Flow distribution obtaining method and device for hemodynamic analysis and medium

Abstract

The invention relates to a flow distribution obtaining method and device for hemodynamic analysis and a medium. The method comprises the following steps: acquiring a lesion artery model, inlet flow of the lesion artery and a circuit model of each downstream branch; parameter values of elements of the circuit model are obtained through flow distribution of downstream branches of the healthy artery; based on the inlet flow, the lesion artery model and the circuit model are adopted for computational fluid dynamics simulation, and a first inlet blood pressure prediction value of the lesion artery and a first flow distribution prediction value of each downstream branch of the lesion artery are obtained through prediction; if the error between the first inlet blood pressure predicted value and the inlet blood pressure, obtained through actual measurement, of the lesion artery is within a preset range, the first flow distribution predicted value is determined as target flow distribution. By adopting the method, the accuracy of a prediction result of multi-scale simulation can be improved.

Description

Technical Field

[0001] The present application relates to the field of fluid simulation technology, and in particular to a flow distribution acquisition method, device and medium 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 traditional technology, when performing computational fluid dynamics (CFD) simulation on an artery to predict the hemodynamic characteristics 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 the flow distribution. Summary of the invention

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

[0005] In a first aspect, the present application provides a flow distribution acquisition method for hemodynamic analysis, comprising: Obtaining a diseased artery model, an inlet flow of the diseased artery, and a circuit model of each downstream branch; parameter values ​​of components of the circuit model are obtained by flow distribution of each downstream branch of a healthy artery; 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.

[0006] In one embodiment, obtaining the diseased artery model includes: 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.

[0007] In one embodiment, obtaining the circuit model of each downstream branch includes: Repairing the diseased artery model to obtain a healthy artery model; Determine an outlet boundary condition, and based on the inlet flow, perform computational fluid dynamics simulation using a healthy artery model and the outlet boundary condition to predict a second inlet blood pressure prediction value of the healthy artery model and an outlet blood pressure prediction value of each downstream branch of the healthy artery and a second flow distribution prediction value; Setting a guessed value of the inlet blood pressure 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 parameter values ​​of components in a 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; A circuit model is obtained based on the parameter values ​​of each element in the characterizing circuit.

[0008] In one embodiment, determining the exit boundary condition includes: 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.

[0009] In one embodiment, the circuit model is a single-element Windkessel circuit model, the element is a remote resistor, and the parameter value is a remote impedance; The method of performing computational fluid dynamics simulation based on the inlet flow rate by 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 includes: 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 a first inlet blood pressure prediction value of the diseased artery model and a first flow distribution prediction value of each downstream branch.

[0010] In one embodiment, obtaining parameter values ​​of components in the characterization 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 inlet blood pressure guess value includes: Obtaining the pressure drop of each downstream branch of the healthy artery based on the second inlet blood pressure prediction value and the outlet blood pressure prediction value; Determine each of the pressure drops as the pressure drops of each downstream branch of the diseased artery, and obtain a guessed value of the outlet blood pressure of each downstream branch of the diseased artery based on the guessed value of the inlet blood pressure and the pressure drops; Parameter values ​​of elements in the characterization circuit corresponding to each downstream branch of the diseased artery are obtained based on the outlet blood pressure guess value and the second flow distribution prediction value.

[0011] In one embodiment, after the computational fluid dynamics simulation is performed based on the inlet flow rate 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, the method further comprises: 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.

[0012] In one embodiment, the error is determined by: 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.

[0013] In a second aspect, the present application also provides a flow distribution acquisition device for hemodynamic analysis, comprising: An acquisition module, used to acquire a diseased artery model, an inlet flow of the diseased artery, and a circuit model of each downstream branch; parameter values ​​of components of the circuit model are obtained by flow distribution of each downstream branch of a healthy artery; A simulation module, configured to perform computational fluid dynamics simulation based on the inlet flow rate 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; The determination module is configured to determine the first flow distribution prediction value as the target flow distribution 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.

[0014] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented: Obtaining a diseased artery model, an inlet flow of the diseased artery, and a circuit model of each downstream branch; parameter values ​​of components of the circuit model are obtained by flow distribution of each downstream branch of a healthy artery; 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.

[0015] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented: Obtaining a diseased artery model, an inlet flow of the diseased artery, and a circuit model of each downstream branch; parameter values ​​of components of the circuit model are obtained by flow distribution of each downstream branch of a healthy artery; 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.

[0016] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor: Obtaining a diseased artery model, an inlet flow of the diseased artery, and a circuit model of each downstream branch; parameter values ​​of components of the circuit model are obtained by flow distribution of each downstream branch of a healthy artery; 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.

