Method and system for acquiring biological material mechanical property of blood vessel, and method and system for analyzing biological material mechanical property of blood vessel

Abstract

Disclosed in the present invention is a method for acquiring the biological material mechanical property of a blood vessel, the method comprising: obtaining a first image of a blood vessel, and acquiring HU values of the blood vessel in different biological regions on the basis of the first image; acquiring material mechanical property parameters of the blood vessel in the different biological regions in an ex-vivo state; and on the basis of the HU values and the material mechanical property parameters, obtaining a correspondence between the HU values and the material mechanical property parameters. The correspondence between the HU values and the material mechanical property parameters that is constructed by the method in the present invention has universality. Moreover, the subsequent personalized evaluation on blood vessels of specific individuals and reflection of plaque conditions are achieved in a case-specific manner, such that accurate results for the biological material mechanical analysis of the blood vessels are obtained. Further disclosed in the present invention are a system for acquiring the biological material mechanical property of a blood vessel, a method and system for analyzing the biological material mechanical property of a blood vessel, and an electronic device and a computer-readable storage medium.

Description

Method, analysis method and system for obtaining biomechanical properties of blood vessels TECHNICAL FIELD

[0001] The present application relates to the technical field of biomechanics of blood vessels, and in particular to a method and system for obtaining biomechanical properties of blood vessels, a method and system for analyzing biomechanical properties of blood vessels, an electronic device and a computer-readable storage medium. BACKGROUND

[0002] Cardiovascular disease is the leading cause of morbidity and mortality in China. The key to its high mortality and disability rate lies in the rupture of coronary atherosclerotic plaques. Plaque rupture is a critical event in coronary artery disease, which can lead to heart attack or stroke. Plaque rupture usually occurs on atherosclerotic plaques in the coronary artery, 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 leakage of lipid core material into the vascular lumen, thereby maintaining the normal function of the blood vessel. The root cause of plaque rupture is the imbalance between plaque stress and the load-carrying capacity of the plaque fibrous cap: for example, with each beat of the heart, the coronary artery experiences periodic stretching, compression and shear forces. If these mechanical stresses exceed the load-carrying capacity of the fibrous cap, the plaque can rupture. Therefore, biomechanical evaluation of the plaque is crucial.

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

[0004] Ideally, a constitutive model describing the mechanical properties of coronary material is constructed based on experimental data obtained from multi-axial testing, to reflect the nonlinear and anisotropic material characteristics of biological materials. 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, and chemical environment. In solid and fluid mechanics, constitutive models are fundamental tools that link stress (or strain) and strain (or stress), which are the core of mechanical behavior. In the field of cardiovascular medicine, especially in the study of atherosclerotic plaques, constitutive models are crucial for simulating the mechanical behavior of plaques. Since plaques are composed of multiple components, including lipid cores, fibrous caps, and calcified foci, each component has its unique mechanical properties, so a complex constitutive model is needed to accurately describe these properties. Multi-axial testing is a material testing method used to evaluate the mechanical properties of materials under different directions and loading conditions. In multi-axial testing, materials are subjected to multiple forces such as tension, compression, and shear in different directions. In the field of cardiovascular medicine, multi-axial testing is particularly important for understanding the mechanical behavior of vascular tissues. For example, the arterial wall is subjected to complex stresses from various directions, including circumferential (around the circumference of the blood vessel) and axial (along the length of the blood vessel) stresses. Through multi-axial testing, researchers can better simulate these stress states, thus more accurately evaluating the mechanical properties of the arterial wall, which is crucial for studying the development mechanisms of atherosclerosis, aneurysm, and other vascular diseases. Nonlinearity refers to the fact that biological materials do not have a simple linear relationship between stress or strain, but rather change gradually as stress or strain increases. Anisotropy refers to the fact that the mechanical properties of biological materials are different in different directions, meaning they 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, media, and adventitia for in vitro multi-axial testing in a laboratory environment. By measuring the mechanical response of each component under different loads, stress-strain curves are drawn, and various models are used to fit and analyze the data to describe the material mechanical properties. However, most of these tests are conducted under simplified ex vivo conditions, which cannot well reflect the state of the plaque. More importantly, this approach requires the removal of the coronary artery, making it impossible to apply to in vivo assessment; at the same time, the material mechanical properties obtained are only applicable to specific experimental samples, and the differences between individuals and the diversity of tissue structures in 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] CT as a non-invasive imaging technique, its economy enables it to be widely used in the preliminary screening of potential groups. The traditional coronary CT-based biomechanical analysis method first realizes the segmentation of different types of tissues including fiber, lipid and calcification through a segmentation algorithm, and assigns uniform material mechanics properties to different types of tissues based on empirical values for subsequent biomechanical analysis. However, this method ignores the differences in material mechanics properties between different samples. This "one set of material mechanics properties for all data" setting inevitably affects the accuracy of the biomechanical analysis results. Taking calcified plaque as an example, it shows complex diversity, including fibrous dense calcification, lipid-containing pool calcification, and necrotic core-containing calcification. The material mechanics properties of these different types of calcified plaques are different, resulting in significant differences in their biomechanical analysis results. Our pre-experiment compared the biomechanical simulation results of high-hardness calcified plaques (simulating fibrous dense calcified plaques) and low-hardness calcified plaques (simulating lipid-containing pool calcified plaques). It can be found that even if they are all defined as calcified plaques, there are still significant differences in the biomechanical simulation results between calcified plaques with different material mechanics properties. This further confirms that specific material mechanics properties are essential for accurate biomechanical evaluation.

[0007] The existing technology has the technical problem that the biomechanical property parameters of biological materials obtained according to a specific experimental sample are specific and cannot be universally applied to the remaining experimental objects; only the ex vivo blood vessels of the target analysis object can be analyzed, and the in vivo evaluation of the target analysis object cannot be applied, nor can the state of the plaque be reflected. When analyzing the biomechanical properties of biological materials of blood vessels through non-invasive imaging technology CT, directly assigning uniform material mechanics properties to different types of tissues based on empirical values for subsequent biomechanical analysis fails to analyze specific problems, resulting in inaccurate biomechanical analysis results of blood vessels. SUMMARY

[0008] The purpose of the present application is to solve the above technical problems.

[0009] In the first aspect, the present application provides a method for obtaining the biomechanical properties of biological materials of blood vessels. The method establishes a correspondence between the HU values and the material mechanics properties of blood vessels of multiple experimental samples. This correspondence is universal and can be applied to all target analysis objects, i.e., specific individuals. Specifically, since the CT images of the target analysis objects reflect individual differences, i.e., HU values have individual differences, the correspondence between HU values and material mechanics properties can take into account the specific individual differences of the target analysis objects, and is universal.

