Methods, apparatus, computer equipment, and storage media for determining the characteristics of blood vessel wall materials

Abstract

This application relates to a method, apparatus, computer device, and storage medium for determining the characteristics of blood vessel wall materials. The method includes: generating a blood vessel wall surface mesh model based on a four-dimensional CT angiography image; determining the displacement field information of a reference region and the region to be analyzed in the blood vessel wall surface mesh model; determining a target mapping function, the equivalent stress to be analyzed, and the equivalent strain to be analyzed in the region to be analyzed in the reference three-dimensional blood vessel wall model, based on the displacement field information and the blood vessel wall surface mesh model; mapping the mechanical characteristic data of the blood vessel wall in the region to be analyzed from the scan image to be corrected to the blood vessel wall surface mesh model, based on the target mapping function, to determine the stress mapping data and strain mapping data of the region to be analyzed; and determining the characteristic parameters of the blood vessel wall material based on the stress mapping data, strain mapping data, equivalent stress to be analyzed, and equivalent strain to be analyzed. This method can improve the accuracy and cross-batch comparability of blood vessel wall material characteristic data across multiple batches.

Description

Technical Field

[0001] This application relates to the field of biological simulation analysis technology, and in particular to a method, apparatus, computer equipment, and storage medium for determining the characteristics of blood vessel wall materials. Background Technology

[0002] The material properties of blood vessel walls, including core parameters such as elastic modulus and stress-strain relationship, are key quantitative indicators for accurately characterizing the physiological and pathological physical state of blood vessels, and have irreplaceable application value in fields such as biomechanical simulation analysis and efficacy evaluation of vascular lesions. Currently, commonly used methods for assessing blood vessel wall elasticity mainly include pulse wave velocity (PWV), echo-tracking (ET) technology, enhancement index (AI), and cardio-ankle vascular index (CAVI). These methods indirectly reflect the stiffness or elasticity of the blood vessel wall by collecting signals such as pulse wave, blood pressure, and dynamic changes in vessel diameter. Their measurement results are all significantly dependent on the baseline blood pressure level at the time of measurement. Building upon this, with the development of imaging technology and computational biomechanics, a new type of method has emerged for quantitative inversion of material properties based on blood vessel wall image data. For example, a three-dimensional model of the blood vessel wall can be constructed using image data, the stress-strain distribution of the blood vessel wall can be obtained using finite element analysis, and material parameters such as elastic modulus can be determined using the initial slope of the fitted stress-strain curve. The calculation logic depends on the boundary conditions input during image acquisition, with blood pressure serving as the core physiological boundary condition, playing a decisive role in the accuracy and consistency of the calculation results. Under normal physiological conditions, human blood pressure fluctuates dynamically due to various internal and external factors, including activity intensity, emotional fluctuations, medication intervention, and / or dietary changes. These blood pressure variations directly alter the stress field distribution on the blood vessel walls, leading to non-physiological shifts in the stress-strain curves of the same blood vessel segment during biomechanical simulation analysis of vascular imaging data acquired at different time points or under different blood pressure conditions. Furthermore, due to inherent differences in blood pressure levels at different scanning times, the core characteristic data such as stress values ​​and elastic modulus output by simulation software for the same blood vessel segment exhibit significant systematic biases. These issues cause existing methods for determining the properties of blood vessel walls to be highly dependent on acquisition boundary conditions such as blood pressure. Blood pressure fluctuations can cause non-physiological shifts and systematic biases in the calculation results, and there is a lack of effective correction methods, making it impossible for multiple batches of blood vessel wall scan data to accurately reflect the state of the blood vessel walls. Therefore, how to eliminate data bias caused by blood pressure fluctuations and improve the accuracy and cross-batch comparability of multi-batch blood vessel wall material characteristic data is a problem that needs to be solved. Summary of the Invention

[0003] Therefore, it is necessary to provide a method, apparatus, computer equipment, and storage medium for determining vascular wall material characteristics that can avoid data deviations caused by blood pressure fluctuations and improve the accuracy and cross-batch comparability of characteristic data of vascular wall materials from multiple batches.

[0004] In a first aspect, this application provides a method for determining the characteristics of blood vessel wall materials, the method comprising:

[0005] A four-dimensional CT angiography image of the vessel wall to be analyzed is acquired, and a vessel wall surface mesh model is generated based on the four-dimensional CT angiography image; the four-dimensional CT angiography image includes a set of mapping reference scan images and at least two sets of scan images to be corrected; the vessel wall surface mesh model includes a reference three-dimensional vessel wall model and a three-dimensional vessel wall model to be corrected.

[0006] The surface mesh model of the blood vessel wall is divided into a reference region and a region to be analyzed, and the displacement field information of the reference region and the region to be analyzed is determined; the displacement field information includes the displacement field information corresponding to the reference three-dimensional model of the blood vessel wall and the displacement field information corresponding to the three-dimensional model of the blood vessel wall to be corrected.

[0007] Based on the displacement field information and the vessel wall surface mesh model, the target mapping function, the equivalent stress to be analyzed, and the equivalent strain to be analyzed in the region to be analyzed of the reference three-dimensional vessel wall model are determined; the target mapping function includes the equivalent strain mapping function and the equivalent stress mapping function.

[0008] According to the target mapping function, the vascular wall mechanical feature data of the region to be analyzed in the scan image to be corrected is mapped to the vascular wall surface mesh model, and the stress mapping data and strain mapping data corresponding to the region to be analyzed in the scan image to be corrected are determined; the vascular wall mechanical feature data includes the equivalent stress and equivalent strain to be mapped.

[0009] Based on the stress mapping data, the strain mapping data, the equivalent stress to be analyzed, and the equivalent strain to be analyzed, the characteristic parameters of the blood vessel wall material in the region to be analyzed in the blood vessel wall surface mesh model are determined; the characteristic parameters of the blood vessel wall material are used to characterize the elastic properties of the blood vessel wall to be analyzed.

[0010] In one embodiment, determining the displacement field information of the reference region and the region to be analyzed includes:

[0011] Select an image phase from the time-series images of the four-dimensional CT angiography images as a reference phase;

[0012] A non-rigid image registration method is used to align the three-dimensional images of the remaining time phases of the time series images (excluding the reference time phase) with the three-dimensional image of the reference time phase, thereby determining the deformation field characterizing the displacement of each voxel.

[0013] The surface mesh model of the blood vessel wall and the deformation field are spatially mapped, and the displacement vectors of each mesh node in the surface mesh model of the blood vessel wall under different image phases are extracted by interpolation method to determine the displacement field information of the reference area and the area to be analyzed.

[0014] In one embodiment, based on the displacement field information and the vessel wall surface mesh model, the target mapping function, the equivalent stress to be analyzed and the equivalent strain to be analyzed in the region to be analyzed of the baseline three-dimensional vessel wall model are determined, including:

[0015] Based on the displacement field information and the vessel wall surface mesh model, the target equivalent strain and target equivalent stress of the vessel wall surface mesh model are determined. The target equivalent strain includes the first equivalent strain of the reference region corresponding to the reference vessel wall 3D model, the equivalent strain to be analyzed of the region to be analyzed corresponding to the reference vessel wall 3D model, and the second equivalent strain of the reference region corresponding to the vessel wall 3D model to be corrected. The target equivalent stress includes the first equivalent stress of the reference region corresponding to the reference vessel wall 3D model, the equivalent stress to be analyzed of the region to be analyzed corresponding to the reference vessel wall 3D model, and the second equivalent stress of the reference region corresponding to the vessel wall 3D model to be corrected.

[0016] The equivalent strain mapping function is determined based on the first equivalent strain and the second equivalent strain, and the equivalent stress mapping function is determined based on the first equivalent stress and the second equivalent stress.

[0017] In one embodiment, determining the target equivalent strain and target equivalent stress of the blood vessel wall surface mesh model based on the displacement field information and the blood vessel wall surface mesh model includes:

[0018] Based on the displacement field information, the vessel wall strain tensor of the vessel wall surface mesh model is determined; the vessel wall strain tensor includes the first strain tensor of the reference region in the vessel wall surface mesh model and the second strain tensor of the region to be analyzed in the vessel wall surface mesh model.

[0019] Based on the displacement field information and the vessel wall surface mesh model, the temporal vessel wall model for different expansion stages is determined;

[0020] The vessel wall stress tensor of the time-series vessel wall model is determined by forward finite element analysis. The vessel wall stress tensor includes the first stress tensor of the reference region in the time-series vessel wall model and the second stress tensor of the region to be analyzed in the time-series vessel wall model.

[0021] Based on the strain tensor and stress tensor of the blood vessel wall, the target equivalent strain and target equivalent stress of the surface mesh model of the blood vessel wall are determined.

[0022] In one embodiment, based on the stress mapping data, the strain mapping data, the equivalent stress to be analyzed, and the equivalent strain to be analyzed, the characteristic parameters of the blood vessel wall material of the region to be analyzed in the blood vessel wall surface mesh model are determined, including:

[0023] Based on the nonlinear hyperelastic constitutive model, the characteristic fitting coefficients of the blood vessel wall material are determined according to the strain tensor and stress tensor of the blood vessel wall.

[0024] Based on the fitting coefficients of the blood vessel wall material characteristics, the stress mapping data, the strain mapping data, the equivalent stress to be analyzed, and the equivalent strain to be analyzed, the blood vessel wall material characteristic parameters of the region to be analyzed in the blood vessel wall surface mesh model are determined.

[0025] In one embodiment, the above-mentioned method for determining the characteristics of blood vessel wall material further includes:

[0026] The reference 3D model of the blood vessel wall and the 3D model of the blood vessel wall to be corrected are divided into regions to determine the target reference model sub-region and the model to be corrected sub-region.

[0027] Determine the average parameter values ​​of the vascular wall material characteristic parameters of the sub-region of the baseline model, and the average parameter values ​​of the vascular wall material characteristic parameters of the sub-region of the model to be corrected.

