An individualized cerebral blood flow simulation method combining cerebral vascular morphology and functionality

Abstract

The application discloses a kind of individualized cerebral blood flow simulation methods of fusing brain vascular morphology and function, obtains the three-dimensional reconstruction of the multimodal medical image data of target patient, obtains cerebral vascular three-dimensional geometric model;Standard cerebral vascular field atlas is accurately mapped to individualized brain tissue model, and weighted mechanism is added, and the blood supply area of brain blood vessel is accurately partitioned;According to the geometric shape characteristics of brain blood vessel and the function information of brain tissue, set boundary condition in line with physiological characteristics;Based on cerebral vascular three-dimensional geometric model and cerebral blood supply functional area, the flow of blood flow in cerebral blood vessel is simulated by using computational fluid dynamics method, and blood flow analysis result is obtained.The application not only can reconstruct individualized brain vascular morphology boundary and brain tissue function area, but also can accurately distribute cerebral blood flow numerical impedance parameters based on standard cerebral vascular field atlas, finally realize individual physiological characteristics, more accurate and individualized cerebral blood flow simulation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cerebral blood flow simulation, and particularly relates to a personalized cerebral blood flow simulation method and system fusing cerebral vascular morphology and functional information, a terminal and a computer readable storage medium. BACKGROUND

[0002] Cerebral hemodynamics refers to the flow behavior of blood in the cerebral vascular system, which is crucial for maintaining the normal function of brain tissue. The specificity and self-regulation function of cerebral blood flow make it a challenging research topic to accurately simulate the flow process of cerebral blood flow.

[0003] Current cerebral blood flow simulation methods are only based on the geometric morphology of cerebral blood vessels, ignoring the functional information highly related to cerebral hemodynamics, such as brain tissue characteristics and blood supply areas. This neglect leads to deviations between simulation results and actual cerebral blood flow states, especially in cases of significant individual differences.

[0004] The functionality of cerebral blood flow is determined by multiple factors, including the geometric morphology of blood vessels, the functional demand of blood supply areas, and the physiological state of individuals. Traditional cerebral blood flow simulation methods mainly rely on morphological characteristics, failing to fully utilize the functional information contained in medical images. The limitations of this method are reflected in the difficulty of accurately predicting the blood flow distribution in different functional areas and the regulation mechanism of cerebral blood flow, especially when simulating the changes in cerebral blood flow under pathological conditions.

[0005] Existing cerebral blood flow simulation methods mainly rely on three-dimensional cerebral vascular geometry reconstructed from medical images. CTA or MRA is used to obtain the three-dimensional geometry of cerebral blood vessels, and then the area surrounded by the vascular geometry is used as the calculation domain. The Windkessel model is used to assign resistance parameters and other parameters to the calculation domain. This method simplifies hemodynamics into several impedance units, and determines the simulation parameters based on morphological characteristics such as vessel outlet area. However, since only the geometric characteristics of each outlet are considered, the functional information of brain tissue is not considered, resulting in limitations in simulation.

[0006] The existing technology has the following main limitations in cerebral blood flow simulation: insufficient consideration of individualized blood flow regulation mechanism, difficulty in accurately reflecting the self-regulation function of cerebral blood flow, inability to accurately capture the differentiated functional characteristics of the downstream vascular network of each truncated outlet, lack of full use of brain tissue functional information, and inability to accurately reflect the individualized blood flow regulation mechanism in the simulation results.

[0007] Therefore, the existing technology still needs to be improved and developed. SUMMARY

[0008] The main purpose of the present application is to provide a personalized cerebral blood flow simulation method, system, terminal and computer readable storage medium that combines cerebral vascular morphology and functional, aiming to solve the problem of insufficient consideration of individualized blood flow regulation mechanism in cerebral blood flow simulation in the prior art, which is difficult to accurately reflect the self-regulation function of cerebral blood flow.

[0009] To achieve the above purpose, the present application provides a personalized cerebral blood flow simulation method that combines cerebral vascular morphology and functional, which comprises the following steps:

[0010] Obtain multi-modal medical image data of a target patient, perform three-dimensional reconstruction according to the multi-modal medical image data, and obtain a cerebral vascular three-dimensional geometric model;

[0011] Accurately map a standard cerebral vascular field atlas to an individualized brain tissue model through geometric deformation, and introduce a weighted mechanism of morphological characteristics to accurately partition the cerebral vascular blood supply area;

[0012] According to the geometric morphological characteristics of the cerebral blood vessels and the functional information of the brain tissue, set boundary conditions that conform to physiological characteristics, perform first-level distribution of blood flow impedance parameters according to the size of the individualized cerebral vascular blood supply area, and perform second-level distribution in the separate cerebral vascular blood supply area according to the morphological characteristics of the distal outlet of the cerebral blood vessels.

[0013] Based on the cerebral vascular three-dimensional geometric model and the cerebral blood supply functional area, use computational fluid dynamics method to numerically simulate the flow of blood in the cerebral blood vessels, and obtain blood flow analysis results.

[0014] Optionally, the personalized cerebral blood flow simulation method that combines cerebral vascular morphology and functional, wherein the multi-modal medical image data of the target patient is obtained, the three-dimensional geometric model of the cerebral blood vessels is obtained by performing three-dimensional reconstruction according to the multi-modal medical image data, and specifically comprises:

[0015] Obtain multi-modal medical image data of a target patient through MRA technology, and the multi-modal medical image data includes cerebral vascular and cerebral functional area image data;

[0016] Segment and classify the cerebral vascular and cerebral functional area image data through medical image processing software, extract the geometric morphological characteristics of the cerebral blood vessels and the spatial distribution of the brain tissue respectively, perform three-dimensional reconstruction according to the geometric morphological characteristics of the cerebral blood vessels and the spatial distribution of the brain tissue, and obtain a cerebral vascular three-dimensional geometric model.

