A user state determination method, apparatus, and electronic device

Abstract

The application provides a user state determination method and device and electronic equipment, including: based on the pulmonary artery three-dimensional model, cardiac output and hematology parameters of a target user, applying a preset microvessel bifurcation growth rule, a personalized whole circulation fluid network model extending from the main pulmonary artery to the distal microcirculation is constructed; taking the pulmonary artery pressure data and the microcirculation blood flow resistance value corresponding to each outlet as the boundary condition, hemodynamic numerical simulation is performed on the model, and the pressure value of each pulmonary artery outlet is calculated. According to the pressure value of each outlet and the pulmonary artery pressure data, the blood flow reserve fraction of each outlet is determined; the blood flow reserve fractions of all outlets and the occluded pulmonary artery branches identified in the three-dimensional model are comprehensively calculated to obtain the pulmonary artery system average blood flow reserve fraction reflecting the blood flow function state of the whole system, and the pulmonary artery state attribute information of the user is determined accordingly. The scheme realizes accurate quantitative evaluation of the influence of pulmonary embolism on the whole blood flow function.

Description

Technical Field

[0001] The embodiments of the present invention relate to the fields of human physiology and hemodynamics, and particularly to a method, apparatus and electronic device for determining user status. Background Technology

[0002] Currently, the core of assessing the severity of pulmonary embolism lies in quantifying the impact of pulmonary artery stenosis on pulmonary blood flow function, and non-invasive fractional flow reserve calculation technology based on medical imaging provides a potential solution to this need.

[0003] Currently, the usual method for assessing the severity of pulmonary embolism is to reconstruct a three-dimensional geometric model of the patient's pulmonary artery based on computed tomography angiography images, and then further calculate the fractional blood flow reserve downstream of a specific stenotic site through computational fluid dynamics simulation, thereby quantitatively assessing the hemodynamic impact of local vascular stenosis through the fractional blood flow reserve.

[0004] However, the above methods have significant limitations when applied to different types of pulmonary embolism patients. Figure 1 A comparative schematic diagram of two types of pulmonary artery stenosis three-dimensional models, such as... Figure 1 The left side shows a blockage in the main branch of the pulmonary artery. Figure 1 The pulmonary artery terminal branch occlusion shown on the right, despite the different anatomical locations of the embolisms and their actual clinical significance in affecting overall blood flow, both show lesion flow reserve scores close to zero calculated based on this local assessment scheme. This makes it impossible to distinguish the differences in the degree of overall pulmonary blood flow function impairment between the two based on this indicator, contradicting the understanding that a comprehensive evaluation of the global impact of embolism is necessary in clinical diagnosis and treatment. Therefore, it is difficult to use as a reliable indicator for grading the risk of pulmonary embolism. Summary of the Invention

[0005] This application provides a user status determination method, device, and electronic device to overcome the limitations of existing local assessment methods that cannot distinguish the differences in the impact of complete occlusion of blood vessels at different locations on overall blood flow function, and to achieve a more comprehensive and clinically consistent quantitative assessment of the risk of pulmonary embolism.

[0006] In a first aspect, embodiments of this application provide a method for determining user status, the method comprising: Obtain the target user's pulmonary artery 3D model, pulmonary artery pressure data, cardiac output, and hematological parameters; Based on the pulmonary artery 3D model, cardiac output, and hematological parameters, a full-circulation fluid network model extending from the main pulmonary artery to the distal microcirculation is constructed using preset microvascular bifurcation growth rules; wherein, the full-circulation fluid network model includes the microcirculation blood flow resistance value corresponding to each pulmonary artery outlet; Using the pulmonary artery pressure data and the microcirculatory blood flow resistance values ​​at each pulmonary artery outlet as boundary conditions, a hemodynamic numerical simulation is performed on the whole circulation fluid network model to determine the pressure value at each pulmonary artery outlet in the whole circulation fluid network model. For each of the pulmonary artery outlets, the fractional flow reserve of the pulmonary artery outlet is determined based on the pressure value of the pulmonary artery outlet and the pulmonary artery pressure data; The average fractional flow reserve of the pulmonary artery system is determined based on the fractional flow reserve of each pulmonary artery outlet and the occluded pulmonary artery branches identified from the three-dimensional model of the pulmonary artery. Based on the mean fractional blood flow reserve, the pulmonary artery status attribute information of the target user is determined.

[0007] Secondly, embodiments of this application also provide a user status determination device, the device comprising: The data acquisition module is used to acquire the target user's pulmonary artery 3D model, pulmonary artery pressure data, cardiac output, and hematological parameters. The circulation model construction module is used to construct a full circulation fluid network model extending from the main pulmonary artery to the distal microcirculation based on the pulmonary artery three-dimensional model, the cardiac output, and the hematological parameters, and through preset microvascular bifurcation growth rules; wherein, the full circulation fluid network model includes the microcirculation blood flow resistance value corresponding to each pulmonary artery outlet; The outlet pressure determination module is used to perform hemodynamic numerical simulation on the whole circulation fluid network model by using the pulmonary artery pressure data and the microcirculation blood flow resistance value of each pulmonary artery outlet as boundary conditions, and to determine the pressure value of each pulmonary artery outlet in the whole circulation fluid network model. A blood flow reserve determination module is used to determine the blood flow reserve fraction of each pulmonary artery outlet based on the pressure value of the pulmonary artery outlet and the pulmonary artery pressure data. The mean blood flow reserve determination module is used to determine the mean blood flow reserve of the pulmonary artery system based on the blood flow reserve fraction of each pulmonary artery outlet and the occluded pulmonary artery branches identified from the pulmonary artery three-dimensional model. The pulmonary artery status determination module is used to determine the pulmonary artery status attribute information of the target user based on the mean fractional flow reserve.

[0008] Thirdly, embodiments of this application also provide an electronic device, which includes: One or more processors; Storage device for storing one or more programs. When one or more programs are executed by one or more processors, the one or more processors implement a user state determination method as described in any of the embodiments of this application.

[0009] Fourthly, embodiments of this application also provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform any of the user state determination methods described in embodiments of this application.

[0010] This application provides a method for determining user status, comprising: acquiring a three-dimensional model of the pulmonary artery, pulmonary artery pressure data, cardiac output, and hematological parameters of a target user; constructing a full-circulation fluid network model extending from the main pulmonary artery to the distal microcirculation based on the three-dimensional model of the pulmonary artery, cardiac output, and hematological parameters, using preset microvascular bifurcation growth rules; wherein the full-circulation fluid network model includes the microcirculation blood flow resistance value corresponding to each pulmonary artery outlet; performing hemodynamic numerical simulation on the full-circulation fluid network model using the pulmonary artery pressure data and the microcirculation blood flow resistance value of each pulmonary artery outlet as boundary conditions to determine the pressure value of each pulmonary artery outlet in the full-circulation fluid network model; determining the fractional blood flow reserve (FVR) of each pulmonary artery outlet based on the pressure value of the pulmonary artery outlet and the pulmonary artery pressure data; determining the average FVR of the pulmonary artery system based on the FVR of each pulmonary artery outlet and the occluded pulmonary artery branches identified from the three-dimensional model of the pulmonary artery; and determining the pulmonary artery status attribute information of the target user based on the average FVR. The technical solution of this application constructs a personalized full-circulation fluid network model extending from the main pulmonary artery to the distal microcirculation by acquiring multi-dimensional physiological and imaging data of the target user. Based on this model, hemodynamic simulation is performed to calculate the fractional blood flow reserve at all pulmonary artery outlets. Furthermore, by integrating the fractions at each outlet and taking into account the influence of occluded branches, the average fractional blood flow reserve, which reflects the blood flow function status of the entire pulmonary artery system, is determined. This overcomes the limitation of existing local assessment methods that cannot distinguish the differences in the impact of complete occlusion of vessels at different locations on overall blood flow function, and achieves a more comprehensive and clinically consistent quantitative assessment of the risk of pulmonary embolism. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the exemplary embodiments of this application, the accompanying drawings used in describing the embodiments are briefly introduced below. Obviously, the accompanying drawings described are only a portion of the embodiments to be described in this application, and not all of them. For those skilled in the art, other drawings can be obtained from these drawings without any creative effort.

[0012] Figure 1 This is a comparative schematic diagram of two types of pulmonary artery stenosis involved in this embodiment; Figure 2 A flowchart illustrating a user status determination method provided in an embodiment of this application; Figure 3 This is a schematic diagram of the pulmonary artery three-dimensional model built based on CTA images involved in this embodiment; Figure 4 This is a schematic diagram of the numerical simulation model and numerical simulation results involved in this embodiment; Figure 5 A flowchart illustrating yet another user status determination method provided in an embodiment of this application; Figure 6 This is a graph showing the relationship between the average pulmonary artery FFR and the length of hospital stay for patients involved in this embodiment; Figure 7 This is the ROC curve used in this embodiment to determine whether hospitalization for more than one week is required based on the average pulmonary artery FFR. Figure 8 This is a schematic diagram of a user status determination device provided in an embodiment of this application; Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0013] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present application, not the entire structure.

[0014] Before introducing the technical solutions provided in the embodiments of this application, the application scenarios of the solutions can be explained first. This embodiment is applicable to scenarios that require accurate risk assessment and classification of patients with pulmonary embolism.

[0015] Pulmonary embolism (PE) is a common and highly fatal / disabling disease in the emergency department, requiring timely and accurate treatment. Death from PE typically occurs within weeks of diagnosis, with significant variations in short-term mortality: the mortality rate is less than 2% in many patients with non-severe PE, while it can exceed 95% for those experiencing cardiopulmonary arrest. Therefore, accurate prediction of the prognosis of acute PE has extremely high clinical value.

