Method, device and computer equipment for determining cerebral vascular blood flow parameters

Abstract

The application relates to a cerebral blood vessel blood flow parameter determination method and device, computer equipment and a storage medium. The method comprises the following steps: constructing a corresponding cerebral blood vessel geometric model according to a cerebral medical image of a target object; constructing a corresponding grid model of the cerebral blood vessel geometric model; inputting the cerebral blood vessel geometric model into a pre-trained model typifier to obtain a model type corresponding to the cerebral blood vessel geometric model; determining a corresponding calculation model and boundary conditions according to the grid model and the model type; and determining the blood flow parameter of the cerebral blood vessel of the target object through the calculation model and the boundary conditions. The method can provide comprehensive hemodynamic evaluation data.

Description

TECHNICAL FIELD

[0001] The present application relates to the medical technical field, and in particular to a brain blood vessel blood flow parameter determination method and device, computer equipment, storage medium and computer program product. BACKGROUND

[0002] The lesions of intracranial blood vessels include aneurysm and arterial stenosis, which have a significant impact on the life and health of patients. The hemodynamic parameters of intracranial blood vessels are important functional indicators for analyzing intracranial blood vessel lesions, and therefore, how to obtain the hemodynamic parameters becomes a major research focus.

[0003] However, it is difficult to model the intracranial blood vessels, which have complex structures and numerous pathways, and it is extremely difficult to simulate the hemodynamics thereof. At present, the analysis methods for intracranial blood vessel lesions mainly include structural assessment, functional assessment, and grading assessment schemes. However, these traditional technical means are greatly affected by image quality, and are often limited to local lesion areas, and cannot provide comprehensive and accurate assessment. SUMMARY

[0004] Therefore, it is necessary to provide a brain blood vessel blood flow parameter determination method and device, computer equipment, computer readable storage medium and computer program product to solve the technical problem that the above method cannot accurately assess the intracranial blood vessels.

[0005] In a first aspect, the present application provides a brain blood vessel blood flow parameter determination method. The method comprises:

[0006] constructing a corresponding brain blood vessel geometric model based on a brain medical image of a target object;

[0007] constructing a grid model corresponding to the brain blood vessel geometric model, and inputting the brain blood vessel geometric model into a pre-trained model typifier to obtain a model typing corresponding to the brain blood vessel geometric model;

[0008] determining a corresponding calculation model and boundary conditions according to the grid model and the model typing;

[0009] determining the blood flow parameters of the brain blood vessels of the target object through the calculation model and the boundary conditions.

[0010] In one embodiment, the construction of the grid model corresponding to the brain blood vessel geometric model comprises:

[0011] performing grid division on the brain blood vessel geometric model to obtain a divided model;

[0012] determining the inlet and outlet regions and wall surface regions of the divided model;

[0013] Based on the import and export area and the wall area, the divided model is tetrahedralized to obtain a corresponding mesh model.

[0014] In one of the embodiments, the determining of the corresponding calculation model and boundary condition according to the mesh model and the model parting includes:

[0015] Determining a connected domain of the brain blood vessel geometric model according to the mesh model and the model parting, and taking the connected domain as a calculation model.

[0016] Determining a boundary parameter of the connected domain, and taking the boundary parameter as a boundary condition of the calculation model.

[0017] In one of the embodiments, the determining of the boundary parameter of the connected domain includes:

[0018] Determining an inlet parameter of a blood flow inlet, an outlet parameter of a blood flow outlet, and a blood vessel wall model and a blood model of the connected domain.

[0019] Taking the inlet parameter, the outlet parameter, and the blood vessel wall model and the blood model as the boundary parameter.

[0020] In one of the embodiments, the determining of the blood flow parameter of the brain blood vessel of the target object through the calculation model and the boundary condition includes:

[0021] Obtaining a control equation of fluid mechanics.

[0022] Determining a discrete equation set based on the calculation model, the boundary condition and the control equation.

[0023] Solving the discrete equation set through a numerical calculation method to determine the blood flow parameter of the brain blood vessel of the target object.

[0024] In one of the embodiments, after the determining of the blood flow parameter of the brain blood vessel of the target object through the calculation model and the boundary condition, the method further includes:

[0025] Drawing a cloud chart of the brain blood vessel of the target object based on the blood flow parameter.

[0026] Determining the blood flow parameter at an arbitrary position of the brain blood vessel based on the cloud chart.

