate distribution.

[0044] In a fifth aspect, the present application further provides a computer program product, including a computer program. When the computer program is executed by a processor, the following steps are implemented:

[0045] Obtain a diseased artery model, the inlet flow rate of the diseased artery, and the circuit models of each downstream branch; the parameter values of the components of the circuit model are obtained through the flow rate distribution of each downstream branch of the healthy artery;

[0046] Based on the inlet flow rate, perform computational fluid dynamics simulation using the diseased artery model and the circuit model to predict the first inlet blood pressure prediction value of the diseased artery and the first flow rate distribution prediction values of each downstream branch of the diseased artery;

[0047] If the error between the predicted value of the first inlet blood pressure and the measured inlet blood pressure of the diseased artery is within a preset range, then the first predicted flow rate distribution is determined as the target flow rate distribution.

[0048] The above-mentioned method, device and medium for obtaining flow rate distribution for hemodynamic analysis are based on the discovery that the resistance of the corresponding downstream branches to blood flow does not change whether it is a diseased artery or a healthy artery. By first obtaining the parameter values of the components of the circuit model of each downstream branch from the flow rate distribution of the downstream branches of the healthy artery, and then obtaining the circuit model, and then using the circuit model as the outlet boundary condition and the inlet flow rate of the diseased artery as the inlet boundary condition, computational fluid dynamics simulation is carried out using the diseased artery model and the circuit model, so as to obtain the flow rate of each downstream branch that conforms to the actual situation of the diseased artery, that is, to obtain a more accurate flow rate distribution, thereby improving the accuracy of the prediction results of multi-scale simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0050] Figure 1 It is a structural diagram of a multi-scale simulation related to an embodiment of the present application;

[0051] Figure 2 It is a schematic flow chart of a method for obtaining flow rate distribution for hemodynamic analysis in one embodiment;

[0052] Figure 3 It is a schematic comparison diagram of the empirical flow rate distribution ratio and the target flow rate distribution ratio corresponding to a healthy artery in one embodiment;

[0053] Figure 4 It is a schematic flow chart of a method for obtaining flow rate distribution for hemodynamic analysis in another embodiment;

[0054] Figure 5a It is a front and rear view of a diseased artery model with an arterial dissection as a lesion related to an embodiment of the present application;

[0055] Figure 5b It is a front and rear view of a healthy artery model related to an embodiment of the present application;

[0056] Figure 6a It is a structural diagram of a single-element Windkessel circuit model related to an embodiment of the present application;

[0057] Figure 6b This is a structural diagram of a three - element Windkessel circuit model related to the embodiments of the present application;

[0058] Figure 7 This is a schematic flowchart of a method for obtaining flow distribution for hemodynamic analysis in another embodiment;

[0059] Figure 8 This is another schematic flowchart of a method for obtaining flow distribution for hemodynamic analysis in another embodiment;

[0060] Figure 9 This is a structural block diagram of a device for obtaining flow distribution for hemodynamic analysis in an embodiment;

[0061] Figure 10 This is an internal structural diagram of a computer device in an embodiment; Specific Embodiments

[0062] In order to make the objectives, technical solutions and advantages of the present application clearer, the following explains the technical terms that may be used in the embodiments of the present application:

[0063] Multi - scale simulation: By dividing the system into different levels or scales and using appropriate modeling and simulation methods at each level to reveal the internal correlations and characteristics of the system. The system in the embodiments of the present application refers to the blood circulation system, specifically to study the hemodynamic characteristics of the arterial system in the blood circulation system. The research process involves arteries and their downstream branches. Among them, the arteries are represented by three - dimensional models, and each downstream branch is represented by a Windkessel circuit model.

[0064] Hemodynamics: The mechanics of blood flow in the cardiovascular system, and its research contents include blood flow volume, blood flow resistance, blood pressure and their mutual relationships.

[0065] Windkessel circuit model: It regards the arterial system as an elastic cavity (bellows) with a terminal impedance. During the cardiac systolic phase, when the arterial system pressure rises, due to the compliance of the arteries (especially the aorta and large arteries), the arterial system diameter expands, playing a good "pressure storage" role. In the cardiac diastolic phase, the arterial diameter retracts and releases pressure. This role of first "storing pressure" and then "releasing pressure" ensures the continuity and stability of blood supply at the end of the arterial system. This model usually refers to a circuit representation containing one or more of the elements of resistance, capacitance, and inductance, and is used to represent the distal arteries, arterioles, capillaries, etc.

[0066] Steady state: In hemodynamics, at a certain moment or within a certain period of time, parameters such as blood flow volume, blood flow resistance, and blood pressure in the blood circulation system remain relatively stable and do not change significantly over time. In steady-state multiscale simulations, the Windkessel circuit model used is generally a single-element Windkessel circuit model.

[0067] Single-element Windkessel circuit model: A single element refers to one component, that is, the single-element Windkessel circuit model is a representative circuit containing one component. The advantage of using the single-element Windkessel circuit model is that the calculation process of the parameter values of the component is simple, and the simulation process is also simple. Of course, the data contained in the simulation results is also less.

[0068] Transient state: In hemodynamics, parameters such as blood flow volume, blood flow resistance, and blood pressure in the blood circulation system change over time. In transient multiscale simulations, the Windkessel circuit model used is generally a three-element Windkessel circuit model.

[0069] Three-element Windkessel circuit model: Three elements refer to three components, that is, the single-element Windkessel circuit model is a representative circuit containing three components. For example, the three components are two resistors and one capacitor. Among them, one resistor is used to represent the distal resistance, the other resistor is used to represent the proximal resistance, and the capacitor is used to represent the compliance of blood flow. The advantage of using the three-element Windkessel circuit model is that it can simulate the changes of parameters such as blood flow volume, blood flow resistance, and blood pressure over time. Therefore, the data contained in the simulation results is also more.

[0070] Flow distribution: It refers to the outflow distribution of the inlet flow for an artery when blood flows into the artery from the inlet and flows out through the artery to each downstream branch. It can be a specific flow value. For example, the inlet flow is 0.2 kg / s, there are 2 downstream branches, namely downstream branch 1 and downstream branch 2, and the flow rate allocated to downstream branch 1 is 0.05 kg / s, and the flow rate allocated to downstream branch 2 is 0.15 kg / s; it can also be a flow distribution ratio. For example, there are 3 downstream branches, namely downstream branch 1, downstream branch 2, and downstream branch 3, and the flow distribution ratio is 1:1:2. That is to say, the flow distribution can refer to both the flow value and the flow distribution ratio.

[0071] It can be understood that the flow distribution ratio can be calculated through the flow values of each downstream branch, and the flow values of each downstream branch can be calculated through the flow distribution ratio and the inlet flow.