[0028] In one embodiment, the reference 3D model of the blood vessel wall and the 3D model of the blood vessel wall to be corrected are divided into regions to determine the target reference model sub-region and the model to be corrected sub-region, including:

[0029] The reference blood vessel wall three-dimensional model is divided into regions to determine the reference model sub-regions;

[0030] The benchmark model sub-region is divided into regions using a clustering algorithm to determine the target benchmark model sub-region and its cluster center point.

[0031] The three-dimensional model of the blood vessel wall to be corrected is spatially registered with the reference three-dimensional model of the blood vessel wall, and the spatially registered three-dimensional model of the blood vessel wall to be corrected is divided into regions based on the cluster center points to determine the sub-regions of the model to be corrected.

[0032] Secondly, this application also provides a device for determining the characteristics of blood vessel wall materials, the device comprising:

[0033] The model building module is used to acquire four-dimensional CT angiography images of the vessel wall to be analyzed, and generate a vessel wall surface mesh model based on the four-dimensional CT angiography images; the four-dimensional CT angiography images include a set of mapping reference scan images and at least two sets of scan images to be corrected; the vessel wall surface mesh model includes a reference three-dimensional vessel wall model and a three-dimensional vessel wall model to be corrected.

[0034] The displacement field information determination module is used to divide the blood vessel wall surface mesh model into a reference region and an analysis region, and to determine the displacement field information of the reference region and the analysis region; the displacement field information includes the displacement field information corresponding to the reference blood vessel wall three-dimensional model and the displacement field information corresponding to the blood vessel wall three-dimensional model to be corrected;

[0035] The mapping function determination module is used to determine the target mapping function, the equivalent stress to be analyzed, and the equivalent strain to be analyzed in the region to be analyzed of the reference three-dimensional model of the blood vessel wall, based on the displacement field information and the blood vessel wall surface mesh model; the target mapping function includes an equivalent strain mapping function and an equivalent stress mapping function;

[0036] The mapping data determination module is used to map the vascular wall mechanical feature data of the region to be analyzed in the scan image to the vascular wall surface mesh model according to the target mapping function, and determine the stress mapping data and strain mapping data corresponding to the region to be analyzed in the scan image to be corrected; the vascular wall mechanical feature data includes the equivalent stress and equivalent strain to be mapped;

[0037] The feature parameter determination module is used to determine the feature parameters of the blood vessel wall material of the region to be analyzed in the blood vessel wall surface mesh model based on the stress mapping data, the strain mapping data, the equivalent stress to be analyzed, and the equivalent strain to be analyzed; the feature parameters of the blood vessel wall material are used to characterize the elastic properties of the blood vessel wall to be analyzed.

[0038] Thirdly, this application also provides a computer device, the computer device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0039] A four-dimensional CT angiography image of the vessel wall to be analyzed is acquired, and a vessel wall surface mesh model is generated based on the four-dimensional CT angiography image; the four-dimensional CT angiography image includes a set of mapping reference scan images and at least two sets of scan images to be corrected; the vessel wall surface mesh model includes a reference three-dimensional vessel wall model and a three-dimensional vessel wall model to be corrected.

[0040] The surface mesh model of the blood vessel wall is divided into a reference region and a region to be analyzed, and the displacement field information of the reference region and the region to be analyzed is determined; the displacement field information includes the displacement field information corresponding to the reference three-dimensional model of the blood vessel wall and the displacement field information corresponding to the three-dimensional model of the blood vessel wall to be corrected.

[0041] Based on the displacement field information and the vessel wall surface mesh model, the target mapping function, the equivalent stress to be analyzed, and the equivalent strain to be analyzed in the region to be analyzed of the reference three-dimensional vessel wall model are determined; the target mapping function includes the equivalent strain mapping function and the equivalent stress mapping function.

[0042] According to the target mapping function, the vascular wall mechanical feature data of the region to be analyzed in the scan image to be corrected is mapped to the vascular wall surface mesh model, and the stress mapping data and strain mapping data corresponding to the region to be analyzed in the scan image to be corrected are determined; the vascular wall mechanical feature data includes the equivalent stress and equivalent strain to be mapped.

[0043] Based on the stress mapping data, the strain mapping data, the equivalent stress to be analyzed, and the equivalent strain to be analyzed, the characteristic parameters of the blood vessel wall material in the region to be analyzed in the blood vessel wall surface mesh model are determined; the characteristic parameters of the blood vessel wall material are used to characterize the elastic properties of the blood vessel wall to be analyzed.

[0044] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0045] A four-dimensional CT angiography image of the vessel wall to be analyzed is acquired, and a vessel wall surface mesh model is generated based on the four-dimensional CT angiography image; the four-dimensional CT angiography image includes a set of mapping reference scan images and at least two sets of scan images to be corrected; the vessel wall surface mesh model includes a reference three-dimensional vessel wall model and a three-dimensional vessel wall model to be corrected.

[0046] The surface mesh model of the blood vessel wall is divided into a reference region and a region to be analyzed, and the displacement field information of the reference region and the region to be analyzed is determined; the displacement field information includes the displacement field information corresponding to the reference three-dimensional model of the blood vessel wall and the displacement field information corresponding to the three-dimensional model of the blood vessel wall to be corrected.

[0047] Based on the displacement field information and the vessel wall surface mesh model, the target mapping function, the equivalent stress to be analyzed, and the equivalent strain to be analyzed in the region to be analyzed of the reference three-dimensional vessel wall model are determined; the target mapping function includes the equivalent strain mapping function and the equivalent stress mapping function.

[0048] According to the target mapping function, the vascular wall mechanical feature data of the region to be analyzed in the scan image to be corrected is mapped to the vascular wall surface mesh model, and the stress mapping data and strain mapping data corresponding to the region to be analyzed in the scan image to be corrected are determined; the vascular wall mechanical feature data includes the equivalent stress and equivalent strain to be mapped.

[0049] Based on the stress mapping data, the strain mapping data, the equivalent stress to be analyzed, and the equivalent strain to be analyzed, the characteristic parameters of the blood vessel wall material in the region to be analyzed in the blood vessel wall surface mesh model are determined; the characteristic parameters of the blood vessel wall material are used to characterize the elastic properties of the blood vessel wall to be analyzed.

[0050] The aforementioned method, apparatus, computer equipment, and storage medium for determining the characteristics of blood vessel wall materials acquire four-dimensional CT angiography images of the blood vessel wall to be analyzed, and generate a blood vessel wall surface mesh model based on the four-dimensional CT angiography images. The four-dimensional CT angiography images include a set of mapping reference scan images and at least two sets of scan images to be corrected. The blood vessel wall surface mesh model includes a reference three-dimensional model of the blood vessel wall and a three-dimensional model of the blood vessel wall to be corrected. The blood vessel wall surface mesh model is divided into a reference region and an analysis region, and the displacement field information between the reference region and the analysis region is determined. The displacement field information includes the displacement field information corresponding to the reference three-dimensional model of the blood vessel wall and the displacement field information corresponding to the three-dimensional model of the blood vessel wall to be corrected. Based on the displacement field information and the blood vessel wall surface mesh model, a target mapping is determined. The method involves analyzing the equivalent stress and equivalent strain of the region to be analyzed in the 3D model of the blood vessel wall, using a function and a benchmark blood vessel wall model. The target mapping function includes an equivalent strain mapping function and an equivalent stress mapping function. Based on the target mapping function, the mechanical characteristic data of the blood vessel wall in the region to be analyzed in the scan image to be corrected are mapped to a blood vessel wall surface mesh model, determining the stress mapping data and strain mapping data corresponding to the region to be analyzed in the scan image. The mechanical characteristic data of the blood vessel wall includes the equivalent stress and equivalent strain to be mapped. Based on the stress mapping data, the strain mapping data, the equivalent stress to be analyzed, and the equivalent strain to be analyzed, the material characteristic parameters of the blood vessel wall in the region to be analyzed in the blood vessel wall surface mesh model are determined. These material characteristic parameters characterize the elastic properties of the blood vessel wall to be analyzed. This method solves the problem that existing methods for determining the material properties of blood vessels are highly dependent on boundary conditions such as blood pressure, which can lead to non-physiological shifts and systematic biases in the calculation results due to blood pressure fluctuations, and lack effective correction methods, making it impossible for multiple batches of blood vessel wall scan data to truly reflect the state of the blood vessel wall. The above scheme, based on multiple batches of four-dimensional images, constructs a dedicated mechanical mapping function through partitioning and displacement field solving to achieve normalization correction of vascular mechanical data from different scanning batches, eliminating data deviations caused by differences in scanning conditions and boundary environments, and ensuring the uniformity of comparison of multiple sets of mechanical parameters. Based on the corrected stress and strain data, the characteristic parameters of vascular wall materials are determined, which can eliminate data deviations caused by blood pressure fluctuations and improve the accuracy and cross-batch comparability of vascular wall material characteristic data from multiple batches. Attached Figure Description

[0051] Figure 1 This is an application environment diagram of the method for determining the characteristics of blood vessel wall materials in one embodiment;

[0052] Figure 2 This is a flowchart illustrating a method for determining the characteristics of blood vessel wall material in one embodiment;

[0053] Figure 3This is an example diagram of region division for a blood vessel wall surface mesh model in one embodiment;

[0054] Figure 4 This is a flowchart illustrating a method for determining a target mapping function in one embodiment;

[0055] Figure 5 This is a flowchart illustrating a method for determining characteristic parameters of blood vessel wall material in one embodiment;

[0056] Figure 6 Here is an example diagram of the SSI distribution cloud map corresponding to the pre-mapping vessel wall mechanical parameters in one embodiment;

[0057] Figure 7 This is an example diagram of the SSI distribution cloud map corresponding to the mapped blood vessel wall mechanical parameters in one embodiment.

