Hemodynamic parameter determination method and apparatus, electronic device, and storage medium

Abstract

The application discloses a kind of hemodynamic parameter determination method, device, electronic equipment and storage medium.The method comprises the following steps: obtaining three-dimensional blood vessel segment model, two-dimensional mapping is carried out to the three-dimensional blood vessel segment model, and two-dimensional blood vessel segment model is obtained;The two-dimensional blood vessel segment model is dimensionless, and the two-dimensional blood vessel segment model after dimensionless is obtained;Based on the two-dimensional blood vessel segment model after dimensionless, dimensionless hemodynamic parameter is determined;Based on preset dimensionless coefficient, the dimensionless hemodynamic parameter is converted into actual hemodynamic parameter.The above technical solution simplifies the calculation of hemodynamic parameter by dimensionless, and improves the determination speed of hemodynamic parameter.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a hemodynamic parameter determination method and device, an electronic device, and a storage medium. BACKGROUND

[0002] Vascular diseases, such as coronary artery-related diseases, cerebrovascular diseases, and aortic diseases, have gradually become a threat to human health. Based on the above situation, analysis of vascular hemodynamic parameters has become a key research direction.

[0003] In the process of implementing the present application, it is found that the existing technology at least has the following technical problems: At present, the determination method of the vascular hemodynamic parameter has the problem of slow calculation speed. SUMMARY

[0004] The present application provides a hemodynamic parameter determination method and device, an electronic device, and a storage medium to improve the determination speed of the vascular hemodynamic parameter.

[0005] According to an aspect of the present application, a hemodynamic parameter determination method is provided, comprising:

[0006] obtaining a three-dimensional vascular segment model, performing two-dimensional mapping on the three-dimensional vascular segment model to obtain a two-dimensional vascular segment model;

[0007] dimensionless processing the two-dimensional vascular segment model to obtain a dimensionless two-dimensional vascular segment model;

[0008] determining a dimensionless hemodynamic parameter based on the dimensionless two-dimensional vascular segment model;

[0009] converting the dimensionless hemodynamic parameter into an actual hemodynamic parameter based on a preset dimensionless coefficient.

[0010] According to another aspect of the present application, a hemodynamic parameter determination device is provided, comprising:

[0011] a model two-dimensional mapping module for obtaining a three-dimensional vascular segment model, performing two-dimensional mapping on the three-dimensional vascular segment model to obtain a two-dimensional vascular segment model;

[0012] a model dimensionless processing module for dimensionless processing the two-dimensional vascular segment model to obtain a dimensionless two-dimensional vascular segment model;

[0013] a dimensionless parameter determination module for determining a dimensionless hemodynamic parameter based on the dimensionless two-dimensional vascular segment model;

[0014] an actual parameter determination module for converting the dimensionless hemodynamic parameter into an actual hemodynamic parameter based on a preset dimensionless coefficient.

[0015] According to another aspect of the present application, there is provided an electronic device, comprising:

[0016] at least one processor;

[0017] and a memory connected to the at least one processor in communication;

[0018] wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the method for determining hemodynamic parameters according to any one of the embodiments of the present application.

[0019] According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for enabling a processor to implement the method for determining hemodynamic parameters according to any one of the embodiments of the present application when executed by the processor.

[0020] The technical solution of the embodiments of the present application obtains a three-dimensional blood vessel segment model, performs two-dimensional mapping on the three-dimensional blood vessel segment model to obtain a two-dimensional blood vessel segment model, then performs dimensionless processing on the two-dimensional blood vessel segment model to obtain a dimensionless two-dimensional blood vessel segment model, then determines a dimensionless hemodynamic parameter based on the dimensionless two-dimensional blood vessel segment model, and converts the dimensionless hemodynamic parameter into an actual hemodynamic parameter based on a preset dimensionless coefficient. The above technical solution simplifies the calculation of the hemodynamic parameter through dimensionless processing, and improves the determination speed of the hemodynamic parameter.

[0021] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent through the following description. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0023] Figure 1 is a flow chart of a method for determining hemodynamic parameters according to an embodiment of the present application;

[0024] Figure 2 is a structural schematic diagram of a three-dimensional blood vessel segment model provided by an embodiment of the present application. The two-dimensional blood vessel segment model refers to a two-dimensional blood vessel segment model;

[0025] Figure 3 A structural schematic diagram of a two-dimensional blood vessel segment model provided by an embodiment of the present application;

[0026] Figure 4 A flow chart of a blood flow dynamics parameter determination method provided by an embodiment two of the present application;

[0027] Figure 5 A flow chart of a blood flow dynamics parameter determination method provided by an embodiment three of the present application;

[0028] Figure 6 A flow chart of a blood flow dynamics parameter determination method provided by an embodiment four of the present application;

[0029] Figure 7 A flow chart of a blood flow dynamics parameter determination method provided by an embodiment five of the present application;

[0030] Figure 8 A flow chart of a blood flow dynamics parameter determination method provided by an embodiment six of the present application;

[0031] Figure 9 A structural schematic diagram of a blood flow dynamics parameter determination device provided by an embodiment seven of the present application;

[0032] Figure 10 A structural schematic diagram of an electronic device implementing a blood flow dynamics parameter determination method of an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to make the personnel in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below by combining the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by the personnel in the art without creative labor should belong to the protection scope of the present application.