[0017] The above-mentioned flow distribution acquisition method, device and medium for hemodynamic analysis are based on the discovery that the resistance of the corresponding downstream branches to blood flow does not change regardless of whether it is a diseased artery or a healthy artery. The parameter values ​​of the components of the circuit model of each downstream branch are obtained by first distributing the flow of each downstream branch of the healthy artery, and then the circuit model is obtained. The circuit model is then used as the outlet boundary condition, and the inlet flow of the diseased artery is used as the inlet boundary condition. The diseased artery model and the circuit model are used to perform computational fluid dynamics simulation, thereby obtaining the flow of each downstream branch that conforms to the actual situation of the diseased artery, that is, obtaining a more accurate flow distribution, thereby improving the accuracy of the prediction results of the multi-scale simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments of the present application or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A structural diagram of a multi-scale simulation involved in an embodiment of the present application; Figure 2 It is a flowchart of a flow distribution acquisition method for hemodynamic analysis in one embodiment; Figure 3 A schematic diagram showing a comparison between an empirical flow distribution ratio and a target flow distribution ratio corresponding to a healthy artery in an embodiment; Figure 4 It is a flow chart of a flow distribution acquisition method for hemodynamic analysis in another embodiment; Figure 5a Front and rear views of a diseased artery model in which the disease is arterial dissection according to an embodiment of the present application; Figure 5b It is a front and back view of a healthy artery model involved in an embodiment of the present application; Figure 6a A structural diagram of a single-element Windkessel circuit model involved in an embodiment of the present application; Figure 6b A structural diagram of a three-element Windkessel circuit model involved in an embodiment of the present application; Figure 7 A schematic flow chart of a flow distribution acquisition method for hemodynamic analysis in yet another embodiment; Figure 8 It is another flow chart of a flow distribution acquisition method for hemodynamic analysis in yet another embodiment; Fig. 9 is a structural block diagram of a flow distribution acquisition device for hemodynamic analysis in one embodiment; Fig.10 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present application clearer, the following is an explanation of the technical terms that may be used in the embodiments of the present application: Multi-scale simulation: By dividing the system into different levels or scales and using appropriate modeling and simulation methods at each level, the inherent connections and characteristics of the system are revealed. The system in the embodiment of the present application refers to the blood circulation system, specifically the hemodynamic characteristics of the arterial system in the blood circulation system, and the arteries and their downstream branches are involved in the research process. The arteries are represented by a three-dimensional model, and each downstream branch is represented by a Windkessel circuit model.

[0021] Hemodynamics: The mechanics of blood flow in the cardiovascular system, which includes blood flow, blood flow resistance, blood pressure and their relationship.

[0022] Windkessel circuit model: It regards the arterial system as an elastic cavity (bellows) with terminal impedance. During cardiac systole, when the pressure in the arterial system increases, the diameter of the arterial system expands due to the compliance of the arteries (especially the aorta and large arteries), which plays a good "pressure storage" role. During cardiac diastole, the diameter of the artery shrinks and the pressure is released. This effect of "storing pressure" first and then "releasing" ensures the continuity and stability of blood supply at the end of the arterial system. This model usually refers to a representation circuit containing one or more components of resistance, capacitance, and inductance, and is used to refer to distal arteries, arterioles, capillaries, etc.

[0023] Steady state: refers to the fact that in hemodynamics, at a certain moment or over a period of time, the blood flow, blood flow resistance, blood pressure and other parameters in the blood circulation system remain relatively stable and do not change significantly over time. In steady-state multi-scale simulation, the Windkessel circuit model used is generally a single-element Windkessel circuit model.

[0024] Single-element Windkessel circuit model: A single element refers to one component, that is, a single-element Windkessel circuit model is a representation circuit containing one component. The advantage of using a single-element Windkessel circuit model is that the calculation process of the component parameter value is simple, and the simulation process is simple. Of course, the simulation results contain less data.

[0025] Transient: refers to the changes in blood flow, blood flow resistance, blood pressure and other parameters in the blood circulation system over time in hemodynamics. In transient multi-scale simulation, the Windkessel circuit model used is generally a three-element Windkessel circuit model.

[0026] Three-element Windkessel circuit model: Three elements refer to three components, that is, the single-element Windkessel circuit model is a representation circuit containing three components, for example, the three components are two resistors and one capacitor, where one resistor is used to refer to the distal resistance, the other resistor is used to refer to the proximal resistance, and the capacitor is used to refer to 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, blood flow resistance, and blood pressure over time, so the simulated results also contain more data.

[0027] Flow distribution: refers to the outflow distribution of the inlet flow for the 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 0.2kg / s, there are 2 downstream branches, namely downstream branch 1 and downstream branch 2, where the flow allocated to downstream branch 1 is 0.05kg / s, and the flow allocated to downstream branch 2 is 0.15kg / 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, flow distribution can refer to both flow value and flow distribution ratio.

[0028] It can be understood that the flow distribution ratio can be calculated through the flow value of each downstream branch, and the flow value of each downstream branch can be calculated through the flow distribution ratio and the inlet flow.

[0029] The following further describes the conventional technology involved in the embodiments of the present application: When performing computational fluid dynamics simulation to predict the hemodynamic characteristics of an artery, it is necessary to specify the boundary conditions of each opening. The artery refers to the aorta, coronary artery, etc., and the opening refers to the inlet and outlet of the artery, wherein the outlet generally includes multiple ones, and the outlet is the inlet of each downstream branch of the artery.

[0030] For example, the entrance of the aorta is the exit of the ascending aorta, and the exit of the aorta includes the entrance of the renal artery, the entrance of the left common carotid artery, and the like.

[0031] Therefore, the boundary conditions of the opening include inlet boundary conditions and outlet boundary conditions.

[0032] For the inlet boundary conditions, they are usually specified as empirical flow curves or specific flow curves obtained from clinical measurements.

[0033] For the outlet boundary conditions, they are usually specified as zero pressure at the outlet plane of the artery, pulsating pressure at the outlet plane of the artery, flow distribution ratio of each outlet of the artery, or Windkessel circuit model.