[0058] Figure 8 This is a flowchart illustrating a method for determining the characteristics of blood vessel wall material in one embodiment;

[0059] Figure 9 This is a schematic diagram of the region division of the vessel wall surface mesh model generated from the four-dimensional CT angiography images obtained from each batch of scans in one embodiment;

[0060] Figure 10 This is a structural block diagram of a device for determining the characteristics of blood vessel wall material in one embodiment;

[0061] Figure 11 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0063] The method for determining the characteristics of blood vessel wall materials provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on a cloud or other network server. Server 104 acquires a four-dimensional CT angiography image of the vessel wall to be analyzed and generates a vessel wall surface mesh model based on the four-dimensional CT angiography image. The four-dimensional CT angiography image includes a set of mapping reference scan images and at least two sets of scan images to be corrected. The vessel wall surface mesh model includes a reference three-dimensional vessel wall model and a three-dimensional vessel wall model to be corrected. The vessel wall surface mesh model is divided into a reference region and an analysis region, and the displacement field information of the reference region and the analysis region is determined. The displacement field information includes the displacement field information corresponding to the reference three-dimensional vessel wall model and the displacement field information corresponding to the three-dimensional vessel wall model to be corrected. Based on the displacement field information and the vessel wall surface mesh model, the target mapping function and the analysis region of the reference three-dimensional vessel wall model are determined. The system analyzes the equivalent stress and equivalent strain of the region to be analyzed. The target mapping function includes an equivalent strain mapping function and an equivalent stress mapping function. Based on the target mapping function, the mechanical characteristic data of the blood vessel wall in the region to be analyzed is mapped to the blood vessel wall surface mesh model, determining the stress mapping data and strain mapping data corresponding to the region to be analyzed in each scan image to be corrected. The mechanical characteristic data of the blood vessel wall includes the equivalent stress and equivalent strain to be mapped. Based on the stress mapping data and the strain mapping data, the material characteristic parameters of the blood vessel wall in the region to be analyzed in the blood vessel wall surface mesh model are determined. The material characteristic parameters of the blood vessel wall are used to characterize the elastic properties of the blood vessel wall to be analyzed, and are sent to the terminal 102 via a communication network. The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can be smart speakers, smart TVs, smart air conditioners, smart vehicle devices, etc. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers.

[0064] In one embodiment, such as Figure 2 As shown, a method for determining the characteristics of blood vessel wall materials is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0065] S210. Obtain a four-dimensional CT angiography image of the vessel wall to be analyzed, and generate a vessel wall surface mesh model based on the four-dimensional CT angiography image.

[0066] The four-dimensional CT angiography image includes a set of baseline scan images and at least two sets of scan images to be corrected; the vessel wall surface mesh model includes a baseline three-dimensional vessel wall model and a three-dimensional vessel wall model to be corrected.

[0067] Among them, four-dimensional CT angiography images, also known as 4D-CTA time-series images, are dynamic angiography data that integrates three-dimensional spatial information and the temporal dimension. They can acquire the continuous anatomical morphology of the same vascular segment under different cardiac phases and different instantaneous blood pressures, providing a temporal imaging basis for vascular biomechanical analysis. A set of four-dimensional CT angiography images contains multiple imaging phases, each corresponding to a moment of cardiac systole or diastole, with the blood vessel exhibiting different degrees of expansion under different imaging phases.

[0068] Specifically, multiple batches of four-dimensional CT angiography images of the vessel wall to be analyzed are acquired, and a vessel wall surface mesh model is generated based on these images using medical image analysis software.

[0069] For example, the selection method for the mapping reference scan image can be as follows: acquire multiple batches of four-dimensional CT angiography images of the vessel wall to be analyzed, summarize the image acquisition time and blood pressure level corresponding to each batch of four-dimensional CT angiography images, and select the scan data under specific blood pressure conditions as the mapping reference scan image based on the blood pressure level. For example, the four-dimensional CT angiography image corresponding to the lowest blood pressure can be selected as the mapping reference scan image; or the first acquired four-dimensional CT angiography image can be selected as the mapping reference scan image according to the image acquisition time, so as to establish a unified numerical reference framework.

[0070] S220. Divide the blood vessel wall surface mesh model into a reference region and an analysis region, and determine the displacement field information of the reference region and the analysis region.

[0071] The displacement field information includes the displacement field information corresponding to the reference three-dimensional model of the blood vessel wall and the displacement field information corresponding to the three-dimensional model of the blood vessel wall to be corrected.

[0072] The displacement field refers to the set of displacement vectors for each spatial point moving from its original position to a new position within the entire spatial range of the mesh model on the surface of the blood vessel wall.

[0073] It should be noted that the reference region is selected based on its relatively stable mechanical response characteristics, which can provide a reliable reference for subsequent data mapping. The region to be analyzed, however, may exhibit non-uniform stress-strain distribution due to structural differences or changes in the mechanical environment, making it susceptible to interference from boundary condition differences when directly comparing across batches. Normal vascular segments in the vascular wall surface mesh model that exhibit stable mechanical response, are free from lesion interference, and maintain regular mechanical properties under different blood pressures can be selected as the reference region; abnormal vascular segments in the vascular wall surface mesh model that have complex structures, uneven stress-strain distribution, or lesions can be selected as the region to be analyzed.

[0074] For example, when the region to be analyzed is located in a branch of the aorta (i.e., a region outside the aortic trunk), the reference region is generally selected from the corresponding segment of the main aorta, such as the abdominal aorta. Taking the superior mesenteric artery as the region to be analyzed for changes in the mechanical environment, its region division method is as follows: Figure 3 As shown. In contrast, when the region to be analyzed is located in the aortic segment, an uninterrupted segment of the aorta is selected as the reference region. For example, for the vessel wall surface mesh model of the aortic arch region, an aortic segment without implants can be used as the reference region.

[0075] For example, determining the displacement field information of the reference region and the region to be analyzed includes:

[0076] A reference phase is selected from the time-series images of 4D CT angiography. A non-rigid image registration method is used to align the 3D images of the remaining time-series images (excluding the reference phase) with the 3D image of the reference phase, thus determining the deformation field characterizing the displacement of each voxel. The vessel wall surface mesh model and the deformation field are spatially mapped. For each mesh node in the vessel wall surface mesh model, based on its position in image space, the displacement vector of each mesh node in the vessel wall surface mesh model under different image phases is extracted using interpolation, thus determining the displacement field information of the reference region and the region to be analyzed.

[0077] Among them, non-rigid image registration methods are deformation alignment algorithms adapted to the elastic deformation of soft tissues. These methods can characterize local stretching and contraction deformation of organs and solve for the displacement field, achieving accurate registration of multiple batches of medical images with morphological differences. Non-rigid image registration methods can be B-spline registration or Demons registration. A voxel is the basic unit of a three-dimensional image; in four-dimensional CT angiography images, a voxel is the smallest unit constituting the four-dimensional CT angiography image. The cardiac cycle phase at the end of diastole can be selected as the reference phase.

[0078] It should be noted that the vessel wall surface mesh model records the spatial contour and node positions of the vessel wall to be analyzed; the deformation field records the displacement variation patterns at various locations of the vessel wall to be analyzed. Spatial mapping allows each node of the mesh model to correspond to the displacement data in the deformation field. After completing the spatial mapping, the specific displacement of each node on the vessel wall surface mesh model can be clearly defined, providing an accurate spatial correspondence for subsequent extraction of node displacements, fitting of mapping coefficients, and data standardization, ensuring the accuracy of subsequent data calculations.

[0079] Specifically, a time-series image phase from 4D CT angiography images is selected as a unified reference standard. Using non-rigid image registration technology, based on the 3D image of the reference phase, deformation matching and spatial alignment are performed on the corresponding 3D images of all other time-series images to obtain a global deformation field that characterizes the positional shifts of each voxel. A spatial mapping relationship is established between the vessel wall surface mesh model and this deformation field. An interpolation algorithm is used to solve for and extract the displacement vectors of each mesh node in the vessel wall surface mesh model across different image phases. Finally, the complete displacement field information corresponding to the reference region and the region to be analyzed is accurately calculated, achieving a quantitative characterization of the dynamic deformation features of the vessel wall.

[0080] The above scheme, based on 4D-CTA time-series image data and combined with the dynamic motion of the vessel wall during the cardiac cycle, quantitatively solves the displacement field of different functional regions. First, a single cardiac phase is selected from the 4D CT angiography image of the vessel wall as a unified reference. Then, a non-rigid registration algorithm is used to perform deformation alignment and spatial matching on the corresponding 3D images of the remaining images in the 4D CT angiography image of the vessel wall, solving for the continuous deformation field characterizing the positional changes of each voxel. Next, the reconstructed vessel wall surface mesh model is spatially mapped to this deformation field. A spatial interpolation algorithm is used to accurately extract the displacement vectors of each mesh node in the vessel wall under different cardiac phases. Finally, node displacement field datasets corresponding to the reference region and the region to be analyzed are constructed respectively, realizing the quantitative acquisition of the global deformation information of the vessel wall and providing basic deformation parameter support for subsequent stress-strain calculations and multi-batch data correction.

[0081] S230. Based on the displacement field information and the vessel wall surface mesh model, determine the target mapping function, the equivalent stress to be analyzed and the equivalent strain to be analyzed in the region to be analyzed in the reference three-dimensional model of the vessel wall.

[0082] The target mapping function includes the equivalent strain mapping function and the equivalent stress mapping function.

[0083] For example, such as Figure 4 As shown, the methods for determining the target mapping function include:

[0084] S2301. Based on the displacement field information and the blood vessel wall surface mesh model, determine the target equivalent strain and target equivalent stress of the blood vessel wall surface mesh model.

