A method of predicting the growth process of abdominal aortic aneurysms

Abstract

The application discloses a method for predicting the growth process of abdominal aortic aneurysm, and relates to the technical field of aneurysm. The method comprises the following steps: establishing a patient-specific abdominal aortic aneurysm model based on medical image data, dividing the blood vessel wall into a media and an adventitia, and constructing a strain energy function of elastin, smooth muscle and collagen fiber evolution on each layer of blood vessel with time and space; in the growth process of the aneurysm, by adjusting different growth parameters, changing the degradation rate of elastin and the deposition effect of smooth muscle and collagen fiber, the grown aneurysm model can be matched with the model reconstructed based on follow-up data, so that the optimal solution of the growth parameter is obtained; next, the mapping relationship between the growth parameters of multiple aneurysm patients and the medical history information is established, and after verification of the test set, the growth parameters of the newly added aneurysm model are quickly determined, and the growth process of the patient-specific abdominal aortic aneurysm is predicted. The application can greatly improve the efficiency of predicting the growth process of abdominal aortic aneurysm.

Description

Technical Field

[0001] This invention relates to the field of aneurysm technology, and more specifically to a method for predicting the growth process of abdominal aortic aneurysms. Background Technology

[0002] An abdominal aortic aneurysm is the portion of the descending aorta located in the abdominal cavity. Due to the degenerative changes in the elastic fibers of the vessel wall, the vessel wall undergoes irreversible expansion under the pressure within the lumen, forming an abdominal aortic aneurysm.

[0003] Traditional biomechanical models acquire images, build models, and perform numerical simulations to assess the mechanical state of the blood vessel wall at a given moment. They then use hemodynamic or biomechanical indicators to determine the likelihood of aneurysm growth and rupture. However, these methods cannot calculate the aneurysm growth process over large timescales, nor can they quantitatively describe the dynamic evolution of the blood vessel wall components.

[0004] To address the above issues, some researchers, while considering density and morphological changes in the components of the blood vessel wall, have not analyzed the mass transfer between different components. Others have simplified aneurysms into ideal circular tubular models but failed to study the growth process of patient-specific aneurysms. Some researchers have used unidirectional fluid-structure interaction methods to simulate aneurysm growth, but these models are complex and difficult to operate. While all these methods can simulate aneurysm growth, they generally suffer from drawbacks such as simplified models, cumbersome processes, and a lack of validation through in vitro experiments and follow-up data.

[0005] Therefore, it is necessary for researchers in this field to develop a method for predicting the growth process of patient-specific abdominal aortic aneurysms. Summary of the Invention

[0006] In view of the above-mentioned deficiencies of the prior art, the technical problem to be solved by the present invention is to provide a method for predicting the growth process of abdominal aortic aneurysm. By using a strain energy function that evolves over time, the dynamic changes of different components in the vessel wall are coupled into the model to predict the growth process of patient-specific abdominal aortic aneurysm. The method calculates the dynamic evolution of components at different locations of the vessel wall over a large time scale and the growth of the aneurysm, which can greatly improve the efficiency of predicting the growth process of aneurysm.

[0007] To achieve the above-mentioned technical objectives, the present invention provides a method for predicting the growth process of abdominal aortic aneurysms, comprising the following steps:

[0008] S1: The wall of the abdominal aortic aneurysm is defined as being composed of the media and the adventitia;

[0009] S2: Establish the strain energy functions of different components on each layer of blood vessels in the media and adventitia as they evolve over time during the growth and remodeling of aneurysms, and establish the strain energy functions of the media and adventitia respectively.

[0010] S3: Calculate the deformation gradient of each component of the middle and outer membrane during growth or remodeling, and calculate the corresponding elastic deformation gradient based on the deformation gradient to obtain the right Cauchygreen strain tensor corresponding to the elastic deformation of each component.

