Tumor state prediction method, system, equipment and medium

Abstract

The invention relates to the technical field of medical image processing, and discloses a tumor state prediction method, system and device and a medium, and the method comprises the steps: obtaining a CT image group of a lung of a target object in a current respiratory cycle, and constructing a three-dimensional tumor model of a lung tumor based on the CT image group; the lung tumor has a real volume and a real centroid coordinate; performing finite element analysis to obtain a finite element tumor model, and calculating the simulated volume and simulated centroid coordinates of the lung tumor; comparing to obtain a volume retention ratio and a gravity center offset; performing motion state prediction on the lung tumor in the next respiratory cycle to obtain an initial motion state parameter of the lung tumor in the next respiratory cycle; if it is judged that the motion state prediction quality of the lung tumor meets the preset motion state prediction quality based on the volume retention ratio and the gravity center offset, the initial motion state parameters serve as the target motion state parameters, the motion state of the lung tumor is obtained through prediction, and the accuracy and accuracy of radiotherapy irradiation are improved.

Description

Technical Field

[0001] The present application relates to the field of medical image processing technology, and in particular to a tumor status prediction method, system, device and medium. Background Art

[0002] The mechanical properties of the lungs are spatially non-uniform, and the physiological structure of the thorax also restricts the movement of the lungs. Therefore, the movement behaviors of lung tumors in the lungs, such as growth, expansion, or metastasis, are actually anisotropic, showing different characteristics in different directions. However, in related technologies, clinical practice often uses uniformly distributed outward expansion boundaries to describe the movement of lung tumors, which makes the prediction of the movement state of lung tumors inaccurate, and thus makes it difficult for doctors to conform to the actual tumor area when formulating radiation plans. A new tumor state prediction method is needed. Summary of the invention

[0003] The present application provides a tumor status prediction method, system, device and medium, which predicts the movement state of lung tumors by modeling and simulating lung tumors. It solves the technical problem of inaccurate prediction of the movement state of lung tumors in related technologies, and achieves the technical effect of accurately predicting the movement state of lung tumors to improve the accuracy and correctness of radiotherapy irradiation.

[0004] In order to achieve the above objectives, the main technical solutions adopted in this application include:

[0005] In a first aspect, an embodiment of the present application provides a method for predicting tumor status, comprising:

[0006] Acquire a CT image group of the target object's lungs in the current respiratory cycle, and construct a three-dimensional tumor model of the lung tumor based on the CT image group; wherein the lung tumor has a real volume and real centroid coordinates;

[0007] Performing finite element analysis on the three-dimensional tumor model to obtain a finite element tumor model, and calculating a simulated volume and a simulated centroid coordinate of the lung tumor based on the finite element tumor model;

[0008] Comparing the real volume with the simulated volume to obtain a volume retention ratio; and comparing the real center of mass coordinates with the simulated center of mass coordinates to obtain a center of gravity offset;

[0009] Using the finite element tumor model to predict the motion state of the lung tumor in the next respiratory cycle, so as to obtain initial motion state parameters of the lung tumor in the next respiratory cycle;

[0010] If it is determined based on the volume retention ratio and the center of gravity offset that the motion state prediction quality of the lung tumor meets the preset motion state prediction quality, the initial motion state parameters are used as target motion state parameters of the lung tumor in the next respiratory cycle.

[0011] This application uses biophysical modeling and simulation to convert a two-dimensional 4D-CT image of a patient's lung into a three-dimensional tumor model with a sense of space and multi-angle observation and measurement, and performs finite element analysis to simulate the state of the lung tumor under respiratory motion to predict the motion state of the lung tumor. By ensuring that the motion state prediction quality meets the preset motion state prediction quality, the accuracy of the target motion state parameters is guaranteed.

[0012] According to the specific conditions of different target objects, personalized radiotherapy plans are designed to predict the movement trajectory and range of lung tumors of different target objects, and reduce the boundaries of the radiotherapy target area in a timely manner to protect normal tissues. This ensures that lung tumors receive sufficient radiation while reducing damage to normal tissues in the lungs, thereby improving the accuracy and correctness of radiotherapy irradiation.

[0013] This application omits the complex rib movement of the chest cavity, does not have the complex segmentation of the diaphragm, does not consider the influence of posture and gravity load, reduces the modeling difficulty and modeling time cost, ensures the timeliness of radiotherapy treatment, and overcomes the problem of difficulty in modeling and low simulation accuracy in related technologies that lung tumors are extracted by simulating lung respiratory movement.