[0085] The target equivalent strain includes the first equivalent strain of the reference region corresponding to the baseline 3D model of the blood vessel wall, the equivalent strain to be analyzed of the region to be analyzed corresponding to the baseline 3D model of the blood vessel wall, the second equivalent strain of the reference region corresponding to the 3D model of the blood vessel wall to be corrected, and the equivalent strain to be analyzed of the region to be analyzed corresponding to the 3D model of the blood vessel wall to be corrected; the target equivalent stress includes the first equivalent stress of the reference region corresponding to the baseline 3D model of the blood vessel wall, the equivalent stress to be analyzed of the region to be analyzed corresponding to the baseline 3D model of the blood vessel wall, the second equivalent stress of the reference region corresponding to the 3D model of the blood vessel wall to be corrected, and the equivalent stress to be analyzed of the region to be analyzed corresponding to the 3D model of the blood vessel wall to be corrected.

[0086] For example, based on displacement field information and a vessel wall surface mesh model, the target equivalent strain and target equivalent stress of the vessel wall surface mesh model are determined, including:

[0087] Based on the displacement field information, the vessel wall strain tensor of the vessel wall surface mesh model is determined. The vessel wall strain tensor includes the first strain tensor of the reference region in the vessel wall surface mesh model and the second strain tensor of the region to be analyzed in the vessel wall surface mesh model. Based on the displacement field information and the vessel wall surface mesh model, the time-series vessel wall model for different expansion stages is determined. The vessel wall stress tensor of the time-series vessel wall model is determined using forward finite element analysis. The vessel wall stress tensor includes the first stress tensor of the reference region in the time-series vessel wall model and the second stress tensor of the region to be analyzed in the time-series vessel wall model. Based on the vessel wall strain tensor and the vessel wall stress tensor, the target equivalent strain and target equivalent stress of the vessel wall surface mesh model are determined.

[0088] The strain tensor is a second-order physical quantity used to fully describe the complex deformation of an object in three-dimensional space. It is used to accurately characterize all deformation states of a material at any location, including tension, compression, shear, and torsion. The first strain tensor is the strain tensor obtained based on the displacement field of the reference region in the initial blood vessel wall surface mesh model under its corresponding image phase. The second strain tensor is the strain tensor of the region to be analyzed under its corresponding image phase. The stress tensor is a second-order tensor in continuum mechanics that characterizes the internal stress state of a material element. It contains normal stress and shear stress components and can describe the internal mechanical distribution of a structure under multidimensional spatial loads. During the cardiac cycle, the blood vessel wall undergoes different pulsating phases, such as expansion and contraction. The different image phases of the time series image are morphological records of these different pulsating phases, so that the blood vessel geometry under each phase can reflect the expansion or contraction state of the blood vessel wall at the corresponding moment.

[0089] Specifically, based on displacement field information and the displacement vectors of each grid node in the vessel wall surface mesh model, the first strain tensor corresponding to the reference region and the second strain tensor corresponding to the region to be analyzed are calculated. From the displacement field information and the vessel wall surface mesh model, the deformation configurations for different pulsation phases are extracted. Using forward finite element analysis, combined with the intraluminal pressure distribution of the vessel wall at each phase, the vessel wall stress tensor of the vessel wall model at each phase is calculated. The vessel wall stress tensor includes the first stress tensor of the reference region and the second stress tensor of the region to be analyzed in the time-series vessel wall model. The classical von Mises equivalent mechanical criterion can be used to perform scalar dimensionality reduction on the vessel wall strain tensor and the vessel wall stress tensor at each grid node in the time-series vessel wall model, respectively transforming the strain tensor and stress tensor of each node into equivalent scalar forms of target equivalent strain and target equivalent stress.

[0090] The above scheme, based on the field information and the vascular wall mesh model, solves the strain tensor of the reference region and the region to be analyzed in sections. Combined with the vascular wall deformation configuration and intraluminal pressure distribution under different pulsation phases, it accurately obtains the stress tensor of each section through forward finite element analysis. Then, based on the tensor data, it calculates the equivalent strain and equivalent stress. This scheme can comprehensively characterize the three-dimensional deformation and stress characteristics of the vascular wall in multiple time periods and regions, reduce the limitations of single mechanical parameter evaluation, and improve the overall integrity and accuracy of vascular wall mechanical response analysis.

[0091] S2302. Determine the equivalent strain mapping function based on the first equivalent strain and the second equivalent strain, and determine the equivalent stress mapping function based on the first equivalent stress and the second equivalent stress.

[0092] It should be noted that equivalent strain data and equivalent stress data need to be mapped separately. The general mapping form is as follows: Where f represents the mapping function; This represents the coefficients or variables involved in the mapping. Mapping functions can be divided into linear mappings and nonlinear mappings.

[0093] For example, since the equivalent strain data shows relatively regular changes at different blood pressure levels, a linear mapping can be used for processing. The first and second equivalent strains can be substituted into the linear mapping function to fit the linear mapping coefficients. Based on the linear mapping coefficients and the linear mapping function, the equivalent strain mapping function is determined. For example, the linear mapping function is: Where A and b are linear mapping coefficients, A is the linear transformation coefficient matrix, and b is the offset. For the first equivalent effect change, Let v be the second equivalent strain, and v be the quantity of the first equivalent strain, the second equivalent strain, the first equivalent stress, and the second equivalent stress.

[0094] The changes in equivalent stress data affected by blood pressure are more complex and therefore more suitable for nonlinear mapping. The first and second equivalent stresses can be substituted into the nonlinear mapping function to fit the nonlinear mapping coefficients. Based on the nonlinear mapping coefficients and the nonlinear mapping function, the equivalent stress mapping function can be determined. The nonlinear mapping function is as follows: ;in, and The coefficients are nonlinear mapping coefficients, and n is the highest degree of the polynomial. For the first equivalent stress, This is the second equivalent stress.

[0095] By applying the average equivalent strain of the benchmark reference area Average equivalent stress By mapping stresses separately, the mechanical parameters of the blood vessel wall in the reference area of ​​the scanned image to be corrected can be uniformly transformed to the numerical level corresponding to the three-dimensional model of the reference blood vessel wall.

[0096] The above scheme utilizes the relatively stable mechanical state of a reference region to quantify systematic biases caused by acquisition conditions such as blood pressure fluctuations, and accordingly corrects the vascular wall mechanical parameters of the region to be analyzed. This is equivalent to uniformly calibrating different batches of scans to a comparable mechanical baseline, thereby ensuring the accuracy and consistency of data comparison across multiple batches.

[0097] S240. Based on the target mapping function, map the mechanical characteristic data of the blood vessel wall in the region to be analyzed in the blood vessel wall surface mesh model to the blood vessel wall surface mesh model, and determine the stress mapping data and strain mapping data corresponding to the region to be analyzed in the scan image to be corrected.

[0098] The biomechanical characteristics of the blood vessel wall include the equivalent stress and equivalent strain to be mapped.

[0099] Specifically, using the target mapping functions corresponding to equivalent stress and equivalent strain pre-constructed in the reference area as the correction conversion model, the equivalent stress and equivalent strain to be mapped in the region to be analyzed under the scan image to be corrected are uniformly mapped and transformed. The mechanical parameters of the blood vessel wall under different scanning conditions are uniformly transformed to the reference frame of the reference three-dimensional model of the reference blood vessel wall. Finally, the standardized stress mapping data and strain mapping data of the region to be analyzed are obtained, and the unified correction of mechanical parameters between multiple batches of images is completed.

[0100] S250. Based on the stress mapping data and the strain mapping data, determine the characteristic parameters of the blood vessel wall material in the region to be analyzed in the blood vessel wall surface mesh model.

[0101] For example, such as Figure 5 As shown, the methods for determining the characteristic parameters of blood vessel wall materials include:

[0102] S2501. Based on the nonlinear hyperelastic constitutive model, the characteristic fitting coefficients of the blood vessel wall material are determined according to the strain tensor and stress tensor of the blood vessel wall.

[0103] Among them, the nonlinear hyperelastic constitutive model can be a one-dimensional form of the Fung constitutive model. The one-dimensional form of the Fung constitutive model simplifies the Fung hyperelastic constitutive equation, which originally described the complex deformation of three-dimensional soft tissue, into a one-dimensional mathematical expression in a single tensile direction.

[0104] Specifically, the nonlinear hyperelastic constitutive model is as follows: .in, and These are the characteristic fitting coefficients for blood vessel wall materials, used to describe the hyperelastic properties of blood vessel wall materials. Blood vessel wall stress tensor This represents the strain tensor of the blood vessel wall.

[0105] For example, as a contrast, when a constant term is added to the formula corresponding to the nonlinear hyperelastic constitutive model... That is, the nonlinear hyperelastic constitutive model is At this time, some nodes of the same model will show abnormal fitting coefficients, which indicates that although the constant term It can provide more degrees of freedom for the model, but in some cases it may lead to stability issues during the fitting process. Therefore, the solution obtained above... and The optimal fitting coefficients for the characteristics of the blood vessel wall material are given.

[0106] S2502. Based on the fitting coefficients of the blood vessel wall material characteristics, stress mapping data, strain mapping data, equivalent stress to be analyzed, and equivalent strain to be analyzed, determine the blood vessel wall material characteristic parameters of the region to be analyzed in the blood vessel wall surface mesh model.

[0107] The characteristic parameters of the blood vessel wall material are used to characterize the elastic properties of the blood vessel wall to be analyzed.

[0108] Specifically, the formula for calculating the characteristic parameters of the blood vessel wall material is as follows: .in, This represents the maximum equivalent variable value corresponding to the region to be analyzed. This represents the minimum equivalent variable value corresponding to the region to be analyzed. Each region to be analyzed corresponds to one... and SSI stands for Blood Vessel Wall Material Characteristic Parameter. This parameter reflects the actual elastic state of the blood vessel wall. Based on the calculation formula for the blood vessel wall material characteristic parameter, and according to the blood vessel wall material characteristic fitting coefficient, stress mapping data, and strain mapping data, the blood vessel wall material characteristic parameter of the region to be analyzed in the three-dimensional model of the blood vessel wall to be corrected is determined. Based on the blood vessel wall material characteristic fitting coefficient, the equivalent stress to be analyzed, and the equivalent strain to be analyzed, the blood vessel wall material characteristic parameter of the baseline three-dimensional model of the blood vessel wall is determined.