[0027] In a second aspect, the application further provides a determination device of a brain blood vessel blood flow parameter. The device includes:

[0028] A first processing module is configured to construct a corresponding brain blood vessel geometric model according to a brain medical image of a target object.

[0029] a second processing module, configured to construct a mesh model corresponding to the brain blood vessel geometric model, and input the brain blood vessel geometric model into a pre-trained model typifier to obtain a model typing corresponding to the brain blood vessel geometric model;

[0030] a boundary determining module, configured to determine a calculation model and a boundary condition corresponding to the mesh model and the model typing;

[0031] a parameter determining module, configured to determine a blood flow parameter of the brain blood vessel of the target object through the calculation model and the boundary condition.

[0032] In a third aspect, the present application further provides a computer device. The computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0033] constructing a brain blood vessel geometric model corresponding to a brain medical image of a target object;

[0034] constructing a mesh model corresponding to the brain blood vessel geometric model, and inputting the brain blood vessel geometric model into a pre-trained model typifier to obtain a model typing corresponding to the brain blood vessel geometric model;

[0035] determining a calculation model and a boundary condition corresponding to the mesh model and the model typing;

[0036] determining a blood flow parameter of the brain blood vessel of the target object through the calculation model and the boundary condition.

[0037] In a fourth aspect, the present application further provides a computer readable storage medium. The computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the following steps:

[0038] constructing a brain blood vessel geometric model corresponding to a brain medical image of a target object;

[0039] constructing a mesh model corresponding to the brain blood vessel geometric model, and inputting the brain blood vessel geometric model into a pre-trained model typifier to obtain a model typing corresponding to the brain blood vessel geometric model;

[0040] determining a calculation model and a boundary condition corresponding to the mesh model and the model typing;

[0041] determining a blood flow parameter of the brain blood vessel of the target object through the calculation model and the boundary condition.

[0042] In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the following steps:

[0043] constructing a brain blood vessel geometric model corresponding to the brain medical image of the target object;

[0044] constructing a mesh model corresponding to the brain blood vessel geometric model, and inputting the brain blood vessel geometric model into a pre-trained model typifier to obtain a model typing corresponding to the brain blood vessel geometric model;

[0045] determining a calculation model and boundary conditions corresponding to the mesh model and the model typing;

[0046] determining the blood flow parameters of the brain blood vessels of the target object through the calculation model and the boundary conditions.

[0047] The brain blood vessel blood flow parameter determination method, device, computer device, storage medium and computer program product described above, by constructing a brain blood vessel geometric model corresponding to the brain medical image of the target object, and constructing a mesh model corresponding to the brain blood vessel geometric model, and inputting the brain blood vessel geometric model into a pre-trained model typifier to obtain a model typing corresponding to the brain blood vessel geometric model. According to the mesh model and the model typing, a calculation model and boundary conditions corresponding thereto are determined; and through the calculation model and the boundary conditions, the blood flow parameters of the brain blood vessels of the target object are determined. This method realizes the simulation of brain blood vessels by constructing a brain blood vessel geometric model corresponding to the brain medical image, and calculates the blood flow parameters by using the simulated geometric model, so that the calculation result can not be affected by the quality of the brain medical image, thereby ensuring the accuracy of the determined blood flow parameters. At the same time, according to the brain blood vessel geometric model, the calculation model and the boundary conditions are determined, and the blood flow parameters are further determined, which realizes the consideration of the whole brain region, thereby providing comprehensive hemodynamic evaluation data and overcoming the defect that the traditional method is limited to the local lesion area. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 An application environment diagram of the brain blood vessel blood flow parameter determination method in one embodiment;

[0049] Figure 2 A flowchart of the brain blood vessel blood flow parameter determination method in one embodiment;

[0050] Figure 3 A schematic diagram of a cerebral artery ring in one embodiment;

[0051] Figure 4 A flowchart of the brain blood vessel blood flow parameter determination method in another embodiment;

[0052] Figure 5 A structural block diagram of a device for determining a cerebral vascular blood flow parameter in an embodiment;

[0053] Figure 6 An internal structural diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0054] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not used to limit the present application.

