Intracranial aneurysm rupture risk assessment method, device and equipment and storage medium

Abstract

The invention discloses an intracranial aneurysm rupture risk assessment method, device and equipment and a storage medium, and the method comprises the steps: firstly, generating a time sequence image containing flow field information based on the maximum density projection and center line extraction processing of a two-dimensional angiography image sequence in combination with contrast agent concentration change curve analysis; performing three-dimensional reconstruction, region-of-interest extraction and opening extension processing by using the three-dimensional image sequence to obtain detailed morphological parameters and a region-of-interest model after extension processing; secondly, displaying a time sequence image with flow field information and a region-of-interest model of which an opening is not prolonged in an overlapping manner, further optimizing setting of boundary conditions and performing hydrodynamic simulation by calculating a flow field vector in an observation ball and determining an actual blood flow direction, and calculating key hemodynamic parameters; and finally, in combination with the morphological parameters, the hemodynamic parameters and clinical information of the patient, obtaining an evaluation result of the aneurysm rupture risk.

Description

Technical Field

[0001] The present invention relates to the technical field of medical image processing, and particularly to a method, device, equipment and storage medium for evaluating the rupture risk of intracranial aneurysms. Background Art

[0002] An intracranial aneurysm refers to an abnormal bulge in the wall of an intracranial artery. The overall prevalence of this disease in the general population is approximately 3% to 5%. Although most intracranial aneurysms do not rupture during their lifetime, once they rupture and cause subarachnoid hemorrhage, the consequences are extremely serious, and the fatality rate can be as high as about 40%. Therefore, it is particularly important to screen and evaluate the rupture risk of aneurysms in a timely and accurate manner, which not only helps to early detect potential risk factors but also guides clinical practice to take effective preventive measures or treatment plans.

[0003] The risk of aneurysm rupture is not determined by a single factor, but is closely related to the patient's clinical characteristics, the specific morphological characteristics of the aneurysm, and hemodynamic characteristics. In recent years, with the progress of medical imaging technology and the development of computational fluid dynamics (CFD), hemodynamic analysis has been widely used in the evaluation of aneurysm rupture risk. Specifically, researchers usually first reconstruct the vascular tree model based on intracranial medical images; next, select the region of interest (such as the aneurysm and its surrounding environment) from the reconstructed vascular tree model, and determine the inlet and outlet positions of the blood flow on this basis, and apply the corresponding flow or velocity boundary conditions; subsequently, use the computational fluid dynamics method to simulate the selected region to obtain various physical parameters including the velocity field and pressure field. Based on these results, important hemodynamic indices such as wall shear stress (WSS) and oscillatory shear index (OSI) can be further calculated. By combining these hemodynamic parameters with morphological characteristics and the patient's clinical data, and using a machine learning model for comprehensive analysis, the rupture risk level of the aneurysm can be finally obtained.

[0004] However, although this method is highly scientific and practical in theory, there is still a significant problem in its actual application: it assumes the flow pattern of blood in the intracranial blood vessels. In fact, due to the natural complexity of the intracranial vascular system, it contains a rich collateral blood supply mechanism, such as the existence of the anterior communicating artery and the posterior communicating artery. This means that when performing hemodynamic simulations, simply treating certain arteries as fixed inlets or outlets may lead to incorrect judgments of the flow direction. For example, if the anterior communicating artery or the posterior communicating artery is inappropriately set as an outlet during the simulation, the simulated blood flow path may not match the actual situation, thus seriously affecting the accuracy of aneurysm rupture risk assessment. Therefore, how to more accurately reflect the true blood flow pattern of the intracranial blood vessels has become one of the key challenges in improving the accuracy of aneurysm rupture risk assessment. Summary of the Invention

[0005] Based on this, the present invention provides a method, device, equipment and storage medium for assessing the rupture risk of intracranial aneurysms in view of the above technical problems.

