Pulmonary vessel perfusion analysis method based on drainage basin dynamics

Abstract

The invention relates to the technical field of medical image processing, in particular to a pulmonary blood vessel perfusion analysis method based on drainage basin dynamics, which comprises the following steps: acquiring a pulmonary artery blood vessel tree and a pulmonary artery CT film; extracting the pulmonary artery blood vessel tree based on the pulmonary artery blood vessel tree and the pulmonary artery CT film; based on the pulmonary artery blood vessel tree, pressure gradient fields in the pulmonary artery blood vessel tree and on the wall of the pulmonary artery blood vessel tree are calculated; extracting a pulmonary vein basin based on the pressure gradient field; based on the pulmonary vein drainage basin, perfusion analysis is achieved in combination with dynamic hemodynamic simulation; on the basis of perfusion analysis results, lung tissue blood supply conditions are evaluated, a multi-stage connected domain detection method is adopted, the method can more accurately extract a complex pulmonary artery blood vessel tree structure, the actual distribution condition of blood flow can be more truly reflected on the basis of a pressure gradient field drainage basin division method, and dynamic blood flow simulation is introduced, so that the dynamic blood flow simulation effect is improved. By means of the method, perfusion changes in the breathing process can be comprehensively captured, and more comprehensive diagnosis information is provided.

Description

Technical Field

[0001] The present invention relates to the technical field of medical image processing, and more specifically, to a pulmonary vascular perfusion analysis method based on flow field dynamics. Background Art

[0002] With the rapid development of medical imaging technology, pulmonary vascular perfusion analysis plays an increasingly important role in clinical diagnosis and treatment. Traditional pulmonary vascular perfusion analysis methods mainly rely on static CT or magnetic resonance imaging to evaluate pulmonary blood flow by observing the distribution of contrast agents. However, these methods often cannot accurately reflect the dynamic changes of pulmonary blood flow during breathing, resulting in certain limitations in diagnostic results.

[0003] In recent years, with the advancement of computer technology and medical image processing technology, pulmonary vascular perfusion analysis methods based on image segmentation and hemodynamic simulation have gradually emerged. These methods attempt to simulate and analyze pulmonary blood perfusion more accurately by performing complex post-processing on CT or magnetic resonance images and combining hemodynamic models. However, these existing methods still have some obvious shortcomings.

[0004] First, most existing methods still rely on traditional image segmentation algorithms, such as threshold segmentation or region growing methods, in the vascular segmentation stage. These algorithms often have problems with over-segmentation or under-segmentation when dealing with complex pulmonary vascular structures, making it difficult to accurately extract the complete pulmonary artery vascular tree. Second, in terms of hemodynamic simulation, existing methods usually use simplified fluid mechanics models, ignoring the elastic properties of the vascular wall and the non-Newtonian fluid properties of blood, resulting in a large deviation between the simulation results and the actual physiological conditions.

[0005] In addition, existing methods often use simple geometric divisions or empirical divisions based on anatomical structures when analyzing the pulmonary venous basin, which is difficult to accurately reflect the actual blood flow distribution. This rough division method may lead to incorrect assessment of the perfusion conditions of different lobes, affecting the accuracy of clinical diagnosis.

[0006] Finally, most existing pulmonary vascular perfusion analysis methods are limited to static analysis, which makes it difficult to capture the dynamic changes of pulmonary blood flow during breathing. This static analysis method cannot fully reflect the pulmonary perfusion of patients under different respiratory states, which may lead to the neglect of certain pathological conditions.

[0007] In view of the problems existing in the prior art, a method for analyzing pulmonary vascular perfusion more accurately and comprehensively is urgently needed. The pulmonary vascular perfusion analysis method based on flow field dynamics proposed in the present invention is designed to solve these technical problems. Summary of the invention

[0008] The present invention aims to solve the above technical problems and proposes a pulmonary vascular perfusion analysis method based on watershed dynamics. The method of the present invention realizes a comprehensive and dynamic analysis of pulmonary vascular perfusion by innovatively introducing the concept of watershed dynamics, combining advanced image processing technology and precise hemodynamic simulation. This method not only overcomes the shortcomings of the prior art in terms of vascular segmentation, watershed division and dynamic simulation, but also provides richer and more accurate perfusion information, providing strong support for clinical diagnosis and treatment decisions.