[0014] Optionally, constructing a three-dimensional tumor model of a lung tumor based on the CT image group includes:

[0015] constructing a three-dimensional lung model including the lung tumor based on the CT image group;

[0016] The three-dimensional lung model is segmented to obtain a three-dimensional tumor model of the lung tumor.

[0017] Optionally, the true volume and true center of mass coordinates of the lung tumor are determined by:

[0018] The relative position and relative size of the lung tumor in the lung are acquired based on the three-dimensional tumor model, and the real volume and real center of mass coordinates of the lung tumor are calculated based on the relative position and the relative size.

[0019] The real volume and real center of mass coordinates of the lung tumor are calculated by the tools provided by 3D Slicer as the real value of the tumor data. The simulated volume and simulated center of mass coordinates of the lung tumor are calculated in combination with the subsequent finite element tumor model to compare and evaluate the simulation effect and state prediction effect.

[0020] Optionally, performing finite element analysis on the three-dimensional tumor model to obtain a finite element tumor model includes:

[0021] Optimizing the three-dimensional tumor model to obtain a three-dimensional solid tumor model; the optimization process includes at least one of meshing, defect repair, error repair, and precise surface processing;

[0022] The three-dimensional solid tumor model is subjected to finite element analysis to obtain a finite element tumor model.

[0023] In this way, the network of the three-dimensional tumor model is re-divided, and the density or topological structure of the three-dimensional tumor model is changed to optimize the three-dimensional tumor model. The three-dimensional tumor model is subjected to defect repair and error repair processing to ensure the quality and accuracy of the three-dimensional tumor model. The three-dimensional tumor model is subjected to precise surface processing through four steps of detecting contour lines, constructing surface patches, constructing grids, and fitting surfaces to further adjust and optimize the three-dimensional solid tumor model.

[0024] Optionally, the three-dimensional tumor model is meshed in the following manner:

[0025] Grid parameters are set for the three-dimensional tumor model, wherein the grid parameters include a grid density parameter and an edge smoothness parameter, and the three-dimensional tumor model is gridded based on the grid density parameter and the edge smoothness parameter to improve the detail and smoothness of the three-dimensional tumor model.

[0026] Optionally, the calculating the simulated volume and simulated centroid coordinates of the lung tumor based on the finite element tumor model includes:

[0027] Setting an elastic modulus and Poisson's ratio of the lung tumor based on the finite element tumor model, wherein the elastic modulus and Poisson's ratio are used to characterize material properties of the lung tumor;

[0028] The simulated volume and simulated center of mass coordinates of the lung tumor are calculated based on the elastic modulus and the Poisson's ratio.

[0029] Optionally, the initial motion state parameters include stress parameters, strain parameters and displacement parameters;

[0030] The method of using the finite element tumor model to predict the motion state of the lung tumor in the next respiratory cycle includes:

[0031] The analysis duration and step size of the motion state prediction are set, and the motion state of the lung tumor in the next respiratory cycle is predicted using the finite element tumor model based on the analysis duration and the step size.

[0032] In a second aspect, an embodiment of the present application provides a tumor status prediction system, including an acquisition module, an analysis module, a comparison module, a status prediction module, and a quality determination module;

[0033] The acquisition module is configured to acquire a CT image group of the target object's lungs in a current respiratory cycle, and construct a three-dimensional tumor model of the lung tumor based on the CT image group; wherein the lung tumor has a real volume and real centroid coordinates;

[0034] The analysis module is configured to perform finite element analysis on the three-dimensional tumor model to obtain a finite element tumor model, and calculate the simulated volume and simulated centroid coordinates of the lung tumor based on the finite element tumor model;

[0035] The comparison module is configured to compare the real volume with the simulated volume to obtain a volume retention ratio; and compare the real center of mass coordinates with the simulated center of mass coordinates to obtain a center of gravity offset;

[0036] The state prediction module is configured to use the finite element tumor model to predict the motion state of the lung tumor in the next respiratory cycle to obtain initial motion state parameters of the lung tumor in the next respiratory cycle;

[0037] The quality determination module is configured to use the initial motion state parameter as the target motion state parameter of the lung tumor in the next respiratory cycle if it is determined based on the volume retention ratio and the center of gravity offset that the motion state prediction quality of the lung tumor meets the preset motion state prediction quality.

[0038] In a third aspect, an embodiment of the present application provides a computer device, including:

[0039] A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes any one of the above-mentioned tumor status prediction methods by executing the computer instructions.