[0016] To achieve accurate prognostic assessment, the key lies in quantifying the impact of embolic events on overall pulmonary blood flow function. Currently, while fractional flow reserve (FVR) calculation techniques based on computed tomography angiography image reconstruction models combined with computational fluid dynamics simulations can be used to assess the hemodynamic effects of localized vascular stenosis, this method has significant limitations in practical application to pulmonary embolism assessment. Since pulmonary embolism often presents as complete occlusion of multiple branch vessels, and the impact of emboli at different locations on overall pulmonary blood flow function varies significantly, traditional local assessment methods cannot distinguish these differences at the overall level. This leads to assessment results that do not align with clinical perceptions of severity, making them unreliable as a basis for risk grading. Therefore, there is an urgent need for an assessment method that can comprehensively reflect the blood flow function status of the entire pulmonary artery system and accurately quantify the global impact of embolism. This embodiment, based on the construction of a personalized full-circulation fluid network model, calculates the average FVR of the pulmonary artery system. By comprehensively integrating individual physiological parameters and vascular geometric characteristics, it aims to achieve accurate quantitative assessment of the risk level of pulmonary embolism, thereby providing a more reliable basis for clinical prognostic judgment and treatment decisions.

[0017] Example 1 Figure 2 This is a flowchart illustrating a user status determination method provided in an embodiment of this application. This embodiment is applicable to situations involving accurate risk assessment and classification of pulmonary embolism patients. The method can be executed by a user status determination device, which can be implemented in the form of software and / or hardware. The hardware can be a controller, such as a mobile terminal, a PC, or a server.

[0018] like Figure 2 As shown, the user status determination method provided in this embodiment of the invention includes the following steps: S110. Obtain the target user's pulmonary artery 3D model, pulmonary artery pressure data, cardiac output, and hematological parameters.

[0019] Here, the target user refers to the specific object to which this method is implemented, namely, the specific individual who needs to undergo pulmonary artery status assessment. The pulmonary artery 3D model refers to the three-dimensional geometric structure of the target user's pulmonary artery system reconstructed using medical imaging technology; for example, a 3D reconstruction of the target user's CTA (Computed Tomography Angiography) or MRA (Magnetic Resonance Angiography) images can be performed to obtain the pulmonary artery 3D model. Figure 3 A schematic diagram of a pulmonary artery three-dimensional model built based on CTA images.

[0020] Among these, pulmonary artery pressure data refers to the pressure value within the pulmonary artery of the target user obtained through measurement. Cardiac output refers to the total amount of blood pumped by the target user's heart per minute. Hematological parameters refer to parameters related to the physical and rheological properties of the target user's blood.

[0021] Specifically, computed tomography angiography can be used to image the target user's pleural cavity. Image segmentation and 3D reconstruction algorithms then generate a 3D model of the pulmonary artery, including the geometry, luminal radius, and course of the main pulmonary artery and its branches. Under resting or exercise conditions, systolic, diastolic, and mean pressure data within the user's main pulmonary artery and its branches are collected using catheters or non-invasive hemodynamic monitoring devices to generate pulmonary artery pressure data. Cardiac output is obtained by measuring the volume of blood pumped out of the ventricles per unit time using thermodilution, echocardiography, or bioimpedance analysis. Simultaneously, peripheral venous blood samples are drawn, and hemoglobin concentration, hematocrit, plasma viscosity, coagulation parameters, and blood pH are measured in the laboratory, summarizing these into hematological parameters. The target user's pulmonary artery 3D model, pulmonary artery pressure data, cardiac output, and hematological parameters can be stored in a pre-defined storage system. When a pulmonary artery status assessment of the target user is needed, this data can be retrieved from the pre-defined storage system. This data collectively constitutes the input required for subsequently constructing the full circulatory fluid network model.

[0022] S120, based on the pulmonary artery 3D model, cardiac output and hematological parameters, constructs a full-circulation fluid network model extending from the main pulmonary artery to the distal microcirculation through preset microvascular bifurcation growth rules.

[0023] The whole-circulation fluid network model refers to a fluid dynamics calculation model that integrates the complete pulmonary vascular pathway from the main pulmonary artery to the distal microcirculation, and can be used for blood flow simulation. The whole-circulation fluid network model includes the microcirculatory blood flow resistance value corresponding to each pulmonary artery outlet. The pulmonary artery outlet refers to the endpoint of each final terminal pulmonary artery segment connected to the microcirculatory network in this whole-circulation fluid network model. The microcirculatory blood flow resistance value is a quantitative value representing the obstruction to blood flow exerted by the entire microcirculatory vascular network connected downstream of each pulmonary artery outlet.

[0024] Among them, the microvascular bifurcation growth rule is a set of preset algorithms or principles used to simulate the geometric morphology and connection relationships of microvascular networks extending from the main trunk vessels to the capillaries. The main pulmonary artery refers to the largest trunk vessel at the beginning of the pulmonary artery system; the distal microcirculation refers to the capillary bed located deep in the lung tissue, which carries out gas exchange, and its connected smallest vascular network.

[0025] Specifically, the actual anatomical geometry provided by the 3D model of the pulmonary artery can be used as the starting framework, cardiac output as the global flow constraint, and hematological parameters as rheological inputs. According to the preset microvascular bifurcation growth rules, a microcirculation tree structure matching each pulmonary artery outlet can be extended step by step in the digital space. At each bifurcation point, the local resistance is calculated based on the law of viscous fluid resistance and the non-Newtonian rheological correction relationship of blood. Finally, the 3D model of the pulmonary artery, the generated microvascular tree structure and the corresponding microcirculation blood flow resistance value are coupled and associated to form a complete full-circulation fluid network model covering the main pulmonary artery to the distal capillary bed.

[0026] S130. Using pulmonary artery pressure data and microcirculatory blood flow resistance values ​​at each pulmonary artery outlet as boundary conditions, perform hemodynamic numerical simulation on the whole circulation fluid network model to determine the pressure value at each pulmonary artery outlet in the whole circulation fluid network model.

[0027] The pressure value at the pulmonary artery outlet refers to the steady-state blood flow pressure value of each pulmonary artery outlet section under specific boundary conditions in the whole circulation fluid network model, which is obtained through hemodynamic numerical simulation.

[0028] Specifically, the collected pulmonary artery pressure data can be assigned to the inlet section of the whole circulation fluid network model as a fixed pressure boundary condition. At the same time, the calculated microcirculation blood flow resistance value corresponding to each pulmonary artery outlet can be converted into an equivalent flow resistance and assigned to the corresponding outlet section as a downstream flow resistance boundary condition. Based on this, the steady-state numerical solution method can be applied to the whole circulation fluid network model containing the main pulmonary artery and all its branches to solve the fluid dynamics control equations. The internal pressure distribution of the model can be obtained through iterative calculation, thereby obtaining the pressure value at each pulmonary artery outlet section. Figure 4 This is a schematic diagram of the numerical simulation model and the numerical simulation results.

[0029] Based on the above embodiments, optionally, the specific implementation method for determining the pressure value of each pulmonary artery outlet in the whole circulation fluid network model can be as follows: (1) Set the pulmonary artery pressure data as the pressure boundary condition of the inlet section of the whole circulation fluid network model.

[0030] In this embodiment, the measured pulmonary artery pressure data can be used as a fixed pressure value and assigned to the section representing the main pulmonary artery inlet in the whole circulation fluid network model. This ensures that the section maintains this pressure level throughout the hemodynamic numerical simulation, thus becoming the initial pressure constraint driving blood flow within the entire model.

[0031] (2) The microcirculatory blood flow resistance value corresponding to each pulmonary artery outlet is used as the equivalent flow resistance of the microvascular network downstream of the pulmonary artery outlet, and the flow resistance boundary conditions of the outlet section are set based on the equivalent flow resistance.

[0032] In this embodiment, the pre-calculated microcirculatory blood flow resistance value of each pulmonary artery outlet can be regarded as the total resistance generated by the downstream microvascular tree of that outlet on blood flow. Accordingly, in the numerical model, this resistance is transformed into an equivalent flow resistance constraint connected to the outlet section, so that blood must overcome this resistance when flowing out, thereby forming a pressure-flow relationship that conforms to real physiology at the outlet section, which becomes the outlet-side boundary condition driving the pressure distribution in the model.

[0033] (3) Based on the inlet pressure boundary conditions and the flow resistance boundary conditions of each outlet, the fluid dynamics control equations of the main pulmonary artery and branch vessels of the full circulation fluid network model are solved by steady-state numerical solution method, and the pressure values ​​at the outlet sections of each pulmonary artery in the full circulation fluid network model are calculated.

[0034] In this embodiment, under the common constraints of the pre-set inlet pressure boundary conditions and the flow resistance boundary conditions of each outlet, the steady-state numerical solution method is applied to discretize and iteratively calculate the fluid dynamics control equations for the vascular segments from the main pulmonary artery to each level of branches in the full circulation fluid network model. The pressure distribution of each node inside the model is obtained through pressure-velocity coupling processing, and then the pressure value at each pulmonary artery outlet section is extracted.

[0035] S140. For each pulmonary artery outlet, determine the fractional flow reserve at the pulmonary artery outlet based on the pressure value at the pulmonary artery outlet and the pulmonary artery pressure data.

[0036] Fractional flow reserve (FFR) is a percentage ratio of the actual blood flow pressure obtained at a certain pulmonary artery outlet section under current stenosis or occlusion to the pressure that would be obtained if the same outlet were fully patent. It is used to quantify the degree of hemodynamic restriction caused by stenosis. The smaller the value, the more significant the blood flow restriction caused by stenosis.

