Intracranial arterial hemodynamics analysis method, device and computer equipment

Abstract

The application relates to an intracranial artery hemodynamics analysis method, device and computer equipment. The method comprises the following steps: acquiring an aorta-intracranial artery angiography image and constructing a three-dimensional blood vessel model; determining a terminal outlet blood flow distribution; determining an initial pressure according to a steady-state pressure difference, a flow parameter, a diastolic pressure and a distal reference pressure; setting an inlet flow boundary condition, and performing hemodynamics analysis with a three-unit wind cavity model as an outer peripheral boundary. The above scheme can reduce the dependence on patient-specific measured flow data, and improve the accuracy and convergence efficiency of hemodynamics analysis.

Description

Technical Field

[0001] This application relates to the field of hemodynamic analysis technology, and in particular to methods, devices and computer equipment for intracranial arterial hemodynamic analysis. Background Technology

[0002] Currently, in predicting intracranial arterial hemodynamics using computational fluid dynamics, it is necessary to define the boundary conditions for each opening of intracranial vessels. The inlet boundary of intracranial vessels is often determined based on experience or clinically measured flow curves from patients. The outlet boundary of intracranial vessels is often determined using a three-unit windkessel model, i.e., a three-unit Windkessel model that simulates the pressure-flow relationship in the arterial system. Among them, the three-unit Windkessel model, consisting of two resistors and one capacitor, has good accuracy in reconstructing inlet blood pressure and flow distribution. However, existing methods for hemodynamic simulation have the following limitations: First, the outlet boundary is highly dependent on measured flow data. Without patient-specific data, only general empirical values ​​can be used, leading to significant deviations between simulation results and actual physiological states, making it difficult to meet the needs of individualized clinical assessment. Second, the cerebral arterial circle, i.e., the intracranial Willis circle, is a physiological closed loop with characteristics such as flow compensation, bidirectional flow, and blood flow convergence. Traditional flow allocation methods based on the open-loop vascular assumption cannot adapt to the blood flow allocation rules of the loop, resulting in severe distortion of flow allocation. Third, the three-unit Windkessel model is a relatively accurate outlet boundary model for simulating distal microcirculation. Traditional methods have unreasonable initial pressure settings, which differ greatly from physiological states. This means that the vascular compliance unit needs to be filled for multiple cardiac cycles, resulting in a long baseline pressure stabilization period and slow transient response. Fourth, when the intracranial closed-loop structure is coupled with the Windkessel boundary, traditional initial conditions further exacerbate convergence difficulties, significantly increase computational costs, and easily lead to systematic deviations in key hemodynamic indicators such as pressure, flow velocity, and wall shear stress. Distortion problems are even more prominent under complex vascular structures. Therefore, reducing the cost of intracranial arterial hemodynamic analysis and improving the accuracy of the results are problems that need to be solved. Summary of the Invention

[0003] Therefore, it is necessary to provide a method, device, and computer equipment for intracranial arterial hemodynamic analysis that can reduce the dependence on individual-specific measured flow data and improve simulation convergence efficiency and result accuracy, in order to address the above-mentioned technical problems.

[0004] In a first aspect, this application provides a method for intracranial arterial hemodynamic analysis, the method comprising:

[0005] Obtain angiographic images of the aorta-intracranial artery in the aorta-intracranial artery region related to the user, and construct a three-dimensional vascular model of the aorta-intracranial artery based on the angiographic images;

[0006] Determine the blood flow distribution at the vascular terminal outlets of the branch vessels supplying blood from the aortic arch to the intracranial cavity in the three-dimensional vascular model.

[0007] Based on the blood flow distribution, steady-state calculations are performed on the three-dimensional vascular model to determine the steady-state pressure difference between the inlet of the ascending aorta and the outlet of the descending aorta.

[0008] The initial pressure is determined based on empirical coefficients, average flow rate during the cycle, average flow rate during systole, the steady-state pressure difference, diastolic pressure, and reference pressure.

[0009] Using a three-unit air cavity model, intracranial arterial hemodynamic analysis was performed based on the blood flow distribution, initial pressure, and inlet flow boundary conditions to determine the intracranial arterial hemodynamic data.

[0010] In one embodiment, angiographic images of the aorta-intracranial artery in the aorta-intracranial artery region related to the user are acquired, and a three-dimensional vascular model of the aorta-intracranial artery is constructed based on the angiographic images, including:

[0011] Obtain angiographic images of the aorta-intracranial artery in the aorta-intracranial artery region related to the user's aorta, and based on the angiographic images, determine the aortic arch, the relevant branches of the aortic arch, the model's starting point, and the model's ending point; the model's starting point is the ascending aorta, and the ending point is the proximal descending aorta.

[0012] A three-dimensional vascular model of the aorta-intracranial artery is constructed based on the aortic arch, its related branches, the model's starting end, and the model's ending end.

[0013] In one embodiment, determining the blood flow distribution at the vascular terminal outlet of the branch vessels supplying blood from the aortic arch to the intracranial cavity in the three-dimensional vascular model includes:

[0014] Using medical image analysis software, the center line of the blood vessels is extracted from the three-dimensional blood vessel model, and sampling points on the center line of the blood vessels are determined.

[0015] Calculate the cross-sectional area of ​​the branch vessels of the aortic arch supplying blood to the intracranial cavity at the sampling point, and determine the cross-sectional area sequence;

[0016] The minimum cross-sectional area of ​​the branch vessels supplying blood from the aortic arch to the intracranial cavity at the sampling point is determined from the cross-sectional area sequence. Based on the minimum cross-sectional area and the connectivity of the vascular network in the three-dimensional vascular model, a Markov chain transition model including bifurcation nodes and closed-loop connected nodes is constructed.

[0017] Based on the Markov chain transfer model, the blood flow distribution at the vascular terminal outlet of the branch vessels supplying blood from the aortic arch to the intracranial cavity is determined.

[0018] In one embodiment, the blood flow distribution at the vascular terminal exit of the branch vessels supplying blood from the aortic arch to the intracranial cavity is determined according to the Markov chain transfer model, including:

[0019] For a bifurcation node, the model transition probability of the Markov chain transition model of the aortic arch supplying blood to the intracranial cavity is determined based on the proportion of the minimum cross-sectional area to the sum of the minimum cross-sectional areas of the branch vessels of the aortic arch connected to the bifurcation node.

[0020] For a closed-loop connected node, based on the minimum cross-sectional area of ​​the branch vessels of the aortic arch that supply blood to the intracranial cavity and the vessel connectivity relationship connected to the closed-loop connected node, the corresponding bidirectional state transition edge and the model transition probability of the Markov chain transition model are set.

[0021] The vascular terminal outlet of the branch vessels supplying blood from the aortic arch to the intracranial cavity is set as the model absorption state of the Markov chain transition model. Based on the model absorption state and the model transition probability, the blood flow distribution at the vascular terminal outlet of the branch vessels supplying blood from the aortic arch to the intracranial cavity is determined.

[0022] In one embodiment, based on the blood flow distribution, steady-state calculations are performed on the three-dimensional vascular model to determine the steady-state pressure difference between the inlet of the ascending aorta and the outlet of the descending aorta, including:

[0023] The blood flow distribution at the vascular terminal outlet of the branch vessels supplying blood from the aortic arch to the intracranial cavity is used as the outlet flow boundary condition of the three-dimensional vascular model.

[0024] Based on the aforementioned outlet flow boundary conditions and flow conservation law, the three-dimensional vascular model is subjected to steady-state calculations using hemodynamic steady-state equations to determine the inlet pressure of the ascending aorta and the outlet pressure of the descending aorta.

[0025] The steady-state pressure difference between the inlet of the ascending aorta and the outlet of the descending aorta is determined based on the inlet pressure of the ascending aorta and the outlet pressure of the descending aorta.

