Vascular roughness quantification method and device based on triangular infinitesimal distribution characteristics and fractal law, medium and program product

Abstract

The invention provides a blood vessel roughness quantification method and device based on triangular infinitesimal distribution characteristics and fractal rules, a medium and a program product, and relates to the field of intelligent medical treatment. The method comprises the following steps: discretizing a blood vessel wall surface of a blood vessel geometric model into continuous triangular units by adopting different sampling intervals, and calculating an effective shear dip angle and an effective dip angle of each triangular unit; calculating the ratio A theta * of the area of the triangular unit with the effective inclination angle of the blood vessel wall larger than the effective shear inclination angle to the total area, and calculating a fitting coefficient according to A theta *; calculating the average inclination angle of the wall surface in the specified blood flow direction under different sampling intervals; quantifying the relationship between the average inclination angle of the wall surface and the corresponding sampling interval to obtain fractal dimensions; and quantifying the roughness of the blood vessel according to the fractal dimension. The wall surface of the blood vessel is regarded as a geometry with self-similarity and fractal characteristics, and the details of the surface of the blood vessel are analyzed by using the fractal theory and triangular infinitesimal distribution, so that roughness quantification with higher precision than that of a traditional method can be provided.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent medical treatment, and more particularly, to a blood vessel roughness quantification method, device, medium and program product based on triangular microelement distribution characteristics and fractal law. BACKGROUND

[0002] Vascular disease, particularly atherosclerosis, is one of the major health problems leading to death and disability worldwide. Research on atherosclerosis and blood vessel surface roughness has important clinical significance, especially in the early diagnosis of vascular disease. Atherosclerosis is one of the main causes of cardiovascular disease, characterized by the formation of plaques in the arterial wall, leading to stenosis of the blood vessel lumen, obstruction of blood flow, and even potentially triggering serious cardiac events such as myocardial infarction and stroke. The nature of the plaque and the microstructure changes of the blood vessel wall directly affect the patency of the blood vessel and hemodynamics, therefore, monitoring the changes in blood vessel surface roughness is crucial for assessing arterial health. In-depth study of the relationship between the changes in blood vessel surface roughness, plaque morphology and hemodynamics not only helps to detect vascular disease early, but also improves the prediction of plaque rupture and cardiovascular events, providing a more accurate assessment method for clinical practice.

[0003] Currently, in the process of studying the relationship between the changes in blood vessel surface roughness, plaque morphology and hemodynamics, fractal theory has been applied in blood vessel research, especially in describing the complexity of blood vessel network and blood vessel wall surface. The fractal characteristics of the blood vessel wall surface can be used to assess the early manifestations of vascular lesions. Existing fractal analysis methods mainly focus on the fractal features of the blood vessel lumen morphology or the fractal dimension of the blood vessel network, providing some help in solving the complexity of the blood vessel network. Although these methods can reveal the rules of blood vessel morphology changes, there are still limitations in accurately quantifying the micro-roughness of the blood vessel wall and early diagnosis of atherosclerosis. SUMMARY

[0004] The present application aims to at least solve one of the technical problems existing in the prior art. To this end, the present application provides a blood vessel roughness quantification method, device, medium and program product based on triangular microelement distribution characteristics and fractal law.

[0005] The first aspect of the present application discloses a blood vessel roughness quantification method based on triangular microelement distribution characteristics and fractal law, the method comprising:

[0006] S101, obtaining a geometric model of a blood vessel;

[0007] S102, discretizing the blood vessel wall surface of the geometric model into continuous triangular elements using different sampling intervals to obtain a wall surface topography model showing continuous triangular elements;

[0008] S103, Calculate the effective shear dip angle of a single triangular element based on the wall topography model. And the effective tilt angle; calculate the ratio A of the area of ​​the triangular unit whose effective tilt angle of the vessel wall is greater than the effective shear tilt angle to the total area. θ* According to A θ* Calculate the fitting coefficient C;

[0009] S104, based on the effective shear tilt angle The fitting coefficient C is used to calculate the average wall inclination angle for a specified blood flow direction at different sampling intervals. The relationship between the average inclination angle of the wall and the corresponding sampling interval is quantified to obtain the fractal dimension D; the roughness of the blood vessel is quantified based on the fractal dimension D.