[0006] On the one hand, the present invention provides a method for assessing the rupture risk of intracranial aneurysms, the method comprising: Obtain a two-dimensional angiographic image sequence and a three-dimensional image sequence of blood vessels related to an intracranial aneurysm; Based on the two-dimensional angiographic image sequence, perform maximum intensity projection and centerline extraction processing on it, and analyze in combination with the contrast agent concentration change curve to generate a time series image containing flow field information; Based on the three-dimensional image sequence, perform three-dimensional reconstruction, region of interest extraction and opening extension processing on it to obtain morphological parameters of the blood vessels in the region of interest and a region of interest model after opening extension processing; Overlap and display the time series image with flow field information and the region of interest model without extended opening, make the viewing angles consistent, and automatically generate observation spheres at all opening positions of the region of interest model without extended opening. By calculating the flow field vector sum within each observation sphere, determine the actual blood flow direction at the opening position of the region of interest model with extended opening, and finally complete the determination of boundary conditions and the construction of the flow field; Based on the boundary conditions and the flow field, automatically mesh the region of interest model with extended opening, and perform fluid mechanics simulation and calculate hemodynamic parameters; According to the morphological parameters and hemodynamic parameters, combined with the clinical information of the patient, obtain an assessment of the aneurysm rupture risk.

[0007] In one embodiment, the method of generating a time series image containing flow field information based on a two-dimensional contrast image sequence by performing maximum intensity projection and centerline extraction on the sequence and analyzing the contrast agent concentration change curve includes: Performing maximum intensity projection on all time frames of the two-dimensional contrast image sequence to obtain a projection image, and extracting the two-dimensional vascular tree contour on the projection image; Extracting the centerline based on the two-dimensional vascular tree contour and superimposing these centerlines on the original two-dimensional contrast image sequence to form a new image sequence with highlighted contours and centerlines; In the newly generated image sequence, sampling points are uniformly set along the centerline, the change curve of the contrast agent concentration over time at each sampling point is recorded, the time to peak is calculated through fitting analysis of these curves, and the blood flow direction at each sampling point is judged according to the trend of the contrast agent concentration change to construct a flow field along the centerline, and finally a time series image containing flow field information is generated.

[0008] In one embodiment, the method of performing three-dimensional reconstruction, region of interest extraction, and opening extension on a three-dimensional image sequence to obtain morphological parameters of blood vessels in the region of interest and a region of interest model after opening extension includes: Constructing a first vascular tree model based on the three-dimensional image sequence and extracting the region of interest in the first vascular tree model to obtain a second vascular tree model; Performing calculations on the second vascular tree model to obtain morphological parameters of blood vessels in the region of interest; Extending all openings on the second vascular tree model to obtain a region of interest model after opening extension.

[0009] In one embodiment, the method of performing calculations on the second vascular tree model to obtain morphological parameters of blood vessels in the region of interest includes: Selecting a point at an arbitrary position on the surface of the aneurysm cavity of the second vascular tree model, automatically separating the aneurysm cavity from the blood vessel, and automatically calculating the morphological parameters.

[0010] In one embodiment, the method of determining the actual blood flow direction at the opening position and finally completing the determination of boundary conditions and the construction of the flow field includes: Calculating the vector sum of the flow fields in each observation sphere, and the direction of the vector sum is the blood flow direction at the opening position. After obtaining the blood flow direction, set the flow rate at each inlet position; Among them, the inlet flow rate is obtained by transcranial Doppler measurement. The inlet is defined as the opening where the blood flow direction is from the outside of the model to the inside of the model, and vice versa is the outlet. The outlet flow rate is jointly determined by the law of conservation of flow rate and Murray's law.

[0011] In one embodiment, the center of the observation sphere is the geometric center of the opening, and the radius is 2 to 5 times the equivalent radius of the opening area.

[0012] In one embodiment, automatically meshing the region of interest model of the extended opening, performing fluid dynamics simulation and calculating hemodynamic parameters, including: Automatically meshing the region of interest model of the extended opening, performing fluid dynamics simulation calculation, extracting the calculation results of the last cardiac cycle as the final simulation results, and the calculation results are the velocity distribution and pressure distribution at different moments within the last cardiac cycle; Using the velocity distribution and pressure distribution at different moments, calculating the hemodynamic parameter distribution and animation on the surface of the vascular tree model; Combining with the aneurysm cavity of the blood vessel in the region of interest, automatically calculating the hemodynamic parameters of the aneurysm cavity.