[0055] The method for determining a cerebral vascular blood flow parameter provided by the embodiments of the present application can be applied in an application environment as shown in Figure 1 The server 102 communicates with the medical scanning device 104 through a network. A data storage system can store data required to be processed by the server 102. The data storage system can be integrated on the server 102, or placed on a cloud or other network server. In the application scenario of the present application, the medical image of the brain of a target object is collected by the medical scanning device 104, the server 102 can obtain the medical image of the brain of the target object from the medical scanning device 104 through the network, construct a corresponding cerebral vascular geometric model, construct a grid model corresponding to the cerebral vascular geometric model, and input the cerebral vascular geometric model into a pre-trained model classifier to obtain a model classification corresponding to the cerebral vascular geometric model; further, according to the grid model and the model classification, a corresponding calculation model and boundary conditions are determined; finally, the blood flow parameter of the cerebral vascular of the target object is determined through the boundary conditions.

[0056] The medical scanning device 104 can be a medical device such as a Computed Tomography (CT), a Magnetic Resonance (MR), a Digital Subtraction Angiography (DSA), etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0057] In an embodiment, as shown in Figure 2 , a method for determining a cerebral vascular blood flow parameter is provided. Taking the server 102 in Figure 1 as an example, the method includes the following steps:

[0058] In step S210, a cerebral vascular geometric model corresponding to the medical image of the brain of the target object is constructed.

[0059] The target object can be an object including a brain of a living body.

[0060] The brain medical image is an image acquired by a medical scanning device, for example, the brain medical image can be a CT image, an MR image, a DSA image, and the like.

[0061] The brain blood vessel geometric model is a model used for describing and analyzing the morphological structure and geometric characteristics of the cerebral blood vessel system.

[0062] In a specific implementation, the brain blood vessel geometric model can be constructed based on a blood vessel segmentation of a brain medical image of the target object. The blood vessel segmentation refers to a process of accurately extracting blood vessels from a medical image. The blood vessel segmentation can be implemented by using technologies such as threshold-based segmentation, edge detection, region growing, horizontal line transformation, and active contour model. The brain blood vessels can be automatically segmented by using a convolutional neural network based on deep learning. After the segmentation is completed, surface reconstruction and geometric modeling can be performed based on the segmentation result to obtain the brain blood vessel geometric model.

[0063] In step S220, a mesh model corresponding to the brain blood vessel geometric model is constructed, and the brain blood vessel geometric model is input into a pre-trained model typifier to obtain a model type corresponding to the brain blood vessel geometric model.

[0064] In a specific implementation, the mesh model corresponding to the brain blood vessel geometric model can be constructed by using a finite element method, and the model type corresponding to the brain blood vessel geometric model can be obtained by inputting the brain blood vessel geometric model into a pre-trained model typifier. More specifically, the training of the model typifier can include: obtaining a sample brain blood vessel geometric model, labeling the sample brain blood vessel geometric model, and training the model typifier based on the sample brain blood vessel geometric model and the corresponding label. For example, the sample brain blood vessel geometric model can be input into the model typifier to obtain a predicted model type as output. The predicted model type is compared with a true model type labeled in advance, and the model typifier is trained based on the difference between the two until the difference converges or is less than a threshold value, thereby obtaining a trained model typifier.

[0065] The model typifier determines the model type corresponding to the brain blood vessel geometric model by analyzing the Willis circle (cerebral arterial circle) in the brain blood vessel geometric model, and specifically determines the model type according to the opening state of the anterior communicating artery and the posterior communicating artery in the Willis circle. Figure 3The Willis circle, also known as the cerebral arterial circle, is located below the brain base and above the sella turcica, around the optic chiasm, the mamillary body and the tuberculum sellae, and is formed by the anterior communicating artery, the initial segments of the bilateral anterior cerebral arteries, the terminal segments of the bilateral internal carotid arteries, the bilateral posterior communicating arteries and the initial segments of the bilateral posterior cerebral arteries.

[0066] When the anterior communicating artery and the posterior communicating artery of the cerebral arterial circle are both open according to the cerebral vascular geometry model, the model type is determined to be the first type; when the anterior communicating artery of the cerebral arterial circle is open and the posterior communicating artery is not open according to the cerebral vascular geometry model, the model type is determined to be the second type; when the anterior communicating artery of the cerebral arterial circle is not open and the posterior communicating artery is unilaterally open according to the cerebral vascular geometry model, the model type is determined to be the third type; when the anterior communicating artery of the cerebral arterial circle is not open and the posterior communicating artery is bilaterally open according to the cerebral vascular geometry model, the model type is determined to be the fourth type; and when the anterior communicating artery and the posterior communicating artery of the cerebral arterial circle are both not open according to the cerebral vascular geometry model, the model type is determined to be the fifth type.