[0010] In some embodiments, the A θ* The calculation method is as follows: ;

[0011] In the formula, A0 is the ratio of the total area of ​​all vessel wall units with an inclination angle greater than 0 to the total surface area of ​​the vessel wall; θ * max is the maximum effective shear tilt angle of the blood vessel wall unit; C is the formula fitting coefficient, which describes the unit angle distribution;

[0012] Optionally, the effective shear tilt angle The calculation method is as follows: ;

[0013] In the formula, tanθ is obtained by converting cosθ; , The effective tilt angle of the vessel wall micro-element. denoted as , where is the angle between the inclination of the vessel wall and the vessel centerline; t is the direction vector of the vessel centerline; n is the external normal vector of the element; n0 is the external normal vector of the blood flow direction plane; and n1 is the projection vector of the blood flow direction onto the blood flow plane.

[0014] Optionally, the average inclination angle of the wall surface The calculation method is as follows: .

[0015] In some embodiments, the fractal dimension D is calculated as follows: (Equation 6); where, The actual contact joint roughness parameter when the sampling interval is δ. ; For an index with a measurement scale of 1 D is the fractal dimension;

[0016] Optionally, the calculation method for the fractal dimension D further includes: ;

[0017] Optional sampling intervals include: 0.01mm, 0.1mm, 0.25mm, 0.5mm, and 1mm.

[0018] In some embodiments, between S101 and S102, the method further includes: extracting the centerline of the target blood vessel based on a geometric model; the centerline is the main trunk centerline after removing irrelevant branches, which helps to reduce interference from details, so that subsequent blood vessel wall roughness quantification can focus on the analysis of the aortic trunk;

[0019] Optionally, the methods for extracting the centerline include any one or more of the following: manual calibration method, topology refinement method, distance transformation method, and minimum cost path algorithm;

[0020] Optionally, between S101 and S102, the method further includes: denoising the geometric model to effectively remove high-frequency noise from the blood vessel model, making the model smoother and more stable.

[0021] In some embodiments, between S101 and S102, the method further includes: extracting the target blood vessel region from the geometric model and discretizing the blood vessel wall of the target blood vessel region into continuous triangular units using different sampling intervals.

[0022] In some embodiments, the method further includes S105, calculating a roughness index based on the fractal dimension D and the average inclination angle of the wall when the sampling interval is 1;

[0023] Optionally, the roughness index is calculated as the product of the fractal dimension D and the average inclination angle of the wall when the sampling interval is 1.

[0024] Optionally, the method further includes: assessing the local wall shear force of the blood vessel based on a roughness index; the larger the roughness index, the greater the wall shear force.

[0025] A second aspect of this application discloses a computer device, the device comprising: a memory and a processor; the memory being used to store a computer program; and the processor executing the computer program to implement the steps of the above-described method.

[0026] A third aspect of this application discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0027] The fourth aspect of this application discloses a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0028] The research significance, main objectives, and beneficial effects of this application are as follows:

[0029] 1. Significance of the study

[0030] 1.1 Innovative Application of Triangular Infinite Element Distribution Characteristics and Fractal Laws This study proposes a method for quantifying vascular wall roughness based on the distribution characteristics of triangular infinitesimals and fractal laws. By treating the vascular wall as a geometric body with self-similarity and fractal characteristics, and utilizing fractal theory and triangular infinitesimal element distribution analysis, it can provide roughness quantification with higher accuracy than traditional methods. Fractal theory can effectively capture the irregularities of the vascular surface at different scales. Changes in this self-similarity in early lesions are important early warning signals for the development of diseases such as atherosclerosis.

[0031] 1.2 Precise Identification of Early Lesions Increased arterial wall roughness is often an early manifestation of atherosclerosis and other vascular diseases. Traditional imaging methods struggle to accurately quantify this roughness, especially in the early stages of lesions. Quantification methods based on the distribution characteristics and fractal patterns of triangular micro-elements can analyze changes in the microstructure of the vascular wall at high resolution, identifying early lesions such as endothelial damage or lipid deposition. This helps physicians detect problems in the early stages of disease and take timely intervention measures.

[0032] 1.3 Personalized Treatment and Risk Assessment: Quantifying vascular wall roughness enables personalized treatment. In the treatment of vascular diseases, understanding the changes in vascular microstructure and roughness in each patient helps to develop more precise treatment plans. For example, for high-risk patients, this precise quantitative analysis can assess changes in hemodynamics, predict the risk of thrombus or plaque formation, and evaluate the effectiveness of different treatment methods (such as drug intervention, stent implantation, etc.). Through detailed roughness analysis, risks can be predicted in the early stages of disease and treatment effects can be dynamically monitored, thereby reducing complications and mortality caused by vascular diseases.