[0040] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute any one of the above-mentioned tumor status prediction methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 A schematic diagram of the steps of a tumor status prediction method provided by an embodiment of the present invention;

[0043] Figure 2 This is a flow chart of 3D Slicer processing a CT image group in an embodiment of the present invention;

[0044] Figure 3a Schematic diagram of a slice image in a cross-sectional direction displayed by 3D Slicer software in an embodiment of the present invention;

[0045] Figure 3b Schematic diagram of a slice image in the coronal direction displayed by 3D Slicer software in an embodiment of the present invention;

[0046] Figure 3c Schematic diagram of a slice image in the sagittal direction displayed by 3D Slicer software in an embodiment of the present invention;

[0047] Figure 3d Schematic diagram of a three-dimensional tumor model displayed by 3D Slicer software in an embodiment of the present invention;

[0048] Figure 4 This is a schematic diagram of an STL format file of the tumor-side lung and tumor of two target objects in an embodiment of the present invention;

[0049] Figure 5 This is a flow chart of the optimization processing of STL format files by Geomagic Wrap software in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0051] The mechanical properties of the lungs are spatially non-uniform, and the physiological structure of the thorax also restricts the movement of the lungs. Therefore, the movement behaviors of lung tumors in the lungs, such as growth, expansion, or metastasis, are actually anisotropic, showing different characteristics in different directions. However, in related technologies, clinical practice often uses uniformly distributed outward expansion boundaries to describe the movement of lung tumors, which makes the prediction of the movement state of lung tumors inaccurate, and thus makes it difficult for doctors to conform to the actual tumor area when formulating radiation plans, resulting in poor radiotherapy treatment effects.

[0052] In the related art, there are two main methods for simulating the respiratory motion state of lung tumors. One is the image registration method, which directly extracts the motion field from the 4D image to simulate the motion field, and uses the similarity measurement between the single 3D frames of the maximized image sequence for registration. The image registration method will affect the extraction of motion data due to artifacts such as fuzzy 4D-CT images, repeated structures, overlapping structures, and incomplete structures, and the respiratory physiological process and the physical characteristics of the organs are usually not considered. This method has artifact errors and makes the simulation inaccurate.

[0053] The second is the biophysical modeling method, which is to model the lungs and lung tumors, analyze the physiological and physical factors of lung respiratory movement, and describe the movement in a physics-based mathematical formula. The process is relatively complicated, and it takes a lot of time to deal with irregularly shaped lungs and lung tumors. According to the volume changes of the lungs and tumors, there is no completely linear relationship between the tumor and the lungs, but most of the related technologies indirectly calculate the movement of the tumor by applying force to the lungs. This method is difficult to model, takes a long time, and the simulation is not accurate enough.

[0054] Reference Figure 1 The present application embodiment provides a method for predicting tumor status, comprising:

[0055] S100, obtaining a CT image group of the target object's lungs in a current respiratory cycle, and constructing a three-dimensional tumor model of the lung tumor based on the CT image group; wherein the lung tumor has a real volume and real centroid coordinates.

[0056] Among them, the CT image group of the target object's lungs in the current respiratory cycle can be a 4D-CT lung image, which is a CT image scanned by combining four-dimensional imaging technology. The embodiment of the present application selects three-dimensional anatomical images of ten consecutive phases to form a complete respiratory cycle. Each 4D-CT data consists of 10 phases spanning a respiratory cycle. The scanner tube potential and current are 140kVp and 120mAs / slicer respectively, and the layer thickness of all scans is 3mm. It is used to describe the positional changes of the lung organs or lung tumors in the human body during breathing. Different phases can represent different stages in the respiratory cycle, such as the inhalation stage and the exhalation stage.

[0057] Specifically, 4D-CT lung images use specific scanning conditions to ensure that each position has at least one respiratory cycle of data. The collected data includes synchronously acquired patient respiratory signals and anatomical images, which can observe the translation and deformation of lung organs caused by respiratory movement. Using 4D-CT lung images to establish a motion model of lung tumors, visualize the morphology and position changes of lung tumors, and individually formulate the radiotherapy margins of lung tumors for each patient, which is conducive to reducing the irradiation range of radiotherapy and reducing the exposure of normal tissues.

[0058] The CT scan in the related technology is static, usually acquiring a cross-sectional image of the body at a certain moment, while the 4D-CT lung image uses continuous time to acquire images of multiple different respiratory phases to better simulate and analyze the dynamic movement state of lung tumors or lung organs.

[0059] Reference Figure 2 , Figure 2 This is a flowchart of 3D Slicer processing a CT image group. 3D Slicer is an open source, cross-platform medical image processing software. The embodiment of the present application uses 3D Slicer to process the CT image group and constructs a three-dimensional tumor model of the lung tumor based on the CT image group.