[0017] Optionally, the individualized brain blood flow simulation method fusing brain blood vessel morphology and function, wherein the standard brain blood vessel field atlas is accurately mapped onto the individualized brain tissue model through geometric deformation, and a weighted mechanism of morphological characteristics is introduced to accurately partition the brain blood vessel supply area, specifically comprising:

[0018] A standard brain blood vessel field atlas suitable for brain blood flow dynamics simulation is selected, which is used to provide standardized brain region blood supply distribution information;

[0019] A non-rigid registration algorithm is used to obtain a deformation field T(x) to align the standard brain blood vessel field atlas S with the individualized three-dimensional model I, and the deformed point x' is defined as:

[0020] x' = T(x) = x + d(x);

[0021] Wherein x represents a point in the standard brain blood vessel field atlas, d(x) is the corresponding displacement vector, and represents the deformation of point x from the standard brain blood vessel field atlas to the individualized brain tissue model;

[0022] The displacement of the B-spline deformation field is calculated by the control points and the spline function, and is defined as:

[0023]

[0024] Wherein P i represents the displacement of the control point, B i (x) represents the B-spline basis function of the i-th control point, and m is the number of control points;

[0025] In the registration process, a weighted mechanism of morphological characteristics is introduced in the non-rigid registration algorithm to accurately partition the brain blood vessel supply area, and the morphological weighted number E(d) for measuring the quality and rationality of the deformation field d is:

[0026] E(d) = E data (d) + λE smooth (d);

[0027] Wherein E data (d) represents the data matching item, which is used to measure the matching degree between the deformed standard brain blood vessel field atlas and the individualized brain tissue model, E smooth (d) represents the smoothing item, which is used to limit the sharp change of deformation, and the weight coefficient λ is used to balance the importance of the matching item and the smoothing item.

[0028] Optionally, the individualized cerebral blood flow simulation method fusing cerebral vascular morphology and function, wherein the boundary conditions conforming to physiological characteristics are set according to the geometric morphological characteristics of cerebral blood vessels and the functional information of brain tissue, the blood flow resistance parameters are first allocated according to the size of individualized cerebral blood vessel blood supply area, and in the individual cerebral blood vessel blood supply area, the secondary allocation is performed according to the morphological characteristics of the distal outlet of the cerebral blood vessel, and specifically comprises the following steps.

[0029] Calculate total resistance R total and total capacitance C total :

[0030]

[0031]

[0032] Wherein, R total represents the total resistance of the cerebral vascular system, which is obtained by adding the proximal resistance R p and the distal resistance R d , R total = R p + R d , P m represents the mean arterial pressure, which is calculated by the weighted average of the systolic blood pressure SBP and the diastolic blood pressure DBP, Pm=(SBP+2×DBP) / 3, Q total represents the total cerebral blood flow; V b represents the total volume of blood in the systemic arterial system, and P represents the cerebral blood pressure.

[0033] Calculate the blood flow of each blood supply area, divide the cerebral blood vessels into 7 areas D i (i=1,...,7), including left anterior cerebral artery, right anterior cerebral artery, left middle cerebral artery, right middle cerebral artery, left posterior cerebral artery, right posterior cerebral artery and other arterial areas, according to the normal population sample in the statistical data, combined with the individualized cerebral vascular tree, calculate the blood flow Q i of each area D i , the formula is as follows:

[0034] Q i = α i Q total , i=1,...,7,

[0035] Wherein, α i represents the percentage of each blood supply area D i in the total cerebral blood flow published;

[0036] Calculate the resistance and capacitance of each blood supply area, for each blood supply area D i , according to the parallel characteristic of resistance, calculate the sub resistance R iCapacitor C i :

[0037]

[0038]

[0039] Calculate the resistance and capacitance of each outlet in the D region of each blood vessel outlet. i Within, based on the area A of each cerebral blood vessel exit contained in each exit region. j Calculate the resistance R at the corresponding outlet. j and capacitor C j The formula is as follows:

[0040]

[0041]

[0042] Where n represents region D i Export volume within A j This represents the cross-sectional area of ​​the j-th exit.

[0043] Optionally, the personalized cerebral blood flow simulation method integrating cerebral vascular morphology and function, wherein the step of numerically simulating blood flow in cerebral blood vessels using computational fluid dynamics based on the three-dimensional geometric model of cerebral blood vessels and the functional areas supplying blood to the brain to obtain blood flow analysis results, specifically includes:

[0044] Solving the Navier-Stokes equations, which describe the flow behavior of fluids in blood vessels, involves discretizing the Navier-Stokes equations using the finite element method or the finite volume method. This divides the continuous blood flow domain into discrete grid cells, and numerical calculations are performed on each grid cell to obtain the velocity distribution and pressure distribution of blood in the coronary arteries and cerebral blood vessels, as well as the flow of blood in different branch vessels.

[0045] During the simulation, boundary conditions are defined based on the patient's individual physiological parameters, and the velocity and pressure distribution of blood flow are dynamically simulated to output blood flow analysis results.

[0046] Optionally, the individualized cerebral blood flow simulation method integrating cerebral vascular morphology and function employs the three-dimensional unsteady and incompressible Navier-Stokes equations as the governing equations describing coronary blood flow:

[0047]

[0048] Where u represents blood flow velocity, denotes the Cauchy stress tensor, p denotes the pressure, μ denotes the blood flow viscosity coefficient, I denotes a 3 by 3 identity matrix, t denotes time, denotes the gradient operator, Ω denotes the computational domain consisting of the three-dimensional geometry of the blood vessel and the inlet and outlet boundaries;

[0049] The blood vessel is truncated when constructing the computational domain, and artificial boundary conditions are given on the truncation surface. Under the assumption of rigid blood vessel wall, the boundary condition at ΓW on the blood vessel wall is:

[0050] u = 0 on Γ W ;

[0051] The cerebral blood flow inlet is located in the carotid artery, and the clinical carotid ultrasound provides the blood flow velocity g in the complete cardiac cycle and is applied on the inlet boundary ΓI:

[0052] u = g on Γ I ;

[0053] Two-stage boundary conditions are applied to the outlet boundary ΓO of the cerebral blood vessel. At time t, the time-varying blood pressure p k (t) of the kth cerebral blood vessel outlet is:

[0054]

[0055] where R k denotes the resistance, Q k denotes the volume flow rate through the kth outlet, C k is used to characterize the capacitance of the artery deformation, p k (0) and respectively denote the blood pressure at the kth arterial outlet at the initial time and the downstream distal pressure, denotes the downstream distal pressure of the kth arterial outlet at time t, s denotes the integral variable from 0 to t, which is used to reflect the cumulative effect in the dynamic process, τ k = R k C k .

[0056] Optionally, the individualized cerebral blood flow simulation method fusing cerebral blood vessel morphology and functionality, wherein the blood flow in the cerebral blood vessel is numerically simulated based on the cerebral blood vessel three-dimensional geometric model and the cerebral blood supply functional area using the computational fluid dynamics method to obtain blood flow analysis results, and then further comprising:

[0057] The blood flow analysis results are compared and verified with the clinical data measured by transcranial Doppler ultrasound to evaluate the simulation accuracy.

