A method, device and storage medium for generating a blood flow model

Abstract

The application provides a blood flow model generation method, device and equipment and a storage medium. The method comprises the following steps: obtaining three-dimensional angiography data, constructing a three-dimensional blood vessel model based on the three-dimensional angiography data; obtaining two-dimensional angiography data, simulating the flow of contrast agent in the three-dimensional blood vessel model based on the two-dimensional angiography data, and obtaining a simulation blood flow model; projecting the simulation blood flow model in the projection direction of the two-dimensional angiography data to obtain two-dimensional projection data; calculating the difference between the two-dimensional projection data and the two-dimensional angiography data, iteratively adjusting the model parameters of the simulation blood flow model based on the difference until the difference meets a preset convergence condition; determining the contrast agent concentration distribution weight of each position at different times in the iteratively adjusted simulation blood flow model; and based on the concentration distribution weight, projecting the gray scale change of the contrast agent at different times in the two-dimensional angiography data to each position of the three-dimensional blood vessel model to generate a target blood flow model.

Description

Technical Field

[0001] This application relates to the field of medical image processing technology, and in particular to a method, apparatus, device and storage medium for generating a blood flow model. Background Technology

[0002] Digital subtraction angiography (DSA) is an important imaging technique in the diagnosis and treatment of vascular diseases. It involves injecting a contrast agent into blood vessels and using X-ray imaging to observe the morphology and blood flow, providing crucial information for clinical diagnosis and treatment. Traditional two-dimensional digital subtraction angiography (2D-DSA) captures vascular morphology and blood flow characteristics through dynamic imaging, but its spatial resolution is limited, making it difficult to fully present the complex three-dimensional anatomical structure of blood vessels. Therefore, three-dimensional digital subtraction angiography (3D-DSA) technology has been developed. 3D-DSA generates high-resolution 3D vascular models through rotational acquisition and 3D reconstruction techniques, significantly improving the detection rate of lesions such as aneurysms and arteriovenous malformations, providing more intuitive and comprehensive imaging information for clinical diagnosis. However, 3D-DSA is limited to static 3D imaging and cannot capture the crucial physiological information of dynamic changes in blood flow.

[0003] To overcome this limitation, four-dimensional digital subtraction angiography (4D-DSA) technology was developed. 4D-DSA introduces a temporal dimension into three-dimensional space. Through high-speed rotating X-ray acquisition and time-resolved reconstruction algorithms, it generates a blood flow model that continuously records the flow of contrast agent within blood vessels, forming a dynamic three-dimensional image sequence with temporal information. This technology not only effectively preserves the high spatial resolution of 3D-DSA but also provides blood flow information, offering a new perspective for vascular function assessment, assisting clinicians in making better surgical decisions, and improving diagnostic accuracy.

[0004] Currently, the generation of blood flow models in 4D-DSA technology typically involves directly filling the time axis of a 3D model with contrast agent grayscale information from 2D angiography data. However, in actual angiography, multiple overlapping blood vessels may occur, and 2D angiography data cannot effectively identify this overlap, leading to inaccurate identification of blood flow location and problems such as blood flow misalignment. For example, two distant blood vessels may appear as overlapping vessels in 2D angiography data. If contrast agent flows into one vessel first, it may be mistakenly identified as having contrast agent flowing into the other vessel, thus misleading clinicians and affecting diagnostic accuracy. Summary of the Invention

[0005] To overcome the problems existing in related technologies, this application provides a method, apparatus, device and storage medium for generating a blood flow model.

[0006] According to a first aspect of the embodiments of this application, a method for generating a blood flow model is provided, the method comprising:

[0007] Acquire three-dimensional angiography data and construct a three-dimensional vascular model based on the three-dimensional angiography data;

[0008] Two-dimensional angiography data is acquired, and based on the two-dimensional angiography data, the flow of contrast agent is simulated in the three-dimensional vascular model to obtain a simulated blood flow model.

[0009] The simulated blood flow model is projected onto the projection direction of the two-dimensional angiography data to obtain two-dimensional projection data.

