Vessel reconstruction and centerline driven hemodynamics simulation method and system
By constructing a three-dimensional vascular model and performing dynamic simulation based on vascular reconstruction and centerline driving, the problems of real-time interaction of hemodynamics and vascular structure modeling in existing technologies are solved, realizing synchronous dynamic simulation of blood flow and vascular geometry and real-time query of local blood flow parameters.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHANDONG UNIV
- Filing Date
- 2026-05-28
- Publication Date
- 2026-08-04
AI Technical Summary
Existing technologies are insufficient for real-time interaction of hemodynamics and three-dimensional modeling of vascular structures in clinical practice. They lack a unified modeling mechanism for blood flow propagation paths and cannot reflect the dynamic changes in vascular pulsation.
A method based on vascular reconstruction and centerline driving is adopted. By preprocessing digital subtraction angiography and computed tomography angiography data, a three-dimensional vascular model is constructed, the centerline is extracted and the arc length parameter is calculated, a flow velocity estimation model is established, and multi-frame three-dimensional sub-models are generated by DSA sequence matching to achieve synchronous dynamic simulation of blood flow and vascular geometry.
It achieves synchronous dynamic simulation of blood flow and vascular geometry, enables real-time querying of local blood flow parameters, and supports local magnification observation, meeting the needs of real-time clinical interaction.
Smart Images
Figure CN122263749B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of dynamic simulation, specifically relating to a hemodynamic simulation method and system based on vascular reconstruction and centerline driving. Background Technology
[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.
[0003] The occurrence and development of cardiovascular and cerebrovascular diseases are closely related to hemodynamic status. For example, factors such as abnormal local flow velocity, changes in pressure gradient, and periodic stress fluctuations in the vessel wall can all have a significant impact on the formation of vascular lesions (such as aneurysms and stenosis). Therefore, modeling vascular structure and its blood flow status based on medical imaging is of great significance for clinical diagnosis and treatment.
[0004] Currently, clinical practice mainly relies on two types of imaging data: digital subtraction angiography (DSA) and computed tomography angiography (CTA). DSA, through continuous X-ray imaging combined with contrast agent injection, can record blood flow processes at a temporal resolution of 10–30 frames per second, clearly reflecting the inflow, diffusion, and elution of contrast agents in blood vessels, exhibiting significant dynamic characteristics. However, DSA is essentially a two-dimensional projection image, lacking three-dimensional spatial information, making it difficult to accurately obtain vascular geometric parameters.
[0005] CTA, on the other hand, acquires three-dimensional volumetric data through multi-slice spiral CT scanning. It has high spatial resolution and can accurately reconstruct vascular morphology and measure geometric information such as vessel diameter and volume. However, CTA usually only provides static data at a single moment and cannot reflect the dynamic process of blood flow changes over time.
[0006] In existing technologies, blood flow analysis primarily relies on computational fluid dynamics (CFD) methods to obtain velocity and pressure field distributions by solving the Navier-Stokes equations. However, this method requires complex mesh generation and boundary condition settings, resulting in high computational complexity and typically long computation times, making it difficult to meet the needs of real-time clinical interaction. Furthermore, CFD methods are highly dependent on inlet flow rate and pressure boundary conditions, which are often difficult to obtain accurately in actual clinical settings.
[0007] On the other hand, existing vascular analysis systems generally have the following shortcomings: they lack a unified blood flow propagation path modeling mechanism, and there is a lack of constraint relationship between blood flow direction and vascular structure; blood flow parameters are difficult to query in real time at any location; and they lack vascular pulsation modeling based on real image data, which cannot reflect the dynamic changes of blood vessels with the cardiac cycle. Summary of the Invention
[0008] To address the aforementioned problems, this invention proposes a hemodynamic simulation method and system based on vascular reconstruction and centerline driving. This invention achieves unified modeling and interactive analysis of blood flow propagation, vascular structure, and vascular pulsation while ensuring computational efficiency.