[0034] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices. The acquisition, storage, use, processing and other data in the technical solutions of the present application comply with the relevant provisions of national laws and regulations.

[0035] Embodiment one

[0036] Figure 1 A flowchart of a hemodynamic parameter determination method provided for the first embodiment of the present application. The present embodiment can be applicable to the case of simulating and calculating hemodynamic parameters. The method can be executed by a hemodynamic parameter determination device, which can be realized in the form of hardware and / or software, and can be configured in an electronic device such as a terminal or a server. As shown in the figure, the method comprises: Figure 1

[0037] S110, obtaining a three-dimensional blood vessel segment model, performing two-dimensional mapping on the three-dimensional blood vessel segment model to obtain a two-dimensional blood vessel segment model.

[0038] In the embodiments of the present application, the three-dimensional blood vessel segment model refers to a blood vessel segment model segmented from a three-dimensional blood vessel model. For example, Figure 2 A structural schematic diagram of a three-dimensional blood vessel segment model provided for the embodiments of the present application. The two-dimensional blood vessel segment model refers to a two-dimensional blood vessel segment model. For example, Figure 3 A structural schematic diagram of a two-dimensional blood vessel segment model provided for the embodiments of the present application.

[0039] For example, a computed tomography (CT) image or a contrast image can be three-dimensionally reconstructed to obtain a three-dimensional blood vessel model, and then the blood vessels in the three-dimensional blood vessel model are segmented into a plurality of blood vessel segments, thereby obtaining a plurality of three-dimensional blood vessel segment models corresponding to the blood vessel segments. For any three-dimensional blood vessel segment model corresponding to a blood vessel segment, the three-dimensional blood vessel segment model can be two-dimensionally projected and mapped, and a two-dimensional blood vessel segment model corresponding to the blood vessel segment can be obtained.

[0040] ​S120, dimensionless the two-dimensional blood vessel segment model to obtain a dimensionless two-dimensional blood vessel segment model.

[0041] In the embodiments of the present application, dimensionless refers to converting the physical quantity with dimension in the two-dimensional blood vessel segment model into dimensionless quantity, so as to simplify the calculation of the two-dimensional blood vessel segment model for solving the hemodynamic parameters, thereby improving the speed of determining the hemodynamic parameters.

[0042] For example, the two-dimensional blood vessel segment model can be subjected to dimensionless processing such as extremization, standardization, mean value, or standard deviation, and the physical quantity with dimension in the two-dimensional blood vessel segment model can include but is not limited to the vessel segment inlet diameter, the vessel segment outlet diameter, and the vessel segment centerline length, without specific limitation here.

[0043] S130, determining dimensionless hemodynamic parameters based on the dimensionless two-dimensional blood vessel segment model.

[0044] In some embodiments, the dimensionless two-dimensional blood vessel segment model can be subjected to computational fluid dynamics simulation, thereby obtaining the dimensionless hemodynamic parameters. In some embodiments, the geometric parameters of the dimensionless two-dimensional blood vessel segment model can also be input into a pre-trained physical information neural network to predict the dimensionless hemodynamic parameters, and the geometric parameters can include but are not limited to the vessel diameter and the vessel centerline length.

[0045] S140, converting the dimensionless hemodynamic parameters into actual hemodynamic parameters based on a preset dimensionless coefficient.

[0046] In the embodiments of the present application, the dimensionless hemodynamic parameters can include dimensionless vessel segment flow and dimensionless vessel segment pressure, and the actual hemodynamic parameters refer to the hemodynamic parameters with dimension, which can include the vessel segment flow with dimension and the vessel segment pressure with dimension. The preset dimensionless coefficient can include length coefficient, density coefficient, and time coefficient, without specific limitation here.

[0047] For example, the dimensionless vessel segment flow can be 1, the dimensionless vessel segment pressure can be 1, the length coefficient can be 10, the density coefficient can be 1000, and the time coefficient can be 10, and the specific conversion formula can be:

[0048]

[0049] wherein q represents the dimensionless vessel segment flow, p represents the dimensionless vessel segment pressure, C l represents the length coefficient, C ρ represents the density coefficient, and C t represents the time coefficient, Q represents the vessel segment flow with dimension, and P represents the vessel segment pressure with dimension.

[0050] The technical scheme of the embodiment of the present application obtains a three-dimensional blood vessel segment model, performs two-dimensional mapping on the three-dimensional blood vessel segment model to obtain a two-dimensional blood vessel segment model, then performs dimensionless processing on the two-dimensional blood vessel segment model to obtain a dimensionless two-dimensional blood vessel segment model, then determines a dimensionless blood flow dynamics parameter based on the dimensionless two-dimensional blood vessel segment model, and converts the dimensionless blood flow dynamics parameter into an actual blood flow dynamics parameter based on a preset dimensionless coefficient. The above technical scheme simplifies the calculation of the blood flow dynamics parameter through dimensionless processing, and improves the determination speed of the blood flow dynamics parameter.

