Hemodynamic-based Lung Flow Field Analysis Method

Abstract

The present invention relates to the technical field of medical image analysis methods, and more specifically, to a hemodynamic-based pulmonary flow domain analysis method, comprising: acquiring pulmonary CT image data; acquiring pulmonary anatomical data and clinical medical knowledge; generating a pulmonary artery vascular tree model based on the pulmonary CT image data; establishing a hemodynamic model based on the pulmonary artery vascular tree model; calculating the blood flow velocity field and pressure gradient field based on the hemodynamic model; executing a watershed flow domain segmentation algorithm based on the pressure gradient field to obtain pulmonary flow domain division results; and generating an analysis report containing the pulmonary flow domain division results. By employing advanced image processing techniques and vascular tracing algorithms, the segmentation accuracy of the pulmonary vascular network is significantly improved. By dynamically adjusting boundary conditions and blood parameters, the method can more accurately reflect individual differences and blood flow characteristics under pathological conditions. At the same time, the use of an efficient numerical solution algorithm significantly reduces computational complexity.

Description

Technical Field

[0001] The present invention relates to the technical field of medical image analysis methods, and more particularly to a pulmonary flow domain analysis method based on hemodynamics. Background Art

[0002] With the rapid development of medical imaging technology and computer-aided diagnosis, significant progress has been made in the diagnosis and treatment of lung diseases. However, existing technologies still have many limitations when it comes to complex pulmonary hemodynamic analysis. Traditional lung imaging analysis relies primarily on static images, which make it difficult to accurately reflect the dynamic characteristics of blood flow. Although some blood flow imaging technologies based on CT or MRI have emerged in recent years, these methods can only provide local or fragmentary blood flow information and cannot fully assess the blood flow status of the entire lung.

[0003] Currently, the closest existing technology typically uses computational fluid dynamics (CFD) simulation combined with medical image segmentation to analyze pulmonary blood flow. Although this method can theoretically provide detailed blood flow information, it faces many challenges in practical application. First, existing vascular segmentation algorithms often make errors when dealing with complex pulmonary vascular networks, especially when it comes to identifying and tracking small blood vessels. Second, the boundary conditions and blood parameters used in CFD simulations are often based on empirical values or simplified assumptions, making it difficult to accurately reflect individual differences and pathological conditions. In addition, existing methods have high computational complexity and time-consuming process in the construction and solution of hemodynamic models, making it difficult to meet the needs of clinical real-time analysis.

[0004] More critically, existing technologies are significantly inadequate in translating hemodynamic analysis results into clinically meaningful diagnostic information. Most methods provide only basic parameters such as blood flow velocity and pressure, lacking the systematic identification and classification of abnormal blood flow patterns. Furthermore, existing analysis methods often overlook the multi-scale nature of pulmonary blood flow, making it difficult to simultaneously analyze both macrovascular and microcirculatory processes.

[0005] Furthermore, existing technologies generally lack effective validation mechanisms. Due to the lack of reliable methods for directly measuring pulmonary blood flow, the accuracy of CFD simulation results is difficult to fully verify. This severely limits the application and promotion of these methods in clinical practice.

[0006] Given these challenges, a new method is urgently needed to comprehensively, accurately, and efficiently analyze pulmonary hemodynamic characteristics. This method should overcome the shortcomings of existing technologies in vessel segmentation, model construction, and anomaly identification, while also providing a reliable verification mechanism to meet the needs of clinical diagnosis and research. Summary of the Invention

[0007] This paper addresses these limitations of existing technologies and proposes a hemodynamic-based pulmonary flow domain analysis method. This method innovatively combines high-precision vessel segmentation, adaptive CFD simulation, multi-scale flow domain delineation, and abnormal blood flow identification to achieve a comprehensive and in-depth analysis of pulmonary blood flow.

[0008] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0009] Pulmonary flow analysis methods based on hemodynamics, including:

[0010] The acquisition steps include:

[0011] Acquire lung CT image data;

[0012] Obtain lung anatomy data and clinical medical knowledge;

[0013] Processing steps include:

[0014] generating a pulmonary artery vascular tree model based on the lung CT image data;

[0015] Establishing a hemodynamic model based on the pulmonary artery vascular tree model;

[0016] Calculating the blood flow velocity field and pressure gradient field based on the hemodynamic model;

[0017] executing a watershed watershed segmentation algorithm according to the pressure gradient field to obtain a lung watershed segmentation result;

[0018] Output steps include:

[0019] An analysis report including the lung watershed division results is generated.

[0020] Preferably, generating the pulmonary artery vascular tree model specifically includes:

[0021] reconstructing the lung CT image data into three-dimensional data;

[0022] extracting pulmonary blood vessels based on the three-dimensional data;

[0023] According to the pulmonary anatomical data, identifying the bifurcation of the main pulmonary artery;

[0024] A pulmonary artery vascular tree structure is generated based on the bifurcation of the pulmonary artery trunk.

[0025] Preferably, the establishing of the hemodynamic model specifically includes:

[0026] Set blood physical parameters, including blood density, dynamic viscosity, static viscosity, volume fraction and thermal conductivity;

[0027] Set the wall conditions, flow field boundary conditions, and lung boundary conditions;

[0028] Based on Darcy's law, LM equation, continuity equation and Bernoulli equation, a group of hemodynamic equations was constructed.

[0029] Preferably, the calculating of the blood flow velocity field and the pressure gradient field specifically includes:

[0030] Dividing the computational domain of the hemodynamic model into upstream and downstream parts;

[0031] applying an inlet boundary condition to the upstream boundary;

[0032] applying an outlet boundary condition to the downstream boundary;

[0033] The hemodynamic equations are solved using a computational fluid dynamics (CFD) algorithm to obtain a blood flow velocity field and a pressure gradient field.

[0034] Preferably, the executing watershed segmentation algorithm specifically includes:

[0035] Setting a seed point in the pressure gradient field;

[0036] Based on the seed point, executing a region growing algorithm;

[0037] Determine the watershed boundary based on the pressure gradient threshold;

[0038] Generate lung watershed division results.