[0009] In order to solve the above technical problems, the present invention adopts the following technical solutions: A pulmonary vascular perfusion analysis method based on watershed dynamics, comprising: The acquisition steps include: Obtain CT images of the pulmonary artery tree and pulmonary arteries; Processing steps include: Extracting the pulmonary artery vascular tree based on the pulmonary artery vascular tree and the pulmonary artery CT angiography; Based on the pulmonary artery vascular tree, calculating the pressure gradient field inside the pulmonary artery vascular tree and on the vessel wall; extracting a pulmonary vein watershed based on the pressure gradient field; Based on the pulmonary venous flow area, a perfusion analysis is realized in combination with a dynamic hemodynamic simulation; Output steps include: Based on the perfusion analysis results, the blood supply to the lung tissue is assessed.

[0010] Preferably, the extracting of the pulmonary artery vascular tree specifically includes: Segmenting the pulmonary artery tree region based on the pulmonary artery tree and the pulmonary artery CT angiography image; extracting a candidate region around the pulmonary artery vascular tree region; Extracting seed pixels in the candidate area; Based on the seed pixel points, region growing is performed to obtain a connected domain of the pulmonary artery vascular tree; Performing cluster marking on the connected domains of the pulmonary artery vascular tree; The largest connected region is extracted from the cluster labeled region as the pulmonary artery vascular tree.

[0011] Preferably, the calculating of the pressure gradient field inside the pulmonary artery tree and on the wall specifically includes: Using finite element analysis method to mesh the pulmonary artery tree; Set boundary conditions, apply fluid boundary conditions and wall boundary conditions; Compute the pressure gradient vector, momentum flux, fluid pressure distribution, and total pressure for each cell; Calculate the momentum derivative of blood flow at the vessel wall.

[0012] Preferably, the extracting of the pulmonary vein watershed specifically includes: Extract the initial streamline set of blood flow at the current time point; Calculating a first pressure gradient between adjacent streamline clusters in the initial streamline set; Calculate a second pressure gradient formed by connecting the current point with other points in the initial streamline set; comparing an absolute value of a difference between the first pressure gradient and the second pressure gradient; Based on the comparison result, determining whether the point is in a blood flow transition region or a blood flow stability region; Based on the determination result, the shunt boundary of the pulmonary vein basin is determined.

[0013] Preferably, the method of implementing perfusion analysis in combination with dynamic hemodynamics simulation specifically includes: The entrance of the pulmonary vein is used as the exit point to obtain the streamline information of the previous frame; The lowest pressure point on the wall of the pulmonary artery vascular tree was searched as the downstream point; Taking the downstream point as the starting point of the next frame, determining a new downstream point on the wall surface of the next frame; Repeat the above process until the downstream point of the next frame cannot be found on the new frame; The position where the pressure gradient of the blood vessel wall is zero is calculated as the end point; The entire streamline formed from the initial streamline to the end point on the pulmonary artery wall is extracted as the perfusion path of the pulmonary artery vascular tree.

[0014] Preferably, the evaluating the blood supply to lung tissue specifically includes: Multiple seed points were randomly set outside the boundary of the pulmonary vein watershed; Using the determined perfusion path as the seed point, the seed point inside the vascular tree is marked by the marking search method; Extract all perfusion paths containing the marked points to obtain a perfusion path set; Calculate the average blood flow rate of each perfusion pathway; Select the blood flow at different lung locations to calculate the range of lung ischemia; The range of lung ischemia at different time points is calculated to obtain the dynamic range of lung ischemia during the patient's breathing process.

[0015] Preferably, the method further comprises performing denoising on the pulmonary artery CT angiography film, wherein the denoising comprises: Based on the pulmonary artery CT angiography, extracting the largest connected area in the CT angiography; Segmenting the maximum connected area to obtain a vascular tree area; A background is randomly selected outside the pulmonary artery region and assigned a value so that a binary image is formed inside and outside the maximum connected region; The boundary of the binary image is smoothed to obtain a denoised pulmonary artery vascular tree region.

[0016] Preferably, the method further comprises performing frequency reduction processing on the pulmonary artery CT angiography film, wherein the frequency reduction processing comprises: Perform frequency reduction processing on the denoised pulmonary artery vascular tree area; Perform edge detection on the pulmonary artery vascular tree area after frequency reduction processing; Based on the watershed algorithm, the grayscale image after edge detection is segmented by watershed to obtain each connected domain image; Region growing is performed on each connected domain image within the vascular region to generate a pulmonary artery CT image.

[0017] Preferably, the frequency reduction process adopts a minimum mean filtering method.