[0109] For example, taking multiple batches of four-dimensional CT angiography images of a superior mesenteric artery model, the blood pressure readings corresponding to each four-dimensional CT angiography image are 160 / 100 mmHg, 147 / 78 mmHg, 128 / 86 mmHg, and 127 / 100 mmHg, respectively. The vessel wall surface mesh model corresponding to the last scanned four-dimensional CT angiography image is selected as the baseline three-dimensional vessel wall model, and the vessel wall surface mesh models corresponding to the other four-dimensional CT angiography images are used as the three-dimensional vessel wall models to be corrected. Figure 6 The image shows the SSI distribution cloud map corresponding to the mechanical parameters of the blood vessel wall in the region to be analyzed in the three-dimensional model of the blood vessel wall to be corrected before mapping. Figure 7 The image shows the SSI distribution cloud map corresponding to the mechanical parameters of the blood vessel wall after mapping the analyzed area of ​​the 3D model of the blood vessel wall to be corrected. It can be seen that the SSI distribution after mapping shows a more consistent spatial distribution pattern across different scanning batches, effectively reducing the numerical fluctuations caused by blood pressure differences, and making the feature data output from each batch more comparable.

[0110] In the above method for determining the characteristics of blood vessel wall materials, a four-dimensional CT angiography image of the blood vessel wall to be analyzed is acquired, and a blood vessel wall surface mesh model is generated based on the four-dimensional CT angiography image. The four-dimensional CT angiography image includes a set of mapping reference scan images and at least two sets of scan images to be corrected. The blood vessel wall surface mesh model includes a reference three-dimensional model of the blood vessel wall and a three-dimensional model of the blood vessel wall to be corrected. The blood vessel wall surface mesh model is divided into a reference region and an analysis region, and the displacement field information between the reference region and the analysis region is determined. The displacement field information includes the displacement field information corresponding to the reference three-dimensional model of the blood vessel wall and the displacement field information corresponding to the three-dimensional model of the blood vessel wall to be corrected. Based on the displacement field information and the blood vessel wall surface mesh model, the target mapping function and the reference blood vessel wall are determined. The method involves analyzing the equivalent stress and equivalent strain of the region to be analyzed in the 3D model of the blood vessel wall. The target mapping function includes an equivalent strain mapping function and an equivalent stress mapping function. Based on the target mapping function, the vascular wall mechanical characteristic data of the region to be analyzed in the scan image to be calibrated is mapped to the vascular wall surface mesh model, determining the stress mapping data and strain mapping data corresponding to the region to be analyzed in the scan image. The vascular wall mechanical characteristic data includes the equivalent stress and equivalent strain to be mapped. Based on the stress mapping data, the strain mapping data, the equivalent stress to be analyzed, and the equivalent strain to be analyzed, the vascular wall material characteristic parameters of the region to be analyzed in the vascular wall surface mesh model are determined. The vascular wall material characteristic parameters are used to characterize the elastic properties of the vascular wall to be analyzed. This method solves the problem that existing methods for determining vascular wall material properties are highly dependent on acquisition boundary conditions such as blood pressure, which can lead to non-physiological shifts and systematic biases in calculation results due to blood pressure fluctuations, and lack effective correction methods, making it impossible for multiple batches of vascular wall scan data to truly reflect the state of the vascular wall. The above scheme, based on multiple batches of four-dimensional images, constructs a dedicated mechanical mapping function through partitioning and displacement field solving to achieve normalization correction of vascular mechanical data from different scanning batches, eliminating data deviations caused by differences in scanning conditions and boundary environments, and ensuring the uniformity of comparison of multiple sets of mechanical parameters. Based on the corrected stress and strain data, the characteristic parameters of vascular wall materials are determined, which can eliminate data deviations caused by blood pressure fluctuations and improve the accuracy and cross-batch comparability of vascular wall material characteristic data from multiple batches.

[0111] For example, such as Figure 8 The above-mentioned method for determining the characteristics of blood vessel wall materials also includes:

[0112] S310. Divide the reference 3D model of the blood vessel wall and the 3D model of the blood vessel wall to be corrected into regions, and determine the target reference model sub-region and the model to be corrected sub-region.

[0113] For example, the three-dimensional model of the reference blood vessel wall and the three-dimensional model of the blood vessel wall to be corrected are divided into regions to determine the target reference model sub-region and the model to be corrected sub-region, including:

[0114] The reference 3D model of the blood vessel wall is divided into regions to determine the reference model sub-regions. The reference model sub-regions are further divided using a clustering algorithm to determine the target reference model sub-regions and their cluster centers. The 3D model of the blood vessel wall to be corrected is spatially registered with the reference 3D model of the blood vessel wall, and the spatially registered 3D model of the blood vessel wall to be corrected is divided into regions based on the cluster centers to determine the sub-regions of the model to be corrected.

[0115] Specifically, based on the structural morphological characteristics of the baseline 3D blood vessel wall model, the model can be manually divided into regions to determine the baseline model sub-regions, thus establishing the regional boundary references for subsequent automated processing. Based on the baseline model sub-regions obtained from the initial region division, a clustering algorithm is used to further divide the baseline model sub-regions, determining the target baseline model sub-region and its cluster center points.

[0116] Clustering analysis algorithms automatically group model nodes by evaluating the similarity between data points, thereby improving the granularity of region partitioning. Various clustering algorithms can be used in this process, such as K-means clustering, hierarchical clustering, and DBSCAN. Taking K-means clustering as an example, the goal of K-means clustering is to assign n data points to K clusters, ensuring that each point belongs to its nearest cluster center, while minimizing the sum of the distances from points within a cluster to the cluster center. This is represented by the objective function: .

[0117] Where J is the objective function; K is the number of clusters; Let x be the set of points in the i-th cluster; x is the cluster to which x belongs. point; It is clustering The center; Representing point x and its cluster center The square of the Euclidean distance between them. After obtaining the target baseline model sub-region, determine the cluster centers of the target baseline model sub-region, and assign a region label to each cluster center.

[0118] Furthermore, the 3D model of the vessel wall to be corrected is spatially registered with the reference 3D model of the vessel wall, so that the 3D model of the vessel wall to be corrected coincides with the reference 3D model of the vessel wall in terms of anatomical spatial position and morphological contour. After registration, the spatially registered 3D model of the vessel wall to be corrected is divided into regions based on the cluster center point, and the sub-regions of the model to be corrected are determined.

[0119] The above method ensures that the region division of the three-dimensional model of the blood vessel wall to be corrected is consistent with that of the benchmark three-dimensional model of the blood vessel wall, thus guaranteeing the uniformity of the region division standard among different models.

[0120] S320. Determine the average value of the characteristic parameters of the blood vessel wall material in the sub-region of the reference model, and the average value of the characteristic parameters of the blood vessel wall material in the sub-region of the model to be corrected.

[0121] Understandably, this involves determining the average values ​​of the vascular wall material characteristic parameters for the sub-regions of the baseline model and the sub-regions of the model to be calibrated. Since the sub-regions of the model to be calibrated are obtained by partitioning after registration with the baseline model, the sub-regions of each batch of models maintain a one-to-one correspondence in anatomical location, thus avoiding data bias introduced by partitioning misalignment. This allows the calculated average values ​​of the parameters for each sub-region to be directly used for cross-batch horizontal comparisons and subsequent calibration analyses.

[0122] For example, based on the above embodiments, the method for determining the characteristics of blood vessel wall materials includes:

[0123] Multiple batches of 4D CT angiography images of the vessel wall to be analyzed are acquired. Using medical image analysis software, a vessel wall surface mesh model is generated based on these images. The 4D CT angiography images are time-series images (4D-CTA images), comprising one set of baseline images and at least two sets of images to be corrected. The vessel wall surface mesh model includes a baseline 3D model and a 3D model to be corrected. The baseline images can be selected by: acquiring multiple batches of 4D CT angiography images of the vessel wall to be analyzed, summarizing the image acquisition time and blood pressure level corresponding to each batch; selecting scan data under specific blood pressure conditions as the baseline images, for example, choosing the 4D CT angiography image corresponding to the lowest blood pressure; or selecting the first acquired 4D CT angiography image as the baseline image based on the image acquisition time, thus establishing a unified numerical reference framework.

[0124] The blood vessel wall surface mesh model is divided into a reference region and an analysis region. The reference region is selected based on its relatively stable mechanical response characteristics, providing a reliable reference for subsequent data mapping. The analysis region, however, may exhibit non-uniform stress-strain distribution due to structural differences or changes in the mechanical environment, making it susceptible to interference from boundary condition differences during direct cross-batch comparisons. Normal blood vessel segments with stable mechanical responses, no lesion interference, and consistent mechanical properties under different blood pressures can be selected as the reference region. Abnormal blood vessel segments with complex structures, uneven stress-strain distribution, or lesions can be selected as the analysis region.

[0125] A single image phase is selected from the time-series images of 4D CT angiography as a unified reference standard. Using non-rigid image registration techniques, based on the 3D image of the reference phase, deformation matching and spatial alignment are performed on the corresponding 3D images of all other time-series images to obtain a global deformation field characterizing the positional shifts of each voxel. A spatial mapping relationship is established between the vessel wall surface mesh model and this deformation field. An interpolation algorithm is used to solve for and extract the displacement vectors of each mesh node in the vessel wall surface mesh model across different image phases. Finally, the complete displacement field information corresponding to the reference region and the region to be analyzed is accurately calculated, achieving a quantitative characterization of the dynamic deformation features of the vessel wall. The displacement field information includes the displacement field information corresponding to the reference 3D vessel wall model and the displacement field information corresponding to the 3D vessel wall model to be corrected. The non-rigid image registration method can be either B-spline registration or Demons registration.

