Acquisition method, analysis method and system of biological material mechanical property of blood vessel

Abstract

The invention discloses a blood vessel biomaterial mechanical property acquisition method which comprises the following steps: acquiring a first image of a blood vessel, and acquiring HU values of the blood vessel in different biological areas based on the first image; acquiring material mechanical property parameters of different biological areas of the blood vessel in an in-vitro state; and based on the HU value and the material mechanical property parameter, obtaining a corresponding relation between the HU value and the material mechanical property parameter. The corresponding relation between the HU value and the mechanical property parameters of the material, which is constructed by the method, has universality. The blood vessels of subsequent specific individuals are subjected to personalized evaluation, the plaque state is reflected, specific problems are specifically analyzed, and an accurate result is obtained through biological material mechanical analysis of the blood vessels. The invention further discloses a system for acquiring the biological material mechanical property of the blood vessel, a method and a system for analyzing the biological material mechanical property of the blood vessel, electronic equipment and a computer readable storage medium.

Description

Technical Field

[0001] The present invention relates to the technical field of vascular biomaterial mechanics, and specifically to a method and system for acquiring vascular biomaterial mechanical properties, a method and system for analyzing vascular biomaterial mechanical properties, an electronic device, and a computer-readable storage medium. Background Art

[0002] Cardiovascular disease is the disease with the highest morbidity and mortality in my country. The key to its high mortality and disability rate lies in the rupture of coronary atherosclerotic plaques. Plaque rupture is a key event in coronary artery disease, which may lead to heart attack or stroke. Plaque rupture usually occurs in atherosclerotic plaques in the coronary arteries, which are composed of a lipid core and a fibrous cap covering it. The main function of the fibrous cap is to protect the plaque and prevent the lipid core material from leaking into the vascular lumen, thereby maintaining the normal function of the blood vessel. The root cause of plaque rupture lies in the imbalance between plaque stress and the carrying capacity of the plaque fibrous cap: for example, in every beat of the heart, the coronary artery will experience cyclical stretching, compression and shear forces. If these mechanical stresses exceed the carrying capacity of the fibrous cap, the plaque may rupture. Therefore, biomechanical evaluation of plaques is crucial.

[0003] In theory, the biomechanical process of plaque rupture can be simulated by using precise modeling and simulation technology, and the prerequisite for obtaining accurate biomechanical results is to obtain accurate material mechanical properties. However, the current field of plaque biomechanical analysis lacks plaque material mechanical property parameters. The biomechanical analysis errors caused by the lack of plaque material mechanical property parameters have become the main obstacle to biomechanical analysis.

[0004] Ideally, a constitutive model describing the mechanical properties of coronary materials is constructed based on the experimental data obtained from multiaxial testing to reflect the nonlinear and anisotropic material characteristics of biomaterials. Among them, the constitutive model is a mathematical model that describes how a material or substance responds to external forces and other external conditions (such as temperature, humidity, chemical environment, etc.). In solid mechanics and fluid mechanics, constitutive models are basic tools for linking stress (or strain) and strain (or stress), which are the core part of mechanical behavior. In the cardiovascular field, especially in the study of atherosclerotic plaques, constitutive models are crucial for simulating the mechanical behavior of plaques. Since plaques are composed of many different components, including lipid core, fibrous cap, calcification foci, etc., each component has its own unique mechanical properties, so complex constitutive models are required to accurately describe these properties. Multiaxial testing is a material testing method used to evaluate the mechanical properties of materials in different directions and loading conditions. In multiaxial testing, the material is subjected to multiple forces such as tension, compression, and shear in different directions at the same time. In the cardiovascular field, multiaxial testing is particularly important for understanding the mechanical behavior of vascular tissue. For example, the arterial wall is subject to complex stresses from all directions, including circumferential (around the circumference of the vessel) and axial (along the length of the vessel) stresses. Through multi-axial testing, researchers can better simulate these stress states and thus more accurately evaluate the mechanical properties of the arterial wall, which is critical for studying the development mechanisms of atherosclerosis, aneurysms and other vascular diseases. Nonlinearity means that when a biomaterial is subjected to stress or strain, it is not a simple linear relationship, but gradually changes as the stress or strain increases. Anisotropy means that the mechanical properties of biomaterials are different in different directions, which means that they will respond differently when subjected to forces in different directions.

[0005] Existing research methods rely on extracting coronary arteries from autopsy samples, i.e., ex vivo blood vessels, and carefully separating various plaques in the intima, the middle membrane, and the outer membrane, and performing in vitro multiaxial tests in a laboratory environment. By measuring the mechanical response of each separated component under different loads, the stress-strain curve is drawn, and the obtained data is fitted and analyzed with the help of various models to describe its material mechanical properties. However, most of the above tests are carried out under simplified ex vivo conditions and cannot well reflect the state of the plaque. More importantly, this approach requires the removal of the coronary artery, so it cannot be used for internal evaluation; at the same time, the mechanical properties of the materials obtained are limited to specific experimental samples. The differences between different individuals and the diversity of tissue structures under disease states mean that these data lack universal applicability. Therefore, the difficulty of current plaque biomechanical evaluation lies in how to efficiently and accurately obtain patient-specific plaque material mechanical properties without changing the existing clinical operation process.

[0006] As a non-invasive imaging technology, CT is economical and can be widely used in the preliminary screening of potential groups. The traditional biomechanical analysis method based on coronary CT first uses a segmentation algorithm to segment different types of tissues, including fibers, lipids, and calcifications, and assigns uniform material mechanical properties to different types of tissues based on empirical values ​​for subsequent biomechanical analysis. However, this method ignores the differences in material mechanical properties between different samples. This setting method of "one set of material mechanical properties applies to all data" will inevitably affect the accuracy of biomechanical analysis results. Taking calcified plaques as an example, it shows complex diversity, covering multiple types such as fibrous dense calcification, lipid pool calcification, and necrotic core calcification. The material mechanical properties of these different types of calcified plaques are different, resulting in obvious differences in their biomechanical analysis results. We pre-experimentally compared the biomechanical simulation results of high-hardness calcified plaques (simulating fibrous dense calcified plaques) and low-hardness calcified plaques (simulating lipid pool calcified plaques). It can be found that even if they are all defined as calcified plaques, there are large differences in the biomechanical simulation results between calcification types with different material mechanical properties. This further confirms that in order to achieve accurate biomechanical evaluation, it is crucial to obtain specific material mechanical properties.

[0007] The technical problem with existing technologies is that the biomaterial mechanical property parameters obtained from specific experimental samples are specific and cannot be generally applied to other experimental subjects; they can only be used to analyze the ex vivo blood vessels of the target analysis object, and cannot be applied to the in vivo evaluation of the target analysis object, nor can they reflect the state of plaques. When the biomaterial mechanical property analysis of blood vessels is performed using non-invasive imaging technology CT, uniform material mechanical properties are directly assigned to different types of tissues based on empirical values ​​for subsequent biomechanical analysis, which fails to analyze specific problems specifically, resulting in inaccurate results of the biomaterial mechanical analysis of blood vessels. Summary of the invention

[0008] The purpose of the present invention is to solve the aforementioned technical problems.

[0009] In the first aspect, the method for obtaining the mechanical properties of biological materials of blood vessels provided by the present invention constructs a correspondence between HU values ​​and material mechanical properties through blood vessels of multiple experimental samples. This correspondence is universal and can be applied to all target analysis objects, that is, specific individuals. Specifically, since the CT images of the target analysis objects reflect individual differences, that is, the HU values ​​have individual differences, the specific individual differences of the target analysis objects can be taken into account when applying the correspondence between HU values ​​and material mechanical properties, and it has universality.