[0018] Preferably, in the step of extracting the pulmonary vein watershed, the step further includes: Set a threshold; filtering the pressure gradient calculation results according to the threshold value, and retaining only the points where the pressure is greater than the pressure gradient threshold value; The retained points are used as the outer boundaries of the blood flow domain; Taking the outer boundary as the center, the pressure gradient at the boundary is gradually determined to be zero by using a dichotomy method, thereby determining a boundary line; The boundary line is used as the boundary line of the pulmonary venous blood flow area; Randomly select a seed point outside the boundary line; Connect each seed point on the boundary line to the nearest point on the pulmonary artery wall; Search outside the boundary until no downstream point can be found for the next frame; The position where the pressure gradient of the blood vessel wall is zero is calculated as the end point; The entire streamline formed from the initial streamline to the end point on the vessel wall is extracted as the perfusion path of the pulmonary artery vascular tree.

[0019] The method of the present invention has the following significant technical effects: The method of the present invention has made significant progress in improving the accuracy of pulmonary vascular segmentation, optimizing watershed division, and realizing dynamic perfusion analysis. By adopting a multi-level connected domain detection method, the present method can more accurately extract the complex pulmonary artery vascular tree structure. The watershed division method based on the pressure gradient field can more realistically reflect the actual distribution of blood flow. The introduction of dynamic blood flow simulation enables the present method to fully capture the perfusion changes during the respiratory process and provide more comprehensive diagnostic information.

[0020] In addition, the method of the present invention has also made important innovations in perfusion assessment. By introducing an assessment method based on perfusion pathways, the present invention can quantitatively analyze the blood supply of different lung regions, providing a more objective and accurate basis for clinical diagnosis. This assessment method can not only identify potential lesion areas, but also evaluate the treatment effect, which is of great significance for guiding clinical treatment.

[0021] In general, the pulmonary vascular perfusion analysis method based on watershed dynamics proposed in the present invention realizes a more accurate, comprehensive and dynamic analysis of pulmonary vascular perfusion through the organic combination of multiple technological innovations. This method not only overcomes many limitations of existing technologies, but also provides more reliable and rich information support for clinical diagnosis and treatment. It is expected to play an important role in improving the accuracy of lung disease diagnosis and optimizing the formulation of treatment plans, and has broad clinical application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The figure is an overall flow chart of the method of the present invention.

[0023] Figure 2 The flowchart of the present invention is to extract the pulmonary artery vascular tree. DETAILED DESCRIPTION

[0024] like Figure 1-2 As shown, the present invention proposes a pulmonary vascular perfusion analysis method based on flow field dynamics. The method makes full use of fluid mechanics and medical image processing technology to achieve accurate analysis of pulmonary vascular perfusion. The technical solution of the present invention will be described in detail below.

[0025] First, the method of the present invention includes an acquisition step, a processing step and an output step. In the acquisition step, the pulmonary artery vascular tree and the pulmonary artery CT angiography are acquired. These raw data lay the foundation for subsequent analysis.

[0026] Next, in the processing step, the method of the present invention extracts the pulmonary artery vascular tree based on the acquired pulmonary artery vascular tree and the pulmonary artery CT angiography. This step is crucial for accurately analyzing the pulmonary vascular structure. Preferably, the present invention uses a multi-level connected domain detection method to extract the pulmonary artery vascular tree, which can better process the complex pulmonary vascular branching structure than the traditional method.

[0027] After extracting the pulmonary artery vascular tree, the method of the present invention calculates the pressure gradient field inside the pulmonary artery vascular tree and the wall based on the extracted pulmonary artery vascular tree. This step is the key to achieve accurate perfusion analysis. The present invention preferably uses finite element analysis to calculate the pressure gradient field. This method can fully consider the elastic properties of the vascular wall and the non-Newtonian fluid properties of the blood, thereby obtaining a more accurate pressure distribution.

[0028] After obtaining the pressure gradient field, the method of the present invention extracts the pulmonary venous flow area based on the calculated pressure gradient field. This step is of great significance for distinguishing the perfusion areas of different lobes. The present invention innovatively proposes a method based on streamline clusters and pressure gradient analysis to determine the flow area boundary, which can more accurately reflect the actual blood flow situation.

[0029] Subsequently, the method of the present invention realizes perfusion analysis based on the extracted pulmonary venous flow area and combined with dynamic hemodynamic simulation. This step takes into account the changes in hemodynamics during breathing and can obtain a more realistic perfusion situation. The present invention uses a streamline tracing-based method to analyze the perfusion path, which can intuitively display the blood flow process in the lungs.

