Method, computing device, and medium for determining blood pressure in true and false lumens of blood vessels

Abstract

The present invention relates to a method, a computing device, and a medium for determining the blood pressure of the true and false lumens of a blood vessel. The method includes: obtaining fluid correlation data of the blood vessel of a detection object, where the fluid correlation data at least indicates blood pressure and / or flow rate; determining the blood pressure and flow rate of boundary discrete points in a blood vessel reconstruction model based on the reference relationship of the blood pressure, flow rate, and time of the blood vessel and the obtained fluid correlation data, where the boundary discrete points include inlet discrete points and outlet discrete points; determining a blood vessel pressure distribution model based on the blood vessel reconstruction model and the determined blood pressure and flow rate of the boundary discrete points; and determining prediction data regarding the true lumen blood pressure and false lumen blood pressure in the blood vessel based on the determined blood vessel pressure distribution model. The present invention can reduce the cost of determining blood pressure and avoid harm to the detection object, and at the same time can accurately and comprehensively reflect the pressure conditions of the true and false lumens of the blood vessel.

Description

Technical Field

[0001] Embodiments of the present invention generally relate to the field of medical information processing, and more particularly to a method, a computing device, and a medium for determining the blood pressure of true and false lumens of blood vessels. Background Art

[0002] The true and false lumens of blood vessels generally refer to the true and false lumens of aortic dissection. Due to factors such as metabolic disorders or genetics, problems such as stiffness and dilation may occur in the aortic blood vessels, which may further lead to the rupture of elastic fibers in the middle layer of the aorta, thus triggering the tearing of the aortic intima. The blood in the aorta flows through the tear into the middle layer to form a dissecting hematoma. Moreover, the pressure of the blood flow causes the dissecting hematoma in the middle layer of the aorta to continuously expand downward to form a false lumen.

[0003] In traditional methods for determining the blood pressure of true and false lumens of blood vessels, professional devices such as pressure wires are often used. The pressure wire is invasively introduced into the true and false lumens inside the aortic blood vessels to detect the corresponding blood pressure. However, this method has too high an operation difficulty and causes relatively high harm to the detection object. At the same time, in order to reduce the invasive harm to the detection object, the position where the pressure wire is introduced into the aorta is generally small, resulting in relatively one-sided pressure acquisition data and being unable to accurately and comprehensively reflect the blood pressure distribution of the true and false lumens of the aorta.

[0004] In summary, the deficiencies of traditional methods for determining the blood pressure of true and false lumens of blood vessels are as follows: the cost of determining blood pressure is too high and the harm to the detection object is relatively large, and at the same time, the determined results are difficult to accurately and comprehensively reflect the pressure conditions of the true and false lumens of blood vessels. Summary of the Invention

[0005] In view of the above problems, the present invention provides a method, a computing device, and a medium for determining the blood pressure of true and false lumens of blood vessels, which can reduce the cost of determining blood pressure and avoid harm to the detection object, and at the same time can accurately and comprehensively reflect the pressure conditions of the true and false lumens of blood vessels.

[0006] According to a first aspect of the present invention, there is provided a method for determining the blood pressure of true and false lumens of blood vessels, the method comprising: obtaining fluid correlation data of the blood vessels of a detection object, the fluid correlation data at least indicating blood pressure and / or flow rate; determining the blood pressure and flow rate of boundary discrete points in a blood vessel reconstruction model based on a reference relationship of blood pressure, flow rate, and time of the blood vessels and the obtained fluid correlation data, the boundary discrete points including inlet discrete points and outlet discrete points; determining a blood pressure distribution model based on the blood vessel reconstruction model and the determined blood pressure and flow rate of the boundary discrete points; and determining prediction data regarding the true lumen blood pressure and false lumen blood pressure in the blood vessels based on the determined blood pressure distribution model.

[0007] In some embodiments, the method further includes: obtaining a scanned image of a tissue site including blood vessels, the scanned image being a three-dimensional voxel image; determining voxel points corresponding to the blood vessels based on the brightness of the voxel points in the obtained scanned image; and determining a blood vessel reconstruction model based on the determined voxel points corresponding to the blood vessels.

[0008] In some embodiments, obtaining fluid correlation data of a blood vessel of a detection object includes: obtaining pulsation correlation detection data of the detection object; adjusting a reference relationship between blood flow rate and time based on the obtained pulsation correlation detection data so as to obtain an adjusted relationship between blood flow rate and time for the detection object; and obtaining the flow velocity of the blood vessel of the detection object based on the obtained relationship between blood flow rate and time for the detection object.

[0009] In some embodiments, the method further includes: discretizing, in a spatial dimension, a reference relationship between blood pressure, flow velocity, and time of a blood vessel based on volume elements so as to determine boundary discretization points; discretizing, in a time dimension, a reference relationship between blood pressure, flow velocity, and time of a blood vessel based on a time step so as to determine a calculation interval of the boundary discretization points; and determining boundary discretization points corresponding to a time point based on the discretization results in the spatial dimension and the time dimension.

[0010] In some embodiments, determining the blood pressure and flow velocity of boundary discretization points in a blood vessel reconstruction model based on a reference relationship between blood pressure, flow velocity, and time of a blood vessel and the obtained fluid correlation data includes: determining a set of initial blood pressures and a set of initial flow velocities of a set of boundary discretization points corresponding to a selected time point based on the reference relationship between blood pressure, flow velocity, and time of the blood vessel and the obtained fluid correlation data; determining a set of first errors of a set of boundary discretization points based on the determined set of initial blood pressures and the set of initial flow velocities; determining a second error for the selected time point based on the determined set of first errors; and in response to the determined second error satisfying a convergence condition, determining a set of blood pressures and a set of flow velocities of a set of boundary discretization points based on the determined set of initial blood pressures and the set of initial flow velocities of the set of boundary discretization points.

[0011] In some embodiments, the fluid correlation data further includes the density and viscosity of the fluid in the blood vessel, and the method further includes: determining a rate of change of momentum for the fluid in the blood vessel based on the flow velocity and density of the blood vessel; determining a force on the fluid in the blood vessel based on the blood pressure, density, viscosity, and flow velocity of the blood vessel; and determining a reference relationship between blood pressure, flow velocity, and time of the blood vessel based on an identity relationship between the determined rate of change of momentum and the determined force.