[0010] An embodiment of the present invention discloses a method for obtaining the biomaterial mechanical properties of a blood vessel, including: obtaining a first image of the blood vessel, and obtaining HU values ​​of the ex vivo blood vessel in different biological regions based on the first image, wherein the first image is a CT image; obtaining material mechanical property parameters of the blood vessel in different biological regions in an ex vivo state; and obtaining the correspondence between the HU values ​​and the material mechanical property parameters based on the HU values ​​of the blood vessel in different biological regions and their corresponding material mechanical property parameters of the blood vessel in different biological regions in an ex vivo state.

[0011] By adopting the above technical scheme, the HU values ​​of different biological regions and the corresponding material mechanical property parameters of isolated blood vessels in different biological regions are obtained through CT images of blood vessels of multiple experimental samples, and the corresponding relationship between HU values ​​and material mechanical properties is constructed. This corresponding relationship is universal and can be applied to all target analysis objects, that is, specific individuals. In addition, personalized evaluation of the blood vessels of specific individuals is carried out in the future to reflect the status of plaques, so as to analyze specific problems specifically and obtain accurate results of the biomaterial mechanical analysis of blood vessels.

[0012] It should be noted that the biomaterial mechanical property acquisition method and system, biomaterial mechanical property analysis method and system, electronic device and computer storage medium provided by the present invention all have non-therapeutic purposes.

[0013] The biomaterial mechanical property acquisition method and biomaterial mechanical property analysis method provided by the present invention do not involve surgical methods, but are based on the processing of medical images, that is, their direct objects are medical images, such as CT images.

[0014] The biomaterial mechanical property acquisition system, biomaterial mechanical property analysis system, electronic device and computer storage medium provided by the present invention are also not devices related to surgical methods, but are related devices based on the processing of medical images, that is, their direct objects are medical images, such as CT images.

[0015] According to another specific embodiment of the present invention, the material mechanical property parameters of different biological regions of the blood vessel in the ex vivo state are obtained, including: obtaining the material mechanical property parameters of different biological regions of the blood vessel in the ex vivo state through iterative finite element simulation or in vitro multi-axis testing; or, based on the HU values ​​of the blood vessels in different biological regions and the corresponding material mechanical property parameters of different biological blood vessel regions of the blood vessels in the ex vivo state, obtaining the correspondence between the HU values ​​and the material mechanical property parameters, including: based on the HU values ​​of the blood vessels in different biological regions and the corresponding material mechanical property parameters of different biological blood vessel regions of the blood vessels in the ex vivo state, obtaining the correspondence between the HU values ​​and the material mechanical property parameters through a parameter association analysis method.

[0016] According to another specific embodiment of the present invention, the material mechanical properties of different biological blood vessel regions are represented by a constitutive model, wherein the constitutive model includes at least one of the following: a Mooney-Rivlin model, a Fung exponential model, and a Gasser model.

[0017] In the second aspect, an embodiment of the present invention discloses a system for acquiring the biomaterial mechanical properties of a blood vessel, comprising: a first image acquisition module, used to acquire a first image of the blood vessel, and acquire HU values ​​of the blood vessel in different biological regions based on the first image, wherein the first image is a CT image; a material mechanical property parameter acquisition module, used to acquire material mechanical property parameters of different biological regions of the blood vessel in an ex vivo state; and a parameter association module, used to obtain the correspondence between HU values ​​and material mechanical property parameters based on the HU values ​​of the blood vessel in different biological regions and the corresponding material mechanical property parameters of different biological blood vessel regions of the blood vessel in an ex vivo state.

[0018] By adopting the above technical solution, the present invention obtains the corresponding relationship between the HU value and the material mechanical property parameter under the joint action of the first image acquisition module, the material mechanical property parameter acquisition module and the parameter association module. This corresponding relationship is universal and can be applied to all target analysis objects, i.e., specific individuals. The blood vessels of specific individuals are subsequently evaluated in a personalized manner to reflect the state of plaques, so that specific problems can be analyzed specifically, so that the biomaterial mechanical analysis of blood vessels can obtain accurate results.

[0019] According to another specific embodiment of the present invention, the material mechanical property parameter acquisition module is used to obtain the material mechanical property parameters of different biological regions of the blood vessel in an ex vivo state through iterative finite element simulation or in vitro multi-axis testing, or the parameter association module is used to obtain the correspondence between the HU value and the material mechanical property parameters through a parameter association analysis method based on the HU value of the blood vessel in different biological regions and the corresponding material mechanical property parameters of different biological blood vessel regions of the blood vessel in an ex vivo state.

[0020] According to another specific embodiment of the present invention, the material mechanical property parameter acquisition module represents the material mechanical properties of different biological blood vessel regions through a constitutive model, wherein the constitutive model includes at least one of the following: Mooney-Rivlin model, Fung index model and Gasser model.

[0021] In a third aspect, an embodiment of the present invention discloses a method for analyzing the mechanical properties of biological materials of blood vessels, comprising: obtaining a second image of the target analysis object, the second image being a CT image; identifying and segmenting the second image through an image processing algorithm to obtain a segmentation result of the biological blood vessel in the second image; reconstructing a specific geometric model based on the segmentation result; obtaining specific material mechanical property parameters of the biological blood vessel region of the target analysis object according to the specific geometric model and the correspondence between the HU value and the material mechanical properties; constructing a finite element model according to the specific geometric model and the specific material mechanical property parameters, performing finite element simulation calculations on the finite element model, and obtaining the stress and strain distribution of the region of interest.

[0022] By adopting the above technical scheme, the present invention provides a method for analyzing the mechanical properties of vascular biomaterials. The method is universal, can perform in vivo evaluation of specific target analysis objects, reflect the state of plaques, and can perform personalized analysis based on individual differences of the target analysis objects, thereby achieving specific analysis of specific problems and making the mechanical analysis results of vascular biomaterials accurate.

[0023] According to another specific embodiment of the present invention, a method for analyzing the mechanical properties of blood vessel biomaterials includes: obtaining blood pressure information of a target analysis object; constructing a finite element model based on a specific geometric model, specific material mechanical property parameters, and blood pressure information, performing finite element simulation calculations on the finite element model, and obtaining stress and strain distributions in a region of interest.

[0024] According to another specific embodiment of the present invention, the image processing algorithm is a deep learning algorithm, and the deep learning algorithm includes a deep neural network with 3D-UNet as the backbone network.

[0025] In a fourth aspect, an embodiment of the present invention discloses a system for analyzing the mechanical properties of biological materials of a blood vessel, comprising: a second image acquisition module, used to obtain a second image of a target analysis object, wherein the second image is a CT image; an image processing module, used to identify and segment the second image through an image processing algorithm, and obtain a segmentation result of a biological blood vessel in the second image; a three-dimensional geometric model establishment module, used to reconstruct a specific geometric model based on the segmentation result; a specific material mechanical property parameter calculation module, used to obtain the material mechanical property parameters of the biological blood vessel region of the target analysis object according to the correspondence between the HU value and the material mechanical property parameters and the specific geometric model obtained by the three-dimensional geometric model establishment module; a finite element model module, used to construct a finite element model according to the segmentation result of the biological blood vessels in the second image, the specific geometric model and the material mechanical property parameters of the biological blood vessel region of the target analysis object, perform finite element simulation calculations on the finite element model, and obtain the stress and strain distribution of the region of interest.