[0011] S4: Combining the strain energy functions of the middle and outer membranes and the partial derivatives of the right Cauchy-Green strain tensor, construct the corresponding Cauchy stress matrix and stiffness matrix; where the first-order partial derivative of the strain energy function with respect to the right Cauchy-Green strain tensor is the second-kind Piola–Kirchhof stress tensor:

[0012]

[0013] In the formula, Let be the strain energy function. Let the right Cauchygreen strain tensor be the strain tensor.

[0014] The Cauchy stress matrix is:

[0015]

[0016] In the formula, For deformation gradient, For the deformable gradient determinant, This is the transpose of the deformation gradient;

[0017] The stiffness matrix is ​​the second-order partial derivative of the strain energy function with respect to the right Cauchy-Green strain tensor:

[0018]

[0019] The Cauchy stress matrix and stiffness matrix change during aneurysm growth as different components are deposited and remodeled on the blood vessel.

[0020] S5: Define the different component parameters on each layer of blood vessels, update the stress matrix and stiffness matrix during the growth process, so that the different components of the blood vessel wall change with time and space, thereby realizing the growth and remodeling of aneurysms.

[0021] S6: Compare the simulation results with the model obtained based on follow-up data, obtain the optimal solution of growth parameters through inversion, and predict the growth process of abdominal aortic aneurysm.

[0022] In a preferred embodiment of the present invention, in step S2, the different components on each layer of blood vessels in the media and adventitia include elastin, smooth muscle and collagen fibers.

[0023] In a preferred embodiment of the present invention, the density derivative of the elastin with respect to time during the growth and remodeling of aneurysms is: This is used to simulate the degradation of elastin;

[0024] During the growth and remodeling of aneurysms, the derivative of the density of the collagen fibers with respect to time is: ,in, and This represents the current stress and steady-state stress of collagen fibers;

[0025] During the growth and remodeling of the aneurysm, the derivative of the density of the smooth muscle with respect to time is: , and This represents the current stress and steady-state stress of the smooth muscle.

[0026] The total density of elastin, collagen fibers, and smooth muscle in the blood vessels at different times was:

[0027] .

[0028] In a preferred embodiment of the present invention, the strain energy functions corresponding to the media and adventitia of the abdominal aortic aneurysm are respectively:

[0029]

[0030]

[0031] in, This indicates the total density of elastin, collagen fibers, and smooth muscle in blood vessels at different times. and These represent the fractions of elastin in the middle and outer membranes, respectively. and These represent the mass fractions of smooth muscle in the media and adventitia, respectively. and The mass fractions of collagen fibers in the media and adventitia were determined by histological experiments on blood vessels. , , and These represent the strain energy functions of elastin, the strain energy functions generated by passive force and active contraction of smooth muscle, and the strain energy function corresponding to each bundle of collagen fibers, respectively.

[0032] The strain energy function corresponding to elastin is:

[0033]

[0034] In the formula, This indicates the material parameters corresponding to elastin. Let represent the first invariant of the deviatoric strain tensor that does not involve volume change;

[0035] The strain energy functions corresponding to the passive stress on smooth muscle and collagen fibers are:

[0036]

[0037]

[0038] In the formula, and These represent the stress-related material parameters for a bundle of smooth muscle and each bundle of collagen fibers, respectively. and These are dimensionless material parameters. and This represents the fourth invariant corresponding to the stretching direction of smooth muscle and collagen fibers;

[0039] The strain energy function generated during active contraction of smooth muscle is:

[0040]

[0041] In the formula, where, It is the amount of active stretching in the fiber direction. and These are the maximum stretch and minimum contraction of smooth muscle tissue. It is the maximum stress generated by the active contraction of smooth muscle.

[0042] In a preferred embodiment of the present invention, in step S3, the derivative of the deformation gradient of collagen fibers and smooth muscle due to inelastic growth with respect to time is: The deformation gradient caused by tissue remodeling is: ,in, For the direction of growth of each type of tissue, This indicates the amount of stretching that different components undergo during remodeling.