[0058] In addition, to achieve the above object, the present application also provides an individualized cerebral blood flow simulation system combining cerebral vascular morphology and function, wherein the individualized cerebral blood flow simulation system combining cerebral vascular morphology and function comprises:

[0059] a three-dimensional reconstruction module, configured to acquire multi-modal medical image data of a target patient, perform three-dimensional reconstruction according to the multi-modal medical image data, and obtain a cerebral vascular three-dimensional geometric model;

[0060] a atlas mapping module, configured to accurately map a standard cerebral vascular field atlas to an individualized brain tissue model through geometric deformation, introduce a weighted mechanism of morphological characteristics, and accurately partition a cerebral vascular blood supply area;

[0061] a boundary condition setting module, configured to set a boundary condition conforming to a physiological characteristic according to geometric morphological characteristics of the cerebral vascular and functional information of the brain tissue, perform first-level distribution of blood flow impedance parameters according to a size of the individualized cerebral vascular blood supply area, and perform second-level distribution according to morphological characteristics of a distal outlet of the cerebral vascular in the individualized cerebral vascular blood supply area;

[0062] a cerebral blood flow numerical simulation module, configured to perform numerical simulation of blood flow in the cerebral vascular by using a computational fluid dynamics method based on the cerebral vascular three-dimensional geometric model and the cerebral blood supply functional area, and obtain a blood flow analysis result.

[0063] In addition, to achieve the above object, the present application also provides a terminal, wherein the terminal comprises a memory, a processor, and an individualized cerebral blood flow simulation program combining cerebral vascular morphology and function stored in the memory and capable of running on the processor, and the individualized cerebral blood flow simulation program combining cerebral vascular morphology and function implements steps of the individualized cerebral blood flow simulation method combining cerebral vascular morphology and function when executed by the processor.

[0064] In addition, to achieve the above object, the present application also provides a computer readable storage medium, wherein the computer readable storage medium stores an individualized cerebral blood flow simulation program combining cerebral vascular morphology and function, and the individualized cerebral blood flow simulation program combining cerebral vascular morphology and function implements steps of the individualized cerebral blood flow simulation method combining cerebral vascular morphology and function when executed by a processor.

[0065] In the present application, multi-modal medical image data of a target patient is acquired, three-dimensional reconstruction is performed according to the multi-modal medical image data, and a cerebral vascular three-dimensional geometric model is obtained; a standard cerebral vascular field atlas is accurately mapped onto an individualized brain tissue model through geometric deformation, and a weighting mechanism of morphological characteristics is introduced to accurately partition the cerebral vascular blood supply area; according to the geometric and morphological characteristics of the cerebral vascular and the functional information of the brain tissue, boundary conditions conforming to physiological characteristics are set, the blood flow impedance parameters are first allocated according to the size of the individualized cerebral vascular blood supply area, and in the individual cerebral vascular blood supply area, secondary allocation is performed according to the morphological characteristics of the distal outlet of the cerebral vascular; based on the cerebral vascular three-dimensional geometric model and the cerebral blood supply functional area, the flow of blood in the cerebral vascular is numerically simulated by using the computational fluid dynamics method, and blood flow analysis results are obtained. The present application not only can reconstruct the individualized morphological boundary of the cerebral vascular and the functional area of the brain tissue, but also can accurately allocate the numerical impedance parameters of the cerebral blood flow based on the standard cerebral vascular field atlas, and finally realize the cerebral blood flow simulation conforming to the individual physiological characteristics, which is more accurate and individualized. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 is a flow chart of a preferred embodiment of the individualized cerebral blood flow simulation method of the present application fusing the morphological and functional characteristics of the cerebral vascular;

[0067] Figure 2 is a schematic diagram of the reconstruction of the cerebral vascular three-dimensional geometric model and the brain tissue based on the nuclear magnetic image in the preferred embodiment of the individualized cerebral blood flow simulation method of the present application fusing the morphological and functional characteristics of the cerebral vascular;

[0068] Figure 3 is a schematic diagram of the blood supply area partitioning of the individualized brain tissue and the pre-allocation of the cerebral blood flow by the standard cerebral vascular field atlas in the preferred embodiment of the individualized cerebral blood flow simulation method of the present application fusing the morphological and functional characteristics of the cerebral vascular;

[0069] Figure 4 is a structure diagram of a preferred embodiment of the individualized cerebral blood flow simulation system of the present application;

[0070] Figure 5 is a structure diagram of a preferred embodiment of the terminal of the present application. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical scheme and advantages of the present application more clear and explicit, the present application is further described in detail below with reference to the drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0072] In order to improve the accuracy of simulation, the application combines medical images and computational fluid dynamics technology to develop a cerebral blood flow simulation method that can integrate individualized cerebral vascular geometry and brain functional area information. This technology can not only reconstruct the individualized cerebral vascular morphological boundary and brain tissue functional area, but also accurately distribute the cerebral blood flow numerical impedance parameters based on the standard cerebral vascular field atlas, and finally realize the cerebral blood flow simulation conforming to the individual physiological characteristics. That is, through medical image data and computational fluid dynamics technology, the individual cerebral blood flow is accurately simulated. This method not only considers the geometric morphological characteristics of the cerebral blood vessels, but also integrates the functional information of the brain tissue into the simulation process to improve the accuracy and clinical relevance of the simulation results.

[0073] The individualized cerebral blood flow simulation method integrating cerebral vascular morphology and functional science according to the preferred embodiment of the application, as shown in Figure 1 The individualized cerebral blood flow simulation method integrating cerebral vascular morphology and functional science includes the following steps:

[0074] Step S10, acquiring multi-modal medical image data of a target patient, and performing three-dimensional reconstruction according to the multi-modal medical image data to obtain a cerebral vascular three-dimensional geometric model.