[0060] S110. Import 4D-CT lung images into 3D Slicer software to construct a three-dimensional tumor model.

[0061] Reference Figure 3a to Figure 3d , the 3D Slicer software interface displays slice images in three orthogonal directions, including transverse direction, coronal direction and sagittal direction. In the 3D Slicer software, you can control the direction of the moving slice view by dragging the mouse or keyboard. Figure 3a This is a schematic diagram of the slice image in the cross-section direction displayed by the 3D Slicer software. Figure 3b This is a schematic diagram of the coronal slice image displayed by the 3DSlicer software. Figure 3c This is a schematic diagram of the sagittal slice image displayed by the 3D Slicer software. Figure 3d This is a schematic diagram of the three-dimensional tumor model displayed by the 3D Slicer software. The green part is the left lung, the yellow part is the right lung, and the red part is the lung tumor.

[0062] S130. Based on the three-dimensional tumor model of the lung tumor, calculate the true volume and true center of mass coordinates of the lung tumor.

[0063] S150, after completing the calculation of the real volume and the real center of mass coordinates of the lung tumor, save the three-dimensional tumor model of the lung tumor as an STL format file.

[0064] Among them, the STL file is a 3D printing file format that describes the surface geometry of a 3D model. It is usually composed of a series of triangular facets and can be used to describe anatomical structures such as bones, organs or tissues for 3D reconstruction, visualization and analysis of medical images.

[0065] S200, performing finite element analysis on the three-dimensional tumor model to obtain a finite element tumor model, and calculating a simulated volume and simulated centroid coordinates of the lung tumor based on the finite element tumor model.

[0066] Finite element analysis can be performed using HyperMesh software. Finite element analysis is used to establish a geometric model of biological tissues and convert the shape, size, and structure of biological tissues, i.e., lung tumors, into finite element tumor models. The material properties of lung tumors (such as elastic modulus, Young's modulus, etc.) are key parameters of finite element analysis. These material properties can be obtained through experimental measurements, literature reports, or calculations, and used in finite element analysis simulations. When simulating the mechanical behavior of lung tumors, appropriate boundary conditions need to be defined. Boundary conditions include loads or constraints applied to the surface of lung tumors, such as external forces, pressures, displacements, or other fixed boundary conditions.

[0067] Furthermore, before conducting finite element analysis, it is necessary to define the physical and mechanical properties of lung tumors, including material properties such as elastic modulus and Poisson's ratio. The specific elastic modulus and Poisson's ratio have individual differences. For example, the Poisson's ratio will change dynamically between 0.3 and 0.4.

[0068] S300, comparing the real volume and the simulated volume to obtain a volume retention ratio; and comparing the real center of mass coordinates and the simulated center of mass coordinates to obtain a center of gravity offset.

[0069] The volume retention ratio can be the ratio of the real volume to the simulated volume, which is used to evaluate how much real geometric information the three-dimensional tumor model retains during the finite element analysis process. If the volume retention ratio is close to 100%, it means that the finite element tumor model accurately reflects the real size of the lung tumor. If the ratio is lower than 100%, it means that the finite element tumor model may have certain errors in the size or shape of the tumor.

[0070] The center of gravity offset can be the difference between the real center of mass coordinates and the simulated center of mass coordinates. The center of gravity offset can be calculated using the Euclidean distance formula to reflect the accuracy of the spatial position of the finite element tumor model. If the center of gravity offset is close to zero, it means that the position of the finite element tumor model is very close to that of the real tumor, and the finite element tumor model can better reproduce the position of the tumor in three-dimensional space. Conversely, if the center of gravity offset is large, it means that the finite element tumor model has a significant deviation in position.

[0071] S400, predicting the motion state of the lung tumor in the next respiratory cycle using the finite element tumor model to obtain initial motion state parameters of the lung tumor in the next respiratory cycle.

[0072] The finite element tumor model can be dynamically simulated by time step to simulate the deformation and movement of lung tissue, tumor and other related structures in the respiratory cycle. Based on the current lung and tumor state (such as displacement, stress, strain, etc.), the initial state of the tumor in the next respiratory cycle is predicted, and historical data (such as the frequency and amplitude of the respiratory cycle) can also be used to estimate the initial movement state of the tumor in the next cycle.

[0073] S500: If it is determined based on the volume retention ratio and the center of gravity offset that the motion state prediction quality of the lung tumor meets the preset motion state prediction quality, the initial motion state parameters are used as target motion state parameters of the lung tumor in the next respiratory cycle.