[0043] Furthermore, based on the deformation gradient calculation, the elastic deformation gradients corresponding to the middle and outer membranes are calculated using the following formula:

[0044]

[0045] The total deformation gradient, The gradient is formed by the combined effects of growth and remodeling. .

[0046] Furthermore, the formula for calculating the right Cauchygreen strain tensor corresponding to elastic deformation is:

[0047] .

[0048] In a preferred embodiment of the present invention, step S6 involves obtaining the optimal solution for the growth parameters using the following steps:

[0049] S61: After setting the growth parameters and material parameters for each component, perform a numerical simulation of the growth of the abdominal aortic aneurysm and obtain the simulation results;

[0050] S62: Compare the simulation results with the model reconstructed from the medical imaging data at the second follow-up time. When the proportion of nodes with a distance < 0.5 mm between the two is above 80%, the corresponding growth parameters are the optimal solution.

[0051] Furthermore, the process also includes a step to verify the optimal solution for the growth parameters: the selected growth parameters are used to continue the simulation, and the simulation results are compared with the model reconstructed from the image data at the third follow-up time. When the proportion of nodes with a distance of < 0.5 mm between the two is more than 70%, it indicates that the optimal solution for the growth parameters has been verified.

[0052] Furthermore, once the optimal solution for growth parameters is validated, the following method is used to predict the degradation and deposition trends of different components on each layer of the blood vessel in the media and adventitia during aneurysm growth:

[0053] Using a validated growth model, growth parameters for multiple abdominal aortic aneurysm cases were obtained through inversion.

[0054] The cases were divided into a training set and a test set. The training set was used to construct a mathematical regression relationship between growth parameters and the characteristic information of medical history, and the test set was used to verify the above regression relationship.

[0055] Growth parameters were quickly determined by collecting medical history information and combining it with validated regression relationships. Numerical simulations were then used to obtain the aneurysm growth process and the degradation and deposition trends of different components on each layer of blood vessels in the media and adventitia.

[0056] The medical history information includes: age, gender, height, weight, blood pressure, blood sugar, and whether the patient smokes or drinks alcohol.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] (1) During the growth of aneurysm, elastin will degrade, and collagen fibers and smooth muscle will be deposited. Under pressure, aneurysm will grow and remodel. This invention uses a strain energy function that evolves over time to couple the dynamic changes of different components in the blood vessel wall into the growth model, which can predict the growth process of patient-specific abdominal aortic aneurysm and realize the dynamic evolution of components at different locations of the blood vessel wall on a large time scale as well as the growth of aneurysm.

[0059] (2) This application determines the growth parameters corresponding to patients based on medical imaging data containing two or more follow-up time points, and uses the follow-up data at the third time point to verify the growth model. Based on the sensitivity analysis of growth parameters, the growth parameters corresponding to elastin, collagen fibers and smooth muscle for each case are determined by comparing with the follow-up data. The optimal solution of the growth parameters is used to simulate the continued growth of the aneurysm, and the simulation results are verified using the model at the third time point. Based on the model reconstructed from the follow-up data, the case-specific growth parameters are obtained through reverse derivation, and the accuracy of the growth model is verified.

[0060] (3) This application constructs a functional relationship between medical history information (smoking, drinking, hypertension, etc.) and growth parameters, which can quickly calculate growth parameters and realize the simulation of aneurysm growth process, saving a lot of numerical simulation time, improving computational efficiency, and increasing the clinical applicability of the method.

[0061] The following will further explain the concept, specific structure, and technical effects of the present invention in conjunction with the accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Attached Figure Description

[0062] Figure 1 This diagram illustrates the construction and verification of the aneurysm growth model disclosed in this embodiment of the invention, as well as the construction of the relationship between medical history information and growth parameters.