[0026] By adopting the above-mentioned technical scheme, the vascular biomaterial mechanical property analysis system provided by the present invention, under the joint action of the second image acquisition module, the image processing module, the three-dimensional geometric model establishment module, the specific material mechanical property parameter calculation module and the finite element model module, can perform in vivo evaluation of specific target analysis objects, reflect the state of plaques, and can perform personalized analysis according to the individual differences of the target analysis objects, realize specific analysis of specific problems, make the vascular biomaterial mechanical analysis results accurate, and the system has universal applicability.

[0027] According to another specific embodiment of the present invention, a blood vessel biomaterial mechanical property analysis system includes: a blood pressure acquisition module, used to obtain blood pressure information of a target analysis object; a finite element model module, used to construct a finite element model based on a specific geometric model, specific material mechanical property parameters, and blood pressure information, perform finite element simulation calculations on the finite element model, and obtain stress and strain distributions in a region of interest.

[0028] According to another specific embodiment of the present invention, the image processing algorithm is a deep learning algorithm, and the deep learning algorithm includes a deep neural network with 3D-UNet as the backbone network.

[0029] In a fifth aspect, an embodiment of the present invention discloses an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for acquiring the biomaterial mechanical properties of a blood vessel in any embodiment of the first aspect is implemented, or the method for analyzing the biomaterial mechanical properties of a blood vessel in any embodiment of the third aspect is implemented.

[0030] By adopting the above technical solution, the electronic device constructs the correspondence between HU value and material mechanical properties through the blood vessels of multiple experimental samples. This correspondence is universal and can be applied to all target analysis objects, that is, specific individuals. It also conducts personalized evaluation of the blood vessels of specific individuals and reflects the status of plaques, so as to analyze specific problems specifically and obtain accurate results for the mechanical analysis of biomaterials of blood vessels.

[0031] In a sixth aspect, an embodiment of the present invention discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a method for acquiring the mechanical properties of biomaterials of a blood vessel in any embodiment of the first aspect or implements a method for analyzing the mechanical properties of biomaterials of a blood vessel in any embodiment of the third aspect.

[0032] By adopting the above technical solution, the computer-readable storage medium realizes the correspondence between the HU values ​​of the blood vessels of multiple experimental samples and the mechanical properties of the materials. This correspondence is universal and can be applied to all target analysis objects, i.e., specific individuals. The blood vessels of specific individuals are subsequently evaluated in a personalized manner to reflect the state of plaques, so that specific problems can be analyzed specifically, so that the mechanical analysis of the biomaterials of the blood vessels can obtain accurate results.

[0033] The beneficial effects of the present invention are as follows:

[0034] 1. The method and system for obtaining the mechanical properties of biomaterials of blood vessels provided by the present invention construct the correspondence between HU values ​​and material mechanical properties through blood vessels of multiple experimental samples. This correspondence is universal and can be applied to all target analysis objects, that is, specific individuals. Specifically, since the CT images of the target analysis objects reflect individual differences, that is, the HU values ​​have individual differences, the specific individual differences of the target analysis objects can be taken into account when applying the correspondence between HU values ​​and material mechanical properties, and it has universality. Furthermore, it realizes the subsequent personalized evaluation of the blood vessels of specific individuals, reflects the status of plaques, and makes specific analysis of specific problems, so as to obtain accurate results through the mechanical analysis of biomaterials of blood vessels.

[0035] 2. The method and system for analyzing the mechanical properties of vascular biomaterials provided by the present invention are universal, capable of performing in vivo evaluation of specific target analysis objects, reflecting the state of plaques, and capable of performing personalized analysis based on individual differences of the target analysis objects, thereby achieving specific analysis of specific problems and making the results of the mechanical analysis of vascular biomaterials accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1A The biomechanical simulation results of fibrous dense plaque in the prior art are shown;

[0037] Figure 1B The biomechanical simulation results of lipid-containing plaques in the prior art are shown;

[0038] Figure 2 A flow chart showing a method for obtaining mechanical properties of biological materials of blood vessels in an embodiment of the present invention;

[0039] Figure 3 A schematic diagram showing a system for acquiring mechanical properties of biological materials of blood vessels according to an embodiment of the present invention;

[0040] Figure 4 A flow chart showing a method for analyzing the mechanical properties of biological materials of a blood vessel in an embodiment of the present invention;

[0041] Figure 5A schematic diagram showing a biomechanical simulation model of a section of atherosclerotic coronary artery in an embodiment of the present invention;

[0042] Figure 6 Schematic diagram 1 showing a system for analyzing the mechanical properties of biological materials of blood vessels according to an embodiment of the present invention;

[0043] Figure 7 Schematic diagram showing a blood vessel biomaterial mechanical property analysis system according to an embodiment of the present invention Figure 2 ;

[0044] Figure 8 A schematic diagram of the structure of an electronic device showing a method for acquiring and analyzing mechanical properties of biological materials of a blood vessel in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following specific embodiments illustrate the implementation of the present invention, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Although the description of the present invention will be introduced in conjunction with the preferred embodiment, this does not mean that the features of this invention are limited to this implementation. On the contrary, the purpose of introducing the invention in conjunction with the implementation is to cover other options or modifications that may extend based on the claims of the present invention. In order to provide a deep understanding of the present invention, many specific details will be included in the following description. The present invention can also be implemented without using these details. In addition, in order to avoid confusion or blurring the focus of the present invention, some specific details will be omitted in the description. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0046] It should be noted that in this specification, similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0047] The terms “first”, “second”, etc. are only used for distinguishing descriptions and should not be understood as indicating or implying relative importance.

[0048] In the description of this embodiment, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this embodiment can be understood according to specific circumstances.

[0049] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0050] The material mechanical properties in the present invention refer to the response characteristics of the blood vessel wall when subjected to mechanical loads, and these properties are crucial to maintaining the functional and structural integrity of the blood vessels.

[0051] The mechanical properties of blood vessels are affected by many factors, including age, disease state, blood pressure, changes in tissue composition, etc. The blood vessel wall is mainly composed of three layers: the intima, the media, and the adventitia, and the mechanical properties of each layer are different. Usually, the media contains a large number of smooth muscle cells and collagen fibers, which is the main part that determines the mechanical properties of blood vessels.

[0052] The main mechanical properties of vascular materials include elasticity, elastic modulus, Poisson's ratio, yield stress, fracture stress, toughness, stiffness, compliance, creep, relaxation, shear modulus, viscoelasticity, etc.

[0053] Among them, elasticity refers to the ability of the blood vessel wall to deform under the action of external force and return to its original shape after the external force is removed. Elastic modulus refers to an indicator of the rigidity of the blood vessel wall, which is defined as the ratio of stress to strain. The elastic modulus of blood vessels is usually between thousands and tens of thousands of Pascals (Pa). Poisson's ratio describes the relationship between the transverse strain and the longitudinal strain of the blood vessel during stretching. Yield stress refers to the minimum stress value at which the blood vessel wall begins to permanently deform. Fracture stress refers to the maximum stress that the blood vessel wall can withstand before it breaks. Toughness refers to the amount of energy that the blood vessel wall can absorb before breaking, which is the area under the stress-strain curve. Stiffness refers to the ability of the blood vessel wall to resist deformation, which is usually related to the elastic modulus. Compliance refers to the ability of the blood vessel wall to change volume under pressure changes, and is an important indicator of vascular dilation and contraction function. Creep refers to the slow deformation of the blood vessel wall over time under continuous stress. Relaxation refers to the decrease in blood vessel wall stress over time under constant strain conditions. Shear modulus refers to the ability of the blood vessel wall to resist shear force. Viscoelasticity means that the blood vessel wall exhibits both elastic and viscous properties, that is, it has both immediate deformation and deformation that develops over time under stress.