[0033] 1.4 Promoting the Development of Early Diagnosis and Treatment Technologies for Vascular Diseases With the continuous advancement of vascular disease treatment, especially in interventional vascular procedures (such as stent implantation and vascular repair), the precise quantification of vascular wall roughness is crucial for developing treatment plans and evaluating the effectiveness of interventional treatments. This method can provide more accurate vascular wall roughness data, which helps optimize the design of interventional procedures and improve treatment outcomes. Particularly in dynamically monitoring the progression of vascular lesions and assessing the compatibility between vascular implants and the vascular wall, this method can provide more forward-looking and accurate technical support for clinical practice.

[0034] 1.5 Establishing the Relationship between Vascular Roughness and Hemodynamics Vascular wall roughness is closely related to hemodynamics, especially in areas of vascular stenosis, plaque formation, or thrombosis. Quantifying vascular wall roughness allows for further research into the mechanical properties of the vascular wall, such as wall shear force and pressure distribution. This provides a more detailed biomechanical understanding of the pathogenesis of vascular diseases and offers a new theoretical basis for the prevention and intervention of vascular diseases.

[0035] Defects to be overcome:

[0036] 2. Shortcomings overcome: 2.1 Neglecting changes in the microscopic surface features of the vessel wall: Judging changes in the vessel wall based on imaging is subjective, and existing methods often ignore changes in the microscopic surface features of the vessel wall, lacking detailed quantitative means for measuring vessel wall roughness, which may lead to biased diagnostic results due to subjective judgment. This patent, by dividing the vessel wall surface into continuous triangular units, can conduct in-depth research on the influence of local features on vascular dynamics, providing more accurate roughness analysis.

[0037] 2.2 Limited Scope and Accuracy of Fractal Theory: Although fractal theory holds potential in vascular morphology analysis, existing fractal analysis methods are primarily used for qualitative descriptions of vascular network complexity, making it difficult to effectively quantify the microscopic details of the vessel wall and local lesions. Fractal analysis typically focuses on macroscopic structure and cannot accurately capture changes in the microscopic roughness of the vessel wall. This patent overcomes this deficiency by precisely quantifying the roughness of the vessel wall.

[0038] 2.3 Lack of integration with quantitative analysis and clinical application: Existing methods for assessing vascular roughness mostly focus on qualitative analysis, making it difficult to directly integrate with hemodynamics, vascular implant design, and clinical treatment planning. This patent can quantitatively describe vascular wall roughness and, combined with a hemodynamic model, provide more precise data support for personalized treatment and disease intervention.

[0039] 3. Based on the shortcomings of the existing technology mentioned above, the main purpose of this patent is:

[0040] 3.1 Precise quantification of vessel wall micro-roughness: By continuously dividing the vessel wall surface into triangular units, this patent can accurately describe the micro-roughness of the vessel wall and study the influence of local features on vascular dynamics, thereby overcoming the shortcomings of existing methods in that they lack effective quantification of subtle roughness changes.

[0041] 3.2 Improving the accuracy of fractal theory application: This patent, by combining fractal theory, accurately quantifies the micro-roughness of the blood vessel wall, breaking through the limitation of existing fractal analysis methods that are limited to macroscopic structural description. It can effectively capture and quantify the complexity of the micro-surface of the blood vessel wall and local lesions, providing new theoretical support for the early diagnosis of vascular diseases such as atherosclerosis.

[0042] 3.3 Promoting the Integration of Vascular Roughness with Clinical Treatment Plans: This patent can quantitatively describe vascular wall roughness and combine it with hemodynamic models to provide a basis for personalized treatment. This method not only improves the accuracy of early disease diagnosis but also provides more precise data support for vascular implant design, treatment plan optimization, and disease intervention, promoting the development of precision medicine for vascular diseases.

[0043] 4. Beneficial effects

[0044] Advantage 1: Precise Quantification of Blood Vessel Wall Micro-Roughness. This invention discretizes the blood vessel wall surface into continuous triangular units and performs fine analysis based on the distribution characteristics of these triangular micro-elements. This allows for precise description of the micro-morphology of the blood vessel wall and effective quantification of roughness. Compared with traditional methods, this invention not only provides higher resolution roughness data but also identifies more subtle lesion features, significantly improving the accuracy and practicality of roughness assessment.