[0075] Specifically, the multi-modal medical image data of the target patient is acquired by MRA technology (Magnetic resonance angiography, magnetic resonance angiography), and the multi-modal medical image data includes cerebral vascular and brain functional area image data. MRA can clearly show the geometric morphological information of the cerebral arterial cerebral blood vessels and obtain brain tissue volume information, which provides basic data for the integration of brain functional area and hemodynamics. The cerebral vascular and brain functional area image data are segmented and classified by medical image processing software to extract the geometric morphological characteristics of the cerebral blood vessels and the spatial distribution of the brain tissue, which helps to realize more accurate hemodynamic analysis in subsequent cerebral blood flow simulation. The cerebral vascular three-dimensional geometric model is obtained by three-dimensional reconstruction according to the geometric morphological characteristics of the cerebral blood vessels and the spatial distribution of the brain tissue, as shown in Figure 2 Figure 2 The upper part shows the reconstruction of the cerebral vascular three-dimensional geometric model based on the nuclear magnetic image, Figure 2 The lower part shows the brain tissue. In the reconstruction process, the three-dimensional geometric reconstruction of the cerebral blood vessels and the brain tissue is separated, which can be considered as finding the areas of the cerebral blood vessels and the brain tissue from the images respectively, and performing three-dimensional geometric reconstruction respectively.

[0076] ​The application utilizes multi-modal medical image data such as CTA, magnetic resonance angiography (MRA) and the like to perform three-dimensional reconstruction of individualized cerebral vessels and brain tissue. The purpose of the three-dimensional reconstruction based on the image is to obtain a three-dimensional model of the cerebral vessels and brain tissue, wherein the three-dimensional model of the cerebral vessels is a calculation region for cerebral blood flow simulation, and the three-dimensional model of the brain tissue is used to further divide the functional areas of the brain tissue and provide quantitative parameters (volumes of different brain tissue functional areas) of the boundary conditions of the cerebral blood flow simulation; different brain tissue functional areas are marked, and the brain tissue reconstruction model is optimized based on individual physiological characteristics (such as age, gender, genetic factors) to make it more consistent with the physiological characteristics of the individual.

[0077] In step S20, the standard cerebral vessel field atlas is accurately mapped to the individualized brain tissue model through geometric deformation, and a weighting mechanism of morphological characteristics is introduced to accurately partition the cerebral vessel blood supply area.

[0078] Specifically, in order to realize accurate partitioning of the cerebral vessel blood supply area, the application proposes a mapping method based on a non-rigid registration algorithm. The method accurately maps the standard cerebral vessel field atlas to the individualized brain tissue model through geometric deformation, and introduces a weighting mechanism of morphological characteristics in the process to ensure high consistency of the atlas with the anatomical and functional characteristics of the individual (i.e., to ensure that the standard atlas can adapt to the anatomical differences of different individuals), thereby realizing accurate partitioning of the cerebral vessel blood supply area.

[0079] First, a standard cerebral vessel field atlas suitable for cerebral blood flow dynamics simulation is selected. The standard cerebral vessel field atlas is used to provide standardized cerebral region blood supply distribution information, covering the blood supply areas of the main cerebral vessels, including the main distribution ranges of the anterior cerebral artery, the middle cerebral artery and the posterior cerebral artery.

[0080] Second, in order to accurately match the standard cerebral vessel field atlas with the individualized three-dimensional cerebral vessel model (i.e., the three-dimensional geometric model of the cerebral vessels), the application uses a non-rigid registration algorithm, such as a registration method based on B-spline or Thin Plate Spline (TPS). The goal of non-rigid registration is to obtain a deformation field T(x) that aligns the standard cerebral vessel field atlas S with the individualized three-dimensional model I, and the deformed point x' is defined as:

[0081] x' = T(x) = x + d(x);

[0082] wherein x represents a point in the standard cerebral vessel field atlas, d(x) is the corresponding displacement vector, and represents the deformation of the point x from the standard cerebral vessel field atlas to the individualized brain tissue model. The displacement of the B-spline deformation field is calculated through control points and spline functions, and is defined as follows:

[0083]

[0084] Among them, P i B represents the displacement of the control point. i (x) represents the B-spline basis function of the i-th control point, and m is the number of control points.

[0085] This registration technique effectively handles morphological differences between individual models and standard atlases. Through deformation adjustments, it maintains a high degree of consistency between the standard atlas and the anatomical structure of the individual brain tissue, ensuring accurate mapping of each blood supply region within the individual model. During registration, the algorithm introduces a weighted mechanism for morphological features, assigning higher weights to key locations (such as vascular bifurcation points and major feeding arteries). This weighting mechanism prioritizes the geometric features of these key locations, ensuring correct pairing of cerebral vascular branch structures even during deformation. In the non-rigid registration algorithm, a weighted mechanism for morphological features is introduced to precisely partition the cerebral vascular blood supply regions. The morphological weighting factor E(d) that measures the quality and rationality of the deformation field d is:

[0086] E(d)=E data (d)+λE smooth (d);

[0087] Among them, E data (d) represents the data matching item, used to measure the degree of matching between the deformed standard cerebrovascular domain atlas and the individualized brain tissue model, E smooth (d) represents the smoothing term, used to limit drastic changes in deformation. The weighting coefficient λ is used to balance the importance of the matching term and the smoothing term. The weighting mechanism assigns higher weights to important locations (such as vascular bifurcation points) to ensure that the geometric features of these locations remain consistent during the mapping process, conforming to the individual differences in the shape and function of cerebral blood vessels, thereby achieving accurate division of the blood supply area.

[0088] like Figure 3 As shown, Figure 3 Standard cerebrovascular field atlas ( Figure 3 The left side can be used to divide the blood supply areas of individualized brain tissue. Figure 3 (in the middle) and pre-allocate cerebral blood flow ( Figure 3 (to the right).

[0089] Through the above mapping algorithm, this invention can flexibly adapt standardized cerebrovascular domain maps to individualized anatomical structures, achieve precise blood supply area division of brain functional areas, and provide a solid foundation for further cerebral hemodynamic analysis.

[0090] Step S30, according to the geometric features of the cerebral blood vessels and the functional information of the brain tissue, set the boundary conditions conforming to the physiological characteristics, according to the size of the individualized cerebral blood supply area, the blood flow impedance parameters are first allocated, in the individual cerebral blood supply area, according to the morphological characteristics of the distal outlet of the cerebral blood vessels for secondary allocation.

[0091] Specifically, in the numerical simulation of cerebral blood flow, the accurate setting of boundary conditions is the key. For this purpose, the present application proposes a two-level vascular resistance and compliance distribution algorithm based boundary condition setting method, which not only considers the geometric features of the cerebral blood vessels, but also combines the functional information of the brain tissue, to realize more physiological characteristics of cerebral blood flow simulation. First, according to the size of the individualized cerebral blood supply area, the blood flow impedance parameters are first allocated, and then in the individual cerebral blood supply area, according to the morphological characteristics of the distal outlet of the cerebral blood vessels for secondary allocation.