[0009] According to some embodiments, the present invention adopts the following technical solution: A hemodynamic simulation method based on vascular reconstruction and centerline driving includes the following steps: The acquired digital subtraction angiography and computed tomography angiography data were preprocessed and segmented to construct a three-dimensional vascular model. Extract the vessel centerline and calculate the arc length parameter along the centerline. Calculate the vessel radius distribution through the distance field and establish a flow velocity estimation model using the continuity equation. Based on the flow velocity estimation model, a time-dependent blood flow front function is constructed to represent the dynamic propagation of blood flow in blood vessels; Multi-frame three-dimensional sub-models are generated by matching digital subtraction angiography sequences, and the deformation information of blood vessels over time is extracted to construct a periodic pulsation model. By coupling the blood flow front function with the periodic pulsation model, dynamic simulation of synchronous changes in blood flow and vascular geometry is achieved, and local blood flow parameters can be queried and visualized through an interactive mechanism.
[0010] As an alternative implementation, the preprocessing of the acquired digital subtraction angiography and computed tomography angiography data includes: smoothing the acquired digital subtraction angiography and computed tomography angiography data using Gaussian filtering, and enhancing the image using a contrast-limited adaptive histogram equalization method to make the gray-level distribution of the vascular region more concentrated.
[0011] As an alternative implementation, the process of segmenting blood vessels from the acquired digital subtraction angiography and computed tomography angiography data includes: extracting high-density blood vessel regions from the preprocessed image using a threshold-based segmentation method to achieve preliminary segmentation; performing morphological processing on the preliminary segmentation results to retain only the largest connected blood vessel regions; and generating a mesh structure composed of vertex sets, edge sets, and triangular facet sets by constructing isosurfaces in a voxel mesh to convert the voxel data into a three-dimensional surface mesh model.
[0012] As an alternative implementation, the process of extracting the vessel centerline and calculating the arc length parameter along the centerline includes: constructing a distance field within the vessel, i.e. calculating the minimum distance from each voxel point to the vessel boundary; extracting local extreme paths based on the distance field; and generating a centerline network from the vessel inlet to the end of each branch by combining Dijkstra's shortest path algorithm; representing the centerline as one or more parameterized curves; and performing directional unification processing on all branches.
[0013] As an alternative implementation method, the process of calculating the blood vessel radius distribution through the distance field and establishing a flow velocity estimation model using the continuity equation includes: defining the blood vessel radius as the shortest Euclidean distance from the centerline point to the blood vessel boundary; directly obtaining the radius information based on the distance field, that is, the distance field value of the centerline point is the local radius of that point; for each centerline point, mapping its radius value to the vertex of the blood vessel surface; and using the neighborhood smoothing method to process the vertex radius, that is, performing a weighted average of each vertex and its neighboring vertices to obtain a continuous and smooth radius distribution.
[0014] As an alternative implementation, a time-dependent blood flow front function is constructed based on a flow velocity estimation model to represent the dynamic propagation of blood flow in blood vessels. This includes: estimating blood flow velocity based on a continuity equation, estimating pressure based on a simplified Bernoulli equation, normalizing the centerline arc length parameter, defining a blood flow front function that varies with time, wherein the blood flow front function is inversely proportional to the total number of frames in the inflow phase, and calculating the blood flow tail based on the blood flow front function and the blood flow length. Based on the blood flow tail and blood flow front functions, a blood flow filling function is constructed, and a smoothing function is used to process the filling boundary of the blood flow filling function. Based on the estimated blood flow velocity and the processed blood flow filling function, a front enhancement term is introduced to obtain the dynamic flow velocity.
[0015] As a further defined implementation method, the centerline arc length parameter is normalized to: s ; Define the blood flow front function head(t) as a function of time t: ; Where T is the total number of frames in the inflow phase, and tail(t) is defined as the blood flow tail: ; in, Indicates the length of blood flow; Construct the blood flow filling function: ; A smoothing function is used to process the filling boundary; Introducing frontier enhancement terms : ; The dynamic flow rate is obtained: ; in, For the estimated speed, This is the blood flow propagation velocity coefficient. This represents the standard deviation of the filter kernel.