[0074] Furthermore, if it is determined that the motion state prediction quality of the lung tumor based on the volume retention ratio and the center of gravity offset does not meet the preset motion state prediction quality, the parameters of the finite element tumor model are adjusted to adjust the obtained initial motion state parameters, and then judgment is made again after the adjustment until it is determined that the motion state prediction quality of the lung tumor meets the preset motion state prediction quality.

[0075] This application uses biophysical modeling and simulation to convert a two-dimensional 4D-CT image of a patient's lung into a three-dimensional tumor model with a sense of space and multi-angle observation and measurement, and performs finite element analysis to simulate the state of the lung tumor under respiratory motion to predict the motion state of the lung tumor. By ensuring that the motion state prediction quality meets the preset motion state prediction quality, the accuracy of the target motion state parameters is guaranteed.

[0076] According to the specific conditions of different target objects, personalized radiotherapy plans are designed to predict the movement trajectory and range of lung tumors of different target objects, and reduce the boundaries of the radiotherapy target area in a timely manner to protect normal tissues. This ensures that lung tumors receive sufficient radiation while reducing damage to normal tissues in the lungs, thereby improving the accuracy and correctness of radiotherapy irradiation.

[0077] This application omits the complex rib movement of the chest cavity, does not have the complex segmentation of the diaphragm, does not consider the influence of posture and gravity load, reduces the difficulty and time cost of modeling, ensures the timeliness of radiotherapy treatment, and overcomes the problem of difficulty in modeling and low simulation accuracy caused by extracting lung tumors by simulating lung breathing movement in related technologies. At the same time, it also reduces the occurrence of inaccurate simulation due to artifacts such as blur, repeated structure, overlapping structure and incomplete structure in 4D-CT images.

[0078] As an implementation method, a three-dimensional tumor model of a lung tumor is constructed based on a CT image group, including:

[0079] S112. Import the 4D-CT lung image into the 3D Slicer software to construct a three-dimensional lung model including the lung tumor.

[0080] S114. Segment the three-dimensional lung model to obtain a three-dimensional tumor model of the lung tumor.

[0081] S152. After the calculation of the real volume and the real center of mass coordinates of the lung tumor is completed, the segmented three-dimensional tumor model is saved as an STL format file.

[0082] For details, please refer to Figure 4 , Figure 4 Schematic diagram of the tumor-side lung and the STL file of the tumor for two target objects.

[0083] As an implementation, the true volume and true center of mass coordinates of the lung tumor are determined by:

[0084] S132. Obtain the relative position and relative size of the lung tumor in the lung based on the three-dimensional tumor model, and calculate the true volume and true center of mass coordinates of the lung tumor based on the relative position and relative size.

[0085] Specifically, the real volume and real center of mass coordinates of the lung tumor are calculated as the real value of the tumor data through the tools provided by 3D Slicer, and the simulated volume and simulated center of mass coordinates of the lung tumor are calculated in combination with the subsequent finite element tumor model to compare and evaluate the simulation effect and state prediction effect.

[0086] As an implementation manner, performing finite element analysis on a three-dimensional tumor model to obtain a finite element tumor model includes:

[0087] S210, optimizing the three-dimensional tumor model to obtain a three-dimensional solid tumor model; the optimization process includes at least one of meshing, defect repair, error repair, and precise surface processing.

[0088] Specifically, the STL format file is relatively rough, and Geomagic Wrap software is used to optimize the rough STL format file, that is, to optimize the three-dimensional tumor model to obtain a high-quality three-dimensional solid tumor model.

[0089] In this embodiment, refer to Figure 5 , Figure 5 This is a flow chart of the optimization processing of STL format files by Geomagic Wrap software in an embodiment of the present invention.

[0090] S211. Import STL format files into Geomagic Wrap software.

[0091] S212. Re-divide the network of the three-dimensional tumor model, and change the density or topological structure of the three-dimensional tumor model to optimize the three-dimensional tumor model.

[0092] S213, performing defect repair and error repair processing on the three-dimensional tumor model. The defect repair and error repair processing may be identifying and filling holes, detecting and repairing discontinuous surfaces, removing other unnecessary details, performing smoothing and trimming operations, or automatically adjusting the topological structure and vertex distribution of the three-dimensional tumor model to ensure the quality and accuracy of the three-dimensional tumor model.

[0093] S214, performing precise surface processing on the three-dimensional tumor model through four steps: detecting contour lines, constructing surface patches, constructing grids, and fitting surfaces.

[0094] S215. Save the optimized three-dimensional solid tumor model as an IGS format file. When performing numerical simulation of finite element analysis (FEA), accurate geometric description is usually required. The IGS file is a file format that can exchange geometric data between different CAD software and can provide geometric data.