[0045] Results: Through precise micro-element discretization and morphological sampling, this invention can more accurately assess the roughness of the blood vessel wall at different scales, especially in the identification of microlesions and early atherosclerotic plaques, providing more refined and reliable quantitative data. This technology can significantly improve the early diagnosis of vascular diseases and provide doctors with more valuable references.

[0046] Advantage 2: Combining fractal theory to consider roughness variations under different sampling intervals. This invention uses fractal theory to analyze the roughness of the blood vessel wall and introduces fractal features under different sampling intervals. Combined with the fractal dimension D, an accurate roughness index is calculated. This method can quantify the roughness changes of the blood vessel wall at different scales and reveal the influence of different sampling intervals on the microstructure.

[0047] Results: The introduction of fractal theory enables this invention to accurately capture the roughness changes of the blood vessel wall surface at multiple scales. By analyzing different sampling intervals, this invention can effectively identify the microscopic changes of the blood vessel wall at different scales, especially in the early stages of disease. Through fractal dimension analysis, it can reveal the details and development trends of vascular lesions, providing a basis for the prediction and personalized treatment of vascular diseases.

[0048] Advantage 3: Provides refined quantification of blood vessel wall roughness and disease risk assessment

[0049] This invention, by precisely quantifying blood vessel wall roughness and combining it with fractal analysis at different scales, can provide more detailed evidence for early disease diagnosis, risk assessment, and surgical planning. Through roughness indices... The quantification of this information can accurately assess the microscopic changes in the blood vessel wall, providing doctors with personalized treatment plans.

[0050] Effects: This advantage enables the present invention to identify potential lesion areas in the early stages of vascular disease, predict disease progression, and provide targeted treatment recommendations. Particularly in patients with vascular stenosis and atherosclerosis, the present invention can provide more refined risk assessment data, helping to develop personalized intervention plans, reduce the incidence of complications, and improve patients' quality of life. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a schematic diagram of the method flow provided in the first aspect of the present invention;

[0053] Figure 2 This is a schematic diagram of a vascular roughness quantification system based on the distribution characteristics and fractal laws of triangular micro-elements provided in the second aspect of the present invention;

[0054] Figure 3 This is a schematic diagram of a computer device provided in an embodiment of the present invention;

[0055] Figure 4 This is a schematic diagram of the architecture of an exemplary computing device provided in an embodiment of the present invention;

[0056] Figure 5 This is a schematic diagram of the storage medium provided in an embodiment of the present invention;

[0057] Figure 6 This is a schematic diagram of the 3D model before and after denoising and smoothing provided in an embodiment of the present invention. Figure 6 A represents the value before noise reduction. Figure 6 B represents the result after noise reduction and smoothing;

[0058] Figure 7 This is a diagram showing the centerline extraction result of the blood vessel model provided in this embodiment of the invention. Figure 7 A represents the centerline without removing irrelevant branches. Figure 7 B is the centerline after irrelevant branches have been removed;

[0059] Figure 8 This is a schematic diagram of a three-dimensional blood vessel model under different sampling intervals provided in an embodiment of the present invention;

[0060] Figure 9This is a schematic diagram of the effective shear tilt angle geometry provided in an embodiment of the present invention; wherein, the left figure is a vascular mesh topography, the middle figure is a local magnified view, and the right figure is a triangular micro-element tilt angle model;

[0061] Figure 10 In the downward tilt angle distribution function of a specific blood flow direction provided in the embodiments of the present invention and Relationship;

[0062] Figure 11 This is provided by the embodiments of the present invention. and Relationship;

[0063] Figure 12 This is a schematic diagram of dividing blood vessels into 10 parts and calculating roughness index provided in an embodiment of the present invention;

[0064] Figure 13 This is a schematic diagram of different roughness levels of segment W3 provided in an embodiment of the present invention;

[0065] Figure 14 These are cloud maps of different roughness W3 segments provided in embodiments of the present invention;

[0066] Figure 15 This invention provides the correlation between different roughness indices and hemodynamic parameters. Detailed Implementation

[0067] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0068] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] Definitions:

[0071] Triangular mesh elements (often simply called triangular elements or triangular units) are one of the most basic and commonly used element types in Finite Element Analysis (FEA) and Computational Geometry. They divide (discrete) a complex geometric region (such as the surface or volume of an object) into many small, simple, interconnected triangular regions. Each such small triangular region is called an "element" or "unit".