[0026] In one embodiment, the reference pressure is a distal reference pressure. The initial pressure is determined based on an empirical coefficient, the average flow rate during the period, the average flow rate during systole, the steady-state pressure difference, the diastolic pressure, and the reference pressure, including:

[0027] An empirical coefficient is determined, and the ratio of the average flow rate during the period to the average flow rate during the systolic phase is determined; the empirical coefficient is determined based on the calibration results of preliminary experiments or sample data;

[0028] The product of the empirical coefficient, the flow ratio, and the steady-state pressure difference is used as the redundant pressure;

[0029] Determine the pressure difference between the diastolic pressure and the distal reference pressure, and use the sum of the redundant pressure and the pressure difference as the initial pressure.

[0030] In one embodiment, using a three-unit air cavity model, intracranial arterial hemodynamic analysis is performed based on the blood flow distribution, the initial pressure, and the inlet flow boundary conditions to determine the intracranial arterial hemodynamic data, including:

[0031] The differential model of the three-unit air cavity model is transformed into an integral model through integration by parts;

[0032] The blood flow allocation is used as the outlet boundary condition, and the inlet flow boundary condition is set.

[0033] Using the integral model of the three-unit air cavity model, based on the initial pressure and the inlet flow boundary conditions, multi-cardiac cycle hemodynamic analysis is performed on the intracranial artery to determine the hemodynamic data of the intracranial artery.

[0034] Secondly, this application also provides an intracranial arterial hemodynamic analysis device, the device comprising:

[0035] The three-dimensional model construction module is used to acquire angiographic images of the aorta-intracranial artery in the aorta-intracranial artery related region of the user, and to construct a three-dimensional vascular model of the aorta-intracranial artery based on the angiographic images.

[0036] The blood flow distribution determination module is used to determine the blood flow distribution at the vascular terminal outlet of the branch vessels supplying blood from the aortic arch to the intracranial cavity in the three-dimensional vascular model.

[0037] The steady-state pressure difference determination module is used to perform steady-state calculations on the three-dimensional vascular model based on the blood flow distribution to determine the steady-state pressure difference between the inlet of the ascending aorta and the outlet of the descending aorta.

[0038] The initial pressure determination module is used to determine the initial pressure based on an empirical coefficient, the average flow rate during the cycle, the average flow rate during systole, the steady-state pressure difference, the diastolic pressure, and the reference pressure.

[0039] The inlet flow boundary condition setting module is used to perform intracranial arterial hemodynamic analysis based on the blood flow distribution, the initial pressure, and the inlet flow boundary conditions using a three-unit air cavity model, and to determine the intracranial arterial hemodynamic data.

[0040] Thirdly, this application also provides a computer device, the computer device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0041] Obtain angiographic images of the aorta-intracranial artery in the aorta-intracranial artery region related to the user, and construct a three-dimensional vascular model of the aorta-intracranial artery based on the angiographic images;

[0042] Determine the blood flow distribution at the vascular terminal outlets of the branch vessels supplying blood from the aortic arch to the intracranial cavity in the three-dimensional vascular model.

[0043] Based on the blood flow distribution, steady-state calculations are performed on the three-dimensional vascular model to determine the steady-state pressure difference between the inlet of the ascending aorta and the outlet of the descending aorta.

[0044] The initial pressure is determined based on empirical coefficients, average flow rate during the cycle, average flow rate during systole, the steady-state pressure difference, diastolic pressure, and reference pressure.

[0045] Using a three-unit air cavity model, intracranial arterial hemodynamic analysis was performed based on the blood flow distribution, initial pressure, and inlet flow boundary conditions to determine the intracranial arterial hemodynamic data.

[0046] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0047] Obtain angiographic images of the aorta-intracranial artery in the aorta-intracranial artery region related to the user, and construct a three-dimensional vascular model of the aorta-intracranial artery based on the angiographic images;

[0048] Determine the blood flow distribution at the vascular terminal outlets of the branch vessels supplying blood from the aortic arch to the intracranial cavity in the three-dimensional vascular model.

[0049] Based on the blood flow distribution, steady-state calculations are performed on the three-dimensional vascular model to determine the steady-state pressure difference between the inlet of the ascending aorta and the outlet of the descending aorta.

[0050] The initial pressure is determined based on empirical coefficients, average flow rate during the cycle, average flow rate during systole, the steady-state pressure difference, diastolic pressure, and reference pressure.

[0051] Using a three-unit air cavity model, intracranial arterial hemodynamic analysis was performed based on the blood flow distribution, initial pressure, and inlet flow boundary conditions to determine the intracranial arterial hemodynamic data.

[0052] The aforementioned intracranial arterial hemodynamic analysis method, device, and computer equipment acquire angiographic images of the aorta-intracranial artery related region from the user, and construct a three-dimensional vascular model of the aorta-intracranial artery based on the angiographic images; determine the blood flow distribution at the terminal outlets of the intracranial blood supply branches in the three-dimensional vascular model; perform steady-state calculations based on the blood flow distribution to determine the steady-state pressure difference between the ascending aorta inlet and the descending aorta outlet; determine the initial pressure based on empirical coefficients, flow parameters, diastolic pressure, and distal reference pressure; set inlet flow boundary conditions, and conduct hemodynamic analysis using a three-unit wind cavity model as the peripheral boundary. This addresses the problems of existing intracranial hemodynamic analysis methods, such as excessive reliance on measured flow data for the simulated outlet boundary, insufficient accuracy of hemodynamic analysis results when individualized data is lacking, inability to adapt to the closed-loop blood flow characteristics of the circle of Willis, severe distortion of flow distribution, initial pressure settings in the Windkessel model that do not conform to physiological reality, slow simulation convergence and long stabilization period, and deviations and distortions in multiple hemodynamic parameters caused by coupling the closed-loop vascular structure with the model. The above approach can reduce the reliance on patient-specific measured flow data, improve the flow distribution distortion problem in closed-loop structures, and improve simulation convergence speed and result accuracy. Attached Figure Description

[0053] Figure 1 This is a diagram illustrating the application environment of an intracranial arterial hemodynamics analysis method in one embodiment.

[0054] Figure 2 This is a flowchart illustrating an intracranial arterial hemodynamic analysis method in one embodiment;

[0055] Figure 3 This is an example diagram of a three-dimensional vascular model of the aorta-intracranial artery in one embodiment;

[0056] Figure 4 This is a flowchart illustrating a method for determining blood flow distribution at the outlet of a blood vessel terminal in one embodiment.

[0057] Figure 5 This is an example diagram showing the simulation results of a three-unit Windkessel model in one embodiment;

[0058] Figure 6 Example diagram of simulation results for a three-unit Windkessel model in another embodiment;

[0059] Figure 7 This is a structural block diagram of an intracranial arterial hemodynamic analysis device in one embodiment;

[0060] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0062] The intracranial arterial hemodynamic analysis method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on a cloud or other network server. Server 104 acquires angiographic images of the aorta-intracranial artery region related to the user's aorta-intracranial artery, and constructs a three-dimensional vascular model of the aorta-intracranial artery based on the angiographic images; determines the blood flow distribution at the vascular terminal outlets of the branch vessels supplying blood from the aortic arch to the intracranial cavity in the three-dimensional vascular model; performs steady-state calculations on the three-dimensional vascular model based on the blood flow distribution to determine the steady-state pressure difference between the ascending aorta inlet and the descending aorta outlet; determines the initial pressure based on empirical coefficients, average flow rate during the cycle, average systolic flow rate, the steady-state pressure difference, diastolic pressure, and reference pressure; performs intracranial artery hemodynamic analysis using a three-unit wind cavity model, based on the blood flow distribution, the initial pressure, and the inlet flow boundary conditions, to determine the intracranial artery hemodynamic data, and sends the hemodynamic data to terminal 102 via the communication network. The inlet flow boundary condition is either the measured clinical blood flow or a preset inlet pulsating flow curve determined based on the average flow within the cycle.