[0010] The embodiment of the present application discloses a method for obtaining biomechanical properties of a blood vessel, comprising: obtaining a first image of the blood vessel, obtaining HU values of different biological regions of the blood vessel in an ex vivo state based on the first image, the first image being a CT image; obtaining material mechanical property parameters of different biological regions of the blood vessel in the ex vivo state; and obtaining a corresponding relationship between the HU values and the material mechanical property parameters based on the HU values of the different biological regions of the blood vessel and the corresponding material mechanical property parameters of the different biological regions of the blood vessel in the ex vivo state.

[0011] The above technical solution is adopted to obtain the HU values of different biological regions and the corresponding material mechanical property parameters of different biological regions of the blood vessel in the ex vivo state through the CT images of the blood vessels of multiple experimental samples, and a corresponding relationship between the HU values and the material mechanical properties is constructed. This corresponding relationship is universal and can be applied to all target analysis objects, i.e., specific individuals. The state of the plaque is reflected through personalized evaluation of the blood vessels of the specific individuals, and specific problems are analyzed to obtain accurate results of the biomechanical analysis of the blood vessels.

[0012] It should be noted that the method and system for obtaining biomechanical properties, the method and system for analyzing biomechanical properties, the electronic device, and the computer storage medium provided by the present application all have non-therapeutic purposes.

[0013] The method for obtaining biomechanical properties and the method for analyzing biomechanical properties provided by the present application do not involve surgical methods, but are based on processing of medical images, i.e., the direct object is a medical image such as a CT image.

[0014] The system for obtaining biomechanical properties and the system for analyzing biomechanical properties provided by the present application are also not devices related to surgical methods, but are devices related to processing of medical images, i.e., the direct object is a medical image such as a CT image.

[0015] According to another specific embodiment of the present application, the material mechanical property parameters of different biological regions of the blood vessel in the ex vivo state are obtained by: 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 obtaining a corresponding relationship between the HU values and the material mechanical property parameters based on the HU values of the different biological regions of the blood vessel and the corresponding material mechanical property parameters of the different biological regions of the blood vessel in the ex vivo state, comprising: obtaining the corresponding relationship between the HU values and the material mechanical property parameters through a parameter correlation analysis method based on the HU values of the different biological regions of the blood vessel and the corresponding material mechanical property parameters of the different biological regions of the blood vessel in the ex vivo state.

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

[0017] In a second aspect, embodiments of the present application disclose a system for obtaining material mechanical properties of a blood vessel, comprising: a first image acquisition module configured to acquire a first image of the blood vessel, and obtain HU values of different biological regions of the blood vessel based on the first image, wherein the first image is a CT image; a material mechanical property parameter acquisition module configured to obtain material mechanical property parameters of the different biological regions of the blood vessel in an ex vivo state; and a parameter correlation module configured to obtain a corresponding relationship between the HU values and the material mechanical property parameters based on the HU values of the different biological regions of the blood vessel and the material mechanical property parameters of the different biological regions of the blood vessel in the ex vivo state.

[0018] According to the above technical solution, the corresponding relationship between the HU values and the material mechanical property parameters is obtained by the first image acquisition module, the material mechanical property parameter acquisition module and the parameter correlation module, and the corresponding relationship is universal and applicable to all target analysis objects, i.e., specific individuals. The blood vessels of the specific individuals can be evaluated individually, the state of the plaque can be reflected, and the specific problem can be analyzed, so that the biological material mechanical analysis of the blood vessels can obtain accurate results.

[0019] According to another specific embodiment of the present application, the material mechanical property parameter acquisition module is configured to obtain the material mechanical property parameters of the different biological regions of the blood vessel in the ex vivo state by iterative finite element simulation or in-vitro multi-axial testing, or the parameter correlation module is configured to obtain the corresponding relationship between the HU values and the material mechanical property parameters by a parameter correlation analysis method based on the HU values of the different biological regions of the blood vessel and the material mechanical property parameters of the different biological regions of the blood vessel in the ex vivo state.

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

[0021] In a third aspect, the embodiments of the present application disclose a method for analyzing biomechanical properties of blood vessels, comprising: obtaining a second image of a target analysis object, the second image being a CT image; identifying and segmenting the second image by using an image processing algorithm to obtain a segmentation result of biological blood vessels of the second image; reconstructing a specific geometric model based on the segmentation result; obtaining specific material mechanical property parameters of a biological blood vessel region of the target analysis object according to the specific geometric model and a corresponding relationship between HU values and material mechanical properties; and constructing a finite element model according to the specific geometric model and the specific material mechanical property parameters, performing finite element simulation calculation on the finite element model, and obtaining stress and strain distribution of a region of interest.

[0022] By using the technical scheme, the method for analyzing biomechanical properties of blood vessels has universality, can evaluate specific target analysis objects in vivo, reflect the state of plaques, can perform individualized analysis according to individual differences of target analysis objects, realize specific analysis of specific problems, and make the biomechanical analysis result of blood vessels accurate.

[0023] According to another specific embodiment of the present application, the method for analyzing biomechanical properties of blood vessels comprises: obtaining blood pressure information of the target analysis object; constructing a finite element model according to the specific geometric model, the specific material mechanical property parameters and the blood pressure information, performing finite element simulation calculation on the finite element model, and obtaining stress and strain distribution of a region of interest.

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

[0025] In a fourth aspect, the embodiments of the present application disclose a system for analyzing biomechanical properties of blood vessels, comprising: a second image acquisition module configured to obtain a second image of a target analysis object, the second image being a CT image; an image processing module configured to identify and segment the second image by using an image processing algorithm to obtain a segmentation result of biological blood vessels of the second image; a three-dimensional geometric model establishing module configured to reconstruct a specific geometric model based on the segmentation result; a specific material mechanical property parameter calculation module configured to obtain material mechanical property parameters of a biological blood vessel region of the target analysis object according to a corresponding relationship between HU values and material mechanical property parameters and the specific geometric model obtained by the three-dimensional geometric model establishing module; and a finite element model module configured to construct a finite element model according to the segmentation result of biological blood vessels of 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 calculation on the finite element model, and obtain stress and strain distribution of a region of interest.

[0026] The blood vessel biomaterial mechanical property analysis system provided by the application can evaluate a specific target analysis object in vivo, reflect the state of a plaque, and perform individualized analysis according to individual differences of the target analysis object, so that the blood vessel biomaterial mechanical analysis result is accurate, and the system is universal.