[0051] Embodiment Two

[0052] Figure 4 A flowchart of a blood flow dynamics parameter determination method provided by Embodiment Two of the present application, the method of the present embodiment can be combined with each optional scheme in the blood flow dynamics parameter determination method provided in the above embodiments. The blood flow dynamics parameter determination method provided by the present embodiment further optimizes the two-dimensional mapping. As shown in the figure, the method comprises the following steps. Figure 4

[0053] S210, obtaining a three-dimensional blood vessel segment model.

[0054] S220, determining a first port normal vector and a second port normal vector of the three-dimensional blood vessel segment model.

[0055] The first port normal vector can be a normal vector of a blood vessel segment inlet and is perpendicular to a blood vessel segment inlet cross section. The second port normal vector can be a normal vector of a blood vessel segment outlet and is perpendicular to a blood vessel segment outlet cross section. In some embodiments, the first port can also be an outlet, and the second port can also be an inlet, which is not limited herein.

[0056] S230, determining a normal vector of a mapping plane based on the first port normal vector and the second port normal vector.

[0057] Exemplarily, the normal vector obtained by cross multiplication of the normal vector of the blood vessel segment inlet and the normal vector of the blood vessel segment outlet can be taken as the normal vector of the mapping plane.

[0058] S240, determining a first port diameter, a second port diameter, a blood vessel center line and a blood vessel wall surface of the three-dimensional blood vessel segment model.

[0059] The first port diameter can be a blood vessel segment inlet diameter, and the second port diameter can be a blood vessel segment outlet diameter.

[0060] Exemplarily, the blood vessel segment inlet diameter, the blood vessel segment outlet diameter, the blood vessel center line and the blood vessel wall surface of the three-dimensional blood vessel segment model can be recognized through an image processing method. ​

[0061] S250, mapping the first port diameter, the second port diameter, the vessel centerline and the vessel wall surface of the three-dimensional vessel segment model onto the mapping plane to obtain a two-dimensional vessel segment model.

[0062] The geometric parameters of the two-dimensional vessel segment model can include the first port diameter mapped onto the mapping plane, the second port diameter mapped onto the mapping plane, the vessel centerline mapped onto the mapping plane, and the vessel wall surface mapped onto the mapping plane.

[0063] Exemplarily, as shown in the figure, the left edge of the two-dimensional vessel segment model represents the first port diameter mapped onto the mapping plane, the right edge of the two-dimensional vessel segment model represents the second port diameter mapped onto the mapping plane, the upper and lower edges of the two-dimensional vessel segment model represent the vessel wall surface mapped onto the mapping plane, and the symmetry axis (not shown) of the two-dimensional vessel segment model represents the vessel centerline mapped onto the mapping plane. Figure 3

[0064] S260, non-dimensionalizing the two-dimensional vessel segment model to obtain a non-dimensionalized two-dimensional vessel segment model.

[0065] S270, determining a non-dimensional blood flow dynamics parameter based on the non-dimensionalized two-dimensional vessel segment model.

[0066] S280, converting the non-dimensional blood flow dynamics parameter into an actual blood flow dynamics parameter based on a preset non-dimensional coefficient.

[0067] The technical scheme of the embodiment of the present application determines the first port normal vector and the second port normal vector of the three-dimensional vessel segment model, and then determines the normal vector of the mapping plane based on the first port normal vector and the second port normal vector, determines the first port diameter, the second port diameter, the vessel centerline and the vessel wall surface of the three-dimensional vessel segment model, and then maps the first port diameter, the second port diameter, the vessel centerline and the vessel wall surface of the three-dimensional vessel segment model onto the mapping plane to obtain a two-dimensional vessel segment model, thereby realizing automatic mapping of the three-dimensional vessel segment model to the two-dimensional vessel segment model, converting a three-dimensional calculation problem into a two-dimensional axisymmetric problem, simplifying the subsequent blood flow dynamics parameter determination step, and improving the determination speed of the blood flow dynamics parameter.

[0068] Embodiment Three

[0069] Figure 5 A flowchart of a blood flow dynamics parameter determination method provided by the third embodiment of the present application, the method of the present embodiment can be combined with the various optional schemes of the blood flow dynamics parameter determination method provided in the above embodiments. The blood flow dynamics parameter determination method provided by the present embodiment further optimizes the non-dimensionalization.

[0070] As​Figure 5 As shown in the figure, the method comprises:

[0071] S310, a three-dimensional blood vessel segment model is acquired, and the three-dimensional blood vessel segment model is two-dimensionally mapped to obtain a two-dimensional blood vessel segment model.

[0072] S320, the first port diameter, the second port diameter, the blood vessel center line and the blood vessel wall surface of the two-dimensional blood vessel segment model are standardized to obtain a two-dimensional blood vessel segment model after non-dimensionalization.