[0063] In one embodiment, such as Figure 2 As shown, a method for intracranial arterial hemodynamic analysis is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0064] S210. Obtain angiographic images of the aorta-intracranial artery in the aorta-intracranial artery region related to the user's aorta, and construct a three-dimensional vascular model of the aorta-intracranial artery based on the angiographic images.

[0065] Among them, angiography images are CTA (Computed Tomographic Angiography) images.

[0066] It should be noted that the three-dimensional vascular model starts from the ascending aorta, with three or four branches extending upwards from the aortic arch, ending at the proximal descending aorta. Specifically, if there are three branches at the aortic arch, these branches include the brachiocephalic trunk, the left common carotid artery, and the left subclavian artery; if there are four branches at the aortic arch, these branches include the right subclavian artery, the right common carotid artery, the left common carotid artery, and the left subclavian artery.

[0067] Specifically, angiographic images of the aorta-intracranial artery region related to the user are acquired. These images are then processed through image segmentation, vascular contour extraction, and 3D reconstruction to construct a 3D vascular model of the aorta-intracranial artery corresponding to the anatomical structure. For example, the 3D vascular model is as follows: Figure 3 As shown.

[0068] For example, acquiring angiographic images of the aorta-intracranial artery region related to the user's aorta-intracranial artery, and constructing a three-dimensional vascular model of the aorta-intracranial artery based on the angiographic images, including:

[0069] Obtain angiographic images of the aorta-intracranial artery region related to the user's aorta-intracranial artery, and based on the angiographic images, determine the aortic arch, related branches of the aortic arch, the model's starting end, and the model's ending end; the model's starting end is the ascending aorta, and the ending end is the proximal descending aorta; construct a three-dimensional vascular model of the aorta-intracranial artery based on the aortic arch, related branches of the aortic arch, the model's starting end, and the model's ending end.

[0070] The ascending aorta is the blood vessel directly connected to the left ventricle of the heart. It is the first major blood vessel that pumps blood from the heart into the systemic circulation and is the source of blood flow in the three-dimensional vascular model. The descending aorta is an extension of the ascending aorta, and the proximal descending aorta refers to the segment of the descending aorta closest to the ascending aorta and the aortic arch.

[0071] Specifically, the aortic arch can have either three or four branches: three branches include the brachiocephalic trunk, left common carotid artery, and left subclavian artery; four branches include the right subclavian artery, right common carotid artery, left common carotid artery, and left subclavian artery. CTA images of the aorta-intracranial artery region are acquired from the user. Image recognition is performed on the CTA images to determine the aortic arch, its branches, the model's starting point, and the model's ending point. The model's starting point is the ascending aorta, and the model's ending point is the proximal descending aorta. Based on the determined anatomical structures and key locations, a three-dimensional vascular model of the aorta-intracranial artery region is constructed using medical image processing tools and 3D reconstruction tools.

[0072] The above scheme, by determining the aortic arch, its branches, and the beginning and ending points of the model to construct a three-dimensional vascular model, can ensure that the anatomical structure of the three-dimensional vascular model is accurate and the boundaries are clear, providing a reliable model basis for subsequent hemodynamic simulation.

[0073] S220. Determine the blood flow distribution at the vascular terminal outlets of the branch vessels supplying blood from the aortic arch to the intracranial cavity in the three-dimensional vascular model.

[0074] It should be noted that the blood pumped by the heart passes through the aortic arch and is distributed to different intracranial vessels such as the anterior cerebral artery, middle cerebral artery, and posterior cerebral artery. Blood flow distribution refers to the proportion of blood flow from the aortic arch into each intracranial branch vessel, that is, how much blood each intracranial branch vessel receives, and what proportion of the total flow is given to each vessel.

[0075] Specifically, based on the constructed three-dimensional vascular model of the aorta-intracranial artery, the vascular terminal outlets of each branch vessel supplying blood from the aortic arch to the intracranial cavity are determined, and the blood flow distribution corresponding to each vascular terminal outlet is determined.

[0076] For example, such as Figure 4 As shown, determining the blood flow distribution at the vascular terminal outlet of the branch vessels supplying blood from the aortic arch to the intracranial cavity in the three-dimensional vascular model includes:

[0077] S2201. Using medical image analysis software, extract the center line of the blood vessels from the three-dimensional blood vessel model and determine the sampling points on the center line of the blood vessels.

[0078] Sampling points refer to multiple discrete detection points taken along the direction of the blood vessel.

[0079] Specifically, the constructed three-dimensional vascular model of the aorta-intracranial artery is processed using medical image analysis software. The centerline of the blood vessel is extracted from the three-dimensional vascular model, and sampling points are selected on the centerline. The centerline is then segmented based on the sampling points to sample and calculate the cross-sectional area of ​​the blood vessel at that location, forming a cross-sectional area sequence. This provides a data foundation for the subsequent construction of the Markov chain transfer model and the calculation of blood flow distribution.

[0080] S2202. Calculate the cross-sectional area of ​​the branch vessels of the aortic arch supplying blood to the intracranial cavity at the sampling point, and determine the cross-sectional area sequence.

[0081] Specifically, for each sampling point of the three-dimensional vascular model, the cross-sectional area of ​​the branch vessels supplying blood from the aortic arch to the intracranial cavity is calculated at the corresponding location, and the cross-sectional areas of each sampling point are arranged in ascending order to form a cross-sectional area sequence. Each branch vessel corresponds to its own cross-sectional area sequence.

[0082] S2203. Determine the minimum cross-sectional area of ​​the branch vessels supplying blood from the aortic arch to the intracranial cavity at the sampling point from the cross-sectional area sequence, and construct a Markov chain transition model including bifurcation nodes and closed-loop connected nodes based on the minimum cross-sectional area and the connectivity of the vascular network in the three-dimensional vascular model.

[0083] The Markov chain transition model is a state transition model that describes the flow path and distribution ratio of blood in a vascular network including bifurcation nodes and closed-loop connected nodes, based on the minimum cross-sectional area of ​​each branch vessel and the connectivity of the vascular network. It is used to quantitatively determine the blood flow distribution at the terminal outlet of each branch vessel.

[0084] Specifically, from the obtained sequence of cross-sectional areas corresponding to each branch vessel, the smallest cross-sectional area for each branch vessel is selected and determined as the minimum cross-sectional area for each branch vessel. Based on the minimum cross-sectional area and the vessel connectivity, a Markov chain transition model of the vascular network in the three-dimensional vascular model is constructed. That is, using the minimum cross-sectional area of ​​each branch vessel as the construction basis, the vascular network is abstracted into a Markov chain state transition structure. Based on the minimum cross-sectional area and the vessel connectivity, the transition probability or flow distribution relationship between each branch in the vascular network is determined, and corresponding bidirectional state transition edges are set at the closed-loop connected nodes of the Willis ring, thereby completing the construction of the Markov chain transition model of the entire vascular network.

[0085] S2204. Based on the Markov chain transfer model, determine the blood flow distribution at the vascular terminal outlet of the branch vessels supplying blood from the aortic arch to the intracranial cavity.

[0086] Specifically, based on the completed Markov chain transfer model, the blood flow distribution at the terminal outlets of the various branches of the aortic arch supplying blood to the intracranial cavity is ultimately determined by the vascular network state transfer relationships reflected in the model.