[0027] According to another specific embodiment of the application, the blood vessel biomaterial mechanical property analysis system comprises: a blood pressure acquisition module configured to acquire blood pressure information of the target analysis object; and a finite element model module configured to construct a finite element model according to the specific geometric model, the specific material mechanical property parameters and the blood pressure information, perform finite element simulation calculation on the finite element model, and acquire stress and strain distribution of a region of interest.

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

[0029] In a fifth aspect, an embodiment of the application discloses an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the blood vessel biomaterial mechanical property acquisition method in any of the embodiments of the first aspect or implements the blood vessel biomaterial mechanical property analysis method in any of the embodiments of the third aspect.

[0030] According to the above technical solution, the electronic device establishes a corresponding relationship between HU values and material mechanical properties of blood vessels of multiple experimental samples, and the corresponding relationship is universal and can be applied to all target analysis objects, i.e., specific individuals. The blood vessels of the specific individuals are evaluated and the state of a plaque is reflected, so that the blood vessel biomaterial mechanical analysis obtains accurate results.

[0031] In a sixth aspect, an embodiment of the application discloses a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the blood vessel biomaterial mechanical property acquisition method in any of the embodiments of the first aspect or implement the blood vessel biomaterial mechanical property analysis method in any of the embodiments of the third aspect.

[0032] The computer readable storage medium realizes the corresponding relationship between the HU value of the blood vessel constructed by the multiple experimental samples and the material mechanical property, the corresponding relationship has universality and can be applied to all target analysis objects, i.e., specific individuals.

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

[0034] 1. The method and system for obtaining the biological material mechanical property of the blood vessel provided by the present application realize the corresponding relationship between the HU value of the blood vessel constructed by the multiple experimental samples and the material mechanical property, the corresponding relationship has universality and can be applied to all target analysis objects, i.e., specific individuals. Specifically, the CT image of the target analysis object reflects individual differences, i.e., the HU value has individual differences, and the corresponding relationship between the HU value and the material mechanical property can consider the specific individual differences of the target analysis object and has universality. Further, the subsequent individualized evaluation of the blood vessel of the specific individual is realized, the state of the plaque is reflected, specific problems are analyzed, and accurate results are obtained through the biological material mechanical analysis of the blood vessel.

[0035] 2. The method and system for analyzing the biological material mechanical property of the blood vessel provided by the present application have universality, can evaluate the specific target analysis object in vivo, reflect the state of the plaque, can perform individualized analysis according to the individual differences of the target analysis object, realize specific problem analysis, and make the biological material mechanical analysis result of the blood vessel accurate. BRIEF DESCRIPTION OF DRAWINGS

[0036] FIG. 1A shows the biomechanical simulation result of a fibrous dense plaque in the prior art;

[0037] FIG. 1B shows the biomechanical simulation result of a lipid-containing plaque in the prior art;

[0038] FIG. 2 shows a flowchart of the method for obtaining the biological material mechanical property of the blood vessel in the embodiment of the present application;

[0039] FIG. 3 shows a schematic diagram of the system for obtaining the biological material mechanical property of the blood vessel in the embodiment of the present application;

[0040] FIG. 4 shows a flowchart of the method for analyzing the biological material mechanical property of the blood vessel in the embodiment of the present application;

[0041] FIG. 5 shows a schematic diagram of a biomechanical simulation model of a segment of atherosclerotic coronary blood vessel in the embodiment of the present application;

[0042] Fig. 6 shows a schematic diagram of a system for analyzing biomechanical properties of a blood vessel according to an embodiment of the present application;

[0043] Fig. 7 shows a schematic diagram of a system for analyzing biomechanical properties of a blood vessel according to an embodiment of the present application;

[0044] Fig. 8 shows a schematic diagram of an electronic device for acquiring and analyzing biomechanical properties of a blood vessel according to an embodiment of the present application. DETAILED DESCRIPTION

[0045] The advantages and effects of the present application can be easily understood by those skilled in the art from the description of the specific embodiments. Although the description of the present application will be introduced in combination with the preferred embodiments, it does not mean that the features of the present application are limited to the embodiments. On the contrary, the purpose of introducing the embodiments in combination with the present application is to cover other options or modifications that can be extended based on the claims of the present application. In order to provide a deep understanding of the present application, many specific details will be included in the following description. The present application can also be implemented without using these details. In addition, in order to avoid confusion or obscure the focus of the present application, some specific details will be omitted in the description. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

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

[0047] The terms "first", "second", and the like are only used for differentiation and description, and cannot be understood as indicating or implying relative importance.

[0048] In the description of the present embodiment, it should also be noted that unless otherwise explicitly specified and limited, the terms "set", "connected", and "connected" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be connected inside two elements. For those skilled in the art, the specific meaning of the above terms in the present embodiment can be understood according to the specific circumstances.

[0049] In order to make the purpose, technical solutions and advantages of the present application clearer, the embodiments of the present application will be further described in detail below with reference to the drawings.

[0050] The biomechanical properties in the present application refer to the reaction characteristics of the blood vessel wall of the blood vessel when subjected to mechanical load, which are crucial for maintaining the function and structural integrity of the blood vessel.

[0051] The material mechanical properties of blood vessels are influenced by a variety of factors, including age, disease state, blood pressure, changes in tissue composition, etc. The blood vessel wall is mainly composed of three layers: Intima, Media and Adventitia, and each layer has different mechanical properties. Generally, the Media layer contains a large number of smooth muscle cells and collagen fibers, and is the main part that determines the mechanical properties of blood vessels.

[0052] The main material mechanical properties of blood vessels 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 external force and return to its original state after the external force is removed. Elastic modulus is a measure of the stiffness of the blood vessel wall, defined as the ratio of stress to strain. The elastic modulus of the blood vessel is usually between several thousand and several tens of thousands of Pascals (Pa). Poisson's ratio describes the relationship between the lateral strain and the longitudinal strain of the blood vessel during stretching. The yield stress is the minimum stress value at which the blood vessel wall begins to exhibit permanent deformation. The fracture stress is the maximum stress that the blood vessel wall can withstand before it breaks. Toughness is 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, which is an important indicator of blood vessel 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 stress of the blood vessel wall over time under constant strain. Shear modulus refers to the ability of the blood vessel wall to resist shear force. Viscoelasticity refers to the blood vessel wall exhibiting both elastic and viscous properties, i.e. both immediate deformation and deformation over time under stress.

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

[0055] Figures 1A and 1B show the same geometry with different material mechanical properties in the prior art.