[0067] In this step, by pre-training a model type classifier, when determining the model type corresponding to the cerebral vascular geometry model, the cerebral vascular geometry model is input into the pre-trained model type classifier to obtain the model type corresponding to the cerebral vascular geometry model, which can improve the efficiency and accuracy of determining the model type.

[0068] In step S230, the corresponding calculation model and boundary condition are determined according to the grid model and the model type.

[0069] The calculation model represents any connected domain contained in the cerebral vascular geometry model, and the boundary condition represents the boundary parameter of the connected domain.

[0070] In a specific implementation, after obtaining the grid model and the model type corresponding to the cerebral vascular geometry model, the connected domain of the cerebral vascular geometry model can be determined according to the grid model and the model type, and the connected domain is taken as the calculation model. At the same time, the boundary parameter of the connected domain is determined, and the boundary parameter is taken as the boundary condition of the calculation model, so as to further calculate the blood flow parameter of the cerebral vascular according to the boundary condition.

[0071] In step S240, the blood flow parameter of the cerebral vascular of the target object is determined by the calculation model and the boundary condition.

[0072] In a specific implementation, the blood flow parameter of the cerebral vascular of the target object can be calculated based on the fluid mechanics algorithm, the calculation model and the boundary condition.

[0073] Blood flow parameters of cerebral vessels may include blood pressure, vessel wall shear stress, and blood flow velocity. In one embodiment, blood flow reserve can also be calculated based on blood pressure, and this reserve can also be used as a blood flow parameter. Specifically, the calculation of blood flow reserve at any point in a cerebral vessel can be determined based on the blood pressure at that point and the pressure at the blood inlet of the selected calculation model, and can be expressed by the following formula:

[0074]

[0075] in, Indicates the location of blood vessels in the brain Blood flow reserve at the site Indicates the location of blood vessels in the brain Blood pressure at the site, This represents the pressure at the blood flow inlet of the computational model corresponding to the blood vessels in the brain.

[0076] In the aforementioned method for determining cerebral vascular blood flow parameters, a corresponding cerebral vascular geometric model and a corresponding mesh model are constructed using medical images of the target brain. Simultaneously, the cerebral vascular geometric model is input into a pre-trained model classifier to obtain the corresponding model classification. Based on the mesh model and model classification, the corresponding computational model and boundary conditions are determined. Through the computational model and boundary conditions, the cerebral blood flow parameters of the target brain are determined. This method simulates cerebral blood vessels by constructing a cerebral vascular geometric model corresponding to a medical brain image. Calculating blood flow parameters using the simulated geometric model ensures that the calculation results are unaffected by the quality of the medical brain image, thus guaranteeing the accuracy of the determined blood flow parameters. Furthermore, by determining the computational model and boundary conditions based on the cerebral vascular geometric model, and further determining the blood flow parameters, the method considers the entire brain region, providing comprehensive hemodynamic assessment data and overcoming the limitations of traditional methods that are limited to local lesion areas.

[0077] In an exemplary embodiment, the step S220 above, which constructs a mesh model corresponding to the geometric model of brain blood vessels, includes: dividing the geometric model of brain blood vessels into a mesh to obtain a divided model; determining the inlet and outlet regions and wall regions of the divided model; and performing tetrahedral subdivision on the divided model based on the inlet and outlet regions and wall regions to obtain a corresponding mesh model.

[0078] In the specific implementation, the geometric model of brain blood vessels is meshed to obtain the meshed model. For example, the geometric model of brain blood vessels is triangulated using a triangulation algorithm to obtain a triangulated model, which is used as the meshed model. The inlet and outlet regions and wall regions of the meshed model are determined. Based on the inlet and outlet regions and wall regions, the meshed model is tetrahedralized to obtain the corresponding mesh model.

[0079] Before performing tetrahedral subdivision on the divided model, the divided model can be preprocessed, such as performing mesh optimization, local smoothing, and local refinement to obtain a preprocessed model. The preprocessed model is then tetrahedral subdivided to obtain the corresponding mesh model.

[0080] In this embodiment, by converting the geometric model of the brain's blood vessels into a mesh model, it is easier to perform subsequent hemodynamic simulation and analysis. It is also easier to understand the flow of blood in the brain's vascular system, such as blood flow velocity and pressure distribution, by performing numerical calculations on the mesh model.