[0126] Based on displacement field information and the dynamic displacement vectors of each grid node in the vessel wall surface mesh model, the first strain tensor corresponding to the reference region and the second strain tensor corresponding to the region to be analyzed are calculated. From the displacement field information and the vessel wall surface mesh model, time-series vessel wall models at different expansion stages are extracted. Using forward finite element analysis, the vessel wall stress tensor of each time-series vessel wall model is calculated. The vessel wall stress tensor includes the first stress tensor of the reference region and the second stress tensor of the region to be analyzed. The multidimensional components of the vessel wall strain tensor and the multidimensional components of the vessel wall stress tensor at each grid node in the time-series vessel wall model are integrated and reduced in dimension, transforming the vessel wall strain tensor and vessel wall stress tensor into dimensionless scalar forms of target equivalent strain and target equivalent stress. The target equivalent stress includes the first equivalent stress of the reference region corresponding to the reference 3D model of the reference vessel wall, the equivalent stress to be analyzed of the region to be analyzed corresponding to the reference 3D model of the reference vessel wall, and the second equivalent stress of the reference region corresponding to the 3D model of the vessel wall to be corrected.

[0127] It is necessary to map the equivalent strain data and equivalent stress data separately. The general mapping form is as follows: Where f represents the mapping function; This represents the coefficients or variables involved in the mapping. Mapping functions can be divided into linear mappings and nonlinear mappings. Since the changes in equivalent strain data at different blood pressure levels are relatively regular, linear mapping can be used. The first and second equivalent strains can be substituted into the linear mapping function to fit the linear mapping coefficients. Based on the linear mapping coefficients and the linear mapping function, the equivalent strain mapping function is determined. For example, the linear mapping function is: Where A and b are linear mapping coefficients, A is the linear transformation coefficient matrix, and b is the offset. For the first equivalent effect change, Let v be the second equivalent strain, and v be the quantity of the first and second equivalent strains, the first equivalent stress, and the second equivalent stress. The changes in equivalent stress data due to blood pressure are more complex and are better suited to a nonlinear mapping. The first and second equivalent stresses can be substituted into the nonlinear mapping function to fit the nonlinear mapping coefficients. Based on the nonlinear mapping coefficients and the nonlinear mapping function, the equivalent stress mapping function is determined. The nonlinear mapping function is: ;in, and The coefficients are nonlinear mapping coefficients, and n is the highest degree of the polynomial. For the first equivalent stress, This is the second equivalent stress.

[0128] Using pre-constructed target mapping functions corresponding to equivalent stress and equivalent strain as correction conversion models, a unified mapping transformation is performed on the equivalent stress and equivalent strain to be mapped in the region to be analyzed under the scanned images to be corrected. This normalizes and matches the vascular wall mechanical parameters under different scanning conditions to the reference framework of the baseline vascular wall 3D model. Finally, standardized stress mapping data and strain mapping data of the region to be analyzed are obtained, completing the cross-batch unified correction of mechanical parameters among multiple batches of images. The vascular wall mechanical characteristic data includes the equivalent stress and equivalent strain to be mapped.

[0129] Based on a nonlinear hyperelastic constitutive model, the characteristic fitting coefficients of the blood vessel wall material are determined according to the strain tensor and stress tensor of the blood vessel wall. The nonlinear hyperelastic constitutive model is as follows: .in, and These are the characteristic fitting coefficients for blood vessel wall materials, used to describe the hyperelastic properties of blood vessel wall materials. Blood vessel wall stress tensor Let be the strain tensor of the blood vessel wall. The formula for calculating the characteristic parameters of the blood vessel wall material is: .in, This represents the maximum equivalent variable value corresponding to the region to be analyzed. This represents the minimum equivalent variable value corresponding to the region to be analyzed. Each region to be analyzed corresponds to one... and SSI stands for Blood Vessel Wall Material Characteristic Parameter. This parameter reflects the actual elastic state of the blood vessel wall. Based on the calculation formula for the blood vessel wall material characteristic parameter, and according to the blood vessel wall material characteristic fitting coefficient, stress mapping data, and strain mapping data, the blood vessel wall material characteristic parameter of the region to be analyzed in the three-dimensional model of the blood vessel wall to be corrected is determined. Based on the blood vessel wall material characteristic fitting coefficient, the equivalent stress to be analyzed, and the equivalent strain to be analyzed, the blood vessel wall material characteristic parameter of the baseline three-dimensional model of the blood vessel wall is determined.

[0130] Furthermore, based on the structural and morphological characteristics of the baseline 3D blood vessel wall model, the baseline 3D blood vessel wall model is manually divided into regions to determine the baseline model sub-regions, thus establishing the region boundary references for subsequent automated processing. Based on the baseline model sub-regions obtained from the initial region division, a clustering algorithm is used to further divide the baseline model sub-regions, determining the target baseline model sub-regions and their cluster center points. The 3D blood vessel wall model to be corrected is spatially registered with the baseline 3D blood vessel wall model, ensuring that the 3D blood vessel wall model to be corrected coincides with the baseline 3D blood vessel wall model in anatomical spatial position and morphological contour. After registration, using the cluster center points as the basis for region division, the spatially registered 3D blood vessel wall model to be corrected is further divided into regions to determine the sub-regions of the model to be corrected.

[0131] The average values ​​of the vascular wall material characteristic parameters of the baseline model sub-region and the average values ​​of the vascular wall material characteristic parameters of the model sub-region to be calibrated are determined. By utilizing a unified registration and replication partitioning rule, the sub-region ranges of models from different scanning batches are strictly correlated, effectively improving the accuracy and consistency of cross-batch region division, eliminating data bias caused by region misalignment and differences in region division, and providing standardized and reliable statistical data support for subsequent cross-sectional comparison, quantitative analysis, and data correction of vascular mechanics and structural characteristics across multiple batches.

[0132] For example, Figure 9 This diagram illustrates the region division of the vessel wall surface mesh model generated from the four-dimensional CT angiography images obtained in various batches of scanning of a superior mesenteric artery. The vessel wall surface mesh model generated from the four-dimensional CT angiography image corresponding to scan batch 4 is the baseline three-dimensional vessel wall model. After mapping processing, the fluctuation range of SSI values ​​in the corresponding regions of the mechanical feature data of each corrected scan image is reduced, and the temporal trend of the feature data shows a smoother transition.

[0133] In the above method for determining the characteristics of blood vessel wall materials, a four-dimensional CT angiography image of the blood vessel wall to be analyzed is acquired, and a blood vessel wall surface mesh model is generated based on the four-dimensional CT angiography image. The four-dimensional CT angiography image includes a set of mapping reference scan images and at least two sets of scan images to be corrected. The blood vessel wall surface mesh model includes a reference three-dimensional model of the blood vessel wall and a three-dimensional model of the blood vessel wall to be corrected. The blood vessel wall surface mesh model is divided into a reference region and an analysis region, and the displacement field information between the reference region and the analysis region is determined. The displacement field information includes the displacement field information corresponding to the reference three-dimensional model of the blood vessel wall and the displacement field information corresponding to the three-dimensional model of the blood vessel wall to be corrected. Based on the displacement field information and the blood vessel wall surface mesh model, the target mapping function and the reference blood vessel wall are determined. The method involves analyzing the equivalent stress and equivalent strain of the region to be analyzed in the 3D model of the blood vessel wall. The target mapping function includes an equivalent strain mapping function and an equivalent stress mapping function. Based on the target mapping function, the vascular wall mechanical characteristic data of the region to be analyzed in the scan image to be calibrated is mapped to the vascular wall surface mesh model, determining the stress mapping data and strain mapping data corresponding to the region to be analyzed in the scan image. The vascular wall mechanical characteristic data includes the equivalent stress and equivalent strain to be mapped. Based on the stress mapping data, the strain mapping data, the equivalent stress to be analyzed, and the equivalent strain to be analyzed, the vascular wall material characteristic parameters of the region to be analyzed in the vascular wall surface mesh model are determined. The vascular wall material characteristic parameters are used to characterize the elastic properties of the vascular wall to be analyzed. This method solves the problem that existing methods for determining vascular wall material properties are highly dependent on acquisition boundary conditions such as blood pressure, which can lead to non-physiological shifts and systematic biases in calculation results due to blood pressure fluctuations, and lack effective correction methods, making it impossible for multiple batches of vascular wall scan data to truly reflect the state of the vascular wall. The above scheme, based on multiple batches of four-dimensional images, constructs a dedicated mechanical mapping function through partitioning and displacement field solving to achieve normalization correction of vascular mechanical data from different scanning batches, eliminating data deviations caused by differences in scanning conditions and boundary environments, and ensuring the uniformity of comparison of multiple sets of mechanical parameters. Based on the corrected stress and strain data, the characteristic parameters of vascular wall materials are determined, which can eliminate data deviations caused by blood pressure fluctuations and improve the accuracy and cross-batch comparability of vascular wall material characteristic data from multiple batches.

[0134] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0135] Based on the same inventive concept, this application also provides a vessel wall material feature determination device for implementing the above-described method for determining vessel wall material features. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the vessel wall material feature determination device provided below can be found in the limitations of the vessel wall material feature determination method described above, and will not be repeated here.

[0136] In one embodiment, such as Figure 10 As shown, a device for determining the characteristics of blood vessel wall materials is provided, including: a model construction module 401, a displacement field information determination module 402, a mapping function determination module 403, a mapping data determination module 404, and a feature parameter determination module 405, wherein:

[0137] The model building module 401 is used to acquire a four-dimensional CT angiography image of the vessel wall to be analyzed, and generate a vessel wall surface mesh model based on the four-dimensional CT angiography image; the four-dimensional CT angiography image includes a set of mapping reference scan images and at least two sets of scan images to be corrected; the vessel wall surface mesh model includes a reference three-dimensional vessel wall model and a three-dimensional vessel wall model to be corrected.