[0087] For example, based on the Markov chain transfer model, determining the blood flow distribution at the vascular terminal exit of the branch vessels supplying blood from the aortic arch to the intracranial cavity includes:

[0088] For bifurcation nodes, the model transition probability of the Markov chain transition model for the aortic arch branch vessels supplying blood to the intracranial cavity is determined based on the proportion of the minimum cross-sectional area to the sum of the minimum cross-sectional areas of the aortic arch branch vessels connected to the bifurcation node. For closed-loop connected nodes, corresponding bidirectional state transition edges and the model transition probability of the Markov chain transition model are set based on the minimum cross-sectional area of ​​the aortic arch branch vessels supplying blood to the intracranial cavity and the vessel connectivity relationship connected to the closed-loop connected node. The vessel terminal outlet of the aortic arch branch vessels supplying blood to the intracranial cavity is set as the model absorption state of the Markov chain transition model. Based on the model absorption state and the model transition probability, the blood flow distribution at the vessel terminal outlet of the aortic arch branch vessels supplying blood to the intracranial cavity is determined.

[0089] It should be noted that setting the terminal outlet of the branch vessels supplying blood from the aortic arch to the intracranial cavity as the model absorption state of the Markov chain transfer model ensures that the computational logic of the Markov chain transfer model is completely consistent with the actual flow of blood, and ensures that subsequent calculations are based on the probability of blood flowing to each final outlet.

[0090] Specifically, in the Markov chain transition model of the vascular network of the constructed 3D vascular model, for each vascular bifurcation node in the aortic arch blood supply branch network and the closed-loop connected node in the Willis ring, the minimum cross-sectional area of ​​each branch vessel connected to that node is extracted, and the minimum cross-sectional area is used as the basic allocation weight of the corresponding branch. That is, the larger the value of the minimum cross-sectional area, the stronger the blood supply capacity of the corresponding vascular branch, and the more blood flow should be allocated. For each connected branch at the same node, the minimum cross-sectional area values ​​are summed to obtain the cross-sectional area sum, and the ratio between the minimum cross-sectional area of ​​a single branch and the cross-sectional area sum is used as the model transition probability corresponding to that branch, so that the sum of the model transition probabilities of all branches at the same node is 1. For closed-loop connected nodes, corresponding bidirectional state transition edges can be set according to the vascular connectivity relationship. The branch vessel transition probability is assigned to the state transition relationship from the corresponding node to the corresponding vascular branch in the Markov chain transition model to determine the model transition probability at each node. The vascular terminal outlets of the branch vessels supplying blood from the aortic arch to the intracranial cavity are set as the model absorption states of the Markov chain transition model. By combining the model transition probabilities and calculating the state transitions of the Markov chain transition model in the vascular network, the blood flow state eventually converges to each model absorption state, thereby determining the blood flow distribution corresponding to each vascular terminal outlet.

[0091] Understandably, the above scheme can make the blood flow distribution result highly matched with the vascular anatomy and blood supply capacity, providing stable flow boundary conditions for subsequent hemodynamic simulation.

[0092] The above scheme obtains the cross-sectional area sequence by extracting the vascular centerline and sampling points and determines the minimum cross-sectional area. Based on this, a Markov chain transfer model is constructed to determine the blood flow distribution. This enables the blood flow distribution at the terminal outlets of the various branches of the aortic arch supplying blood to the intracranial cavity to better conform to the actual anatomical structure of the blood vessels, thereby improving the accuracy and rationality of blood flow distribution.

[0093] S230. Based on blood flow distribution, perform steady-state calculations on the three-dimensional vascular model to determine the steady-state pressure difference between the inlet of the ascending aorta and the outlet of the descending aorta.

[0094] Specifically, based on the three-dimensional aortic-intracranial artery vascular model with completed blood flow distribution, the periodic average flow rate at the inlet of the ascending aorta is input into the model as a given parameter. Fluid dynamic steady-state calculations are performed on the three-dimensional vascular model, and the pressure difference between the inlet of the ascending aorta and the outlet of the descending aorta is obtained through calculation. This pressure difference is the steady-state pressure difference between the inlet of the ascending aorta and the outlet of the descending aorta.

[0095] For example, based on the blood flow distribution, steady-state calculations are performed on the three-dimensional vascular model to determine the steady-state pressure difference between the inlet of the ascending aorta and the outlet of the descending aorta, including:

[0096] The blood flow distribution at the terminal outlet of the branch vessels supplying blood from the aortic arch to the intracranial cavity is used as the outlet flow boundary condition for the three-dimensional vascular model. Based on the outlet flow boundary condition and the law of flow conservation, steady-state calculations are performed on the three-dimensional vascular model using hemodynamic steady-state equations to determine the inlet pressure of the ascending aorta and the outlet pressure of the descending aorta. Based on the inlet pressure of the ascending aorta and the outlet pressure of the descending aorta, the steady-state pressure difference between the inlet of the ascending aorta and the outlet of the descending aorta is determined.

[0097] Specifically, the calculated blood flow distribution at the outlets of the aortic arch's branches supplying blood to the intracranial cavity is assigned as the flow boundary conditions at the corresponding vessel outlets in the three-dimensional aortic-intracranial artery model. Based on these vessel outlet flow boundary conditions, and following the law of flow conservation, the hemodynamic steady-state equations are substituted into the model to perform a full-scale hemodynamic steady-state calculation. The pressure values ​​at the ascending aorta inlet and descending aorta outlet are obtained by solving the hemodynamic steady-state equations. The difference between the calculated ascending aorta inlet pressure and descending aorta outlet pressure is then calculated; the result represents the steady-state pressure difference between the ascending aorta inlet and descending aorta outlet.

[0098] The above scheme uses the distribution of blood flow at the outlet of the intracranial blood supply branch as the boundary condition of the model outlet flow. It combines the law of flow conservation and the steady-state equation of hemodynamics to carry out steady-state calculations and solve the steady-state pressure difference, ensuring the accuracy and reliability of the steady-state pressure difference. At the same time, it provides accurate and effective preliminary data for subsequent calculation of initial pressure of intracranial arteries and transient simulation of fluid dynamics, which helps to improve the efficiency and rationality of hemodynamic analysis.

[0099] S240. Determine the initial pressure based on empirical coefficients, average flow rate during the cycle, average flow rate during systole, steady-state pressure difference, diastolic pressure, and reference pressure.

[0100] For example, the initial pressure can be determined in the following ways:

[0101] Determine the empirical coefficient and the ratio of the average flow rate during the period to the average flow rate during the systolic phase; the empirical coefficient is determined based on the calibration results of pre-experiments or sample data; the product of the empirical coefficient, the flow rate ratio, and the steady-state pressure difference is used as the redundant pressure; the pressure difference between the diastolic pressure and the distal reference pressure is determined, and the sum of the redundant pressure and the pressure difference is used as the initial pressure.

[0102] Wherein, the empirical coefficient α is a coefficient obtained from pre-experiment or sample data calibration; the reference pressure is the distal reference pressure; in one embodiment, the empirical coefficient α = -0.5.

[0103] For example, the formula for calculating the initial pressure is shown below:

[0104]

[0105] in, As the initial pressure, The steady-state pressure difference; Q mean Q is the average flow rate over the period. mean_sys The average flow rate during the systolic phase. For diastolic pressure, For reference pressure.

[0106] The above scheme calculates redundant pressure by combining empirical coefficients obtained from pre-experimental or sample data with the ratio of average flow rate during the cycle to average flow rate during systole and steady-state pressure difference, and then determines the initial pressure by combining the difference between diastolic pressure and distal reference pressure. This approach can provide a more reasonable initial pressure value that fits the physiological state for the transient fluid dynamics simulation of the three-element Windkessel model, thereby improving the convergence speed of the reference pressure and enhancing the efficiency and accuracy of intracranial arterial hemodynamic simulation.

[0107] S250. Using a three-unit air cavity model, intracranial arterial hemodynamic analysis is performed based on blood flow distribution, initial pressure, and inlet flow boundary conditions to determine intracranial arterial hemodynamic data.