[0039] Preferably, the method further includes the step of identifying abnormal blood flow:

[0040] Calculate outlier discrimination parameters, including flow, density, velocity, vessel radius, and vessel length;

[0041] Set the abnormality judgment threshold;

[0042] identifying an abnormal blood flow pattern based on the abnormal point discrimination parameter and the abnormality judgment threshold;

[0043] The abnormal blood flow pattern is added to the analysis report.

[0044] Preferably, the abnormal blood flow pattern includes:

[0045] When the flow rate is less than the first preset threshold and the density is less than the second preset threshold, it is determined to be an embolism point;

[0046] When the density is less than the second preset threshold and the blood vessel radius is less than the third preset threshold, it is determined to be a stenosis point;

[0047] When the density is less than the second preset threshold and the blood vessel radius is greater than the fourth preset threshold, it is determined to be a dilation point.

[0048] As an advantage, it also includes a dynamic adjustment step:

[0049] Calculate the velocity distribution factor, pressure drop factor and resistance gradient factor of normal blood flow in the current flow area;

[0050] calculating a probability distribution of the factor;

[0051] Based on the maximum point of the probability distribution, the threshold of the watershed segmentation algorithm is dynamically adjusted.

[0052] Preferably, the lung watershed division result includes:

[0053] First-order drainage basin: the area including the right ventricular pulmonary artery;

[0054] Second-level basin: a branch of the first-level basin;

[0055] Third-level basin: a branch of the second-level basin;

[0056] Fourth-level basin: a branch of the third-level basin.

[0057] As an advantage, the step of verifying the result is also included:

[0058] Fluorescent tracer dynamic data is used to locate and quantitatively measure blood flow distribution;

[0059] calculating actual blood viscosity based on the dynamic data of the fluorescent tracer;

[0060] comparing the actual blood viscosity with a blood viscosity parameter in the hemodynamic model;

[0061] According to the comparison results, the parameters of the hemodynamic model are adjusted.

[0062] The method of the present invention has the following significant technical effects:

[0063] First, by employing advanced image processing techniques and vessel tracking algorithms, the accuracy of segmentation of the pulmonary vascular network was significantly improved, with a particular breakthrough achieved in the identification of small vessels. This laid a solid foundation for subsequent hemodynamic analysis.

[0064] Secondly, this invention innovatively introduces adaptive CFD simulation technology. By dynamically adjusting boundary conditions and blood parameters, this method can more accurately reflect individual differences and blood flow characteristics under pathological conditions. Furthermore, the use of an efficient numerical solution algorithm significantly reduces computational complexity, enabling real-time analysis.

[0065] More importantly, the multi-scale flow domain partitioning method proposed in this paper cleverly solves the problem of unifying macrovascular and microcirculatory analysis. By dividing the pulmonary vascular network into four levels of flow domains, this method can capture hemodynamic characteristics at different scales, providing a new perspective for comprehensively assessing pulmonary blood flow.

[0066] In terms of abnormal blood flow identification, this invention has developed a systematic parameter system and judgment mechanism that can automatically identify and classify various abnormal blood flow patterns, such as embolism, stenosis, and dilation. This greatly improves the clinical value of the analysis results and provides important support for early diagnosis and precise treatment.

[0067] This invention also innovatively introduces a verification mechanism based on fluorescent tracers. By comparing and optimizing CFD simulation results with actual measurement data, the reliability and accuracy of the analysis results are significantly improved. This not only solves the verification difficulties encountered in existing technologies but also provides a path for continuous model optimization.

[0068] In summary, the hemodynamic-based pulmonary flow analysis method proposed in this invention achieves comprehensive, accurate, and efficient analysis of pulmonary blood flow through the organic combination of multiple innovative technologies. This method not only overcomes many limitations of existing technologies but also achieves significant improvements in accuracy, efficiency, and clinical practicality. This provides a powerful tool for the diagnosis, treatment evaluation, and research of lung diseases, and is expected to play an important role in clinical practice and promote the further development of pulmonary medicine. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 The figure is an overall flow chart of the method of the present invention.

[0070] Figure 2 This is a flow chart of generating a pulmonary artery vascular tree model according to the present invention.

[0071] Figure 3 The flowchart of establishing the hemodynamic model of the present invention is shown in FIG.

[0072] Figure 4 This is a flowchart of the watershed segmentation algorithm of the present invention. DETAILED DESCRIPTION

[0073] like Figure 1-4 As shown, the present invention provides a pulmonary flow domain analysis method based on hemodynamics. The method comprises the following steps:

[0074] First, obtain lung CT image data, along with relevant lung anatomy data and clinical medical knowledge. Preferably, the CT image data should have a resolution of no less than 512x512 pixels, with a slice thickness no greater than 1 mm, to ensure the accuracy of subsequent analysis. In one embodiment of the present invention, a multi-slice CT scanner can be used to acquire spiral CT data of the entire lung, with scanning parameters set to 120 kV and 250 mAs. This setting can minimize radiation dose while maintaining image quality.

[0075] Next, a pulmonary artery vascular tree model is generated based on the acquired CT image data. Specifically, this method first reconstructs the two-dimensional CT image into a three-dimensional dataset. Then, a vascular enhancement algorithm based on the Hessian matrix is used to enhance the vascular structure. The mathematical expression of this algorithm is as follows:

[0076] ,

[0077] in, Represent the second-order partial derivatives of the image in the x, y, and z directions, respectively. 、 、 Represents mixed partial derivatives. By analyzing the eigenvalues of the Hessian matrix, vascular structures can be effectively distinguished from other tissues.

[0078] Preferably, the present invention uses a multi-scale analysis method to calculate the Hessian matrix at different Gaussian scales to accommodate blood vessels of different calibers. The scale range is usually set to 0.5mm to 2mm, which covers the diameter of most blood vessels in the lungs.

[0079] After vessel enhancement, this method uses a region growing algorithm to extract vascular structure. The selection of seed points is crucial. The present invention preferably begins with the pulmonary artery trunk and automatically selects the point with the highest grayscale value as the initial seed point. The growing threshold can be adaptively adjusted based on image contrast, typically set to the mean grayscale value plus 1.5 times the standard deviation to effectively distinguish blood vessels from surrounding tissue.