[0013] On the other hand, the present invention provides an intracranial aneurysm rupture risk assessment device, and the device includes: An image sequence acquisition module, configured to acquire a two-dimensional angiographic image sequence and a three-dimensional image sequence of blood vessels related to an intracranial aneurysm; A first image sequence processing module, configured to perform maximum intensity projection and centerline extraction processing on the two-dimensional angiographic image sequence based on it, and generate a time series image containing flow field information by combining the analysis of the contrast agent concentration change curve; A second image sequence processing module, configured to perform three-dimensional reconstruction, region of interest extraction, and opening extension processing on the three-dimensional image sequence based on it, and obtain the morphological parameters of the blood vessels in the region of interest and the region of interest model after the opening extension processing; A boundary condition determination module, configured to overlap and display the time series image with flow field information and the region of interest model without extended opening to make the viewing angles consistent, and automatically generate observation spheres at all opening positions of the region of interest model without extended opening, and determine the actual blood flow direction at the opening position of the region of interest model with extended opening by calculating the sum of the flow field vectors in each observation sphere, and finally complete the boundary condition determination and flow field construction; A hemodynamic parameter calculation module, configured to automatically mesh the region of interest model with extended opening based on the boundary conditions and the flow field, and perform fluid dynamics simulation and calculate hemodynamic parameters; An evaluation module, configured to obtain an evaluation of the aneurysm rupture risk according to the morphological parameters and hemodynamic parameters, in combination with the clinical information of the patient.

[0014] On another aspect, the present invention provides a computer device, including a memory and a processor, and when the processor executes the computer program, the following steps are implemented: Obtain a two-dimensional angiographic image sequence and a three-dimensional image sequence of the blood vessels related to the intracranial aneurysm; Based on the two-dimensional angiographic image sequence, perform maximum intensity projection and centerline extraction processing on it, and combine the analysis of the contrast agent concentration change curve to generate a time series image containing flow field information; Based on the three-dimensional image sequence, perform three-dimensional reconstruction, region of interest extraction, and opening extension processing on it to obtain the morphological parameters of the blood vessels in the region of interest and the region of interest model after opening extension processing; Overlay and display the time series image with flow field information and the region of interest model without extended opening to make the viewing angles consistent, and automatically generate viewing spheres at all opening positions of the region of interest model without extended opening. By calculating the sum of the flow field vectors in each viewing sphere, determine the actual blood flow direction at the opening position of the region of interest model with extended opening, and finally complete the determination of boundary conditions and the construction of the flow field; Based on the boundary conditions and the flow field, automatically mesh the region of interest model with extended opening, and perform fluid mechanics simulation and calculate hemodynamic parameters; Based on the morphological parameters and hemodynamic parameters, combined with the clinical information of the patient, obtain an assessment of the aneurysm rupture risk.

[0015] On the other hand, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: Obtain a two-dimensional angiographic image sequence and a three-dimensional image sequence of the blood vessels related to the intracranial aneurysm; Based on the two-dimensional angiographic image sequence, perform maximum intensity projection and centerline extraction processing on it, and combine the analysis of the contrast agent concentration change curve to generate a time series image containing flow field information; Based on the three-dimensional image sequence, perform three-dimensional reconstruction, region of interest extraction, and opening extension processing on it to obtain the morphological parameters of the blood vessels in the region of interest and the region of interest model after opening extension processing; Overlay and display the time series image with flow field information and the region of interest model without extended opening to make the viewing angles consistent, and automatically generate viewing spheres at all opening positions of the region of interest model without extended opening. By calculating the sum of the flow field vectors in each viewing sphere, determine the actual blood flow direction at the opening position of the region of interest model with extended opening, and finally complete the determination of boundary conditions and the construction of the flow field; Based on the boundary conditions and the flow field, automatically mesh the region of interest model with extended opening, and perform fluid mechanics simulation and calculate hemodynamic parameters; Based on morphological parameters and hemodynamic parameters, combined with the clinical information of the patient, an assessment of the aneurysm rupture risk is obtained.

[0016] Compared with the prior art, the present invention performs maximum intensity projection and centerline extraction based on a two-dimensional angiographic image sequence, and generates a time series image containing flow field information by analyzing the contrast agent concentration change curve, so as to accurately capture the actual blood flow direction. Three-dimensional reconstruction, region of interest extraction, and opening extension processing are performed using a three-dimensional image sequence to ensure that the model can more realistically reflect the vascular structure. By overlapping and displaying the flow field information with the three-dimensional model and applying the observation sphere technique to determine the actual blood flow direction, the boundary conditions are set. This method automatically determines the flow pattern of intracranial blood vessels, effectively overcomes the limitation of the traditional method that ignores the complex collateral blood supply mechanism, makes the result of hemodynamic simulation more reliable, and significantly improves the accuracy of aneurysm rupture risk assessment. Brief Description of the Drawings

[0017] Figure 1 It is a schematic flow chart of a method for assessing the risk of intracranial aneurysm rupture in an embodiment.