[0016] As an alternative implementation method, the process of generating multi-frame three-dimensional sub-models by matching digital subtraction angiography sequences, extracting the deformation information of blood vessels over time, and constructing a periodic pulsation model includes: extracting multiple key images from the original digital subtraction angiography sequence, matching them with the projection of the three-dimensional blood vessel model, and constructing a corresponding set of three-dimensional sub-models. By analyzing the radius changes of each three-dimensional sub-model at different time points, the deformation characteristics of blood vessels in the time dimension are obtained. The deformation characteristics are fitted with a periodic function to construct a time function of blood vessel radius, thereby realizing the coupling of blood flow and blood vessel wall deformation.
[0017] As an alternative implementation, the process of querying and visualizing local blood flow parameters through an interactive mechanism includes: responding to a user's instruction to change the input position in a three-dimensional view, emitting a ray from the viewpoint, calculating the intersection of the ray with the blood vessel surface, finding the vertex closest to the intersection in the grid, obtaining the radius, flow velocity, and pressure of the vertex, and displaying them; A local magnification observation mechanism is introduced. By setting up a second camera, which is always pointed at the currently selected position and kept at a certain distance, a local area can be magnified and displayed.
[0018] A hemodynamic simulation system based on vascular reconstruction and centerline driving includes: The 3D vascular model construction module is configured to preprocess and segment the acquired digital subtraction angiography and computed tomography angiography data to construct a 3D vascular model. The centerline driving module is configured to extract the vessel centerline and calculate the arc length parameter along the centerline, calculate the vessel radius distribution through the distance field, and establish a flow velocity estimation model using the continuity equation. The dynamic propagation module is configured to construct a time-dependent blood flow front function based on a flow velocity estimation model to represent the dynamic propagation of blood flow in blood vessels. The dynamic pulsation module is configured to generate multi-frame three-dimensional sub-models by matching digital subtraction angiography sequences, extract the deformation information of blood vessels over time, and construct a periodic pulsation model. The interaction and visualization module is configured to couple the blood flow front function with the periodic pulsation model to achieve dynamic simulation of synchronous changes in blood flow and vascular geometry, and to realize the query and visualization of local blood flow parameters through the interaction mechanism.
[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention utilizes the three-dimensional structural information provided by CTA to establish a vascular geometric model, uses the centerline to describe the blood flow propagation path, and realizes the dynamic propagation of blood flow by simplifying the fluid model and time function. At the same time, it combines DSA sequence to correct the vascular geometric changes, thereby realizing the dynamic simulation of blood flow coupling with the vascular wall.
[0020] This invention introduces a local magnification observation mechanism. By setting up a second camera, which is always pointed at the currently selected location and maintained at a certain distance, a magnified display of a local area can be achieved. This mechanism allows users to simultaneously observe global blood flow distribution and local detailed changes.
[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description
[0022] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.
[0023] Figure 1 A general flowchart of a method provided for one embodiment. Detailed Implementation
[0024] The present invention will be further described below with reference to the accompanying drawings and embodiments.
[0025] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.
[0026] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.
[0027] Where there is no conflict, the embodiments and features described in this application may be combined with each other.
[0028] Example 1 A multimodal vascular reconstruction and centerline-driven hemodynamic simulation method based on DSA / CTA, such as Figure 1 As shown, it includes the following steps: Step S1: Vascular modeling process In this embodiment, CTA three-dimensional volume data is first acquired, with a typical resolution of 512. The voxel spacing is 0.5. The unit is mm. Due to noise and tissue interference in CTA images, the raw data is first preprocessed.
[0029] Specifically, a Gaussian filter is used to smooth the image, and the standard deviation of the filter kernel is set to... To suppress high-frequency noise, a contrast-limited adaptive histogram equalization method was used to enhance the image and make the gray-level distribution of the vascular region more concentrated.