[0030] Finally, in the output step, the method of the present invention evaluates the blood supply of lung tissue based on the obtained perfusion analysis results. This step provides an important basis for clinical diagnosis and treatment. The present invention proposes an evaluation method based on perfusion pathways, which can quantitatively analyze the blood supply of different lung regions.

[0031] Next, the present invention will describe in detail the specific steps of extracting the pulmonary artery vascular tree. First, based on the acquired pulmonary artery vascular tree and the pulmonary artery CT angiography film, the pulmonary artery vascular tree region is segmented. This step is usually implemented using image segmentation algorithms such as threshold segmentation or region growing. Preferably, the present invention uses an adaptive threshold segmentation algorithm, and the threshold can be automatically adjusted according to the grayscale distribution of the image, and the typical threshold range is 100-200 HU (Hounsfield Unit).

[0032] After segmenting the pulmonary artery vascular tree region, the method of the present invention extracts a candidate region around the segmented pulmonary artery vascular tree region. Preferably, the size of the extracted candidate region is 3×3 pixels. This size is selected based on experience and can reduce the amount of calculation while ensuring the extraction accuracy.

[0033] Subsequently, the method of the present invention extracts seed pixels in the extracted candidate region. Usually, pixels with a gray value of zero are selected as seed pixels. This is because in a CT image, the HU value of air is close to -1000, and after normalization, its gray value is close to zero.

[0034] Based on the extracted seed pixel points, the method of the present invention performs region growing to obtain the connected domain of the pulmonary artery vascular tree. During the region growing process, the growth threshold is usually set to ±10% of the gray value of the seed point. The selection of this range can effectively control the growth process and avoid overgrowth or undergrowth.

[0035] After obtaining the connected domain, the method of the present invention performs clustering and marking on the obtained pulmonary artery vascular tree connected domain. Preferably, a K-means clustering algorithm is used, and the number of clusters K is usually set to 3-5. This range is selected based on the anatomical structure of the pulmonary blood vessels, and can better distinguish the main blood vessel branches.

[0036] Finally, the method of the present invention extracts the largest connected domain from the marked cluster region as the pulmonary artery vascular tree. This step can effectively remove some small isolated regions and obtain a complete pulmonary artery vascular tree structure.

[0037] Next, the present invention will describe in detail the specific steps of calculating the pressure gradient field inside the pulmonary artery tree and the wall. First, the extracted pulmonary artery tree is meshed using the finite element analysis method. Preferably, a tetrahedral mesh is used, and the mesh size is usually set to 0.1-0.5 mm. The selection of this range can achieve a balance between ensuring calculation accuracy and calculation efficiency.

[0038] After the meshing is completed, the method of the present invention sets boundary conditions, applies fluid boundary conditions and wall boundary conditions. Usually, a constant flow boundary condition is set at the entrance of the pulmonary artery, and the flow value can be set according to the patient's physiological parameters, with a typical value of 4-6 L / min. A pressure boundary condition is set at the outlet of the pulmonary vein, and the pressure value is usually set to 0-5 mmHg.

[0039] Subsequently, the method of the present invention calculates the pressure gradient vector, momentum flux, fluid pressure distribution and total pressure for each unit. These calculations are based on the Navier-Stokes equations, which are in the following form: , , in, is the blood density, is the blood velocity vector, For time, For pressure, is blood viscosity, is the volume force. Finally, the method of the present invention calculates the momentum derivative of blood flow at the vessel wall. This step is of great significance for evaluating the shear stress of the vessel wall. The calculation formula of the momentum derivative is as follows: , in, is the wall normal vector, , , are the components of velocity in the x, y, and z directions respectively.

[0040] Through the above steps, the method of the present invention can obtain a detailed pressure gradient field inside the pulmonary artery vascular tree and the wall, laying the foundation for subsequent perfusion analysis. This analysis method based on flow field dynamics can more accurately reflect the actual situation of pulmonary blood perfusion compared to traditional methods, and provides important support for clinical diagnosis and treatment. Next, the present invention will describe in detail the specific steps of extracting the pulmonary venous flow field. First, the method extracts the initial streamline set of blood flow at the current time point. This step usually uses a numerical integration method, such as the fourth-order Runge-Kutta method, to track the trajectory of blood particles. Preferably, in one embodiment of the present invention, the number of initial streamlines is set to 100-500, and this range can achieve a good balance between computational efficiency and result accuracy.