[0080] This method then uses a minimum spanning tree algorithm to construct vascular connections, forming a complete pulmonary artery vascular tree structure. This process uses the Kruskal algorithm to construct the minimum spanning tree, with a time complexity of O(ElogE), where E is the number of edges. This method can effectively handle complex vascular branching structures.

[0081] After obtaining the pulmonary artery tree model, the method of the present invention further establishes a hemodynamic model. This model is based on classical fluid mechanics theory and incorporates the specific characteristics of pulmonary blood flow. Specifically, this method considers the following key equations:

[0082] 1. Continuity equation:

[0083] ,

[0084] in, is the blood density, v is the velocity vector, For time.

[0085] 2. Momentum equation (Navier-Stokes equation):

[0086] ,

[0087] For pressure, is the dynamic viscosity of blood, is the volume force.

[0088] 3. Energy equation:

[0089] ,

[0090] in, is the specific heat capacity, is the temperature, is the thermal conductivity coefficient, is the viscous dissipation function.

[0091] When setting the parameters of these equations, the present invention preferably adopts the following empirical values: blood density kg / , dynamic viscosity Pa·s. These values are based on a large number of physiological studies and can better reflect the characteristics of human blood.

[0092] It is worth noting that one of the innovations of the present invention is that it takes into account the non-Newtonian properties of blood. The Carreau-Yasuda model is used to describe the shear thinning properties of blood:

[0093] ,

[0094] in, and are the viscosities at infinite shear rate and zero shear rate, respectively, is the characteristic time, is the shear rate, and is the model parameter. This model can more accurately describe the rheological properties of blood in different blood vessel diameters and flow rates.

[0095] Regarding boundary conditions, this method uses a periodic flow boundary condition at the inlet to simulate the effects of cardiac pulsation on pulmonary artery blood flow. A zero pressure gradient condition is used at the outlet. The vessel wall is treated as a rigid no-slip boundary, taking into account the relatively stiff nature of the pulmonary artery wall.

[0096] Based on the above hemodynamic model, this method next calculates the blood flow velocity field and pressure gradient field. In this step, the governing equations are discretized using the finite volume method. When generating the mesh, a boundary layer mesh is preferably used near the vessel wall to better capture the velocity gradient near the wall. The number of mesh cells typically ranges from 1 million to 5 million, depending on the complexity of the vascular tree.

[0097] During the calculation process, the SIMPLE (Semi-Implicit Method for Pressure Linked Equations) algorithm is used to solve the velocity and pressure fields. The selection of the time step is crucial to the accuracy and stability of the calculation results. This paper recommends setting the time step to 1 / 100 of the cardiac cycle to better capture the transient characteristics of blood flow.

[0098] After obtaining the blood flow velocity field and pressure gradient field, this method performs a watershed segmentation algorithm to obtain the lung watershed delineation results. The core concept of the watershed algorithm is to treat the image as a topographic map, with grayscale values representing altitude. The present invention innovatively applies this concept to the pressure gradient field, treating the magnitude of the pressure gradient as altitude.

[0099] The specific steps of the watershed algorithm are as follows:

[0100] 1. Initialization: Mark the local minimum points in the pressure gradient field with different labels.

[0101] 2. Immersion process: gradually increase the water level to simulate the submergence process.

[0102] 3. When two areas with different labels are about to meet, a dam is built between them, which is the watershed line.

[0103] 4. Repeat steps 2 and 3 until the entire image is completely submerged.

[0104] In practice, to avoid over-segmentation, this method uses a marker-controlled watershed algorithm. Markers are pre-set based on the hierarchical structure of vascular branches, typically at the bifurcation points. This ensures that the segmentation results are consistent with the actual anatomical structure.

[0105] Finally, this method generates an analysis report containing the results of the pulmonary flow domain delineation. This report includes not only the spatial distribution of the flow domains but also quantitative analysis results for each flow domain, such as the volume, mean blood flow velocity, and mean pressure. This information can help clinicians gain a more comprehensive understanding of the patient's pulmonary blood flow status.

[0106] Through the above steps, the method provided by the present invention can accurately analyze and divide pulmonary blood flow based on hemodynamic principles. Compared with traditional methods based solely on anatomical structure, this method can provide more functional information, which helps to improve the diagnostic accuracy and treatment effect evaluation of lung diseases. The method of the present invention further includes specific steps for generating a pulmonary artery vascular tree model. These steps are designed to accurately extract the pulmonary artery structure from CT image data, laying the foundation for subsequent hemodynamic analysis.

[0107] First, the method reconstructs the lung CT image data into three-dimensional data. Preferably, a voxel-based reconstruction technique is used, such as a volume rendering algorithm (Volume Rendering) or a surface rendering algorithm (Surface Rendering). In one embodiment of the present invention, the volume rendering algorithm uses the following transfer function:

[0108] ,

[0109] in, is the output image intensity, and are the color and opacity of the i-th voxel, and n is the number of voxels that the light passes through. This method can well preserve the three-dimensional structural information of the blood vessels.

[0110] Next, the method extracts the pulmonary vessels based on the reconstructed 3D data. In this step, the present invention preferably uses a multi-scale vessel enhancement filter. This filter is based on the eigenvalue analysis of the Hessian matrix, and its response function can be expressed as:

[0111] ,

[0112] in, is the eigenvalue of the Hessian matrix .

[0113] It is a control parameter, usually taken as half of the maximumHessian norm.

[0114] After the blood vessel extraction, the present method further identifies the bifurcation of the pulmonary artery trunk. Preferably, a method based on skeleton extraction and topological analysis is adopted. First, a thinning algorithm is used to extract the blood vessel skeleton, and then the bifurcation point is identified by analyzing the connectivity of the skeleton points. After the blood vessel extraction, the present method further identifies the bifurcation of the pulmonary artery trunk. Preferably, a method based on skeleton extraction and topological analysis is adopted. First, a thinning algorithm is used to extract the blood vessel skeleton, and then the bifurcation point is identified by analyzing the connectivity of the skeleton points. In one embodiment of the present invention, the Zhang-Suen thinning algorithm is adopted, and the iterative process of the algorithm is as follows:

[0115] 1. For each boundary point , check its 8 neighboring points .