[0054] This application mainly focuses on the material mechanical property parameters of blood vessels such as elastic modulus and shear modulus.

[0055] Figure 1A and Figure 1B It is shown that the same geometric structure in the prior art has different material mechanical properties.

[0056] The traditional biomechanical analysis method based on coronary CT first uses a segmentation algorithm to segment different types of tissues including fibers, lipids, and calcifications, and assigns uniform material mechanical properties to these tissues based on empirical values ​​for subsequent biomechanical analysis. However, this method ignores the differences in material mechanical properties between different samples. This setting method of "one set of material mechanical properties applies to all data" will inevitably affect the accuracy of the biomechanical analysis results. Taking calcified plaque as an example, it shows complex diversity, covering multiple types such as fibrous dense calcification, lipid pool calcification, and necrotic core calcification. These different types of calcified plaques have different material mechanical properties, which leads to obvious differences in their biomechanical analysis results. In our preliminary experiment, we compared high-hardness calcified plaques, that is, simulated fibrous dense calcified plaques (such as Figure 1A ) and low-hardness calcified plaques that simulate calcified plaques containing lipid pools (such as Figure 1B Biomechanical simulation results of the

[0057] refer to Figure 1A and Figure 1B , where the arrow indicates the location of the maximum plaque structural stress; the circle indicates the plaque type; different colors indicate different stress values ​​of the plaque obtained by finite element simulation. When the circle area is set to a harder fibrous dense calcified plaque, the maximum plaque structural stress obtained by finite element simulation is 7.5kPa, while when the circle area is set to a softer lipid pool calcified plaque, the maximum plaque stress obtained by finite element simulation is 10.2kPa.

[0058] It can be found that even if they are all defined as calcified plaques, there are large differences in the biomechanical simulation results between calcification types with different material mechanical properties. In other words, even if they have exactly the same geometric structure, due to different material mechanical property settings, such as Figure 1A The harder fibrous dense calcified plaques and Figure 1B Calcified plaques containing lipid pools are shown to also lead to large differences between biomechanical analysis results.

[0059] It can be seen that in the prior art, the uniform material mechanical properties of tissues in different biological regions of blood vessels are assigned based on empirical values ​​for subsequent biomechanical analysis, which cannot achieve personalized evaluation of specific target analysis objects, resulting in inaccurate biomechanical analysis. This further confirms that in order to achieve accurate biomechanical evaluation, it is crucial to obtain specific material mechanical properties.

[0060] In order to directly obtain the material mechanical properties of the specific target analysis object, the inventors constructed the correspondence between the HU value and the material mechanical properties through the blood vessels of multiple experimental samples. Since the CT images of the target analysis object reflect individual differences, that is, the HU value has individual differences, the specific individual differences of the target analysis object can be taken into account when applying the correspondence between the HU value and the material mechanical properties, which has universality. The correspondence between the HU value and the material mechanical properties can be applied to all target analysis objects, that is, specific individuals. And then conduct personalized assessments of the blood vessels of specific individuals, reflect the status of plaques, and analyze specific problems specifically, so that accurate results can be obtained for the biomaterial mechanical analysis of blood vessels.

[0061] The biomaterial mechanical property acquisition method and system, biomaterial mechanical property analysis method and system, electronic device and computer storage medium provided by the present invention all have non-therapeutic purposes.

[0062] The biomaterial mechanical property acquisition method and biomaterial mechanical property analysis method provided by the present invention do not involve surgical methods, but are based on the processing of medical images, that is, their direct objects are medical images, such as CT images.

[0063] The biomaterial mechanical property acquisition system, biomaterial mechanical property analysis system, electronic device and computer storage medium provided by the present invention are also not devices related to surgical methods, but are related devices based on the processing of medical images, that is, their direct objects are medical images, such as CT images.

[0064] First, refer to Figure 2 The present invention provides a method for obtaining the mechanical properties of a biological material of a blood vessel, comprising:

[0065] S01: Obtain a first image of a blood vessel, and obtain HU values ​​of the blood vessel in different biological regions based on the first image, where the first image is a CT image.

[0066] In this embodiment, a plurality of blood vessels are collected as experimental samples, and CT images of the blood vessels are obtained. The HU values ​​and numerical distribution of different biological regions are obtained based on the obtained CT images of the blood vessels. In this embodiment, the CT images can be CT images of ex vivo blood vessels or CT images of in vivo blood vessels.

[0067] S02: Obtain material mechanical property parameters of different biological regions of the blood vessel in vitro, so as to provide data preparation for the subsequent acquisition of the corresponding relationship between the HU value and the material mechanical property parameters.

[0068] S03: Based on the HU values ​​of the blood vessels in different biological regions and the corresponding material mechanical property parameters of the blood vessels in different biological regions in an ex vivo state, a corresponding relationship between the HU values ​​and the material mechanical property parameters is obtained.

[0069] It should be noted that the material mechanical property parameters of different biological regions of the blood vessels need to be obtained based on the blood vessels in the ex vivo state. In other words, the blood vessels here need to be ex vivo blood vessels, not in vivo blood vessels. The reason is that the analysis of the material mechanical property parameters of different biological regions using in vivo blood vessels will be affected by the contraction and relaxation of the heart during the cardiac cycle. The in vivo blood vessels will deform due to pressure changes, which will cause the imaging position of the blood vessels to deform, resulting in inaccurate analysis of the material mechanical property parameters of different biological regions of the blood vessels.

[0070] By adopting the above technical scheme, the HU values ​​of different biological regions and the corresponding material mechanical property parameters of isolated blood vessels in different biological regions are obtained through CT images of blood vessels of multiple experimental samples, and the corresponding relationship between HU values ​​and material mechanical properties is constructed. This corresponding relationship is universal and can be applied to all target analysis objects, that is, specific individuals. In addition, personalized evaluation of the blood vessels of specific individuals is carried out in the future to reflect the status of plaques, so as to analyze specific problems specifically and obtain accurate results of the biomaterial mechanical analysis of blood vessels.

[0071] In some possible embodiments provided by the present invention, the correspondence between the HU value and the material mechanical property parameters can be obtained through in vitro experiments.

[0072] It should be noted that the method for obtaining the mechanical properties of vascular biomaterials, the system for obtaining the mechanical properties of vascular biomaterials, the method for analyzing the mechanical properties of vascular biomaterials, and the system for analyzing the mechanical properties of vascular biomaterials provided by the present invention are not only applicable to coronary arteries, but also to other biological tissues, such as the myocardium, aorta, renal artery, popliteal artery, etc.

[0073] In some possible embodiments provided by the present invention, S02: obtaining material mechanical property parameters of different biological regions of the blood vessel in an ex vivo state, including: obtaining material mechanical property parameters of different biological regions of the blood vessel in an ex vivo state through iterative finite element simulation or in vitro multi-axis testing.

[0074] In this embodiment, obtaining material mechanical property parameters of different biological regions of the blood vessel in an ex vivo state includes obtaining an intraluminal image, a CT image, or a magnetic resonance image of the ex vivo blood vessel.

[0075] Among them, iterative finite element simulation is a method based on computer-aided engineering (CAE), which uses mathematical models to simulate and analyze the behavior of materials under different loads and boundary conditions. Exemplarily, iterative finite element simulation can obtain the mechanical properties of vascular materials in the following steps: Establish a three-dimensional geometric model of the blood vessel, usually through CT (Computed Tomography) or MRI (Magnetic Resonance Imaging) scanning data. Use finite element software to mesh the three-dimensional geometric model of the blood vessel and divide the three-dimensional geometric model of the blood vessel into multiple small elements for numerical analysis. Define the mechanical properties of the material, including elastic modulus, Poisson's ratio, etc. Set loading conditions and boundary conditions to simulate the stress conditions of the blood vessel in a real environment. Run the simulation and record the mechanical responses such as stress and strain under different loads. According to the simulation results, adjust the material property parameters and perform iterative optimization until satisfactory simulation results are obtained.