[0037] Specifically, the fractional flow reserve (FVR) process for each pulmonary artery outlet is consistent. To clearly illustrate this technical solution, we will use one pulmonary artery outlet as an example. For a specific pulmonary artery outlet in the whole-circulation fluid network model, the pressure value at the outlet section obtained from numerical simulation is compared with the corresponding driving pressure in the inlet pulmonary artery pressure data. The ratio between the two is calculated according to the definition of FVR, thereby quantifying the degree of blood flow restriction caused by stenosis or occlusion at that outlet, forming the unique FVR for that pulmonary artery outlet. Based on the same processing method, the FVR for each pulmonary artery outlet can be obtained.

[0038] S150. Based on the fractional flow reserve of each pulmonary artery outlet and the occluded pulmonary artery branches identified from the 3D model of the pulmonary artery, the average fractional flow reserve of the pulmonary artery system is determined.

[0039] In this context, occluded pulmonary artery branches refer to pulmonary artery branches in the target user's 3D pulmonary artery model whose lumens are completely blocked and blood flow is interrupted due to pathological conditions such as embolism. Mean fractional flow reserve (FFR) is a comprehensive indicator used to quantitatively assess the blood flow function status of the entire pulmonary artery system, calculated by mathematically averaging the FFRs of all functional pulmonary artery outlets after excluding the influence of all identified occluded pulmonary artery branches.

[0040] Specifically, firstly, the geometric area of ​​each pulmonary artery outlet can be extracted from the geometric data of the 3D pulmonary artery model. Then, the occluded pulmonary artery branches are marked within the 3D pulmonary artery model, and the area of ​​the occluded branch at its proximal cross-section is measured. Subsequently, based on preset geometric similarity criteria, the normal pulmonary artery branch that is closest in morphology to the occluded branch is retrieved, and the area of ​​the normal branch at its proximal cross-section is obtained. The geometric similarity ratio coefficient is calculated by comparing the occluded branch area with the normal branch area. Using this coefficient, the geometric area of ​​each outlet downstream of the normal branch is scaled to obtain the equivalent outlet area set corresponding to the occluded branch. The blood flow reserve fraction of all outlets in this set is forcibly set to the preset occlusion blood flow reserve fraction value. Finally, using the cube of the radius of each pulmonary artery outlet as the weight, the blood flow reserve fraction of all pulmonary artery outlets, including the equivalent outlet area set, is weighted and averaged to obtain the average blood flow reserve fraction reflecting the overall degree of pulmonary artery stenosis.

[0041] S160. Based on the mean fractional blood flow reserve, determine the pulmonary artery status attribute information of the target user.

[0042] Among them, pulmonary artery status attribute information refers to the descriptive conclusions about the overall blood flow function status of the target user's pulmonary artery system and its corresponding clinical severity or risk level, based on the comprehensive quantitative indicator of the calculated average fractional blood flow reserve.

[0043] In this embodiment, the calculated average fractional blood flow reserve can be compared with preset low-risk, intermediate-risk, and high-risk threshold ranges. Combined with clinical decision rules, the target user's current pulmonary artery system risk level, whether emergency intervention is needed, follow-up period, and prognostic assessment conclusion can be automatically mapped to form pulmonary artery status attribute information including risk level and treatment recommendations, and then output to the clinical decision system.

[0044] This application provides a method for determining user status, comprising: acquiring a three-dimensional model of the pulmonary artery, pulmonary artery pressure data, cardiac output, and hematological parameters of a target user; constructing a full-circulation fluid network model extending from the main pulmonary artery to the distal microcirculation based on the three-dimensional model of the pulmonary artery, cardiac output, and hematological parameters, using preset microvascular bifurcation growth rules; wherein the full-circulation fluid network model includes the microcirculation blood flow resistance value corresponding to each pulmonary artery outlet; performing hemodynamic numerical simulation on the full-circulation fluid network model using the pulmonary artery pressure data and the microcirculation blood flow resistance value of each pulmonary artery outlet as boundary conditions to determine the pressure value of each pulmonary artery outlet in the full-circulation fluid network model; determining the fractional blood flow reserve (FVR) of each pulmonary artery outlet based on the pressure value of the pulmonary artery outlet and the pulmonary artery pressure data; determining the average FVR of the pulmonary artery system based on the FVR of each pulmonary artery outlet and the occluded pulmonary artery branches identified from the three-dimensional model of the pulmonary artery; and determining the pulmonary artery status attribute information of the target user based on the average FVR. The technical solution of this application constructs a personalized full-circulation fluid network model extending from the main pulmonary artery to the distal microcirculation by acquiring multi-dimensional physiological and imaging data of the target user. Based on this model, hemodynamic simulation is performed to calculate the fractional blood flow reserve at all pulmonary artery outlets. Furthermore, by integrating the fractions at each outlet and taking into account the influence of occluded branches, the average fractional blood flow reserve, which reflects the blood flow function status of the entire pulmonary artery system, is determined. This overcomes the limitation of existing local assessment methods that cannot distinguish the differences in the impact of complete occlusion of vessels at different locations on overall blood flow function, and achieves a more comprehensive and clinically consistent quantitative assessment of the risk of pulmonary embolism.

[0045] Example 2 Figure 5 This is a schematic diagram of a user status determination method provided in an embodiment of this application. Based on the foregoing embodiments, this embodiment provides a more detailed description of step S120. For specific implementation details, please refer to the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0046] like Figure 5 As shown, the method specifically includes the following steps: S210. Obtain the target user's pulmonary artery 3D model, pulmonary artery pressure data, cardiac output, and hematological parameters.

[0047] S220. Based on cardiac output, determine the bifurcation level coefficient of the pulmonary artery vascular tree.

[0048] Among them, the bifurcation hierarchy coefficient of the pulmonary artery vascular tree refers to the model parameter used to quantify the degree of refinement of the downstream vascular network from the main pulmonary artery. It determines the number of iterations or the hierarchy depth of the microvascular tree structure growing and bifurcerating downstream from each pulmonary artery outlet when constructing a whole-circulation fluid network model.

[0049] In this embodiment, the simulated total blood flow and cardiac output calculated by the numerical model of the pulmonary artery and microvessel coupling can be compared and matched through iterative optimization. This allows for automatic adjustment and final determination of the most suitable bifurcation level coefficient that makes the error between the two less than a preset threshold.

[0050] In this embodiment, optionally, the specific implementation steps for determining the bifurcation level coefficient of the pulmonary artery vascular tree based on cardiac output may include: (1) Set the initial value of the bifurcation level coefficient.

[0051] Specifically, before starting iterative calibration, an empirical starting value is assigned to the bifurcation level coefficient of the pulmonary artery vascular tree. This value is used to construct the numerical model of the coupling between the pulmonary artery and microvessels for the first time, and serves as a benchmark for subsequent gradual correction through cardiac output comparison and numerical optimization methods, so as to ensure that the entire calibration process can start from a reasonable and convergent starting point.

[0052] (2) Construct an initial numerical model of pulmonary artery and microvascular coupling based on initial values.

[0053] In this embodiment, a corresponding level of microvascular tree structure can be generated at each outlet of the pulmonary artery three-dimensional model according to the initial values. These microvascular tree structures are then coupled with their respective outlet radii, supply lung volumes, and hematological parameters. The microcirculatory blood flow resistance values ​​of each outlet are calculated based on the law of viscous fluid resistance and the non-Newtonian rheological correction relationship of blood. This connects the macroscopic pulmonary artery network and downstream microcirculatory resistance into a complete numerical calculation framework, forming an initial pulmonary artery and microvascular coupling numerical model that can run hemodynamic simulations.

[0054] (3) Perform hemodynamic numerical simulation on the numerical model of pulmonary artery and microvessel coupling to determine the simulated total blood flow calculated by the numerical model of pulmonary artery and microvessel coupling.

[0055] The simulated total blood flow refers to the total blood flow rate obtained by performing hemodynamic numerical simulation calculations on a numerical model of pulmonary artery and microvessel coupling constructed based on the current bifurcation level coefficients, which is the total blood flow rate from the inlet of the model into the entire simulated vascular network.

[0056] In this embodiment, on the constructed numerical model of pulmonary artery and microvessel coupling, with the set inlet pressure and the microcirculation blood flow resistance of each outlet as boundary conditions, the fluid dynamics control equation is discretely iteratively calculated using the steady-state solution method to obtain the pressure and velocity distribution inside the model. Then, the products of the velocity and area of ​​all pulmonary artery outlet sections are summed to obtain the simulated total blood flow that the model can deliver under numerical simulation conditions.

[0057] (4) Calculate the relative error between the simulated total blood flow and cardiac output; if the relative error is less than the preset error threshold, then the current bifurcation level coefficient is determined as the final bifurcation level coefficient.

[0058] In this embodiment, the absolute value of the difference between the simulated total blood flow obtained from the hemodynamic numerical simulation and the measured cardiac output can be divided by the cardiac output to obtain the relative error value. When this value is lower than the preset error threshold, it is considered that the current bifurcation level coefficient has made the model output close enough to the real physiological flow, and no further correction is needed. The current bifurcation level coefficient can be directly used as the final bifurcation level coefficient.

[0059] (5) If the relative error value is greater than or equal to the preset error threshold, the bifurcation level coefficient is corrected according to the numerical optimization method, and the numerical model of pulmonary artery and microvessel coupling is reconstructed based on the corrected bifurcation level coefficient. The above hemodynamic numerical simulation, calculation of relative error value and judgment steps are repeated until the relative error value is less than the preset error threshold, and the corresponding bifurcation level coefficient is determined as the final bifurcation level coefficient.