[0012] In some embodiments, the outlet discrete points include discrete points indicating the outlet of a branch vessel of a blood vessel, and determining the blood pressure and flow rate of the boundary discrete points in the blood vessel reconstruction model based on the reference relationship of the blood pressure, flow rate, and time of the blood vessel and the acquired fluid correlation data includes: determining the blood pressure and flow rate of the inlet discrete points in the blood vessel reconstruction model based on the reference relationship of the blood pressure, flow rate, and time of the blood vessel and the acquired fluid correlation data; and determining the blood pressure and flow rate of the outlet discrete points in the blood vessel reconstruction model based on the resistance element, elastic element, and the determined blood pressure and flow rate of the inlet discrete points in the equivalent circuit corresponding to the blood vessel elastic cavity model.

[0013] In some embodiments, determining the prediction data regarding the true lumen blood pressure and false lumen blood pressure in the blood vessel based on the determined blood vessel pressure distribution model includes: determining the first blood pressure at the first position and the second blood pressure at the second position based on the determined blood vessel pressure distribution model; and determining the prediction data regarding the true lumen blood pressure and false lumen blood pressure in the blood vessel in response to the difference between the determined first blood pressure and the second blood pressure being higher than the difference threshold.

[0014] According to a second aspect of the present invention, there is provided a computing device. The computing device includes: at least one processor; and at least one memory, the at least one memory being coupled to the at least one processor and storing instructions for execution by the at least one processor, the instructions when executed by the at least one processor causing the computing device to perform the steps of the method according to the first aspect of the present invention.

[0015] According to a third aspect of the present invention, there is provided a computer-readable storage medium having computer program code stored thereon, the computer program code when run performing the steps of the method according to the first aspect of the present invention.

[0016] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. Description of the Drawings

[0017] The present invention will be better understood by referring to the following description of the specific embodiments of the present invention given in the accompanying drawings, and other objects, details, features, and advantages of the present invention will become more apparent.

[0018] Figure 1 A schematic diagram of a system for a method of determining the blood pressure of the true and false lumens of a blood vessel according to some embodiments of the present invention;

[0019] Figure 2 A flowchart of a method for determining the blood pressure of the true and false lumens of a blood vessel according to some embodiments of the present invention;

[0020] Figure 3 A schematic diagram showing the blood flow curves of some embodiments of the present invention;

[0021] Figure 4 A schematic diagram showing the blood vessel pressure distribution model of some embodiments of the present invention;

[0022] Figure 5 A schematic diagram showing the equivalent circuit of the blood vessel elastic chamber model of some embodiments of the present invention;

[0023] Figure 6 A schematic diagram showing the process for determining the blood pressure of the true and false lumens of a blood vessel in some embodiments of the present invention; and

[0024] Figure 7 A step diagram schematically showing an electronic device suitable for implementing the embodiments of the present invention.

[0025] In each of the drawings, the same or corresponding reference numerals represent the same or corresponding parts. Detailed Description of the Embodiments

[0026] The preferred embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0027] In the following description, certain specific details are set forth for the purpose of explaining various embodiments of the invention to provide a thorough understanding of the various embodiments of the invention. However, those skilled in the relevant art will recognize that the embodiments can be practiced without one or more of these specific details. In other instances, well-known devices, structures, and techniques associated with the present application may not be shown or described in detail so as not to unnecessarily obscure the description of the embodiments.

[0028] Unless the context requires otherwise, throughout the specification and claims, the words "comprise" and its variations, such as "comprising" and "having", should be understood in an open, inclusive sense, i.e., should be interpreted as "including, but not limited to".

[0029] References to "one embodiment" or "some embodiments" throughout the specification mean that the particular features, structures, or characteristics described in connection with the embodiments are included in at least one embodiment. Thus, the appearances of "in one embodiment" or "in some embodiments" throughout the specification do not necessarily all refer to the same embodiment. Additionally, the particular features, structures, or characteristics may be combined in any manner in one or more embodiments.

[0030] In addition, the terms "first", "second", etc. used in the specification and claims are only for the sake of clear description to distinguish each object, and do not limit the size or other order of the objects they describe.

[0031] As described above, the deficiencies of the traditional method for determining the blood pressure of the true and false lumens of blood vessels are as follows: the cost of determining the blood pressure is too high and the harm to the detection object is relatively large, and at the same time, the determined result is difficult to accurately and comprehensively reflect the pressure conditions of the true and false lumens of the blood vessels.

[0032] To at least partially solve one or more of the above problems and other potential problems, the present invention provides a method for determining the blood pressure of the true and false lumens of blood vessels. In this method, by collecting fluid-related data such as the blood pressure and blood flow rate of the blood vessels of the detection object, and substituting the fluid-related data into the reference relationship of blood pressure, flow rate, and time to calculate the blood pressure and flow rate of the boundary discrete points at both ends of the blood vessel reconstruction model, and then based on the blood pressure and flow rate of the boundary discrete points of the blood vessel reconstruction model, performing a simulation on the discrete points within the boundary, so as to obtain a blood vessel pressure distribution model, and further determining prediction data regarding the blood pressure of the true and false lumens based on the blood vessel pressure distribution model. In the present invention, only by collecting the fluid-related data of the object to be detected can the prediction data regarding the blood pressure of the true and false lumens in the blood vessel be determined, without the need to invasively introduce a pressure guide wire into the blood vessel, reducing the cost of determining the blood pressure and the harm to the detection object. At the same time, the obtained blood vessel pressure distribution model can reflect the blood pressure at each position of the blood vessel, so as to accurately and comprehensively reflect the distribution status of the prediction data of the blood pressure of the true and false lumens of the blood vessel. Therefore, the present invention can reduce the cost of determining the blood pressure and avoid harm to the detection object, and at the same time can accurately and comprehensively reflect the distribution status of the prediction data of the blood pressure of the true and false lumens of the blood vessel.