[0072] The following further elaborates on the traditional technologies involved in the embodiments of the present application:

[0073] When performing computational fluid dynamics simulations to predict the hemodynamic characteristics of arteries, boundary conditions for each opening need to be specified. Among them, arteries refer to the aorta, coronary arteries, etc., and openings refer to the inlet and outlet of the artery. Among them, there are generally multiple outlets, and the outlet is the inlet of each downstream branch of the artery.

[0074] For example, the inlet of the aorta is the outlet of the ascending aorta, and the outlets of the aorta include the inlets of the renal arteries, the inlet of the left common carotid artery, etc.

[0075] Therefore, the boundary conditions of the opening include inlet boundary conditions and outlet boundary conditions.

[0076] For the inlet boundary conditions, they are usually specified as empirical flow curves or specific flow curves obtained from clinical measurements.

[0077] For the outlet boundary conditions, they are usually specified as the pressure at the outlet plane of the artery being zero, the pressure at the outlet plane of the artery being pulsating pressure, the flow distribution ratio of each outlet of the artery, or the Windkessel circuit model.

[0078] And since specifying the Windkessel circuit model as the outlet boundary condition can make the inlet blood pressure and the outlet flow distribution ratio in the predicted hemodynamic characteristics simultaneously satisfy the specified values, so that the obtained hemodynamic characteristics are considered to be closer to the real situation of the downstream branches, the Windkessel circuit model is widely used in hemodynamic research.

[0079] As Figure 1 shown, a schematic diagram of multi-scale simulation is provided. It can be seen that what is simulated is the aorta (aortic arch) and its downstream branches. Among them, the aorta is represented by a three-dimensional model, and each downstream branch is represented by a three-element Windkessel model.

[0080] It should be noted that before using the three-element Windkessel model for multi-scale simulation, it is necessary to determine the parameter values of each element in the three-element Windkessel model. The parameter values include the impedance of the proximal resistor, the impedance of the distal resistor, and the capacitance value. In the process of determining the parameter values, it is necessary to use the inlet blood pressure and the flow of each outlet as input conditions, and complete the estimation of the parameter values through manual parameter adjustment or iterative parameter adjustment methods.

[0081] So far, the description of the traditional technology is completed.

[0082] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0083] As mentioned in the background art, when predicting the hemodynamic characteristics of an artery through computational fluid dynamics (CFD) simulation of the artery, it is necessary to obtain the flow distribution of each downstream branch of the artery. However, there are great difficulties in obtaining this flow distribution.

[0084] In order to make the hemodynamic characteristics predicted during multi-scale simulation conform to the actual situation of the diseased artery, it is necessary to measure the intracavitary blood pressure curve corresponding to the diseased artery and the flow curves of each downstream branch, so as to complete the estimation of parameter values through methods such as manual parameter adjustment or iterative parameter adjustment. Among them, the intracavitary blood pressure curve is the above-mentioned inlet blood pressure, and the flow curve is the above-mentioned flow rate.

[0085] However, on the one hand, it is almost impossible to perform invasive measurements. On the other hand, even if measurements are performed, their accuracy is relatively low, specifically referring to the low measurement accuracy of the blood flow in each downstream branch such as arterioles.

[0086] Therefore, in practical applications, simplified data and empirical data are usually used as input conditions, that is, generally the upper arm blood pressure (systolic blood pressure and diastolic blood pressure) corresponding to the diseased artery and the empirical flow distribution of each downstream branch corresponding to the healthy artery are used.

[0087] However, diseased arteries usually cause obvious changes in the shape of the artery, and this change will affect the flow distribution of each downstream branch. That is, there is a difference between the flow distribution of each downstream branch corresponding to the diseased artery and the empirical flow distribution. Therefore, using empirical data as input conditions for multi-scale simulation will result in an obvious deviation between the predicted result and the actual situation, and further lead to a low accuracy of the predicted result of the multi-scale simulation.

[0088] In order to improve the accuracy of the predicted results of multi-scale simulation, the applicant has found through research that lesions generally occur in the artery or the first-level branches close to the artery, such as the aorta, coronary artery, etc., while lesions in the distal artery branches are relatively rare. Therefore, it can be considered that the resistance of the distal branches to blood flow remains unchanged before and after the occurrence of the arterial lesion. That is, whether it is a diseased artery or a healthy artery, the resistance of the corresponding downstream branches to blood flow does not change. Based on this finding, in an exemplary embodiment, such as Figure 2As shown, a method for obtaining flow distribution for hemodynamic analysis is provided. This method first obtains the parameter values of the components of the circuit model of each downstream branch from the flow distribution of each downstream branch of a healthy artery, and then obtains the circuit model. Subsequently, using this circuit model as the outlet boundary condition and the inlet flow of the diseased artery as the inlet boundary condition, computational fluid dynamics simulation is performed using the diseased artery model and the circuit model, so as to obtain the flow of each downstream branch that conforms to the actual situation of the diseased artery, that is, to obtain a more accurate flow distribution, thereby improving the accuracy of the prediction results of multi-scale simulation.

[0089] This method includes the following steps 202 to step 206. Among them:

[0090] Step 202, obtain a diseased artery model, the inlet flow of the diseased artery, and the circuit model of each downstream branch; the parameter values of the components of the circuit model are obtained from the flow distribution of each downstream branch of a healthy artery.

[0091] Among them, the diseased artery model is a three-dimensional model representing the diseased artery. This three-dimensional model can be pre-constructed or constructed based on the computed tomography angiography (CTA) images of the diseased artery, that is, reconstruct the three-dimensional model of the diseased artery through the CTA images of the diseased artery.

[0092] Among them, the diseased artery is an artery with lesions, and these lesions include arterial dissection, aneurysm, etc. Correspondingly, the healthy artery is an artery without lesions.

[0093] Among them, the inlet flow refers to the flow of blood flowing into the diseased artery, which can be the mean blood flow during the systolic phase of the artery.

[0094] Exemplarily, if the mean blood flow is not measured, the corresponding empirical value of the healthy artery can be taken, or the cardiac output can be calculated using an empirical formula based on the body surface area of a person (whose artery is diseased), and the corresponding empirical value of the healthy artery is scaled.

[0095] Among them, the circuit model includes but is not limited to a single-element Windkessel circuit model, a three-element Windkessel circuit model, and other circuit models, as long as it can be used to represent the downstream branch.