[0060] Specifically, if the relative error between the simulated total blood flow and the measured cardiac output does not reach the allowable range, the value of the bifurcation level coefficient can be automatically adjusted according to the built-in numerical optimization strategy. The adjusted coefficient is used to regenerate the microvascular tree and update the numerical model of pulmonary artery and microvascular coupling. Steps (1) to (5) are executed again. This process is repeated until the relative error value drops below the preset error threshold. The correction is then stopped and the last bifurcation level coefficient is used as the final bifurcation level coefficient.

[0061] S230. Based on the geometric structure of the pulmonary artery three-dimensional model, determine the overall lung volume corresponding to the pulmonary artery three-dimensional model.

[0062] In this embodiment, the spatial distribution and lumen contour of the pulmonary artery vessels contained in the three-dimensional model can be analyzed based on the geometric structure of the pulmonary artery model, and the total volume of lung tissue in the perfusion area can be indirectly calculated or correlated with the model, which is the overall lung volume corresponding to the three-dimensional model of the pulmonary artery.

[0063] S240. For each pulmonary artery outlet in the 3D model of the pulmonary artery, determine the supply lung volume allocated to the pulmonary artery outlet based on the overall lung volume and the bifurcation hierarchy coefficient.

[0064] The blood supply lung volume allocated to the pulmonary artery outlet refers to the volume of lung tissue region perfused and supplied by the downstream microvascular tree structure, allocated to each pulmonary artery outlet based on the overall lung volume and the bifurcation hierarchy coefficient.

[0065] In this embodiment, the obtained total lung volume can be used as the total amount. Then, based on the radius weighting rule implied by the bifurcation hierarchy coefficient, the radius of each pulmonary artery outlet is nonlinearly amplified to a specified power. The proportion of the nth power of the outlet radius to the sum of the nth powers of all outlet radii is calculated. This proportion is used to divide the total lung volume, thereby obtaining the blood supply lung volume that matches each outlet.

[0066] S250. For each pulmonary artery outlet, based on the initial radius of the pulmonary artery outlet and its corresponding supply lung volume, an iterative geometric bifurcation growth operation is performed according to the preset microvascular bifurcation growth rules to generate the corresponding microvascular tree structure.

[0067] Among them, the microvascular tree structure refers to the tree-like geometric shape and connection relationship of the microvascular network in the distal lung, which is generated by the preset microvascular bifurcation growth rules, and is formed by gradually branching and extending from the pulmonary artery outlet.

[0068] Specifically, for each pulmonary artery outlet, with its initial lumen radius and the volume of the lung it supplies as input conditions, a pre-defined geometric growth rule describing how microvessels branch off at different levels is applied. By repeatedly executing a series of geometric construction steps such as branching, extension, and adjustment of the vessel diameter, a dendritic microvascular network structure with specific levels and morphology is gradually generated, starting from the pulmonary artery outlet and extending downstream until it fills the volume of the lung it supplies.

[0069] Specifically, the pre-defined microvascular bifurcation growth rules include the following steps: (1) The volume of the blood-supplying lung corresponding to the pulmonary artery outlet is equivalent to a sphere, and the starting point corresponding to the pulmonary artery outlet is determined on the surface of the sphere.

[0070] In this embodiment, the blood supply lung volume allocated to the outlet can be abstracted as an ideal sphere with the same volume. Based on the orientation of the pulmonary artery outlet in the real anatomical space, a starting point coinciding with the center coordinates of the outlet is located on the surface of this ideal sphere. This serves as the spatial origin of the hierarchical branching growth of the microvascular tree, ensuring that the subsequently generated branching vascular segments extend from this point on the sphere towards the centroid of the sphere, thereby maintaining the correspondence between the vascular orientation and the spatial distribution of lung tissue.

[0071] (2) Based on the starting point, the sphere is divided into two sub-spheres of equal volume to be processed.

[0072] In this embodiment, the sphere can be divided into two non-overlapping sub-spheres with the same volume according to a preset spatial segmentation algorithm, based on the determined starting point of the sphere. Each sub-sphere has its own independent centroid and boundary, thereby providing an equivalent blood supply area for the subsequent generation of two symmetrical bifurcated blood vessel segments.

[0073] (3) Determine the centroid of each subsphere to be processed, and connect the starting point with each centroid to generate two bifurcated blood vessel segments.

[0074] Specifically, after the sphere is divided into two sub-spheres of equal volume, the geometric center, i.e. the centroid, of each sub-sphere is first calculated. Then, taking the starting point of the original sphere as the common point, straight line segments are drawn to these two centroids respectively, forming two vascular paths with direction and length. These serve as the geometric framework of the first-level branching of the microvascular tree, thereby ensuring that each newly formed vascular segment points to the center of the corresponding sub-sphere, so that the blood flow distribution matches the volume of the sub-sphere.

[0075] (4) Determine the radius of each bifurcation vessel segment according to the preset bifurcation radius attenuation coefficient.

[0076] The bifurcation radius attenuation coefficient is determined based on the bifurcation hierarchy coefficient. Optionally, Where n represents the bifurcation level coefficient, This represents the attenuation coefficient of the bifurcation radius.

[0077] In this embodiment, the current radius of the previous-level vessel segment can be used as a reference, and the previous-level radius can be multiplied by the ratio specified by the bifurcation radius attenuation coefficient derived from the bifurcation hierarchy coefficient. This allows for the calculation of the new radii of the two bifurcated vessel segments at the next level, ensuring that the vessel diameter continuously decreases during the hierarchical bifurcation process and conforms to the physiological law of blood flow distribution and resistance matching.

[0078] (5) The volume of each sub-sphere to be processed is taken as the blood supply lung volume downstream of the corresponding bifurcation segment.

[0079] Specifically, after the sphere is segmented, the volume of space contained inside each subsphere to be processed can be directly assigned to the bifurcation vessel segment derived from the centroid of that subsphere as the total volume of lung tissue that must be supplied with blood when it continues to extend downwards. This ensures that the blood supply area of ​​each branch of the microvascular tree corresponds one-to-one with the upstream vessel segment, maintaining the consistency of blood flow distribution and geometric division.

[0080] (6) For each bifurcation segment, determine whether the radius of the bifurcation segment is greater than the preset microvascular termination radius threshold.

[0081] Optionally, the preset microvessel termination radius threshold is set between 0.005 mm and 0.01 mm.

[0082] Specifically, after each bifurcation, the current radius of the newly generated blood vessel segment is read and compared with a pre-set microvessel termination radius threshold. For example, the microvessel termination radius threshold is limited to the range of 0.005 mm to 0.01 mm. If the radius of the blood vessel segment is still larger than this tiny scale, it is allowed to continue as the starting point for the next round of spherical segmentation and bifurcation. Otherwise, further growth is stopped, thereby ensuring that the microvessel tree automatically terminates when its diameter approaches the physiological limit of capillaries, avoiding infinite subdivision.

[0083] (7) If so, the bifurcation vessel segment is used as the new starting vessel segment, and the sub-sphere to be processed corresponding to the bifurcation vessel segment is used as the new equivalent sphere. The above segmentation, generation and judgment steps are repeated until the radius of all bifurcation vessel segments is not greater than the microvessel termination radius threshold.

[0084] Specifically, if the radius of the current bifurcation vessel segment is still greater than the preset microvessel termination radius threshold, the vessel segment is regarded as the starting vessel segment of a new growth cycle, and its corresponding sub-sphere to be processed is re-equivalent to a complete sphere. The entire process of sphere segmentation, centroid connection, radius decay, volume allocation, and termination judgment is executed in sequence. This process is recursively repeated layer by layer to continuously extend the microvessel tree until the radius of all terminal bifurcation vessel segments decays to no greater than the microvessel termination radius threshold, thereby completing the continuous geometric construction from the pulmonary artery outlet to the capillary scale.

[0085] S260. Based on the microvascular tree structure corresponding to each generated pulmonary artery outlet, and combined with hematological parameters, the microcirculatory blood flow resistance value corresponding to each pulmonary artery outlet is calculated according to the law of viscous fluid resistance and the correction relationship of non-Newtonian rheology of blood.

[0086] In this embodiment, for each microvascular tree structure corresponding to the pulmonary artery outlet, the basic blood flow resistance of its terminal microvascular segment is first calculated based on the viscous fluid resistance law, combined with the blood characteristics reflected by hematological parameters. Then, this basic resistance is corrected by the non-Newtonian rheological correction relationship of blood to obtain the equivalent blood flow resistance. Finally, by integrating the equivalent blood flow resistance of all vascular segments in the microvascular tree structure step by step, the overall microcirculation blood flow resistance value corresponding to the pulmonary artery outlet is calculated.

[0087] Based on the above embodiments, optionally, the specific implementation method for determining the pressure value of each pulmonary artery outlet in the whole circulation fluid network model can be as follows: (1) Determine the viscosity characteristics of blood based on hematological parameters.

[0088] In this embodiment, key component indicators affecting blood viscosity can be extracted from the acquired hematological parameters, and the viscosity value of the current target user's blood under the current physiological state can be calculated or obtained by looking up a table based on the established correspondence between these indicators and viscosity, so as to accurately reflect the blood flow resistance characteristics in the subsequent microvascular segment resistance calculation.

[0089] (2) For the terminal microvascular segment in each microvascular tree structure, the basic blood flow resistance of the terminal microvascular segment is calculated based on the geometric radius and length of the terminal microvascular segment and the viscous characteristic parameters, using the law of viscous fluid resistance.