[0076] In vitro multi-axial testing is an experimental method that directly applies different loads to an ex vivo vascular sample to measure its mechanical response in different directions. Exemplarily, in vitro multi-axial testing generally includes the following steps: Prepare the vascular sample and ensure that its surface is clean and free of any damage. Fix the vascular sample on a multi-axial testing machine to ensure good contact between the sample and the testing machine. Apply different loads, including forces in axial, radial, and tangential directions. Record mechanical responses such as stress and strain under different loads. Analyze the test results and extract the mechanical property parameters of the blood vessel in different directions.

[0077] Whether it is iterative finite element simulation or in vitro multiaxial testing, the material mechanical property parameters obtained can be used to establish a constitutive model that describes the mechanical properties of vascular materials, thereby providing a basis for biomechanical analysis and clinical applications. These two methods usually complement each other. Iterative finite element simulation can help optimize experimental design, while in vitro multiaxial testing can verify the accuracy of the simulation model.

[0078] In some other possible embodiments provided by the present invention, S03: based on the HU values ​​of blood vessels in different biological regions and the corresponding material mechanical property parameters of different biological blood vessel regions of the blood vessels in vitro, the correspondence between the HU values ​​and the material mechanical property parameters is obtained, including: based on the HU values ​​of blood vessels in different biological regions and the corresponding material mechanical property parameters of different biological blood vessel regions of the blood vessels in vitro, the correspondence between the HU values ​​and the material mechanical property parameters is obtained through a parameter association analysis method.

[0079] In some possible embodiments provided by the present invention, the parameter association analysis method includes: linear regression, nonlinear regression, and principal component analysis.

[0080] Among them, linear regression is a statistical method used to estimate the linear relationship between two or more variables. In this method, the data is assumed to be linearly related, that is, the value of one variable can be linearly predicted by the value of another variable. The linear regression model is usually expressed as y=a+bx, where y is the dependent variable, x is the independent variable, a is the intercept, and b is the slope. Linear regression analysis can be used to predict the value of a variable or to test whether there is a significant linear relationship between two variables. Linear regression can explore the direct linear relationship between HU values ​​and material mechanical property parameters in a relatively simple and direct way. Specifically, multiple data of HU values ​​and corresponding mechanical property parameters are collected; SPSS (Statistical Package for the Social Sciences) and Stata and other statistical software are used to perform linear fitting on the collected data to obtain regression coefficients (such as intercept and slope) and R 2 , and based on R 2 Evaluate the fit. 2 It is an important indicator for evaluating the goodness of fit of the linear regression model, and its value is between 0 and 1. 2 The closer the value of is to 1, the stronger the explanatory power of the model is and the better the fitting effect is. On the contrary, if R 2 If the value of is close to 0, it means that the explanatory power of the model is weak.

[0081] Nonlinear regression is an extension of linear regression and is used to estimate the nonlinear relationship between two or more variables. This method takes into account the nonlinear relationships between data and uses mathematical models to describe these relationships. Nonlinear regression models can more accurately describe complex data relationships, but they are usually more complex than linear regression models, requiring more data points and more complex mathematical models, and are therefore more suitable for predicting complex dependencies between HU values ​​and material mechanical properties. Select a suitable nonlinear model (such as a polynomial model, exponential model) based on the expected data distribution, and use numerical methods including least squares to optimize the nonlinear model parameters. Specifically, if the data presents a curvilinear trend, a polynomial model can be considered. For example, a quadratic polynomial model (y = a + bx + cx^2) can be used to describe a curve that opens up or down; when the data grows or decreases over time and shows an accelerating or decelerating trend, an exponential model (such as y = ae^(bx)) may be a suitable choice. Least squares is a commonly used optimization method for fitting nonlinear models. It finds the optimal values ​​of model parameters by minimizing the sum of squares of the errors. This can be implemented using software packages (such as MATLAB, Python's SciPy library, R language, etc.).

[0082] Principal component analysis (PCA) is a statistical method used to extract a set of linearly unrelated variables from a set of possibly related variables. These variables are called principal components, which are linear combinations of the original variables. The main purpose of PCA is to reduce the dimensionality of the data set while retaining as much information as possible from the original data. Through PCA, we can identify and remove redundant information in the data, simplify the complexity of the data, and better understand the main structure of the data. Principal component analysis can effectively reduce the dimensionality of the data while retaining as much variation information as possible. When the number of biomechanical parameters is large (≥10), principal component analysis can be used to explore the relationship between HU values ​​and material mechanical properties.

[0083] These techniques have different applications in different data analysis and modeling scenarios. Linear regression and nonlinear regression are used to build prediction models, while principal component analysis is used for data dimensionality reduction and feature extraction.

[0084] In some other possible embodiments provided by the present invention, the material mechanical properties of different biological blood vessel regions are represented by a constitutive model.

[0085] A constitutive model is a mathematical model that describes how a material or substance responds to external forces and other external conditions (such as temperature, humidity, chemical environment, etc.). In solid mechanics and fluid mechanics, constitutive models are basic tools for linking stress (or strain) and strain (or stress), which are the core part of mechanical behavior. In the cardiovascular field, especially in the study of atherosclerotic plaques, constitutive models are crucial for simulating the mechanical behavior of plaques. Since plaques are composed of many different components, including lipid cores, fibrous caps, calcification foci, etc., each component has its own unique mechanical properties, so complex constitutive models are required to accurately describe these properties.

[0086] In some other possible embodiments provided by the present invention, the constitutive model includes at least one of the following: a Mooney-Rivlin model, a Fung exponential model, and a Gasser model.

[0087] Exemplarily, the Mooney-Rivlin model is:

[0088] W=c1(I1-3)+c2(I2-3)+D1[exp[D2(I1-3)]-1] Formula 1

[0089] Where W is the strain energy density, I1 and I2 are the first and second strain invariants respectively, c1, c2, D1 and D2 are material mechanical property parameters, e is a natural constant, exp represents the natural exponential function, and exp[D2(I1-3)]-1 is e to the power of [D2(I1-3)]-1.

[0090] An example of the correspondence between the HU value and the material mechanical properties obtained according to the Mooney-Rivlin model is shown in Table 1.

[0091] Table 1

[0092]

[0093] Exemplarily, the Fung index model is:

[0094]

[0095] Where W is the strain energy density, C is the material mechanical property parameter, Q is the intermediate quantity, and e is the natural constant.

[0096]

[0097] In the formula, E ij is the Green strain tensor (i, j = R, θ, Z), R, θ, Z are the radial, circumferential and axial coordinate variables in the cylindrical coordinate system, b1 to b9 are the material mechanical property parameters, E RR represents the Green strain tensor in the radial direction R in the cylindrical coordinate system, E θθ represents the Green strain tensor in the circumferential direction θ in the cylindrical coordinate system, E ZZ represents the Green strain tensor in the axial direction Z in the cylindrical coordinate system, E Rθ It represents the Green strain tensor in the radial direction R and the circumferential direction θ in the cylindrical coordinate system, E θZ It represents the Green strain tensor in the circumferential direction θ and the axial direction Z in the cylindrical coordinate system, E RZ represents the Green's strain tensor in the radial direction R and the axial direction Z in the cylindrical coordinate system.