[0034] Since the Windkessel circuit model is specified as the outlet boundary condition, the inlet blood pressure and outlet flow distribution ratio in the predicted hemodynamic characteristics can simultaneously meet the specified values, so that the obtained hemodynamic characteristics are considered to be closer to the actual situation of the downstream branches. Therefore, the Windkessel circuit model is widely used in hemodynamic research.

[0035] like Figure 1 As shown in FIG. 1 , a schematic diagram of a multi-scale simulation is provided, and it can be seen that the simulation is of the aorta (aortic arch) and its downstream branches. The aorta is represented by a three-dimensional model, and the downstream branches are represented by a three-element Windkessel model.

[0036] 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, including the impedance of the proximal resistor, the impedance of the distal resistor, and the capacitance value. In the process of determining the parameter value, it is necessary to use the inlet blood pressure and the flow rate of each outlet as input conditions, and complete the estimation of the parameter value through manual parameter adjustment or iterative parameter adjustment method.

[0037] This completes the description of the conventional technology.

[0038] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with 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.

[0039] As mentioned in the background art, when performing computational fluid dynamics (CFD) simulation on an artery to predict the hemodynamic characteristics 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 the flow distribution.

[0040] In order to ensure that the predicted hemodynamic characteristics can match the actual situation of the diseased artery during multi-scale simulation, it is necessary to measure the intraluminal blood pressure curve corresponding to the diseased artery and the flow curve of each downstream branch, so as to complete the estimation of parameter values ​​through manual parameter adjustment or iterative parameter adjustment. Among them, the intraluminal blood pressure curve is the above-mentioned inlet blood pressure, and the flow curve is the above-mentioned flow.

[0041] However, on the one hand, it is almost impossible to perform invasive measurement, and on the other hand, even if the measurement is performed, its accuracy is low, specifically, the measurement accuracy of the blood flow in each downstream branch such as small arteries is low.

[0042] Therefore, in practical applications, simplified data and empirical data are usually used as input conditions, that is, the upper arm blood pressure (systolic and diastolic blood pressure) corresponding to the diseased artery and the empirical flow distribution of each downstream branch corresponding to the healthy artery are generally used.

[0043] However, diseased arteries usually cause significant changes in the shape of the arteries, which 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 lead to a significant deviation between the predicted results and the actual situation, which in turn leads to low accuracy of the prediction results of multi-scale simulation.

[0044] In order to improve the accuracy of the prediction results of multi-scale simulation, the applicant found that lesions generally occur in arteries or primary branches close to arteries, such as the aorta, coronary artery, etc., while lesions in distal arterial branches are relatively rare. Therefore, it can be considered that the resistance of the distal branches to blood flow remains unchanged before and after arterial lesions occur. 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, Figure 2 As shown, a flow distribution acquisition method for hemodynamic analysis is provided. The 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 the healthy artery, and then obtains the circuit model. The circuit model is then used as the outlet boundary condition, and the inlet flow of the diseased artery is used as the inlet boundary condition. The diseased artery model and the circuit model are used to perform computational fluid dynamics simulation, thereby obtaining the flow of each downstream branch that conforms to the actual situation of the diseased artery, that is, obtaining a more accurate flow distribution, thereby improving the accuracy of the prediction results of the multi-scale simulation.

[0045] The method includes the following steps 202 to 206. Among them: Step 202, obtaining a diseased artery model, an inlet flow of the diseased artery, and a circuit model of each downstream branch; parameter values ​​of components of the circuit model are obtained by flow distribution of each downstream branch of a healthy artery.

[0046] The diseased artery model refers to a three-dimensional model of the diseased artery. The three-dimensional model may be constructed in advance, or may be constructed based on a computed tomography angiography (CTA) image of the diseased artery, that is, the three-dimensional model of the diseased artery is reconstructed using the CTA image of the diseased artery.

[0047] The diseased artery is an artery with a disease, and the disease includes arterial dissection, aneurysm, etc. Correspondingly, the healthy artery is an artery without a disease.

[0048] The inlet flow rate refers to the flow rate of blood flowing into the diseased artery, which can be the average blood flow rate during the systolic period of the artery.

[0049] For example, if the average blood flow is not measured, an empirical value corresponding to a healthy artery can be used, or the blood output of the heart can be calculated using an empirical formula based on the body surface area of ​​a person (whose artery is diseased) and the empirical value corresponding to a healthy artery can be scaled to obtain the blood output.

[0050] 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, and can be used to refer to downstream branches.

[0051] In addition, the parameter values ​​of the components of the circuit model can be obtained by performing relevant calculations based on the actually measured inlet blood pressure of the healthy artery and the flow rate allocated to each downstream branch; or the inlet blood pressure guess value corresponding to the healthy artery can be set and calculated based on the flow rate allocated to each downstream branch. It can be understood that since the inlet blood pressure guess value may not necessarily reflect the inlet blood pressure under actual conditions, when performing computational fluid dynamics simulation based on the circuit model obtained based on the inlet blood pressure guess value, it may be necessary to use an iterative parameter adjustment method to determine the target flow rate distribution.

[0052] Step 204: Based on the inlet flow, 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.

[0053] The inlet boundary condition of the computational fluid dynamics simulation is the inlet flow rate, and the outlet boundary condition is the circuit model.