[0096] In addition, the parameter values of the components of the circuit model can be obtained through relevant calculations using the actually measured inlet blood pressure of a healthy artery and the flow rates distributed to each downstream branch; or they can be obtained through relevant calculations by setting a guessed value of the inlet blood pressure corresponding to the healthy artery and combining the flow rates distributed to each downstream branch. It can be understood that since the guessed value of the inlet blood pressure may not necessarily reflect the actual inlet blood pressure, when performing computational fluid dynamics simulation based on the circuit model obtained from this guessed value of the inlet blood pressure subsequently, an iterative parameter adjustment method may be required to determine the target flow rate distribution.

[0097] Step 204: Based on the inlet flow rate, perform computational fluid dynamics simulation using the diseased artery model and the circuit model to predict the first predicted value of the inlet blood pressure of the diseased artery and the first predicted values of the flow rate distributions of each downstream branch of the diseased artery.

[0098] Among them, the inlet boundary condition of this computational fluid dynamics simulation is the inlet flow rate, and the outlet boundary condition is the circuit model.

[0099] It can be understood that since the inlet flow rate is known, the predicted values of the flow rate distribution, whether they are flow rate values or flow rate distribution ratios, actually contain the data of the other party. For example, if the inlet flow rate is 0.2 kg / s, there are 3 downstream branches, namely downstream branch 1, downstream branch 2, and downstream branch 3, the predicted value of the flow rate distribution is the flow rate distribution ratio, and this ratio is 1:1:2, then the flow rates distributed to downstream branch 1, downstream branch 2, and downstream branch 3 are 0.05 kg / s, 0.05 kg / s, and 0.1 kg / s respectively.

[0100] Exemplarily, when performing computational fluid dynamics simulation, it can simulate the steady state or the transient state.

[0101] It should be noted that the distribution of blood flow is mainly determined by the resistance and compliance of each branch, where the influence of resistance is greater than that of compliance, and the compliance of the arterial system is mostly concentrated in the aorta (accounting for more than 50% of the entire arterial system). Therefore, after predicting the impedance of the distal branches through steady state simulation and then combining the diseased aorta model, the flow rate distribution of the downstream branches can be predicted.

[0102] Step 206: If the error between the first predicted value of the inlet blood pressure and the actually measured inlet blood pressure of the diseased artery is within the preset range, then determine the first predicted value of the flow rate distribution as the target flow rate distribution.

[0103] It can be understood that if the error is within the preset range, it indicates that the simulated blood flow in the diseased artery model is relatively consistent with the actual situation of blood flow in the diseased artery. In this case, both the input and output data are relatively in line with the actual situation. Therefore, when the error is within the preset range, it can be considered that the first flow distribution prediction value is relatively in line with the actual situation, that is, the first flow distribution prediction value can be determined as the target flow distribution, which is also the actual flow distribution of each downstream branch of the diseased artery.

[0104] Exemplarily, as Figure 3 shown, Figure 3 The comparison between the empirical flow distribution ratio corresponding to the healthy artery and the target flow distribution ratio is given. It can be seen that there are differences between the two. Since the target flow distribution ratio is more in line with the actual situation, when using the target flow distribution ratio for computational fluid dynamics simulation, the information on the predicted hemodynamic characteristics is more accurate.

[0105] In an exemplary embodiment, as Figure 4 shown, the parameter values of the components of the above circuit model can be obtained through relevant calculations by setting the guessed value of the inlet blood pressure corresponding to the healthy artery and combining the flow rates distributed to each downstream branch, so as to obtain the circuit model. That is, the steps of obtaining the circuit model of each downstream branch include steps 402 - step 410, where:

[0106] Step 402, repair the diseased artery model to obtain a healthy artery model.

[0107] Exemplarily, repair means repairing the defects of the healthy artery caused by the disease in the diseased artery model through specific repair principles.

[0108] In one embodiment, the specific repair principles may include the following steps:

[0109] Determine the approximate skeleton of the healthy artery model through the three-dimensional rendering of the diseased artery model and the CTA image; approximate the diameter of the blood vessel affected by the disease by the diameter of the blood vessel not affected by the disease within the same anatomical segment of the diseased artery model; repair the bifurcation of the blood vessel; perform appropriate shrinking and smoothing operations on the diseased artery model repaired through the above steps, so that the repaired diseased artery model is closer to the shape of the healthy artery, and finally obtain the healthy artery model. It can be understood that the purpose of the repair is to obtain a healthy artery model corresponding to the shape and size of the healthy artery.

[0110] Among them, due to the anatomical characteristics of the artery along the center line, such as curvature, being considered to be little affected by the disease, the approximate skeleton of the healthy artery model can be obtained.

[0111] Among them, arterial lesions are generally considered to cause varying degrees of arterial dilation. For example, arterial dissection can lead to slight dilation, while aneurysm can cause more obvious dilation. For the diameter of blood vessels unaffected by lesions, the diameters of blood vessels within the same anatomical segment are consistent. Therefore, the diameter of the blood vessel affected by the lesion can be approximated by the diameter of the blood vessel unaffected by the lesion within the same anatomical segment.

[0112] Among them, arterial lesions are considered to have little impact on the position and geometry of blood vessel bifurcation points. Therefore, the position and direction of the bifurcation points of the healthy arterial model can be determined in the diseased arterial model. This principle is also widely used in the repair of saccular aneurysms. Based on this, the repair of arteries with bifurcations involved in the lesion can be completed, such as repairing the lesion at the bifurcation of the iliac artery.

[0113] It can be understood that since the changes in arteries caused by different lesions may be different, appropriate repair principles can be selected for the repair of specific diseased arterial models, such as adding repair steps or reducing repair steps.

[0114] Such as Figure 5a and Figure 5b As shown, front and rear views of a diseased arterial model and a healthy arterial model with the lesion being arterial dissection are respectively provided.

[0115] Step 404, determine the outlet boundary conditions, and based on the inlet flow rate, perform computational fluid dynamics simulation using the healthy arterial model and the outlet boundary conditions to predict the second inlet blood pressure prediction value of the healthy arterial model, as well as the outlet blood pressure prediction values and the second flow distribution prediction values of each downstream branch of the healthy artery.

[0116] In one embodiment, the outlet boundary conditions can be specified as the pressure at the outlet plane of the artery being zero and the pressure at the outlet plane of the artery being pulsating pressure.

[0117] In another embodiment, the outlet boundary conditions can be specified as the flow distribution ratio, that is, the process of determining the outlet boundary conditions is to obtain the flow distribution ratios of each downstream branch of the healthy artery and determine the flow distribution ratios as the outlet boundary conditions.