[0108] Among them, the three-unit wind cavity model, also known as the three-unit Windkessel model, is a lumped-parameter hemodynamic model based on vascular physiological characteristics. It is used to simulate the pressure-flow dynamic relationship between aorta and peripheral blood vessels. It is also a commonly used outlet boundary condition in vascular computational fluid dynamics simulation and can replace complex distal vascular networks to simplify calculations.

[0109] For example, inlet flow boundary conditions can be set for a three-dimensional vascular model, wherein the inlet flow boundary conditions are either the measured clinical blood flow or a preset inlet pulsatile flow curve determined based on the average flow rate within the cycle. Using a three-unit wind cavity model as the outer boundary, intracranial arterial hemodynamic analysis is performed based on blood flow distribution, initial pressure, and the inlet flow boundary conditions to determine the hemodynamic data of the intracranial arteries.

[0110] Specifically, the calculated blood flow distribution at the outlet of the aortic arch supplying intracranial blood vessels is used as the outlet-side flow constraint input, and the initial pressure is used as the initial pressure value at t=0. Inlet flow boundary conditions are set; these can be either the measured clinical blood flow or an inlet pulsating flow curve obtained by scaling a preset pulsating flow curve based on the average flow over a period. Subsequently, the blood flow distribution, initial pressure, and inlet flow boundary conditions are substituted into a three-element Windkessel model. Based on the differential or integral equations reflecting the pressure-flow dynamic relationship in this model, transient hemodynamic simulation calculations of the intracranial arteries are performed. Through simulation analysis, relevant hemodynamic data such as pressure, velocity, and flow distribution of the intracranial arteries are obtained, completing the intracranial artery hemodynamic analysis.

[0111] For example, intracranial artery hemodynamic data includes three core basic data: pressure, flow rate, and flow velocity, as well as characteristic analysis data derived from them, such as blood flow shear stress, vascular compliance, blood flow resistance, and pressure reflection coefficient. It also includes convergence and stability characteristic values ​​of relevant data under each cardiac cycle.

[0112] For example, using a three-unit wind cavity model, intracranial arterial hemodynamic analysis is performed based on blood flow distribution, initial pressure, and inlet flow boundary conditions to determine intracranial arterial hemodynamic data, including:

[0113] The differential model of the three-unit air cavity model is transformed into an integral model through integration by parts; blood flow distribution is used as the outlet boundary condition, and inlet flow boundary conditions are set; based on the initial pressure and the inlet flow boundary conditions, multi-cardiac cycle hemodynamic analysis is performed on the intracranial artery using the integral model of the three-unit air cavity model to determine the hemodynamic data of the intracranial artery.

[0114] For example, the differential model of the three-unit Windkessel model is shown below:

[0115]

[0116] The differential model of the three-unit air cavity model is transformed into an integral model by integration by parts, as shown below:

[0117]

[0118] Where i is the outlet number; Pl,i(t) is the near-end pressure of the three-element Windkessel model at outlet i, i.e., the CFD (Computational Fluid Dynamics) boundary pressure, in Pa or mmHg; Qi(t) is the volumetric flow rate at outlet i, i.e., given by the CFD solver or used as a boundary variable, in m³ / s; R c,l,i This refers to characteristic impedance, also known as near-end impedance or characteristic resistance, measured in Pa·s / m³; R p,l,i C is the peripheral resistance, i.e., the end resistance, with units of Pa·s / m³. i Compliant capacitance is an equivalent electrical element that transforms the arterial compliance of blood vessels through a hydraulic-electrical analogy, measured in m³ / Pa; P ref The distal reference pressure is represented by venous pressure or gauge pressure, in Pa or mmHg; t represents time, in seconds.

[0119] Based on the transformed three-unit Windkessel model integral form, i.e., the integral model, the blood flow distribution from the aortic arch to the intracranial blood supply branches, initial pressure, and inlet flow boundary conditions are substituted. Based on the pressure-flow dynamic correlation reflected in the integral model, hemodynamic transient simulations of intracranial arteries over multiple cardiac cycles are performed. During the simulation, the integral model reflects the influence of initial values ​​and the negative exponential convergence of pressure over time, allowing the baseline pressure to stabilize quickly and reducing the number of iterations. Finally, core basic data such as pressure, flow rate, and velocity at each segment, bifurcation point, and terminal outlet of the intracranial artery, as well as derived characteristic analysis data such as blood flow shear stress, blood flow resistance, and vascular compliance, are extracted from the simulation results to complete the hemodynamic analysis of the intracranial arteries. When measured clinical blood flow is available, it can be used as the inlet flow boundary condition; when no patient-specific measured flow is available, a preset inlet pulsating flow curve determined based on the average flow within the cycle can be used to complete the simulation.

[0120] In the above-mentioned intracranial arterial hemodynamic analysis method, angiographic images of the aorta-intracranial artery in the relevant region of the user's aorta-intracranial artery are acquired, and a three-dimensional vascular model of the aorta-intracranial artery is constructed based on the angiographic images; the blood flow distribution at the vascular terminal outlets of the branch vessels supplying blood from the aortic arch to the intracranial cavity in the three-dimensional vascular model is determined; steady-state calculations are performed on the three-dimensional vascular model according to the blood flow distribution to determine the steady-state pressure difference between the ascending aorta inlet and the descending aorta outlet; the initial pressure is determined based on empirical coefficients, average flow rate during the cycle, average systolic flow rate, the steady-state pressure difference, diastolic pressure, and reference pressure; inlet flow boundary conditions are set, and with a three-unit wind cavity model as the outer boundary, intracranial arterial hemodynamic analysis is performed based on the blood flow distribution, the initial pressure, and the inlet flow boundary conditions to determine the intracranial arterial hemodynamic data. The above-mentioned scheme constructs an individualized three-dimensional model based on angiography images and combines it with a blood flow distribution method adapted to a closed-loop structure. It can complete the simulation by combining preset inlet flow boundary conditions when patient-specific measured flow data is lacking, thereby reducing the dependence on clinical measured flow data. At the same time, it rationally determines the initial pressure by using steady-state pressure difference, flow ratio and physiological pressure parameters, so that the initial conditions are more in line with the physiological state, shorten the benchmark pressure stabilization time, accelerate the convergence speed, and improve the accuracy and reliability of the simulation results.

[0121] For example, based on the above embodiments, the intracranial arterial hemodynamic analysis method includes:

[0122] CTA images of the aorta-intracranial artery region were acquired from the user. Image recognition was performed on the CTA images to determine the aortic arch, its branches, the model's initiation point, and its termination point. The model's initiation point was the ascending aorta, and the model's termination point was the proximal descending aorta. Based on the determined anatomical structures and key locations, a three-dimensional vascular model of the aorta-intracranial artery region was constructed using medical image processing tools and 3D reconstruction tools. There are two possibilities for the aortic arch: three branches (brachiocephalic trunk, left common carotid artery, left subclavian artery); four branches (right subclavian artery, right common carotid artery, left common carotid artery, left subclavian artery).

[0123] Based on the constructed three-dimensional vascular model of the aorta-intracranial artery, the vascular terminal outlets of each branch vessel supplying blood from the aortic arch to the intracranial cavity are determined, and the blood flow distribution corresponding to each vascular terminal outlet is determined.