[0010] The difference between the two-dimensional projection data and the two-dimensional angiography data is calculated, and the model parameters of the simulated blood flow model are iteratively adjusted based on the difference until the difference meets the preset convergence condition.

[0011] In the iteratively adjusted simulated blood flow model, the weights of the contrast agent concentration distribution at each location at different times are determined.

[0012] Based on the concentration distribution weights, the grayscale changes of the contrast agent in the two-dimensional angiography data at different times are back-projected onto various positions of the three-dimensional vascular model to generate the target blood flow model.

[0013] According to a second aspect of the embodiments of this application, a blood flow model generation apparatus is provided, the apparatus comprising:

[0014] A three-dimensional vascular model construction module is used to acquire three-dimensional angiography data and construct a three-dimensional vascular model based on the three-dimensional angiography data.

[0015] The simulated blood flow model generation module is used to acquire two-dimensional angiography data and, based on the two-dimensional angiography data, simulate the flow of contrast agent in the three-dimensional vascular model to obtain a simulated blood flow model.

[0016] The projection data generation module is used to project the simulated blood flow model onto the projection direction of the two-dimensional angiography data to obtain two-dimensional projection data.

[0017] The model parameter adjustment module is used to calculate the difference between the two-dimensional projection data and the two-dimensional angiography data, and iteratively adjust the model parameters of the simulated blood flow model based on the difference until the difference meets the preset convergence condition.

[0018] The contrast agent concentration distribution weight determination module is used to determine the contrast agent concentration distribution weight at different locations and times in the iteratively adjusted simulated blood flow model.

[0019] The target blood flow model generation module is used to backproject the grayscale changes of the contrast agent in the two-dimensional angiography data at different times onto various positions of the three-dimensional blood vessel model based on the concentration distribution weights, thereby generating the target blood flow model.

[0020] According to a third aspect of the embodiments of this application, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect.

[0021] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method described in the first aspect.

[0022] The technical solutions provided in this application embodiment may include the following beneficial effects:

[0023] In this embodiment, a simulated blood flow model is obtained by simulating the flow of contrast agent in a three-dimensional vascular model. Then, based on the difference between the projection of the simulated blood flow model and the two-dimensional angiography data, the model parameters of the simulated blood flow model are iteratively adjusted to accurately determine the concentration distribution weight of the contrast agent at different locations at different times. Finally, based on this weight, the grayscale changes of the two-dimensional angiography data are back-projected onto the three-dimensional vascular model to generate a more accurate blood flow model. This effectively avoids the blood flow misalignment problem caused by overlapping blood vessels in the two-dimensional angiography data, providing more reliable blood flow information for clinical practice and improving the accuracy of vascular disease diagnosis and treatment efficacy.

[0024] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0025] The accompanying drawings, which are incorporated in and form part of this application, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0026] Figure 1 This is a flowchart illustrating a method for generating a blood flow model according to an exemplary embodiment of this application.

[0027] Figure 2 This is a schematic diagram of a three-dimensional blood vessel model according to an exemplary embodiment of this application.

[0028] Figure 3 This is a schematic diagram of two-dimensional angiography data according to an exemplary embodiment of this application.

[0029] Figure 4This is a structural block diagram of a blood flow model generation device according to an exemplary embodiment of this application.

[0030] Figure 5 This is a structural block diagram of a computer device according to an exemplary embodiment of this application. Detailed Implementation

[0031] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0032] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0033] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0034] Digital subtraction angiography (DSA) is an important imaging technique in the diagnosis and treatment of vascular diseases. It involves injecting a contrast agent into blood vessels and using X-ray imaging to observe the morphology and blood flow, providing crucial information for clinical diagnosis and treatment. Traditional two-dimensional digital subtraction angiography (2D-DSA) captures vascular morphology and blood flow characteristics through dynamic imaging, but its spatial resolution is limited, making it difficult to fully present the complex three-dimensional anatomical structure of blood vessels. Therefore, three-dimensional digital subtraction angiography (3D-DSA) technology has been developed. 3D-DSA generates high-resolution 3D vascular models through rotational acquisition and 3D reconstruction techniques, significantly improving the detection rate of lesions such as aneurysms and arteriovenous malformations, providing more intuitive and comprehensive imaging information for clinical diagnosis. However, 3D-DSA is limited to static 3D imaging and cannot capture the crucial physiological information of dynamic changes in blood flow.