[0118] It should be noted that the second inlet blood pressure prediction value is related to the inlet flow rate and is different from the inlet blood pressure of the actual healthy artery. And in the simulation process, the factor that the heart pumping blood will generate pressure on the blood vessels is not considered. However, through the second inlet blood pressure prediction value and the outlet blood pressure prediction values of each downstream branch, the pressure drop of each downstream branch can be calculated, and the actual outlet blood pressure of each downstream branch can be calculated through this pressure drop and the actual inlet blood pressure of the healthy artery.

[0119] Exemplarily, when performing computational fluid dynamics simulations, either steady state or transient state can be simulated.

[0120] Step 406: Set a guessed value for the inlet blood pressure of the healthy artery model;

[0121] Exemplarily, for the same reason as the intravascular blood pressure curve corresponding to the above-mentioned unmeasurable diseased artery, the actual inlet blood pressure of the healthy artery cannot be measured either. Therefore, in this embodiment, a guessed value is set and applied to the subsequent simulation process, and the target flow distribution is obtained by means of iterative parameter adjustment. For the specific implementation process, please refer to the following text.

[0122] For the setting of the guessed value of the inlet blood pressure, it is generally set to a relatively reasonable blood pressure instead of being set randomly, so as to reduce the number of iterations of iterative parameter adjustment and thus improve the efficiency.

[0123] Exemplarily, arterial lesions generally lead to an increase in blood pressure. Based on this, after measuring the inlet blood pressure of the diseased artery, the guessed value of the inlet blood pressure is set according to the measured blood pressure.

[0124] For example, if the measured inlet blood pressure (mean blood pressure) of the diseased artery is 103 mmHg, the guessed value of the inlet blood pressure can be set to a value less than 103 mmHg, such as 80 mmHg.

[0125] Step 408: Based on the second predicted value of the inlet blood pressure, the predicted value of the outlet blood pressure, the second predicted value of the flow distribution, and the guessed value of the inlet blood pressure, obtain the parameter values of the components in the representation circuit corresponding to each downstream branch of the diseased artery; the representation circuit is used to represent the functional state of the downstream branch.

[0126] Among them, when performing computational fluid dynamics simulations using the diseased artery model and the circuit model, if simulating the steady state, that is, performing steady-state computational fluid dynamics simulations using the diseased artery model and the circuit model, the component in the representation circuit is one, which is the distal resistor, and its parameter value is the distal impedance; if simulating the transient state, that is, performing transient computational fluid dynamics simulations using the diseased artery model and the circuit model, the components in the representation circuit can be three, namely the proximal resistor, the distal resistor, and the capacitor, and their parameter values are the proximal impedance, the distal impedance, and the capacitance value representing compliance.

[0127] In one embodiment, in the case of simulating the steady state, the circuit model is a single-element Windkessel circuit model, the component is the distal resistor, and the parameter value is the distal impedance;

[0128] Based on the inlet flow rate, computational fluid dynamics simulation is performed using the diseased artery model and the circuit model to predict the first predicted value of the inlet blood pressure of the diseased artery and the first predicted value of the flow rate distribution of each downstream branch of the diseased artery, including:

[0129] Based on the inlet flow rate, steady-state computational fluid dynamics simulation is performed using the diseased artery model and the single-element Windkessel circuit model to predict the first predicted value of the inlet blood pressure of the diseased artery model and the first predicted value of the flow rate distribution of each downstream branch.

[0130] Among them, the distal impedance can be calculated through the following steps:

[0131] Based on the second predicted value of the inlet blood pressure and the predicted value of the outlet blood pressure, the pressure drops of each downstream branch of the healthy artery are obtained; the pressure drops are respectively determined as the pressure drops of each downstream branch of the diseased artery, and the predicted values of the outlet blood pressure of each downstream branch of the diseased artery are obtained based on the guessed value of the inlet blood pressure and the pressure drops; based on the predicted values of the outlet blood pressure and the second predicted value of the flow rate distribution, the parameter values of the elements in the corresponding representation circuit of each downstream branch of the diseased artery are obtained. Among them, for the downstream branch, since it is minimally affected by the artery disease, the pressure drops of each downstream branch can be considered to be basically unchanged for both the diseased artery and the healthy artery.

[0132] The calculation steps of the distal impedance can be implemented through at least two implementation methods. Among them:

[0133] Implementation method one:

[0134] The distal impedance ( ) can be calculated through the following formula (1):

[0135] (1)

[0136] Among them, P in,r,guess is the guessed value of the inlet blood pressure, P in,r and P out,r,i are the second predicted value of the inlet blood pressure and the predicted value of the outlet blood pressure of each downstream branch respectively, Q r,i is the second predicted value of the flow rate distribution; among them, i is the serial number of the downstream branch.

[0137] Implementation method two:

[0138] The distal impedance ( ) can be calculated through the following formula (2):

[0139] (2)

[0140] Wherein, P in,r,guess is the guessed value of the inlet blood pressure, P in,r and P out,r,i are respectively the second predicted value of the inlet blood pressure and the predicted values of the outlet blood pressures of each downstream branch as described above, Q r,i is the second predicted value of the flow distribution; wherein, i is the serial number of the downstream branch, a is the weight coefficient, a is a positive number, used to adjust the pressure drop according to experience to make the distal impedance more accurate.

[0141] In another embodiment, in the case of simulating a transient state, the circuit model may be a three-element Windkessel circuit model, and the components are respectively a proximal resistor, a distal resistor, and a capacitor, and the parameter values are respectively a proximal impedance, a distal impedance, and a capacitance value.

[0142] Performing computational fluid dynamics simulation based on the inlet flow rate by using the diseased artery model and the circuit model to predict the first predicted value of the inlet blood pressure of the diseased artery and the first predicted value of the flow distribution of each downstream branch of the diseased artery includes:

[0143] Performing transient computational fluid dynamics simulation based on the inlet flow rate by using the diseased artery model and the three-element Windkessel circuit model to predict the first predicted value of the inlet blood pressure of the diseased artery model and the first predicted value of the flow distribution of each downstream branch.

[0144] Wherein, the parameter values of each component can be obtained by methods in practical applications, and the method of simulating a transient state can also be carried out by methods in practical applications, which will not be elaborated here.

[0145] Step 410, obtaining a circuit model based on the parameter values of the components in the characterization circuit.

[0146] Wherein, as Figure 6a shows, a single-element Windkessel circuit model is provided, wherein the outlet refers to the outlet of the artery, that is, the inlet corresponding to the downstream branch, R i is the distal resistor corresponding to the downstream branch.

[0147] Wherein, as Figure 6bAs shown, a three-element Windkessel circuit model is provided, where the outlet refers to the outlet of the artery, i.e., the inlet corresponding to the downstream branch. R c,i is the proximal resistance of the corresponding downstream branch. R p,i is the distal resistance of the corresponding downstream branch. C i is the capacitance of the corresponding downstream branch.