[0041] After obtaining the initial streamline set, the method of the present invention calculates the first pressure gradient between adjacent streamline clusters in the initial streamline set. The calculation formula of the pressure gradient is as follows: , in, For pressure, Represent three spatial directions respectively.

[0042] Subsequently, the method calculates the second pressure gradient formed by the current point and the lines connecting other points in the initial streamline set. The purpose of this step is to capture the local pressure change. Preferably, in an embodiment of the present invention, points within a range of 5-10 mm from the current point are selected for calculation. The selection of this range can effectively reflect the local pressure change without introducing too much noise.

[0043] Next, the method of the present invention compares the absolute value of the difference between the first pressure gradient and the second pressure gradient. This step is the key to determine the blood flow state. Specifically, the method adopts the following judgment criteria: ,

[0044] in, is the first pressure gradient, is the second pressure gradient, Preferably, The value of is set to 0.1 mmHg / mm. This threshold is selected based on a large amount of experimental data and can effectively distinguish the stable domain and transition domain of blood flow.

[0045] Based on the above comparison results, the method of the present invention determines whether the point is in the blood flow transition domain or the blood flow stability domain. If the absolute value of the difference is less than or equal to the threshold, the point is judged to be in the blood flow transition domain; otherwise, the point is judged to be in the blood flow stability domain. This judgment method can effectively identify different states of blood flow and provide an important basis for subsequent flow domain analysis.

[0046] Finally, based on the judgment result, the method determines the shunt boundary of the pulmonary vein flow basin. Preferably, in one embodiment of the present invention, a level set method is used to track the evolution of the shunt boundary. Level set function The evolution equation of is as follows: , in, is the velocity field, which can be constructed based on the pressure gradient information.

[0047] Through the above steps, the method of the present invention can accurately extract the pulmonary vein watershed, laying the foundation for subsequent perfusion analysis. Compared with traditional methods, this analysis method based on watershed dynamics can more accurately divide the perfusion areas of different lobes, which helps to improve the accuracy of diagnosis.

[0048] Next, the present invention will describe in detail the specific steps of realizing perfusion analysis in combination with dynamic hemodynamic simulation. First, the method uses the entrance of the pulmonary vein as the exit point to obtain the streamline information of the previous frame. This step provides the initial conditions for dynamic simulation. Preferably, in an embodiment of the present invention, a particle tracking method is used to obtain streamline information, and the number of particles is usually set to 1000-5000. This range can achieve a good balance between computational efficiency and result accuracy.

[0049] Subsequently, the method of the present invention searches for the lowest pressure point on the wall of the pulmonary artery vascular tree as the downstream point. This step is based on the principle of fluid mechanics, and the lowest pressure point usually corresponds to the position with the fastest blood flow velocity. Preferably, the method uses a gradient descent algorithm for searching, and the search step size is set to 0.1-0.5 mm, which can ensure the accuracy and efficiency of the search.

[0050] Next, the method uses the downstream point as the starting point of the next frame and determines a new downstream point on the wall surface of the next frame. This step realizes the continuity of dynamic simulation. Preferably, in one embodiment of the present invention, the frame interval time is set to 0.01-0.05 seconds, which can better capture the dynamic changes of blood flow.

[0051] The method of the present invention repeats the above process until the downstream point of the next frame cannot be found on the new frame. This termination condition ensures the integrity of the simulation process. Preferably, when no new downstream point can be found for 5 consecutive frames, it is considered that the termination condition has been reached.

[0052] When the termination condition is reached, the method calculates the position where the pressure gradient of the blood vessel wall is zero as the end point. This step is based on fluid mechanics theory. The position where the pressure gradient is zero usually corresponds to the position where the blood flow is stagnant or bifurcated. Specifically, the calculation formula of the pressure gradient is as follows: , in, It is a small positive number, usually set to 0.01 mmHg / mm.

[0053] Finally, the method of the present invention extracts the entire streamline formed from the initial streamline to the end point on the pulmonary artery wall as the perfusion path of the pulmonary artery tree. These perfusion paths fully reflect the flow of blood in the lungs and provide an important basis for evaluating the blood supply to lung tissue.

[0054] Through the above steps, the method of the present invention realizes the organic combination of dynamic blood flow simulation and perfusion analysis. Compared with the static analysis method, it can more realistically reflect the changes in pulmonary blood perfusion during breathing, which helps to improve the accuracy and comprehensiveness of diagnosis.