[0056] Traditional coronary CT-based biomechanical analysis methods first implement segmentation of different types of tissue including fiber, lipid and calcification by segmentation algorithms, and assign uniform material mechanics properties to these tissues based on empirical values for subsequent biomechanical analysis. However, this method ignores the differences in material mechanics properties between different samples, and this "one set of material mechanics properties for all data" setting inevitably affects the accuracy of the biomechanical analysis results. For example, calcified plaques exhibit complex diversity, covering fiber-dense calcification, lipid-containing pool calcification, and necrotic core-containing calcification. The material mechanics properties of these different types of calcified plaques are different, resulting in significant differences in their biomechanical analysis results. Our pre-experiment compared the biomechanical simulation results of high-hardness calcified plaques, i.e., simulating fiber-dense calcified plaques (as shown in FIG. 1A), and low-hardness calcified plaques, i.e., simulating lipid-containing pool calcified plaques (as shown in FIG. 1B).

[0057] Referring to FIGS. 1A and 1B, the arrows indicate the location where the maximum plaque structural stress occurs; the circular portion indicates the plaque type; and different gray colors represent different stress values of the plaque obtained by finite element simulation. Specifically, as shown in FIGS. 1A and 1B, the circular portion includes the area with the darkest gray color, i.e., the area with the largest stress value, which indicates the presence of a plaque at that location. The arrow indicates the Max△PSS, i.e., the difference in maximum plaque structural stress, which represents the difference between the maximum and minimum PSS values during a cardiac cycle. If the pressure changes significantly during a cardiac cycle (i.e., the difference between the maximum and minimum blood pressures is large), the maximum△PSS will also be large, indicating that the plaque experiences a large stress fluctuation during a cardiac cycle. A larger maximum△PSS can increase the risk of plaque rupture, as it means that the plaque material needs to withstand a larger dynamic stress range. The area indicated by the arrow in FIGS. 1A and 1B includes the area with the lightest gray color. When the circular area is set to a harder fiber-dense calcified plaque, the maximum△PSS obtained by finite element simulation is 7.5 kPa, while when the circular area is set to a softer lipid-containing pool calcified plaque, the maximum△PSS obtained by finite element simulation is 10.2 kPa.

[0058] It can be found that even if they are all defined as calcified plaques, there are significant differences in the biomechanical simulation results between calcification types with different material mechanics properties. That is, even if they have exactly the same geometric structure, due to the different material mechanics properties, i.e., the harder fiber-dense calcified plaque shown in FIG. 1A and the lipid-containing pool calcified plaque shown in FIG. 1B, there are significant differences between the biomechanical analysis results.

[0059] Therefore, in the prior art, the uniform material mechanical properties are assigned to different biological regions of the blood vessel 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, the acquisition of specific material mechanical properties is crucial.

[0060] In order to directly acquire the material mechanical properties of the specific target analysis object, the inventors establish the correspondence between the HU values and the material mechanical properties of the blood vessels of multiple experimental samples. Since the CT image of the target analysis object reflects individual differences, that is, the HU values have individual differences, the correspondence between the HU values and the material mechanical properties can take into account the specific individual differences of the target analysis object and has universality. The correspondence between the HU values and the material mechanical properties can be applied to all target analysis objects, that is, specific individuals. And the subsequent evaluation of the blood vessels of the specific individuals reflects the state of the plaque, and the specific problem is analyzed, so that the biomechanical analysis of the blood vessels obtains accurate results.

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

[0062] The biological material mechanical property acquisition method and the biological material mechanical property analysis method provided by the present application do not involve surgical methods, but are based on processing of medical images, that is, the direct object is a medical image such as a CT image.

[0063] The biological material mechanical property acquisition system, the biological material mechanical property analysis system, the electronic device and the computer storage medium provided by the present application are also not devices related to surgical methods, but are devices related to processing of medical images, that is, the direct object is a medical image such as a CT image.

[0064] In a first aspect, referring to FIG. 2, the present application provides a biological material mechanical property acquisition method of a blood vessel, comprising:

[0065] S01: obtaining a first image of a blood vessel, obtaining HU values of different biological regions of the blood vessel based on the first image, and the first image being a CT image.

[0066] In this embodiment, a plurality of blood vessels as experimental samples are collected, and CT images of the blood vessels are obtained. The HU value size and numerical distribution of different biological regions are obtained according to the obtained CT images of the blood vessels. In this embodiment, the CT image can be a CT image of an ex vivo blood vessel, or a CT image of an in vivo blood vessel.

[0067] S02: Obtain the material mechanical property parameters of the different biological regions of the blood vessel in the ex vivo state. Provide data preparation for the subsequent correspondence between the HU value and the material mechanical property parameters.

[0068] S03: Obtain the correspondence between the HU value and the material mechanical property parameters based on the HU values of the different biological regions of the blood vessel and the corresponding material mechanical property parameters of the different biological regions of the blood vessel in the ex vivo state.

[0069] It should be noted that the material mechanical property parameters of the different biological regions of the blood vessel need to be obtained from the blood vessel in the ex vivo state. That is, the blood vessel here needs to be an ex vivo blood vessel, not an in vivo blood vessel. The reason is that the analysis of the material mechanical property parameters of the different biological regions using the in vivo blood vessel will be affected by the heart contraction and relaxation in the cardiac cycle. The in vivo blood vessel will deform due to the pressure change, thereby causing the imaging position of the blood vessel to deform, resulting in inaccurate analysis of the material mechanical property parameters of the different biological regions of the blood vessel.

[0070] By using the above technical solution, the HU values of the different biological regions and the corresponding material mechanical property parameters of the ex vivo blood vessel in the different biological regions are obtained through the CT images of the blood vessels of multiple experimental samples, and the correspondence between the HU value and the material mechanical property is constructed. This correspondence is universal and can be applied to all target analysis objects, i.e., specific individuals. It can also be used for personalized evaluation of the blood vessels of specific individuals, reflecting the state of the plaque, and achieving specific analysis of specific problems, so that the biological material mechanical analysis of the blood vessel can obtain accurate results.

[0071] In some possible embodiments provided by the present application, 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 blood vessel biological material mechanical property acquisition method, the blood vessel biological material mechanical property acquisition system, the blood vessel biological material mechanical property analysis method, and the blood vessel biological material mechanical property analysis system provided by the present application are not only suitable for coronary arteries, but also can be applied to other biological tissues, such as myocardium, aorta, renal artery, popliteal artery, etc.