[0148] Furthermore, for the above-mentioned target flow distribution obtained by iterative parameter adjustment, when the error between the predicted value of the first inlet blood pressure and the measured inlet blood pressure of the diseased artery is within the preset range, the first predicted value of the flow distribution can be directly used as the target flow distribution.

[0149] However, when the error between the predicted value of the first inlet blood pressure and the measured inlet blood pressure of the diseased artery is outside the preset range, the step of returning the guessed value of the inlet blood pressure of the set healthy artery model is performed until the error between the predicted value of the first inlet blood pressure and the measured inlet blood pressure of the diseased artery is within the preset range.

[0150] That is, iterative parameter adjustment is performed on the guessed value of the inlet blood pressure, and after each parameter adjustment, the guessed value of the inlet blood pressure, the predicted value of the second inlet blood pressure, the predicted value of the outlet blood pressure, and the predicted value of the second flow distribution are used to obtain the parameter values of the elements in the corresponding representation circuit of each downstream branch of the diseased artery, and a circuit model is obtained based on the parameter values of the elements in the representation circuit.

[0151] Thus, based on the inlet flow rate, computational fluid dynamics simulation is performed using the diseased artery model and the circuit model to predict the first predicted value of the inlet blood pressure of the diseased artery and the first predicted value of the flow distribution of each downstream branch of the diseased artery until the error between the predicted value of the first inlet blood pressure and the measured inlet blood pressure of the diseased artery is within the preset range.

[0152] Among them, when adjusting the parameters, based on the above-mentioned method of setting the guessed value of the inlet blood pressure, a certain rule can be adopted for parameter adjustment. For example, according to experience, the guessed value of the inlet blood pressure can be adjusted by the difference between the measured inlet blood pressure of the diseased artery and the first predicted value of the inlet blood pressure predicted in this round, such as adding the difference to the guessed value of the inlet blood pressure.

[0153] Among them, the measured inlet blood pressure of the diseased artery ( P mean ) can be calculated by the following formula (III):

[0154] (III)

[0155] Among them,P sys is the systolic blood pressure of the upper arm, P dia is the diastolic blood pressure of the upper arm.

[0156] For example, if the measured inlet blood pressure (mean blood pressure) of the diseased artery is 103 mmHg, the guessed value of the inlet blood pressure can be set to 80 mmHg, and the predicted first inlet blood pressure prediction value is 88 mmHg, which is less than the inlet blood pressure of the diseased artery, and the difference is 15 mmHg. Therefore, based on 15 mmHg, the initial guessed value of the inlet blood pressure can be adjusted. For example, on the basis of 80 mmHg, increase it by 14 mmHg to obtain the guessed value of the inlet blood pressure at the second round of iteration as 94 mmHg. After performing the corresponding operations again, the predicted first inlet blood pressure prediction value is 103.14 mmHg. At this time, the error between the first inlet blood pressure prediction value and the actually measured inlet blood pressure of the diseased artery is within the preset range.

[0157] Among them, the preset range in the above example is < 1%.

[0158] Exemplarily, the method for determining the error is as follows: obtain the inlet blood pressure of the diseased artery; determine the absolute value of the difference between the inlet blood pressure of the diseased artery and the first inlet blood pressure prediction value; obtain the error based on the absolute value and the inlet blood pressure of the diseased artery.

[0159] In one embodiment, the error ( ) can be specifically calculated by the following formula (IV):

[0160] (IV)

[0161] Among them, P in,1 refers to the first inlet blood pressure prediction value.

[0162] So far, the description of how to obtain the first flow distribution prediction value of each downstream branch of the diseased artery in the embodiments of the present application is completed.

[0163] The following is an explanation of the process of obtaining information on hemodynamic characteristics by performing transient computational fluid dynamics simulations using the diseased artery model and the three-element Windkessel circuit model based on the first flow distribution prediction value:

[0164] First, perform steady-state computational fluid dynamics simulation on the diseased artery model, specify the inlet boundary condition as the hourly inlet flow rate, and the outlet boundary condition as the target flow distribution, that is, the target flow distribution ratio, and predict the inlet and outlet blood pressures and the flow values of each downstream branch ( Q i), and then calculate the geometric impedance of each downstream branch through the following Formula Five ( R g,1,i ):

[0165] (Five)

[0166] Where, P in,1 is the inlet blood pressure, P out,1,i is the outlet blood pressure.

[0167] After that, based on the measured inlet blood pressure of the diseased artery, the flow distribution and geometric impedance of each downstream branch, determine the total impedance of the Windkessel circuit model at each outlet through the following Formula Six R WK,i :

[0168] (Six)

[0169] Where, T refers to the period of the heart beat.

[0170] Based on this, the systolic blood pressure of the Windkessel model can be calculated through the following Formula Seven P sys ,i , and the diastolic blood pressure of the Windkessel model can be calculated through the following Formula Eight P dia ,i :

[0171] (Seven)

[0172] (Eight)

[0173] Where, P ref is the reference pressure, and this reference pressure is an empirical value.

[0174] Finally, use the global optimization algorithm to find the optimal parameters (proximal impedance R c,i , distal impedance R p,i , compliance C i ) for each Windkessel model to match the above systolic and diastolic blood pressures. The control equation of the Windkessel model (ordinary differential equation (ODE)) is:

[0175]

[0176] The objective function adopted is:

[0177]

[0178] In an exemplary embodiment, as Figure 7 and Figure 8 shown, an alternative embodiment of a method for obtaining flow distribution for hemodynamic analysis is provided. The method includes the following steps:

[0179] Step 1: Construct a diseased aortic model from CTA images and repair the diseased aortic model to obtain a healthy aortic model.

[0180] Step 2: Perform iterative steady-state computational fluid dynamics simulations.

[0181] Among them, Step 2 includes: Based on the repaired aortic model (healthy aortic model), predict the inlet pressure (blood pressure) and flow distribution; calculate the distal impedance of each downstream branch; based on the diseased aortic model, predict the inlet pressure and flow distribution; if the relative error between the mean blood pressure (measured upper arm blood pressure) and the predicted value < 1%, then execute Step 3; if the relative error between the mean blood pressure (measured upper arm blood pressure) and the predicted value ≥ 1%, then return to calculate the distal impedance of each downstream branch.

[0182] Step 3: Determine the flow distribution of each branch downstream of the diseased aorta.

[0183] Step 4: Estimate the Windkessel model parameter values.

[0184] Step 5: Perform multi-scale simulations of the aortic model + outlet Windkessel model to obtain aortic hemodynamic characteristics.

[0185] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps in other steps.