[0018] Figure 2 It is a schematic diagram of a two-dimensional vascular tree contour with sampling points in an embodiment.

[0019] Figure 3 It is a schematic diagram of the contrast agent concentration change curve in an embodiment.

[0020] Figure 4 It is a schematic diagram of the flow field and the observation sphere in an embodiment. Detailed Embodiment

[0021] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0022] As Figure 1 shown, a method for assessing the risk of intracranial aneurysm rupture according to the present invention includes the following steps: Step S100, obtaining a two-dimensional angiographic image sequence and a three-dimensional image sequence of blood vessels related to an intracranial aneurysm.

[0023] In step S100, blood vessel images are read, including a two-dimensional angiographic image sequence (time series) and a three-dimensional image sequence, which are respectively denoted as the first time series and the first spatial sequence.

[0024] Step S200: Based on the two-dimensional contrast image sequence, perform maximum intensity projection and centerline extraction on it, and combine the analysis of the contrast agent concentration change curve to generate a time series image containing flow field information.

[0025] This step S200 specifically includes the following sub-steps: S210: Perform maximum intensity projection on all time frames of the two-dimensional contrast image sequence to obtain a projection image, and extract the two-dimensional vascular tree contour on the projection image. Specifically, perform maximum intensity projection on all time frames of the two-dimensional contrast image sequence, that is, the first time series, to obtain a projection image, and then extract the 2D vascular tree contour on the projection image.

[0026] S220: Extract the centerline based on the two-dimensional vascular tree contour and superimpose these centerlines on the original two-dimensional contrast image sequence to form a new image sequence with highlighted contours and centerlines. Specifically, use the maximum inscribed circle algorithm to obtain the centerline of the vascular tree contour, and superimpose the contour and centerline on the first time series to obtain a new image sequence with highlighted contours and highlighted centerlines, that is, the second time series.

[0027] S230: In the newly generated image sequence, uniformly set sampling points along the centerline, record the change curve of the contrast agent concentration over time at each sampling point, perform fitting analysis on these curves, calculate the time to peak, and judge the blood flow direction at each sampling point according to the trend of the contrast agent concentration change, construct the flow field along the centerline, and finally generate a time series image containing flow field information.

[0028] Specifically, in the new image sequence, that is, the second time series, uniformly set sampling points along the centerline, and the spacing between sampling points is 1 mm, as Figure 2 shown. At the same time, record the change curve of the contrast agent concentration over time at each sampling point. Assuming there are n sampling points, there are n curves. Usually, due to the continuous injection of the contrast agent, each curve will show an ascending segment, a plateau segment, and a descending segment, as Figure 3 shown. Fit the curves. The fitting function can be a simple piecewise linear function or other complex functions. Calculate the time corresponding to the intersection of the ascending segment and the plateau segment on the fitted curve, that is, the time to peak TTP.

[0029] Calculate the blood flow direction of each sampling point in turn. The calculation method is as follows: If , then the blood flow direction is ; otherwise, . After traversing and calculating all sampling points, obtain the flow field along the centerline. The flow field is represented by assigning a unit vector to each sampling point. Display the flow field in the second time series to obtain a time series image with the flow field, that is, the third time series.

[0030] Step S300: Based on the three-dimensional image sequence, perform three-dimensional reconstruction, region of interest extraction, and opening extension processing to obtain the morphological parameters of the blood vessels in the region of interest and the region of interest model after opening extension processing.

[0031] This step S300 specifically includes the following sub-steps: Step S310: Construct a first blood vessel tree model based on the three-dimensional image sequence, and extract the region of interest in the first blood vessel tree model to obtain a second blood vessel tree model. Specifically, based on the three-dimensional image sequence, i.e., the first spatial sequence, perform three-dimensional reconstruction to obtain the first blood vessel tree model. The reconstruction algorithm can be the traditional threshold method, the display algorithm based on active contours, the implicit algorithm based on level sets, or the algorithm based on an artificial intelligence model. Extract the region of interest in the first blood vessel tree model, denoted as the second blood vessel tree model, where the method of extracting the region of interest can be a cropping sphere, a cropping cuboid, or a point-selection-based cropping method.