[0033] Figure 1 A schematic diagram of a system 100 for a method of determining the blood pressure of the true and false lumens of blood vessels according to some embodiments of the present invention is shown. Refer to Figure 1 , the system 100 includes a computing device 102, a tomography device 112, a physiological information detection device 114, and a prediction data display device 116. In some embodiments, the physiological information detection device 114 and the computing device 102 may be independent devices from each other. Alternatively or additionally, the physiological information detection device 114 and the computing device 102 may include different threads of the same device. For example, the thread for calculating the flow rate in the physiological information detection device 114 and the computing device 102 may be two different threads of the same computer.

[0034] Regarding the tomographic scanning device 112, it is connected to the computing device 102 to collect scanning data of the tissue part of the blood vessel and send it to the computing device 102. In some embodiments, the tomographic scanning device 112 penetrates the tissue part through X-rays, Y-rays, ultrasonic waves, etc. to obtain cross-sectional scanning data. In some embodiments, the tomographic scanning device 112 includes a transmitting end (such as an X-ray generator), a receiving end, and a mechanical component (such as a turntable, a guide rail, a base), etc.

[0035] Regarding the physiological information detection device 114, it is connected to the computing device 102 to collect fluid-related data such as blood pressure and flow rate for a blood vessel (such as the aorta), as well as pulsation-related detection data such as heart rate, stroke volume, and systolic ratio (the fluid-related data and the pulsation-related detection data can be collectively referred to as the physiological information of the detection object) and send it to the computing device 102. Among them, blood pressure is the pressure of the blood in the blood vessel on the side wall of the blood vessel, the flow rate is the flow velocity of the blood in the blood vessel, the heart rate can be, for example, the number of pulsations per minute, the stroke volume is the amount of blood discharged per pulsation, and the systolic ratio is the ratio of the systolic period to the pulsation period in a pulsation cycle.

[0036] Regarding the computing device 102, it can be implemented by an MCU (Micro Controller Unit), a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-purpose Computing on Graphics Processing Units), an FPGA (Field Programmable Gate Array), or other programmable logic devices, an ASIC (Application Specific Integrated Circuit), discrete gate or transistor logic devices, discrete hardware components, etc. The computing device 102 can have one or more processing units, including dedicated processing units such as GPUs, FPGAs, and ASICs, and general-purpose processing units such as CPUs. The computing device 102 can receive the scan data sent by the tomography device 112 and process it to synthesize a scan image and obtain a blood vessel reconstruction model. The computing device 102 can also receive fluid-related data such as blood pressure and flow rate sent by the physiological information detection device 114 and determine prediction data regarding true lumen blood pressure and false lumen blood pressure in the blood vessel based on the fluid-related data and the blood vessel reconstruction model. In addition, the computing device 102 can send the prediction data regarding true lumen blood pressure and false lumen blood pressure in the blood vessel to the prediction data display device 116 so that the prediction data display device 116 can display the above prediction data.

[0037] Regarding the fluid-related data acquisition unit 104, it is used to acquire the fluid-related data of the blood vessels of the detection object, and the fluid-related data at least indicates blood pressure and / or flow rate.

[0038] Regarding the boundary discrete point parameter acquisition unit 106, it is used to determine the blood pressure and flow rate of the boundary discrete points in the blood vessel reconstruction model based on the reference relationship of blood pressure, flow rate, and time of the blood vessel and the acquired fluid-related data. The boundary discrete points include inlet discrete points and outlet discrete points.

[0039] Regarding the blood vessel pressure distribution model determination unit 108, it is used to determine the blood vessel pressure distribution model based on the blood vessel reconstruction model and the determined blood pressure and flow rate of the boundary discrete points.

[0040] Regarding the prediction data determination unit 110, it is used to determine the prediction data regarding true lumen blood pressure and false lumen blood pressure in the blood vessel based on the determined blood vessel pressure distribution model.

[0041] Regarding the predicted data display device 116, it is configured to receive the predicted data of the true lumen blood pressure and the false lumen blood pressure sent by the computing device 102 and perform display. In some embodiments, the predicted data sent by the computing device 102 is organized based on the structure of the vascular reconstruction model, so as to form a distribution model of the predicted data. The predicted data display device 116 can display the distribution model of the predicted data. For example, it can display a stereoscopic image of the distribution model of the predicted data at a corresponding position or in a corresponding direction based on the received position instruction or direction instruction. In some embodiments, different predicted data can be presented by the color or brightness of pixel points in the stereoscopic image of the distribution model of the predicted data.

[0042] The following will combine Figure 2 to describe the method 200 for determining the blood pressure of the true and false lumens of blood vessels in some embodiments of the present invention. Figure 2 FIG. shows a flowchart of the method 200 for determining the blood pressure of the true and false lumens of blood vessels in some embodiments of the present invention. The method 200 can be executed, for example, in Figure 1 the computing device 102 described in Figure 7 or can also be executed in the electronic device 700 shown in

[0043] Before obtaining the fluid association data of the blood vessel of the detection object (i.e., step 202), the computing device 102 can first determine the vascular reconstruction model. Regarding the method for determining the vascular reconstruction model, for example, it includes: obtaining a scanned image of the tissue part containing the blood vessel, where the scanned image is a three-dimensional voxel image; determining the voxel points corresponding to the blood vessel based on the brightness of the voxel points in the obtained scanned image; and determining the vascular reconstruction model based on the determined voxel points corresponding to the blood vessel.

[0044] In some embodiments, referring to Figure 1 the system 100 in

[0045] In the current embodiment, according to the brightness (or color) of the voxel points in the scanned image, the voxel points representing blood vessels are extracted, and based on the voxel points of the blood vessels, the blood vessels are modeled to obtain a blood vessel reconstruction model. In some embodiments, the brightness range of the voxel points of the blood vessels can be determined, the corresponding brightness threshold can be determined, and the voxel points representing blood vessels are screened from the voxel points of the scanned image by means of threshold screening.

[0046] In this way, the voxel points corresponding to blood vessels can be directly extracted from the scanned image and the blood vessels can be quickly modeled, without collecting targeted blood vessel scan data for the blood vessels and performing model reconstruction, reducing the modeling cost of the blood vessels and improving the modeling efficiency of the blood vessels.

[0047] At step 202, the computing device 102 obtains the fluid correlation data of the blood vessels of the detection object, and the fluid correlation data at least indicates blood pressure and / or flow rate.