[0186] Based on the same inventive concept, an embodiment of the present application further provides a flow distribution acquisition device for hemodynamic analysis for implementing the flow distribution acquisition method for hemodynamic analysis involved above. The solution for solving the problem provided by this device is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the flow distribution acquisition device for hemodynamic analysis provided below can refer to the limitations on the flow distribution acquisition method for hemodynamic analysis in the above text, and will not be elaborated here.

[0187] In an exemplary embodiment, as Figure 9 shown, a flow distribution acquisition device for hemodynamic analysis is provided, including: an acquisition module 902, a simulation module 904, and a determination module 906, where:

[0188] The acquisition module 902 is configured to acquire a diseased artery model, the inlet flow of the diseased artery, and the circuit models of each downstream branch; the parameter values of the elements of the circuit model are obtained through the flow distribution of each downstream branch of the healthy artery;

[0189] The simulation module 904 is configured to perform computational fluid dynamics simulation based on the inlet flow, using the diseased artery model and the circuit model, to predict the first inlet blood pressure prediction value of the diseased artery and the first flow distribution prediction values of each downstream branch of the diseased artery;

[0190] The determination module 906 is configured to, if the error between the first inlet blood pressure prediction value and the measured inlet blood pressure of the diseased artery is within a preset range, determine the first flow distribution prediction value as the target flow distribution.

[0191] In one embodiment, the acquisition module 902 is specifically configured to:

[0192] Construct a three-dimensional model of the diseased artery according to the computed tomography angiography image of the diseased artery to obtain the diseased artery model.

[0193] In one embodiment, the acquisition module 902 is further configured to:

[0194] Repair the diseased artery model to obtain a healthy artery model;

[0195] Determine the outlet boundary conditions, and perform computational fluid dynamics simulation based on the inlet flow, using the healthy artery model and the outlet boundary conditions, to predict the second inlet blood pressure prediction value of the healthy artery model, the outlet blood pressure prediction values of each downstream branch of the healthy artery model, and the second flow distribution prediction values;

[0196] Set the guessed value of the inlet blood pressure of the healthy artery model;

[0197] Based on the predicted value of the second inlet blood pressure, the predicted value of the outlet blood pressure, the predicted value of the second flow distribution, and the guessed value of the inlet blood pressure, obtain the parameter values of the components in the representation circuit corresponding to each downstream branch of the diseased artery; the representation circuit is used to represent the functional state of the downstream branch.

[0198] Obtain a circuit model based on the parameter values of the components in the representation circuit.

[0199] In one embodiment, the obtaining module 902 is further configured to:

[0200] Obtain the flow distribution ratio of each downstream branch of the healthy artery, and determine the flow distribution ratio as the outlet boundary condition.

[0201] In one embodiment, the circuit model is a single-element Windkessel circuit model, the component is a distal resistor, and the parameter value is the distal impedance; the simulation module 904 is specifically configured to:

[0202] Based on the inlet flow rate, perform a steady-state computational fluid dynamics simulation using the diseased artery model and the single-element Windkessel circuit model, and predict the first predicted value of the inlet blood pressure of the diseased artery model and the first predicted value of the flow distribution of each downstream branch.

[0203] In one embodiment, the obtaining module 902 is further configured to:

[0204] Obtain the pressure drop of each downstream branch of the healthy artery based on the predicted value of the second inlet blood pressure and the predicted value of the outlet blood pressure;

[0205] Respectively determine the pressure drops of the downstream branches of the diseased artery as the pressure drops of the downstream branches of the diseased artery, and obtain the guessed values of the outlet blood pressure of the downstream branches of the diseased artery based on the guessed value of the inlet blood pressure and the pressure drops;

[0206] Obtain the parameter values of the components in the representation circuit corresponding to each downstream branch of the diseased artery based on the guessed value of the outlet blood pressure and the predicted value of the second flow distribution.

[0207] In one embodiment, the flow distribution obtaining device for hemodynamic analysis further includes:

[0208] A judgment module, configured to, if the error between the first predicted value of the inlet blood pressure and the actually measured inlet blood pressure of the diseased artery is outside the preset range, return to the step of setting the guessed value of the inlet blood pressure of the healthy artery model until the error between the first predicted value of the inlet blood pressure and the actually measured inlet blood pressure of the diseased artery is within the preset range.

[0209] In one embodiment, the flow distribution acquisition device for hemodynamic analysis further includes:

[0210] The acquisition module 902 is further configured to acquire the inlet blood pressure of the diseased artery;

[0211] The determination module 906 is further configured to determine the absolute value of the difference between the inlet blood pressure of the diseased artery and the first predicted value of the inlet blood pressure;

[0212] The calculation module is configured to obtain an error based on the absolute value and the inlet blood pressure of the diseased artery.

[0213] Each module in the above flow distribution acquisition device for hemodynamic analysis can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or stored in a memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0214] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as Figure 10 shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for wired or wireless communication with external terminals, and the wireless method can be implemented through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for acquiring flow distribution for hemodynamic analysis. The display unit of the computer device is used to form a visually visible picture, which may be a display screen, a projection device, or a virtual reality imaging device. The display screen may be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device may be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0215] Those skilled in the art can understand, Figure 10The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0216] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented:

[0217] Obtain a diseased artery model, the inlet flow of the diseased artery, and the circuit models of each downstream branch; the parameter values of the components of the circuit model are obtained through the flow distribution of each downstream branch of the healthy artery;

[0218] Based on the inlet flow, perform computational fluid dynamics simulation using the diseased artery model and the circuit model, and predict the first inlet blood pressure prediction value of the diseased artery and the first flow distribution prediction values of each downstream branch of the diseased artery;

[0219] If the error between the first inlet blood pressure prediction value and the measured inlet blood pressure of the diseased artery is within a preset range, then determine the first flow distribution prediction value as the target flow distribution.

[0220] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0221] Construct a three-dimensional model of the diseased artery according to the computed tomography angiography image of the diseased artery to obtain a diseased artery model.

[0222] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0223] Repair the diseased artery model to obtain a healthy artery model;

[0224] Determine the outlet boundary conditions, and based on the inlet flow, perform computational fluid dynamics simulation using the healthy artery model and the outlet boundary conditions, and predict the second inlet blood pressure prediction value of the healthy artery model, the outlet blood pressure prediction values of each downstream branch of the healthy artery, and the second flow distribution prediction values;

[0225] Set the guessed value of the inlet blood pressure of the healthy artery model;

[0226] Based on the second inlet blood pressure prediction value, the outlet blood pressure prediction value, the second flow distribution prediction value, and the guessed value of the inlet blood pressure, obtain the parameter values of the components in the representation circuit corresponding to each downstream branch of the diseased artery; the representation circuit is used to represent the functional state of the downstream branch;

[0227] A circuit model is obtained based on the parameter values of the components in the characterization circuit.