[0063] Figure 2 The diagram shows the media and adventitia constituting the vessel wall of an abdominal aneurysm, as well as the composition of the vessel wall, as disclosed in an embodiment of the present invention; wherein, 1, aneurysm; 2, vessel wall media; 3, vessel wall adventitia; 4, elastin matrix; 5, smooth muscle; 6, collagen fibers;

[0064] Figure 3 This is a node distance graph between the simulation results corresponding to the optimal solution of growth parameters disclosed in the embodiments of the present invention and the model reconstructed based on the follow-up data at the second time step.

[0065] Figure 4 This is a node distance diagram between the simulation results corresponding to the optimal solution of growth parameters disclosed in the embodiments of the present invention and the model reconstructed based on the follow-up data at the third time step. Detailed Implementation

[0066] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are merely illustrative and not intended to limit the scope of the invention in any way. The present invention can be embodied in many different forms, and the scope of protection of the present invention is not limited to the embodiments mentioned herein.

[0067] In the accompanying drawings, components with the same structure are indicated by the same numerical designation, and components with similar structures or functions are indicated by similar numerical designations. The dimensions and thicknesses of each component shown in the drawings are arbitrary, and the present invention does not limit the dimensions and thicknesses of each component. To make the illustrations clearer, the thickness of some components has been appropriately exaggerated in the drawings.

[0068] like Figure 1 This invention discloses a method for predicting the growth process of abdominal aortic aneurysms, comprising the following steps:

[0069] S1: The abdominal aortic aneurysm wall is defined as being composed of a media and an adventitia.

[0070] S2: Establish the strain energy functions of different components on each layer of blood vessels in the media and adventitia as a function of time during the growth and remodeling of aneurysms, and establish the strain energy functions of the media and adventitia respectively.

[0071] S3: Calculate the deformation gradient of each component of the middle and outer membrane during growth or remodeling, and calculate the corresponding elastic deformation gradient based on the deformation gradient to obtain the right Cauchygreen strain tensor corresponding to the elastic deformation of each component.

[0072] S4: Combining the strain energy functions of the middle and outer membranes and the partial derivatives of the right Cauchy-Green strain tensor, construct the corresponding Cauchy stress matrix and stiffness matrix; where the first-order partial derivative of the strain energy function with respect to the right Cauchy-Green strain tensor is the second-kind Piola–Kirchhof stress tensor:

[0073]

[0074] In the formula, Let be the strain energy function. Let the right Cauchygreen strain tensor be the strain tensor.

[0075] The Cauchy stress matrix is:

[0076]

[0077] In the formula, For deformation gradient, For the deformable gradient determinant, This is the transpose of the deformation gradient;

[0078] The stiffness matrix is ​​the second-order partial derivative of the strain energy function with respect to the right Cauchy-Green strain tensor:

[0079]

[0080] The Cauchy stress matrix and stiffness matrix change during aneurysm growth as different components are deposited and remodeled on the blood vessel.

[0081] S5: Define the different component parameters on each layer of blood vessels, update the stress matrix and stiffness matrix during the growth process, so that the different components of the blood vessel wall change with time and space, thereby realizing the growth and remodeling of aneurysms.

[0082] S6: Compare the numerical simulation results with the model reconstructed based on follow-up data, obtain the optimal solution of growth parameters through inversion, and predict the growth process of abdominal aortic aneurysm.

[0083] The steps in establishing a patient-specific abdominal aortic aneurysm growth model include: reconstructing a patient-specific three-dimensional model based on medical imaging data, and dividing the model into a structured mesh. The aneurysm vessel wall is considered to be composed of a media and an adventitia, with each layer of the vessel containing elastin, smooth muscle, and collagen fibers, such as... Figure 2 As shown. In Figure 2 The strain energy functions corresponding to the middle and outer layers of the blood vessel wall are as follows:

[0084]

[0085] in, This indicates the total density of elastin, collagen fibers, and smooth muscle in blood vessels at different times. and These represent the mass fractions of elastin in the middle and outer membranes, respectively. and These represent the mass fractions of smooth muscle in the media and adventitia, respectively. and The mass fractions of collagen fibers in the media and adventitia, respectively, can be determined through histological experiments on blood vessels. , , and These represent the strain energy functions of elastin, the strain energy functions generated by passive force and active contraction of smooth muscle, and the strain energy function corresponding to each bundle of collagen fibers, respectively.