[0048] Regarding the fluid correlation data, that is, the data related to the dynamics of the blood in the blood vessels, it can at least include blood pressure and / or flow rate. In some embodiments, referring to Figure 1 the system 100 in, the fluid correlation data is collected by the physiological information detection device 114 and sent to the computing device 102.

[0049] Regarding the method for obtaining the fluid correlation data of the blood vessels of the detection object, for example, it includes: obtaining the pulsation correlation detection data of the detection object; based on the obtained pulsation correlation detection data, adjusting the reference relationship between blood flow and time so as to obtain the adjusted relationship between blood flow and time for the detection object; and based on the obtained relationship between blood flow and time for the detection object, obtaining the flow rate of the blood vessels of the detection object.

[0050] Regarding the pulsation correlation detection data, it can be data used to indicate data associated with the pulsation of the detection object, for example, it can include heart rate, stroke volume, and systolic ratio, etc. In some embodiments, in Figure 1 the system 100 shown in, the pulsation correlation detection data can be obtained by the physiological information detection device 114 and sent to the computing device 102.

[0051] Regarding the reference relationship between blood flow and time, for example, it can be the general relationship between blood flow and time of multiple normal objects. In some embodiments, the reference relationship is embodied as a template curve of blood flow and time.

[0052] The following combines Figure 3 to describe the blood flow curve 300 according to some embodiments of the present invention. Figure 3 The schematic diagram of the blood flow curve of some embodiments of the present invention is shown. In some embodiments, referring to Figure 3, the horizontal axis of the blood flow curve is time T, and its unit includes, for example, minutes (min) or seconds (s). The vertical axis of the blood flow curve is flow Q, and its unit includes, for example, liters per minute (L / min) or milliliters per minute (mL / min). The intersection of the horizontal axis and the vertical axis is the origin 0. One cycle of the blood flow curve includes: ① (starting point), ② (point of fastest blood flow increase), ③ (point of highest blood flow), ④ (point of fastest blood flow decrease), ⑤ (end-systolic reflux point, also known as the end of systole or the start of diastole), ⑥ (reflection point), and ⑦ (ending point). The time interval [0, T1] corresponding to between ① and ⑦ is one cycle of the blood flow curve. The time interval [0, T2] corresponding to between ① and ⑤ is the systolic phase of the blood flow curve in one cycle. The time interval [T2, T1] corresponding to between ⑤ and ⑦ is the diastolic phase of the blood flow curve in one cycle.

[0053] In some embodiments, the computing device 102 obtains pulsation-related detection data such as the heart rate, stroke volume, and systolic ratio of the detection object, and adjusts based on the pulsation-related detection data Figure 3 the blood flow curve shown in Figure 3 to obtain an adjusted blood flow curve for the detection object. For example, the position of ③ (point of highest blood flow) shown in

[0054] can be adjusted so that the position where the blood flow peak appears in one pulsation cycle is the same as or close to the actual blood flow peak time of the detection object.

[0055] In this way, an accurate relationship between the blood flow and time for the detection object can be obtained, and expensive instruments such as magnetic resonance imaging devices are not required. Furthermore, the flow rate can be determined based on the blood flow of the detection object efficiently and at low cost, thereby significantly improving the accuracy of determining the flow rate for different individuals.

[0056] Return reference Figure 2 , at step 204, the computing device 102 determines the blood pressure and flow rate of the boundary discrete points in the blood vessel reconstruction model based on the reference relationship between the blood pressure, flow rate, and time of the blood vessel and the acquired fluid-related data. The boundary discrete points include inlet discrete points and outlet discrete points.

[0057] Regarding fluid correlation data, which for example also includes the density and viscosity of the fluid within a blood vessel, and a method for determining the reference relationship of the blood pressure, flow rate, and time of a blood vessel, which for example includes: determining the rate of change of momentum for the fluid within the blood vessel based on the flow rate and density of the blood vessel; determining the force acting on the fluid within the blood vessel based on the blood pressure, density, viscosity, and flow rate of the blood vessel; and determining the reference relationship of the blood pressure, flow rate, and time of the blood vessel based on the identity relationship between the determined rate of change of momentum and the determined force.

[0058] Regarding the reference relationship of the blood pressure, flow rate, and time of a blood vessel, which for example can be the reference equation of the blood pressure, flow rate, and time of blood. In some embodiments, the reference equation of the blood pressure, flow rate, and time of blood is the Navier-Stokes Equations (abbreviated as the NS equation), and its specific expression is as follows:

[0059]

[0060]

[0061] Among them, in the expression represents the gradient, u represents the velocity vector, t represents time, ρ represents density, p represents pressure, g represents the gravitational acceleration constant, and v represents viscosity. In some embodiments, the blood in the blood vessel is approximated as an incompressible Newtonian fluid, the density of the blood is 1060 KG / m 3 , the viscosity is 0.004 Pa·s, and the blood vessel wall is assumed to be a non-slip rigid wall. Multiply both sides of formula (2) by ρ to obtain formula (3):

[0062]

[0063] In formula (3), the left side of the equal sign represents the rate of change of momentum of the blood within the blood vessel, which are the time term and the convective term from left to right in sequence, and the right side of the equal sign represents the force acting on the blood within the blood vessel, which are the pressure term, the body force term, and the viscous force term from left to right in sequence. It can be understood that there is an identity relationship between the rate of change of momentum and the force of the blood within the blood vessel. Therefore, the reference relationship of the blood pressure, flow rate, and time shown in formula (2) or formula (3) can be established based on this identity relationship.

[0064] Regarding the boundary discrete points, which for example refer to the discrete points of the boundary (i.e., the inlet and outlet) in the blood vessel reconstruction model, including the inlet discrete points and the outlet discrete points. It can be understood that the boundary discrete points of the blood vessel reconstruction model are used to characterize a certain local area of the blood vessel at the boundary. For example, if the number of inlet discrete points is 1 million, it represents the inlet of the blood vessel composed of 1 million local areas.

[0065] A method for determining boundary discrete points, for example, includes: discretizing the reference relationship of blood pressure, flow rate, and time of a blood vessel in the spatial dimension based on volume elements to determine boundary discrete points; discretizing the reference relationship of blood pressure, flow rate, and time of a blood vessel in the time dimension based on time steps to determine the calculation interval of boundary discrete points; and determining the boundary discrete points corresponding to time points based on the discretization results in the spatial dimension and time dimension.