[0030] After preprocessing, blood vessel segmentation is performed on the image. In this embodiment, a threshold-based segmentation method is used, with the threshold T set to approximately 100–150 HU based on Hounsfield (HU) units, thereby extracting high-density blood vessel regions. V = {x }; Here, V represents the set of blood vessel voxels. To eliminate noise points and fill small holes, morphological processing is performed on the segmentation results, including closing operations and connected component filtering, retaining only the largest connected blood vessel regions.
[0031] Subsequently, the Marching Cubes algorithm was used to convert the voxel data into a 3D surface mesh model. This process generates a mesh structure consisting of a vertex set V, an edge set E, and a triangular facet set F by constructing isosurfaces within the voxel mesh. In actual experiments, the generated blood vessel model typically contains approximately... vertices of order of magnitude and The number of triangular facets on the order of magnitude provides the basis for subsequent calculations.
[0032] Step S2, Centerline Extraction Process After obtaining the 3D mesh model of the blood vessel, it is necessary to further extract the vessel centerline in order to describe the topological structure of the vessel and the blood flow propagation path. In this embodiment, the centerline is defined as the path inside the blood vessel that is farthest from the boundary, which can effectively reflect the geometric skeleton of the blood vessel.
[0033] In the specific implementation, a distance field is first constructed within the blood vessel, that is, the minimum distance from each voxel point to the blood vessel boundary is calculated. This distance field can be implemented using the Fast Marching Method, with a time complexity of O(n log n). Subsequently, local extreme paths are extracted based on the distance field, and combined with Dijkstra's shortest path algorithm, a centerline network is generated from the blood vessel inlet to the ends of each branch.
[0034] The centerline can be represented as one or more parametric curves: = ( , , ), ; Here, s represents the normalized arc length parameter along the vascular path. In actual calculations, the centerline is usually composed of a discrete point sequence, with each branch containing approximately 100–500 sampling points, and the entire vascular tree structure can contain thousands of centerline points.
[0035] To ensure consistency in the centerline direction, this embodiment unifies the direction of all branches, ensuring that all centerlines point from the inlet to the distal end. This process is crucial for subsequent blood flow propagation modeling.
[0036] Step S3: Calculation of blood vessel radius After the centerline is extracted, the local radius of the blood vessel at each location is further calculated. The blood vessel radius is defined as the shortest Euclidean distance from the centerline point to the blood vessel boundary, and its mathematical expression is: = ; in, It refers to the surface of blood vessels.
[0037] In practical implementation, to improve computational efficiency, this embodiment directly obtains radius information based on the distance field; that is, the distance field value of the centerline point is the local radius of that point. For each centerline point, its radius value is mapped to a vertex on the blood vessel surface. Specifically, for any surface vertex... Find its nearest centerline point The radius value corresponding to the center line point is then assigned to the vertex, thus obtaining the vertex-level radius distribution: ; in: ; To avoid abrupt changes in radius due to segmentation of the centerline, this embodiment further employs a neighborhood smoothing method to process the vertex radius, that is, to perform a weighted average of each vertex and its neighboring vertices, thereby obtaining a continuous and smooth radius distribution.
[0038] Step S4: Calculation of hemodynamic parameters After obtaining the blood vessel radius distribution, this embodiment estimates the blood flow velocity based on the continuity equation. Assuming blood is an incompressible fluid, the flow rate is conserved: ; Wherein the cross-sectional area is: ; Therefore, the flow velocity can be expressed as: ; In practical applications, the inflow rate Q can be set based on clinical experience or typical values; for example, in cerebral blood vessels, Q can be taken as Q = .
[0039] For example, when the blood vessel radius is r = 2 mm, the flow velocity can be calculated to be approximately However, when the radius is reduced to 1 mm, the flow rate will increase to approximately [missing value]. This result is consistent with actual physiological laws.
[0040] Furthermore, this embodiment estimates the pressure based on the simplified Bernoulli equation: ; in, This represents blood density. The model reflects the effect of changes in flow velocity on pressure distribution.