[0054] It can be understood that since the inlet flow is known, the flow distribution prediction value, whether it is the flow value or the flow distribution ratio, actually contains the data of the other party. For example, if the inlet flow is 0.2kg / s, there are 3 downstream branches, namely downstream branch 1, downstream branch 2 and downstream branch 3, and the flow distribution prediction value is the flow distribution ratio, and the ratio is 1:1:2, then the flow rates allocated to downstream branch 1, downstream branch 2 and downstream branch 3 are 0.05kg / s, 0.05kg / s and 0.1kg / s respectively.

[0055] For example, when performing computational fluid dynamics simulation, both steady state and transient state can be simulated.

[0056] It should be noted that the distribution of blood flow is mainly determined by the resistance and compliance of each branch, among which the influence of resistance is greater than 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 the impedance of the distal branch is predicted through steady-state simulation, combined with the diseased aorta model, the flow distribution of the downstream branch can be predicted.

[0057] Step 206: 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.

[0058] It can be understood that if the error is within the preset range, it means that the simulated blood flow in the diseased artery model is more consistent with the actual situation of blood flow in the diseased artery, and the data at this time, including input and output, are more consistent 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 more consistent with the actual situation, that is, the first flow distribution prediction value can be determined as the target flow distribution, that is, determined as the actual flow distribution of each downstream branch of the diseased artery.

[0059] For example, Figure 3 As shown, Figure 3 A comparison is given between the empirical flow distribution ratio and the target flow distribution ratio corresponding to a healthy artery. It can be seen that there is a difference between the two. Since the target flow distribution ratio is more in line with the actual situation, when the target flow distribution ratio is used for computational fluid dynamics simulation, the information on the predicted hemodynamic characteristics is more accurate.

[0060] In an exemplary embodiment, Figure 4 As shown, the parameter values ​​of the components of the above circuit model can be obtained by setting the inlet blood pressure guess value corresponding to the healthy artery and performing relevant calculations in combination with the flow rate allocated to each downstream branch, thereby obtaining the circuit model. That is, the step of obtaining the circuit model of each downstream branch includes steps 402 to 410, wherein: Step 402: repair the diseased artery model to obtain a healthy artery model.

[0061] Exemplarily, repair refers to repairing defects existing in a healthy artery caused by a lesion in a diseased artery model by using specific repair principles.

[0062] In one embodiment, a specific repair principle may include the following steps: The approximate skeleton of the healthy artery model is determined by three-dimensional rendering of the diseased artery model and the CTA image; the diameter of the blood vessel affected by the disease is approximated by the diameter of the blood vessel not affected by the disease in the same anatomical segment of the diseased artery model; the bifurcation of the blood vessel is repaired; the diseased artery model repaired by the above steps is appropriately reduced and smoothed, so that the repaired diseased artery model is closer to the morphology of the healthy artery, and finally the healthy artery model is obtained. It can be understood that the purpose of the repair is to obtain a healthy artery model corresponding to the morphology and size of the healthy artery.

[0063] Among them, since the anatomical features of the artery along the centerline, such as curvature, are considered to be little affected by the lesion, an approximate skeleton of the healthy artery model can be obtained.

[0064] Among them, arterial lesions are generally believed to cause different degrees of arterial dilatation, such as arterial dissection will cause mild dilatation, while aneurysm will cause more obvious dilatation. As for the diameter of blood vessels not affected by lesions, the diameter of blood vessels in the same anatomical segment is consistent. Therefore, the diameter of blood vessels affected by lesions can be approximated by the diameter of blood vessels not affected by lesions in the same anatomical segment.

[0065] Among them, arterial lesions are believed to have little effect on the bifurcation point location and bifurcation geometry of the blood vessels. Therefore, the bifurcation point location and direction of the healthy artery model can be determined in the diseased artery model. This principle is also widely used in the repair of saccular aneurysms. Based on this, the repair of arteries with lesions involving bifurcations can be completed, such as repairing lesions at the bifurcation of the iliac artery.

[0066] It is understandable that, since the changes in the arteries caused by different lesions may be different, an appropriate repair principle may be selected for a specific lesion artery model, such as adding a new repair step or reducing a repair step.

[0067] like Figure 5a and Figure 5b As shown, front and back views of a diseased artery model in which the lesion is arterial dissection and a healthy artery model are provided respectively.

[0068] Step 404, determine the outlet boundary conditions, and based on the inlet flow, use 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.

[0069] In one embodiment, the outlet boundary condition may 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 a pulsating pressure.

[0070] In another embodiment, the outlet boundary condition may be specified as a flow distribution ratio, that is, the process of determining the outlet boundary condition is to obtain the flow distribution ratio of each downstream branch of the healthy artery and determine the flow distribution ratio as the outlet boundary condition.

[0071] It should be noted that the second inlet blood pressure prediction value is related to the inlet flow rate, which is different from the actual inlet blood pressure of a healthy artery, and the factor that the heart pumping blood will produce pressure on the blood vessels is not considered during the simulation process. However, the pressure drop of each downstream branch can be calculated through the second inlet blood pressure prediction value and the outlet blood pressure prediction value of each downstream branch, and the actual outlet blood pressure of each downstream branch can be calculated through the pressure drop and the actual inlet blood pressure of the healthy artery.

[0072] For example, when performing computational fluid dynamics simulation, both steady state and transient state can be simulated.

[0073] Step 406, setting the estimated value of the inlet blood pressure of the healthy artery model; For example, for the same reason as the inability to measure the intraluminal blood pressure curve corresponding to the diseased artery, the actual inlet blood pressure of the healthy artery cannot be measured. Therefore, in this embodiment, a guess value is set and applied to the subsequent simulation process, and the target flow distribution is obtained by iterative parameter adjustment. Please refer to the following for the specific implementation process.