[0098] An example of the correspondence between the HU value and the material mechanical properties obtained according to the Fung index model is shown in Table 2.

[0099] Table 2

[0100]

[0101] Exemplarily, the Gasser model is:

[0102]

[0103] Where W is the strain energy density, μ, k1, k2, k are material mechanical property parameters, I1 is the first strain invariant, I4 is the fourth strain invariant, e is a natural constant, exp represents the natural exponential function, [exp[k2[κ(l1-3)+(1-3κ)(l4-3)] 2]-1] is e's [k2[κ(l1-3)+(1-3κ)(l4-3)] 2 ]-1] power.

[0104] An example of the correspondence between the HU value and the material mechanical properties obtained according to the Gasser model is shown in Table 3.

[0105] Table 3

[0106]

[0107] Second, reference Figure 3 The present invention provides a system 00 for acquiring mechanical properties of biological materials of blood vessels, comprising a first image acquisition module 01, a material mechanical property parameter acquisition module 02 and a parameter association module 03.

[0108] The first image acquisition module 01 is used to acquire a first image of a blood vessel, and acquire HU values ​​of the blood vessel in different biological regions based on the first image. The first image is a CT image.

[0109] The material mechanical property parameter acquisition module 02 is used to acquire the material mechanical property parameters of different biological regions of the blood vessel in an ex vivo state.

[0110] The parameter association module 03 is used to obtain the corresponding relationship between the HU value and the material mechanical property parameters based on the HU value of the blood vessel in different biological regions and the corresponding material mechanical property parameters of the blood vessel in different biological blood vessel regions in an ex vivo state.

[0111] By adopting the above technical solution, the present invention obtains the corresponding relationship between the HU value and the material mechanical property parameter under the joint action of the first image acquisition module 01, the material mechanical property parameter acquisition module 02 and the parameter association module 03. This corresponding relationship is universal and can be applied to all target analysis objects, i.e., specific individuals. The blood vessels of specific individuals are subsequently evaluated in a personalized manner to reflect the state of plaques, so that specific problems can be analyzed specifically, so that the biomaterial mechanical analysis of blood vessels can obtain accurate results.

[0112] In some possible embodiments provided by the present invention, the material mechanical property parameter acquisition module 02 is used to acquire material mechanical property parameters of different biological regions of a blood vessel in an ex vivo state through iterative finite element simulation or in vitro multi-axis testing.

[0113] In some possible embodiments provided by the present invention, the parameter association module 03 is used to obtain the correspondence between the HU values ​​and the material mechanical property parameters based on the HU values ​​of the blood vessels in different biological regions and the corresponding material mechanical property parameters of the blood vessels in different biological blood vessel regions in an in vitro state through a parameter association analysis method.

[0114] In some possible embodiments, the parameter association analysis method includes: linear regression, nonlinear regression, and principal component analysis.

[0115] In some other possible embodiments, the material mechanical property parameter acquisition module represents the material mechanical properties of different biological blood vessel regions through a constitutive model.

[0116] In some possible embodiments, the constitutive model includes at least one of the following: Mooney-Rivlin model, Fung index model and Gasser model. The specific content has been described in detail above and will not be repeated here.

[0117] Thirdly, reference Figure 4 The present invention also provides a method for analyzing the mechanical properties of vascular biomaterials, comprising:

[0118] S1: Obtain a second image of the target analysis object, where the second image is a CT image.

[0119] In this embodiment, an electrocardiogram gated scan is performed on the target analysis object, ie, the patient, by a CT imaging device to obtain a CT image.

[0120] ECG gating is an imaging technique that uses the ECG signal to synchronize the scanning process of medical imaging equipment so that images can be captured at specific stages of the heart. This technique is particularly important in cardiac imaging because the heart is an organ that is constantly moving, and ECG gating can help reduce image blurring caused by heart motion.

[0121] S2: Identify and segment the second image through an image processing algorithm to obtain a segmentation result of the biological blood vessel in the second image.

[0122] In this embodiment, advanced image processing algorithms are used to accurately identify and segment various biological tissues in the image, including but not limited to fibrous tissue, lipid plaques, calcified plaques, vascular media, vascular adventitia, peripheral fat tissue, etc., to obtain segmentation results, which refer to fibrous tissue, lipid plaques, calcified plaques, vascular media, vascular adventitia, peripheral fat tissue, etc.

[0123] S3: Reconstruct a specific geometric model based on the segmentation results.

[0124] In this embodiment, the specific geometric model is a mathematical model, and its format includes but is not limited to STL, IGES, STP, DXF, etc.

[0125] S4: Obtain the correspondence between the HU value and the material mechanical property according to the specific geometric model and the method for obtaining the mechanical properties of biomaterials as described in any embodiment of the first aspect, and obtain the specific material mechanical property parameters of the biological blood vessel region of the target analysis object.

[0126] That is, according to the specific geometric model reconstructed in step S3 and the corresponding relationship between the HU value and the material mechanical property obtained in steps S01 to S03, the specific material mechanical property parameters of the biological blood vessel region of the target analysis object are obtained. In other words, the specific material mechanical property parameters of the blood vessel tissue region of the target analysis object, i.e., the patient (also a specific individual), are obtained.

[0127] S5: Based on the specific geometric model and specific material mechanical property parameters, a finite element model is constructed, and finite element simulation calculations are performed on the finite element model to obtain the stress and strain distribution of the area of ​​interest.

[0128] In this embodiment, a biomechanical simulation model is constructed based on a specific geometric model and specific material mechanical property parameters.

[0129] In some other possible embodiments provided by the present invention, the method for analyzing the mechanical properties of biological materials of blood vessels further includes:

[0130] S6: Obtaining blood pressure information of the target analysis object;

[0131] S7: Based on the specific geometric model, specific material mechanical property parameters, and blood pressure information, a finite element model is constructed, and finite element simulation calculations are performed on the finite element model to obtain the stress and strain distribution of the region of interest.

[0132] Among them, blood pressure information includes systolic pressure, diastolic pressure, blood pressure waveform and other information.

[0133] In this embodiment, the method for analyzing the mechanical properties of biological materials of blood vessels includes steps S1 to S4 in the aforementioned embodiment, and also includes steps S6 and S7 in this embodiment.

[0134] Among them, step S6 is arranged before step S7, but step S6 can be arranged before or after step S1 or S2 or S3 or S4. That is to say, step S6 is arranged at any position before step S7.

[0135] Finite element simulation is a computer-aided engineering analysis technology that solves engineering and physical problems based on the finite element method (FEM). The finite element method is a mathematical modeling technique that discretizes continuous media (such as materials, structures, etc.) into a finite number of units, each of which has a simple geometric shape, such as a triangle or quadrilateral (in two-dimensional problems) or a tetrahedron or hexahedron (in three-dimensional problems). These units are connected by nodes to form a grid system called a finite element model.

[0136] The finite element model is mainly a numerical analysis method, which discretizes a continuous medium (such as materials, structures, etc.) into a finite number of units, applies boundary conditions and loads to the units, and then uses mathematical methods (such as approximate solutions of partial differential equations) to analyze physical quantities such as stress, strain, temperature, etc. in each unit, and finally integrates the results of all units to obtain the response of the entire structure or system. In this embodiment, blood pressure information is used as the load.