[0086] The strain energy function corresponding to elastin is:

[0087]

[0088] In the formula, This indicates the material parameters corresponding to elastin. Let represent the first invariant of the deviatoric strain tensor that does not involve volume change;

[0089] The strain energy functions corresponding to the passive stress on smooth muscle and collagen fibers are:

[0090]

[0091]

[0092] In the formula, and These represent the stress-related material parameters for a bundle of smooth muscle and each bundle of collagen fibers, respectively. and These are dimensionless material parameters. and This represents the fourth invariant corresponding to the stretching direction of smooth muscle and collagen fibers;

[0093] The strain energy function generated during active contraction of smooth muscle is:

[0094]

[0095] In the formula, where, It is the amount of active stretching in the fiber direction. and These are the maximum stretch and minimum contraction of smooth muscle tissue. It is the maximum stress value generated by the active contraction of smooth muscle.

[0096] During aneurysm growth, elastin continuously degrades, and collagen fibers and smooth muscle undergo deposition under stress-mediated effects. As the components of the vessel wall change, the aneurysm continuously grows and remodels. The time derivative of the elastin density in the vessel wall is: This is used to simulate the degradation of elastin. The derivatives of collagen fiber and smooth muscle density with respect to time are as follows: ,in, and This indicates the current stress and steady-state stress of the collagen fiber. , and This represents the current stress and steady-state stress of the smooth muscle.

[0097] The total density of elastin, collagen fibers, and smooth muscle in blood vessels at different times is:

[0098] .

[0099] During growth and remodeling, the time derivative of the deformation gradient caused by inelastic growth in collagen fibers and smooth muscle is: This allows us to link changes in the shape and size of volumetric elements to the quality of degradation or deposition. The deformation gradient resulting from tissue remodeling is: .in, For the direction of growth of each type of tissue, This indicates the amount of stretching that different components undergo during remodeling.

[0100] After obtaining the deformation gradients related to growth and remodeling, the elastic deformation gradient for each component can be obtained as follows: . The total deformation gradient, The gradient is the result of the combined effects of growth and remodeling, where, Next, the right Cauchygreen strain tensor corresponding to the elastic deformation is obtained as follows: By combining the partial derivatives of the strain energy function with respect to the right Cauchy Green strain tensor, we can obtain the Cauchy stress matrix and stiffness matrix corresponding to each composition.

[0101] Define different component parameters on each layer of blood vessels, update the stress matrix and stiffness matrix during growth, so that different components of the blood vessel wall change with time and space, thereby realizing the growth and remodeling of aneurysms.

[0102] Specifically, the parameters are set as shown in Table 1, and the deformation gradient is updated to achieve aneurysm growth. The numerical simulation results are compared with the model reconstructed based on follow-up data, and a set of optimal solutions for growth parameters is obtained through inversion.

[0103] Table 1. Parameter settings in the embodiments:

[0104]

[0105] The simulation results are compared with the model reconstructed based on the second follow-up time. Figure 3 As shown, when the proportion of nodes with a distance of < 0.5 mm between them is more than 80%, the corresponding set of growth parameters is the optimal solution.

[0106] The simulation was continued using the selected growth parameters, and the simulation results were compared with the model reconstructed based on the image data at the third time point. The results are as follows: Figure 4 As shown, this method can be verified when the proportion of nodes with a distance of less than 0.5 mm between them is more than 70%.