[0081] In an exemplary embodiment, step S230 above, which determines the corresponding computational model and boundary conditions based on the mesh model and model classification, includes: determining the connected domain of the cerebral vascular geometric model based on the mesh model and model classification, and using the connected domain as the computational model; determining the boundary parameters of the connected domain, and using the boundary parameters as the boundary conditions of the computational model.

[0082] It should be noted that all cerebral vascular geometric models of any object include connected components, but the number of connected components varies. The number of connected components is related to the model classification corresponding to the cerebral vascular geometric model. Specifically, except for the first type of model classification where both anterior and posterior communicating arteries are open and there is only one connected component, the other second to fourth types of model classifications all have two or more connected components.

[0083] In specific implementation, if the model classification corresponding to the brain blood vessel geometric model determined in step S220 is the first type, the unique connected domain contained in the brain blood vessel geometric model can be determined according to the mesh model and model classification corresponding to the brain blood vessel geometric model, and then the boundary parameters of the connected domain can be determined as the boundary conditions of the calculation model.

[0084] If the determined geometric model of the brain vessels corresponds to a model type other than the first type, since there are multiple connected components corresponding to other types, one connected component can be selected as the computational model each time. After determining the boundary conditions of the computational model, another connected component is selected as the computational model again, and so on, until the boundary conditions corresponding to each connected component as the computational model are determined. Similarly, when calculating blood flow parameters subsequently, the blood flow parameters are calculated based on the boundary conditions of the computational model corresponding to each connected component, thereby obtaining the blood flow parameters of the entire brain vessels.

[0085] In this embodiment, the connected components determined will be different for different model classifications. By determining the boundary parameters for different connected components, the comprehensiveness of the blood flow parameters of the brain vessels of the target object can be guaranteed.

[0086] In an example embodiment, the determining the boundary parameters of the connected domain further comprises: determining an inlet parameter of a blood flow inlet of the connected domain, an outlet parameter of a blood flow outlet, and determining a blood vessel wall model and a blood model; and determining the inlet parameter, the outlet parameter, the blood vessel wall model and the blood model as the boundary parameters.

[0087] In a specific implementation, the determining the boundary parameters of the connected domain specifically comprises: determining an inlet parameter of a blood flow inlet of the connected domain, an outlet parameter of a blood flow outlet, and determining a blood vessel wall model and a blood model. The inlet parameter can be a velocity or a flow rate of the blood flow inlet, and the outlet parameter can be a pressure of the blood flow outlet.

[0088] More specifically, the outlet parameter of the blood flow outlet can be determined according to blood pressure data of the target object or according to a reference pressure boundary.

[0089] The blood vessel wall model can be set as a rigid no-slip wall model.

[0090] The blood model can be set as an incompressible Newtonian fluid, the density can be 1056 kg / m 3 , and the viscosity can be .

[0091] The inlet parameter of the blood flow inlet, since the blood flow inlet of the connected domain is not only one, the determined inlet parameter includes the inlet parameter of each blood flow inlet. Specifically, the total flow rate of all blood flow inlets included in the connected domain can be determined first, and the total flow rate is distributed to each blood flow inlet, thereby obtaining the inlet parameter of each blood flow inlet.

[0092] The total flow rate of all blood flow inlets is determined, including: determining the inlet type of each blood flow inlet of the connected domain; obtaining a preset reference flow rate corresponding to each inlet type; and determining the total flow rate of the blood flow inlet based on each preset reference flow rate, for example, adding each preset reference flow rate to obtain the total flow rate of the blood flow inlet. The preset reference flow rate corresponding to each inlet type includes a left internal carotid artery flow rate , a right internal carotid artery flow rate , a left vertebral artery flow rate , and a right vertebral artery flow rate . It can be understood that the total flow rate is different for different connected domains including different inlet types, for example, a certain connected domain only includes a left internal carotid artery flow rate , a right internal carotid artery flow rate , and a left vertebral artery flow rate , and the total flow rate is Q = + + If a connected domain includes all the inlet types, the total flow of the corresponding calculation model is: .

[0093] The total flow is distributed to each blood flow inlet, including: determining the area of each blood flow inlet included in the connected domain; distributing the total flow to each blood flow inlet based on the area of each blood flow inlet. Specifically, the inlet flow of each blood flow inlet can be determined according to the power law. Wherein the flow of each blood flow inlet is positively correlated with the area, which can be expressed by the relationship: , where k can be selected as an empirical value, d is a parameter associated with the area of the blood flow inlet, represents the flow of the blood flow inlet. .