[0055] Next, the present invention will describe in detail the specific steps of evaluating the blood supply of lung tissue. First, the method randomly sets multiple seed points outside the boundary of the pulmonary vein basin. These seed points will serve as reference points for evaluating the blood supply. Preferably, in one embodiment of the present invention, the number of seed points is set to 100-500, which can achieve a good balance between computational efficiency and evaluation accuracy.

[0056] Subsequently, the method of the present invention uses the determined perfusion path as a seed point and marks the seed point inside the vascular tree by a marking search method. This step can effectively identify different perfusion areas. Preferably, the method uses a breadth-first search algorithm for marking, and the search radius is set to 1-3 mm, which can better cover the local vascular structure.

[0057] Next, the method extracts all perfusion paths containing the marker points to obtain a set of perfusion paths. These perfusion path sets fully reflect the flow of blood in the lungs. Preferably, in an embodiment of the present invention, the perfusion paths are smoothed using a cubic spline interpolation method, and the control point spacing is set to 0.5-1 mm, which can strike a balance between maintaining the path shape and reducing noise.

[0058] Then, the method of the present invention calculates the average blood flow rate of each perfusion path. The calculation of blood flow rate is based on Poiseuille's law: , in, is the average flow velocity, is the vessel radius, is blood viscosity, is the pressure difference, is the blood vessel length.

[0059] Next, the method selects blood flow at different lung locations to calculate the range of lung ischemia. Preferably, in one embodiment of the present invention, the area where the blood flow is 50% lower than the normal value is defined as the ischemic area. The selection of this threshold is based on a large amount of clinical data and can effectively identify potential lesion areas.

[0060] Finally, the method of the present invention calculates the range of lung ischemia at different time points to obtain the dynamic range of lung ischemia during the patient's breathing process. This step can fully reflect the patient's lung blood supply during the entire respiratory cycle. Preferably, the method performs sampling and analysis on 10-20 respiratory time points, and this sampling frequency can better capture the changes in blood supply during the respiratory process.

[0061] Through the above steps, the method of the present invention realizes a comprehensive evaluation of the blood supply to the lung tissue. Compared with the traditional static analysis method, this evaluation method based on dynamic perfusion analysis can more accurately reflect the actual physiological state of the patient, and provides an important basis for clinical diagnosis and treatment. Next, the present invention will describe in detail the specific steps of down-converting the pulmonary artery CT film. First, the method performs down-converting processing on the pulmonary artery vascular tree area after denoising. The purpose of this step is to reduce the high-frequency noise in the image while retaining important structural information. Preferably, in one embodiment of the present invention, a Gaussian low-pass filter is used for down-converting processing, and the standard deviation σ of the filter is set to 0.5-1.5 pixels. The selection of this range can achieve a good balance between the denoising effect and the retention of details.

[0062] Subsequently, the method of the present invention performs edge detection on the pulmonary artery vascular tree region after the frequency reduction processing. Edge detection is an important basis for subsequent segmentation. Preferably, the method adopts the Canny edge detection operator, and its mathematical expression is as follows: , in, is the image grayscale function, is the edge strength. The low threshold of the Canny operator is usually set to 1 / 2 or 1 / 3 of the high threshold. This setting can effectively detect the main edges and suppress noise.

[0063] Next, the method of the present invention performs watershed segmentation on the grayscale image after edge detection based on the watershed algorithm to obtain each connected domain image. The watershed algorithm can effectively process complex vascular branch structures. Preferably, in an embodiment of the present invention, a marker-controlled watershed algorithm is used, and the selection of marker points is based on local minimum values. This method can effectively avoid the problem of over-segmentation.

[0064] Finally, the method performs region growing on each connected domain image within the vascular region to generate a pulmonary artery CT angiogram. During the region growing process, the growth criterion is usually based on the pixel grayscale value and spatial continuity. Preferably, in one embodiment of the present invention, the growth threshold is set to ±15% of the seed point grayscale value. This range can effectively control the growth process and avoid missed segmentation or over-segmentation.

[0065] Through the above steps, the method of the present invention realizes effective frequency reduction processing of pulmonary artery CT angiography. This processing method can not only effectively remove image noise, but also retain important vascular structure information, providing high-quality image data for subsequent perfusion analysis.