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

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

[0075] Among them, the iterative finite element simulation is a computer aided engineering (CAE) based method that uses mathematical models to simulate and analyze the behavior of materials under different loads and boundary conditions. Illustratively, the iterative finite element simulation can obtain the mechanical properties of the vascular material according to the following steps: a three-dimensional geometric model of the blood vessel is established, usually through CT (Computed Tomography) or MRI (Magnetic Resonance Imaging) scan data. The three-dimensional geometric model of the blood vessel is meshed using finite element software, and the three-dimensional geometric model of the blood vessel is divided into a plurality of small elements for numerical analysis. The mechanical properties of the material, including the elastic modulus, Poisson's ratio, etc. are defined. The loading conditions and boundary conditions are set to simulate the stress of the blood vessel in the 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 iterate and optimize until a satisfactory simulation result is obtained.

[0076] In vitro multi-axial testing is an experimental method that directly applies different loads to an isolated blood vessel sample to measure its mechanical response in different directions. Illustratively, in vitro multi-axial testing generally includes the following steps: preparing the blood vessel sample, ensuring that the surface is clean and free of any damage. The blood vessel sample is fixed on the multi-axial testing machine to ensure good contact between the sample and the testing machine. Different loads are applied, including axial, radial and tangential forces. Record the 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 multi-axial testing, the material mechanical property parameters obtained can be used to establish a constitutive model describing the mechanical properties of the blood vessel material, thereby providing a basis for biomechanical analysis and clinical application. The two methods usually complement each other, and iterative finite element simulation can help optimize experimental design, while in vitro multi-axial testing can verify the accuracy of the simulation model.

[0078] In some other possible embodiments provided by the present application, 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 of the blood vessels in the ex vivo state, the corresponding relationship between the HU values and the material mechanical property parameters is obtained, including: 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 of the blood vessels in the ex vivo state, the corresponding relationship between the HU values and the material mechanical property parameters is obtained by a parameter correlation analysis method.

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

[0080] where linear regression is a statistical method used to estimate the linear relationship between two or more variables. In this method, data is assumed to be in a linear relationship, meaning the value of one variable can be linearly predicted from the value of another variable. Linear regression models are typically represented 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 be used to explore the direct linear relationship between HU values and material mechanical property parameters in a relatively simple and direct manner. Specifically, multiple data of HU values and corresponding mechanical property parameters are collected; statistical software such as SPSS (Statistical Package for the Social Sciences), Stata, etc. is used to perform linear fitting on the collected data to obtain regression coefficients (such as intercept and slope) and R 2 , and the fitting effect is evaluated based on R 2 . R 2 is an important indicator for evaluating the goodness of fit of a linear regression model, and its value is between 0 and 1. The closer the value of R 2 to 1, the stronger the explanatory power of the model and the better the fitting effect. Conversely, if the value of R 2 is close to 0, the explanatory power of the model is weak.

[0081] Nonlinear regression is an extension of linear regression, used to estimate the nonlinear relationship between two or more variables. This method takes into account the nonlinear relationship between data and uses mathematical models to describe these relationships. Nonlinear regression models can more accurately describe complex data relationships, but they are generally more complex than linear regression models, requiring more data points and more complex mathematical models, and are more suitable for predicting complex dependence relationships between HU values and material mechanical properties. Choose the appropriate nonlinear model (such as polynomial model, exponential model) according to the expected data distribution, and use numerical methods including least squares method to optimize the nonlinear model parameters. Specifically, if the data shows a curved trend, a polynomial model can be considered. For example, a quadratic polynomial model (y = a + bx + cx^2) can be used to describe an upward or downward curve; when 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. The least squares method is a commonly used optimization method for fitting nonlinear models. It finds the best values of model parameters by minimizing the sum of squares of errors. Software packages such as MATLAB, Python's SciPy library, R language, etc. can be used to implement it.

[0082] Principal Component Analysis (PCA) is a statistical method used to extract a set of linearly uncorrelated variables from a set of possibly correlated 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 a dataset 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 the most variation information as much 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 and nonlinear regression are used to establish 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 application, the material mechanical properties of different biological vascular 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 fundamental tools that link stress (or strain) and strain (or stress), which are the core parts of mechanical behavior. In the field of cardiovascular, especially in the study of atherosclerotic plaque, constitutive models are crucial for simulating the mechanical behavior of plaque. Since the plaque is composed of multiple different components, including lipid core, fibrous cap, calcification, etc., each component has its unique mechanical properties, so a complex constitutive model is needed to accurately describe these properties.

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

[0087] For example, the Mooney-Rivlin model is:

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

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

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

[0091] Table 1

[0092] Exemplarily, the Fung exponential model is:

[0093] In the formula, W is the strain energy density, C is the material mechanical property parameter, Q is the intermediate quantity, and e is the natural constant.

[0094] In the formula, E ij is the Green strain tensor (i,j = R, θ, Z) in the cylindrical coordinate system, R, θ, and Z are the coordinate variables in the radial direction, the circumferential direction, and the axial direction of 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 of the cylindrical coordinate system, E θθ represents the Green strain tensor in the circumferential direction θ of the cylindrical coordinate system, E ZZ represents the Green strain tensor in the axial direction Z of the cylindrical coordinate system, E Rθ represents the Green strain tensor in the radial direction R and the circumferential direction θ of the cylindrical coordinate system, E θ Z represents the Green strain tensor in the circumferential direction θ and the axial direction Z of the cylindrical coordinate system, E RZ represents the Green strain tensor in the radial direction R and the axial direction Z of the cylindrical coordinate system.

[0095] An example of the correspondence between the HU value obtained according to the Fung exponential model and the material mechanical property is shown in Table 2.

[0096] Table 2

[0097] Exemplarily, the Gasser model is:

[0098] In the formula, W is the strain energy density, μ, k1, k2, and k are the material mechanical property parameters, I1 is the first strain invariant, I4 is the fourth strain invariant, e is the natural constant, exp represents the natural exponential function, and [exp[k2[k(I1-3)+(1-3k)(I4-3)] 2 ]-1] is e raised to the power of [k2[k(I1-3)+(1-3k)(I4-3)] 2 ]-1.

[0099] An example of the correspondence between the HU value obtained according to the Gasser model and the material mechanical property is shown in Table 3.

[0100] Table 3

[0101] In a second aspect, referring to FIG. 3, the present application provides a system for obtaining biomechanical properties of a blood vessel 00, comprising a first image acquisition module 01, a material mechanical property parameter acquisition module 02, and a parameter correlation module 03.

[0102] The first image acquisition module 01 is configured to acquire a first image of the blood vessel, and obtain HU values of different biological regions of the blood vessel based on the first image, wherein the first image is a CT image.

[0103] The material mechanical property parameter acquisition module 02 is configured to obtain material mechanical property parameters of different biological regions of the blood vessel in an ex vivo state.