[0094] In this embodiment, by determining the inlet parameters of the blood flow inlets of the connected domain, the outlet parameters of the blood flow outlets, and determining the blood vessel wall model and the blood model as boundary parameters, the blood flow parameter information of the calculation model corresponding to the connected domain is determined, and multiple dimensions of information are considered comprehensively, which can ensure the accuracy of the determined blood flow parameters.

[0095] In an exemplary embodiment, the blood flow parameters of the brain blood vessels of the target object are determined by the calculation model and the boundary conditions in step S240, including: obtaining the control equation of fluid mechanics; determining the discrete equation set based on the calculation model, the boundary conditions and the control equation; determining the blood flow parameters of the brain blood vessels of the target object by solving the discrete equation set by a numerical calculation method.

[0096] In a specific implementation, the boundary conditions of the calculation model are composed of the boundary parameters of the connected domain corresponding to the calculation model, so when determining the blood flow parameters, the control equation of fluid mechanics can be obtained, the boundary conditions are substituted into the control equation to obtain the discrete equation set, and the blood flow parameters of the brain blood vessels of the target object are obtained by solving the discrete equation set by a computational fluid algorithm.

[0097] Wherein, the control equation of fluid mechanics is as follows:

[0098]

[0099]

[0100] Wherein, u represents the blood velocity, p represents the blood density, p represents the blood pressure, μ represents the blood viscosity, and f represents the external force.

[0101] The fluid mechanics parameters obtained by solving the above control equation include blood pressure, wall shear stress of blood vessels, blood flow rate, etc. Therefore, further solving blood flow reserve according to blood pressure and pressure at the blood flow inlet of the calculation model is needed, and blood pressure, wall shear stress of blood vessels, blood flow rate and blood flow reserve are collectively taken as blood flow parameters of the brain blood vessels of the target object.

[0102] Computational Fluid Dynamics (CFD) is applied to the biomedical field to study hemodynamics, which provides a unique perspective and role for clinical diagnosis and treatment practice. It obtains the flow field distribution and pressure distribution of the target region through modeling and calculation, and then obtains or calculates the corresponding hemodynamic indicators or clinically valuable parameters. In this embodiment, the application of computational fluid technology to solve blood flow parameters can realize the calculation of non-invasive blood flow parameters.

[0103] In an exemplary embodiment, after determining the blood flow parameters of the brain blood vessels of the target object based on the calculation model and the boundary conditions in the above step S240, the method further includes: drawing a cloud chart of the brain blood vessels of the target object based on the blood flow parameters; and determining the blood flow parameters at any position of the brain blood vessels based on the cloud chart.

[0104] In a specific implementation, after obtaining the blood flow parameters at each position of the brain blood vessels of the target object, the blood flow parameters at each position can be mapped back to the calculation model, and a cloud chart of the brain blood vessels can be drawn. For example, the cloud chart of the brain blood vessels can be drawn through data visualization software or tools. This enables the blood flow parameters at any position of the brain blood vessels to be quickly determined based on the cloud chart.

[0105] In one implementation, different colors, sizes and shapes can also be used to represent different blood parameters in order to intuitively display the state of the brain blood vessels. In addition, the cloud chart of the brain blood vessels can also be updated and adjusted according to new blood flow parameters to reflect the latest state of the brain blood vessels.

[0106] In this embodiment, the cloud chart of the brain blood vessels of the target object is drawn based on the blood flow parameters, so that the blood flow parameters at each position of the brain blood vessels can be intuitively displayed through the cloud chart, enabling medical personnel to intuitively and quickly understand the state of the brain blood vessels of the target object, and facilitating more accurate assessment of the function of the brain blood vessels.