[0137] A biomechanical simulation model is a mathematical model that is specifically used to simulate the dynamic behavior and response of an organism or its partial structure. It needs to consider factors such as the anatomical structure, material properties, physiological mechanisms, and mechanical behavior of the organism. Biomechanical simulation models are widely used in fields such as medicine, sports science, bioengineering, and neuroscience. For example, a biomechanical simulation model can be opened and analyzed in finite element simulation software such as Abaqus and Ansys. Biomechanical simulation models such as Figure 5 shown. Figure 5 The biomechanical simulation results of a certain section of atherosclerotic coronary artery are shown, where different colors at various locations correspond to different sizes of plaque structural stress. For example, Figure 5 In the middle, V shows the plaque structural stress of the vascular adventitia; W shows the plaque structural stress of the vascular middle membrane; S, Z, and Y all show the plaque structural stress of the vascular wall, among which the plaque structural stress at the S and Y positions is the largest.

[0138] The region of interest may be, for example, a vascular fibrous cap region, a fibrous tissue region, a lipid plaque region, a calcified plaque region, a vascular media region, a vascular adventitia region, and a peripheral adipose tissue region.

[0139] In this embodiment, the purpose of establishing a biomechanical simulation model is to obtain the stress and strain distribution of the region of interest.

[0140] By adopting the above-mentioned technical scheme, the method for analyzing the mechanical properties of vascular biomaterials provided by the present invention is universal, can perform in vivo evaluation of specific target analysis objects, reflect the state of plaques, and can perform personalized analysis based on the individual differences of the target analysis objects, thereby achieving specific analysis of specific problems and making the results of the mechanical analysis of vascular biomaterials accurate.

[0141] In some possible embodiments provided by the present invention, the image processing algorithm is a deep learning algorithm.

[0142] In some possible embodiments, the deep learning algorithm includes a deep neural network with 3D-UNet as the backbone network. The trained deep neural network with 3D-UNet as the backbone network is used to automatically segment CTA images, wherein the deep neural network avoids the complex manual feature design process in the traditional method, improves the accuracy and generalization ability of segmentation, and can be applied to CTA images under different instruments and acquisition parameters, providing strong support for subsequent coronary reconstruction and geometric model construction.

[0143] It should be pointed out here that both CT images and CTA images are imaging results based on CT (computed tomography) technology, but there are some differences between them. CT images, or computed tomography images, are two-dimensional images obtained through CT scanning. The image can be used to reconstruct the internal structure of an object and provide detailed information about the internal structure of the object, such as bones, soft tissues, blood vessels, etc. CT images are often used to diagnose various diseases, such as fractures, tumors, infections, etc. CTA images, or computed tomography angiography images, are a special type of CT image that focuses on showing vascular structures. CTA enhances the visibility of blood vessels in the image by injecting blood containing contrast agents into the patient. Then, CT scans are used to capture blood flow and vascular structure. CTA can provide high-resolution vascular images for the diagnosis of vascular diseases such as arterial stenosis, hemangiomas, thrombi, etc. In general, CT images are a broad imaging technology used to display internal structures, while CTA images are a CT imaging method specifically used to display vascular structures.

[0144] Fourthly, reference Figure 6 The present invention provides a vascular biomaterial mechanical property analysis system 10, including a second image acquisition module 11, an image processing module 12, a three-dimensional geometric model establishment module 13, a specific material mechanical property parameter calculation module 14 and a finite element model module 15.

[0145] The second image acquisition module 11 is used to obtain a second image of the target analysis object, and the second image is a CT image.

[0146] The image processing module 12 is used to identify and segment the second image through an image processing algorithm to obtain a segmentation result of the biological blood vessels of the second image.

[0147] The three-dimensional geometric model building module 13 is used to reconstruct a specific geometric model based on the segmentation result; the specific material mechanical property parameter calculation module is used to obtain the material mechanical property parameters of the biological blood vessel region of the target analysis object according to the corresponding relationship between the HU value and the material mechanical property parameter obtained by the parameter association module 03 of the biomaterial mechanical property acquisition system obtained in any embodiment of the second aspect and the specific geometric model obtained by the three-dimensional geometric model building module 13.

[0148] The finite element model module 15 is used to construct a finite element model based on the segmentation result of the biological blood vessels in the second image obtained by the image processing module 12, the specific geometric model obtained by the three-dimensional geometric model establishment module 13, and the material mechanical property parameters of the biological blood vessel area of ​​the target analysis object obtained by the specific material mechanical property parameter calculation module 14, and perform finite element simulation calculation on the finite element model to obtain the stress and strain distribution of the area of ​​interest.

[0149] By adopting the above-mentioned technical scheme, the blood vessel biomaterial mechanical property analysis system provided by the present invention, under the joint action of the second image acquisition module 11, the image processing module 12, the three-dimensional geometric model establishment module 13, the specific material mechanical property parameter calculation module 14 and the finite element model module 15, can perform in vivo evaluation of specific target analysis objects, reflect the state of plaques, and can perform personalized analysis according to the individual differences of the target analysis objects, realize specific analysis of specific problems, make the results of the blood vessel biomaterial mechanical analysis accurate, and the system has universal applicability.

[0150] In some other possible embodiments provided by the present invention, reference is made to Figure 7 The blood vessel biomaterial mechanical property analysis system 10 includes a blood pressure acquisition module 16 and a finite element model module 15. The blood pressure acquisition module 16 is used to obtain blood pressure information of the target analysis object. The finite element model module 16 is used to construct a finite element model based on a specific geometric model, specific material mechanical property parameters, and blood pressure information, perform finite element simulation calculations on the finite element model, and obtain stress and strain distributions in the region of interest.

[0151] The blood pressure information includes systolic pressure, diastolic pressure, blood pressure waveform, etc. The systolic pressure and diastolic pressure can be obtained by the cuff pressure method.

[0152] In this embodiment, the blood pressure information of the target analysis object is obtained as the load of the finite element model. The finite element model is constructed by the systolic pressure, diastolic pressure of the target analysis object, the specific geometric model, and the specific material mechanical property parameters, and the finite element simulation calculation is performed on the finite element model to obtain the stress and strain distribution of the region of interest. The in vivo evaluation of the specific target analysis object is realized to reflect the state of the plaque, and personalized analysis can be performed according to the individual differences of the target analysis object, so as to realize specific analysis of specific problems, so that the results of the mechanical analysis of the biomaterials of the blood vessels are accurate, and the system has universal applicability.

[0153] According to another specific embodiment of the present invention, the image processing algorithm is a deep learning algorithm.

[0154] According to another specific embodiment of the present invention, the deep learning algorithm includes a deep neural network with 3D-UNet as the backbone network. The specific content has been described in detail above and will not be repeated here.

[0155] Fifth, reference Figure 8 The present invention provides an electronic device 2, comprising a memory 201, a processor 202, and a computer program stored in the memory 201 and executable on the processor 202. When the processor 202 executes the computer program, the method for obtaining the mechanical properties of a biomaterial of a blood vessel in any embodiment of the first aspect or the method for analyzing the mechanical properties of a biomaterial of a blood vessel in any embodiment of the third aspect is implemented. The memory 201 may include, for example, a system memory, a fixed non-volatile storage medium, etc. The system memory may store, for example, an operating system, an application program, a boot loader, and other programs.

[0156] In this embodiment, the electronic device constructs the correspondence between the HU value and the material mechanical properties through the blood vessels of multiple experimental samples. This correspondence is universal and can be applied to all target analysis objects, that is, specific individuals. The blood vessels of specific individuals are subsequently evaluated in a personalized manner to reflect the state of plaques, so that specific problems can be analyzed specifically, so that the biomaterial mechanical analysis of blood vessels can obtain accurate results.

[0157] In a sixth aspect, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for acquiring the mechanical properties of biomaterials of a blood vessel in any embodiment of the first aspect or implements the method for analyzing the mechanical properties of biomaterials of a blood vessel in any embodiment of the third aspect.