[0073] S330, a non-dimensional blood flow dynamics parameter is determined based on the two-dimensional blood vessel segment model after non-dimensionalization.

[0074] S340, the non-dimensional blood flow dynamics parameter is converted into an actual blood flow dynamics parameter based on a preset non-dimensional coefficient.

[0075] In some embodiments, the blood vessel segment inlet diameter can be equivalent to 1 or other preset numerical value, and the blood vessel segment outlet diameter, the blood vessel center line and the blood vessel wall surface can be proportionally scaled according to the equivalent value of the blood vessel segment inlet diameter, so as to complete the standardization of the two-dimensional blood vessel segment model.

[0076] The technical scheme of the embodiment of the application, by standardizing at least one of the first port diameter, the second port diameter, the blood vessel center line and the blood vessel wall surface of the two-dimensional blood vessel segment model, obtaining the two-dimensional blood vessel segment model after non-dimensionalization, simplifies the geometric parameters of the two-dimensional blood vessel segment model, and can effectively improve the determination speed of the subsequent blood flow dynamics parameter.

[0077] Embodiment four

[0078] Figure 6 A flowchart of a blood flow dynamics parameter determination method provided by the fourth embodiment of the application, the method of the present embodiment can be combined with each optional scheme in the blood flow dynamics parameter determination method provided in the above embodiments. The blood flow dynamics parameter determination method provided by the present embodiment further limits the determination of the non-dimensional blood flow dynamics parameter. As shown in the figure, the method comprises: Figure 6

[0079] S410, a three-dimensional blood vessel segment model is acquired, and the three-dimensional blood vessel segment model is two-dimensionally mapped to obtain a two-dimensional blood vessel segment model.

[0080] S420, the two-dimensional blood vessel segment model is non-dimensionalized to obtain a two-dimensional blood vessel segment model after non-dimensionalization.

[0081] S430, the two-dimensional blood vessel segment model after non-dimensionalization is matched in a model library to obtain a target two-dimensional blood vessel segment model, wherein the model library comprises a plurality of two-dimensional blood vessel segment models that have completed computational fluid dynamics simulation.​

[0082] The target two-dimensional vascular segment model is the same as or similar to the dimensionless two-dimensional vascular segment model.

[0083] For example, the dimensionless two-dimensional blood vessel segment model and / or boundary conditions can be matched in a model library to obtain the target two-dimensional blood vessel segment model, wherein the boundary conditions can be inlet flow velocity or outlet pressure, etc.

[0084] S440. Obtain the flow field information and pressure field information of the target two-dimensional blood vessel segment model. Based on the flow field information and pressure field information of the target two-dimensional blood vessel segment model, perform computational fluid dynamics simulation on the dimensionless two-dimensional blood vessel segment model to obtain dimensionless hemodynamic parameters.

[0085] For example, since the target two-dimensional blood vessel segment model is a model that has already completed computational fluid dynamics simulation, the flow field information and pressure field information of the target two-dimensional blood vessel segment model can be directly applied to the computational fluid dynamics simulation of the dimensionless two-dimensional blood vessel segment model. This can simplify the steps of computational fluid dynamics simulation of the dimensionless two-dimensional blood vessel segment model, thereby improving the calculation speed of dimensionless hemodynamic parameters.

[0086] S450. Based on a preset dimensionless coefficient, the dimensionless hemodynamic parameters are converted into actual hemodynamic parameters.

[0087] The technical solution of this invention simplifies the computational fluid dynamics simulation steps of the dimensionless two-dimensional blood vessel segment model by applying the flow field and pressure field information of the target two-dimensional blood vessel segment model to the computational fluid dynamics simulation of the dimensionless two-dimensional blood vessel segment model, thereby improving the calculation speed of dimensionless hemodynamic parameters.

[0088] Example 5

[0089] Figure 7 This is a flowchart of a method for determining hemodynamic parameters according to Embodiment 5 of the present invention. The method of this embodiment can be combined with various optional schemes in the hemodynamic parameter determination methods provided in the above embodiments. The hemodynamic parameter determination method provided in this embodiment, "determining dimensionless hemodynamic parameters," has been further defined.

[0090] like Figure 7 As shown, the method includes:

[0091] S510. Obtain a three-dimensional vascular segment model, and perform two-dimensional mapping on the three-dimensional vascular segment model to obtain a two-dimensional vascular segment model.

[0092] S520, dimensionless the two-dimensional blood vessel segment model to obtain a dimensionless two-dimensional blood vessel segment model.

[0093] S530, obtain the geometric parameters and boundary conditions of the dimensionless two-dimensional blood vessel segment model.

[0094] The geometric parameters of the dimensionless two-dimensional blood vessel segment model can include coordinate information corresponding to an inlet, an outlet, a blood vessel center line and a blood vessel wall surface. The boundary conditions can be an inlet flow rate and an outlet pressure.

[0095] S540, input the geometric parameters and boundary conditions of the dimensionless two-dimensional blood vessel segment model into a pre-trained physical information neural network to obtain a dimensionless blood flow dynamics parameter.