[0032] Step S320: Calculate on the second blood vessel tree model to obtain the morphological parameters of the blood vessels in the region of interest. Specifically, select a point at any position on the surface of the aneurysm cavity of the second blood vessel tree model, automatically separate the aneurysm cavity from the blood vessels, and automatically calculate the morphological parameters. The morphological parameters include: aneurysm inflow angle, aneurysm inclination angle, blood vessel angle, maximum height of the aneurysm, middle diameter of the aneurysm, neck diameter of the aneurysm, diameter of the parent artery, vertical height of the aneurysm, surface area of the aneurysm, volume of the aneurysm, size ratio, aspect ratio, bottle-neck ratio, ellipticity index, non-spherical index, aneurysm morphological irregularity index, etc.

[0033] Step S330: Extend all the openings on the second blood vessel tree model to obtain the region of interest model after opening extension processing. In this step, after extracting the region of interest in step S320, automatically extend all the openings on the second blood vessel tree model. The extension length of the opening is 3 times the diameter of the blood vessel at the opening position to obtain the region of interest model with the extended openings, denoted as the third blood vessel tree model.

[0034] Step S400: Overlap and display the time series image with flow field information and the region of interest model without extended openings, make the viewing angles consistent, and automatically generate observation spheres at all the opening positions of the region of interest model without extended openings. By calculating the sum of the flow field vectors in each observation sphere, determine the actual blood flow direction at the opening positions of the region of interest model with extended openings, and finally complete the determination of boundary conditions and the construction of the flow field.

[0035] In step S400, the camera is rotated for the region-of-interest model with the extended opening, i.e., the third blood vessel tree model, so that the viewing angle is consistent with the angiography angle of the third time series. Then, the third blood vessel tree model and the third time series are overlapped and displayed. Observation spheres are automatically generated at all the opening positions of the third blood vessel tree model. The center of the sphere of the observation sphere is the geometric center of the opening, and the radius is 2 - 5 times the equivalent radius of the opening area, as Figure 4 shown. Calculate the vector sum of the flow fields within each observation sphere. The direction of the vector sum is the blood flow direction at the opening position. After obtaining the blood flow direction, set the flow rate at each inlet position. The inlet flow rate can be obtained through transcranial Doppler measurement. An inlet is defined as an opening where the blood flow direction is from outside the model to inside the model, and vice versa for the outlet. The outlet flow rate is jointly determined by the law of conservation of flow rate and Murray's law.

[0036] Step S500: Based on the boundary conditions and the flow field, automatically mesh the region-of-interest model with the extended opening, and perform a fluid dynamics simulation and calculate the hemodynamic parameters.

[0037] This step S500 specifically includes the following sub-steps: Step S510: Automatically mesh the region-of-interest model with the extended opening, perform a fluid dynamics simulation calculation, and extract the calculation results of the last cardiac cycle as the final simulation results. The calculation results are the velocity distribution and pressure distribution at different times within the last cardiac cycle. Specifically, automatically mesh the third blood vessel tree model, and then use the finite volume method or the finite element method to perform a computational fluid dynamics simulation calculation. After calculations over multiple cardiac cycles, the simulation results tend to be stable. Extract the calculation results of the last cardiac cycle as the final simulation results. The calculation results are the velocity distribution and pressure distribution at different times within the last cardiac cycle.

[0038] Step S520: Using the velocity distribution and pressure distribution at different times, calculate the hemodynamic parameter distribution and animations on the surface of the blood vessel tree model, including: pressure distribution animation, WSS distribution animation, OSI distribution animation. In addition, calculate the streamline distribution, pathline animation, particle animation, and velocity isosurface animation within the lumen. Animation refers to the change effect of the parameter distribution over time within one cardiac cycle. For the particle animation, the density of the particles can be adjusted. For the pathline animation, the density and length of the pathlines can be adjusted. For the velocity isosurface animation, the display range of the velocity isosurface can be adjusted.