[0041] Furthermore, to characterize the stress on the blood vessel wall, this embodiment further defines a wall load index: ; This indicator takes into account both flow velocity and vascular scale, and has certain indicative significance for potential risk areas.
[0042] Step S5: Blood Flow Dynamic Simulation Process To simulate the dynamic propagation of contrast agent in DSA, this embodiment constructs a blood flow propagation model based on centerline parameters. Specifically, the centerline arc length parameter is normalized as follows: s ; And define a time-varying blood flow front function: ; Where T represents the total number of frames in the inflow phase. The blood flow tail is also defined: ; in, Indicates the length of blood flow.
[0043] Based on the above definition, construct the blood flow filling function: ; In practical implementation, to avoid abrupt changes, this embodiment uses a smoothing function to process the filling boundary.
[0044] Further introduction of frontier enhancement terms: ; in, For the estimated speed, This is the blood flow propagation velocity coefficient. This represents the standard deviation of the filter kernel.
[0045] Thus, the dynamic flow velocity is obtained: ; This model can simulate the entire process of blood flow "gradually advancing - filling - washing away".
[0046] Step S6: DSA-based modeling of vascular pulsation Building upon the blood flow simulation described above, this embodiment introduces DSA sequence-driven vascular pulsation modeling to further enhance the model's realism. Specifically, approximately 40 key images are selected from the original DSA sequence, and a corresponding set of three-dimensional sub-models is constructed by matching their projections with the projections of the three-dimensional vascular model.
[0047] By analyzing the radius changes of these sub-models at different time points, the deformation characteristics of blood vessels in the time dimension are obtained. Subsequently, a periodic function is used to fit this change, constructing a time function for the blood vessel radius: ; in, This is a function of the static radius of the blood vessel. The number of heartbeats per unit of time. The phase shift, α, is typically 2%–10%. Based on this, the blood flow velocity further varies with time: ; This achieves the coupling of blood flow and vessel wall deformation.
[0048] Step S7: Interactive Diagnosis and Local Magnification Process In this embodiment, a ray-based interactive mechanism is used to achieve local blood flow analysis. Specifically, when the user moves the mouse in the 3D view, a ray is emitted from the viewpoint: = o + td; Where o is the current viewpoint or camera position, and d is the ray direction vector.
[0049] Calculate the intersection point of the ray and the surface of the blood vessel, and find the vertex in the mesh that is closest to the intersection point: ; This is the intersection of the ray and the surface of the blood vessel.
[0050] The parameters such as radius, flow velocity, and pressure of the vertex are then read and displayed in real time.
[0051] To further enhance diagnostic capabilities, this embodiment introduces a local magnification observation mechanism. By setting up a second camera (ZoomCamera) that is always pointed at the currently selected location and maintained at a certain distance, a magnified display of the local area is achieved. This mechanism allows users to simultaneously observe global blood flow distribution and localized detailed changes.
[0052] In actual testing, the response time of this interaction process is less than 50 ms, which can meet the requirements of real-time interaction.
[0053] Example 2 A hemodynamic simulation system based on vascular reconstruction and centerline driving includes: The 3D vascular model construction module is configured to preprocess and segment the acquired digital subtraction angiography and computed tomography angiography data to construct a 3D vascular model. The centerline driving module is configured to extract the vessel centerline and calculate the arc length parameter along the centerline, calculate the vessel radius distribution through the distance field, and establish a flow velocity estimation model using the continuity equation. The dynamic propagation module is configured to construct a time-dependent blood flow front function based on a flow velocity estimation model to represent the dynamic propagation of blood flow in blood vessels. The dynamic pulsation module is configured to generate multi-frame three-dimensional sub-models by matching digital subtraction angiography sequences, extract the deformation information of blood vessels over time, and construct a periodic pulsation model. The interaction and visualization module is configured to couple the blood flow front function with the periodic pulsation model to achieve dynamic simulation of synchronous changes in blood flow and vascular geometry, and to realize the query and visualization of local blood flow parameters through the interaction mechanism.