[0104] The parameter correlation module 03 is configured to obtain a corresponding relationship between the HU values and the material mechanical property parameters based on the HU values of different biological regions of the blood vessel and the material mechanical property parameters of different biological regions of the blood vessel in the ex vivo state.

[0105] With the above technical solutions, the corresponding relationship between the HU values and the material mechanical property parameters is obtained through the joint action of the first image acquisition module 01, the material mechanical property parameter acquisition module 02, and the parameter correlation module 03. This corresponding relationship is universal and can be applied to all target analysis objects, i.e., specific individuals. It can also be used to evaluate the state of the plaque for specific individuals, analyze specific problems, and obtain accurate results of the biomechanical analysis of the blood vessel.

[0106] In some possible embodiments of the present application, the material mechanical property parameter acquisition module 02 is configured to obtain 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-axial testing.

[0107] In some possible embodiments of the present application, the parameter correlation module 03 is configured to obtain the corresponding relationship between the HU values and the material mechanical property parameters through a parameter correlation analysis method based on the HU values of different biological regions of the blood vessel and the material mechanical property parameters of different biological regions of the blood vessel in the ex vivo state.

[0108] In some possible embodiments, the parameter correlation analysis method includes linear regression, nonlinear regression, and principal component analysis.

[0109] In some other possible embodiments, the material mechanical property parameter acquisition module represents the material mechanical properties of different biological regions of the blood vessel through a constitutive model.

[0110] In some possible embodiments, the constitutive model comprises at least one of the following: a Mooney-Rivlin model, a Fung exponential model, and a Gasser model. Details have been described above, and will not be repeated here.

[0111] In a third aspect, referring to FIG. 4, the present application further provides a method for analyzing biomechanical properties of blood vessels, comprising:

[0112] S1: obtaining a second image of a target analysis object, the second image being a CT image.

[0113] In this embodiment, the target analysis object, i.e., a patient, is subjected to ECG-gated scanning by a CT imaging device to obtain a CT image.

[0114] ECG-gated scanning is an imaging technique that uses electrocardiogram (ECG) signals to synchronize the scanning process of a medical imaging device, so as to capture images at specific stages of the heart. This technique is particularly important in cardiac imaging, because the heart is a constantly moving organ, and ECG-gated scanning can help reduce image blurring caused by heart movement.

[0115] S2: identifying and segmenting the second image by an image processing algorithm to obtain a segmentation result of biological blood vessels in the second image.

[0116] 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 plaque, calcified plaque, vascular media, adventitia, peripheral adipose tissue, etc., to obtain a segmentation result, which refers to fibrous tissue, lipid plaque, calcified plaque, vascular media, adventitia, peripheral adipose tissue, etc.

[0117] S3: reconstructing a specific geometric model based on the segmentation result.

[0118] 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.

[0119] S4: obtaining a correspondence between HU values and material mechanical properties according to the specific geometric model and the method for obtaining biomechanical properties of biological materials according to any one of the preceding embodiments of the first aspect, and obtaining specific material mechanical property parameters of a biological blood vessel region of the target analysis object.

[0120] That is, according to the specific geometric model reconstructed in step S3 and the correspondence between HU values and material mechanical properties 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. That is, the specific material mechanical property parameters of the blood vessel tissue region of the target analysis object, i.e., a patient (also a specific individual), are obtained.

[0121] S5: Constructing a finite element model according to the specific geometric model and the specific material mechanical property parameters, performing finite element simulation calculation on the finite element model, and obtaining the stress and strain distribution of the region of interest.

[0122] In this embodiment, a biomechanics simulation model is constructed according to the specific geometric model and the specific material mechanical property parameters.

[0123] In some other possible embodiments provided by the present application, the blood vessel biological material mechanical property analysis method further comprises:

[0124] S6: Obtaining blood pressure information of the target analysis object;

[0125] S7: Constructing a finite element model according to the specific geometric model, the specific material mechanical property parameters, and the blood pressure information, performing finite element simulation calculation on the finite element model, and obtaining the stress and strain distribution of the region of interest.

[0126] The blood pressure information includes systolic pressure, diastolic pressure, and waveform information of blood pressure.

[0127] In this embodiment, the blood vessel biological material mechanical property analysis method comprises steps S1 to S4 in the foregoing embodiments, and further comprises steps S6 and S7 in this embodiment.

[0128] 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, step S6 is arranged at any position before step S7.

[0129] Finite element simulation calculation is a computer-aided engineering analysis technology based on the finite element method (FEM) to solve engineering and physical problems. The finite element method is a mathematical modeling technique that discretizes continuous media (such as materials, structures, etc.) into a finite number of elements, each with a simple geometric shape such as a triangle or quadrilateral (in two-dimensional problems) or tetrahedron or hexahedron (in three-dimensional problems). These elements are connected by nodes to form a mesh system, called a finite element model.

[0130] The finite element model is mainly a numerical analysis method that discretizes continuous media (such as materials, structures, etc.) into a finite number of elements and applies boundary conditions and loads on the elements, then uses mathematical methods (such as approximate solutions of partial differential equations) to analyze the stress, strain, temperature, and other physical quantities within each element, and finally synthesizes the results of all elements to obtain the response of the entire structure or system. In this embodiment, the blood pressure information is used as the load.

[0131] A biomechanical simulation model is a mathematical model that specifically simulates the dynamic behavior and response of a biological body or part of its structure. It needs to consider factors such as the anatomical structure, material properties, physiological mechanisms, and mechanical behavior of the biological body. Biomechanical simulation models have a wide range of applications in the fields of 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, Ansys, etc. The biomechanical simulation model is shown in Figure 5. Figure 5 shows the biomechanical simulation results of a certain segment of atherosclerotic coronary artery, where different gray colors at different positions correspond to different sizes of plaque structure stress. For example, Figure 5 shows the plaque structure stress of the adventitia of the blood vessel at V; the plaque structure stress of the media of the blood vessel at W; the plaque structure stress of the blood vessel wall at S, Z, and Y, where the plaque structure stress at positions S and Y is the largest.

[0132] Region of interest, for example, such as blood vessel fibrous cap region, fibrous tissue region, lipid plaque region, calcified plaque region, media region, adventitia region, peripheral adipose tissue region.

[0133] In this embodiment, the purpose of establishing a biomechanical simulation model is to obtain the stress and strain distribution of the region of interest

[0134] With the above technical solution, the biological material mechanical property analysis method of the blood vessel provided by the present application has universality, can evaluate the target analysis object in vivo, reflect the state of the plaque, can perform personalized analysis according to the individual differences of the target analysis object, realize specific problem specific analysis, and make the biological material mechanical analysis result of the blood vessel accurate.