[0090] In this embodiment, at the very end of the constructed microvascular tree, the geometric radius and length of the terminal microvascular segment are read. Combined with the viscous characteristic parameters determined by hematological parameters, the relationship between resistance and tube diameter, tube length and viscosity given by the law of viscous fluid resistance is applied to calculate the basic blood flow resistance of the terminal vascular segment without considering non-Newtonian effects. This provides an initial resistance value for subsequent hierarchical integration, which is the basic blood flow resistance of the terminal microvascular segment.

[0091] (3) Based on the non-Newtonian rheological correction relationship of blood, the basic blood flow resistance is corrected to obtain the equivalent blood flow resistance of the terminal microvascular segment.

[0092] In this embodiment, based on the obtained basic blood flow resistance, a non-Newtonian rheological correction relationship for blood is introduced. The shear thinning effect caused by changes in blood viscosity with tube diameter, shear rate, and hematocrit is considered, and the basic resistance is proportionally adjusted to obtain an equivalent blood flow resistance of the terminal microvascular segment that is closer to the actual blood rheological characteristics. This is used for subsequent resistance integration and blood flow calculation.

[0093] (4) Integrate the equivalent blood flow resistance of all vascular segments in each microvascular tree structure in series and parallel relationships, up to the pulmonary artery outlet, to obtain the microcirculation blood flow resistance value corresponding to the pulmonary artery outlet.

[0094] In this embodiment, starting from the terminal microvascular segment, based on the anatomical order of blood flow in the microvascular tree being first connected in parallel and then in series, the resistance of the two downstream branches at each bifurcation node is merged according to the parallel rule, and then added to the resistance of the upstream vascular segment according to the series rule. This process is then summarized in reverse step by step to the pulmonary artery outlet, finally obtaining the complete microcirculatory blood flow resistance value faced by the outlet, which is used for subsequent hemodynamic calculations.

[0095] S270. Couple and associate each pulmonary artery outlet in the pulmonary artery 3D model with its corresponding microvascular tree structure and microcirculatory blood flow resistance value to construct a full circulation fluid network model.

[0096] Specifically, in the digital space, the entry nodes of each generated microvascular tree structure are precisely connected to the center of the cross section of the corresponding outlet of the pulmonary artery three-dimensional model according to spatial coordinates. At the same time, the microcirculation blood flow resistance value calculated step by step is assigned as the downstream boundary attribute to the outlet, so that the macropulmonary artery segment and the microvascular tree form a continuous and interconnected blood flow channel with resistance connection within the same fluid network framework. This results in the construction of a unified full circulation fluid network model covering the main pulmonary artery to the capillary terminal, which is used for subsequent hemodynamic numerical simulation.

[0097] S280. Using pulmonary artery pressure data and microcirculatory blood flow resistance values ​​at each pulmonary artery outlet as boundary conditions, perform hemodynamic numerical simulation on the whole circulation fluid network model to determine the pressure value at each pulmonary artery outlet in the whole circulation fluid network model.

[0098] S290. For each pulmonary artery outlet, determine the fractional blood flow reserve (FCR) of the pulmonary artery outlet based on the pressure value of the pulmonary artery outlet and the pulmonary artery pressure data; determine the average fractional blood flow reserve of the pulmonary artery system based on the fractional blood flow reserve of each pulmonary artery outlet and the occluded pulmonary artery branches identified from the 3D model of the pulmonary artery, so as to determine the pulmonary artery status attribute information of the target user based on the average fractional blood flow reserve.

[0099] The technical solution of this application, when constructing a full-circulation fluid network model extending from the main pulmonary artery to the distal microcirculation, adaptively determines the bifurcation level coefficient by iteratively matching the measured cardiac output with the model's simulated total blood flow. Based on the individualized three-dimensional geometry of the pulmonary artery and the overall lung volume, it allocates a physiologically reasonable blood supply area to each pulmonary artery outlet. Then, it uses preset growth rules to generate an anatomically reasonable microvascular tree structure. Finally, it accurately calculates the downstream microcirculation blood flow resistance by combining individual blood characteristics. This constructs a highly personalized full-circulation fluid network model that reflects the complete blood flow path from the main pulmonary artery to the distal microcirculation and has physiologically reliable boundary conditions. This lays a reliable model foundation for subsequent accurate hemodynamic simulation and calculation of functional indicators.

[0100] Example 3 Based on the foregoing embodiments, this embodiment provides a more detailed description of S150, and its specific implementation can be found in the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here. The method specifically includes the following steps: S310. Obtain the target user's pulmonary artery 3D model, pulmonary artery pressure data, cardiac output, and hematological parameters.

[0101] S320, based on the pulmonary artery three-dimensional model, cardiac output and hematological parameters, constructs a full-circulation fluid network model extending from the main pulmonary artery to the distal microcirculation through preset microvascular bifurcation growth rules.

[0102] The whole-circulation fluid network model includes the microcirculation blood flow resistance value corresponding to each pulmonary artery outlet.

[0103] S330. Using pulmonary artery pressure data and microcirculatory blood flow resistance values ​​at each pulmonary artery outlet as boundary conditions, perform hemodynamic numerical simulation on the whole circulation fluid network model to determine the pressure value at each pulmonary artery outlet in the whole circulation fluid network model.

[0104] S340. For each pulmonary artery outlet, determine the fractional flow reserve at the pulmonary artery outlet based on the pressure value at the pulmonary artery outlet and the pulmonary artery pressure data.

[0105] S350. Based on the fractional flow reserve of each pulmonary artery outlet and the occluded pulmonary artery branches identified from the 3D model of the pulmonary artery, the average fractional flow reserve of the pulmonary artery system is determined.

[0106] Based on the above embodiments, optionally, determining the specific implementation of the mean fractional flow reserve of the pulmonary artery system may include the following steps: (1) Based on the geometric data of the three-dimensional model of the pulmonary artery, determine the geometric area of ​​each pulmonary artery outlet.

[0107] In this embodiment, the anatomical morphology information of all terminal outlets can be extracted from the constructed 3D model of the pulmonary artery. An algorithm is used to identify and measure the boundary contour of each outlet cross-section in 3D space, and then calculate its corresponding cross-sectional area. Based on the same processing method, the geometric area of ​​each pulmonary artery outlet can be obtained.

[0108] (2) In the three-dimensional model of the pulmonary artery, the pulmonary artery branch as the occluded branch is determined, and the occluded branch area of ​​the proximal section of the pulmonary artery branch is obtained.

[0109] Specifically, the constructed 3D model of the pulmonary artery can be used to identify pulmonary artery branches that are completely or partially blocked due to thrombosis or stenosis, and to accurately locate the complete cross-section of the nearest upstream end of the occlusion site. Then, the geometric boundary data of the cross-section can be extracted, and its corresponding area value can be calculated. This area is the area of ​​the occluded branch, which is used for subsequent geometric similarity comparison with normal branches and equivalent area estimation.

[0110] Based on the above embodiments, optionally, the specific implementation steps for determining the pulmonary artery branch as an occluded branch in the pulmonary artery three-dimensional model may include: S1. Based on the geometric data of the pulmonary artery three-dimensional model, extract the centerline of each branch of the pulmonary artery and the distribution data of the intravascular radius along the centerline.

[0111] Specifically, the central path of the tree-like structure of each blood vessel can be traced from the constructed 3D model of the pulmonary artery through topology refinement and skeletonization algorithms, resulting in a continuous central line located inside the lumen. Samples are taken along this central line with a very small step size, and the shortest distance from the inner wall of the lumen to the central line is automatically measured at each sampling point, forming the distribution data of the inner radius of the blood vessel corresponding to the central line point by point. Thus, the spatial orientation information of the blood vessel and the characteristics of the change in the lumen radius along its course can be obtained at the same time.

[0112] S2. For each pulmonary artery branch, based on the centerline and intravascular radius distribution data, generate a continuous vascular detection segment of a preset length along the centerline.

[0113] In this embodiment, guided by the extracted centerline of the branch, a continuous path interval containing multiple radius sampling points is sequentially extracted on the centerline according to a set fixed length scale. This makes the start and end points of the interval and each point inside correspond to the obtained intravascular radius distribution data, thereby forming a local vascular segment that has both spatial orientation and radius information, which serves as a calculation unit for the ratio of the front-end radius to the end-end radius.

[0114] S3. Determine the front and rear radii of the continuous blood vessel detection segment, and calculate the ratio between the front and rear radii.

[0115] Specifically, within the generated continuous vascular detection segment, taking the starting direction of the centerline as the front and the ending direction as the end, the vascular radius distribution data corresponding to the sampling point at the front end of the detection segment is directly read as the front radius, and the vascular radius distribution data corresponding to the sampling point at the end end is read as the end radius. Then, the front radius and the end radius are directly compared, and the degree of reduction of the front radius relative to the end radius is used as a geometric indicator to measure whether there is abnormal stenosis or occlusion tendency in this segment of the blood vessel, providing a quantitative basis for subsequent comparison with the preset occlusion judgment threshold.

[0116] S4. If the ratio is less than the preset occlusion determination threshold, then the pulmonary artery branch is determined to be an occluded branch.

[0117] Specifically, the ratio between the front radius and the terminal radius can be directly compared with a pre-set occlusion determination threshold. When the ratio is lower than the threshold, it indicates that the vessel segment has a sharp reduction in inner diameter that meets the occlusion criteria along the blood flow direction. Thus, the entire pulmonary artery branch is marked as an occluded branch, which can be used to locate the lesion in the three-dimensional model and participate in geometric similarity analysis and equivalent area calculation.

[0118] (3) Based on the preset geometric similarity judgment conditions, find the normal pulmonary artery branch with similar geometric shape to the occluded branch in the pulmonary artery three-dimensional model, and obtain the normal branch area of ​​the proximal section of the normal pulmonary artery branch.