[0035] To overcome this limitation, four-dimensional digital subtraction angiography (4D-DSA) technology was developed. 4D-DSA introduces a temporal dimension into three-dimensional space. Through high-speed rotating X-ray acquisition and time-resolved reconstruction algorithms, it generates a blood flow model that continuously records the flow of contrast agent within blood vessels, forming a dynamic three-dimensional image sequence with temporal information. This technology not only effectively preserves the high spatial resolution of 3D-DSA but also provides blood flow information, offering a new perspective for vascular function assessment, assisting clinicians in making better surgical decisions, and improving diagnostic accuracy.

[0036] Currently, the generation of blood flow models in 4D-DSA technology typically involves directly filling the time axis of a 3D model with contrast agent grayscale information from 2D angiography data. However, in actual angiography, multiple overlapping blood vessels may occur, and 2D angiography data cannot effectively identify this overlap, leading to inaccurate identification of blood flow location and problems such as blood flow misalignment. For example, two distant blood vessels may appear as overlapping vessels in 2D angiography data. If contrast agent flows into one vessel first, it may be mistakenly identified as having contrast agent flowing into the other vessel, thus misleading clinicians and affecting diagnostic accuracy.

[0037] To address the problems existing in related technologies, this application provides a method for generating a blood flow model. This method simulates the flow of contrast agent in a three-dimensional vascular model to obtain a simulated blood flow model. Then, based on the difference between the projection of the simulated blood flow model and the two-dimensional contrast data, the model parameters of the simulated blood flow model are iteratively adjusted to accurately determine the concentration distribution weights of the contrast agent at different locations at different times. Finally, based on these weights, the grayscale changes of the two-dimensional contrast data are back-projected onto the three-dimensional vascular model to generate a more accurate blood flow model. This effectively avoids the blood flow misalignment problem caused by overlapping blood vessels in the two-dimensional contrast data, providing more reliable blood flow information for clinical practice and improving the accuracy of vascular disease diagnosis and treatment outcomes.

[0038] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0039] Figure 1 This is a flowchart illustrating a method for generating a blood flow model according to an exemplary embodiment of this application. Figure 1 As shown, the method includes steps S101 to S106.

[0040] Step S101: Obtain three-dimensional angiography data and construct a three-dimensional vascular model based on the three-dimensional angiography data.

[0041] In this step, three-dimensional angiography data refers to image data that provides detailed anatomical information about blood vessels in three-dimensional space. This data can include multi-dimensional information such as the morphology, orientation, branch distribution, and surface features of the vessel wall. Specifically, three-dimensional angiography data can be acquired using 3D-DSA technology. During the acquisition process, X-ray imaging equipment can be used to rotate and scan the blood vessels from multiple different angles to obtain a series of image data covering the three-dimensional spatial distribution of the blood vessels.

[0042] After obtaining 3D angiography data, it can be processed using vessel segmentation algorithms, such as threshold segmentation, region growing, or deep learning segmentation methods, to separate the vessels from the complex background tissue. Then, based on geometric modeling techniques, a 3D geometric model of the vessels can be reconstructed. Figure 2 As shown, this allows the model to accurately recreate the spatial morphology and topology of blood vessels. In this process, morphological parameters of the vessel wall, such as diameter, curvature, and bifurcation angle, can be further extracted. Based on the biomechanical properties of the vessel wall, a digital model of the vessel wall with elastic mechanical properties can be established, making the constructed three-dimensional vessel model closer to the real physiological state and providing a more accurate foundation for subsequent blood flow simulation and analysis.

[0043] Step S102: Obtain two-dimensional angiography data, and based on the two-dimensional angiography data, simulate the flow of contrast agent in a three-dimensional vascular model to obtain a simulated blood flow model.