[0095] First, the contour of the surface is drawn on the surface of the three-dimensional tumor model by detecting the contour line to determine the approximate shape and size of the surface. Next, the surface patches are constructed to connect the drawn contour lines while ensuring the continuity and smoothness between the surface patches to obtain a high-quality surface model. Then, the grid is constructed to decompose the surface patches into smaller areas to more finely control the shape and curvature of the surface model. Finally, the surface is fitted for further adjustment and optimization.

[0096] S230, performing finite element analysis on the three-dimensional solid tumor model to obtain a finite element tumor model, and calculating a simulated volume and simulated centroid coordinates of the lung tumor based on the finite element tumor model.

[0097] Specifically, through the analysis of Geomagic Wrap software, the simulated volume and simulated center of mass coordinates of the lung tumor were calculated based on the finite element tumor model. After comparing with the actual volume and actual center of mass coordinates of the lung tumor, the volume retention ratio and center of gravity offset were obtained to compare and evaluate the simulation effect and state prediction effect.

[0098] As an implementation method, the three-dimensional tumor model is meshed in the following manner:

[0099] Mesh parameters are set for the three-dimensional tumor model, the mesh parameters include a mesh density parameter and an edge smoothness parameter, and the three-dimensional tumor model is meshed based on the mesh density parameter and the edge smoothness parameter to improve the detail and smoothness of the three-dimensional tumor model.

[0100] As an implementation method, calculating the simulated volume and simulated centroid coordinates of the lung tumor based on the finite element tumor model includes:

[0101] The elastic modulus and Poisson's ratio of the lung tumor are set based on the finite element tumor model. The elastic modulus and Poisson's ratio are used to characterize the material properties of the lung tumor.

[0102] The simulated volume and simulated center of mass coordinates of the lung tumor are calculated based on the elastic modulus and Poisson's ratio.

[0103] As an implementation method, HyperMesh is a finite element modeling and mesh generation software, which is used to generate the mesh required for finite element analysis and obtain the boundary conditions of lung tumor movement to import into finite element software for simulation.

[0104] S401. Select surface elements: In HyperMesh, select appropriate surface elements to represent the surface of the lung tumor, and select triangles (TRIA) to construct a finite element tumor model.

[0105] S402. Create a surface mesh: Use a meshing tool to create a surface mesh of the lung tumor based on the selected surface units, and ensure that the mesh covers the entire surface of the lung tumor.

[0106] S403, extract nodes: After creating the surface mesh, use HyperMesh's node query tool to extract surface nodes, query the location of surface nodes, import the coordinates of some selected nodes into Excel for summary, so as to facilitate subsequent coordinate value comparison and calculation of node displacement.

[0107] S404, saving results: After extracting the surface nodes, the results are saved as an INP format file so as to be imported into the Abaqus finite element analysis software for subsequent analysis or post-processing.

[0108] As an implementation method, S410, finite element analysis can be performed using Abaqus software. The geometric characteristics of the lung tumor are determined. Lung tumor disease can cause changes in mechanical properties such as soft tissue elasticity and hardness. A key parameter in biomechanical modeling is to simulate the elastic distribution of soft tissue, and apply mechanical parameters to finite element simulation, such as biomechanical properties such as nonlinearity, incompressibility, viscoelasticity, plasticity and anisotropy.

[0109] S420, determine the load and boundary conditions, set the node displacement calculated from HyperMesh as the displacement load, use the Boundary command to define the displacement boundary condition, and apply the displacement load by specifying the node and the displacement direction and magnitude. type indicates the displacement boundary condition, node set specifies the node set, and DOF1, DOF2, and DOF3 represent the displacement in the X, Y, and Z directions, respectively.

[0110] S430. After completing the settings, submit the "job" file. Warnings and other serious error messages can be viewed in the task manager. Resolve these error warnings until the calculation of the finite element tumor model converges. The solver performs numerical calculations on the finite element tumor model and generates initial motion state parameters. After the analysis is completed, check the initial motion state parameters, including stress parameters, strain parameters, and displacement parameters, and generate various charts, animations, profiles, etc. to better understand and interpret the initial motion state parameters.

[0111] S500: If it is determined based on the volume retention ratio and the center of gravity offset that the motion state prediction quality of the lung tumor meets the preset motion state prediction quality, the initial motion state parameters are used as target motion state parameters of the lung tumor in the next respiratory cycle.

[0112] As an implementation manner, the initial motion state parameters include stress parameters, strain parameters and displacement parameters.