[0116] 2. If the following conditions are met, then mark To be deleted: a) ; b) ; c) or ;d) or ,in, for The number of non-zero neighbors of For arrive The number of 0-1 transitions when traversing in order.

[0117] Finally, the method generates a complete pulmonary artery vascular tree structure based on the identified pulmonary artery bifurcations. In this step, an adaptive region growing algorithm is preferably used. The core idea of this algorithm is to start from the bifurcation point of the main trunk and gradually expand outward until a preset stopping condition is reached.

[0118] The method presented in this paper prioritizes small vessels and diseased areas when generating a pulmonary artery tree model. For vessels with diameters less than 1 mm, super-resolution reconstruction techniques are used to enhance their visibility. For areas potentially containing disease, such as stenosis or dilation, this method combines morphological manipulation with local contrast enhancement techniques to ensure accurate identification and modeling of these areas.

[0119] Through the above steps, the method of the present invention can generate a high-precision and high-completeness pulmonary artery vascular tree model from CT image data, providing a reliable geometric basis for subsequent hemodynamic analysis.

[0120] Next, the method of the present invention further elaborates on the process of establishing the hemodynamic model, which involves the setting of multiple key parameters and is crucial for accurately simulating pulmonary blood flow.

[0121] First, the method sets the physical properties of blood. Under standard physiological conditions, the blood density is usually 1060 kg / m³. However, taking into account the individual differences between different patients, in a preferred embodiment of the present invention, the density value can be adjusted within the range of 1050-1070 kg / m³. The dynamic viscosity of blood is a more complex parameter because blood exhibits non-Newtonian fluid properties. At low shear rates, the blood viscosity is high, while the viscosity decreases at high shear rates. To accurately describe this behavior, the method uses the Carreau-Yasuda model:

[0122] ,

[0123] in, is the viscosity at infinite shear rate (about 0.0035 Pa·s), is the viscosity at zero shear rate (about 0.016 Pa·s), is the characteristic time (about 3.313s), is the shear rate, and These parameter values are obtained by fitting a large amount of experimental data and can well describe the rheological properties of human blood.

[0124] Static viscosity is another important parameter that reflects the viscosity of blood at rest. In this method, the static viscosity is set to 1.5 times the dynamic viscosity to take into account the effect of red blood cell aggregation at rest.

[0125] The blood volume fraction primarily refers to the hematocrit (hematocrit). Under normal physiological conditions, this value is approximately 45%. However, in certain lung diseases, such as pulmonary edema or anemia, this value can vary significantly. Therefore, the method of the present invention allows for adjustment of this parameter based on the patient's specific condition, typically within a range of 35% to 55%.

[0126] Finally, the thermal conductivity of blood is typically taken as 0.52 W / (m·K). This parameter is kept constant in most blood flow simulations unless the study is specifically interested in heat transfer processes.

[0127] Next, the method sets the wall conditions. In standard cases, the vessel wall is considered a rigid, no-slip boundary. However, considering the elastic properties of actual blood vessels, a preferred embodiment of the present invention uses an elastic wall model. This model is based on the following equation:

[0128] ,

[0129] in, is the vascular wall density, is the displacement vector, is the stress tensor, The Young's modulus of the blood vessel wall is usually taken as 1 MPa, and the Poisson's ratio is 0.45.

[0130] When setting the flow field boundary conditions, this method uses the velocity inlet condition at the inlet. The velocity profile can be described using the Womersley analytical solution:

[0131] ,

[0132] in, is the zero-order Bessel function, is the Womersley number, is the vessel radius, is the angular frequency.

[0133] At the outlet boundary, this method uses a pressure outlet condition. Considering the particularity of the pulmonary circulation, the outlet pressure is set to 10-25 mmHg, which covers the normal physiological state to mild pulmonary hypertension.

[0134] Finally, this method also takes into account the special boundary conditions of the lungs. The pulmonary blood vessels are affected by respiratory motion, so a periodic external pressure change is introduced into the model. This pressure change can be described by the following function:

[0135] ,

[0136] in, is the average thoracic pressure (about -4 mmHg), is the pressure wave amplitude (about 2 mmHg), is the respiratory rate (about 0.25 Hz).

[0137] By setting these detailed parameters and conditions, the method of the present invention can establish a highly accurate pulmonary hemodynamic model. This model not only takes into account the complex rheological properties of blood, but also incorporates the unique physiological characteristics of the lungs, providing a reliable foundation for subsequent blood flow analysis.

[0138] After establishing the hemodynamic model, the method of the present invention further elaborates on the calculation process of the blood flow velocity field and pressure gradient field. This process is the core of the entire analysis and directly affects the accuracy of the subsequent flow domain division.

[0139] First, this method uses computational fluid dynamics (CFD) to solve the hemodynamic equations. Among the many CFD methods, the present invention prefers the finite volume method (FVM). The core concept of FVM is to discretize the computational domain into a series of control volumes and then apply conservation laws to each control volume. This method inherently ensures the conservation of mass, momentum, and energy, making it particularly suitable for dealing with complex vascular networks.

[0140] During the discretization process, this method uses a strategy that combines structured and unstructured grids. For main vessels, a hexahedral structured grid is used, as this grid type is computationally efficient and has minimal numerical diffusion. For branches and bends, a tetrahedral unstructured grid is used to better accommodate complex geometries. During mesh generation, special attention is paid to increasing the density of the grid near the vessel wall to accurately capture the boundary layer flow characteristics. Typical mesh sizes are approximately 0.1 mm in the center of the vessel and can reach 0.01 mm near the wall.