[0124] The constructed three-dimensional aortic-intracranial artery vascular model was processed using medical image analysis software. The centerline of the vessels was extracted from the model, and sampling points were selected along this centerline. The centerline was then segmented based on these sampling points to calculate the cross-sectional area of ​​the vessels at each location, forming a cross-sectional area sequence. This sequence provides the data foundation for subsequent construction of a Markov chain transfer model and calculation of blood flow distribution. For each sampling point in the three-dimensional vascular model, the cross-sectional area of ​​the branch vessels supplying blood from the aortic arch to the intracranial cavity was calculated at the corresponding location. The cross-sectional areas of each sampling point were arranged in ascending order to form a cross-sectional area sequence. Each branch vessel corresponds to its own cross-sectional area sequence. From the obtained cross-sectional area sequences corresponding to each branch vessel, the smallest cross-sectional area was selected as the minimum cross-sectional area for each branch vessel. Based on this minimum cross-sectional area and the vessel connectivity, a Markov chain transfer model of the vascular network in the three-dimensional vascular model was constructed. This method uses the minimum cross-sectional area of ​​each branch vessel as the basis for construction, abstracting the vascular network into a Markov chain state transition structure. Based on the minimum cross-sectional area and vessel connectivity, the transition probabilities or flow distribution relationships between branches in the vascular network are determined. Corresponding bidirectional state transition edges are set at the closed-loop connected nodes of the Willis ring, thus completing the construction of the Markov chain transition model for the entire vascular network. In the Markov chain transition model of the constructed 3D vascular network, for each branch node in the aortic arch blood supply branch network and the closed-loop connected node in the Willis ring, the minimum cross-sectional area of ​​each branch vessel connected to that node is extracted and used as the basic allocation weight for each branch. For closed-loop connected nodes, corresponding bidirectional state transition edges can be set according to the vessel connectivity. At the same node, the minimum cross-sectional area values ​​of all connected branches are summed to obtain the cross-sectional area sum, and the ratio between the minimum cross-sectional area of ​​a single branch and the sum of the cross-sectional areas is used as the model transition probability corresponding to that branch, ensuring that the sum of the model transition probabilities of all branches at the same node is 1. The vascular terminal outlets of the branch vessels supplying blood from the aortic arch to the intracranial cavity are set as the model absorption states of the Markov chain transition model. By combining the model transition probabilities and calculating the state transitions of the Markov chain transition model in the vascular network, the blood flow state eventually converges to each model absorption state, thereby determining the blood flow distribution corresponding to each vascular terminal outlet.

[0125] The calculated blood flow distribution at the outlets of the aortic arch's branches supplying blood to the intracranial cavity is assigned as the flow boundary conditions at the corresponding vessel outlets in the three-dimensional aortic-intracranial artery model. Based on these flow boundary conditions, and following the law of flow conservation, the hemodynamic steady-state equations are substituted into the model to perform a full-scale hemodynamic steady-state calculation. The pressure values ​​at the ascending aortic inlet and descending aortic outlet are obtained by solving the hemodynamic steady-state equations. The difference between the calculated ascending aortic inlet pressure and descending aortic outlet pressure is then calculated, and the result represents the steady-state pressure difference between the ascending aortic inlet and descending aortic outlet.

[0126] Based on the calibration results of pre-experimental or sample data, determine the empirical coefficient and the flow ratio of the average flow rate during the cycle to the average flow rate during the systolic phase; use the product of the empirical coefficient, the flow ratio, and the steady-state pressure difference as the redundant pressure; determine the pressure difference between the diastolic pressure and the distal reference pressure, and use the sum of the redundant pressure and the pressure difference as the initial pressure.

[0127] The inlet flow boundary conditions of the three-dimensional vascular model are set, wherein the inlet flow boundary conditions can be the measured clinical blood flow or a preset inlet pulsatile flow curve determined based on the average flow within a period. The calculated blood flow distribution result of the aortic arch to the terminal outlet of the intracranial blood supply branch is used as the outlet-side flow constraint input, and the aforementioned initial pressure is used as the initial pressure value at t=0. The blood flow distribution, initial pressure, and inlet flow boundary conditions are substituted into the three-element Windkessel model. The differential model of the three-element wind cavity model is transformed into an integral model through integration by parts. Using the three-element wind cavity model as the outer boundary, intracranial arterial hemodynamic analysis is performed based on the blood flow distribution, initial pressure, and inlet flow boundary conditions through the integral model of the three-element wind cavity model to determine the intracranial arterial hemodynamic data.

[0128] It should be noted that the simulation calculations are first performed using the differential form of the three-element Windkessel model, and then transformed into an integral form through integration by parts. Since P(t) in the integral form is a negative exponential function, the larger t is, the closer P(t) is to stability. Therefore, a reasonable initial pressure value at t=0, i.e., the calculated initial pressure, can significantly improve the efficiency of reaching a steady state with the reference pressure. For example... Figure 5 and Figure 6 As shown, compared to the case where it takes five cardiac cycles to stabilize when the initial pressure is set to 0 Pa, using the calculated initial pressure value as the initial pressure at t=0 only requires three cardiac cycles to stabilize the reference pressure, significantly improving the efficiency and stability of the simulation calculation. Figure 5The simulation results are for a three-element Windkessel model with an initial pressure of 0 Pa. Figure 6 The simulation results of the three-element Windkessel model are given when the initial pressure is the calculated initial pressure.

[0129] In the above-mentioned intracranial arterial hemodynamic analysis method, angiographic images of the aorta-intracranial artery in the relevant region of the user's aorta-intracranial artery are acquired, and a three-dimensional vascular model of the aorta-intracranial artery is constructed based on the angiographic images; the blood flow distribution at the vascular terminal outlets of the branch vessels supplying blood from the aortic arch to the intracranial cavity in the three-dimensional vascular model is determined; steady-state calculations are performed on the three-dimensional vascular model according to the blood flow distribution to determine the steady-state pressure difference between the ascending aorta inlet and the descending aorta outlet; the initial pressure is determined based on empirical coefficients, average flow rate during the cycle, average systolic flow rate, the steady-state pressure difference, diastolic pressure, and reference pressure; inlet flow boundary conditions are set, and with a three-unit wind cavity model as the outer boundary, intracranial arterial hemodynamic analysis is performed based on the blood flow distribution, the initial pressure, and the inlet flow boundary conditions to determine the intracranial arterial hemodynamic data. The above scheme employs a loop-adapted Markov chain flow allocation method, using vascular bifurcation nodes and closed-loop connectivity nodes as the basis for state transitions, the terminal outlet as the absorption state, and the minimum cross-sectional area to characterize blood flow capacity, thus better adapting to the blood flow compensation and return characteristics of the Willis ring. Simultaneously, the flow allocation method is coupled with a three-element Windkessel model, with the former responsible for blood flow allocation in the proximal vascular network and the latter responsible for simulating distal microcirculatory resistance and compliance, forming a more realistic outlet boundary. By jointly calculating the initial pressure based on steady-state pressure difference, the ratio of average flow to systolic flow, diastolic pressure, and distal reference pressure, the simulation starting point can be closer to the physiological steady state, suppressing capacitance filling drift and achieving rapid convergence. Even in the absence of patient-specific measured flow rates, simulations can still be completed by combining preset inlet flow boundary conditions, thus balancing computational efficiency, data availability, and individualized accuracy.

[0130] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0131] Based on the same inventive concept, this application also provides an intracranial arterial hemodynamic analysis device for implementing the aforementioned intracranial arterial hemodynamic analysis method. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations of one or more embodiments of the intracranial arterial hemodynamic analysis device provided below can be found in the limitations of the intracranial arterial hemodynamic analysis method described above, and will not be repeated here.

[0132] In one embodiment, such as Figure 7 As shown, an intracranial arterial hemodynamic analysis device is provided, comprising: a three-dimensional model construction module 701, a blood flow distribution determination module 702, a steady-state pressure difference determination module 703, an initial pressure determination module 704, and an inlet flow boundary condition setting module 705, wherein:

[0133] The three-dimensional model construction module 701 is used to acquire angiographic images of the aorta-intracranial artery in the aorta-intracranial artery related region of the user, and to construct a three-dimensional vascular model of the aorta-intracranial artery based on the angiographic images.