[0044] In this step, two-dimensional angiography data refers to dynamic two-dimensional imaging data that can capture the flow of contrast agent within blood vessels in real time. This data can include a series of continuous two-dimensional images, such as... Figure 3 As shown, these images reflect the distribution and flow state of the contrast agent in blood vessels, including information such as the injection time, flow velocity, and concentration changes. By analyzing the time series of these two-dimensional images, the dynamic characteristics of the contrast agent flow can be obtained. Specifically, two-dimensional angiography data can be acquired using 2D-DSA technology. During the acquisition process, the contrast agent is injected into the blood vessel, and X-ray imaging equipment is used to continuously capture images of the blood vessel at a fixed angle, resulting in a series of two-dimensional images.

[0045] After obtaining two-dimensional angiography data, the flow of contrast agent can be simulated in a three-dimensional vascular model based on this data to obtain a simulated blood flow model.

[0046] First, by detecting and identifying the location and time point at which the contrast agent first appears in the two-dimensional angiography data, the initial location and initial time of the contrast agent's flow in the blood vessel can be determined.

[0047] Subsequently, based on the initial position and time of the contrast agent's flow in the blood vessel, the model parameters of the simulated blood flow model are initialized. Specifically, the internal space of the three-dimensional blood vessel model can be defined as a fluid domain, which is used to simulate the actual flow space of blood. Within the fluid domain, a pure water environment is set as the fluid medium, and boundary conditions are set based on clinical experience, such as inlet pressure (simulating the periodic fluctuations of aortic blood pressure) and outlet impedance (reflecting peripheral vascular resistance). This constructs a water-contrast agent two-material multiphase flow model as the basic framework of the simulated blood flow model. Simultaneously, based on the initial time of the contrast agent's flow in the blood vessel, a contrast agent injection time window is set in the simulated blood flow model, and a strict temporal mapping relationship is established between this time window and the initial time to ensure that the time reference of the simulation process is consistent with the actual two-dimensional angiography data. Based on this, injection parameters are initialized, such as determining the initial flow rate of the contrast agent based on the blood vessel cross-sectional area and empirical flow velocity values. At the same time, the material property parameters of the contrast agent, including viscosity coefficient, density, and other physical properties, are defined to make the simulation model closer to the physiological reality.

[0048] After initializing the model parameters, computational fluid dynamics (CFD) methods are used to simulate the flow of contrast agent in a three-dimensional vascular model. For example, the finite element method is used to solve the fluid dynamics equations, and combined with the geometry of the blood vessel and the mechanical properties of the vessel wall, hemodynamic parameters such as the concentration distribution, velocity field, and pressure field of the contrast agent in the three-dimensional vascular model are calculated, thus obtaining a simulated blood flow model. This simulated blood flow model can dynamically display the flow process of contrast agent in the blood vessel in a three-dimensional form, providing an important foundation for subsequent blood flow analysis.

[0049] Step S103: Project the simulated blood flow model onto the projection direction of the two-dimensional angiography data to obtain two-dimensional projection data.

[0050] In this step, since the two-dimensional angiography data is obtained by taking pictures of blood vessels in a specific projection direction using X-ray imaging equipment, it reflects the distribution and flow of contrast agent in the blood vessels as observed from that specific angle. Therefore, in order to establish a spatial consistency relationship between the simulation model and the actual angiography image, and to enable the simulated blood flow model to be compared with the actual two-dimensional angiography data in the same spatial reference system, the simulated blood flow model can be projected in the projection direction of the two-dimensional angiography data to obtain two-dimensional projection data. This converts the simulated blood flow model in three-dimensional space into two-dimensional data with the same viewpoint and imaging method as the two-dimensional angiography data, so as to facilitate subsequent difference analysis and model correction.

[0051] Specifically, the contrast agent concentration values ​​of each voxel in the three-dimensional simulated blood flow model can be integrated along the projection direction using the ray projection method to generate a two-dimensional projection image of the same size as the two-dimensional angiography data. This image reflects the contrast agent concentration distribution of the simulation model in the corresponding projection direction, thereby achieving spatial alignment between the simulation data and the actual angiography data.