[0074] The estimated value of the inlet blood pressure is generally set to a more reasonable blood pressure rather than an arbitrary setting, thereby reducing the number of iterations of iterative parameter adjustment and improving efficiency.

[0075] Exemplarily, arterial disease generally leads to increased blood pressure. Based on this, after the inlet blood pressure of the diseased artery is measured, the inlet blood pressure guess value is set according to the measured blood pressure.

[0076] For example, if the inlet blood pressure (average blood pressure) of the diseased artery is measured to be 103 mmHg, the inlet blood pressure guess value may be set to a value less than 103 mmHg, such as 80 mmHg.

[0077] Step 408, 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, obtain 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 branches.

[0078] Among them, when the diseased artery model and the circuit model are used for computational fluid dynamics simulation, if the steady state is simulated, that is, the diseased artery model and the circuit model are used for steady-state computational fluid dynamics simulation, then the element characterizing the circuit is one, which is the distal resistance, and its parameter value is the distal impedance; if the transient state is simulated, that is, the diseased artery model and the circuit model are used for transient computational fluid dynamics simulation, then the elements characterizing the circuit can be three, namely the proximal resistance, the distal resistance and the capacitance, and their parameter values ​​are the proximal impedance, the distal impedance and the capacitance value characterizing compliance.

[0079] In one embodiment, in the case of simulating a steady state, the circuit model is a single-element Windkessel circuit model, the element is a remote resistor, and the parameter value is a remote impedance; The method of performing computational fluid dynamics simulation based on the inlet flow rate by 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 includes: 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 a first inlet blood pressure prediction value of the diseased artery model and a first flow distribution prediction value of each downstream branch.

[0080] The far-end impedance can be calculated by the following steps: 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 determined as the pressure drop of each downstream branch of the diseased artery, and the outlet blood pressure prediction 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 value of the element in the characterization circuit corresponding to each downstream branch of the diseased artery is obtained. Among them, for the downstream branches, since they are minimally affected by the lesions of the artery, the pressure drop of each downstream branch can be considered to be basically unchanged regardless of whether it is a diseased artery or a healthy artery.

[0081] The step of calculating the far-end impedance can be implemented in at least two ways. Implementation method 1: Far-end impedance ( ) can be calculated by the following formula (I): (one) in, P in,r,guess is the estimated value of the inlet blood pressure, P in,r and Pout,r,i are the predicted value of the second inlet blood pressure and the predicted value of the outlet blood pressure of each downstream branch, respectively. Q r,i Assign a predicted value to the second flow; wherein, i The sequence number of the downstream branch.

[0082] Implementation method 2: Far-end impedance ( ) can be calculated by the following formula (II): (two) in, P in,r,guess is the estimated value of the inlet blood pressure, P in,r and P out,r,i are the predicted value of the second inlet blood pressure and the predicted value of the outlet blood pressure of each downstream branch, respectively. Q r,i Assign a predicted value to the second flow; wherein, i is the sequence number of the downstream branch, a is the weight coefficient, a It is a positive number used to adjust the voltage drop based on experience to make the far-end impedance more accurate.

[0083] In another embodiment, in the case of transient simulation, the circuit model may be a three-element Windkessel circuit model, wherein the elements are proximal resistance, distal resistance and capacitance, and the parameter values ​​are proximal impedance, distal impedance and capacitance.

[0084] The method of performing computational fluid dynamics simulation based on the inlet flow rate by 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 includes: Based on the inlet flow, transient computational fluid dynamics simulation is performed using the diseased artery model and the three-element Windkessel circuit model to predict a first inlet blood pressure prediction value of the diseased artery model and a first flow distribution prediction value of each downstream branch.

[0085] The parameter value of each component can be obtained through methods in practical applications, and the method of simulating transient state can also be performed through methods in practical applications, which will not be described in detail here.

[0086] Step 410: Obtain a circuit model based on the parameter values ​​of each component in the characterization circuit.

[0087] Among them, Figure 6aAs shown, a single-element Windkessel circuit model is provided, wherein the outlet refers to the outlet of the artery, that is, the inlet of the corresponding downstream branch, R i is the far-end resistance of the corresponding downstream branch.

[0088] Among them, Figure 6b As shown, a three-element Windkessel circuit model is provided, wherein the outlet refers to the outlet of the artery, that is, the inlet of the corresponding downstream branch, R c,i is the proximal resistance of the corresponding downstream branch, R p,i is the remote resistance of the corresponding downstream branch, C i is the capacitance of the corresponding downstream branch.

[0089] Furthermore, for the above-mentioned target flow distribution obtained by iterative parameter adjustment, when 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, the first flow distribution prediction value can be directly used as the target flow distribution.

[0090] When 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, 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 measured inlet blood pressure of the diseased artery is within the preset range.

[0091] That is, the inlet blood pressure guess value is iteratively adjusted, and after each parameter adjustment, the inlet blood pressure guess value, the second inlet blood pressure prediction value, the outlet blood pressure prediction value and the second flow distribution prediction value are used to obtain the parameter values ​​of the components in the characterization circuit corresponding to each downstream branch of the diseased artery, and the circuit model is obtained based on the parameter values ​​of each component in the characterization circuit.

[0092] Based on the inlet flow, 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, until 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.