[0134] The blood flow distribution determination module 702 is used to determine the blood flow distribution at the vascular terminal outlet of the branch vessels supplying blood from the aortic arch to the intracranial cavity in the three-dimensional vascular model.

[0135] The steady-state pressure difference determination module 703 is used to perform steady-state calculations on the three-dimensional vascular model based on the blood flow distribution to determine the steady-state pressure difference between the inlet of the ascending aorta and the outlet of the descending aorta.

[0136] The initial pressure determination module 704 is used to determine the initial pressure based on an empirical coefficient, the average flow rate during the cycle, the average flow rate during systole, the steady-state pressure difference, the diastolic pressure, and the reference pressure.

[0137] The inlet flow boundary condition setting module 705 is used to perform intracranial arterial hemodynamic analysis based on the blood flow distribution, the initial pressure, and the inlet flow boundary conditions using a three-unit air cavity model, and to determine the intracranial arterial hemodynamic data.

[0138] For example, the 3D model building module 701 is specifically used for:

[0139] Obtain angiographic images of the aorta-intracranial artery in the aorta-intracranial artery region related to the user's aorta, and based on the angiographic images, determine the aortic arch, the relevant branches of the aortic arch, the model's starting point, and the model's ending point; the model's starting point is the ascending aorta, and the ending point is the proximal descending aorta.

[0140] A three-dimensional vascular model of the aorta-intracranial artery is constructed based on the aortic arch, its related branches, the model's starting end, and the model's ending end.

[0141] For example, the blood flow allocation determination module 702 is specifically used for:

[0142] Using medical image analysis software, the center line of the blood vessels is extracted from the three-dimensional blood vessel model, and sampling points on the center line of the blood vessels are determined.

[0143] Calculate the cross-sectional area of ​​the branch vessels of the aortic arch supplying blood to the intracranial cavity at the sampling point, and determine the cross-sectional area sequence;

[0144] The minimum cross-sectional area of ​​the branch vessels supplying blood from the aortic arch to the intracranial cavity at the sampling point is determined from the cross-sectional area sequence. Based on the minimum cross-sectional area and the connectivity of the vascular network in the three-dimensional vascular model, a Markov chain transition model including bifurcation nodes and closed-loop connected nodes is constructed.

[0145] Based on the Markov chain transfer model, the blood flow distribution at the vascular terminal outlet of the branch vessels supplying blood from the aortic arch to the intracranial cavity is determined.

[0146] For example, the blood flow allocation determination module 702 is also specifically used for:

[0147] For a bifurcation node, the model transition probability of the Markov chain transition model of the aortic arch supplying blood to the intracranial cavity is determined based on the proportion of the minimum cross-sectional area to the sum of the minimum cross-sectional areas of the branch vessels of the aortic arch connected to the bifurcation node.

[0148] For a closed-loop connected node, based on the minimum cross-sectional area of ​​the branch vessels of the aortic arch that supply blood to the intracranial cavity and the vessel connectivity relationship connected to the closed-loop connected node, the corresponding bidirectional state transition edge and the model transition probability of the Markov chain transition model are set.

[0149] The vascular terminal outlet of the branch vessels supplying blood from the aortic arch to the intracranial cavity is set as the model absorption state of the Markov chain transition model. Based on the model absorption state and the model transition probability, the blood flow distribution at the vascular terminal outlet of the branch vessels supplying blood from the aortic arch to the intracranial cavity is determined.

[0150] For example, the steady-state pressure difference determination module 703 is specifically used for:

[0151] The blood flow distribution at the vascular terminal outlet of the branch vessels supplying blood from the aortic arch to the intracranial cavity is used as the outlet flow boundary condition of the three-dimensional vascular model.

[0152] Based on the aforementioned outlet flow boundary conditions and flow conservation law, the three-dimensional vascular model is subjected to steady-state calculations using hemodynamic steady-state equations to determine the inlet pressure of the ascending aorta and the outlet pressure of the descending aorta.

[0153] The steady-state pressure difference between the inlet of the ascending aorta and the outlet of the descending aorta is determined based on the inlet pressure of the ascending aorta and the outlet pressure of the descending aorta.

[0154] For example, the initial pressure determination module 704 is specifically used for:

[0155] An empirical coefficient is determined, and the ratio of the average flow rate during the period to the average flow rate during the systolic phase is determined; the empirical coefficient is determined based on the calibration results of preliminary experiments or sample data;

[0156] The product of the empirical coefficient, the flow ratio, and the steady-state pressure difference is used as the redundant pressure;

[0157] Determine the pressure difference between the diastolic pressure and the distal reference pressure, and use the sum of the redundant pressure and the pressure difference as the initial pressure.

[0158] For example, the ingress flow boundary condition setting module 705 is specifically used for:

[0159] The differential model of the three-unit air cavity model is transformed into an integral model through integration by parts;

[0160] The blood flow allocation is used as the outlet boundary condition, and the inlet flow boundary condition is set.

[0161] Using the integral model of the three-unit air cavity model, based on the initial pressure and the inlet flow boundary conditions, multi-cardiac cycle hemodynamic analysis is performed on the intracranial artery to determine the hemodynamic data of the intracranial artery.

[0162] Each module in the aforementioned intracranial arterial hemodynamic analysis device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0163] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 8As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for intracranial arterial hemodynamic analysis. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or a key vector, trackball, or touchpad set on the computer device casing, or an external key vector disk, touchpad, or mouse, etc.

[0164] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0165] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0166] Step 1: Obtain angiographic images of the aorta-intracranial artery in the relevant region of the user's aorta-intracranial artery, and construct a three-dimensional vascular model of the aorta-intracranial artery based on the angiographic images;

[0167] Step 2: Determine the blood flow distribution at the vascular terminal outlets of the branch vessels supplying blood from the aortic arch to the intracranial cavity in the three-dimensional vascular model.

[0168] Step 3: Based on the blood flow distribution, perform steady-state calculations on the three-dimensional vascular model to determine the steady-state pressure difference between the inlet of the ascending aorta and the outlet of the descending aorta;

[0169] Step 4: Determine the initial pressure based on the empirical coefficient, the average flow rate during the cycle, the average flow rate during systole, the steady-state pressure difference, the diastolic pressure, and the reference pressure;

[0170] Step 5: Using a three-unit air cavity model, based on the blood flow distribution, the initial pressure, and the inlet flow boundary conditions, perform intracranial arterial hemodynamic analysis to determine the intracranial arterial hemodynamic data.

[0171] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0172] Step 1: Obtain angiographic images of the aorta-intracranial artery in the relevant region of the user's aorta-intracranial artery, and construct a three-dimensional vascular model of the aorta-intracranial artery based on the angiographic images;

[0173] Step 2: Determine the blood flow distribution at the vascular terminal outlets of the branch vessels supplying blood from the aortic arch to the intracranial cavity in the three-dimensional vascular model.

[0174] Step 3: Based on the blood flow distribution, perform steady-state calculations on the three-dimensional vascular model to determine the steady-state pressure difference between the inlet of the ascending aorta and the outlet of the descending aorta;

[0175] Step 4: Determine the initial pressure based on the empirical coefficient, the average flow rate during the cycle, the average flow rate during systole, the steady-state pressure difference, the diastolic pressure, and the reference pressure;

[0176] Step 5: Using a three-unit air cavity model, based on the blood flow distribution, the initial pressure, and the inlet flow boundary conditions, perform intracranial arterial hemodynamic analysis to determine the intracranial arterial hemodynamic data.

[0177] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0178] Step 1: Obtain angiographic images of the aorta-intracranial artery in the relevant region of the user's aorta-intracranial artery, and construct a three-dimensional vascular model of the aorta-intracranial artery based on the angiographic images;

[0179] Step 2: Determine the blood flow distribution at the vascular terminal outlets of the branch vessels supplying blood from the aortic arch to the intracranial cavity in the three-dimensional vascular model.