[0228] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0229] Obtain the flow distribution ratios of the downstream branches of the healthy artery, and determine the flow distribution ratios as the outlet boundary conditions.

[0230] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0231] Based on the inlet flow rate, perform a steady-state computational fluid dynamics simulation using the diseased artery model and the single-element Windkessel circuit model to predict the first inlet blood pressure prediction value of the diseased artery model and the first flow distribution prediction values of the downstream branches.

[0232] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0233] Obtain the pressure drops of the downstream branches of the healthy artery based on the second inlet blood pressure prediction value and the outlet blood pressure prediction value;

[0234] Respectively determine the pressure drops of the downstream branches of the diseased artery as the pressure drops, and obtain the outlet blood pressure guess values of the downstream branches of the diseased artery based on the inlet blood pressure guess value and the pressure drops;

[0235] Obtain the parameter values of the components in the characterization circuit corresponding to the downstream branches of the diseased artery based on the outlet blood pressure guess value and the second flow distribution prediction value.

[0236] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0237] If the error between the first inlet blood pressure prediction value and the measured inlet blood pressure of the diseased artery is outside the preset range, return to the step of setting the inlet blood pressure guess value of the healthy artery model until the error between the first inlet blood pressure prediction value and the measured inlet blood pressure of the diseased artery is within the preset range.

[0238] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0239] Obtain the inlet blood pressure of the diseased artery;

[0240] Determine the absolute value of the difference between the inlet blood pressure of the diseased artery and the first inlet blood pressure prediction value;

[0241] Obtain the error based on the absolute value and the inlet blood pressure of the diseased artery.

[0242] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0243] Obtain a diseased artery model, the inlet flow of the diseased artery, and the circuit models of each downstream branch; the parameter values of the elements of the circuit model are obtained through the flow distribution of each downstream branch of the healthy artery;

[0244] Based on the inlet flow, perform computational fluid dynamics simulation using the diseased artery model and the circuit model to predict the first inlet blood pressure prediction value of the diseased artery and the first flow distribution prediction values of each downstream branch of the diseased artery;

[0245] If the error between the first inlet blood pressure prediction value and the measured inlet blood pressure of the diseased artery is within a preset range, then determine the first flow distribution prediction value as the target flow distribution.

[0246] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0247] Construct a three-dimensional model of the diseased artery according to the computed tomography angiography image of the diseased artery to obtain a diseased artery model.

[0248] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0249] Repair the diseased artery model to obtain a healthy artery model;

[0250] Determine the outlet boundary conditions, and based on the inlet flow, perform computational fluid dynamics simulation using the healthy artery model and the outlet boundary conditions to predict the second inlet blood pressure prediction value of the healthy artery model, the outlet blood pressure prediction values of each downstream branch of the healthy artery, and the second flow distribution prediction values;

[0251] Set the guessed value of the inlet blood pressure of the healthy artery model;

[0252] Based on the second inlet blood pressure prediction value, the outlet blood pressure prediction values, the second flow distribution prediction values, and the guessed value of the inlet blood pressure, obtain the parameter values of the elements in the representation circuit corresponding to each downstream branch of the diseased artery; the representation circuit is used to represent the functional state of the downstream branch;

[0253] Obtain a circuit model based on the parameter values of each element in the representation circuit.

[0254] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0255] Obtain the flow distribution ratios of the downstream branches of the healthy artery, and determine the flow distribution ratios as the outlet boundary conditions.

[0256] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0257] Based on the inlet flow rate, perform a steady-state computational fluid dynamics simulation using the diseased artery model and the single-element Windkessel circuit model to predict the first inlet blood pressure prediction value of the diseased artery model and the first flow distribution prediction values of each downstream branch.

[0258] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0259] Obtain the pressure drops of the downstream branches of the healthy artery based on the second inlet blood pressure prediction value and the outlet blood pressure prediction value;

[0260] Respectively determine the pressure drops of the downstream branches of the diseased artery as the pressure drops, and based on the guessed value of the inlet blood pressure and the pressure drops, obtain the guessed values of the outlet blood pressures of the downstream branches of the diseased artery;

[0261] Based on the guessed values of the outlet blood pressures and the second flow distribution prediction values, obtain the parameter values of the elements in the corresponding representation circuits of the downstream branches of the diseased artery.

[0262] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0263] If the error between the first inlet blood pressure prediction value and the measured inlet blood pressure of the diseased artery is outside the preset range, return to the step of setting the guessed value of the inlet blood pressure of the healthy artery model until the error between the first inlet blood pressure prediction value and the measured inlet blood pressure of the diseased artery is within the preset range.

[0264] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0265] Obtain the inlet blood pressure of the diseased artery;

[0266] Determine the absolute value of the difference between the inlet blood pressure of the diseased artery and the first inlet blood pressure prediction value;

[0267] Based on the absolute value and the inlet blood pressure of the diseased artery, obtain the error.

[0268] In one embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0269] Obtain a diseased artery model, the inlet flow rate of the diseased artery, and the circuit models of each downstream branch; the parameter values of the components of the circuit model are obtained through the flow distribution of each downstream branch of the healthy artery;

[0270] Based on the inlet flow rate, perform computational fluid dynamics simulation using the diseased artery model and the circuit model to predict the first predicted inlet blood pressure value of the diseased artery and the first predicted flow distribution values of each downstream branch of the diseased artery;

[0271] If the error between the first predicted inlet blood pressure value and the measured inlet blood pressure of the diseased artery is within a preset range, then determine the first predicted flow distribution value as the target flow distribution.

[0272] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0273] Construct a three-dimensional model of the diseased artery based on the computed tomography angiography image of the diseased artery to obtain a diseased artery model.

[0274] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0275] Repair the diseased artery model to obtain a healthy artery model;

[0276] Determine the outlet boundary conditions, and based on the inlet flow rate, perform computational fluid dynamics simulation using the healthy artery model and the outlet boundary conditions to predict the second predicted inlet blood pressure value of the healthy artery model, the outlet blood pressure predicted values of each downstream branch of the healthy artery, and the second predicted flow distribution values;

[0277] Set the guessed value of the inlet blood pressure of the healthy artery model;

[0278] Based on the second predicted inlet blood pressure value, the outlet blood pressure predicted values, the second predicted flow distribution values, and the guessed value of the inlet blood pressure, obtain the parameter values of the components in the circuit representing each downstream branch of the diseased artery; the representing circuit is used to represent the functional state of the downstream branch;

[0279] Obtain the circuit model based on the parameter values of each component in the representing circuit.