[0066] In some embodiments, the Navier - Stokes equation is discretized in the spatial dimension based on the upwind scheme to obtain boundary discrete points. Among them, the upwind scheme can handle the defects of the central difference scheme, including but not limited to the first - order upwind scheme and the second - order upwind scheme. Preferably, the upwind scheme is set to the second - order upwind scheme.

[0067] In some embodiments, a blood vessel reconstruction model can be discretized using volume elements of a certain size to obtain multiple grid cells in the blood vessel reconstruction model. Based on this volume element, the Navier - Stokes equation can be discretized in the spatial dimension to obtain a discretization result, that is, boundary discrete points. In some embodiments, the size and number of volume elements can be set according to the actual situation of the detection object. For example, the volume element is set to a tetrahedral element of 0.5 mm according to the actual situation of the detection object, so as to obtain a blood vessel reconstruction model containing approximately 3.471 million grid cells.

[0068] In some embodiments, the Navier - Stokes equation is discretized in the time dimension based on the first - order implicit Euler scheme to obtain a discretization result according to a certain time step. Among them, this time step can be set according to the actual situation of the detection object, for example, set to 10 ms to achieve time independence.

[0069] In this way, the boundary discrete points corresponding to each time point can be quickly obtained based on the discretization results in space and time to meet the discretization requirements of the blood vessel reconstruction model and improve the accuracy of boundary discrete points.

[0070] In some embodiments, after determining the boundary discrete points, the blood pressure and flow rate of each boundary discrete point are calculated based on the fluid correlation data and the Navier - Stokes equation. For example, if the fluid correlation data for a certain boundary discrete point includes the flow rate at this boundary discrete point, the blood pressure of this boundary discrete point can be calculated based on the Navier - Stokes equation.

[0071] A method for determining blood pressure and flow rate at boundary discrete points in a blood vessel reconstruction model based on a reference relationship of blood pressure, flow rate, and time of a blood vessel and acquired fluid correlation data, for example, includes: determining a set of initial blood pressures and a set of initial flow rates of a set of boundary discrete points corresponding to a selected time point based on the reference relationship of blood pressure, flow rate, and time of the blood vessel and the acquired fluid correlation data; determining a set of first errors of a set of boundary discrete points based on the determined set of initial blood pressures and the set of initial flow rates; determining a second error for the selected time point based on the determined set of first errors; and in response to the determined second error satisfying a convergence condition, determining a set of blood pressures and a set of flow rates of a set of boundary discrete points based on the determined set of initial blood pressures and the set of initial flow rates of the set of boundary discrete points.

[0072] In some embodiments, based on the Navier-Stokes equations and fluid correlation data, a set of boundary discrete points at a selected time point after discretization is determined, and a set of blood pressures and a set of flow rates of the set of boundary discrete points are calculated. Then, based on the blood pressure and flow rate of each boundary discrete point, a residual (which can be referred to as a first error) of the boundary discrete point is determined, obtaining a set of residuals corresponding to the set of boundary discrete points, and further based on the set of residuals, a root mean square error (which can be referred to as a second error) corresponding to the selected time point is calculated. It is determined whether the root mean square error satisfies the convergence condition. When the root mean square error satisfies the convergence condition, the above set of blood pressures and the set of flow rates can be used as the final blood pressures and final flow rates corresponding to the set of boundary discrete points.

[0073] In this way, the errors of the blood pressure and flow rate at the boundary discrete points can be reduced, the accuracy of the blood pressure and flow rate at the boundary discrete points can be improved, and further the accuracy of the blood vessel pressure distribution model can be improved.

[0074] At step 206, the computing device 102 determines a blood vessel pressure distribution model based on the blood vessel reconstruction model and the blood pressure and flow rate of the determined boundary discrete points.

[0075] In some embodiments, after determining the blood pressure and flow rate at the boundary discrete points in the blood vessel reconstruction model, based on the blood vessel reconstruction model and the blood pressure and flow rate of the boundary discrete points, the blood pressure of each discrete point in the blood vessel reconstruction model is simulated, thereby obtaining a blood vessel pressure distribution model.

[0076] The following combines Figure 4 to describe the blood vessel pressure distribution model 400 according to some embodiments of the present invention. Figure 4A schematic diagram of a vascular pressure distribution model 400 showing some embodiments of the present invention is presented. In some embodiments, the vascular pressure distribution model 400 includes a true lumen pressure distribution sub-model 402 of the aorta, a false lumen pressure distribution sub-model 404 of the aorta, and a branch vessel pressure distribution sub-model 406. In some embodiments, the vascular pressure in the vascular pressure distribution model 400 is represented by the gray value of each voxel point. Starting from Figure 4 it can be seen that there are significant differences between the blood pressure in the true lumen and the false lumen. Alternatively or additionally, the blood pressure can also be represented by the color of each voxel point in the vascular pressure distribution model 400 (not shown in the figure). For example, colors such as red, yellow, green, blue, purple, etc. are used for identification in descending order of blood pressure level.

[0077] Regarding the outlet discrete points, which for example include discrete points indicating the branch vessel outlets of the blood vessels, and a method for determining the blood pressure and flow rate of the boundary discrete points in the vascular reconstruction model based on the reference relationship of the blood pressure, flow rate, and time of the blood vessels and the acquired fluid correlation data. For example, it includes: determining the blood pressure and flow rate of the inlet discrete points in the vascular reconstruction model based on the reference relationship of the blood pressure, flow rate, and time of the blood vessels and the acquired fluid correlation data; and determining the blood pressure and flow rate of the outlet discrete points in the vascular reconstruction model based on the resistance element, elastic element in the equivalent circuit corresponding to the vascular elastic cavity model, and the blood pressure and flow rate of the determined inlet discrete points.

[0078] Regarding the vascular elastic cavity model (Windkessel Model), it can be a modeling method for local blood vessels. In this modeling method, the blood vessels are simplified into resistance elements, compliance elements, inertia elements, etc. The vascular elastic cavity model includes a two-element model, a three-element model, and a four-element model, etc. In some embodiments, the vascular elastic cavity model is a two-element model. In the equivalent circuit of the two-element model, all the peripheral resistances of the blood vessels are combined into a resistance element, and the blood vessel elasticity generated in the blood vessels is combined into an elastic element. Alternatively or additionally, the vascular elastic cavity model is a three-element model, and its equivalent circuit adds a shock resistance element compared to the above two-element model to describe the phase difference between the reflected wave and the shock wave in the blood vessels.