[0180] Step 3: Based on the blood flow distribution, perform steady-state calculations on the three-dimensional vascular model to determine the steady-state pressure difference between the inlet of the ascending aorta and the outlet of the descending aorta;

[0181] Step 4: Determine the initial pressure based on the empirical coefficient, the average flow rate during the cycle, the average flow rate during systole, the steady-state pressure difference, the diastolic pressure, and the reference pressure;

[0182] Step 5: Using a three-unit air cavity model, based on the blood flow distribution, the initial pressure, and the inlet flow boundary conditions, perform intracranial arterial hemodynamic analysis to determine the intracranial arterial hemodynamic data.

[0183] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0184] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this 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 memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0185] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0186] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for analyzing intracranial arterial hemodynamics, characterized in that, include: Obtain angiographic images of the aorta-intracranial artery in the aorta-intracranial artery region related to the user, and construct a three-dimensional vascular model of the aorta-intracranial artery based on the angiographic images; Determine the blood flow distribution at the vascular terminal outlets of the branch vessels supplying blood from the aortic arch to the intracranial cavity in the three-dimensional vascular model. Based on the blood flow distribution, steady-state calculations are performed on the three-dimensional vascular model to determine the steady-state pressure difference between the inlet of the ascending aorta and the outlet of the descending aorta. The initial pressure is determined based on empirical coefficients, average flow rate during the cycle, average flow rate during systole, the steady-state pressure difference, diastolic pressure, and reference pressure. Using a three-unit air cavity model, intracranial arterial hemodynamic analysis was performed based on the blood flow distribution, initial pressure, and inlet flow boundary conditions to determine the intracranial arterial hemodynamic data.

2. The method according to claim 1, characterized in that, The process of acquiring angiographic images of the aorta-intracranial artery region related to the user's aorta-intracranial artery, and constructing a three-dimensional vascular model of the aorta-intracranial artery based on the angiographic images, includes: Obtain angiographic images of the aorta-intracranial artery in the aorta-intracranial artery region related to the user's aorta, and based on the angiographic images, determine the aortic arch, the relevant branches of the aortic arch, the model's starting point, and the model's ending point; the model's starting point is the ascending aorta, and the ending point is the proximal descending aorta. A three-dimensional vascular model of the aorta-intracranial artery is constructed based on the aortic arch, its related branches, the model's starting end, and the model's ending end.

3. The method according to claim 1, characterized in that, Determining the blood flow distribution at the vascular terminal outlets of the aortic arch branch vessels supplying blood to the intracranial cavity in the three-dimensional vascular model includes: Using medical image analysis software, the center line of the blood vessels is extracted from the three-dimensional blood vessel model, and sampling points on the center line of the blood vessels are determined. Calculate the cross-sectional area of ​​the branch vessels of the aortic arch supplying blood to the intracranial cavity at the sampling point, and determine the cross-sectional area sequence; The minimum cross-sectional area of ​​the branch vessels supplying blood from the aortic arch to the intracranial cavity at the sampling point is determined from the cross-sectional area sequence. Based on the minimum cross-sectional area and the connectivity of the vascular network in the three-dimensional vascular model, a Markov chain transition model including bifurcation nodes and closed-loop connected nodes is constructed. Based on the Markov chain transfer model, the blood flow distribution at the vascular terminal outlet of the branch vessels supplying blood from the aortic arch to the intracranial cavity is determined.

4. The method according to claim 3, characterized in that, The determination of blood flow distribution at the vascular terminal outlets of the branch vessels supplying blood from the aortic arch to the intracranial cavity, based on the Markov chain transfer model, includes: For a bifurcation node, the model transition probability of the Markov chain transition model of the aortic arch supplying blood to the intracranial cavity is determined based on the proportion of the minimum cross-sectional area to the sum of the minimum cross-sectional areas of the branch vessels of the aortic arch connected to the bifurcation node. For a closed-loop connected node, based on the minimum cross-sectional area of ​​the branch vessels of the aortic arch that supply blood to the intracranial cavity and the vessel connectivity relationship connected to the closed-loop connected node, the corresponding bidirectional state transition edge and the model transition probability of the Markov chain transition model are set. The vascular terminal outlet of the branch vessels supplying blood from the aortic arch to the intracranial cavity is set as the model absorption state of the Markov chain transition model. Based on the model absorption state and the model transition probability, the blood flow distribution at the vascular terminal outlet of the branch vessels supplying blood from the aortic arch to the intracranial cavity is determined.

5. The method according to claim 1, characterized in that, The step of performing steady-state calculations on the three-dimensional vascular model based on the blood flow distribution to determine the steady-state pressure difference between the inlet of the ascending aorta and the outlet of the descending aorta includes: The blood flow distribution at the vascular terminal outlet of the branch vessels supplying blood from the aortic arch to the intracranial cavity is used as the outlet flow boundary condition of the three-dimensional vascular model. Based on the aforementioned outlet flow boundary conditions and flow conservation law, the three-dimensional vascular model is subjected to steady-state calculations using hemodynamic steady-state equations to determine the inlet pressure of the ascending aorta and the outlet pressure of the descending aorta. The steady-state pressure difference between the inlet of the ascending aorta and the outlet of the descending aorta is determined based on the inlet pressure of the ascending aorta and the outlet pressure of the descending aorta.

6. The method according to claim 1, characterized in that, The reference pressure is the distal reference pressure. The initial pressure is determined based on empirical coefficients, the average flow rate during the period, the average flow rate during systole, the steady-state pressure difference, the diastolic pressure, and the reference pressure, including: An empirical coefficient is determined, and the ratio of the average flow rate during the period to the average flow rate during the systolic phase is determined; the empirical coefficient is determined based on the calibration results of preliminary experiments or sample data; The product of the empirical coefficient, the flow ratio, and the steady-state pressure difference is used as the redundant pressure; Determine the pressure difference between the diastolic pressure and the distal reference pressure, and use the sum of the redundant pressure and the pressure difference as the initial pressure.

7. The method according to claim 1, characterized in that, The intracranial artery hemodynamic analysis is performed using a three-unit air cavity model, based on the blood flow distribution, initial pressure, and inlet flow boundary conditions, to determine the intracranial artery hemodynamic data, including: The differential model of the three-unit air cavity model is transformed into an integral model through integration by parts; The blood flow allocation is used as the outlet boundary condition, and the inlet flow boundary condition is set. Using the integral model of the three-unit air cavity model, based on the initial pressure and the inlet flow boundary conditions, multi-cardiac cycle hemodynamic analysis is performed on the intracranial artery to determine the hemodynamic data of the intracranial artery.

8. An intracranial arterial hemodynamic analysis device, characterized in that, The intracranial arterial hemodynamic analysis device includes: The three-dimensional model construction module is used to acquire angiographic images of the aorta-intracranial artery in the aorta-intracranial artery related region of the user, and to construct a three-dimensional vascular model of the aorta-intracranial artery based on the angiographic images. The blood flow distribution determination module is used to determine the blood flow distribution at the vascular terminal outlet of the branch vessels supplying blood from the aortic arch to the intracranial cavity in the three-dimensional vascular model. The steady-state pressure difference determination module is used to perform steady-state calculations on the three-dimensional vascular model based on the blood flow distribution to determine the steady-state pressure difference between the inlet of the ascending aorta and the outlet of the descending aorta. The initial pressure determination module is used to determine the initial pressure based on an empirical coefficient, the average flow rate during the cycle, the average flow rate during systole, the steady-state pressure difference, the diastolic pressure, and the reference pressure. The inlet flow boundary condition setting module is used to perform intracranial arterial hemodynamic analysis based on the blood flow distribution, the initial pressure, and the inlet flow boundary conditions using a three-unit air cavity model, and to determine the intracranial arterial hemodynamic data.

9. 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 according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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