[0092] The setting of boundary conditions directly affects the accuracy of the numerical simulation of cerebral blood flow. For this purpose, the present application designs a multi-level vascular resistance and compliance distribution algorithm, and combines the functional information to set the boundary conditions more in line with the physiological characteristics. The specific steps are as follows:

[0093] (1) Calculate the total resistance R total and total capacitance C total :

[0094]

[0095]

[0096] Wherein, R total represents the total resistance of the cerebral vascular system, which is obtained by adding the proximal resistance R p and the distal resistance R d , R total = R p + R d , P m represents the mean arterial pressure, which is calculated by the weighted average of the systolic blood pressure SBP and diastolic blood pressure DBP, Pm=(SBP+2×DBP) / 3, Q total represents the total cerebral blood flow; V b represents the total volume of blood in the systemic arterial system, and P represents the cerebral blood pressure.

[0097] (2) Calculate the blood flow of each blood supply area:

[0098] The cerebral blood vessels are divided into 7 regions D i(i = 1,..., 7), including the left anterior cerebral artery (Left ACA), the right anterior cerebral artery (Right ACA), the left middle cerebral artery (Left MCA), the right middle cerebral artery (Right MCA), the left posterior cerebral artery (Left PCA), the right posterior cerebral artery (Right PCA), and other arterial regions, according to the normal population sample in the statistical data, combined with the individualized cerebral vascular tree, the blood flow Q of each region D i i is calculated as follows:

[0099] Q i = a i Q total , i = 1,..., 7,

[0100] wherein a i represents the percentage of the total cerebral blood flow occupied by each blood supply region D i .

[0101] (3) Calculate the resistance and capacitance of each blood supply region (first distribution) :

[0102] For each blood supply region D i , according to the parallel characteristics of resistance, the sub-resistance R i and the sub-capacitance C i are calculated:

[0103]

[0104] (4) Calculate the resistance and capacitance of each outlet (second distribution) :

[0105] In each blood vessel outlet region D i (i.e. blood supply region), according to the area A j of each cerebral blood vessel outlet contained in each outlet region, the resistance R j and the capacitance C j of the corresponding outlet are calculated, and the formula is as follows:

[0106]

[0107] wherein n represents the number of outlets in the region D i , and A j represents the cross-sectional area of the jth outlet.

[0108] Step S40, based on the cerebral vascular three-dimensional geometric model and the cerebral blood supply functional area, the flow of blood flow in the cerebral blood vessels is numerically simulated by using the computational fluid dynamics method, and the blood flow analysis result is obtained.

[0109] ​Specifically, on the basis of the cerebral blood vessel three-dimensional model obtained in the above steps and the cerebral blood supply functional area, the flow of blood in the cerebral blood vessels is numerically simulated by using a computational fluid dynamics (CFD) method. The core of the cerebral blood flow simulation is to solve the Navier-Stokes equation for describing the flow behavior of fluid in the blood vessels (used to describe the momentum conservation and mass conservation of fluid, and usually solved by using a numerical method). In the blood flow dynamics, the Navier-Stokes equation needs to be discretized, and the commonly used methods include a finite element method (FEM) or a finite volume method (FVM). The Navier-Stokes equation is discretized, the continuous blood flow field is divided into discrete grid cells, and the numerical calculation is performed on each grid cell to obtain the flow velocity distribution, pressure distribution of blood in the coronary artery and cerebral blood vessels, and the flow of blood in different branch blood vessels. In the simulation process, the boundary conditions are defined in combination with the personalized physiological parameters of the patient, and the flow velocity and pressure distribution of the blood flow are dynamically simulated (that is, the solution of the Navier-Stokes equation includes the velocity field and pressure field in the blood vessels. This means that by solving the equation, the flow velocity distribution, pressure distribution of blood in the complex blood vessel geometry such as the coronary artery and cerebral blood vessels, and the flow of blood in different branch blood vessels can be obtained. In the simulation process, the boundary conditions are defined in combination with the personalized physiological parameters (such as blood pressure, heart rate, etc.) of the patient, and the flow velocity, pressure distribution, etc. of the blood flow are dynamically simulated), and the blood flow analysis result is output.

[0110] In this process, the present application adopts a three-dimensional incompressible Navier-Stokes equation to describe the flow of blood, and the two-level boundary conditions above. The specific process is as follows:

[0111] The present application adopts a three-dimensional unsteady incompressible Navier-Stokes equation as a control equation for describing the flow of blood in the coronary artery:

[0112]

[0113] Wherein, u represents the blood flow velocity, represents the Cauchy stress tensor, p represents the pressure, μ represents the blood flow viscosity coefficient, I represents a 3 by 3 unit matrix, and t represents time, represents the gradient operator, and Ω represents a calculation domain constituted by the three-dimensional geometry of the blood vessels and the inlet and outlet boundaries.

[0114] In terms of boundary conditions, due to the complexity of the cerebral blood vessels, the entire coronary artery vascular network cannot be simulated under the existing imaging technology and calculation conditions, so the blood vessels need to be truncated when constructing the calculation domain, and artificial boundary conditions are given on the truncation surface. Under the assumption of a rigid blood vessel wall, the boundary condition of ГW at the blood vessel wall is:

[0115] u = 0 on Γ W ;

[0116] The cerebral blood flow inlet is located in the carotid artery, and the clinical carotid ultrasound provides the blood flow velocity g in the complete cardiac cycle and is applied to the inlet boundary ΓI:

[0117] u = g on Γ I ;

[0118] Two-stage boundary conditions are applied to the cerebral blood vessel outlet boundary ΓO. At time t, the time-varying blood pressure p k (t) of the kth cerebral blood vessel outlet:

[0119]

[0120] where R k represents resistance, Q k represents the volumetric flow rate through the kth outlet, C k represents the capacitance for characterizing arterial deformation, p k (0) and represent the blood pressure at the kth arterial outlet and the downstream distal pressure at the initial time, respectively, represents the downstream distal pressure of the kth arterial outlet at time t, s represents the integral variable from 0 to t, and is used to reflect the cumulative effect in the dynamic process, i.e., how the previous blood flow affects the blood pressure at the current time, τ k = R k C k .