[0039] Step S530: Automatically calculate the hemodynamic parameters of the aneurysm cavity in combination with the aneurysm cavity of the blood vessels in the region of interest. Using the aneurysm cavity obtained in Step S320, automatically calculate the hemodynamic parameters of the aneurysm cavity, including: mean wall shear stress (WSS) of the aneurysm, normalized mean WSS of the aneurysm, mean WSS of the parent artery, maximum WSS of the aneurysm, proportion of the area of the high-WSS region, oscillatory shear index (OSI) of the aneurysm, maximum OSI of the aneurysm, relative retention time of the aneurysm, wall shear stress gradient (WSSG) of the aneurysm, gradient oscillation factor of the aneurysm, proportion of the area of the low-WSS region, and minimum WSS of the aneurysm.

[0040] Step S600: Obtain an assessment of the rupture risk of the aneurysm based on the morphological parameters and hemodynamic parameters, in combination with the patient's clinical information. In this step, use the aneurysm morphological parameters and aneurysm hemodynamic parameters calculated in the previous steps, in combination with the patient's clinical information, and use a machine learning model to predict the rupture risk of the aneurysm. Specifically, a machine learning model can be used to predict the rupture resemblance score (RRS) of the aneurysm as a quantitative representation of the rupture risk of the aneurysm. Further, calculate the morphological RRS by predicting solely using morphological parameters, calculate the hemodynamic RRS by predicting solely using hemodynamic parameters, and calculate the composite RRS by predicting in combination with morphological parameters and hemodynamic parameters.

[0041] It should be understood that although Figure 1 the steps in the flowchart in Figure 1 are shown sequentially according to the indication of the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover,

[0042] In one embodiment, the present invention provides an intracranial aneurysm rupture risk assessment device, including: an image sequence acquisition module, a first image sequence processing module, a second image sequence processing module, a boundary condition determination module, a hemodynamic parameter calculation module, and an evaluation module, where: The image sequence acquisition module is used to acquire a two-dimensional angiographic image sequence and a three-dimensional image sequence of the blood vessels related to the intracranial aneurysm.

[0043] The first image sequence processing module is used to perform maximum intensity projection and centerline extraction processing on a two-dimensional contrast image sequence, and generate a time series image containing flow field information by combining the analysis of the contrast agent concentration change curve.

[0044] The second image sequence processing module is used to perform three-dimensional reconstruction, region of interest extraction, and opening extension processing on a three-dimensional image sequence, and obtain the morphological parameters of the blood vessels in the region of interest and the region of interest model after opening extension processing.

[0045] The boundary condition determination module is used to overlap and display the time series image with flow field information and the region of interest model without extended opening, make the viewing angles consistent, automatically generate observation spheres at all opening positions of the region of interest model without extended opening, determine the actual blood flow direction at the opening position of the region of interest model with extended opening by calculating the sum of flow field vectors in each observation sphere, and finally complete the boundary condition determination and flow field construction.

[0046] The hemodynamic parameter calculation module is used to automatically mesh the region of interest model with extended opening based on the boundary conditions and flow field, perform fluid mechanics simulation, and calculate hemodynamic parameters.

[0047] The evaluation module is used to obtain an evaluation of the aneurysm rupture risk according to the morphological parameters and hemodynamic parameters, combined with the patient's clinical information.

[0048] For the specific limitations of the intracranial aneurysm rupture risk assessment device, reference can be made to the limitations of the intracranial aneurysm rupture risk assessment method in the above text, which will not be elaborated here. Each module in the above intracranial aneurysm rupture risk assessment device can be implemented in whole or in part through software, hardware, and their combinations. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so as to facilitate the processor to call and execute the operations corresponding to the above modules.

[0049] In one embodiment, a computer device is provided. The computer device may be a terminal, which includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for assessing the risk of intracranial aneurysm rupture. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device may be a touch layer covering the display screen, or may be a button, a trackball, or a touchpad provided on the housing of the computer device, or may also be an external keyboard, touchpad, or mouse, etc.

[0050] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented: Step S100, obtain a two-dimensional angiographic image sequence and a three-dimensional image sequence of blood vessels related to an intracranial aneurysm.

[0051] Step S200, based on the two-dimensional angiographic image sequence, perform maximum intensity projection and centerline extraction processing on it, and combine the analysis of the contrast agent concentration change curve to generate a time-series image containing flow field information.

[0052] Step S300, based on the three-dimensional image sequence, perform three-dimensional reconstruction, region of interest extraction, and opening extension processing on it to obtain morphological parameters of the blood vessels in the region of interest and a region of interest model after opening extension processing.