[0079] Next, in combination with Figure 5 the equivalent circuit 500 of the vascular elastic cavity model according to some embodiments of the present invention is described. Figure 5 A schematic diagram of the equivalent circuit of the vascular elastic cavity model showing some embodiments of the present invention is presented. In some embodiments, in the equivalent circuit 500 of the vascular elastic cavity model, Qin is the blood inlet of the aorta, Qout is the blood outlet of the aorta, Pin is the blood pressure at the aorta inlet, Pout is the blood pressure at the aorta outlet, Rp is the proximal oscillatory resistance element, C is the elastic element at the branch vessel inlet, and Rd is the distal resistance element.

[0080] In some embodiments, first, according to the Navier-Stokes equations and fluid correlation data, the blood pressure and flow rate of the inlet discrete points in the blood vessel reconstruction model are calculated. Then, based on the blood pressure and flow rate of the inlet discrete points and the resistance element and elastic element in the equivalent circuit corresponding to the blood vessel elastic cavity model, the blood pressure and flow rate of the outlet discrete points in the blood vessel reconstruction model are calculated. Then, based on the blood vessel reconstruction model, the blood pressure of the inlet discrete points, and the blood pressure and flow rate of the outlet discrete points, the blood pressure of each discrete point in the blood vessel reconstruction model is simulated, thereby obtaining a blood vessel pressure distribution model.

[0081] In some embodiments, first, according to the Navier-Stokes equations and fluid correlation data, the blood pressure and flow rate of the inlet discrete points and outlet discrete points of the aorta in the blood vessel reconstruction model are calculated. Then, based on the blood pressure and flow rate of the inlet discrete points and outlet discrete points of the aorta, and the resistance element and elastic element in the equivalent circuit corresponding to the blood vessel elastic cavity model, the blood pressure and flow rate of the outlet discrete points of the branch blood vessels in the blood vessel reconstruction model are calculated. Then, based on the blood vessel reconstruction model, the blood pressure of the inlet discrete points and outlet discrete points of the aorta, and the blood pressure and flow rate of the outlet discrete points of the branch blood vessels, the blood pressure of each discrete point in the blood vessel reconstruction model is simulated, thereby obtaining a blood vessel pressure distribution model.

[0082] In some embodiments, the parameters of the blood vessel elastic cavity model are calibrated with multiple sample data, where the sample data at least includes data such as systolic blood pressure, diastolic blood pressure, and blood flow at the outlet of the branch blood vessels. By calibrating the parameters of the blood vessel elastic cavity model, it can be ensured that the systolic blood pressure, diastolic blood pressure, and the distribution of blood flow at the outlet of the branch blood vessels simulated and determined based on the calibrated blood vessel elastic cavity model are consistent with the actual data.

[0083] In some embodiments, after determining the blood vessel pressure distribution model, the residuals of each discrete point in the blood vessel pressure distribution model at a time point of a time step can be determined, and further, the root mean square error of the blood vessel pressure distribution model for this time point can be calculated based on the residuals of each discrete point. If the root mean square error meets the convergence condition, it can be determined that the blood vessel pressure distribution model reaches a steady state. In some embodiments, when the root mean square error reaches the error threshold 10 -5 it is determined that the root mean square error reaches the convergence condition. In some embodiments, the simulation process of the computing device 102 can continue for at least 3 cardiac cycles, so as to ensure that the blood vessel pressure distribution model reaches a steady state, and the blood vessel pressure distribution model determined in the last cardiac cycle is used as the simulation result.

[0084] Return reference Figure 2 , at step 208, based on the determined blood vessel pressure distribution model, prediction data regarding the true lumen blood pressure and false lumen blood pressure in the blood vessel is determined.

[0085] A method for determining prediction data on true lumen blood pressure and false lumen blood pressure in a blood vessel based on the determined blood vessel pressure distribution model, for example, includes: determining a first blood pressure at a first position and a second blood pressure at a second position based on the determined blood vessel pressure distribution model; and determining prediction data on true lumen blood pressure and false lumen blood pressure in the blood vessel in response to the difference between the determined first blood pressure and second blood pressure being higher than a difference threshold.

[0086] In some embodiments, the blood pressures at two positions are determined according to the blood vessel pressure distribution model, and whether there are true and false lumens in the blood vessel is provided with reference information based on whether the blood pressure difference between the two positions is higher than the difference threshold. It should be understood that other factors need to be considered to determine whether there are true and false lumens in the blood vessel. Further, the prediction data on true lumen blood pressure and false lumen blood pressure in the blood vessel is determined based on the comparison result between the blood pressure difference between the above two positions and the difference threshold. It should also be understood that the prediction data does not directly point to the diagnostic result regarding true and false lumens, and other factors, such as blood vessel caliber, the active period of blood vessel dilation and constriction, blood flow direction, blood flow velocity change, etc., need to be further considered.

[0087] In the above solution, the present invention can collect fluid-related data such as blood pressure and blood flow velocity of the blood vessel of the detection object, substitute the fluid-related data into the reference relationship of blood pressure, flow velocity, and time to calculate the blood pressure and flow velocity of the boundary discrete points at both ends of the blood vessel reconstruction model, and then perform simulation on the discrete points within the boundary based on the blood pressure and flow velocity of the boundary discrete points of the blood vessel reconstruction model, so as to obtain the blood vessel pressure distribution model, and further determine the prediction data on true and false lumen blood pressure based on the blood vessel pressure distribution model. In the present invention, only the fluid-related data of the object to be detected needs to be collected to determine the prediction data on true and false lumen blood pressure in the blood vessel, and there is no need to invasively introduce a pressure guide wire into the blood vessel interior, reducing the cost of determining blood pressure and the harm to the detection object. At the same time, the obtained blood vessel pressure distribution model can reflect the blood pressure at each position of the blood vessel, so as to accurately and comprehensively reflect the distribution status of the prediction data on true and false lumen blood pressure. Therefore, the present invention can reduce the cost of determining blood pressure and avoid harm to the detection object, and at the same time can accurately and comprehensively reflect the distribution status of the prediction data on true and false lumen blood pressure.