[0096] In the embodiment of the present application, the physical information neural network training process comprises: obtaining a plurality of sets of geometric parameters and boundary conditions of the dimensionless two-dimensional blood vessel segment model, taking the plurality of sets of geometric parameters and boundary conditions of the dimensionless two-dimensional blood vessel segment model as input data of the physical information neural network, taking the Navier-Stokes equation as a loss function to adjust the network parameters of the physical information neural network until the training stop condition is met, and obtaining the trained physical information neural network.

[0097] S550, convert the dimensionless blood flow dynamics parameter into an actual blood flow dynamics parameter based on a preset dimensionless coefficient.

[0098] The technical scheme of the embodiment of the present application inputs the geometric parameters and boundary conditions of the dimensionless two-dimensional blood vessel segment model into the pre-trained physical information neural network, compared with the prior art, the dimensionless blood flow dynamics parameter can be predicted without complex logical operation, and the determination speed of the blood flow dynamics parameter is improved.

[0099] Embodiment six

[0100] Figure 8A flowchart of a blood flow parameter determination method provided in Embodiment Six of the present application, the method of the present embodiment can be combined with the various optional schemes of the blood flow parameter determination methods provided in the above embodiments. The blood flow parameter determination method provided in the present embodiment is further optimized. Optionally, after determining the dimensionless blood flow parameter based on the dimensionless two-dimensional blood vessel segment model, the method further includes: obtaining the irregularity of the blood vessel lumen of the three-dimensional blood vessel segment model, the dimensionless blood vessel segment length of the three-dimensional blood vessel segment model, and the dimensionless blood vessel segment length of the two-dimensional blood vessel segment model; inputting the irregularity of the blood vessel lumen of the three-dimensional blood vessel segment model, the dimensionless blood vessel segment length of the three-dimensional blood vessel segment model, and the dimensionless blood vessel segment length of the two-dimensional blood vessel segment model into a blood flow parameter correction model to obtain a blood flow parameter correction coefficient; determining a corrected dimensionless blood flow parameter based on the blood flow parameter correction coefficient and the dimensionless blood flow parameter; and correspondingly, converting the dimensionless blood flow parameter into an actual blood flow parameter based on a preset dimensionless coefficient, including: converting the corrected dimensionless blood flow parameter into an actual blood flow parameter based on a preset dimensionless coefficient.

[0101] As shown in Figure 8 the method includes:

[0102] S610, obtaining a three-dimensional blood vessel segment model, performing two-dimensional mapping on the three-dimensional blood vessel segment model to obtain a two-dimensional blood vessel segment model.

[0103] S620, dimensioning the two-dimensional blood vessel segment model to obtain a dimensionless two-dimensional blood vessel segment model.

[0104] S630, determining a dimensionless blood flow parameter based on the dimensionless two-dimensional blood vessel segment model.

[0105] S640, obtaining the irregularity of the blood vessel lumen of the three-dimensional blood vessel segment model, the dimensionless blood vessel segment length of the three-dimensional blood vessel segment model, and the dimensionless blood vessel segment length of the two-dimensional blood vessel segment model.

[0106] In the present embodiment, the irregularity of the blood vessel lumen refers to the irregularity of the blood vessel lumen profile compared with a regular circle.

[0107] Specifically, the blood vessel lumen profile of the three-dimensional blood vessel segment model is sampled to obtain a plurality of blood vessel lumen profile points; the distances between the plurality of blood vessel lumen profile points and the blood vessel centerline are determined; and the irregularity of the blood vessel lumen of the three-dimensional blood vessel segment model is determined based on the distances between the plurality of blood vessel lumen profile points and the blood vessel centerline.

[0108] Exemplarily, the irregularity of the blood vessel lumen of the three-dimensional blood vessel segment model can include irregularity of the blood vessel outlet lumen and irregularity of the blood vessel inlet lumen. Specifically, 36 points are sampled on the blood vessel inlet lumen profile of the three-dimensional blood vessel segment model to obtain 36 blood vessel inlet lumen profile points, the distances between the 36 blood vessel inlet lumen profile points and the blood vessel centerline are determined, and the standard deviation of the distances between the 36 blood vessel inlet lumen profile points and the blood vessel centerline is determined as the irregularity of the blood vessel inlet lumen of the three-dimensional blood vessel segment model. Similarly, the irregularity of the blood vessel outlet lumen is calculated in the same way as the irregularity of the blood vessel inlet lumen, and details are not repeated here.

[0109] In the embodiments of the present application, the dimensionless blood vessel segment length of the three-dimensional blood vessel segment model refers to the dimensionless blood vessel segment length measured under the three-dimensional blood vessel segment model. The dimensionless blood vessel segment length of the two-dimensional blood vessel segment model refers to the dimensionless blood vessel segment length measured under the two-dimensional blood vessel segment model.

[0110] S650, inputting the irregularity of the blood vessel lumen of the three-dimensional blood vessel segment model, the dimensionless blood vessel segment length of the three-dimensional blood vessel segment model and the dimensionless blood vessel segment length of the two-dimensional blood vessel segment model into a hemodynamic parameter correction model to obtain a hemodynamic parameter correction coefficient.