[0093] When adjusting the parameters, based on the above-mentioned method of setting the estimated inlet blood pressure value, certain rules can be used to adjust the parameters. For example, based on experience, the estimated inlet blood pressure value can be adjusted by the difference between the measured inlet blood pressure of the diseased artery and the first inlet blood pressure predicted value obtained in this round of prediction, such as adding the difference to the estimated inlet blood pressure value.

[0094] Among them, the measured inlet blood pressure of the diseased artery ( P mean ) can be calculated by the following formula (III): (three) in, P sys is the systolic blood pressure in the upper arm, P dia It is the diastolic blood pressure in the upper arm.

[0095] For example, if the measured inlet blood pressure (average blood pressure) of the diseased artery is 103 mmHg, the estimated inlet blood pressure value can be set to 80 mmHg, and the predicted first inlet blood pressure predicted value is 88 mmHg, which is less than the inlet blood pressure of the diseased artery, and the difference is 15 mmHg. Therefore, the initial estimated inlet blood pressure value can be adjusted on the basis of 15 mmHg, for example, 14 mmHg is added to 80 mmHg, and the estimated inlet blood pressure value in the second round of iteration is 94 mmHg. After re-performing the corresponding operation, the predicted first inlet blood pressure predicted value is 103.14 mmHg, at which time the error between the first inlet blood pressure predicted value and the measured inlet blood pressure of the diseased artery is within the preset range.

[0096] The preset range in the above example is <1%.

[0097] Exemplarily, the error is determined by: obtaining the inlet blood pressure of the diseased artery; determining the absolute value of the difference between the inlet blood pressure of the diseased artery and the first inlet blood pressure prediction value; and obtaining the error based on the absolute value and the inlet blood pressure of the diseased artery.

[0098] In one embodiment, the error ( ) can be calculated by the following formula (IV): (Four) in, P in,1 Refers to the first entrance blood pressure prediction value.

[0099] So far, the description of how to obtain the first flow distribution prediction value of each downstream branch of the diseased artery in the embodiment of the present application is completed.

[0100] The following is an explanation of the process of performing transient computational fluid dynamics simulation based on the first flow distribution prediction value using a diseased artery model and a three-element Windkessel circuit model to obtain information on hemodynamic characteristics: First, a steady-state computational fluid dynamics simulation was performed on the diseased artery model. The inlet boundary condition was specified as the inlet hourly flow rate, and the outlet boundary condition was specified as the target flow distribution, that is, the target flow distribution ratio. The inlet and outlet blood pressures and the flow values ​​of each downstream branch were predicted ( Q i ), and then the geometric impedance of each downstream branch is calculated by the following formula 5 ( R g,1,i ): (five) in, P in,1 is the entrance blood pressure, P out,1,i is the outlet blood pressure.

[0101] Then, based on the measured inlet blood pressure of the diseased artery, the flow distribution of each downstream branch and the geometric impedance, the total impedance of the Windkessel circuit model at each outlet is determined by the following formula 6: R WK,i : (six) Here, T refers to the heart beat period.

[0102] Based on this, the systolic pressure of the Windkessel model can be calculated by the following formula 7: P sys ,i , the diastolic pressure of the Windkessel model is calculated by the following formula 8 P dia ,i : (seven) (eight) in, P ref is the reference pressure, which is an empirical value.

[0103] Finally, a global optimization algorithm is used to find the optimal parameters (proximal impedance R c,i , far-end impedance R p,i , compliance C i ) to match the systolic and diastolic pressures mentioned above. The governing equation (ordinary differential equation (ODE)) of the Windkessel model is:

[0104] The objective function used is: In an exemplary embodiment, if Figure 7 and Figure 8 As shown, an optional embodiment of a flow distribution acquisition method for hemodynamic analysis is provided. The method comprises the following steps: Step 1: A diseased aorta model is constructed through CTA images, and the diseased aorta model is repaired to obtain a healthy aorta model.

[0105] Step 2: Perform iterative steady-state computational fluid dynamics simulation.

[0106] Among them, step 2 includes: based on the repaired aorta model (healthy aorta model), predicting the inlet and outlet pressures (blood pressure) and flow distribution; calculating the distal impedance of each downstream branch; based on the diseased aorta model, predicting the inlet and outlet pressures and flow distribution; if the relative error between the average blood pressure (measured upper arm blood pressure) and the predicted value is less than 1%, executing step 3; if the relative error between the average blood pressure (measured upper arm blood pressure) and the predicted value is ≥1%, returning to calculate the distal impedance of each downstream branch.

[0107] Step 3: Determine the flow distribution of each branch downstream of the diseased aorta.

[0108] Step 4: Estimation of Windkessel model parameter values.

[0109] Step 5: Perform multi-scale simulation of the aorta model + outlet Windkessel model to obtain the aortic hemodynamic characteristics.

[0110] It should be understood that, although the steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0111] Based on the same inventive concept, the embodiment of the present application also provides a flow distribution acquisition device for hemodynamic analysis for implementing the flow distribution acquisition method for hemodynamic analysis involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the flow distribution acquisition device for hemodynamic analysis provided below can refer to the limitations of the flow distribution acquisition method for hemodynamic analysis above, and will not be repeated here.