[0053] Step S400, overlap and display the time-series image with flow field information and the region of interest model without extended opening, make the viewing angles consistent, and automatically generate viewing spheres at all opening positions of the region of interest model without extended opening. By calculating the sum of flow field vectors within each viewing sphere, determine the actual blood flow direction at the opening position of the region of interest model with extended opening, and finally complete the determination of boundary conditions and the construction of the flow field.

[0054] Step S500, based on the boundary conditions and the flow field, automatically perform mesh generation on the region of interest model with extended opening, and perform fluid mechanics simulation and calculate hemodynamic parameters.

[0055] Step S600, according to the morphological parameters and hemodynamic parameters, combined with the clinical information of the patient, obtain an assessment of the aneurysm rupture risk.

[0056] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: Step S100: Obtain a two-dimensional angiographic image sequence and a three-dimensional image sequence of blood vessels related to an intracranial aneurysm.

[0057] Step S200: Based on the two-dimensional angiographic image sequence, perform maximum intensity projection and centerline extraction processing on it, and combine the analysis of the contrast agent concentration change curve to generate a time-series image containing flow field information.

[0058] Step S300: Based on the three-dimensional image sequence, perform three-dimensional reconstruction, region of interest extraction, and opening extension processing on it to obtain morphological parameters of the blood vessels in the region of interest and a region of interest model after opening extension processing.

[0059] Step S400: Overlap and display the time-series image with flow field information and the region of interest model without extended opening, make the viewing angles consistent, and automatically generate viewing spheres at all opening positions of the region of interest model without extended opening. By calculating the sum of flow field vectors within each viewing sphere, determine the actual blood flow direction at the opening position of the region of interest model with extended opening, and finally complete the determination of boundary conditions and the construction of the flow field.

[0060] Step S500: Based on the boundary conditions and the flow field, automatically mesh the region of interest model with extended opening, and perform fluid mechanics simulation and calculate hemodynamic parameters.

[0061] Step S600: According to the morphological parameters and hemodynamic parameters, combined with the clinical information of the patient, obtain an assessment of the aneurysm rupture risk.

[0062] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided by the present invention can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0063] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered as the scope described in this specification.

[0064] The above-described embodiments merely represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the invention patent should be subject to the appended claims.

Claims

1. A method for assessing the risk of intracranial aneurysm rupture, characterized in that, The method includes: Obtaining a two-dimensional angiographic image sequence and a three-dimensional image sequence of the blood vessels related to the intracranial aneurysm; Based on the two-dimensional angiographic image sequence, performing maximum intensity projection and centerline extraction processing on it, and combining with the analysis of the contrast agent concentration change curve to generate a time series image containing flow field information; Based on the three-dimensional image sequence, performing three-dimensional reconstruction, region of interest extraction, and opening extension processing on it to obtain the morphological parameters of the blood vessels in the region of interest and the region of interest model after opening extension processing; Overlapping and displaying the time series image with flow field information and the region of interest model without opening extension, making the viewing angles consistent, and automatically generating viewing spheres at all opening positions of the region of interest model without opening extension. By calculating the sum of the flow field vectors within each viewing sphere, determining the actual blood flow direction at the opening positions of the region of interest model with opening extension, and finally completing the determination of boundary conditions and the construction of the flow field; Based on the boundary conditions and the flow field, automatically meshing the region of interest model with opening extension, and performing fluid mechanics simulation and calculating hemodynamic parameters; Based on the morphological parameters and hemodynamic parameters, and combining with the clinical information of the patient, obtaining an assessment of the aneurysm rupture risk.

2. The intracranial aneurysm rupture risk assessment method according to claim 1, wherein The step of, based on the two-dimensional angiographic image sequence, performing maximum intensity projection and centerline extraction processing on it, and combining with the analysis of the contrast agent concentration change curve to generate a time series image containing flow field information includes: Performing maximum intensity projection on all time frames of the two-dimensional angiographic image sequence to obtain a projection image, and extracting the two-dimensional blood vessel tree contour on the projection image; Extracting the centerline based on the two-dimensional blood vessel tree contour, and superimposing these centerlines on the original two-dimensional angiographic image sequence to form a new image sequence with highlighted contours and centerlines; In the newly generated image sequence, uniformly setting sampling points along the centerline, recording the change curve of the contrast agent concentration over time at each sampling point, calculating the time to peak through fitting analysis of these curves, and judging the blood flow direction at each sampling point according to the trend of the contrast agent concentration change, constructing the flow field along the centerline, and finally generating a time series image containing flow field information.