[0052] Step S104: Calculate the difference between the two-dimensional projection data and the two-dimensional angiography data, and iteratively adjust the model parameters of the simulated blood flow model based on the difference until the difference meets the preset convergence condition.

[0053] The two-dimensional projection data obtained through the above steps is projected from the simulated blood flow model based on the initial model parameters. It reflects the model's simulation result of the real scene. However, due to improper parameter settings, it may fail to accurately simulate and reproduce the real blood flow. Two-dimensional angiography data, on the other hand, reflects the dynamic distribution of contrast agent in real blood flow. Therefore, by calculating the difference between the two-dimensional projection data and the two-dimensional angiography data, the deviation between the simulation model and the actual angiography data can be intuitively reflected, and the accuracy of the simulation model can be evaluated. Furthermore, the model parameters of the simulated blood flow model can be iteratively adjusted based on this difference to continuously reduce the discrepancy, making the simulation model closer to the actual blood flow situation and improving the model's accuracy and reliability.

[0054] Specifically, calculating the difference between two-dimensional projection data and two-dimensional angiography data may include the following steps:

[0055] First, the grayscale distribution features of the two-dimensional angiography data are extracted. The grayscale values ​​in a two-dimensional angiography image reflect the concentration distribution of the contrast agent in the blood vessel; a higher grayscale value indicates a higher contrast agent concentration. By analyzing the time series of the two-dimensional angiography data, the grayscale distribution features of the contrast agent at different times and locations can be obtained.

[0056] Secondly, the contrast agent concentration distribution is extracted from the two-dimensional projection data. Two-dimensional projection data is obtained by projecting a simulated blood flow model onto a specific projection direction, and it contains information about the contrast agent concentration distribution within the simulation model. By calculating the contrast agent concentration at each voxel point, the concentration distribution corresponding to the two-dimensional projection data can be obtained.

[0057] Then, the Euclidean distance between the grayscale distribution characteristics and the contrast agent concentration distribution is calculated as the difference. Euclidean distance is a commonly used metric to measure the degree of difference between two data points. By calculating the Euclidean distance between the grayscale distribution characteristics in the two-dimensional angiography data and the contrast agent concentration distribution in the two-dimensional projection data, a quantitative index of difference can be obtained. This index reflects the degree of deviation between the simulation model and the actual observed data, providing a basis for subsequent model parameter adjustments.

[0058] Based on the differences calculated above, iterative optimization algorithms (such as the Levenberg-Marquardt algorithm) can be used to iteratively adjust the model parameters of the simulated blood flow model. These model parameters can include: contrast agent injection time delay, flow velocity profile parameters, and / or vessel wall compliance parameters. Adjusting the contrast agent injection time delay parameter can correct errors in the timing of contrast agent injection; adjusting the flow velocity profile parameters can optimize the flow velocity distribution of the contrast agent in the blood vessel; and adjusting the vessel wall compliance parameters can better reflect the influence of the vessel wall's elastic properties on blood flow. During the iterative adjustment process, after each adjustment of the model parameters, it is necessary to recalculate the two-dimensional projection data of the simulated blood flow model and the differences between it and the two-dimensional angiography data, i.e., repeat steps S103 and S104 until the differences meet preset convergence conditions, such as the difference value being less than a preset convergence threshold or the number of iterations reaching a preset threshold. At this point, the optimization of the model parameters can be stopped, and the simulated blood flow model at this stage is an optimized model that highly matches the actual blood flow state.

[0059] Step S105: In the iteratively adjusted simulated blood flow model, determine the weight of the contrast agent concentration distribution at each location at different times.

[0060] In this step, the contrast agent concentration distribution weights can be used to quantify the relative concentrations of contrast agents at different spatial locations in the 3D vascular model at different time points. Through normalization, the absolute concentration values ​​are mapped to the [0,1] interval, reflecting the relative importance of the contrast agent concentration relative to the maximum concentration at a specific time and location. This normalization effectively eliminates overall concentration differences caused by factors such as contrast agent injection dosage and blood flow velocity, enabling accurate mapping of grayscale changes in 2D contrast data to the relative concentration distribution in 3D space. This provides a spatial-temporal weight reference for the subsequent backprojection process.