[0112] In an exemplary embodiment, Fig. 9 As shown, a flow distribution acquisition device for hemodynamic analysis is provided, comprising: an acquisition module 902, a simulation module 904 and a determination module 906, wherein: An acquisition module 902 is used to acquire a diseased artery model, an inlet flow of the diseased artery, and a circuit model of each downstream branch; parameter values ​​of components of the circuit model are obtained by flow distribution of each downstream branch of a healthy artery; A simulation module 904 is used to perform computational fluid dynamics simulation based on the inlet flow rate 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; The determination module 906 is configured to determine the first flow distribution prediction value as the target flow distribution 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.

[0113] In one embodiment, the acquisition module 902 is specifically used for: 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.

[0114] In one embodiment, the acquisition module 902 is further used for: Repairing the diseased artery model to obtain a healthy artery model; Determine an outlet boundary condition, and based on the inlet flow, perform computational fluid dynamics simulation using a healthy artery model and the outlet boundary condition to predict a second inlet blood pressure prediction value of the healthy artery model and an outlet blood pressure prediction value of each downstream branch of the healthy artery and a second flow distribution prediction value; Setting a guessed value of the inlet blood pressure 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 parameter values ​​of components in a 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; A circuit model is obtained based on the parameter values ​​of each element in the characterizing circuit.

[0115] In one embodiment, the acquisition module 902 is further used for: 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.

[0116] In one embodiment, the circuit model is a single-element Windkessel circuit model, the element is a remote resistor, and the parameter value is a remote impedance; the simulation module 904 is specifically used for: 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 a first inlet blood pressure prediction value of the diseased artery model and a first flow distribution prediction value of each downstream branch.

[0117] In one embodiment, the acquisition module 902 is further used for: Obtaining the pressure drop of each downstream branch of the healthy artery based on the second inlet blood pressure prediction value and the outlet blood pressure prediction value; Determine each of the pressure drops as the pressure drops of each downstream branch of the diseased artery, and obtain a guessed value of the outlet blood pressure of each downstream branch of the diseased artery based on the guessed value of the inlet blood pressure and the pressure drops; Parameter values ​​of elements in the characterization circuit corresponding to each downstream branch of the diseased artery are obtained based on the outlet blood pressure guess value and the second flow distribution prediction value.

[0118] In one embodiment, the flow distribution acquisition device for hemodynamic analysis further includes: The judgment module is used to return to the step of setting the inlet blood pressure guessing value of the healthy artery model if the error between the first inlet blood pressure prediction value and the inlet blood pressure of the diseased artery actually measured is outside a preset range, until the error between the first inlet blood pressure prediction value and the inlet blood pressure of the diseased artery actually measured is within a preset range.

[0119] In one embodiment, the flow distribution acquisition device for hemodynamic analysis further includes: The acquisition module 902 is also used to acquire the inlet blood pressure of the diseased artery; The determination module 906 is further configured to determine an absolute value of a difference between the inlet blood pressure of the diseased artery and the first inlet blood pressure prediction value; A calculation module is used to obtain an error based on the absolute value and the inlet blood pressure of the diseased artery.

[0120] Each module in the above-mentioned flow distribution acquisition device for hemodynamic analysis can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0121] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Fig.10 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. 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. 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 to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a flow distribution acquisition method for hemodynamic analysis is realized. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0122] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0123] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented: Obtaining a diseased artery model, an inlet flow of the diseased artery, and a circuit model of each downstream branch; parameter values ​​of components of the circuit model are obtained by flow distribution of each downstream branch of a healthy artery; 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.

[0124] In one embodiment, when the processor executes the computer program, the following steps are also implemented: 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.

[0125] In one embodiment, when the processor executes the computer program, the following steps are also implemented: Repairing the diseased artery model to obtain a healthy artery model; Determine an outlet boundary condition, and based on the inlet flow, perform computational fluid dynamics simulation using a healthy artery model and the outlet boundary condition to predict a second inlet blood pressure prediction value of the healthy artery model and an outlet blood pressure prediction value of each downstream branch of the healthy artery and a second flow distribution prediction value; Setting a guessed value of the inlet blood pressure 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 parameter values ​​of components in a 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; A circuit model is obtained based on the parameter values ​​of each element in the characterizing circuit.

[0126] In one embodiment, when the processor executes the computer program, the following steps are also implemented: 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.

[0127] In one embodiment, when the processor executes the computer program, the following steps are also implemented: 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 a first inlet blood pressure prediction value of the diseased artery model and a first flow distribution prediction value of each downstream branch.

[0128] In one embodiment, when the processor executes the computer program, the following steps are also implemented: Obtaining the pressure drop of each downstream branch of the healthy artery based on the second inlet blood pressure prediction value and the outlet blood pressure prediction value; Determine each of the pressure drops as the pressure drops of each downstream branch of the diseased artery, and obtain a guessed value of the outlet blood pressure of each downstream branch of the diseased artery based on the guessed value of the inlet blood pressure and the pressure drops; Parameter values ​​of elements in the characterization circuit corresponding to each downstream branch of the diseased artery are obtained based on the outlet blood pressure guess value and the second flow distribution prediction value.

[0129] In one embodiment, when the processor executes the computer program, the following steps are also implemented: 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.

[0130] In one embodiment, when the processor executes the computer program, the following steps are also implemented: 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.

[0131] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Obtaining a diseased artery model, an inlet flow of the diseased artery, and a circuit model of each downstream branch; parameter values ​​of components of the circuit model are obtained by flow distribution of each downstream branch of a healthy artery; 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.

[0132] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: 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.