[0111] In the embodiments of the present application, the training process of the hemodynamic parameter correction model includes: taking the irregularity of a plurality of blood vessel outlet lumens, the irregularity of a plurality of blood vessel inlet lumens, and the ratio of the dimensionless blood vessel segment length of the three-dimensional blood vessel segment model to the dimensionless blood vessel segment length of the two-dimensional blood vessel segment model as the input of a neural network model, and taking the hemodynamic parameter correction coefficient as the label; further, training the neural network model based on the training samples until the model training stop condition is met, to obtain the hemodynamic parameter correction model, wherein the neural network model can be a deep learning model of any network architecture, which is not specifically limited here.

[0112] Exemplarily, the calculation formula of the hemodynamic parameter correction coefficient is as follows:

[0113] c=f(std1,std2,A / B);

[0114] Wherein, c represents the hemodynamic parameter correction coefficient, f represents the hemodynamic parameter correction model, std1 represents the irregularity of the inlet blood vessel lumen, std2 represents the irregularity of the outlet blood vessel lumen, A represents the dimensionless blood vessel segment length of the three-dimensional blood vessel segment model, and B represents the dimensionless blood vessel segment length of the two-dimensional blood vessel segment model.

[0115] S660, determining a corrected dimensionless hemodynamic parameter based on the hemodynamic parameter correction coefficient and the dimensionless hemodynamic parameter.

[0116] Exemplarily, the calculation formula of the corrected dimensionless blood flow dynamics parameter is as follows:

[0117] Corrected dimensionless blood flow dynamics parameter = dimensionless blood flow dynamics parameter × c.

[0118] S670, converting the corrected dimensionless blood flow dynamics parameter into an actual blood flow dynamics parameter based on a preset dimensionless coefficient.

[0119] The technical scheme of the embodiment of the present application corrects the dimensionless blood flow dynamics parameter by the blood flow dynamics parameter correction coefficient, and effectively improves the accuracy of the dimensionless blood flow dynamics parameter.

[0120] Embodiment seven

[0121] Figure 9 A structural schematic diagram of a blood flow dynamics parameter determination device provided by the embodiment seven of the present application is shown in FIG. 7. As shown in the figure, the device comprises: Figure 9 A model two-dimensional mapping module 710 is configured to obtain a three-dimensional blood vessel segment model, perform two-dimensional mapping on the three-dimensional blood vessel segment model, and obtain a two-dimensional blood vessel segment model.

[0122] A model dimensionless module 720 is configured to perform dimensionless processing on the two-dimensional blood vessel segment model, and obtain a dimensionless two-dimensional blood vessel segment model.

[0123] A dimensionless parameter determination module 730 is configured to determine a dimensionless blood flow dynamics parameter based on the dimensionless two-dimensional blood vessel segment model.

[0124] An actual parameter determination module 740 is configured to convert the dimensionless blood flow dynamics parameter into an actual blood flow dynamics parameter based on a preset dimensionless coefficient.

[0125] The technical scheme of the embodiment of the present application obtains a three-dimensional blood vessel segment model, performs two-dimensional mapping on the three-dimensional blood vessel segment model to obtain a two-dimensional blood vessel segment model, then performs dimensionless processing on the two-dimensional blood vessel segment model to obtain a dimensionless two-dimensional blood vessel segment model, and then determines a dimensionless blood flow dynamics parameter based on the dimensionless two-dimensional blood vessel segment model, and converts the dimensionless blood flow dynamics parameter into an actual blood flow dynamics parameter based on a preset dimensionless coefficient. The above technical scheme simplifies the calculation of the blood flow dynamics parameter by dimensionless processing, and improves the determination speed of the blood flow dynamics parameter.

[0126] In some optional embodiments, the model two-dimensional mapping module 710 can be specifically configured to:

[0127]

[0128] ​determine a first port normal vector and a second port normal vector of the three-dimensional vessel segment model;

[0129] determine a normal vector of a mapping plane based on the first port normal vector and the second port normal vector;

[0130] determine a first port diameter, a second port diameter, a vessel centerline and a vessel wall surface of the three-dimensional vessel segment model;

[0131] map the first port diameter, the second port diameter, the vessel centerline and the vessel wall surface of the three-dimensional vessel segment model onto the mapping plane to obtain a two-dimensional vessel segment model.

[0132] In some optional embodiments, the model dimensionless module 720 can be specifically configured to:

[0133] standardize the first port diameter, the second port diameter, the vessel centerline and the vessel wall surface of the two-dimensional vessel segment model to obtain a dimensionless two-dimensional vessel segment model.

[0134] In some optional embodiments, the dimensionless parameter determination module 730 can be specifically configured to:

[0135] match the dimensionless two-dimensional vessel segment model in a model library to obtain a target two-dimensional vessel segment model, wherein the model library comprises a plurality of two-dimensional vessel segment models that have completed computational fluid dynamics simulation;

[0136] obtain flow field information and pressure field information of the target two-dimensional vessel segment model, and perform computational fluid dynamics simulation on the dimensionless two-dimensional vessel segment model based on the flow field information and the pressure field information of the target two-dimensional vessel segment model to obtain a dimensionless blood flow hemodynamic parameter.