[0135] In some possible embodiments provided by the present application, the image processing algorithm is a deep learning algorithm.

[0136] In some possible embodiments, the deep learning algorithm includes a deep neural network with a 3D-UNet as the backbone network. The trained deep neural network with a 3D-UNet as the backbone network is used to automatically segment the CTA image, wherein the deep neural network avoids the complex manual feature design process in traditional methods, 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.

[0137] It should be noted 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, i.e. computed tomography images, are two-dimensional images obtained by CT scanning. This image can be used to reconstruct the internal structure of the object, providing detailed information about the internal structure of the object, such as bones, soft tissues, blood vessels, etc. CT images are commonly used to diagnose various diseases, such as fractures, tumors, infections, etc. CTA images, i.e. computed tomography angiography images, are a special type of CT image that focuses on displaying blood vessel structures. CTA enhances the visibility of blood vessels in the image by injecting blood containing contrast agents into the patient. Then, CT scanning is used to capture blood flow and blood vessel structure. CTA can provide high-resolution blood vessel images for diagnosing blood vessel diseases such as arterial stenosis, angioma, thrombus, etc. In summary, CT images are a widely used imaging technique for displaying internal structures, while CTA images are a CT imaging method specifically designed to display blood vessel structures.

[0138] In a fourth aspect, referring to FIG. 6, the present application provides a biological material mechanical property analysis system 10 of a blood vessel, comprising 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.

[0139] The second image acquisition module 11 is used to obtain the second image of the target analysis object, and the second image is a CT image.

[0140] The image processing module 12 is used to identify and segment the second image by image processing algorithm, and obtain the segmentation result of the biological blood vessel of the second image.

[0141] The three-dimensional geometric model establishment module 13 is used to reconstruct the specific geometric model based on the segmentation result; the specific material mechanical property parameter calculation module is used to obtain the corresponding relationship between the HU value and the material mechanical property parameter obtained by the parameter correlation module 03 of the biological material mechanical property acquisition system according to any embodiment of the second aspect and the specific geometric model obtained by the three-dimensional geometric model establishment module 13, and obtain the material mechanical property parameter of the biological blood vessel region of the target analysis object.

[0142] The finite element model module 15 is used to construct a finite element model according to the segmentation result of the biological blood vessel of 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 parameter of the biological blood vessel region of the target analysis object obtained by the specific material mechanical property parameter calculation module 14, perform finite element simulation calculation on the finite element model, and obtain the stress and strain distribution of the region of interest.

[0143] According to the technical scheme, the biological material mechanical property analysis system of the blood vessel can evaluate the specific target analysis object in vivo, reflect the state of the plaque, and can perform individualized analysis according to the individual differences of the target analysis object, so that the biological material mechanical analysis result of the blood vessel is accurate, and the system is universal.

[0144] In some other possible embodiments of the present application, referring to FIG. 7, the biological material mechanical property analysis system 10 of the blood vessel includes a blood pressure acquisition module 16 and a finite element model module 15. The blood pressure acquisition module 16 is configured to acquire blood pressure information of the target analysis object. The finite element model module 16 is configured to construct a finite element model according to the specific geometric model, the specific material mechanical property parameters and the blood pressure information, perform finite element simulation calculation on the finite element model, and acquire stress and strain distribution of a region of interest.

[0145] The blood pressure information includes systolic pressure, diastolic pressure and blood pressure waveform information. The systolic pressure and the diastolic pressure can be obtained by a cuff pressure method.

[0146] In this embodiment, the blood pressure information of the target analysis object is acquired as a load of the finite element model. The finite element model is constructed according to the systolic pressure and the diastolic pressure of the target analysis object and 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 acquire the stress and strain distribution of the region of interest. The specific target analysis object can be evaluated in vivo, the state of the plaque can be reflected, individualized analysis can be performed according to the individual differences of the target analysis object, specific problems can be analyzed, the biological material mechanical analysis result of the blood vessel is accurate, and the system is universal.

[0147] According to another specific embodiment of the present application, the image processing algorithm is a deep learning algorithm.

[0148] According to another specific embodiment of the present application, the deep learning algorithm includes a deep neural network with a 3D-UNet as a backbone network. The specific content has been described in detail in the foregoing, and will not be repeated here.

[0149] In a fifth aspect, referring to FIG. 8, the present application 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, wherein the processor 202 implements the method for obtaining the biomechanical properties of the blood vessel according to any one of the embodiments of the first aspect or implements the method for analyzing the biomechanical properties of the blood vessel according to any one of the embodiments of the third aspect. The memory 201 may, for example, include a system memory, a fixed non-volatile storage medium, etc. The system memory may, for example, store an operating system, an application program, a Boot Loader, and other programs, etc.

[0150] In the present embodiment, the electronic device establishes the correspondence between the HU values of the blood vessels constructed from the multiple experimental samples and the material mechanical properties, and this correspondence is universal and can be applied to all target analysis objects, i.e., specific individuals. The blood vessels of the specific individuals are then evaluated individually, the state of the plaque is reflected, and the specific problems are analyzed specifically, so that the biomechanical analysis of the blood vessels obtains accurate results.

[0151] In a sixth aspect, the embodiments of the present application also provide a computer-readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the method for obtaining the biomechanical properties of the blood vessel according to any one of the embodiments of the first aspect or implement the method for analyzing the biomechanical properties of the blood vessel according to any one of the embodiments of the third aspect.

[0152] With the above technical solution, the computer-readable storage medium establishes the correspondence between the HU values of the blood vessels constructed from the multiple experimental samples and the material mechanical properties, and this correspondence is universal and can be applied to all target analysis objects, i.e., specific individuals. The blood vessels of the specific individuals are then evaluated individually, the state of the plaque is reflected, and the specific problems are analyzed specifically, so that the biomechanical analysis of the blood vessels obtains accurate results.

[0153] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0154] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks or in conjunction with the flowchart blocks.

[0155] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks or in conjunction with the flowchart blocks.

[0156] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks or in conjunction with the flowchart blocks.

[0157] Although the present application has been described in terms of certain preferred embodiments, the skilled artisan will appreciate that various modifications, alterations, permutations, and substitutions can be made without departing from the spirit and scope of the application. Accordingly, the application is not limited by the foregoing description, but is only limited by the scope of the patent.