[0133] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: Repairing the diseased artery model to obtain a healthy artery model; Determine an outlet boundary condition, and based on the inlet flow, perform computational fluid dynamics simulation using a healthy artery model and the outlet boundary condition to predict a second inlet blood pressure prediction value of the healthy artery model and an outlet blood pressure prediction value of each downstream branch of the healthy artery and a second flow distribution prediction value; Setting a guessed value of the inlet blood pressure 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 parameter values ​​of components in a 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; A circuit model is obtained based on the parameter values ​​of each element in the characterizing circuit.

[0134] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: 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.

[0135] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: 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 a first inlet blood pressure prediction value of the diseased artery model and a first flow distribution prediction value of each downstream branch.

[0136] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: Obtaining the pressure drop of each downstream branch of the healthy artery based on the second inlet blood pressure prediction value and the outlet blood pressure prediction value; Determine each of the pressure drops as the pressure drops of each downstream branch of the diseased artery, and obtain a guessed value of the outlet blood pressure of each downstream branch of the diseased artery based on the guessed value of the inlet blood pressure and the pressure drops; Parameter values ​​of elements in the characterization circuit corresponding to each downstream branch of the diseased artery are obtained based on the outlet blood pressure guess value and the second flow distribution prediction value.

[0137] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: 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.

[0138] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: 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.

[0139] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps: Obtaining a diseased artery model, an inlet flow of the diseased artery, and a circuit model of each downstream branch; parameter values ​​of components of the circuit model are obtained by flow distribution of each downstream branch of a healthy artery; 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.

[0140] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: 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.

[0141] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: Repairing the diseased artery model to obtain a healthy artery model; Determine an outlet boundary condition, and based on the inlet flow, perform computational fluid dynamics simulation using a healthy artery model and the outlet boundary condition to predict a second inlet blood pressure prediction value of the healthy artery model and an outlet blood pressure prediction value of each downstream branch of the healthy artery and a second flow distribution prediction value; Setting a guessed value of the inlet blood pressure 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 parameter values ​​of components in a 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; A circuit model is obtained based on the parameter values ​​of each element in the characterizing circuit.

[0142] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: 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.

[0143] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: 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 a first inlet blood pressure prediction value of the diseased artery model and a first flow distribution prediction value of each downstream branch.

[0144] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: Obtaining the pressure drop of each downstream branch of the healthy artery based on the second inlet blood pressure prediction value and the outlet blood pressure prediction value; Determine each of the pressure drops as the pressure drops of each downstream branch of the diseased artery, and obtain a guessed value of the outlet blood pressure of each downstream branch of the diseased artery based on the guessed value of the inlet blood pressure and the pressure drops; Parameter values ​​of elements in the characterization circuit corresponding to each downstream branch of the diseased artery are obtained based on the outlet blood pressure guess value and the second flow distribution prediction value.

[0145] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: 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.

[0146] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: 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.

[0147] 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 used 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 must comply with relevant regulations.

[0148] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to the 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), magnetic 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0149] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this application.

[0150] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A flow distribution acquisition method for hemodynamic analysis, characterized in that: The method comprises: Obtaining a diseased artery model, an inlet flow of the diseased artery, and a circuit model of each downstream branch; parameter values ​​of components of the circuit model are obtained by flow distribution of each downstream branch of a healthy artery; 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 2, characterized in that: The obtaining of the circuit model of each downstream branch includes: Repairing the diseased artery model to obtain a healthy artery model; Determine an outlet boundary condition, and based on the inlet flow, perform computational fluid dynamics simulation using a healthy artery model and the outlet boundary condition to predict a second inlet blood pressure prediction value of the healthy artery model and an outlet blood pressure prediction value of each downstream branch of the healthy artery and a second flow distribution prediction value; Setting a guessed value of the inlet blood pressure 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 parameter values ​​of components in a 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; A circuit model is obtained based on the parameter values ​​of each element in the characterizing circuit.

4. The flow distribution acquisition method for hemodynamic analysis according to claim 3, 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.

5. The flow distribution acquisition method for hemodynamic analysis according to claim 3, characterized in that: The circuit model is a single-element Windkessel circuit model, the element is a remote resistor, and the parameter value is a remote impedance; The method of performing computational fluid dynamics simulation based on the inlet flow rate by 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 includes: 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 a first inlet blood pressure prediction value of the diseased artery model and a first flow distribution prediction value of each downstream branch.

6. The flow distribution acquisition method for hemodynamic analysis according to claim 5, characterized in that: The obtaining, 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, parameter values ​​of components in the characterization circuit corresponding to each downstream branch of the diseased artery comprises: Obtaining the pressure drop of each downstream branch of the healthy artery based on the second inlet blood pressure prediction value and the outlet blood pressure prediction value; Determine each of the pressure drops as the pressure drops of each downstream branch of the diseased artery, and obtain a guessed value of the outlet blood pressure of each downstream branch of the diseased artery based on the guessed value of the inlet blood pressure and the pressure drops; Parameter values ​​of elements in the characterization circuit corresponding to each downstream branch of the diseased artery are obtained based on the outlet blood pressure guess value and the second flow distribution prediction value.

7. The flow distribution acquisition method for hemodynamic analysis according to claim 5, 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.

8. The flow distribution acquisition method for hemodynamic analysis according to any one of claims 1 to 7, 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.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: 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 8 are implemented.

10. 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 8 are implemented.

Images