[0138] The displacement field information determination module 402 is used to divide the blood vessel wall surface mesh model into a reference region and an analysis region, and determine the displacement field information of the reference region and the analysis region; the displacement field information includes the displacement field information corresponding to the reference blood vessel wall three-dimensional model and the displacement field information corresponding to the blood vessel wall three-dimensional model to be corrected;

[0139] The mapping function determination module 403 is used to determine the target mapping function, the equivalent stress to be analyzed, and the equivalent strain to be analyzed in the region to be analyzed of the reference three-dimensional model of the blood vessel wall based on the displacement field information and the blood vessel wall surface mesh model; the target mapping function includes the equivalent strain mapping function and the equivalent stress mapping function;

[0140] The mapping data determination module 404 is used to map the vascular wall mechanical feature data of the region to be analyzed in the scan image to the vascular wall surface mesh model according to the target mapping function, and determine the stress mapping data and strain mapping data corresponding to the region to be analyzed in the scan image to be corrected; the vascular wall mechanical feature data includes the equivalent stress and equivalent strain to be mapped;

[0141] The feature parameter determination module 405 is used to determine the feature parameters of the blood vessel wall material of the region to be analyzed in the blood vessel wall surface mesh model based on the stress mapping data, the strain mapping data, the equivalent stress to be analyzed, and the equivalent strain to be analyzed; the feature parameters of the blood vessel wall material are used to characterize the elastic properties of the blood vessel wall to be analyzed.

[0142] For example, the displacement field information determination module 402 is specifically used for:

[0143] Select an image phase from the time-series images of the four-dimensional CT angiography images as a reference phase;

[0144] A non-rigid image registration method is used to align the three-dimensional images of the remaining time phases of the time series images (excluding the reference time phase) with the three-dimensional image of the reference time phase, thereby determining the deformation field characterizing the displacement of each voxel.

[0145] The surface mesh model of the blood vessel wall and the deformation field are spatially mapped, and the displacement vectors of each mesh node in the surface mesh model of the blood vessel wall under different image phases are extracted by interpolation method to determine the displacement field information of the reference area and the area to be analyzed.

[0146] For example, the mapping function determination module 403 is specifically used for:

[0147] Based on the displacement field information and the vessel wall surface mesh model, the target equivalent strain and target equivalent stress of the vessel wall surface mesh model are determined. The target equivalent strain includes the first equivalent strain of the reference region corresponding to the reference vessel wall 3D model, the equivalent strain to be analyzed of the region to be analyzed corresponding to the reference vessel wall 3D model, and the second equivalent strain of the reference region corresponding to the vessel wall 3D model to be corrected. The target equivalent stress includes the first equivalent stress of the reference region corresponding to the reference vessel wall 3D model, the equivalent stress to be analyzed of the region to be analyzed corresponding to the reference vessel wall 3D model, and the second equivalent stress of the reference region corresponding to the vessel wall 3D model to be corrected.

[0148] The equivalent strain mapping function is determined based on the first equivalent strain and the second equivalent strain, and the equivalent stress mapping function is determined based on the first equivalent stress and the second equivalent stress.

[0149] For example, the mapping function determination module 403 is also specifically used for:

[0150] Based on the displacement field information, the vessel wall strain tensor of the vessel wall surface mesh model is determined; the vessel wall strain tensor includes the first strain tensor of the reference region in the vessel wall surface mesh model and the second strain tensor of the region to be analyzed in the vessel wall surface mesh model.

[0151] Based on the displacement field information and the vessel wall surface mesh model, the temporal vessel wall model for different expansion stages is determined;

[0152] The vessel wall stress tensor of the time-series vessel wall model is determined by forward finite element analysis. The vessel wall stress tensor includes the first stress tensor of the reference region in the time-series vessel wall model and the second stress tensor of the region to be analyzed in the time-series vessel wall model.

[0153] Based on the strain tensor and stress tensor of the blood vessel wall, the target equivalent strain and target equivalent stress of the surface mesh model of the blood vessel wall are determined.

[0154] For example, the feature parameter determination module 405 is specifically used for:

[0155] Based on the nonlinear hyperelastic constitutive model, the characteristic fitting coefficients of the blood vessel wall material are determined according to the strain tensor and stress tensor of the blood vessel wall.

[0156] Based on the fitting coefficients of the blood vessel wall material characteristics, the stress mapping data, the strain mapping data, the equivalent stress to be analyzed, and the equivalent strain to be analyzed, the blood vessel wall material characteristic parameters of the region to be analyzed in the blood vessel wall surface mesh model are determined; the blood vessel wall material characteristic parameters are used to characterize the elastic properties of the blood vessel wall to be analyzed.

[0157] For example, the feature parameter determination module 405 is also specifically used for:

[0158] The reference 3D model of the blood vessel wall and the 3D model of the blood vessel wall to be corrected are divided into regions to determine the target reference model sub-region and the model to be corrected sub-region.

[0159] Determine the average parameter values ​​of the vascular wall material characteristic parameters of the sub-region of the baseline model, and the average parameter values ​​of the vascular wall material characteristic parameters of the sub-region of the model to be corrected.

[0160] For example, the feature parameter determination module 405 is also specifically used for:

[0161] The reference blood vessel wall three-dimensional model is divided into regions to determine the reference model sub-regions;

[0162] The benchmark model sub-region is divided into regions using a clustering algorithm to determine the target benchmark model sub-region and its cluster center point.

[0163] The three-dimensional model of the blood vessel wall to be corrected is spatially registered with the reference three-dimensional model of the blood vessel wall, and the spatially registered three-dimensional model of the blood vessel wall to be corrected is divided into regions based on the cluster center points to determine the sub-regions of the model to be corrected.

[0164] Each module in the aforementioned device for determining the characteristics of blood vessel wall materials can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0165] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 11 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for determining the characteristics of blood vessel wall materials. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or a key vector, trackball, or touchpad set on the computer device casing, or an external key vector disk, touchpad, or mouse, etc.

[0166] Those skilled in the art will understand that Figure 11 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0167] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0168] Step 1: Obtain a four-dimensional CT angiography image of the vessel wall to be analyzed, and generate a vessel wall surface mesh model based on the four-dimensional CT angiography image; the four-dimensional CT angiography image includes a set of mapping reference scan images and at least two sets of scan images to be corrected; the vessel wall surface mesh model includes a reference three-dimensional vessel wall model and a three-dimensional vessel wall model to be corrected.

[0169] Step 2: Divide the vessel wall surface mesh model into a reference region and an analysis region, and determine the displacement field information of the reference region and the analysis region; the displacement field information includes the displacement field information corresponding to the reference vessel wall 3D model and the displacement field information corresponding to the vessel wall 3D model to be corrected;

[0170] Step 3: Based on the displacement field information and the vessel wall surface mesh model, determine the target mapping function, the equivalent stress to be analyzed, and the equivalent strain to be analyzed in the region to be analyzed in the reference three-dimensional vessel wall model; the target mapping function includes the equivalent strain mapping function and the equivalent stress mapping function;

[0171] Step 4: Based on the target mapping function, map the vascular wall mechanical feature data of the region to be analyzed in the scan image to the vascular wall surface mesh model, and determine the stress mapping data and strain mapping data corresponding to the region to be analyzed in the scan image; the vascular wall mechanical feature data includes the equivalent stress and equivalent strain to be mapped.

[0172] Step 5: Based on the stress mapping data, the strain mapping data, the equivalent stress to be analyzed, and the equivalent strain to be analyzed, determine the characteristic parameters of the blood vessel wall material in the region to be analyzed of the blood vessel wall surface mesh model; the characteristic parameters of the blood vessel wall material are used to characterize the elastic properties of the blood vessel wall to be analyzed.

[0173] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0174] Step 1: Obtain a four-dimensional CT angiography image of the vessel wall to be analyzed, and generate a vessel wall surface mesh model based on the four-dimensional CT angiography image; the four-dimensional CT angiography image includes a set of mapping reference scan images and at least two sets of scan images to be corrected; the vessel wall surface mesh model includes a reference three-dimensional vessel wall model and a three-dimensional vessel wall model to be corrected.

[0175] Step 2: Divide the vessel wall surface mesh model into a reference region and an analysis region, and determine the displacement field information of the reference region and the analysis region; the displacement field information includes the displacement field information corresponding to the reference vessel wall 3D model and the displacement field information corresponding to the vessel wall 3D model to be corrected;

[0176] Step 3: Based on the displacement field information and the vessel wall surface mesh model, determine the target mapping function, the equivalent stress to be analyzed, and the equivalent strain to be analyzed in the region to be analyzed in the reference three-dimensional vessel wall model; the target mapping function includes the equivalent strain mapping function and the equivalent stress mapping function;

[0177] Step 4: Based on the target mapping function, map the vascular wall mechanical feature data of the region to be analyzed in the scan image to the vascular wall surface mesh model, and determine the stress mapping data and strain mapping data corresponding to the region to be analyzed in the scan image; the vascular wall mechanical feature data includes the equivalent stress and equivalent strain to be mapped.

[0178] Step 5: Based on the stress mapping data, the strain mapping data, the equivalent stress to be analyzed, and the equivalent strain to be analyzed, determine the characteristic parameters of the blood vessel wall material in the region to be analyzed of the blood vessel wall surface mesh model; the characteristic parameters of the blood vessel wall material are used to characterize the elastic properties of the blood vessel wall to be analyzed.