[0137] In some optional embodiments, the dimensionless parameter determination module 730 can be specifically configured to:

[0138] obtain geometric parameters and boundary conditions of the dimensionless two-dimensional vessel segment model;

[0139] input the geometric parameters and the boundary conditions of the dimensionless two-dimensional vessel segment model into a pre-trained physical information neural network to obtain a dimensionless blood flow hemodynamic parameter.

[0140] In some optional embodiments, the blood flow hemodynamic parameter determination apparatus further comprises:

[0141] a blood flow hemodynamic parameter correction data acquisition unit configured to acquire irregularity of a vessel lumen of the three-dimensional vessel segment model, dimensionless vessel segment length of the three-dimensional vessel segment model, and dimensionless vessel segment length of the two-dimensional vessel segment model.

[0142] a hemodynamic parameter correction coefficient determination unit, configured to input the irregularity of the blood vessel lumen of the three-dimensional blood vessel segment model, the dimensionless blood vessel segment length of the three-dimensional blood vessel segment model, and the dimensionless blood vessel segment length of the two-dimensional blood vessel segment model into a hemodynamic parameter correction model to obtain a hemodynamic parameter correction coefficient;

[0143] a dimensionless hemodynamic parameter correction unit, configured to determine a corrected dimensionless hemodynamic parameter based on the hemodynamic parameter correction coefficient and the dimensionless hemodynamic parameter determination.

[0144] Correspondingly, the actual parameter determination module 740 is also configured to:

[0145] convert the corrected dimensionless hemodynamic parameter into an actual hemodynamic parameter based on a preset dimensionless coefficient.

[0146] In some optional embodiments, the hemodynamic parameter correction data acquisition unit can be specifically configured to:

[0147] sample the blood vessel lumen profile of the three-dimensional blood vessel segment model to obtain a plurality of blood vessel lumen profile points;

[0148] determine the distance between each of the plurality of blood vessel lumen profile points and the blood vessel centerline;

[0149] determine the irregularity of the blood vessel lumen of the three-dimensional blood vessel segment model based on the distance between each of the plurality of blood vessel lumen profile points and the blood vessel centerline.

[0150] The hemodynamic parameter determination apparatus provided in the embodiments of the present application can perform the hemodynamic parameter determination method provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of performing the method.

[0151] Embodiment Eight

[0152] Figure 10 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the applications described and / or claimed in this document.

[0153] AsFigure 10 As shown, the electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An I / O interface 15 is also connected to the bus 14.

[0154] Various components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc., an output unit 17, such as various types of displays, a loudspeaker, etc., a storage unit 18, such as a magnetic disk, an optical disk, etc., and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0155] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as a hemodynamic parameter determination method, which includes:

[0156] obtaining a three-dimensional blood vessel segment model, performing two-dimensional mapping on the three-dimensional blood vessel segment model to obtain a two-dimensional blood vessel segment model;

[0157] performing dimensionless processing on the two-dimensional blood vessel segment model to obtain a dimensionless two-dimensional blood vessel segment model;

[0158] determining a dimensionless hemodynamic parameter based on the dimensionless two-dimensional blood vessel segment model;

[0159] converting the dimensionless hemodynamic parameter into an actual hemodynamic parameter based on a preset dimensionless coefficient.

[0160] In some embodiments, the hemodynamic parameter determination method can be implemented as a computer program tangibly embodied in a computer readable storage medium, e.g., storage unit 18. In some embodiments, parts or all of the computer program can be loaded and / or installed onto electronic device 10 via, e.g., ROM 12 and / or communication unit 19. When the computer program is loaded onto RAM 13 and executed by processor 11, one or more steps of the hemodynamic parameter determination method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the hemodynamic parameter determination method by other means, e.g., with the aid of firmware.

[0161] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0162] Computer programs used to implement the processes of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed, can implement the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as part of a standalone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0163] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0164] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0165] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0166] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0167] It should be understood that the various forms of flow shown above can be used to reorder, add or delete steps. For example, each step described in the present application can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, which is not limited herein.

[0168] The above detailed description does not constitute a limitation on the scope of protection of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method of hemodynamic parameter determination, characterized by, The method comprises the following steps: obtaining a three-dimensional blood vessel segment model, performing two-dimensional mapping on the three-dimensional blood vessel segment model to obtain a two-dimensional blood vessel segment model; standardizing a first port diameter, a second port diameter, a blood vessel center line and a blood vessel wall surface of the two-dimensional blood vessel segment model to obtain a dimensionless two-dimensional blood vessel segment model; determining a dimensionless blood flow dynamics parameter based on the dimensionless two-dimensional blood vessel segment model; converting the dimensionless blood flow dynamics parameter into an actual blood flow dynamics parameter based on a preset dimensionless coefficient; the step of determining the dimensionless blood flow dynamics parameter based on the dimensionless two-dimensional blood vessel segment model comprises: matching the dimensionless two-dimensional blood vessel segment model in a model library to obtain a target two-dimensional blood vessel segment model, wherein the model library comprises a plurality of two-dimensional blood vessel segment models that have completed computational fluid dynamics simulation; obtaining flow field information and pressure field information of the target two-dimensional blood vessel segment model, and performing computational fluid dynamics simulation on the dimensionless two-dimensional blood vessel segment model based on the flow field information and the pressure field information of the target two-dimensional blood vessel segment model to obtain the dimensionless blood flow dynamics parameter.