[0141] This method uses the SIMPLE (Semi-Implicit Method for Pressure-Linked Equations) algorithm to solve the problem. This is an iterative algorithm with the following basic steps:

[0142] 1. Assume a pressure field;

[0143] 2. Solve the momentum equation to obtain the velocity field;

[0144] 3. Solve the pressure correction equation;

[0145] 4. Calibrate pressure and speed;

[0146] 5. Solve other scalar equations (such as turbulence equations);

[0147] 6. Check convergence. If not, return to step 2.

[0148] To improve computational efficiency, a preferred embodiment of the present invention employs a multigrid technique. This technique significantly accelerates convergence by alternating between grids of different resolutions. The core concept of the multigrid method can be expressed as the following error equation:

[0149] ,

[0150] ,

[0151] in, and denote the discrete operators on the fine grid and the coarse grid respectively, and is the corresponding error, is the residual, is the restriction operator.

[0152] For time discretization, this method uses a second-order implicit scheme. The choice of time step is crucial, balancing computational accuracy and efficiency. In this embodiment, the time step is set to 1 / 100 of the cardiac cycle, which effectively captures the transient characteristics of blood flow.

[0153] For turbulence simulation, this method uses the k-ωSST (Shear Stress Transport) model. This model combines the advantages of the k-ε model in the free flow region with the k-ω model in the near-wall region, making it particularly suitable for simulating complex flows with adverse pressure gradients and flow separation. The governing equations of the k-ωSST model are as follows:

[0154] ,

[0155] ,

[0156] in, is the turbulent kinetic energy, is the specific dissipation rate, and is the effective diffusion coefficient, and To generate items, and is the dissipation term, is the cross diffusion term, and A user-defined source item.

[0157] By solving the above equations, this method obtains detailed blood flow velocity and pressure gradient fields. These fields contain rich hemodynamic information and lay the foundation for subsequent flow domain division.

[0158] Next, the method of the present invention further describes the specific implementation process of the watershed segmentation algorithm. The core idea of this algorithm is to treat the pressure gradient field as a topographic map and identify different watersheds by simulating the flooding process.

[0159] First, this method sets seed points in the pressure gradient field. The choice of seed points directly impacts the quality of the segmentation results. In a preferred embodiment of the present invention, an automatic seed point selection strategy based on vascular bifurcation points is employed. Specifically, for each major vascular bifurcation, the point with the smallest pressure gradient around it is selected as the seed point. This ensures that each major vascular branch has a corresponding flow basin.

[0160] After the seed point is selected, the method executes the region growing algorithm. The core idea of the algorithm is to start from the seed point and gradually expand the region until it encounters other regions or reaches a preset stopping condition. In the present invention, the region growing process can be described by the following mathematical model:

[0161] ,

[0162] in, represents the pixel set of the ith region at the tth iteration, express The neighborhood pixel set of represents the pressure gradient at pixel p, is the threshold. The choice of is crucial. This method adopts an adaptive threshold strategy:

[0163] ,

[0164] in, and are the average pressure gradient and standard deviation of the current area, To adjust the parameter, the value is usually between 1.5 and 2.5.

[0165] During the region growing process, when two different regions meet, this method establishes a watershed line between them. To avoid the over-segmentation problem, the present invention introduces a region merging strategy. Specifically, when the difference in the average pressure gradient of two adjacent regions is less than a preset threshold, the two regions will be merged. The merging condition can be expressed as:

[0166] ,

[0167] in, and is the average pressure gradient between two adjacent regions, and is the corresponding standard deviation, It is a merging parameter, usually with a value between 0.5 and 1.

[0168] In order to further improve the robustness of the segmentation, the method of the present invention also introduces a morphological post-processing step. First, the opening operation is applied to remove small isolated areas:

[0169] ,

[0170] Among them, A is the segmentation result, B is the structural element, and Denote the erosion and dilation operations respectively. Then, a closing operation is applied to fill the holes:

[0171] ,

[0172] Through these steps, the method of the present invention can obtain a smooth and coherent lung watershed division result.

[0173] Finally, this method generates an analysis report containing the lung watershed delineation results. This report includes not only a visual watershed distribution map but also quantitative analysis results for each watershed. Specifically, for each identified watershed, the report contains the following information:

[0174] 1. Basin volume: calculated by integration, in cubic millimeters.

[0175] 2. Average blood flow velocity: The average velocity of all points in the flow area, in meters per second.

[0176] 3. Average pressure: The average pressure of all points in the basin, in millimeters of mercury.

[0177] 4. Wall shear stress (WSS): Calculates the average shear stress of the vessel wall in Pascals.

[0178] 5. Oscillatory Shear Index (OSI): An indicator that reflects the change in the direction of wall shear stress, dimensionless.

[0179] These quantitative indices provide clinicians with tools to comprehensively assess pulmonary blood flow. For example, abnormally high wall shear stress may indicate the risk of aneurysm, while a high oscillatory shear index may be associated with atherosclerosis.

[0180] Furthermore, the method incorporates anomaly detection results into the report. By comparing the differences between the indicators of each watershed and the normal reference value, it automatically marks areas with potential problems. For example, if the average blood flow velocity in a watershed is less than 50% of the normal value, the area will be marked as a potential area of blood flow obstruction.

[0181] To facilitate physician understanding and usage, the report utilizes a hierarchical structure. The first page provides an overview, including a 3D watershed distribution map and a summary of major anomalies. Subsequent pages detail the specific parameters and analysis results for each watershed. The report also includes interactive features, allowing physicians to click on areas of interest for more detailed information.

[0182] Through this comprehensive, detailed, and intuitive reporting, the method of the present invention provides a powerful tool for pulmonary hemodynamic analysis, potentially significantly improving the diagnosis and treatment of related diseases. The method of the present invention further includes a step for identifying abnormal blood flow, a key innovation that enables direct translation of hemodynamic analysis results into clinically useful information.

[0183] First, the method calculates outlier discrimination parameters. These parameters include flow, density, velocity, vessel radius, and vessel length. The calculation of each parameter is based on the hemodynamic simulation results obtained in the previous step. Specifically:

[0184] 1. The flow rate Q is calculated using the following formula:

[0185] ,

[0186] in, is the velocity vector, and A is the cross-sectional area of the blood vessel. In actual calculations, the integral can be discretized as:

[0187] ,

[0188] Where N is the number of mesh elements in the cross section, and are the velocity and area of the i-th unit respectively.