[0179] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0180] Step 1: Obtain a four-dimensional CT angiography image of the vessel wall to be analyzed, and generate a vessel wall surface mesh model based on the four-dimensional CT angiography image; the four-dimensional CT angiography image includes a set of mapping reference scan images and at least two sets of scan images to be corrected; the vessel wall surface mesh model includes a reference three-dimensional vessel wall model and a three-dimensional vessel wall model to be corrected.

[0181] Step 2: Divide the vessel wall surface mesh model into a reference region and an analysis region, and determine the displacement field information of the reference region and the analysis region; the displacement field information includes the displacement field information corresponding to the reference vessel wall 3D model and the displacement field information corresponding to the vessel wall 3D model to be corrected;

[0182] Step 3: Based on the displacement field information and the vessel wall surface mesh model, determine the target mapping function, the equivalent stress to be analyzed, and the equivalent strain to be analyzed in the region to be analyzed in the reference three-dimensional vessel wall model; the target mapping function includes the equivalent strain mapping function and the equivalent stress mapping function;

[0183] Step 4: Based on the target mapping function, map the vascular wall mechanical feature data of the region to be analyzed in the scan image to the vascular wall surface mesh model, and determine the stress mapping data and strain mapping data corresponding to the region to be analyzed in the scan image; the vascular wall mechanical feature data includes the equivalent stress and equivalent strain to be mapped.

[0184] Step 5: Based on the stress mapping data, the strain mapping data, the equivalent stress to be analyzed, and the equivalent strain to be analyzed, determine the characteristic parameters of the blood vessel wall material in the region to be analyzed of the blood vessel wall surface mesh model; the characteristic parameters of the blood vessel wall material are used to characterize the elastic properties of the blood vessel wall to be analyzed.

[0185] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0186] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0187] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0188] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining the characteristics of blood vessel wall materials, characterized in that, include: A four-dimensional CT angiography image of the vessel wall to be analyzed is acquired, and a vessel wall surface mesh model is generated based on the four-dimensional CT angiography image; the four-dimensional CT angiography image includes a set of mapping reference scan images and at least two sets of scan images to be corrected; the vessel wall surface mesh model includes a reference three-dimensional vessel wall model and a three-dimensional vessel wall model to be corrected. The surface mesh model of the blood vessel wall is divided into a reference region and a region to be analyzed, and the displacement field information of the reference region and the region to be analyzed is determined. Displacement field information includes displacement field information corresponding to the reference 3D model of the blood vessel wall and displacement field information corresponding to the 3D model of the blood vessel wall to be corrected; Based on the displacement field information and the vessel wall surface mesh model, the target mapping function, the equivalent stress to be analyzed, and the equivalent strain to be analyzed in the region to be analyzed of the reference three-dimensional vessel wall model are determined; the target mapping function includes the equivalent strain mapping function and the equivalent stress mapping function. According to the target mapping function, the vascular wall mechanical feature data of the region to be analyzed in the scan image to be corrected is mapped to the vascular wall surface mesh model, and the stress mapping data and strain mapping data corresponding to the region to be analyzed in the scan image to be corrected are determined; the vascular wall mechanical feature data includes the equivalent stress and equivalent strain to be mapped. Based on the stress mapping data, the strain mapping data, the equivalent stress to be analyzed, and the equivalent strain to be analyzed, the characteristic parameters of the blood vessel wall material in the region to be analyzed in the blood vessel wall surface mesh model are determined; the characteristic parameters of the blood vessel wall material are used to characterize the elastic properties of the blood vessel wall to be analyzed.

2. The method according to claim 1, characterized in that, Determining the displacement field information of the reference region and the region to be analyzed includes: Select an image phase from the time-series images of the four-dimensional CT angiography images as a reference phase; A non-rigid image registration method is used to align the three-dimensional images of the remaining time phases of the time series images (excluding the reference time phase) with the three-dimensional image of the reference time phase, thereby determining the deformation field characterizing the displacement of each voxel. The surface mesh model of the blood vessel wall and the deformation field are spatially mapped, and the displacement vectors of each mesh node in the surface mesh model of the blood vessel wall under different image phases are extracted by interpolation method to determine the displacement field information of the reference area and the area to be analyzed.

3. The method according to claim 1, characterized in that, Based on the displacement field information and the vessel wall surface mesh model, the target mapping function, the equivalent stress to be analyzed, and the equivalent strain to be analyzed in the region to be analyzed of the baseline three-dimensional vessel wall model are determined, including: Based on the displacement field information and the vessel wall surface mesh model, the target equivalent strain and target equivalent stress of the vessel wall surface mesh model are determined. The target equivalent strain includes the first equivalent strain of the reference region corresponding to the reference vessel wall 3D model, the equivalent strain to be analyzed of the region to be analyzed corresponding to the reference vessel wall 3D model, and the second equivalent strain of the reference region corresponding to the vessel wall 3D model to be corrected. The target equivalent stress includes the first equivalent stress of the reference region corresponding to the reference vessel wall 3D model, the equivalent stress to be analyzed of the region to be analyzed corresponding to the reference vessel wall 3D model, and the second equivalent stress of the reference region corresponding to the vessel wall 3D model to be corrected. The equivalent strain mapping function is determined based on the first equivalent strain and the second equivalent strain, and the equivalent stress mapping function is determined based on the first equivalent stress and the second equivalent stress.

4. The method according to claim 3, characterized in that, Based on the displacement field information and the vessel wall surface mesh model, the target equivalent strain and target equivalent stress of the vessel wall surface mesh model are determined, including: Based on the displacement field information, the vessel wall strain tensor of the vessel wall surface mesh model is determined; the vessel wall strain tensor includes the first strain tensor of the reference region in the vessel wall surface mesh model and the second strain tensor of the region to be analyzed in the vessel wall surface mesh model. Based on the displacement field information and the vessel wall surface mesh model, the temporal vessel wall model for different expansion stages is determined; The vessel wall stress tensor of the time-series vessel wall model is determined by forward finite element analysis. The vessel wall stress tensor includes the first stress tensor of the reference region in the time-series vessel wall model and the second stress tensor of the region to be analyzed in the time-series vessel wall model. Based on the strain tensor and stress tensor of the blood vessel wall, the target equivalent strain and target equivalent stress of the surface mesh model of the blood vessel wall are determined.

5. The method according to claim 4, characterized in that, Based on the stress mapping data, the strain mapping data, the equivalent stress to be analyzed, and the equivalent strain to be analyzed, the characteristic parameters of the blood vessel wall material in the region to be analyzed in the blood vessel wall surface mesh model are determined, including: Based on the nonlinear hyperelastic constitutive model, the characteristic fitting coefficients of the blood vessel wall material are determined according to the strain tensor and stress tensor of the blood vessel wall. Based on the fitting coefficients of the blood vessel wall material characteristics, the stress mapping data, the strain mapping data, the equivalent stress to be analyzed, and the equivalent strain to be analyzed, the blood vessel wall material characteristic parameters of the region to be analyzed in the blood vessel wall surface mesh model are determined.

6. The method according to claim 1, characterized in that, Also includes: The reference 3D model of the blood vessel wall and the 3D model of the blood vessel wall to be corrected are divided into regions to determine the target reference model sub-region and the model to be corrected sub-region. Determine the average parameter values ​​of the vascular wall material characteristic parameters of the sub-region of the baseline model, and the average parameter values ​​of the vascular wall material characteristic parameters of the sub-region of the model to be corrected.

7. The method according to claim 6, characterized in that, The reference 3D model of the blood vessel wall and the 3D model of the blood vessel wall to be corrected are divided into regions to determine the target reference model sub-region and the model to be corrected sub-region, including: The reference blood vessel wall three-dimensional model is divided into regions to determine the reference model sub-regions; The benchmark model sub-region is divided into regions using a clustering algorithm to determine the target benchmark model sub-region and its cluster center point. The three-dimensional model of the blood vessel wall to be corrected is spatially registered with the reference three-dimensional model of the blood vessel wall, and the spatially registered three-dimensional model of the blood vessel wall to be corrected is divided into regions based on the cluster center points to determine the sub-regions of the model to be corrected.

8. A device for determining the characteristics of blood vessel wall materials, characterized in that, The device for determining the characteristics of blood vessel wall material includes: The model building module is used to acquire four-dimensional CT angiography images of the vessel wall to be analyzed, and generate a vessel wall surface mesh model based on the four-dimensional CT angiography images; the four-dimensional CT angiography images include a set of mapping reference scan images and at least two sets of scan images to be corrected; the vessel wall surface mesh model includes a reference three-dimensional vessel wall model and a three-dimensional vessel wall model to be corrected. The displacement field information determination module is used to divide the blood vessel wall surface mesh model into a reference region and an analysis region, and to determine the displacement field information of the reference region and the analysis region; the displacement field information includes the displacement field information corresponding to the reference blood vessel wall three-dimensional model and the displacement field information corresponding to the blood vessel wall three-dimensional model to be corrected; The mapping function determination module is used to determine the target mapping function, the equivalent stress to be analyzed, and the equivalent strain to be analyzed in the region to be analyzed of the reference three-dimensional model of the blood vessel wall, based on the displacement field information and the blood vessel wall surface mesh model; the target mapping function includes an equivalent strain mapping function and an equivalent stress mapping function; The mapping data determination module is used to map the vascular wall mechanical feature data of the region to be analyzed in the scan image to the vascular wall surface mesh model according to the target mapping function, and determine the stress mapping data and strain mapping data corresponding to the region to be analyzed in the scan image to be corrected; the vascular wall mechanical feature data includes the equivalent stress and equivalent strain to be mapped; The feature parameter determination module is used to determine the feature parameters of the blood vessel wall material of the region to be analyzed in the blood vessel wall surface mesh model based on the stress mapping data, the strain mapping data, the equivalent stress to be analyzed, and the equivalent strain to be analyzed; the feature parameters of the blood vessel wall material are used to characterize the elastic properties of the blood vessel wall to be analyzed.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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