2. The method of claim 1, wherein, the step of performing two-dimensional mapping on the three-dimensional blood vessel segment model to obtain a two-dimensional blood vessel segment model comprises: determining a first port normal vector and a second port normal vector of the three-dimensional blood vessel segment model; determining a normal vector of a mapping plane based on the first port normal vector and the second port normal vector; determining a first port diameter, a second port diameter, a blood vessel center line and a blood vessel wall surface of the three-dimensional blood vessel segment model; mapping the first port diameter, the second port diameter, the blood vessel center line and the blood vessel wall surface of the three-dimensional blood vessel segment model onto the mapping plane to obtain a two-dimensional blood vessel segment model.

3. The method of claim 1, wherein, the step of determining the dimensionless blood flow dynamics parameter based on the dimensionless two-dimensional blood vessel segment model comprises: obtaining geometric parameters and boundary conditions of the dimensionless two-dimensional blood vessel segment model; inputting the geometric parameters and the boundary conditions of the dimensionless two-dimensional blood vessel segment model into a pre-trained physical information neural network to obtain the dimensionless blood flow dynamics parameter.

4. The method according to any one of claims 1 to 3, characterized in that, after the step of determining the dimensionless blood flow dynamics parameter based on the dimensionless two-dimensional blood vessel segment model, the method further comprises: obtaining irregularity of a blood vessel lumen of the three-dimensional blood vessel segment model, dimensionless blood vessel segment length of the three-dimensional blood vessel segment model and dimensionless blood vessel segment length of the two-dimensional blood vessel segment model; inputting the irregularity of the blood vessel lumen of the three-dimensional blood vessel segment model, the dimensionless blood vessel segment length of the three-dimensional blood vessel segment model and the dimensionless blood vessel segment length of the two-dimensional blood vessel segment model into a blood flow dynamics parameter correction model to obtain a blood flow dynamics parameter correction coefficient; determining a corrected dimensionless blood flow dynamics parameter based on the blood flow dynamics parameter correction coefficient and the dimensionless blood flow dynamics parameter; correspondingly, the step of converting the dimensionless blood flow dynamics parameter into an actual blood flow dynamics parameter based on a preset dimensionless coefficient comprises: converting the corrected dimensionless blood flow dynamics parameter into an actual blood flow dynamics parameter based on a preset dimensionless coefficient.

5. The method of claim 4, wherein, The acquiring the irregularity of the blood vessel lumen of the three-dimensional blood vessel segment model comprises: sampling the blood vessel lumen profile of the three-dimensional blood vessel segment model to obtain a plurality of blood vessel lumen profile points; determining the distance between each of the plurality of blood vessel lumen profile points and the blood vessel centerline; determining the irregularity of the blood vessel lumen of the three-dimensional blood vessel segment model based on the distance between each of the plurality of blood vessel lumen profile points and the blood vessel centerline.

6. A hemodynamic parameter determination apparatus, characterized by comprise: a model two-dimensional mapping module configured to acquire a three-dimensional blood vessel segment model, perform two-dimensional mapping on the three-dimensional blood vessel segment model, and obtain a two-dimensional blood vessel segment model; a model dimensionless module configured to standardize a first port diameter, a second port diameter, a blood vessel centerline, and a blood vessel wall surface of the two-dimensional blood vessel segment model, and obtain a dimensionless two-dimensional blood vessel segment model; a dimensionless parameter determination module configured to determine a dimensionless blood flow dynamics parameter based on the dimensionless two-dimensional blood vessel segment model; an actual parameter determination module configured to convert the dimensionless blood flow dynamics parameter into an actual blood flow dynamics parameter based on a preset dimensionless coefficient; the dimensionless parameter determination module is specifically configured to: match the dimensionless two-dimensional blood vessel segment model in a model library to obtain a target two-dimensional blood vessel segment model, wherein the model library comprises a plurality of two-dimensional blood vessel segment models that have completed computational fluid dynamics simulation; acquire flow field information and pressure field information of the target two-dimensional blood vessel segment model, perform computational fluid dynamics simulation on the dimensionless two-dimensional blood vessel segment model based on the flow field information and the pressure field information of the target two-dimensional blood vessel segment model, and obtain a dimensionless blood flow dynamics parameter.

7. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the blood flow dynamics parameter determination method of any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for causing the processor to execute the blood flow dynamics parameter determination method of any one of claims 1-5 when executed.

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