[0121] The present invention can simulate individualized cerebral blood flow dynamics, especially in different functional states of brain regions. The simulation results of the present invention will be compared and verified with clinical data measured by transcranial Doppler ultrasound to evaluate the accuracy improvement compared to traditional methods based only on cerebral vascular morphology.

[0122] Finally, in the present invention, parallel computing algorithms are used to improve the efficiency of cerebral blood flow numerical calculation. Specifically, the area to be calculated is divided into multiple small blocks, just like breaking a complex large task into many smaller tasks. Then, these tasks are assigned to multiple computers or multi-core processors for simultaneous processing. This method not only speeds up the calculation, but also ensures that accurate blood flow analysis results can be obtained in a short time. In order to further optimize the calculation process, advanced algorithms and techniques such as Newton-Krylov-Schwarz algorithm and RAS preconditioning technology are used to solve the cerebral blood flow numerical calculation problem more quickly and efficiently.

[0123] The key points of the present invention are as follows:

[0124] (1) Individualized cerebral hemodynamics simulation method: This method is based on individualized cerebral vascular morphology and functional data, combined with medical imaging, computational fluid dynamics (CFD) and brain vascular field atlas, to realize individualized cerebral blood flow numerical simulation.

[0125] (2) Brain tissue partitioning and cerebral vascular blood supply area division based on functional information: Through medical imaging data reconstruction of individualized brain tissue, combined with brain vascular field atlas, accurate division of cerebral vascular blood supply area is completed, and cerebral blood flow impedance parameter distribution is carried out according to these functional areas.

[0126] (3) Cerebral blood flow boundary condition setting of multi-level parameter distribution mechanism: Through primary and secondary parameter distribution mechanism, according to the geometric characteristics of brain tissue and vascular outlet, blood flow demand, individualized cerebral blood flow simulation outlet parameter setting is realized, to ensure the accuracy and individualized characteristics of numerical simulation.

[0127] The main advantage of the technical scheme of the present application is that it comprehensively considers the geometric shape of the cerebral blood vessels and the functional information of the brain tissue, and realizes more personalized and accurate cerebral blood flow simulation. Through the improved atlas mapping algorithm and boundary condition setting method, the present application can better reflect the physiological characteristics of individuals, especially when simulating the changes of cerebral blood flow under complex pathological conditions, it has obvious advantages. In addition, this method also has strong expansibility and can be applied to cerebral blood flow simulation under different ages, genders and pathological conditions, providing important reference for individualized brain disease diagnosis and treatment.

[0128] The present application has been verified to be feasible through preliminary experiments and numerical simulation, and the experimental results show that the simulation method is superior to the traditional cerebral blood flow simulation method in terms of accuracy and individualized characteristics. Under the premise of using individualized cerebral vascular geometric shape and functional area information, the cerebral blood flow simulation results generated by the method are compared with those of the traditional method. Experiments show that the new simulation method has higher accuracy in blood flow distribution, pressure impedance and blood supply area division. The individualized cerebral vascular blood supply area division registered with the standard brain vascular field atlas is more in line with the physiological characteristics of individuals, and compared with the standard fluid dynamics model, it can better reflect the individualized blood flow regulation mechanism. The simulation results of the method are compared and analyzed with part of the clinical transcranial Doppler ultrasound data, and the results show that the model is consistent with the actual clinical observation in predicting blood flow trend and blood supply area. Especially in the pathological state of arterial stenosis, this method can more accurately simulate the changes of blood flow, proving its applicability in complex pathological conditions.

[0129] Further, as Figure 4As shown, based on the above-mentioned personalized cerebral blood flow simulation method integrating cerebral vascular morphology and function, the present invention also provides a personalized cerebral blood flow simulation system integrating cerebral vascular morphology and function, wherein the personalized cerebral blood flow simulation system integrating cerebral vascular morphology and function includes:

[0130] The three-dimensional reconstruction module 51 is used to acquire multimodal medical image data of the target patient, and perform three-dimensional reconstruction based on the multimodal medical image data to obtain a three-dimensional geometric model of cerebral blood vessels.

[0131] The atlas mapping module 52 is used to accurately map the standard cerebral vascular domain atlas onto an individualized brain tissue model through geometric deformation, and to introduce a weighting mechanism of morphological features to accurately partition the cerebral vascular blood supply area.

[0132] The boundary condition setting module 53 is used to set boundary conditions that conform to physiological characteristics based on the geometric morphological features of cerebral blood vessels and the functional information of brain tissue. It performs primary allocation of blood flow impedance parameters based on the size of the individualized cerebral blood vessel supply area and secondary allocation based on the morphological features of the distal outlet of the cerebral blood vessel in a separate cerebral blood vessel supply area.

[0133] The cerebral blood flow numerical simulation module 54 is used to perform numerical simulation of blood flow in cerebral blood vessels based on the three-dimensional geometric model of cerebral blood vessels and the blood supply functional area of ​​the brain, and to obtain blood flow analysis results.

[0134] Furthermore, such as Figure 5 As shown, based on the above-mentioned personalized cerebral blood flow simulation method and system that integrates cerebral vascular morphology and function, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Figure 5 Only some of the terminal components are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0135] The memory 20 can be an internal storage unit of the terminal in some embodiments, such as a hard disk or a memory of the terminal. The memory 20 can also be an external storage device of the terminal in other embodiments, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal. Further, the memory 20 can include both the internal storage unit and the external storage device of the terminal. The memory 20 is used to store application software and various data installed on the terminal, such as program codes of the terminal, etc. The memory 20 can also be used to temporarily store data that has been output or will be output. In an embodiment, the memory 20 stores a personalized cerebral blood flow simulation program integrating cerebral vascular morphology and function 40, which can be executed by the processor 10 to implement the personalized cerebral blood flow simulation method integrating cerebral vascular morphology and function in the present application.

[0136] The processor 10 can be a central processing unit (CPU), a microprocessor or other data processing chip in some embodiments, which is used to run program codes or process data stored in the memory 20, such as to execute the personalized cerebral blood flow simulation method integrating cerebral vascular morphology and function, etc.

[0137] The display 30 can be an LED display, a liquid crystal display, a touch liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, etc. in some embodiments. The display 30 is used to display information of the terminal and to display a visualized user interface. The components 10-30 of the terminal communicate with each other through a system bus.

[0138] In an embodiment, the steps of the personalized cerebral blood flow simulation method integrating cerebral vascular morphology and function as described above are implemented when the processor 10 executes the personalized cerebral blood flow simulation program integrating cerebral vascular morphology and function 40 in the memory 20.