[0054] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of one or more computer-usable storage media (including, but not limited to, disk storage, etc.) containing computer-usable program code. CD - ROM It takes the form of a computer program product implemented on (such as optical memory, etc.).
[0055] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0056] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0057] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0058] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made by those skilled in the art without creative effort within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for hemodynamic simulation based on vessel reconstruction and centerline driven, characterized in that, Includes the following steps: The acquired digital subtraction angiography and computed tomography angiography data were preprocessed and segmented to construct a three-dimensional vascular model. Extract the vessel centerline and calculate the arc length parameter along the centerline. Calculate the vessel radius distribution through the distance field and establish a flow velocity estimation model using the continuity equation. Based on the flow velocity estimation model, a time-dependent blood flow front function is constructed to represent the dynamic propagation of blood flow in blood vessels; Multi-frame three-dimensional sub-models are generated by matching digital subtraction angiography sequences, and the deformation information of blood vessels over time is extracted to construct a periodic pulsation model. By coupling the blood flow front function with the periodic pulsation model, dynamic simulation of synchronous changes in blood flow and vascular geometry is achieved, and local blood flow parameters can be queried and visualized through an interactive mechanism. Based on the flow velocity estimation model, a time-dependent blood flow front function is constructed to represent the dynamic propagation process of blood flow in blood vessels. This includes: estimating blood flow velocity based on the continuity equation, estimating pressure based on the simplified Bernoulli equation, normalizing the centerline arc length parameter, defining a blood flow front function that varies with time, wherein the blood flow front function is inversely proportional to the total number of frames in the inflow phase, and calculating the blood flow tail based on the blood flow front function and the blood flow length. Based on the blood flow tail and blood flow front functions, a blood flow filling function is constructed, and a smoothing function is used to process the filling boundary of the blood flow filling function. Based on the estimated blood flow velocity and the processed blood flow filling function, a front enhancement term is introduced to obtain the dynamic flow velocity.
2. The hemodynamic simulation method based on vascular reconstruction and centerline driving as described in claim 1, characterized in that, The preprocessing of the acquired digital subtraction angiography and computed tomography angiography data includes: smoothing the acquired digital subtraction angiography and computed tomography angiography data using Gaussian filtering, and enhancing the image using a contrast-limited adaptive histogram equalization method to make the gray-level distribution of the vascular region more concentrated.
3. The method of claim 1, wherein the method further comprises: determining a centerline of the vessel; and determining a blood flow rate along the centerline of the vessel. The process of segmenting blood vessels from acquired digital subtraction angiography and computed tomography angiography data includes: using a threshold-based segmentation method to extract high-density blood vessel regions from the preprocessed image to achieve preliminary segmentation; performing morphological processing on the preliminary segmentation results to retain only the largest connected blood vessel regions; and generating a mesh structure composed of vertex sets, edge sets, and triangular facet sets by constructing isosurfaces in a voxel mesh to convert the voxel data into a three-dimensional surface mesh model.
4. The method of claim 1, wherein the method further comprises: determining a centerline of the vessel; and determining a blood flow rate along the centerline of the vessel. The process of extracting the vessel centerline and calculating the arc length parameter along the centerline includes: constructing a distance field within the vessel, i.e. calculating the minimum distance from each voxel point to the vessel boundary; extracting local extreme paths based on the distance field; and generating a centerline network from the vessel inlet to the end of each branch by combining Dijkstra's shortest path algorithm; representing the centerline as one or more parameterized curves; and unifying the direction of all branches.
5. The method of claim 1, wherein the method further comprises: determining a centerline of the vessel; and determining a blood flow rate along the centerline of the vessel. The process of calculating the blood vessel radius distribution using the distance field and establishing a flow velocity estimation model using the continuity equation includes: defining the blood vessel radius as the shortest Euclidean distance from the centerline point to the blood vessel boundary; directly obtaining radius information based on the distance field, i.e., the distance field value of the centerline point is the local radius of that point; for each centerline point, mapping its radius value to the vertex of the blood vessel surface; and using a neighborhood smoothing method to process the vertex radius, i.e., performing a weighted average of each vertex and its neighboring vertices to obtain a continuous and smooth radius distribution.