[0066] Next, the present invention will describe in detail the minimum mean filtering method used in the frequency reduction process. Minimum mean filtering is a nonlinear filtering method that can effectively remove impulse noise in an image while maintaining the edge information of the image. Specifically, the mathematical expression of minimum mean filtering is as follows: , in, is the original image, is the filtered image, is the filter window, is the number of pixels in the window. Preferably, in the embodiment of the present invention, the filter window size is set to 3×3 or 5×5 pixels. The selection of this window size can achieve a good balance between denoising effect and computational efficiency.

[0067] The core idea of ​​minimum mean filtering is to calculate the mean of multiple sub-windows within the filter window, and then select the smallest mean as the new value of the central pixel. This method can effectively remove large positive impulse noise while maintaining the overall structure of the image.

[0068] It is worth noting that the minimum mean filter has special advantages when processing CT images. Since blood vessels in CT images usually appear as high-brightness areas, while the grayscale values ​​of surrounding tissues are low, the minimum mean filter can effectively preserve the vascular structure while smoothing the surrounding tissues. This is of great significance for subsequent vascular segmentation and perfusion analysis.

[0069] Finally, the present invention will describe in detail some key operations in the step of extracting the pulmonary venous flow area. First, the method sets a threshold. This threshold is used to filter the pressure gradient calculation results and is a key parameter for determining the blood flow area boundary. Preferably, in one embodiment of the present invention, the threshold is set to 20% to 30% of the average pressure gradient. The selection of this range is based on a large amount of experimental data, which can effectively distinguish the main blood flow area from the edge area.

[0070] Subsequently, the method of the present invention filters the pressure gradient calculation results according to the threshold value, and only retains the points where the pressure is greater than the pressure gradient threshold value. This step can effectively remove some noise points and weak blood flow areas. Preferably, the method uses morphological operations to further process the filtered results, such as using a structure element with a radius of 2-3 pixels for opening operations, which can remove some small isolated areas.

[0071] Next, the method uses the retained points as the outer boundary of the blood flow domain. These points constitute the preliminary flow domain boundary. Preferably, in an embodiment of the present invention, spline interpolation is performed on these boundary points to obtain a smooth boundary curve. During interpolation, the distance between control points is usually set to 3-5 pixels, which can strike a balance between maintaining boundary details and smoothness.

[0072] Then, the method of the present invention uses the dichotomy method to gradually determine that the pressure gradient at the boundary is zero, and determines the boundary line, with the outer boundary as the center. This step can accurately locate the actual boundary of the watershed. Preferably, the method sets the number of iterations of the dichotomy method to 10-15 times, which can achieve a good balance between computational efficiency and accuracy.

[0073] Finally, the method uses the boundary line as the boundary line of the pulmonary venous blood flow area, and randomly selects seed points outside the boundary line. These seed points will be used for subsequent perfusion path analysis. Preferably, in one embodiment of the present invention, the number of seed points is set to 1 / 10 to 1 / 5 of the length of the boundary line. This range can ensure sufficient sampling density without introducing excessive computational burden.

[0074] Through the above steps, the method of the present invention realizes accurate pulmonary vein flow area extraction. Compared with the traditional image segmentation method, this method based on pressure gradient and boundary optimization can more accurately reflect the actual blood flow distribution and provide a reliable basis for subsequent perfusion analysis.

[0075] The above description is only a preferred specific implementation manner of the present invention; however, the protection scope of the present invention is not limited thereto; any person familiar with the art who, within the scope disclosed by the present invention, makes equivalent replacements or changes based on the scheme and improved concepts of the present invention shall be covered within the protection scope of the present invention.

Claims

1. A pulmonary vascular perfusion analysis method based on watershed dynamics, characterized in that: include: The acquisition steps include: Obtain CT images of the pulmonary artery tree and pulmonary arteries; Processing steps include: Extracting the pulmonary artery vascular tree based on the pulmonary artery vascular tree and the pulmonary artery CT angiography; Based on the pulmonary artery vascular tree, calculating the pressure gradient field inside the pulmonary artery vascular tree and on the vessel wall; extracting a pulmonary vein watershed based on the pressure gradient field; Based on the pulmonary venous flow area, a perfusion analysis is realized in combination with a dynamic hemodynamic simulation; Output steps include: Based on the perfusion analysis results, the blood supply to the lung tissue is assessed.

2. The method according to claim 1, characterized in that The extracting of the pulmonary artery vascular tree specifically includes: Segmenting the pulmonary artery tree region based on the pulmonary artery tree and the pulmonary artery CT angiography image; extracting a candidate region around the pulmonary artery vascular tree region; Extracting seed pixels in the candidate area; Based on the seed pixel points, region growing is performed to obtain a connected domain of the pulmonary artery vascular tree; Performing cluster marking on the connected domains of the pulmonary artery vascular tree; The largest connected region is extracted from the cluster labeled region as the pulmonary artery vascular tree.