[0119] Optionally, the preset geometric similarity criterion is that the ratio of the proximal cross-sectional areas of the two pulmonary artery branches is between 0.75 and 1.25.

[0120] Specifically, the ratio of the proximal cross-sectional area of ​​two pulmonary artery branches to that of the occluded branch is used as a screening criterion. All non-occluded normal pulmonary artery branches in the model are screened. If the ratio of the proximal cross-sectional area of ​​a normal branch to that of the occluded branch falls within this range, the two are considered to have similar geometric shapes. Then, the proximal cross-sectional geometric data of the normal branch is directly extracted, and the area of ​​the normal branch is calculated.

[0121] (4) Calculate the geometric similarity ratio based on the area of ​​the occluded branch and the area of ​​the normal branch.

[0122] In this embodiment, the ratio of the obtained proximal cross-sectional area of ​​the occluded branch to the screened proximal cross-sectional area of ​​the normal branch can be used as a geometric similarity scaling factor. This factor is used to characterize the degree of difference between the occluded branch and the normal branch on a geometric scale. This scaling factor is subsequently used to adjust the downstream exit area of ​​the normal branch, thereby deriving the equivalent exit area set corresponding to the downstream of the occluded branch.

[0123] (5) Based on the geometric area and geometric similarity ratio coefficient of each outlet included in the downstream of the normal pulmonary artery branch, determine the equivalent outlet area set of the downstream of the occluded branch.

[0124] Specifically, the geometric area of ​​all pulmonary artery outlets downstream of the selected normal pulmonary artery branches can be scaled up as a whole according to the geometric similarity ratio coefficient, so that the scaled area set is numerically equivalent to the downstream outlet area distribution that the occluded branch should have if it is not blocked. This constructs a virtual equivalent outlet area set, which is used to uniformly set the blood flow reserve fraction and participate in the weighted average calculation.

[0125] (6) Set the blood flow reserve fraction corresponding to all exits in the equivalent exit area set to the preset occlusion blood flow reserve fraction value.

[0126] Specifically, the original missing or unmeasurable real blood flow reserve fraction of each outlet in the set of virtual equivalent outlet areas derived from the geometric similarity ratio coefficient can be uniformly and forcibly assigned to 0, so as to reflect the pathological state that the outlet has completely lost its blood flow reserve capacity due to upstream occlusion, and serve as a fixed numerical input representing the occlusion area in the subsequent weighted average calculation.

[0127] (7) Using the cube of the radius of each pulmonary artery outlet as the weight, the blood flow reserve fraction of all pulmonary artery outlets, including the equivalent outlet area set, is calculated by weighted average to obtain the average blood flow reserve fraction of the pulmonary artery system.

[0128] In this embodiment, the cube of the radius of each pulmonary artery outlet can be used as the weighting coefficient of that outlet in the overall average. All outlets, including both real and equivalent virtual ones, are weighted and summed according to this weight, and then divided by the total weight to obtain the average blood flow reserve score that reflects the overall blood flow reserve capacity of the entire pulmonary artery system.

[0129] S360: Based on the average fractional blood flow reserve, determine the pulmonary artery status attribute information of the target user.

[0130] Based on clinical data validation, the mean fractional flow reserve (FFR) shows a clear correlation with patient hospitalization length and possesses good predictive power in determining whether a patient requires hospitalization for more than one week. Figure 6 This is a graph showing the relationship between mean pulmonary artery FFR and patient length of hospital stay. Figure 7 ROC curves are used to determine whether hospitalization for more than one week is required based on the average pulmonary artery FFR.

[0131] The technical solution of this application, when determining the average fractional blood flow reserve of the pulmonary artery system, quantitatively characterizes the functional impact of a completely occluded pulmonary artery branch on its downstream blood supply area by combining the geometric data of the pulmonary artery three-dimensional model: First, the corresponding normal reference branch is found through geometric similarity matching, and the equivalent outlet area set that can equivalently represent the potential vascular bed size downstream of the occluded branch is calculated accordingly, and then the corresponding fractional blood flow reserve is set as the theoretical value; finally, when calculating the average fractional blood flow reserve of the system, the radius cube, which reflects the blood flow carrying capacity, is used as the weight, and this part of the functional loss caused by occlusion is integrated into the weighted average calculation, so that the final average fractional blood flow reserve index can not only reflect the function of the existing unobstructed blood vessels, but also more comprehensively and reasonably quantify and assess the degree of damage to the overall pulmonary artery system blood flow function caused by vascular occlusion, thereby improving the clinical relevance of the assessment of the severity of pulmonary embolism.

[0132] Example 4 Figure 8 This is a schematic diagram of a user status determination device provided in an embodiment of this application. The device includes: The data acquisition module 410 is used to acquire the target user's pulmonary artery three-dimensional model, pulmonary artery pressure data, cardiac output, and hematological parameters. The circulation model construction module 420 is used to construct a full circulation fluid network model extending from the main pulmonary artery to the distal microcirculation based on the pulmonary artery three-dimensional model, the cardiac output, and the hematological parameters, and through preset microvascular bifurcation growth rules; wherein, the full circulation fluid network model includes the microcirculation blood flow resistance value corresponding to each pulmonary artery outlet; The outlet pressure determination module 430 is used to perform hemodynamic numerical simulation on the whole circulation fluid network model by using the pulmonary artery pressure data and the microcirculation blood flow resistance value of each pulmonary artery outlet as boundary conditions, and to determine the pressure value of each pulmonary artery outlet in the whole circulation fluid network model. The blood flow reserve determination module 440 is used to determine the blood flow reserve fraction of each pulmonary artery outlet based on the pressure value of the pulmonary artery outlet and the pulmonary artery pressure data. The mean blood flow reserve determination module 450 is used to determine the mean blood flow reserve of the pulmonary artery system based on the blood flow reserve fraction of each pulmonary artery outlet and the occluded pulmonary artery branches identified from the pulmonary artery three-dimensional model. The pulmonary artery status determination module 460 is used to determine the pulmonary artery status attribute information of the target user based on the mean fractional blood flow reserve.

[0133] This application provides a user status determination device. When applied, the device acquires a three-dimensional model of the pulmonary artery, pulmonary artery pressure data, cardiac output, and hematological parameters of the target user. Based on the pulmonary artery three-dimensional model, cardiac output, and hematological parameters, and using preset microvascular bifurcation growth rules, a full-circulation fluid network model extending from the main pulmonary artery to the distal microcirculation is constructed. The full-circulation fluid network model includes the microcirculation blood flow resistance value corresponding to each pulmonary artery outlet. Using the pulmonary artery pressure data and the microcirculation blood flow resistance values ​​of each pulmonary artery outlet as boundary conditions, hemodynamic numerical simulation is performed on the full-circulation fluid network model to determine the pressure value of each pulmonary artery outlet in the full-circulation fluid network model. For each pulmonary artery outlet, based on the pressure value of the pulmonary artery outlet and the pulmonary artery pressure data, the fractional flow reserve of the pulmonary artery outlet is determined. Based on the fractional flow reserve of each pulmonary artery outlet and the occluded pulmonary artery branches identified from the pulmonary artery three-dimensional model, the average fractional flow reserve of the pulmonary artery system is determined. Based on the average fractional flow reserve, the pulmonary artery status attribute information of the target user is determined. The technical solution of this application constructs a personalized full-circulation fluid network model extending from the main pulmonary artery to the distal microcirculation by acquiring multi-dimensional physiological and imaging data of the target user. Based on this model, hemodynamic simulation is performed to calculate the fractional blood flow reserve at all pulmonary artery outlets. Furthermore, by integrating the fractions at each outlet and taking into account the influence of occluded branches, the average fractional blood flow reserve, which reflects the blood flow function status of the entire pulmonary artery system, is determined. This overcomes the limitation of existing local assessment methods that cannot distinguish the differences in the impact of complete occlusion of vessels at different locations on overall blood flow function, and achieves a more comprehensive and clinically consistent quantitative assessment of the risk of pulmonary embolism.

[0134] Based on the above-mentioned device, optionally, the circulation model construction module 420 is used to: determine the bifurcation level coefficient of the pulmonary artery vascular tree based on the cardiac output; determine the overall lung volume corresponding to the pulmonary artery three-dimensional model based on the geometric structure of the pulmonary artery three-dimensional model; for each pulmonary artery outlet in the pulmonary artery three-dimensional model, determine the supply lung volume allocated to the pulmonary artery outlet based on the overall lung volume and the bifurcation level coefficient; for each pulmonary artery outlet, based on the initial radius of the pulmonary artery outlet and its corresponding supply lung volume, perform iterative geometric bifurcation growth operation through preset microvascular bifurcation growth rules to generate a corresponding microvascular tree structure; based on the generated microvascular tree structure corresponding to each pulmonary artery outlet, and combined with the hematological parameters, calculate the microcirculatory blood flow resistance value corresponding to each pulmonary artery outlet according to the viscous fluid resistance law and the blood non-Newtonian rheological correction relationship; and couple and associate each pulmonary artery outlet in the pulmonary artery three-dimensional model with its corresponding microvascular tree structure and microcirculatory blood flow resistance value to construct the whole circulation fluid network model.