[0158] By adopting the above technical solution, the computer-readable storage medium realizes the correspondence between the HU values ​​of the blood vessels of multiple experimental samples and the mechanical properties of the materials. This correspondence is universal and can be applied to all target analysis objects, i.e., specific individuals. The blood vessels of specific individuals are subsequently evaluated in a personalized manner to reflect the state of plaques, so that specific problems can be analyzed specifically, so that the mechanical analysis of the biomaterials of the blood vessels can obtain accurate results.

[0159] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable non-transient storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0160] The present invention is described with reference to the flowchart and / or block diagram of the method, device (system), and computer program product according to the embodiment of the present invention. 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 produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0161] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0162] These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0163] Although the present invention has been illustrated and described with reference to certain preferred embodiments of the present invention, it should be understood by those skilled in the art that the above is a further detailed description of the present invention in conjunction with specific embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. Those skilled in the art may make various changes in form and details, including making several simple deductions or substitutions, without departing from the spirit and scope of the present invention.

Claims

1. A method for obtaining mechanical properties of vascular biomaterials, characterized in that: include: Obtaining a first image of a blood vessel, and obtaining HU values ​​of the blood vessel in different biological regions based on the first image, wherein the first image is a CT image; Acquiring material mechanical property parameters of different biological regions of the blood vessel in an ex vivo state; Based on the HU values ​​of the blood vessels in different biological regions and the corresponding material mechanical property parameters of the blood vessels in different biological regions in an ex vivo state, a corresponding relationship between the HU values ​​and the material mechanical property parameters is obtained.

2. The method for obtaining mechanical properties of biological materials of blood vessels according to claim 1, characterized in that: The obtaining of material mechanical property parameters of different biological regions of the blood vessel in an ex vivo state includes: Obtaining material mechanical property parameters of different biological regions of the blood vessel in an ex vivo state by iterative finite element simulation or in vitro multi-axis testing; or, The obtaining of the correspondence between the HU value and the material mechanical property parameter based on the HU value of the blood vessel in different biological regions and the corresponding material mechanical property parameter of the blood vessel in different biological blood vessel regions in an ex vivo state comprises: Based on the HU values ​​of the blood vessels in different biological regions and the corresponding material mechanical property parameters of the blood vessels in different biological blood vessel regions in vitro, the corresponding relationship between the HU values ​​and the material mechanical property parameters is obtained through a parameter association analysis method.

3. The method for obtaining mechanical properties of biological materials of blood vessels according to claim 1, characterized in that: The material mechanical properties of the different biological blood vessel regions are represented by a constitutive model, wherein the constitutive model includes at least one of the following: a Mooney-Rivlin model, a Fung exponential model, and a Gasser model.

4. A system for acquiring mechanical properties of biological materials of blood vessels, characterized in that: include: A first image acquisition module, used to acquire a first image of a blood vessel, and acquire HU values ​​of the blood vessel in different biological regions based on the first image, wherein the first image is a CT image; A material mechanical property parameter acquisition module, used to acquire material mechanical property parameters of different biological regions of the blood vessel in an ex vivo state; The parameter association module is used to obtain the corresponding relationship between the HU value and the material mechanical property parameters based on the HU value of the blood vessel in different biological regions and the corresponding material mechanical property parameters of different biological blood vessel regions of the blood vessel in an ex vivo state.

5. The system for acquiring mechanical properties of biological materials of blood vessels according to claim 4, characterized in that: The material mechanical property parameter acquisition module is used to acquire material mechanical property parameters of different biological blood vessel regions of the blood vessel in an ex vivo state through iterative finite element simulation or in vitro multi-axis testing; or The parameter association module is used to obtain the correspondence between the HU value and the material mechanical property parameters through a parameter association analysis method based on the HU value of the blood vessel in different biological regions and the corresponding material mechanical property parameters of different biological blood vessel regions of the blood vessel in vitro.

6. The system for acquiring mechanical properties of biological materials of blood vessels according to claim 5, characterized in that: The material mechanical property parameter acquisition module represents the material mechanical properties of the different biological blood vessel regions through a constitutive model, wherein the constitutive model includes at least one of the following: a Mooney-Rivlin model, a Fung index model, and a Gasser model.

7. A method for analyzing the mechanical properties of vascular biomaterials, characterized in that: include: Obtaining a second image of the target analysis object, wherein the second image is a CT image; Identifying and segmenting the second image by an image processing algorithm to obtain a segmentation result of a biological blood vessel in the second image; reconstructing a specific geometric model based on the segmentation result; Obtain the corresponding relationship between the HU value and the material mechanical property according to the specific geometric model and the method for obtaining the mechanical property of the biomaterial according to any one of claims 1 to 3, and obtain the specific material mechanical property parameters of the biological blood vessel region of the target analysis object; A finite element model is constructed according to the specific geometric model and the specific material mechanical property parameters, and finite element simulation calculation is performed on the finite element model to obtain stress and strain distribution in the area of ​​interest.

8. The method for analyzing mechanical properties of blood vessel biomaterials according to claim 7, characterized in that: include: Acquiring blood pressure information of the target analysis object; A finite element model is constructed according to the specific geometric model, the specific material mechanical property parameters, and the blood pressure information, and finite element simulation calculations are performed on the finite element model to obtain stress and strain distributions in the region of interest.

9. The method for analyzing mechanical properties of blood vessel biomaterials according to claim 7, characterized in that: The image processing algorithm is a deep learning algorithm, which includes a deep neural network with 3D-UNet as the backbone network.

10. A system for analyzing mechanical properties of vascular biomaterials, characterized in that: include: A second image acquisition module, used to obtain a second image of the target analysis object, wherein the second image is a CT image; An image processing module, used for identifying and segmenting the second image through an image processing algorithm to obtain a segmentation result of a biological blood vessel in the second image; A three-dimensional geometric model building module, used for reconstructing a specific geometric model based on the segmentation result; A specific material mechanical property parameter calculation module, used to obtain the material mechanical property parameters of the biological region of the target analysis object according to the correspondence between the HU value and the material mechanical property parameter obtained by the parameter association module of the biomaterial mechanical property acquisition system according to any one of claims 4 to 6 and the specific geometric model obtained by the three-dimensional geometric model establishment module; The finite element model module is used to construct a finite element model according to the specific geometric model and the specific material mechanical property parameters, perform finite element simulation calculations on the finite element model, and obtain the stress and strain distribution of the area of ​​interest.

11. The blood vessel biomaterial mechanical property analysis system according to claim 10, characterized in that: include: A blood pressure acquisition module, used to acquire blood pressure information of the target analysis object; The finite element model module is used to construct a finite element model based on the specific geometric model, the specific material mechanical property parameters, and the blood pressure information, perform finite element simulation calculations on the finite element model, and obtain stress and strain distribution in the area of ​​interest.

12. The blood vessel biomaterial mechanical property analysis system according to claim 10, characterized in that: The image processing algorithm is a deep learning algorithm, which includes a deep neural network with 3D-UNet as the backbone network.

13. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for acquiring the mechanical properties of the biomaterials of a blood vessel as described in any one of claims 1 to 3 or implements the method for analyzing the mechanical properties of the biomaterials of a blood vessel as described in any one of claims 7 to 9 when executing the computer program.

14. A computer storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, it implements the method for acquiring the mechanical properties of biomaterials of a blood vessel as described in any one of claims 1 to 3 or implements the method for analyzing the mechanical properties of biomaterials of a blood vessel as described in any one of claims 7 to 9.

Images