3. The intracranial aneurysm rupture risk assessment method according to claim 1, wherein, The step of, based on the three-dimensional image sequence, performing three-dimensional reconstruction, region of interest extraction, and opening extension processing on it to obtain the morphological parameters of the blood vessels in the region of interest and the region of interest model after opening extension processing includes: Constructing a first blood vessel tree model based on the three-dimensional image sequence, and extracting the region of interest in the first blood vessel tree model to obtain a second blood vessel tree model; Performing calculations on the second blood vessel tree model to obtain the morphological parameters of the blood vessels in the region of interest; Extending all the openings on the second blood vessel tree model to obtain the region of interest model after opening extension processing.

4. The intracranial aneurysm rupture risk assessment method according to claim 3, wherein The step of performing calculations on the second blood vessel tree model to obtain the morphological parameters of the blood vessels in the region of interest includes: Selecting a point at an arbitrary position on the surface of the aneurysm cavity of the second blood vessel tree model, automatically separating the aneurysm cavity from the blood vessel, and automatically calculating the morphological parameters.

5. The intracranial aneurysm rupture risk assessment method according to claim 1, wherein The step of determining the actual blood flow direction at the opening positions, and finally completing the determination of boundary conditions and the construction of the flow field includes: Calculate the vector sum of the flow fields within each observation sphere. The direction of the vector sum is the blood flow direction at the opening position. After obtaining the blood flow direction, set the flow rates at each inlet position. Among them, the inlet flow rate is obtained through transcranial Doppler measurement. The inlet is defined as the opening where the blood flow direction is from outside the model to inside the model, and vice versa for the outlet. The outlet flow rate is jointly determined by the law of conservation of flow rate and Murray's law.

6. The intracranial aneurysm rupture risk assessment method according to claim 1, wherein The center of the observation sphere is the geometric center of the opening, and the radius is 2 - 5 times the equivalent radius of the opening area.

7. The intracranial aneurysm rupture risk assessment method according to claim 1, wherein Automatically mesh the model of the region of interest with the extended opening, and perform fluid mechanics simulation and calculate hemodynamic parameters, including: Automatically mesh the model of the region of interest with the extended opening, perform fluid mechanics simulation calculations, and extract the calculation results of the last cardiac cycle as the final simulation results. The calculation results are the velocity distribution and pressure distribution at different times within the last cardiac cycle. Using the velocity distribution and pressure distribution at different times, calculate the distribution of hemodynamic parameters and animations on the surface of the vascular tree model. Combined with the aneurysm cavity of the blood vessels in the region of interest, automatically calculate the hemodynamic parameters of the aneurysm cavity.

8. An intracranial aneurysm rupture risk assessment device, characterized in that, The device includes: An image sequence acquisition module for acquiring two-dimensional angiographic image sequences and three-dimensional image sequences of blood vessels related to intracranial aneurysms. A first image sequence processing module for performing maximum intensity projection and centerline extraction processing on the two-dimensional angiographic image sequence, and generating a time series image containing flow field information by combining the analysis of the contrast agent concentration change curve. A second image sequence processing module for performing three-dimensional reconstruction, region of interest extraction, and opening extension processing on the three-dimensional image sequence to obtain the morphological parameters of the blood vessels in the region of interest and the model of the region of interest after opening extension processing. A boundary condition determination module for overlapping and displaying the time series image with flow field information and the model of the region of interest without the extended opening to make the viewing angles consistent, and automatically generating observation spheres at all opening positions of the model of the region of interest without the extended opening. By calculating the vector sum of the flow fields within each observation sphere, determine the actual blood flow direction at the opening position of the model of the region of interest with the extended opening, and finally complete the determination of boundary conditions and the construction of the flow field. A hemodynamic parameter calculation module for automatically meshing the model of the region of interest with the extended opening based on the boundary conditions and the flow field, and performing fluid mechanics simulation and calculating hemodynamic parameters. An evaluation module for obtaining an assessment of the aneurysm rupture risk based on the morphological parameters and hemodynamic parameters, combined with the patient's clinical information.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method described in 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 the processor, it implements the steps of the method described in any one of claims 1 to 7.

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