[0107] In an exemplary embodiment, as shown in Figure 4 FIG. 2 is another flowchart of a method for determining blood flow parameters of brain blood vessels, and the method includes the following steps:

[0108] Step S410, constructing a corresponding brain blood vessel geometric model according to a brain medical image of a target object;

[0109] Step S420, a grid model corresponding to the brain blood vessel geometry model is constructed, and the brain blood vessel geometry model is input into a pre-trained model classifier to obtain a model classification corresponding to the brain blood vessel geometry model;

[0110] Step S430, a connected domain of the brain blood vessel geometry model is determined according to the grid model and the model classification, and the connected domain is taken as a calculation model;

[0111] Step S440, an inlet parameter of a blood flow inlet of the connected domain, an outlet parameter of a blood flow outlet, and a blood vessel wall model and a blood model are determined;

[0112] Step S450, the inlet parameter, the outlet parameter, and the blood vessel wall model and the blood model are determined as boundary conditions of the calculation model;

[0113] Step S460, a control equation of fluid mechanics is obtained; a discrete equation set is determined based on the calculation model, the boundary conditions, and the control equation; the blood flow parameter of the brain blood vessel of the target object is determined by solving the discrete equation set through a numerical calculation method;

[0114] Step S470, a cloud chart of the brain blood vessel of the target object is drawn based on the blood flow parameter;

[0115] Step S480, the blood flow parameter at an arbitrary position of the brain blood vessel is determined based on the cloud chart.

[0116] In the embodiment, the brain blood vessel geometry model corresponding to the brain medical image is constructed to realize simulation of the whole brain blood vessel, the blood flow parameter is calculated by using the simulated geometry model, so that the calculation result can not be affected by the quality of the brain medical image, thereby the accuracy of the determined blood flow parameter is ensured, and the calculation model and the boundary conditions are determined according to the brain blood vessel geometry model, and the blood flow parameter is further determined, so that the whole brain region is considered, thereby comprehensive hemodynamic evaluation data is provided, and the defect that the traditional method is limited to a local lesion region is overcome.

[0117] It should be understood that, although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

[0118] Based on the same inventive concept, the embodiments of the present application also provide a device for determining the cerebral vascular blood flow parameter, which is used to implement the method for determining the cerebral vascular blood flow parameter as described above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more device embodiments for determining the cerebral vascular blood flow parameter provided below can refer to the limitations of the method for determining the cerebral vascular blood flow parameter in the above, which will not be described here again.

[0119] In one embodiment, as shown in Figure 5 a device for determining the cerebral vascular blood flow parameter is provided, comprising: a first processing module 510, a boundary determining module 530 and a parameter determining module 540, wherein:

[0120] The first processing module 510 is configured to construct a corresponding cerebral vascular geometric model according to the cerebral medical image of the target object.

[0121] The second processing module 520 is configured to construct a grid model corresponding to the cerebral vascular geometric model, and input the cerebral vascular geometric model into a pre-trained model typifier to obtain a model typing corresponding to the cerebral vascular geometric model.

[0122] The boundary determining module 530 is configured to determine a corresponding calculation model and boundary conditions according to the grid model and the model typing.

[0123] The parameter determining module 540 is configured to determine the blood flow parameter of the cerebral vascular of the target object through the calculation model and the boundary conditions.

[0124] In one embodiment, the second processing module 520 is further configured to perform grid division on the cerebral vascular geometric model to obtain a divided model, determine an import and export area and a wall area of the divided model, and perform tetrahedral division on the divided model based on the import and export area and the wall area to obtain the corresponding grid model.

[0125] In one embodiment, the boundary determining module 530 is further configured to determine a connected domain of the cerebral vascular geometric model according to the grid model and the model typing, take the connected domain as the calculation model, determine a boundary parameter of the connected domain, and take the boundary parameter as the boundary condition of the calculation model.

[0126] In one embodiment, the boundary determining module 530 is further configured to determine an import parameter of a blood flow import of the connected domain, an export parameter of a blood flow export, and a blood vessel wall model and a blood model, and take the import parameter, the export parameter, the blood vessel wall model and the blood model as the boundary parameter.

[0127] In one of the embodiments, the parameter determination module 540 is further configured to acquire a control equation of fluid mechanics; determine a discrete equation set based on the calculation model, the boundary condition and the control equation; and determine the blood flow parameter of the brain blood vessel of the target object by solving the discrete equation set through a numerical calculation method.

[0128] In one of the embodiments, the device further includes a cloud map drawing module configured to draw a cloud map of the brain blood vessel of the target object based on the blood flow parameter; and determine the blood flow parameter at any position of the brain blood vessel based on the cloud map.

[0129] The modules in the device for determining the brain blood vessel blood flow parameter can be implemented by software, hardware or a combination thereof. The modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the modules.

[0130] In one of the embodiments, a computer device is provided, which can be a server. The internal structure diagram of the computer device can be as shown in FIG. 8. Figure 6 The computer device includes a processor, a memory and a network interface connected through a system bus. The processor of the computer device is configured to provide calculation 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, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is configured to store data in the process of determining the brain blood vessel blood flow parameter. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement a method for determining a brain blood vessel blood flow parameter.