[0189] 2. Density ρ is directly extracted from blood flow simulation results and is usually about 1060 kg / m³ under normal physiological conditions.

[0190] 3. The velocity v is taken as the average value in the basin:

[0191] ,

[0192] where V is the volume of the basin.

[0193] 4. The vessel radius R can be estimated from the volume and length of the basin:

[0194] ,

[0195] Where L is the length of the vessel.

[0196] 5. The vessel length L is calculated using a skeletonization algorithm, specifically the Zhang-Suen thinning algorithm.

[0197] Next, the method sets abnormality judgment thresholds. The selection of these thresholds is based on a large amount of clinical data and expert experience. In a preferred embodiment of the present invention, the following thresholds are used:

[0198] Traffic threshold : 50% of normal value; density threshold :1000kg / m³; speed threshold : 30% of normal value; radius threshold : 70% (stenosis) and 150% (dilation) of normal value; length threshold : 120% of normal value;

[0199] These thresholds can be adjusted based on specific clinical needs. For example, the thresholds can be set to be more sensitive for early detection and more specific for definitive diagnosis.

[0200] Based on the above parameters and thresholds, this method identifies abnormal blood flow patterns. The specific judgment logic is as follows:

[0201] 1. Embolism point: When and This is because embolism can lead to a significant reduction in blood flow, and due to the formation of thrombus, the local blood density will also decrease.

[0202] 2. Narrow point: When and Stenosis can cause a decrease in the local vessel radius and, due to an increase in flow velocity, a decrease in local density may occur.

[0203] 3. Expansion point: When and Vascular dilation can cause local blood flow to slow down, density to decrease, and the vascular radius to increase significantly.

[0204] 4. Diversion point: When and This is usually found at a bifurcation and is characterized by a short vessel length and small radius.

[0205] 5. Infusion deficiency: When and In this case, the vessel is shorter but has a larger radius, which may be due to compensatory dilation caused by upstream blood flow obstruction.

[0206] 6. Over-infusion: When This may be due to abnormal angiogenesis or arteriovenous malformations.

[0207] In order to improve the accuracy of judgment, this method also introduces a fuzzy logic system. For example, for the judgment of narrow points, the following fuzzy rules can be used:

[0208] IF (density is low) AND (radius is small) THEN (stenosis is high)

[0209] Among them, low, small and high are all fuzzy sets and can be described by membership functions. This method can better handle the continuous changes of parameters and avoid the judgment errors that may be caused by hard thresholds.

[0210] Finally, this method adds the identified abnormal blood flow patterns to the analysis report. In the report, abnormal points are marked with different colors on the 3D model and accompanied by detailed parameter information and possible pathological explanations. For example, for an identified stenosis point, the report provides information such as the exact location of the point, vessel radius, local flow velocity, pressure gradient, and possible causes of the stenosis (such as atherosclerosis and thrombosis).

[0211] In this way, the method of the present invention not only provides quantitative analysis of hemodynamics, but also directly gives abnormality identification results with clinical significance, greatly improving the practicality and interpretability of the analysis results.

[0212] Next, the method of the present invention introduces an innovative dynamic adjustment step, which aims to improve the adaptability and robustness of the method, especially when dealing with different patients or different pathological conditions.

[0213] First, this method calculates the velocity distribution factor, pressure drop factor, and resistance gradient factor of normal blood flow in the current flow domain. The definitions of these factors are as follows:

[0214] 1. Velocity distribution factor : ,in, is the standard deviation of velocity within the basin, is the average velocity. This factor reflects the uniformity of the velocity distribution.

[0215] 2. Pressure drop factor : ,in, is the pressure drop within the flow domain, and L is the characteristic length of the flow domain. This factor reflects the pressure loss per unit dynamic pressure.

[0216] 3. Resistance gradient factor : , where R is vascular resistance, which can be estimated by the Hagen-Poiseuille equation: , μ is the blood viscosity, and r is the blood vessel radius.

[0217] Next, the method calculates the probability distribution of these factors. In a preferred embodiment of the present invention, the kernel density estimation (KDE) method is used to estimate the probability density function. The mathematical expression of KDE is as follows:

[0218] ,

[0219] Where K is the kernel function and h is the bandwidth parameter. This method uses the Gaussian kernel function:

[0220] ,

[0221] The choice of bandwidth parameter h is crucial. This method adopts Silverman's rule of thumb:

[0222] ,

[0223] in, is the sample standard deviation, and n is the sample size.

[0224] Based on the estimated probability density function, this method extracts the maximum value as the dynamic adjustment threshold. Specifically:

[0225] 1. For velocity distribution factor , take the maximum point of its probability density function , adjust the speed threshold in the watershed algorithm to:

[0226] ,

[0227] Here, α is a tuning parameter, which usually takes a value between 0.5 and 1.5.

[0228] 2. For the pressure drop factor , take the maximum point of its probability density function , adjust the pressure gradient threshold to:

[0229] ,

[0230] Where β is a tuning parameter, which usually takes a value between 0.8 and 1.2.

[0231] 3. For the resistance gradient factor , take the maximum value of its probability density function , adjust the blood vessel radius change threshold to:

[0232] ,

[0233] Here γ is a tuning parameter, which usually takes a value between 0.3 and 0.7.

[0234] Through this dynamic adjustment mechanism, the method of the present invention can adaptively adjust the threshold of the watershed segmentation algorithm to better adapt to individual patient differences and varying pathological conditions. For example, in patients with pulmonary hypertension, due to the high overall pressure gradient, a fixed threshold may lead to oversegmentation. However, the dynamically adjusted threshold can more accurately reflect the patient's actual condition.

[0235] Furthermore, this method incorporates a feedback mechanism. After each analysis, the results are compared with the clinical diagnosis. If significant discrepancies are found, the system automatically adjusts the three parameters α, β, and γ to optimize the next analysis. This mechanism enables the method to learn, allowing its accuracy to improve with use.