[0061] Specifically, for each location in the iteratively adjusted simulated blood flow model, the ratio of the contrast agent concentration at that location to the maximum contrast agent concentration in the entire simulated blood flow model at the same time point can be calculated at different time points, and this ratio can be used as the concentration distribution weight. The calculation formula is as follows:

[0062] W(v,t)=C(v,t) / max(C(t))

[0063] Where W(v,t) represents the contrast agent concentration distribution weight at position v and time t; C(v,t) represents the contrast agent concentration at position v and time t; and max(C(t)) represents the maximum value of the contrast agent concentration at all positions in the entire simulated blood flow model at time t.

[0064] The concentration distribution weights calculated using the above method effectively reflect the relative importance of contrast agent concentrations at different time points and locations, providing a precise weighting basis for subsequent weighted processing of grayscale changes in two-dimensional angiography data. This weighting process can more accurately reflect the actual distribution of contrast agents at different locations and times. Especially when dealing with complex vascular structures, it can effectively distinguish the changes in contrast agent concentration at different locations, avoiding misjudgments caused by overlapping vessels or uneven contrast agent diffusion.

[0065] Step S106: Based on the concentration distribution weight, backproject the grayscale changes of the contrast agent in the two-dimensional angiography data at different times to various locations of the three-dimensional blood vessel model to generate the target blood flow model.

[0066] In this step, although the simulated blood flow model can provide a relatively accurate blood flow simulation through iterative optimization, it is constructed through simulation and will inevitably have modeling errors, and cannot completely replace the actual blood flow situation. On the other hand, two-dimensional angiography data comes from the angiography imaging of real blood vessels and can reflect the real blood flow situation in blood vessels. Therefore, combining two-dimensional angiography data with the simulated blood flow model can generate a more accurate target blood flow model.

[0067] Specifically, the grayscale changes of contrast agent in two-dimensional angiography data can first be weighted based on concentration distribution weights. In this step, the grayscale changes in the two-dimensional angiography data are weighted according to the weights calculated in the simulated blood flow model. This allows for the reasonable allocation of grayscale changes from the two-dimensional projection to their corresponding positions in three-dimensional space. By adjusting the grayscale values ​​at each position and time point in the two-dimensional angiography data, it is made to better reflect actual blood flow conditions and more accurately reflect the actual distribution of contrast agent in blood vessels, avoiding grayscale confusion caused by overlapping blood vessels. For example, when two blood vessels overlap in a two-dimensional projection, the three-dimensional simulation model can distinguish their spatial positions using a weight matrix, ensuring that grayscale changes only correspond to the blood vessels where contrast agent is actually flowing in, avoiding incorrect mapping.

[0068] Furthermore, the grayscale changes of the weighted 2D angiography data can be back-projected onto the corresponding positions of the 3D vascular model to generate the target blood flow model. In this process, each frame of 2D angiography data used for weighting can be back-projected onto the corresponding voxel position in 3D space along the original projection direction using projection methods such as FDK. The calculation formula is as follows:

[0069] Islice(v,t)=Ibaseline+W(v,t)·ΔI(t)

[0070] Where: Islice(v,t) represents the enhanced three-dimensional image at position v and time t, i.e. the generated target blood flow model; Ibaseline represents the base image of the three-dimensional vascular model, i.e. the vascular image without contrast agent; W(v,t) is the concentration distribution weight calculated in step S105; ΔI(t) represents the grayscale change of contrast agent at time t in the two-dimensional angiography data.

[0071] Through the above steps, the generated target blood flow model can be a 4D vascular model with time resolution (3D space + time dimension). It includes the spatial anatomical structure of three-dimensional blood vessels and accurately reflects the flow sequence of contrast agent through grayscale changes in the time dimension. This can effectively solve the problem of blood flow misalignment caused by overlapping blood vessels in two-dimensional images in related technologies, improve the accuracy and reliability of blood flow models, and provide stronger support for clinical diagnosis and treatment.