[0113] The finite element tumor model is used to predict the motion state of the lung tumor in the next respiratory cycle, including:

[0114] The analysis duration and step size of the motion state prediction are set, and the motion state of the lung tumor in the next respiratory cycle is predicted using the finite element tumor model based on the analysis duration and step size.

[0115] Specifically, the finite element analysis in this application is a static analysis, and the analysis duration and step size of the motion state prediction are set. The time step determines the degree of discretization of time during the analysis process.

[0116] The embodiment of the present application provides a tumor status prediction system, including an acquisition module, an analysis module, a comparison module, a status prediction module and a quality determination module;

[0117] An acquisition module is configured to acquire a CT image group of the lungs of the target object in a current respiratory cycle, and construct a three-dimensional tumor model of the lung tumor based on the CT image group; wherein the lung tumor has a real volume and a real centroid coordinate;

[0118] An analysis module is configured to perform finite element analysis on the three-dimensional tumor model to obtain a finite element tumor model, and calculate a simulated volume and a simulated centroid coordinate of the lung tumor based on the finite element tumor model;

[0119] A comparison module configured to compare the real volume with the simulated volume to obtain a volume retention ratio; and to compare the real center of mass coordinates with the simulated center of mass coordinates to obtain a center of gravity offset;

[0120] A state prediction module is configured to use a finite element tumor model to predict the motion state of the lung tumor in the next respiratory cycle to obtain initial motion state parameters of the lung tumor in the next respiratory cycle;

[0121] The quality determination module is configured to use the initial motion state parameters as the target motion state parameters of the lung tumor in the next respiratory cycle if the motion state prediction quality of the lung tumor is determined to meet the preset motion state prediction quality based on the volume retention ratio and the center of gravity offset.

[0122] As an implementation mode, the acquisition module is further configured to

[0123] A three-dimensional lung model including lung tumors was constructed based on the CT image set;

[0124] The three-dimensional lung model is segmented to obtain a three-dimensional tumor model of the lung tumor.

[0125] As an implementation, in the acquisition module, the true volume and true center of mass coordinates of the lung tumor are determined by:

[0126] The relative position and relative size of the lung tumor in the lungs are obtained based on the three-dimensional tumor model, and the real volume and real center of mass coordinates of the lung tumor are calculated based on the relative position and relative size.

[0127] As an implementation mode, the analysis module is further configured to:

[0128] Optimizing the three-dimensional tumor model to obtain a three-dimensional solid tumor model; the optimization process includes at least one of defect repair, error repair, meshing and precise surface processing;

[0129] Finite element analysis is performed on the three-dimensional solid tumor model to obtain a finite element tumor model.

[0130] As an implementation mode, in the analysis module, the three-dimensional tumor model is meshed in the following manner:

[0131] Mesh parameters are set for the three-dimensional tumor model, the mesh parameters include a mesh density parameter and an edge smoothness parameter, and the three-dimensional tumor model is meshed based on the mesh density parameter and the edge smoothness parameter.

[0132] As an implementation mode, the analysis module is further configured to:

[0133] The elastic modulus and Poisson's ratio of the lung tumor are set based on the finite element tumor model. The elastic modulus and Poisson's ratio are used to characterize the material properties of the lung tumor.

[0134] The simulated volume and simulated center of mass coordinates of the lung tumor are calculated based on the elastic modulus and Poisson's ratio.

[0135] As an implementation method, the initial motion state parameters include stress parameters, strain parameters and displacement parameters;

[0136] The state prediction module is further configured to: set the analysis duration and step size of the motion state prediction, and use the finite element tumor model to predict the motion state of the lung tumor in the next respiratory cycle based on the analysis duration and step size.

[0137] The present application provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes a tumor status prediction method provided in the above embodiment by executing the computer instructions.

[0138] The computer device includes: one or more processors, memory, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system).

[0139] The processor may be a central processing unit, a network processor or a combination thereof. The processor may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0140] The memory stores instructions executable by at least one processor, so that the at least one processor executes the method shown in the above embodiment.

[0141] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the storage may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0142] The memory may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid state drive; the memory may also include a combination of the above types of memory.

[0143] The computer device also includes a communication interface, which is used for the computer device to communicate with other devices or a communication network.

[0144] The present application provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute a tumor status prediction method provided in the above embodiment.

[0145] The embodiment of the present application also provides a computer-readable storage medium. The above method according to the embodiment of the present application can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0146] The embodiment of the present application provides a computer program product, which includes computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method of any embodiment of the present application.