[0236] Through this dynamic adjustment and feedback mechanism, the method of the present invention significantly improves its applicability and reliability in different clinical scenarios, providing a powerful tool for personalized pulmonary hemodynamic analysis. The method of the present invention further refines the pulmonary flow domain delineation results, dividing them into four hierarchical levels. This hierarchical structure not only more accurately reflects the complexity of pulmonary blood flow, but also provides more valuable information for clinical diagnosis.

[0237] The first-level flow domain includes the area including the right ventricular exit of the pulmonary artery. This is the starting point of the entire pulmonary circulation and is crucial for understanding the overall pulmonary hemodynamics. In a preferred embodiment of the present invention, the identification of the first-level flow domain adopts the following steps:

[0238] 1. Based on anatomical knowledge, locate the main pulmonary artery at the outlet of the right ventricle.

[0239] 2. Using the vessel tracking algorithm, start from the main pulmonary artery and track downstream until you encounter the first major bifurcation point.

[0240] 3. Define the traced area as the first-level watershed.

[0241] This region typically has the largest blood vessel diameter and the highest blood flow velocity. This method pays particular attention to changes in pressure and flow velocity in this region because they may reflect early signs of right heart function and pulmonary hypertension.

[0242] The second-order drainage basins are branches of the first-order drainage basins. These usually correspond to the major branches of the left and right pulmonary arteries. The process for identifying the second-order drainage basins is as follows:

[0243] 1. Starting from the end point of the first-order basin (i.e., the main bifurcation point), continue vessel tracing.

[0244] 2. Each branch is traced until the next significant bifurcation point is encountered or the vessel diameter decreases below a threshold.

[0245] 3. Define each traced branch as an independent second-level watershed.

[0246] In one embodiment of the present invention, a significant bifurcation point is defined as a bifurcation with a ratio of the diameter of the daughter vessel to the diameter of the parent vessel greater than 0.7. The vessel diameter threshold is usually set to 3 mm, which is determined based on the normal adult pulmonary artery anatomy.

[0247] The third-level basins are branches of the second-level basins. These typically correspond to vessels at the lobar and segmental levels. The identification process is similar to the second-level basins, with the following differences:

[0248] 1. The criteria for determining bifurcation points are more relaxed: a significant bifurcation is considered when the ratio of the daughter vessel diameter to the parent vessel diameter is greater than 0.5.

[0249] 2. The blood vessel diameter threshold is lowered to 1.5 mm.

[0250] The fourth-order basins are branches of the third-order basins and correspond to smaller intrapulmonary vessels. Because the size of these vessels is close to the resolution limit of CT scanning, this method adopts a special processing strategy:

[0251] 1. Use super-resolution reconstruction technology to improve image resolution. The present invention preferably uses a super-resolution algorithm based on deep learning, such as SRCNN (Super-Resolution Convolutional Neural Network).

[0252] 2. Apply a vessel enhancement filter, such as the Frangi filter, to highlight small vessel structures.

[0253] 3. Use more flexible criteria for determining bifurcation, as long as it can be clearly identified as a branching structure.

[0254] 4. The blood vessel diameter threshold is further reduced to 0.5mm.

[0255] To ensure the accuracy and consistency of watershed delineation, this method introduces an innovative verification mechanism based on the following principles:

[0256] 1. Conservation of mass: The total flow of an upstream basin should be equal to the sum of the flows of all its downstream basins.

[0257] 2. Pressure continuity: The pressure between adjacent river basins should change continuously and there should be no sudden changes.

[0258] 3. Morphological consistency: Watersheds of the same level should have similar morphological characteristics (such as length, number of branches, etc.).

[0259] If violations of these principles are detected, the method automatically performs local re-divisions. For example, if a third-level watershed is found to have unusually high flow, the system will re-examine whether the area should be divided into more sub-basins.

[0260] Furthermore, the method of the present invention also takes into account special circumstances under pathological conditions. For example, in patients with pulmonary embolism, certain blood vessels may be completely occluded. In such cases, traditional blood flow-based segmentation methods may fail. To address this, the present method introduces a morphologically-based auxiliary segmentation strategy. Even in the absence of significant blood flow, reasonable flow basin segmentation can be performed based on the anatomical structure of the blood vessels.

[0261] Through this multi-level, adaptive flow domain partitioning method, the present invention can provide a comprehensive and detailed description of pulmonary blood flow, providing a powerful tool for clinical diagnosis and research.

[0262] Finally, the method of this invention introduces an innovative result verification step to further improve the reliability and accuracy of the analysis results. This step uses dynamic data from fluorescent tracers to locate and quantitatively measure blood flow distribution, and uses actual measurement results to verify and optimize the hemodynamic model.

[0263] First, this method uses a fluorescent tracer for dynamic imaging. In a preferred embodiment of the present invention, indocyanine green (ICG) is used as the tracer due to its good biocompatibility and rapid plasma clearance. The ICG injection and imaging process are as follows:

[0264] 1. Rapidly inject ICG solution (dose 0.1 mg / kg body weight) through a peripheral vein.

[0265] 2. Use a near-infrared fluorescence imaging system to continuously acquire images with a sampling rate of 30 frames / s and a duration of 60 seconds.

[0266] Next, this method analyzes the obtained dynamic image sequence to extract blood flow distribution information. The specific steps include:

[0267] 1. Image preprocessing: including denoising, background correction and motion compensation.

[0268] 2. Region of interest (ROI) definition: Based on the watershed demarcation results obtained in the previous step, define the corresponding ROI on the fluorescence image.

[0269] 3. Time-intensity curve (TIC) extraction: For each ROI, extract the curve of fluorescence intensity changing with time.

[0270] 4. Blood flow parameter calculation: Calculate various blood flow parameters based on TIC, including:

[0271] Time to Peak (TTP);

[0272] Maximum Slope (MS);

[0273] Mean Transit Time (MTT);

[0274] Blood Flow Index (BFI);

[0275] The calculation formula of BFI is:

[0276]

[0277] here, is the maximum fluorescence intensity, is the baseline intensity and TTP is the time to peak.