[0072] Furthermore, during the iterative adjustment of model parameters, to improve the efficiency and accuracy of model adjustment, the iteration strategy can be dynamically adjusted based on changes in the differences. Specifically, when the differences have not reached the preset convergence condition and the iteration is in its initial stage, due to the large deviation between the simulated blood flow model and the actual angiographic data, the model needs to be adjusted quickly to approximate the real situation. Therefore, high-frequency iterative adjustments can be adopted, for example, using each frame of two-dimensional angiographic data to calibrate the simulated blood flow model. This can promptly correct large errors in the model and accelerate the speed at which the model parameters approach the true values.

[0073] When the difference gradually decreases over time and convergence is reached, it indicates that the model parameters are close to the true values, and the model is in a stable phase. To further improve the model's accuracy and ensure its temporal consistency with actual angiographic data, the temporal scale of the simulated blood flow model can be adjusted to match that of the two-dimensional angiographic data. Simultaneously, time-related model parameters in the simulated blood flow model, such as contrast agent injection delay and flow velocity time distribution, can be adjusted. This temporal scale synchronization mechanism ensures that the iteratively optimized simulated blood flow model is completely matched to the two-dimensional angiographic data in the time dimension, avoiding calculation deviations in hemodynamic parameters caused by inconsistent time references, and further improving the accuracy and reliability of the target blood flow model.

[0074] Corresponding to the embodiments of the aforementioned methods, this application also provides a blood flow model generation device. Figure 4 This is a structural block diagram of a blood flow model generation device according to an exemplary embodiment of this application. Figure 4 As shown, the device includes:

[0075] The three-dimensional vascular model construction module 401 is used to acquire three-dimensional angiography data and construct a three-dimensional vascular model based on the three-dimensional angiography data.

[0076] The simulated blood flow model generation module 402 is used to acquire two-dimensional angiography data and, based on the two-dimensional angiography data, simulate the flow of contrast agent in a three-dimensional vascular model to obtain a simulated blood flow model.

[0077] The projection data generation module 403 is used to project the simulated blood flow model onto the projection direction of the two-dimensional angiography data to obtain two-dimensional projection data.

[0078] The model parameter adjustment module 404 is used to calculate the difference between the two-dimensional projection data and the two-dimensional angiography data, and iteratively adjust the model parameters of the simulated blood flow model based on the difference until the difference meets the preset convergence condition.

[0079] The contrast agent concentration distribution weight determination module 405 is used to determine the contrast agent concentration distribution weight at different locations at different times in the iteratively adjusted simulated blood flow model.

[0080] The target blood flow model generation module 406 is used to backproject the grayscale changes of the contrast agent in the two-dimensional angiography data at different times onto various positions of the three-dimensional blood vessel model based on the concentration distribution weight, thereby generating a target blood flow model.

[0081] The specific implementation process of the functions and roles of each module in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0082] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0083] Corresponding to the embodiments of the foregoing methods, this application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor; wherein, when the processor executes the computer program, it implements the steps of the method for generating a blood flow model as described in any of the above embodiments.

[0084] For example, processors include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), or field-programmable gate arrays (FPGAs).

[0085] For example, the memory may include at least one type of storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc.

[0086] Figure 5 This is a structural block diagram of a computer device according to an exemplary embodiment of this application. Figure 5 As shown, at the hardware level, the computer device includes a processor 501, an internal bus 502, a network interface 503, memory 504, and non-volatile memory 505, and may also include other hardware required for business operations. One or more embodiments of this application can be implemented in software, for example, the processor 501 reads the corresponding computer program from the non-volatile memory 505 into memory 504 and then runs it. Of course, in addition to software implementation, one or more embodiments of this application do not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the above processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0087] Corresponding to the embodiments of the foregoing methods, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the blood flow model generation method described in any of the above embodiments.

[0088] Corresponding to the embodiments of the foregoing methods, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the blood flow model generation method described in any of the above embodiments.

[0089] The foregoing has described specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0090] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention filed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not claimed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the foregoing claims.