[0147] Although the embodiments of the present application are described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

[0148] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0149] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0150] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0151] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0152] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0153] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0154] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0155] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0156] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

[0157] Although the embodiments of the present application have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for predicting tumor status, characterized in that: include: Acquire a CT image group of the target object's lungs in the current respiratory cycle, and construct a three-dimensional tumor model of the lung tumor based on the CT image group; wherein the lung tumor has a real volume and real centroid coordinates; Performing finite element analysis on the three-dimensional tumor model to obtain a finite element tumor model, and calculating a simulated volume and a simulated centroid coordinate of the lung tumor based on the finite element tumor model; Comparing the real volume with the simulated volume to obtain a volume retention ratio; and comparing the real center of mass coordinates with the simulated center of mass coordinates to obtain a center of gravity offset; Using the finite element tumor model to predict the motion state of the lung tumor in the next respiratory cycle, so as to obtain initial motion state parameters of the lung tumor in the next respiratory cycle; If it is determined based on the volume retention ratio and the center of gravity offset that the motion state prediction quality of the lung tumor meets the preset motion state prediction quality, the initial motion state parameters are used as target motion state parameters of the lung tumor in the next respiratory cycle.

2. The method according to claim 1, characterized in that The constructing of a three-dimensional tumor model of a lung tumor based on the CT image group includes: constructing a three-dimensional lung model including the lung tumor based on the CT image group; The three-dimensional lung model is segmented to obtain a three-dimensional tumor model of the lung tumor.

3. The method according to claim 2, characterized in that The true volume and true center of mass coordinates of the lung tumor are determined by: The relative position and relative size of the lung tumor in the lung are acquired based on the three-dimensional tumor model, and the real volume and real center of mass coordinates of the lung tumor are calculated based on the relative position and the relative size.

4. The method according to claim 1, characterized in that The performing finite element analysis on the three-dimensional tumor model to obtain a finite element tumor model comprises: Optimizing the three-dimensional tumor model to obtain a three-dimensional solid tumor model; the optimization process includes at least one of meshing, defect repair, error repair, and precise surface processing; The three-dimensional solid tumor model is subjected to finite element analysis to obtain a finite element tumor model.

5. The method according to claim 4, characterized in that The three-dimensional tumor model is meshed in the following manner: Grid parameters are set for the three-dimensional tumor model, wherein the grid parameters include a grid density parameter and an edge smoothness parameter, and the three-dimensional tumor model is grid-divided based on the grid density parameter and the edge smoothness parameter.

6. The method according to claim 1, characterized in that The calculating the simulated volume and simulated centroid coordinates of the lung tumor based on the finite element tumor model includes: Setting an elastic modulus and Poisson's ratio of the lung tumor based on the finite element tumor model, wherein the elastic modulus and Poisson's ratio are used to characterize material properties of the lung tumor; The simulated volume and simulated center of mass coordinates of the lung tumor are calculated based on the elastic modulus and the Poisson's ratio.

7. The method according to claim 1 or 6, characterized in that The initial motion state parameters include stress parameters, strain parameters and displacement parameters; The method of using the finite element tumor model to predict the motion state of the lung tumor in the next respiratory cycle includes: The analysis duration and step size of the motion state prediction are set, and the motion state of the lung tumor in the next respiratory cycle is predicted using the finite element tumor model based on the analysis duration and the step size.

8. A tumor status prediction system, characterized in that: It includes an acquisition module, an analysis module, a comparison module, a state prediction module and a quality determination module; The acquisition module is configured to acquire a CT image group of the target object's lungs in a current respiratory cycle, and construct a three-dimensional tumor model of the lung tumor based on the CT image group; wherein the lung tumor has a real volume and real centroid coordinates; The analysis module is configured to perform finite element analysis on the three-dimensional tumor model to obtain a finite element tumor model, and calculate the simulated volume and simulated centroid coordinates of the lung tumor based on the finite element tumor model; The comparison module is configured to compare the real volume with the simulated volume to obtain a volume retention ratio; and compare the real center of mass coordinates with the simulated center of mass coordinates to obtain a center of gravity offset; The state prediction module is configured to use the finite element tumor model to predict the motion state of the lung tumor in the next respiratory cycle to obtain initial motion state parameters of the lung tumor in the next respiratory cycle; The quality determination module is configured to use the initial motion state parameter as the target motion state parameter of the lung tumor in the next respiratory cycle if it is determined based on the volume retention ratio and the center of gravity offset that the motion state prediction quality of the lung tumor meets the preset motion state prediction quality.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes a tumor status prediction method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute a tumor status prediction method according to any one of claims 1 to 7.

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