[0280] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0281] Obtain the flow distribution ratios of each downstream branch of the healthy artery, and determine the flow distribution ratios as the outlet boundary conditions.

[0282] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0283] Based on the inlet flow rate, perform a steady-state computational fluid dynamics simulation using the diseased artery model and the single-element Windkessel circuit model to predict a first inlet blood pressure prediction value of the diseased artery model and first flow rate distribution prediction values of each downstream branch.

[0284] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0285] Obtain the pressure drops of each downstream branch of the healthy artery based on the second inlet blood pressure prediction value and the outlet blood pressure prediction value;

[0286] Respectively determine the pressure drops of each downstream branch of the diseased artery as the pressure drops of each downstream branch of the diseased artery, and obtain guessed outlet blood pressure values of each downstream branch of the diseased artery based on the guessed inlet blood pressure value and the pressure drops;

[0287] Obtain parameter values of elements in the corresponding representation circuit of each downstream branch of the diseased artery based on the guessed outlet blood pressure values and the second flow rate distribution prediction values.

[0288] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0289] If the error between the first inlet blood pressure prediction value and the measured inlet blood pressure of the diseased artery is outside a preset range, return to the step of setting the guessed inlet blood pressure value of the healthy artery model until the error between the first inlet blood pressure prediction value and the measured inlet blood pressure of the diseased artery is within the preset range.

[0290] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0291] Obtain the inlet blood pressure of the diseased artery;

[0292] Determine the absolute value of the difference between the inlet blood pressure of the diseased artery and the first inlet blood pressure prediction value;

[0293] Obtain an error based on the absolute value and the inlet blood pressure of the diseased artery.

[0294] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0295] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0296] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in the present application.

[0297] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A flow distribution acquisition method for hemodynamic analysis, characterized in that: The method comprises: A diseased artery model, an inlet flow of the diseased artery, and a circuit model of each downstream branch are obtained; the parameter values ​​of the elements of the circuit model are obtained through the flow distribution of each downstream branch of the healthy artery; the circuit model of each downstream branch is obtained by: repairing the diseased artery model to obtain a healthy artery model; determining the outlet boundary conditions, and based on the inlet flow, using the healthy artery model and the outlet boundary conditions to perform computational fluid dynamics simulation to predict the second inlet blood pressure prediction value of the healthy artery model and the outlet blood pressure prediction value and the second flow distribution prediction value of each downstream branch of the healthy artery; setting the inlet blood pressure guess value of the healthy artery model; based on the second inlet blood pressure prediction value, the outlet blood pressure prediction value, the second flow distribution prediction value and the inlet blood pressure guess value, obtaining the parameter values ​​of the elements in the characterization circuit corresponding to each downstream branch of the diseased artery; the characterization circuit is used to characterize the functional state of the downstream branch; the circuit model is obtained based on the parameter values ​​of each element in the characterization circuit; the circuit model is a single-element Windkessel circuit model, the element is a distal resistor, and the parameter value is a distal impedance; based on the inlet flow, using the diseased artery model and the outlet boundary conditions to perform computational fluid dynamics simulation to predict the second inlet blood pressure prediction value of the healthy artery model and the outlet blood pressure prediction value and the second flow distribution prediction value of each downstream branch of the healthy artery. The variable artery model and the circuit model are subjected to computational fluid dynamics simulation to predict the first inlet blood pressure prediction value of the diseased artery and the first flow distribution prediction value of each downstream branch of the diseased artery, including: based on the inlet flow, the diseased artery model and the single-element Windkessel circuit model are used to perform steady-state computational fluid dynamics simulation to predict the first inlet blood pressure prediction value of the diseased artery model and the first flow distribution prediction value of each downstream branch; based on the second inlet blood pressure prediction value, the outlet blood pressure prediction value, the second flow distribution prediction value and the inlet blood pressure guess value, the parameter values ​​of the elements in the characterization circuit corresponding to each downstream branch of the diseased artery are obtained, including: based on the second inlet blood pressure prediction value and the outlet blood pressure prediction value, the pressure drop of each downstream branch of the healthy artery is obtained; each of the pressure drops is respectively determined as the pressure drop of each downstream branch of the diseased artery, and the outlet blood pressure guess value of each downstream branch of the diseased artery is obtained based on the inlet blood pressure guess value and the pressure drop; based on the outlet blood pressure guess value and the second flow distribution prediction value, the parameter values ​​of the elements in the characterization circuit corresponding to each downstream branch of the diseased artery are obtained; Based on the inlet flow, a computational fluid dynamics simulation is performed using the diseased artery model and the circuit model to predict a first inlet blood pressure prediction value of the diseased artery and a first flow distribution prediction value of each downstream branch of the diseased artery; If the error between the first inlet blood pressure prediction value and the actually measured inlet blood pressure of the diseased artery is within a preset range, the first flow distribution prediction value is determined as the target flow distribution.

2. The flow distribution acquisition method for hemodynamic analysis according to claim 1, characterized in that: The obtaining of the diseased artery model comprises: A three-dimensional model of the diseased artery is constructed according to the computed tomography angiography image of the diseased artery to obtain a diseased artery model.

3. The flow distribution acquisition method for hemodynamic analysis according to claim 1, characterized in that: The step of determining the exit boundary condition comprises: The flow distribution ratio of each downstream branch of the healthy artery is obtained, and the flow distribution ratio is determined as the outlet boundary condition.

4. The flow distribution acquisition method for hemodynamic analysis according to claim 1, characterized in that: After the method uses the diseased artery model and the circuit model to perform computational fluid dynamics simulation based on the inlet flow rate to predict a first inlet blood pressure prediction value of the diseased artery and a first flow distribution prediction value of each downstream branch of the diseased artery, it also includes: If the error between the first inlet blood pressure prediction value and the inlet blood pressure of the diseased artery measured is outside the preset range, the process returns to the step of setting the inlet blood pressure guessing value of the healthy artery model until the error between the first inlet blood pressure prediction value and the inlet blood pressure of the diseased artery measured is within the preset range.

5. The flow distribution acquisition method for hemodynamic analysis according to any one of claims 1 to 4, characterized in that: The error is determined as follows: Obtain the inlet blood pressure of the diseased artery; determining an absolute value of a difference between an inlet blood pressure of the diseased artery and the first inlet blood pressure prediction value; An error is obtained based on the absolute value and the inlet blood pressure of the diseased artery.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the flow distribution acquisition method for hemodynamic analysis according to any one of claims 1 to 5 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the flow distribution acquisition method for hemodynamic analysis according to any one of claims 1 to 5 are implemented.

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