6. The method of claim 1, wherein the method further comprises: determining a centerline of the vessel; and determining a blood flow rate along the centerline of the vessel. The centerline arc length parameter is normalized to: ； Define the blood flow front function head(t) as a function of time t: ； Where T is the total number of frames in the inflow phase, and tail(t) is defined as the blood flow tail: ； wherein, represents the length of blood flow; Construct the blood flow filling function: ； A smoothing function is used to process the filling boundary; Introducing a front edge enhancement : ； The dynamic flow rate is obtained: ； wherein, is the estimated velocity, is the blood flow propagation velocity coefficient, is the filter kernel standard deviation.
7. The hemodynamic simulation method based on vascular reconstruction and centerline driving as described in claim 1, characterized in that, The process of generating multi-frame 3D sub-models by matching digital subtraction angiography sequences, extracting vascular deformation information over time, and constructing a periodic pulsation model includes: extracting multiple key images from the original digital subtraction angiography sequence, matching them with the projection of the 3D vascular model, and constructing a corresponding set of 3D sub-models. By analyzing the radius changes of each three-dimensional sub-model at different time points, the deformation characteristics of blood vessels in the time dimension are obtained. The deformation characteristics are fitted with a periodic function to construct a time function of blood vessel radius, thereby realizing the coupling of blood flow and blood vessel wall deformation.
8. The hemodynamic simulation method based on vascular reconstruction and centerline driving as described in claim 1, characterized in that, The process of querying and visualizing local blood flow parameters through an interactive mechanism includes: responding to the user's instruction to change the input position in the 3D view, emitting a ray from the viewpoint, calculating the intersection of the ray with the blood vessel surface, finding the vertex closest to the intersection in the grid, obtaining the radius, flow velocity, and pressure of the vertex, and displaying them; A local magnification observation mechanism is introduced. By setting up a second camera, which is always pointed at the currently selected position and kept at a certain distance, a local area can be magnified and displayed.
9. A blood flow dynamics simulation system based on vessel reconstruction and centerline driven, characterized in that, include: The 3D vascular model construction module is configured to preprocess and segment the acquired digital subtraction angiography and computed tomography angiography data to construct a 3D vascular model. The centerline driving module is configured to extract the vessel centerline and calculate the arc length parameter along the centerline, calculate the vessel radius distribution through the distance field, and establish a flow velocity estimation model using the continuity equation. The dynamic propagation module is configured to construct a time-dependent blood flow front function based on a flow velocity estimation model to represent the dynamic propagation of blood flow in blood vessels. The dynamic pulsation module is configured to generate multi-frame three-dimensional sub-models by matching digital subtraction angiography sequences, extract the deformation information of blood vessels over time, and construct a periodic pulsation model. The interaction and visualization module is configured to couple the blood flow front function with the periodic pulsation model to achieve dynamic simulation of synchronous changes in blood flow and vascular geometry, and to realize the query and visualization of local blood flow parameters through the interaction mechanism; Based on the flow velocity estimation model, a time-dependent blood flow front function is constructed to represent the dynamic propagation process of blood flow in blood vessels. This includes: estimating blood flow velocity based on the continuity equation, estimating pressure based on the simplified Bernoulli equation, normalizing the centerline arc length parameter, defining a blood flow front function that varies with time, wherein the blood flow front function is inversely proportional to the total number of frames in the inflow phase, and calculating the blood flow tail based on the blood flow front function and the blood flow length. Based on the blood flow tail and blood flow front functions, a blood flow filling function is constructed, and a smoothing function is used to process the filling boundary of the blood flow filling function. Based on the estimated blood flow velocity and the processed blood flow filling function, a front enhancement term is introduced to obtain the dynamic flow velocity.