[0135] Based on the above device, optionally, the preset microvascular bifurcation growth rule includes: equating the volume of the supply lung corresponding to the pulmonary artery outlet to a sphere, and determining a starting point on the surface of the sphere corresponding to the position of the pulmonary artery outlet; dividing the sphere into two sub-spheres of equal volume based on the starting point; determining the centroid of each sub-sphere and connecting the starting point to each centroid to generate two bifurcation segments; determining the radius of each bifurcation segment according to a preset bifurcation radius attenuation coefficient; wherein the bifurcation radius attenuation coefficient is determined based on the bifurcation hierarchy coefficient; using the volume of each sub-sphere as the supply lung volume downstream of the corresponding bifurcation segment; for each bifurcation segment, determining whether the radius of the bifurcation segment is greater than a preset microvascular termination radius threshold; if so, using the bifurcation segment as a new starting segment and the sub-sphere corresponding to the bifurcation segment as a new equivalent sphere, repeating the above segmentation, generation, and determination steps until the radius of all bifurcation segments is not greater than the microvascular termination radius threshold.

[0136] Based on the above-mentioned device, optionally, the cyclic model construction module 420 is further used to set the initial value of the bifurcation level coefficient; construct an initial pulmonary artery and microvascular coupling numerical model based on the initial value; perform hemodynamic numerical simulation on the pulmonary artery and microvascular coupling numerical model to determine the simulated total blood flow calculated by the pulmonary artery and microvascular coupling numerical model; calculate the relative error value between the simulated total blood flow and cardiac output; if the relative error value is less than a preset error threshold, then the current bifurcation level coefficient is determined as the final bifurcation level coefficient; if the relative error value is greater than or equal to the preset error threshold, then the bifurcation level coefficient is corrected according to the numerical optimization method, and the pulmonary artery and microvascular coupling numerical model is reconstructed based on the corrected bifurcation level coefficient, and the above hemodynamic numerical simulation, calculation of relative error value and judgment steps are repeated until the relative error value is less than the preset error threshold, and the corresponding bifurcation level coefficient at this time is determined as the final bifurcation level coefficient.

[0137] Based on the above-mentioned device, optionally, the circulation model construction module 420 is further used to determine the viscosity characteristics parameters of blood based on the hematological parameters; for each terminal microvascular segment in the microvascular tree structure, based on the geometric radius and length of the terminal microvascular segment and the viscosity characteristics parameters, the basic blood flow resistance of the terminal microvascular segment is calculated using the law of viscous fluid resistance; based on the non-Newtonian rheological correction relationship of blood, the basic blood flow resistance is corrected to obtain the equivalent blood flow resistance of the terminal microvascular segment; and the equivalent blood flow resistance of all vascular segments in each microvascular tree structure is integrated step by step upwards in a series and parallel relationship until the pulmonary artery outlet to obtain the microcirculation blood flow resistance value corresponding to the pulmonary artery outlet.

[0138] Based on the above-mentioned device, optionally, an outlet pressure determination module 430 is used to set the pulmonary artery pressure data as the pressure boundary condition of the inlet section of the whole circulation fluid network model; to use the microcirculation blood flow resistance value corresponding to each pulmonary artery outlet as the equivalent flow resistance of the downstream microvascular network of the pulmonary artery outlet, and to set the flow resistance boundary condition of the outlet section based on the equivalent flow resistance; based on the inlet pressure boundary condition and each outlet flow resistance boundary condition, to use a steady-state numerical solution method to solve the fluid dynamics control equation for the main pulmonary artery and branch vessels of the whole circulation fluid network model, and to calculate the pressure value at each pulmonary artery outlet section in the whole circulation fluid network model.

[0139] Based on the above-mentioned device, optionally, an average blood flow reserve determination module 450 is used to: determine the geometric area of ​​each pulmonary artery outlet based on the geometric data of the pulmonary artery three-dimensional model; identify pulmonary artery branches as occluded branches in the pulmonary artery three-dimensional model and obtain the occluded branch area of ​​the proximal section of the pulmonary artery branch; search for normal pulmonary artery branches with similar geometry to the occluded branch in the pulmonary artery three-dimensional model based on preset geometric similarity judgment conditions, and obtain the normal branch area of ​​the proximal section of the normal pulmonary artery branch; calculate a geometric similarity ratio coefficient based on the occluded branch area and the normal branch area; determine the equivalent outlet area set downstream of the occluded branch based on the geometric area of ​​each outlet included downstream of the normal pulmonary artery branch and the geometric similarity ratio coefficient; set the blood flow reserve fraction corresponding to all outlets in the equivalent outlet area set to a preset occluded blood flow reserve fraction value; and calculate a weighted average of the blood flow reserve fractions of all pulmonary artery outlets, including the equivalent outlet area set, using the cube of the radius of each pulmonary artery outlet as the weight, to obtain the average blood flow reserve fraction of the pulmonary artery system.

[0140] Based on the above-mentioned device, optionally, the mean blood flow reserve determination module 450 is more specifically used to extract the centerline of each branch of the pulmonary artery and the intravascular radius distribution data along the centerline based on the geometric data of the pulmonary artery three-dimensional model; for each branch of the pulmonary artery, based on the centerline and the intravascular radius distribution data, generate a continuous vascular detection segment of a preset length along the centerline; determine the front radius and the terminal radius of the continuous vascular detection segment, and calculate the ratio between the front radius and the terminal radius; if the ratio is less than a preset occlusion determination threshold, then the pulmonary artery branch is determined to be an occluded branch.

[0141] The user status determination device provided in this application embodiment can execute the user status determination method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of the execution method.

[0142] It is worth noting that the various units and modules included in the above system are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the protection scope of the embodiments of this application.

[0143] Example 5 Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 9 A block diagram is shown of an exemplary electronic device 50 suitable for implementing embodiments of the present application. Figure 9 The electronic device 50 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0144] like Figure 9 As shown, the electronic device 50 is represented in the form of a general-purpose computing device. The components of the electronic device 50 may include, but are not limited to: one or more processors or processing units 501, system memory 502, and bus 503 connecting different system components (including system memory 502 and processing unit 501).

[0145] Bus 503 represents one or more of several bus architectures, including memory buses or memory electronics, peripheral buses, graphics acceleration ports, processors, or local buses using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0146] Electronic device 50 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 50, including volatile and non-volatile media, removable and non-removable media.

[0147] System memory 502 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 504 and / or cache memory 505. Electronic device 50 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 506 may be used to read and write non-removable, non-volatile magnetic media (… Figure 9 Not shown; usually referred to as a "hard drive"). Although Figure 9 As not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 503 via one or more data media interfaces. Memory 502 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0148] A program / utility 508 having a set (at least one) of program modules 507 may be stored, for example, in memory 502. Such program modules 507 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 507 typically perform the functions and / or methods described in the embodiments of this application.

[0149] Electronic device 50 can also communicate with one or more external devices 509 (e.g., keyboard, pointing device, display 510, etc.), and with one or more devices that enable a user to interact with the electronic device 50, and / or with any device that enables the electronic device 50 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 511. Furthermore, electronic device 50 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 512. As shown, network adapter 512 communicates with other modules of electronic device 50 via bus 503. It should be understood that, although... Figure 9As not shown, other hardware and / or software modules may be used in conjunction with electronic device 50, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0150] The processing unit 501 executes various functional applications and page processing by running programs stored in the system memory 502, such as implementing the user state determination method provided in the embodiments of this application.

[0151] Example 5 This application also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a user state determination method, the method comprising: Obtain the target user's pulmonary artery 3D model, pulmonary artery pressure data, cardiac output, and hematological parameters; Based on the pulmonary artery 3D model, cardiac output, and hematological parameters, a full-circulation fluid network model extending from the main pulmonary artery to the distal microcirculation is constructed using preset microvascular bifurcation growth rules; wherein, the full-circulation fluid network model includes the microcirculation blood flow resistance value corresponding to each pulmonary artery outlet; Using the pulmonary artery pressure data and the microcirculatory blood flow resistance values ​​at each pulmonary artery outlet as boundary conditions, a hemodynamic numerical simulation is performed on the whole circulation fluid network model to determine the pressure value at each pulmonary artery outlet in the whole circulation fluid network model. For each of the pulmonary artery outlets, the fractional flow reserve of the pulmonary artery outlet is determined based on the pressure value of the pulmonary artery outlet and the pulmonary artery pressure data; The average fractional flow reserve of the pulmonary artery system is determined based on the fractional flow reserve of each pulmonary artery outlet and the occluded pulmonary artery branches identified from the three-dimensional model of the pulmonary artery. Based on the mean fractional blood flow reserve, the pulmonary artery status attribute information of the target user is determined.

[0152] The computer storage medium in this application embodiment can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0153] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0154] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0155] Computer program code for performing the operations of the embodiments of this application can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0156] Note that the above description is merely a preferred embodiment and the technical principles employed in this application. Those skilled in the art will understand that this application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments. Many other equivalent embodiments may be included without departing from the concept of this application, and the scope of this application is determined by the scope of the appended claims.

Claims

1. A method for determining user status, characterized in that, The method includes: Obtain the target user's pulmonary artery 3D model, pulmonary artery pressure data, cardiac output, and hematological parameters; Based on the pulmonary artery 3D model, cardiac output, and hematological parameters, a full-circulation fluid network model extending from the main pulmonary artery to the distal microcirculation is constructed using preset microvascular bifurcation growth rules; wherein, the full-circulation fluid network model includes the microcirculation blood flow resistance value corresponding to each pulmonary artery outlet; Using the pulmonary artery pressure data and the microcirculatory blood flow resistance values ​​at each pulmonary artery outlet as boundary conditions, a hemodynamic numerical simulation is performed on the whole circulation fluid network model to determine the pressure value at each pulmonary artery outlet in the whole circulation fluid network model. For each of the pulmonary artery outlets, the fractional flow reserve of the pulmonary artery outlet is determined based on the pressure value of the pulmonary artery outlet and the pulmonary artery pressure data; The average fractional flow reserve of the pulmonary artery system is determined based on the fractional flow reserve of each pulmonary artery outlet and the occluded pulmonary artery branches identified from the three-dimensional model of the pulmonary artery. Based on the mean fractional blood flow reserve, the pulmonary artery status attribute information of the target user is determined.