3. The method according to claim 1, characterized in that The calculation of the pressure gradient field inside the pulmonary artery tree and on the wall specifically includes: Using finite element analysis method to mesh the pulmonary artery tree; Set boundary conditions, apply fluid boundary conditions and wall boundary conditions; Compute the pressure gradient vector, momentum flux, fluid pressure distribution, and total pressure for each cell; Calculate the momentum derivative of blood flow at the vessel wall.

4. The method according to claim 1, characterized in that: The extracting of the pulmonary vein watershed specifically includes: Extract the initial streamline set of blood flow at the current time point; Calculating a first pressure gradient between adjacent streamline clusters in the initial streamline set; Calculate a second pressure gradient formed by connecting the current point with other points in the initial streamline set; comparing an absolute value of a difference between the first pressure gradient and the second pressure gradient; Based on the comparison result, determining whether the point is in a blood flow transition region or a blood flow stability region; Based on the determination result, the shunt boundary of the pulmonary vein basin is determined.

5. The method according to claim 1, characterized in that The method of realizing perfusion analysis by combining dynamic hemodynamics simulation specifically includes: The entrance of the pulmonary vein is used as the exit point to obtain the streamline information of the previous frame; The lowest pressure point on the wall of the pulmonary artery vascular tree was searched as the downstream point; Taking the downstream point as the starting point of the next frame, determining a new downstream point on the wall surface of the next frame; Repeat the above process until the downstream point of the next frame cannot be found on the new frame; The position where the pressure gradient of the blood vessel wall is zero is calculated as the end point; The entire streamline formed from the initial streamline to the end point on the pulmonary artery wall is extracted as the perfusion path of the pulmonary artery vascular tree.

6. The method according to claim 1, characterized in that The evaluation of the blood supply to lung tissue specifically includes: Multiple seed points were randomly set outside the boundary of the pulmonary vein watershed; Using the determined perfusion path as the seed point, the seed point inside the vascular tree is marked by the marking search method; Extract all perfusion paths containing the marked points to obtain a perfusion path set; Calculate the average blood flow rate of each perfusion pathway; Select the blood flow at different lung locations to calculate the range of lung ischemia; The range of lung ischemia at different time points is calculated to obtain the dynamic range of lung ischemia during the patient's breathing process.

7. The method according to claim 1, characterized in that The method further includes performing denoising on the pulmonary artery CT angiography film, wherein the denoising includes: Based on the pulmonary artery CT angiography, extracting the largest connected area in the CT angiography; Segmenting the maximum connected area to obtain a vascular tree area; A background is randomly selected outside the pulmonary artery region and assigned a value so that a binary image is formed inside and outside the maximum connected region; The boundary of the binary image is smoothed to obtain a denoised pulmonary artery vascular tree region.

8. The method according to claim 1, characterized in that The method further includes performing frequency reduction processing on the pulmonary artery CT angiography film, wherein the frequency reduction processing includes: Perform frequency reduction processing on the denoised pulmonary artery vascular tree area; Perform edge detection on the pulmonary artery vascular tree area after frequency reduction processing; Based on the watershed algorithm, the grayscale image after edge detection is segmented by watershed to obtain each connected domain image; Region growing is performed on each connected domain image within the vascular region to generate a pulmonary artery CT image.

9. The method according to claim 8, characterized in that The frequency reduction process adopts a minimum mean filtering method.

10. The method according to claim 1, characterized in that In the step of extracting the pulmonary vein watershed, the method further includes: Set a threshold; filtering the pressure gradient calculation results according to the threshold value, and retaining only the points where the pressure is greater than the pressure gradient threshold value; The retained points are used as the outer boundaries of the blood flow domain; Taking the outer boundary as the center, the pressure gradient at the boundary is gradually determined to be zero by using a dichotomy method, thereby determining a boundary line; The boundary line is used as the boundary line of the pulmonary venous blood flow area; Randomly select a seed point outside the boundary line; Connect each seed point on the boundary line to the nearest point on the pulmonary artery wall; Search outside the boundary until no downstream point can be found for the next frame; The position where the pressure gradient of the blood vessel wall is zero is calculated as the end point; The entire streamline formed from the initial streamline to the end point on the vessel wall is extracted as the perfusion path of the pulmonary artery vascular tree.

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