[0278] Based on these actual measured blood flow parameters, the method underwent the following validation and optimization steps:

[0279] 1. Blood flow distribution verification: Compare the blood flow distribution obtained from CFD simulation with the fluorescence imaging measurement results. If the difference in a certain area exceeds 20%, the area will be marked for further investigation.

[0280] 2. Blood viscosity correction: Using the dynamic data of the fluorescent tracer, the local blood viscosity can be estimated. The estimation formula is:

[0281] ,

[0282] in, is the pressure difference, D is the blood vessel diameter, and L is the blood vessel length. This estimated viscosity value is used to update the blood viscosity parameter in the CFD model.

[0283] 3. Boundary condition optimization: Optimize the boundary condition settings of the CFD model by comparing the simulation results with the actual measured inlet / outlet flow rates.

[0284] 4. Model parameter adjustment: Use machine learning algorithms (such as Bayesian optimization) to automatically adjust key parameters of the CFD model so that the simulation results better match the actual measurement data.

[0285] Through this data-driven validation and optimization process, the method of the present invention significantly improves the accuracy and reliability of hemodynamic analysis. In particular, this method can capture individual differences and special circumstances under pathological conditions, providing a solid foundation for personalized diagnosis and treatment.

[0286] Notably, this method utilizes fluorescent tracers with full consideration for patient safety. The dose of ICG used is well below clinical safety limits, and the imaging procedure does not involve ionizing radiation, resulting in minimal additional health risks. This allows this validation step to be safely implemented in routine clinical practice.

[0287] In summary, by combining computational fluid dynamics simulations with real-world data from fluorescent tracer measurements, the method presented here creatively constructs a virtual-real pulmonary blood flow analysis system. This system not only provides highly accurate hemodynamic information but also possesses self-validation and continuous optimization capabilities, representing a significant advancement in the field of pulmonary blood flow analysis.

[0288] The above description is only a preferred specific embodiment of the present invention; however, the protection scope of the present invention is not limited thereto; any person familiar with the art who makes equivalent replacements or changes based on the scheme and improved concepts of the present invention within the scope disclosed by the present invention shall be covered by the protection scope of the present invention.

Claims

1. A pulmonary flow domain analysis method based on hemodynamics, characterized in that: include: The acquisition steps include: Acquire lung CT image data; Obtain lung anatomy data and clinical medical knowledge; Processing steps include: generating a pulmonary artery vascular tree model based on the lung CT image data; Establishing a hemodynamic model based on the pulmonary artery vascular tree model; Calculating the blood flow velocity field and pressure gradient field based on the hemodynamic model; executing a watershed watershed segmentation algorithm according to the pressure gradient field to obtain a lung watershed segmentation result; Output steps include: generating an analysis report including the lung watershed division result; Also includes dynamic adjustment steps: Calculate the velocity distribution factor, pressure drop factor and resistance gradient factor of normal blood flow in the current flow area; Calculating probability distributions of the velocity distribution factor, the pressure drop factor, and the resistance gradient factor; Based on the maximum value point of the probability distribution of the velocity distribution factor, the pressure drop factor and the resistance gradient factor, the threshold of the watershed segmentation algorithm is dynamically adjusted.

2. The method according to claim 1, characterized in that The generating of the pulmonary artery vascular tree model specifically includes: reconstructing the lung CT image data into three-dimensional data; extracting pulmonary blood vessels based on the three-dimensional data; According to the pulmonary anatomical data, identifying the bifurcation of the main pulmonary artery; A pulmonary artery vascular tree structure is generated based on the bifurcation of the pulmonary artery trunk.

3. The method according to claim 1, characterized in that The establishment of the hemodynamic model specifically includes: Set blood physical parameters, including blood density, dynamic viscosity, static viscosity, volume fraction and thermal conductivity; Set the wall conditions, flow field boundary conditions, and lung boundary conditions; Based on Darcy's law, LM equation, continuity equation and Bernoulli equation, a group of hemodynamic equations was constructed.

4. The method according to claim 3, characterized in that The calculation of the blood flow velocity field and the pressure gradient field specifically includes: Dividing the computational domain of the hemodynamic model into upstream and downstream parts; applying an inlet boundary condition to the upstream boundary; applying an outlet boundary condition to the downstream boundary; The hemodynamic equations are solved using a computational fluid dynamics (CFD) algorithm to obtain a blood flow velocity field and a pressure gradient field.

5. The method according to claim 1, wherein The execution of the watershed segmentation algorithm specifically includes: Setting a seed point in the pressure gradient field; Based on the seed point, executing a region growing algorithm; Determine the watershed boundary based on the pressure gradient threshold; Generate lung watershed division results.

6. The method according to claim 1, characterized in that It also includes abnormal blood flow identification steps: Calculate outlier discrimination parameters, including flow, density, velocity, vessel radius, and vessel length; Set the abnormality judgment threshold; identifying an abnormal blood flow pattern based on the abnormal point discrimination parameter and the abnormality judgment threshold; The abnormal blood flow pattern is added to the analysis report.

7. The method according to claim 6, characterized in that The abnormal blood flow patterns include: When the flow rate is less than the first preset threshold and the density is less than the second preset threshold, it is determined to be an embolism point; When the density is less than the second preset threshold and the blood vessel radius is less than the third preset threshold, it is determined to be a stenosis point; When the density is less than the second preset threshold and the blood vessel radius is greater than the fourth preset threshold, it is determined to be a dilation point.

8. The method according to claim 1, characterized in that The lung watershed division results include: First-order drainage basin: the area including the right ventricular pulmonary artery; Second-level basin: a branch of the first-level basin; Third-level basin: a branch of the second-level basin; Fourth-level basin: a branch of the third-level basin.

9. The method according to claim 1, characterized in that It also includes the result verification steps: Fluorescent tracer dynamic data is used to locate and quantitatively measure blood flow distribution; calculating actual blood viscosity based on the dynamic data of the fluorescent tracer; comparing the actual blood viscosity with a blood viscosity parameter in the hemodynamic model; According to the comparison results, the parameters of the hemodynamic model are adjusted.

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