2. The method according to claim 1, characterized in that, The method, based on the pulmonary artery three-dimensional model, the cardiac output, and the hematological parameters, constructs a full-circulation fluid network model extending from the main pulmonary artery to the distal microcirculation through a preset microvascular bifurcation growth model, including: Based on the cardiac output, determine the bifurcation level coefficient of the pulmonary artery vascular tree; Based on the geometric structure of the pulmonary artery three-dimensional model, the overall lung volume corresponding to the pulmonary artery three-dimensional model is determined; For each pulmonary artery outlet in the pulmonary artery 3D model, the supply lung volume allocated to the pulmonary artery outlet is determined based on the overall lung volume and the bifurcation hierarchy coefficient. For each pulmonary artery outlet, based on the initial radius of the pulmonary artery outlet and the corresponding blood-supplying lung volume, an iterative geometric bifurcation growth operation is performed according to a preset microvascular bifurcation growth rule to generate a corresponding microvascular tree structure. Based on the microvascular tree structure corresponding to each generated pulmonary artery outlet, and in conjunction with the hematological parameters, the microcirculatory blood flow resistance value corresponding to each pulmonary artery outlet is calculated according to the law of viscous fluid resistance and the correction relationship of non-Newtonian rheology of blood. The pulmonary artery outlet in the 3D model of the pulmonary artery is coupled and associated with its corresponding microvascular tree structure and microcirculatory blood flow resistance value to construct the whole circulation fluid network model.

3. The method according to claim 1 or 2, characterized in that, The preset microvascular bifurcation growth rules include: The volume of the supply lung corresponding to the pulmonary artery outlet is equivalent to a sphere, and a starting point corresponding to the location of the pulmonary artery outlet is determined on the surface of the sphere. Based on the starting point, the sphere is divided into two sub-spheres of equal volume to be processed; The centroid of each of the sub-spheres to be processed is determined, and the starting point is connected to each centroid to generate two bifurcated blood vessel segments; The radius of each bifurcation vessel segment is determined according to a preset bifurcation radius attenuation coefficient; wherein, the bifurcation radius attenuation coefficient is determined based on the bifurcation hierarchy coefficient; The volume of each of the sub-spheres to be processed is taken as the blood supply lung volume downstream of the corresponding bifurcation segment; For each of the bifurcated blood vessel segments, determine whether the radius of the bifurcated blood vessel segment is greater than a preset microvascular termination radius threshold. If so, the bifurcation vessel segment is used as the new starting vessel segment, and the sub-sphere to be processed corresponding to the bifurcation vessel segment is used as the new equivalent sphere. The above segmentation, generation and judgment steps are repeated until the radius of all bifurcation vessel segments is not greater than the microvessel termination radius threshold.

4. The method according to claim 2, characterized in that, The determination of the pulmonary artery vascular tree bifurcation level coefficient based on the cardiac output includes: Set the initial value for the bifurcation level coefficient; An initial numerical model of pulmonary artery and microvascular coupling is constructed based on the initial values; Hemodynamic numerical simulation was performed on the numerical model of pulmonary artery and microvessel coupling to determine the simulated total blood flow calculated by the numerical model of pulmonary artery and microvessel coupling. Calculate the relative error between the simulated total blood flow and cardiac output; If the relative error value is less than the preset error threshold, then the current bifurcation level coefficient is determined as the final bifurcation level coefficient. If the relative error value is greater than or equal to the preset error threshold, the bifurcation level coefficient is corrected according to the numerical optimization method, and the pulmonary artery and microvascular coupling numerical model is reconstructed based on the corrected bifurcation level coefficient. The above hemodynamic numerical simulation, calculation of relative error value and judgment steps are repeated until the relative error value is less than the preset error threshold, and the corresponding bifurcation level coefficient at this time is determined as the final bifurcation level coefficient.

5. The method according to claim 2, characterized in that, Based on the generated microvascular tree structure corresponding to each pulmonary artery outlet, and in conjunction with the hematological parameters, the microcirculatory blood flow resistance value corresponding to each pulmonary artery outlet is calculated according to the viscous fluid resistance law and the non-Newtonian rheological correction relationship of blood, including: Based on the hematological parameters, the viscosity characteristics of the blood are determined; For each terminal microvessel segment in the aforementioned microvascular tree structure, based on the geometric radius and length of the terminal microvessel segment and the viscous characteristic parameters, the basic blood flow resistance of the terminal microvessel segment is calculated using the law of viscous fluid resistance. Based on the aforementioned non-Newtonian rheological correction relationship for blood, the baseline blood flow resistance is corrected to obtain the equivalent blood flow resistance of the terminal microvascular segment. By integrating the equivalent blood flow resistance of all vascular segments in each microvascular tree structure in a series and parallel manner, up to the pulmonary artery outlet, the microcirculatory blood flow resistance value corresponding to the pulmonary artery outlet is obtained.

6. The method according to claim 1, characterized in that, The step of using the pulmonary artery pressure data and the microcirculatory blood flow resistance values ​​at each pulmonary artery outlet as boundary conditions to perform hemodynamic numerical simulation on the whole-circulation fluid network model, and determining the pressure value at each pulmonary artery outlet in the whole-circulation fluid network model, includes: The pulmonary artery pressure data is set as the pressure boundary condition of the inlet section of the full circulation fluid network model; The microcirculatory blood flow resistance value corresponding to each pulmonary artery outlet is used as the equivalent flow resistance of the microvascular network downstream of the pulmonary artery outlet, and the flow resistance boundary conditions of the outlet section are set based on the equivalent flow resistance. Based on the inlet pressure boundary conditions and the flow resistance boundary conditions of each outlet, the fluid dynamics control equations of the main pulmonary artery and branch vessels of the full circulation fluid network model are solved using a steady-state numerical solution method, and the pressure values ​​at the outlet sections of each pulmonary artery in the full circulation fluid network model are calculated.

7. The method according to claim 1, characterized in that, The determination of the average fractional flow reserve of the pulmonary artery system based on the fractional flow reserve of each pulmonary artery outlet and the occluded pulmonary artery branches identified from the three-dimensional model of the pulmonary artery includes: Based on the geometric data of the pulmonary artery three-dimensional model, the geometric area of ​​each pulmonary artery outlet is determined; In the three-dimensional model of the pulmonary artery, the pulmonary artery branch that serves as the occluded branch is identified, and the occluded branch area of ​​the proximal section of the pulmonary artery branch is obtained. Based on preset geometric similarity criteria, a normal pulmonary artery branch with a geometric similarity to the occluded branch is found in the 3D model of the pulmonary artery, and the normal branch area of ​​the proximal section of the normal pulmonary artery branch is obtained. Calculate the geometric similarity ratio coefficient based on the area of ​​the occluded branch and the area of ​​the normal branch; Based on the geometric area of ​​each outlet included downstream of the normal pulmonary artery branch and the geometric similarity ratio coefficient, the set of equivalent outlet areas downstream of the occluded branch is determined. Set the blood flow reserve fraction corresponding to all exits in the equivalent exit area set to a preset occlusion blood flow reserve fraction value. Using the cube of the radius of each pulmonary artery outlet as the weight, a weighted average of the fractional blood flow reserve of all pulmonary artery outlets, including the set of equivalent outlet areas, is calculated to obtain the average fractional blood flow reserve of the pulmonary artery system.

8. The method according to claim 7, characterized in that, The process of identifying the pulmonary artery branch as an occluded branch in the three-dimensional model of the pulmonary artery includes: Based on the geometric data of the pulmonary artery three-dimensional model, the centerline of each branch of the pulmonary artery and the distribution data of the intravascular radius along the centerline are extracted. For each of the pulmonary artery branches, based on the centerline and the distribution data of the vessel radius, a continuous vessel detection segment of a preset length is generated along the centerline; Determine the front radius and the end radius of the continuous blood vessel detection segment, and calculate the ratio between the front radius and the end radius; If the ratio is less than a preset occlusion determination threshold, then the pulmonary artery branch is identified as an occluded branch.

9. A user status determination device, characterized in that, The device includes: The data acquisition module is used to acquire the target user's pulmonary artery 3D model, pulmonary artery pressure data, cardiac output, and hematological parameters. The circulation model construction module is used to construct a full circulation fluid network model extending from the main pulmonary artery to the distal microcirculation based on the pulmonary artery three-dimensional model, the cardiac output, and the hematological parameters, and through preset microvascular bifurcation growth rules; wherein, the full circulation fluid network model includes the microcirculation blood flow resistance value corresponding to each pulmonary artery outlet; The outlet pressure determination module is used to perform hemodynamic numerical simulation on the whole circulation fluid network model by using the pulmonary artery pressure data and the microcirculation blood flow resistance value of each pulmonary artery outlet as boundary conditions, and to determine the pressure value of each pulmonary artery outlet in the whole circulation fluid network model. A blood flow reserve determination module is used to determine the blood flow reserve fraction of each pulmonary artery outlet based on the pressure value of the pulmonary artery outlet and the pulmonary artery pressure data. The mean blood flow reserve determination module is used to determine the mean blood flow reserve of the pulmonary artery system based on the blood flow reserve fraction of each pulmonary artery outlet and the occluded pulmonary artery branches identified from the pulmonary artery three-dimensional model. The pulmonary artery status determination module is used to determine the pulmonary artery status attribute information of the target user based on the mean fractional flow reserve.

10. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the user state determination method according to any one of claims 1-8.

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