[0107] After validating the accuracy of the growth model, growth parameters for multiple abdominal aortic aneurysm cases were obtained through inversion. Cases were divided into training and testing sets. A mathematical regression relationship between growth parameters and characteristic information of the patient's medical history was constructed using the training set, and this regression relationship was validated using the testing set. Subsequently, growth parameters were rapidly determined by combining collected medical history information with the validated regression relationship. Numerical simulations were then used to obtain the aneurysm growth process and the trends in the degradation and deposition of elastin, collagen fibers, and smooth muscle.

[0108] This application establishes a patient-specific abdominal aortic aneurysm model based on medical imaging data. The vessel wall is divided into the media and adventitia, and strain energy functions of elastin, smooth muscle, and collagen fibers in each layer of the vessel are constructed to reflect their evolution over time and space. During aneurysm growth, different growth parameters are adjusted to alter the elastin degradation rate and the deposition effects of smooth muscle and collagen fibers, allowing the grown aneurysm model to match a model reconstructed based on follow-up data, thus obtaining the optimal solution for growth parameters. Next, a mapping relationship between growth parameters and medical history information is established for multiple aneurysm patients. After validation on a test set, the growth parameters of newly added aneurysm models can be quickly determined, predicting the growth process of patient-specific abdominal aortic aneurysms. This invention can significantly improve the efficiency of predicting the growth process of abdominal aortic aneurysms.

[0109] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for predicting the growth process of abdominal aortic aneurysm, characterized in that, Includes the following steps: S1: The abdominal aortic aneurysm wall is defined as being composed of a media and an adventitia. S2: Establish the strain energy functions of different components on each layer of blood vessels in the media and adventitia as they evolve over time during the growth and remodeling of aneurysms, and establish the corresponding strain energy functions for the media and adventitia accordingly. S3: Calculate the deformation gradients of different components in the middle and outer membranes during growth or remodeling, and calculate the corresponding elastic deformation gradients based on the deformation gradients to obtain the right Cauchygreen strain tensor corresponding to the elastic deformation of each component. S4: Combining the strain energy functions of the middle and outer membranes and the partial derivatives of the right Cauchy-Green strain tensor, construct the corresponding Cauchy stress matrix and stiffness matrix; where the first-order partial derivative of the strain energy function with respect to the right Cauchy-Green strain tensor is the second-kind Piola–Kirchhof stress tensor: In the formula, Let be the strain energy function. Let the right Cauchygreen strain tensor be the strain tensor. The Cauchy stress matrix is: In the formula, For deformation gradient, For the deformable gradient determinant, This is the transpose of the deformation gradient; The stiffness matrix is ​​the second-order partial derivative of the strain energy function with respect to the right Cauchy-Green strain tensor: The Cauchy stress matrix and stiffness matrix change during aneurysm growth as different components on the blood vessel grow and remodel. S5: Define the growth parameters and material parameters corresponding to different components on each layer of blood vessels, update the stress matrix and stiffness matrix during the growth process, so that the different components of the blood vessel wall change with time and space, and realize the growth and remodeling of aneurysms under the action of intravascular pressure. S6: Compare the model reconstructed with follow-up data to obtain the optimal solution for growth parameters and predict the growth process of abdominal aortic aneurysm.

2. The method for predicting the growth process of abdominal aortic aneurysm according to claim 1, characterized in that: In step S2, the different components on each layer of blood vessels in the media and adventitia include elastin, smooth muscle and collagen fibers.