[0091] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

[0092] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for generating a blood flow model, characterized in that, include: Acquire three-dimensional angiography data and construct a three-dimensional vascular model based on the three-dimensional angiography data; Two-dimensional angiography data is acquired, and based on the two-dimensional angiography data, the initial position and initial time of contrast agent flow in the blood vessel are determined; Based on the initial position and initial time, the model parameters of the simulated blood flow model are initialized, and based on the initialized model parameters, the flow of contrast agent is simulated in the three-dimensional blood vessel model to obtain the simulated blood flow model. The simulated blood flow model is projected onto the projection direction of the two-dimensional angiography data to obtain two-dimensional projection data. The difference between the two-dimensional projection data and the two-dimensional angiography data is calculated, and the model parameters of the simulated blood flow model are iteratively adjusted based on the difference until the difference meets the preset convergence condition. In the iteratively adjusted simulated blood flow model, the weights of the contrast agent concentration distribution at each location at different times are determined. Based on the concentration distribution weights, the grayscale changes of the contrast agent in the two-dimensional angiography data at different times are back-projected onto various positions of the three-dimensional vascular model to generate the target blood flow model.

2. The method according to claim 1, characterized in that, Calculate the differences between two-dimensional projection data and two-dimensional angiography data, including: Extract the grayscale distribution features from the two-dimensional angiography data; Extract the contrast agent concentration distribution from the two-dimensional projection data; The Euclidean distance between the grayscale distribution characteristics and the contrast agent concentration distribution is calculated as the difference.

3. The method according to claim 1, characterized in that, After the step of iteratively adjusting the model parameters of the simulated blood flow model based on the differences until the differences satisfy a preset convergence condition, the method further includes: The time scale of the simulated blood flow model is adjusted to match the time scale of the two-dimensional angiography data, and the time-related model parameters in the simulated blood flow model are adjusted simultaneously.

4. The method according to claim 1, characterized in that, Based on the iteratively adjusted simulated blood flow model, the weights of contrast agent concentration distribution at different locations and time points were determined, including: For each location in the iteratively adjusted simulated blood flow model, at different time points, the ratio of the contrast agent concentration at that location to the maximum contrast agent concentration in the simulated blood flow model at the same time point is calculated, and the ratio is used as the concentration distribution weight.

5. The method according to claim 1, characterized in that, The step of back-projecting the grayscale changes of the contrast agent in the two-dimensional angiography data at different times onto various locations of the three-dimensional vascular model based on the concentration distribution weights to generate a target blood flow model includes: Based on the concentration distribution weights, the grayscale changes of the contrast agent in the two-dimensional angiography data are weighted. The contrast agent grayscale changes in the weighted two-dimensional angiography data are back-projected onto the corresponding positions of the three-dimensional vascular model to generate the target blood flow model.

6. The method according to claim 1, characterized in that, The model parameters include: contrast agent injection time delay, flow velocity profile parameters, and / or vessel wall compliance parameters.

7. A device for generating a blood flow model, characterized in that, include: A three-dimensional vascular model construction module is used to acquire three-dimensional angiography data and construct a three-dimensional vascular model based on the three-dimensional angiography data. The simulated blood flow model generation module is used to acquire two-dimensional angiography data, and based on the two-dimensional angiography data, determine the initial position and initial time of the contrast agent flow in the blood vessel; based on the initial position and initial time, initialize the model parameters of the simulated blood flow model, and based on the initialized model parameters, simulate the flow of the contrast agent in the three-dimensional blood vessel model to obtain the simulated blood flow model. The projection data generation module is used to project the simulated blood flow model onto the projection direction of the two-dimensional angiography data to obtain two-dimensional projection data. The model parameter adjustment module is used to calculate the difference between the two-dimensional projection data and the two-dimensional angiography data, and iteratively adjust the model parameters of the simulated blood flow model based on the difference until the difference meets the preset convergence condition. The contrast agent concentration distribution weight determination module is used to determine the contrast agent concentration distribution weight at different locations and times in the iteratively adjusted simulated blood flow model. The target blood flow model generation module is used to backproject the grayscale changes of the contrast agent in the two-dimensional angiography data at different times onto various positions of the three-dimensional blood vessel model based on the concentration distribution weights, thereby generating the target blood flow model.

8. A computer device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 6.

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