[0139] The present application also provides a computer readable storage medium, wherein the computer readable storage medium stores a personalized cerebral blood flow simulation program integrating cerebral vascular morphology and function, which, when executed by a processor, implements the steps of the personalized cerebral blood flow simulation method integrating cerebral vascular morphology and function as described above.

[0140] In summary, the present application provides a personalized cerebral blood flow simulation method and system fusing cerebral vascular morphology and function, a terminal and a storage medium, the method comprising: acquiring multi-modal medical image data of a target patient, performing three-dimensional reconstruction according to the multi-modal medical image data to obtain a cerebral vascular three-dimensional geometric model; accurately mapping a standard cerebral vascular field atlas to an individualized brain tissue model through geometric deformation, introducing a weighted mechanism of morphological characteristics, and accurately partitioning a cerebral vascular blood supply area; setting boundary conditions conforming to physiological characteristics according to the geometric morphological characteristics of the cerebral vascular and the functional information of the brain tissue, performing first-level distribution of blood flow impedance parameters according to the size of the individualized cerebral vascular blood supply area, and performing second-level distribution according to the morphological characteristics of the distal outlet of the cerebral vascular in the individual cerebral vascular blood supply area; and performing numerical simulation of blood flow in the cerebral vascular by using a computational fluid dynamics method based on the cerebral vascular three-dimensional geometric model and the cerebral blood supply functional area to obtain blood flow analysis results. The present application not only can reconstruct the individualized cerebral vascular morphological boundary and brain tissue functional area, but also can accurately distribute the cerebral blood flow numerical impedance parameters based on the standard cerebral vascular field atlas, and finally realize the cerebral blood flow simulation conforming to the individual physiological characteristics, which is more accurate and personalized.

[0141] It should be noted that in this document, the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusions, such that processes, methods, articles, or terminals including a series of elements not only include those elements, but also include other elements not explicitly listed, or further include elements inherent to such processes, methods, articles, or terminals. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article, or terminal including the element.

[0142] Of course, those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware (such as a processor, a controller, etc.) to complete, and the program can be stored in a computer-readable computer-readable storage medium, and the program can include the processes of the above-mentioned method embodiments when executed. The computer-readable storage medium can be a memory, a magnetic disc, an optical disc, etc.

[0143] It should be understood that the application is not limited to the above examples, and those skilled in the art can make improvements or changes according to the above description, and all these improvements and changes should belong to the protection scope of the claims of the present application.

Claims

1. A personalized cerebral blood flow simulation method fusing cerebral vascular morphology and functional, characterized in that, The individualized cerebral blood flow simulation method fusing cerebral vascular morphology and functionality comprises the following steps: Obtaining multi-modal medical image data of a target patient, and performing three-dimensional reconstruction according to the multi-modal medical image data to obtain a cerebral vascular three-dimensional geometric model; Mapping a standard cerebral vascular field atlas to an individualized brain tissue model accurately through geometric deformation, and introducing a weighting mechanism of morphological characteristics to accurately partition a cerebral vascular blood supply area; Setting boundary conditions conforming to physiological characteristics according to geometric morphological characteristics of the cerebral vascular and functional information of the brain tissue, and performing first-level distribution of blood flow impedance parameters according to the size of the individualized cerebral vascular blood supply area, and performing second-level distribution according to morphological characteristics of a distal outlet of the cerebral vascular in a separate cerebral vascular blood supply area; Based on the cerebral vascular three-dimensional geometric model and the cerebral blood supply functional area, performing numerical simulation of blood flow in the cerebral vascular by using a computational fluid dynamics method to obtain blood flow analysis results.

2. The individualized cerebral blood flow simulation method fusing brain blood vessel morphology and functional in claim 1, wherein, The method for obtaining multi-modal medical image data of a target patient, and performing three-dimensional reconstruction according to the multi-modal medical image data to obtain a cerebral vascular three-dimensional geometric model comprises the following steps: Obtaining multi-modal medical image data of a target patient by using an MRA technology, wherein the multi-modal medical image data comprises cerebral vascular and cerebral functional area image data; Segmenting and classifying the cerebral vascular and cerebral functional area image data by using a medical image processing software, extracting geometric morphological characteristics of the cerebral vascular and spatial distribution of the brain tissue, and performing three-dimensional reconstruction according to the geometric morphological characteristics of the cerebral vascular and the spatial distribution of the brain tissue to obtain the cerebral vascular three-dimensional geometric model.

3. The individualized cerebral blood flow simulation method fusing brain blood vessel morphology and functional in claim 1, wherein, The method for mapping a standard cerebral vascular field atlas to an individualized brain tissue model accurately through geometric deformation, and introducing a weighting mechanism of morphological characteristics to accurately partition a cerebral vascular blood supply area comprises the following steps: Selecting a standard cerebral vascular field atlas suitable for cerebral blood flow dynamics simulation, wherein the standard cerebral vascular field atlas is used to provide standardized cerebral area blood supply distribution information; A non-rigid registration algorithm is used to obtain a deformation field such that the standard brain atlas in the field of cerebrovascular disease is aligned with the individualized three-dimensional model to define the deformed points is: ； wherein, represents a point in the standard brain vascular field map, is the corresponding displacement vector, representing the point deformation from the standard brain vascular field map to the individualized brain tissue model; Displacement of the B-spline deformation field is calculated by using control points and a spline function, and is defined as follows: ； wherein, denotes the displacement of the control points, denotes the B-spline basis function of the i-th control point, m is the number of control points; In the registration process, a morphological feature weighting mechanism is introduced in the non-rigid registration algorithm to accurately partition the brain blood supply area and measure the quality and rationality of the morphological weighted number of deformation field : ​ ； wherein, represents a data matching term, used to measure the matching degree between the deformed standard brain vessel field atlas and the individualized brain tissue model, represents a smoothing term, used to limit the sharp change of the deformation, and the weight coefficient λ is used to balance the importance of the matching term and the smoothing term.