Claims

1. A method for acquiring biomechanical properties of a blood vessel, characterized by, The method comprises the following steps: obtaining a first image of a blood vessel, obtaining HU values of different biological regions of the blood vessel based on the first image, and the first image being a CT image; obtaining material mechanics attribute parameters of different biological regions of the blood vessel in an ex vivo state; obtaining a corresponding relationship between the HU values and the material mechanics attribute parameters based on the HU values of different biological regions of the blood vessel and the corresponding material mechanics attribute parameters of different biological regions of the blood vessel in the ex vivo state.

2. The method of acquiring biomechanical properties of blood vessels according to claim 1, wherein, The step of obtaining the material mechanics attribute parameters of different biological regions of the blood vessel in the ex vivo state comprises: obtaining the material mechanics attribute parameters of different biological regions of the blood vessel in the ex vivo state through iterative finite element simulation or in-vitro multi-axial testing; or The step of obtaining the corresponding relationship between the HU values and the material mechanics attribute parameters based on the HU values of different biological regions of the blood vessel and the corresponding material mechanics attribute parameters of different biological regions of the blood vessel in the ex vivo state comprises: obtaining the corresponding relationship between the HU values and the material mechanics attribute parameters through a parameter correlation analysis method based on the HU values of different biological regions of the blood vessel and the corresponding material mechanics attribute parameters of different biological regions of the blood vessel in the ex vivo state.

3. The method of claim 1, wherein the step of obtaining the biomechanical properties of the blood vessel comprises the steps of: obtaining a plurality of images of the blood vessel; and obtaining the biomechanical properties of the blood vessel from the plurality of images. The material mechanics attribute of the different biological regions of the blood vessel is represented by a constitutive model, and the constitutive model comprises at least one of a Mooney-Rivlin model, a Fung exponential model and a Gasser model.

4. A system for obtaining the biomaterial mechanical properties of blood vessels, characterized in that, The method comprises the following steps: a first image acquisition module is configured to obtain a first image of a blood vessel, obtain HU values of different biological regions of the blood vessel based on the first image, and the first image being a CT image; a material mechanics attribute parameter acquisition module is configured to obtain material mechanics attribute parameters of different biological regions of the blood vessel in an ex vivo state; a parameter correlation module is configured to obtain a corresponding relationship between the HU values and the material mechanics attribute parameters based on the HU values of different biological regions of the blood vessel and the corresponding material mechanics attribute parameters of different biological regions of the blood vessel in the ex vivo state.

5. The system for acquiring biomechanical properties of blood vessels according to claim 4, wherein, The material mechanics attribute parameter acquisition module is configured to obtain the material mechanics attribute parameters of different biological regions of the blood vessel in the ex vivo state through iterative finite element simulation or in-vitro multi-axial testing; or The parameter correlation module is configured to obtain the corresponding relationship between the HU values and the material mechanics attribute parameters through a parameter correlation analysis method based on the HU values of different biological regions of the blood vessel and the corresponding material mechanics attribute parameters of different biological regions of the blood vessel in the ex vivo state.

6. The system for acquiring biomechanical properties of blood vessels according to claim 5, wherein, The material mechanics attribute parameter acquisition module represents the material mechanics attribute of the different biological regions of the blood vessel by a constitutive model, and the constitutive model comprises at least one of a Mooney-Rivlin model, a Fung exponential model and a Gasser model.

7. A method for analyzing the mechanical properties of biomaterials used in blood vessels, characterized in that, The method comprises the following steps: obtaining a second image of a target analysis object, and the second image being a CT image; identifying and segmenting the second image through an image processing algorithm to obtain a segmentation result of biological blood vessels of the second image; reconstructing a specific geometric model based on the segmentation result; According to the specific geometric model and the biological material mechanics attribute acquisition method in any one of claims 1 to 3, a corresponding relationship between HU values and material mechanics attributes is obtained, and specific material mechanics attribute parameters of a biological blood vessel region of the target analysis object are obtained; According to the specific geometric model and the specific material mechanics attribute parameters, a finite element model is constructed, finite element simulation calculation is performed on the finite element model, and stress and strain distributions of a region of interest are obtained.

8. The method of analyzing biomechanical properties of blood vessels according to claim 7, wherein, The method comprises: Blood pressure information of the target analysis object is obtained; According to the specific geometric model, the specific material mechanics attribute parameters, and the blood pressure information, a finite element model is constructed, finite element simulation calculation is performed on the finite element model, and stress and strain distributions of a region of interest are obtained.

9. The method of analyzing biomechanical properties of blood vessels according to claim 7, wherein, The image processing algorithm is a deep learning algorithm, and the deep learning algorithm comprises a deep neural network taking 3D-UNet as a backbone network.

10. A biomaterial mechanical property analysis system for blood vessels, characterized in that, The method comprises: A second image acquisition module is configured to obtain a second image of a target analysis object, wherein the second image is a CT image; An image processing module is configured to identify and segment the second image by using an image processing algorithm to obtain a segmentation result of a biological blood vessel of the second image; A three-dimensional geometric model establishment module is configured to reconstruct a specific geometric model based on the segmentation result; A specific material mechanics attribute parameter calculation module is configured to obtain material mechanics attribute parameters of a biological region of the target analysis object according to a corresponding relationship between HU values and material mechanics attribute parameters obtained by a parameter correlation module of the biological material mechanics attribute acquisition system in any one of claims 4 to 6 and a specific geometric model obtained by the three-dimensional geometric model establishment module; A finite element model module is configured to construct a finite element model according to the specific geometric model and the specific material mechanics attribute parameters, perform finite element simulation calculation on the finite element model, and obtain stress and strain distributions of a region of interest.

11. The biomechanical property analysis system of blood vessels of claim 10, wherein, The method comprises: A blood pressure acquisition module is configured to obtain blood pressure information of the target analysis object; The finite element model module is configured to construct a finite element model according to the specific geometric model, the specific material mechanics attribute parameters, and the blood pressure information, perform finite element simulation calculation on the finite element model, and obtain stress and strain distributions of a region of interest.

12. The biomechanical property analysis system of blood vessels of claim 10, wherein, The image processing algorithm is a deep learning algorithm, and the deep learning algorithm comprises a deep neural network taking 3D-UNet as a backbone network.

13. An electronic device, comprising: The computer program product comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the blood vessel biological material mechanics attribute acquisition method in any one of claims 1 to 3 or the blood vessel biological material mechanics attribute analysis method in any one of claims 7 to 9 is implemented.

14. A computer storage medium, characterized in that The computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method for obtaining biomechanical properties of a blood vessel according to any one of claims 1 to 3 or the method for analyzing biomechanical properties of a blood vessel according to any one of claims 7 to 9.

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