[0088] The following combines Figure 6 Describe a process 600 for determining the blood pressure of true and false lumens of a blood vessel according to some embodiments of the present invention. Figure 6 The schematic diagram of a process 600 for determining the blood pressure of true and false lumens of a blood vessel according to some embodiments of the present invention is shown. It should be understood that the process 600 can be executed, for example, in Figure 1 the computing device 102 described in Figure 7Execute in the electronic device 700 shown. It should be understood that the process 600 may further include additional actions not shown and / or the actions shown may be omitted, and the scope of the present invention is not limited in this regard.

[0089] In step 604, the computing device 102 extracts a vascular reconstruction model. In some embodiments, the computing device 102 obtains a CT image 602 (which may be referred to as a scanned image), where the CT image 602 is a three-dimensional voxel image of the lesion site of the detection object. Then, according to the CT image, a vascular reconstruction model corresponding to the vascular segment is extracted by means of model annotation, where the model annotation is directed to the brightness of the voxel points in the CT image. In some embodiments, the process of extracting a vascular reconstruction model corresponding to the vascular segment by means of model annotation can be implemented by computer software.

[0090] In step 616, the computing device 102 adjusts the boundary conditions of the vascular reconstruction model. In some embodiments, the computing device 102 obtains the physiological information of the detection object (including blood pressure 612, stroke volume 606, heart rate 608, and systolic ratio 610), and adjusts the boundary conditions of the vascular reconstruction model based on this physiological information. Among them, the boundary conditions refer to the blood pressure and flow velocity conditions of the discrete points at the inlet and outlet in the vascular reconstruction model.

[0091] In some embodiments, according to the stroke volume 606, heart rate 608, and systolic ratio 610 of the detection object, a typical ascending aortic blood flow waveform (which may be referred to as the reference relationship between blood flow and time) is adjusted so that the adjusted waveform satisfies the specific hemodynamic data of the detection object. According to the adjusted ascending aortic blood flow waveform, the inlet velocity curve of the blood vessel can be obtained, and further, the blood flow velocity 614 in the blood vessel can be determined based on this inlet velocity curve. Further, the computing device 102 adjusts the boundary conditions of the vascular reconstruction model according to the blood pressure 612 and the flow velocity 614.

[0092] In some embodiments, the hemodynamic calculation can be based on the vascular reconstruction model, and the instantaneous changes of the hemodynamic parameters (including blood pressure and flow velocity, etc.) of the blood in the blood vessel are calculated by solving the Navier-Stokes equation. The specific expression of the Navier-Stokes equation is as follows:

[0093]

[0094]

[0095] Among them, in the expression represents the gradient, u represents the velocity vector, t represents time, ρ represents density, p represents pressure, g represents the gravitational acceleration constant, and v represents viscosity. In some embodiments, the blood in the blood vessel is approximated as an incompressible Newtonian fluid. The density of the blood is 1060 kg / m3, and the viscosity is 0.004 Pa·s. The blood vessel wall is assumed to be a non-slip rigid wall surface.

[0096] In some embodiments, the Navier-Stokes equations are discretized in space and time based on the second-order upwind scheme and the first-order implicit Euler scheme, respectively. The blood vessel reconstruction model is discretized using tetrahedral elements with a size of 0.5 mm, and the generated model contains approximately 3.471 million elements. The time step is fixed at 10 ms to achieve temporal independence.

[0097] In some embodiments, for the outlet of the branch blood vessels of the blood vessel, the outlet of the branch blood vessels of the blood vessel reconstruction model is coupled with a three-element blood vessel elastic chamber model to obtain reasonable blood pressure changes. Among them, the parameters of the blood vessel elastic chamber model are calibrated so that the systolic blood pressure, diastolic blood pressure, and blood flow distribution at each outlet obtained from the final three-dimensional simulation results match the sample data.

[0098] In step 618, the computing device 102 performs a simulation to obtain a blood vessel pressure distribution model. In some embodiments, after determining the boundary conditions of the blood vessel reconstruction model, the computing device 102 performs a simulation modeling of the blood pressure to obtain a blood vessel pressure distribution model. In some embodiments, when the root mean square error of the blood vessel pressure distribution model within each time step reaches 10 -5 it is determined that the calculation converges. The simulation of the computing device 102 can run for 3 pulsation cycles to reach a periodic steady state, and subsequently, the blood pressure of the blood vessel can be analyzed based on the simulation results of the last cycle.

[0099] In step 620, the computing device 102 determines and compares the predicted data of the blood vessel. In some embodiments, the computing device 102 determines the predicted data of the true lumen blood pressure and false lumen blood pressure in the blood vessel based on the blood vessel pressure distribution model, and compares the predicted data of the true lumen blood pressure and false lumen blood pressure to calculate the data difference.

[0100] Figure 7 A block diagram of an electronic device 700 suitable for implementing the embodiments of the present invention is schematically shown. The electronic device 700 can be used to implement the computing device 102. The electronic device 700 can be a device for implementing the method 200 and process 600 shown in Figure 2 、 Figure 6 As shown in Figure 7As shown, the electronic device 700 includes a central processing unit (i.e., CPU 701), which can execute various appropriate actions and processes according to computer program instructions stored in a read-only memory (i.e., ROM 702) or computer program instructions loaded from a storage unit 708 into a random access memory (i.e., RAM 703). In the RAM 703, various programs and data required for the operation of the electronic device 700 can also be stored. The CPU 701, ROM 702, and RAM 703 are connected to each other via a bus 704. An input / output interface (i.e., I / O interface 705) is also connected to the bus 704.

[0101] Multiple components in the electronic device 700 are connected to the I / O interface 705, including: an input unit 706, an output unit 707, a storage unit 708. The CPU 701 executes the various methods and processes described above, such as Figure 2 , Figure 6 The method 200 and process 600 shown. For example, in some embodiments, the various processes or operations described above can be implemented as a computer software program, which is stored in a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 700 via the ROM 702 and / or a communication unit 709. When the computer program is loaded into the RAM 703 and executed by the CPU 701, the various methods and processes described above can be executed, such as executing Figure 2 , Figure 6 One or more operations of the method 200 and process 600 shown. Alternatively, in other embodiments, the CPU 701 can be configured to execute the various methods and processes described above in any other suitable manner (e.g., by means of firmware), such as executing Figure 2 , Figure 6 One or more actions of the method 200 and process 600 shown.