4. The individualized cerebral blood flow simulation method fusing brain blood vessel morphology and functional in claim 1, wherein, The method for setting boundary conditions conforming to physiological characteristics according to geometric morphological characteristics of the cerebral vascular and functional information of the brain tissue, and performing first-level distribution of blood flow impedance parameters according to the size of the individualized cerebral vascular blood supply area, and performing second-level distribution according to morphological characteristics of a distal outlet of the cerebral vascular in a separate cerebral vascular blood supply area comprises the following steps: Total resistance is calculated R total And total capacitance C total : wherein R total Rtotai represents the total resistance of the cerebrovascular system, which is the sum of the proximal resistance R p Rprox and the distal resistance R d Rdistai R total = R p + R d , P m MAP represents the mean arterial pressure, which is calculated as a weighted average of the systolic blood pressure SBP and the diastolic blood pressure DBP, , Q total CBFtotai represents the total cerebral blood flow; V b Vtotai represents the total volume of blood in the systemic arterial system, P CBP represents the cerebral blood pressure; The blood flow of each blood supply area is calculated, and the cerebral blood vessels are divided into 7 areas D i , i = 1,..., 7, including left anterior cerebral artery, right anterior cerebral artery, left middle cerebral artery, right middle cerebral artery, left posterior cerebral artery, right posterior cerebral artery and other arterial regions, according to the normal population sample in the statistical data, combined with the individualized cerebral blood vessel tree, the blood flow Q i of each area D i is calculated, and the formula is as follows: wherein, represents each blood supply region D published i as a percentage of total cerebral blood flow; The resistance and the capacitance of each blood supply area are calculated, for each blood supply area D i , according to the parallel characteristic of the resistance, the sub-resistance R i and the sub-capacitance C i are calculated ； Calculate the resistance and capacitance of each outlet in the D region of each blood vessel outlet. i Within, based on the area A of each cerebral blood vessel exit contained in each exit region. j Calculate the resistance R at the corresponding outlet. j and capacitor C j The formula is as follows: Where n represents region D i Export volume within A j This represents the cross-sectional area of ​​the j-th exit.

5. The individualized cerebral blood flow simulation method fusing brain blood vessel morphology and functional in claim 1, wherein, The method for performing numerical simulation of blood flow in the cerebral vascular by using a computational fluid dynamics method based on the cerebral vascular three-dimensional geometric model and the cerebral blood supply functional area to obtain blood flow analysis results comprises the following steps: Solving a Navier-Stokes equation, wherein the Navier-Stokes equation is used to describe flow behavior of fluid in a blood vessel, the Navier-Stokes equation is discretized by using a finite element method or a finite volume method, a continuous blood flow field is segmented into discrete grid cells, numerical calculation is performed on each grid cell, and flow velocity distribution, pressure distribution of blood in the coronary artery and the cerebral vascular, and flow behavior of blood in different branch blood vessels are obtained. In the simulation process, the boundary conditions are defined in combination with the personalized physiological parameters of the patient, and the flow velocity and pressure distribution of the blood flow are dynamically simulated, and the blood flow analysis result is output.

6. The individualized cerebral blood flow simulation method fusing brain blood vessel morphology and functional in claim 5, wherein, Adopt three-dimensional unsteady incompressible Navier-Stokes equation as the control equation for describing the coronary blood flow: ； wherein, denotes the blood flow velocity, denotes the Cauchy stress tensor, denotes the pressure, μ denotes the blood flow viscosity coefficient, I' denotes a 3 by 3 identity matrix, t denotes time, denotes the gradient operator, denotes the computational domain constituted by the three-dimensional geometry of the blood vessel and the inlet and outlet boundaries; When constructing the computational domain, the blood vessel is truncated, and artificial boundary conditions are given on the truncation surface. Under the assumption of rigid blood vessel wall, the boundary condition at the blood vessel wall is: W Г = 0. ； The cerebral blood flow inlet is located at the carotid artery, and the clinical carotid ultrasound provides the blood flow velocity g in the complete cardiac cycle and is applied to the inlet boundary Г I Top: ； Applying two-level boundary conditions to the cerebral vessel outlet boundary Г O Above, in time t , the first k time-varying blood pressure at the cerebral vessel outlet : ； wherein R k represents the resistance, Q k represents the volume flow through the first k outlet, C k represents the capacitance for characterizing the arterial deformation, and represent the blood pressure and the downstream distal pressure at the first k arterial outlet at the initial time, respectively, represents t the downstream distal pressure at the first k arterial outlet at the time t, represents the integral variable from 0 to t t, for reflecting the cumulative effect in the dynamic process, τ k = t R k C k .

7. The individualized cerebral blood flow simulation method fusing brain blood vessel morphology and functional in claim 5, wherein, The blood flow analysis result is compared and verified with the clinical data measured by transcranial Doppler ultrasound to evaluate the simulation accuracy. The individualized cerebral blood flow simulation system combining cerebral vascular morphology and function includes:

8. An individualized cerebral blood flow simulation system fusing cerebral vascular morphology and functional, characterized in that, A three-dimensional reconstruction module is configured to acquire multi-modal medical image data of a target patient, perform three-dimensional reconstruction based on the multi-modal medical image data, and obtain a cerebral vascular three-dimensional geometric model. A graph mapping module is configured to accurately map a standard cerebral vascular field graph to an individualized brain tissue model through geometric deformation, introduce a weighted mechanism of morphological characteristics, and accurately partition a cerebral vascular blood supply area. A boundary condition setting module is configured to set boundary conditions in accordance with the geometric morphological characteristics of the cerebral blood vessels and the functional information of the brain tissue, perform first-level distribution of blood flow impedance parameters according to the size of the individualized cerebral vascular blood supply area, and perform second-level distribution according to the morphological characteristics of the distal outlet of the cerebral blood vessels in the individualized cerebral vascular blood supply area. A cerebral blood flow numerical simulation module is configured to perform numerical simulation of the blood flow in the cerebral blood vessels based on the cerebral vascular three-dimensional geometric model and the cerebral blood supply functional area by using computational fluid dynamics method, and obtain a blood flow analysis result. The terminal includes a memory, a processor, and an individualized cerebral blood flow simulation program combining cerebral vascular morphology and function stored on the memory and executable on the processor, and the individualized cerebral blood flow simulation program combining cerebral vascular morphology and function implements the steps of the individualized cerebral blood flow simulation method combining cerebral vascular morphology and function when executed by the processor.

9. A terminal, characterized by comprising: The computer-readable storage medium stores an individualized cerebral blood flow simulation program combining cerebral vascular morphology and function, and the individualized cerebral blood flow simulation program combining cerebral vascular morphology and function implements the steps of the individualized cerebral blood flow simulation method combining cerebral vascular morphology and function when executed by the processor.

10. A computer-readable storage medium, characterized in that, ​

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