3. The method for predicting the growth process of abdominal aortic aneurysm according to claim 2, characterized in that: The time derivative of the density of the elastin during aneurysm growth and remodeling is: This is used to simulate the degradation of elastin; During the growth and remodeling of the aneurysm, the derivative of the collagen fiber density with respect to time is: ,in, and This represents the current stress and steady-state stress of collagen fibers; During the growth and remodeling of the aneurysm, the derivative of the density of the smooth muscle with respect to time is: , and This represents the current stress and steady-state stress of the smooth muscle. The total density of elastin, collagen fibers, and smooth muscle in the blood vessels at different times was: 。 4. The method for predicting the growth process of abdominal aortic aneurysm according to claim 3, characterized in that: The strain energy functions of the superior media and adventitia of the abdominal aortic aneurysm are as follows: in, This indicates the total density of elastin, collagen fibers, and smooth muscle in blood vessels at different times. and These represent the fractions of elastin in the middle and outer membranes, respectively. and These represent the mass fractions of smooth muscle in the media and adventitia, respectively. and The mass fractions of collagen fibers in the media and adventitia were determined by histological experiments on blood vessels. , , and These represent the strain energy functions of elastin, the strain energy functions generated by passive force and active contraction of smooth muscle, and the strain energy functions corresponding to each bundle of collagen fibers on the two layers of blood vessels, respectively. The elastin strain energy function is: In the formula, This indicates the material parameters corresponding to elastin. The first invariant of the deviatoric strain tensor that does not include volume change; The strain energy functions corresponding to smooth muscle and collagen fibers under passive stress are as follows: In the formula, and These represent the stress-related material parameters for each bundle of smooth muscle and each bundle of collagen fibers, respectively. and These are dimensionless material parameters. and This represents the fourth invariant corresponding to the stretching direction of smooth muscle and collagen fibers; The strain energy function generated during active contraction of smooth muscle is: In the formula, where, It is the amount of active stretching in the fiber direction. and These are the maximum stretch and minimum contraction of smooth muscle tissue. It is the maximum stress generated by the active contraction of smooth muscle.

5. The method for predicting the growth process of abdominal aortic aneurysm according to claim 1, characterized in that: In step S3, the derivative of the deformation gradient of collagen fibers and smooth muscle due to inelastic growth with respect to time is: The deformation gradient caused by tissue remodeling is: ,in, For the direction of growth of each type of tissue, This indicates the amount of stretching that different components undergo during remodeling.

6. The method for predicting the growth process of abdominal aortic aneurysm according to claim 5, characterized in that: Based on the deformation gradient calculation, the elastic deformation gradients corresponding to the middle and outer membranes are calculated using the following formula: The total deformation gradient, The gradient is formed by the combined effects of growth and remodeling. .

7. The method for predicting the growth process of abdominal aortic aneurysm according to claim 6, characterized in that: The formula for calculating the right Cauchygreen strain tensor corresponding to elastic deformation is: 。 8. The method for predicting the growth process of abdominal aortic aneurysm according to claim 1, characterized in that: In step S6, the optimal solution for growth parameters is obtained through the following steps: S61: Set the growth parameters and material parameters corresponding to each component, perform numerical simulation of abdominal aortic aneurysm growth, and obtain simulation results; S62: Compare the simulation results with the model reconstructed from the medical imaging data at the second follow-up time. When the proportion of nodes with a distance < 0.5 mm between the two is above 80%, the corresponding growth parameters are the optimal solution.

9. The method for predicting the growth process of abdominal aortic aneurysm according to claim 8, characterized in that, It also includes a step to verify the optimal solution of the growth parameters: continue the simulation using the selected growth parameters, and compare the simulation results with the model reconstructed from the image data at the third follow-up time. When the proportion of nodes with a distance < 0.5 mm between the two is more than 70%, it indicates that the optimal solution of the growth parameters has been verified.

10. The method for predicting the growth process of abdominal aortic aneurysm according to claim 9, characterized in that, Once the optimal solution for growth parameters is validated, the following method is used to predict the degradation and deposition trends of different components on each layer of the blood vessel in the media and adventitia during aneurysm growth: Using a validated growth model, growth parameters for multiple abdominal aortic aneurysm cases were obtained through inversion. The cases were divided into a training set and a test set. The training set was used to construct a mathematical regression relationship between growth parameters and the characteristic information of medical history, and the test set was used to verify the above regression relationship. Growth parameters were quickly determined by collecting medical history information and combining it with validated regression relationships. Numerical simulations were then used to obtain the aneurysm growth process and the degradation and deposition trends of different components on each layer of blood vessels in the media and adventitia.

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