[0102] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded to an external computer or external storage device through a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0103] The computer program instructions for performing the operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine - related instructions, microcode, firmware instructions, state - setting data, or source code or object code written in any combination of one or more programming languages, including object - oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer - readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand - alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it may be connected to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, by using the state information of the computer - readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field - programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer - readable program instructions to implement various aspects of the present invention.

[0104] These computer - readable program instructions can be provided to the processing unit of a processor, a general - purpose computer, a special - purpose computer, or other programmable data - processing devices in a voice interaction device, thereby producing a machine such that when these instructions are executed by the processing unit of the computer or other programmable data - processing devices, a device is produced that implements the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer - readable program instructions can also be stored in a computer - readable storage medium, and these instructions cause the computer, the programmable data - processing device, and / or other devices to work in a specific manner.

[0105] The embodiments of the present invention have been described above. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to technologies in the market, or to enable other ordinary skill in the technical field to understand the embodiments disclosed herein.

[0106] The above are only optional embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for determining the blood pressure of the true and false lumens of a blood vessel, characterized in that: include: Acquiring fluid-related data of a blood vessel of a test subject, wherein the fluid-related data at least indicates blood pressure and / or flow rate; Based on the reference relationship between the blood pressure, flow velocity and time of the blood vessel and the acquired fluid association data, determining the blood pressure and flow velocity of the boundary discrete points in the blood vessel reconstruction model, wherein the boundary discrete points include the inlet discrete points and the outlet discrete points; Determine a vascular pressure distribution model based on the vascular reconstruction model and the blood pressure and flow velocity at the determined discrete boundary points; and Determining predicted data regarding true lumen blood pressure and false lumen blood pressure in the blood vessel based on the determined vascular pressure distribution model; The method also includes: Based on the volume element, the reference relationship between the blood pressure, flow velocity and time of the blood vessel is discretized in the spatial dimension in order to determine the boundary discrete points; Based on the time step, the reference relationship between the blood pressure, flow velocity and time of the blood vessel is discretized in the time dimension so as to determine the calculation interval of the boundary discrete points; and Based on the discrete results in the spatial dimension and the temporal dimension, the boundary discrete points corresponding to the time points are determined; Based on the determined vascular pressure distribution model, the predicted data about the true lumen blood pressure and the false lumen blood pressure in the blood vessel are determined to include: determining a first blood pressure at a first location and a second blood pressure at a second location based on the determined blood vessel pressure distribution model; and In response to the determined difference between the first blood pressure and the second blood pressure being higher than a difference threshold, predicted data regarding true lumen blood pressure and false lumen blood pressure in the blood vessel is determined.

2. The method according to claim 1, characterized in that: Also includes: Acquire a scanned image of a tissue part containing a blood vessel, wherein the scanned image is a three-dimensional voxel image; Determine the voxel point corresponding to the blood vessel based on the brightness of the voxel point in the acquired scanned image; and A blood vessel reconstruction model is determined based on the determined voxel points corresponding to the blood vessels.

3. The method according to claim 1, characterized in that Acquiring fluid-related data of a blood vessel of a detection object includes: Acquire pulse-related detection data of the detection object; Based on the acquired pulsation-related detection data, adjusting the reference relationship between blood flow and time, so as to obtain an adjusted relationship between blood flow and time for the detection object; and Based on the acquired relationship between the blood flow rate and time of the detection object, the flow velocity of the blood vessel of the detection object is acquired.

4. The method according to claim 1, characterized in that: Based on the reference relationship between the blood pressure, flow velocity and time of the blood vessel and the acquired fluid association data, the blood pressure and flow velocity of the discrete boundary points in the blood vessel reconstruction model are determined, including: Determine a set of initial blood pressures and a set of initial flow velocities for a set of discrete boundary points corresponding to a selected time point based on a reference relationship between blood pressure, flow velocity, and time of the blood vessel and the acquired fluid association data; Determining a set of first errors of a set of boundary discrete points based on the determined set of initial blood pressures and the determined set of initial flow rates; determining a second error for a selected point in time based on the determined set of first errors; and In response to the determined second error satisfying a convergence condition, a set of blood pressures and a set of flow rates for a set of boundary discrete points are determined based on the determined set of initial blood pressures and a set of initial flow rates for the set of boundary discrete points.

5. The method according to claim 4, characterized in that The fluid-related data also includes density and viscosity of the intravascular fluid, and the method further includes: determining a rate of change of momentum for the fluid within the blood vessel based on the flow velocity and density of the blood vessel; Determining forces on the fluid within the blood vessel based on the blood pressure, density, viscosity, and flow rate of the blood vessel; and Based on the determined momentum change rate and the determined force identity, a reference relationship between the blood pressure, flow velocity and time of the blood vessel is determined.

6. The method according to claim 1, characterized in that The outlet discrete points include discrete points indicating the outlets of branch vessels of the blood vessels, and based on the reference relationship between the blood pressure, flow rate and time of the blood vessels and the acquired fluid association data, determining the blood pressure and flow rate of the boundary discrete points in the blood vessel reconstruction model includes: Determine the blood pressure and flow rate of the inlet discrete points in the blood vessel reconstruction model based on the reference relationship between the blood pressure, flow rate and time of the blood vessel and the acquired fluid correlation data; and Based on the resistance element, the elastic element and the determined blood pressure and flow rate at the inlet discrete point in the equivalent circuit corresponding to the vascular elastic cavity model, the blood pressure and flow rate at the outlet discrete point in the vascular reconstruction model are determined.

7. A computing device comprising: at least one processor; as well as At least one memory, the at least one memory being coupled to the at least one processor and storing instructions for execution by the at least one processor, the instructions, when executed by the at least one processor, causing the computing device to perform the steps of the method according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program code stored thereon, wherein the computer program code executes the method according to any one of claims 1 to 6 when executed.

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