[0131] Those skilled in the art can understand that, Figure 6 the structure shown in FIG. 8 is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0132] In one of the embodiments, a computer device is also provided, which includes a memory and a processor. The memory stores a computer program. The processor implements the steps in the above method embodiments when executing the computer program.

[0133] In one of the embodiments, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments.

[0134] In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of any of the above method embodiments.

[0135] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0136] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiments. Any reference to memory, database or other medium used in the embodiments provided by the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric 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 but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided by the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided by the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0137] Any technical features in the above embodiments can be combined, and for the sake of brevity, not all possible combinations are described above, however, as long as the combinations of technical features do not have contradictions, they shall be considered within the scope of the present disclosure.

[0138] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it shall not be understood as a limitation on the patent scope of the present application. It shall be pointed out that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, and these shall be within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for determining cerebral vascular blood flow parameters, characterized in that, The method includes: Based on the target subject's brain medical images, construct the corresponding brain blood vessel geometric model; Construct a mesh model corresponding to the geometric model of the brain blood vessels, and input the geometric model of the brain blood vessels into a pre-trained model classifier to obtain the model class corresponding to the geometric model of the brain blood vessels; Based on the mesh model and the model classification, the connected domain of the cerebral vascular geometric model is determined, and the connected domain is used as the computational model; The inlet parameters of the blood flow inlet and the outlet parameters of the blood flow outlet of the connected domain are determined, as well as the blood vessel wall model and the blood model are determined, as the boundary conditions of the calculation model; The blood flow parameters of the brain vessels of the target object are determined by the computational model and the boundary conditions.

2. The method according to claim 1, characterized in that, The construction of the mesh model corresponding to the geometric model of the brain blood vessels includes: The geometric model of the brain blood vessels is divided into meshes to obtain the meshed model; Determine the inlet / outlet area and wall area of ​​the divided model; Based on the inlet / outlet area and the wall area, the divided model is tetrahedralized to obtain the corresponding mesh model.

3. The method according to claim 1, characterized in that, The model classification device determines the model classification corresponding to the brain vascular geometric model by analyzing the opening status of the anterior communicating artery and posterior communicating artery in the circle of arteries of the brain.

4. The method according to claim 1, characterized in that, The method further includes: Determine the total flow rate of all blood flow inlets included in the connected domain, and allocate the total flow rate to each blood flow inlet to obtain the inlet parameters of each blood flow inlet; The outlet parameters of the blood flow outlet are determined based on the blood pressure data of the target object or the reference pressure boundary.

5. The method according to claim 1, characterized in that, The process of determining the blood flow parameters of the brain vessels of the target object using the computational model and the boundary conditions includes: Obtain the governing equations of fluid dynamics; Based on the computational model, the boundary conditions, and the governing equations, a discrete set of equations is determined. The blood flow parameters of the brain vessels of the target object are determined by solving the discrete equations using numerical calculation methods.

6. The method according to any one of claims 1-5, characterized in that, After determining the blood flow parameters of the brain vessels of the target object through the computational model and the boundary conditions, the method further includes: Based on the blood flow parameters, a cloud map of the brain blood vessels of the target object is drawn; Based on the cloud map, blood flow parameters at any location of the cerebral blood vessels are determined.

7. A device for determining cerebral vascular blood flow parameters, characterized in that, The device includes: The first processing module is used to construct a corresponding geometric model of brain blood vessels based on the brain medical image of the target object; The second processing module is used to construct a mesh model corresponding to the brain vascular geometry model, and to input the brain vascular geometry model into a pre-trained model classifier to obtain the model class corresponding to the brain vascular geometry model. The boundary determination module is used to determine the connected domain of the brain blood vessel geometric model based on the mesh model and the model classification, and use the connected domain as the calculation model; determine the inlet parameters of the blood flow inlet and the outlet parameters of the blood flow outlet of the connected domain, and determine the blood vessel wall model and the blood model as the boundary conditions of the calculation model; The parameter determination module is used to determine the blood flow parameters of the brain vessels of the target object through the calculation model and the boundary conditions.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for determining cerebral vascular blood flow parameters as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for determining cerebral vascular blood flow parameters as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method for determining cerebral vascular blood flow parameters as described in any one of claims 1 to 6.

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