[0072] Effective shear angle: The effective shear angle is generally referred to as the angle between the direction of blood flow shear stress and the tangent vector of the vessel wall. This angle reflects the direction of the shearing effect of blood flow on the vessel wall.

[0073] Effective Inclination Angle of the Vessel Wall: This refers to the angle of inclination of the vessel wall relative to a reference plane (usually the blood flow plane). It describes the degree of inclination of the vessel wall in a local area.

[0074] Inclination Angle of the Vessel Wall Element: This refers to the angle between the normal vector of a single triangular mesh element and a reference plane (such as the blood flow plane). It describes the degree of tilt of the element in a local region.

[0075] Angle between Vessel Wall Inclination and Vessel Centerline: This refers to the angle between the normal vector of a point on the vessel wall and the tangent vector to the vessel centerline. It describes the degree of inclination of the vessel wall relative to the vessel centerline at that point.

[0076] Vessel Centerline Direction Vector: This is a unit vector representing the tangent direction of the vessel centerline at a given point, usually denoted by T. It describes the direction of the vessel centerline at that point.

[0077] Element External Normal Vector: This is the unit vector representing the direction of the outward normal of a triangular mesh element, usually denoted as n. It is perpendicular to the element plane and points outward from the vessel wall. The normal vector n is calculated as the cross product of the edge vectors: n = a × b. Let the triangular element be defined by three points P1, P2, and P3, located on the vessel wall. Calculate the edge vectors: Calculate two edge vectors, for example: vector a = P2 - P1; vector b = P3 - P1.

[0078] External Normal Vector of the Blood Flow Plane: This is the unit vector of the external normal direction of the plane containing the blood flow direction (usually a local cross-sectional plane containing the tangent vector of the vessel's centerline). It is perpendicular to the plane and points outward from the vessel.

[0079] Projection vector of blood flow direction in the blood flow plane: This is the projection of the blood flow direction vector onto the blood flow plane. The blood flow plane is typically a local cross-sectional plane containing the tangent vector of the vessel centerline. The projection vector describes the component of the blood flow direction in that plane.

[0080] An aortic mask is a specific region obtained in medical imaging after segmenting and annotating the aortic region using image processing techniques. It is used for precise segmentation and annotation of the aorta.

[0081] Figure 1 This is a schematic flowchart of a method for quantifying vascular roughness based on the distribution characteristics and fractal laws of triangular micro-elements provided in an embodiment of the present invention. Specifically, the method includes the following steps: S101, obtaining the geometric model of the blood vessel;

[0082] In some embodiments, the target blood vessel includes, but is not limited to, the following: aorta, superior vena cava, and inferior vena cava;

[0083] Optionally, the geometric model includes a three-dimensional model.

[0084] In some embodiments, between S101 and S102, the method further includes: extracting the centerline of the target blood vessel based on a geometric model; the centerline is the main trunk centerline after removing irrelevant branches, which helps to reduce interference from details, so that subsequent blood vessel wall roughness quantification can focus on the analysis of the aortic trunk;

[0085] Optionally, the methods for extracting the centerline include any one or more of the following: manual calibration method, topology refinement method, distance transformation method, and minimum cost path algorithm;

[0086] Optionally, between S101 and S102, the method further includes: denoising the geometric model to effectively remove high-frequency noise from the blood vessel model, making the model smoother and more stable.

[0087] In some embodiments, between S101 and S102, the method further includes: extracting the target blood vessel region from the geometric model and discretizing the blood vessel wall of the target blood vessel region into continuous triangular units using different sampling intervals.

[0088] S102, the blood vessel wall of the geometric model is discretized into continuous triangular units by using different sampling intervals, and a wall morphology model displaying continuous triangular units is obtained;

[0089] S103, Calculate the effective shear dip angle of a single triangular element based on the wall topography model. And the effective tilt angle; calculate the ratio A of the area of ​​the triangular unit whose effective tilt angle of the vessel wall is greater than the effective shear tilt angle to the total area. θ* According to A θ* Calculate the fitting coefficient C;

[0090] In some embodiments, the A θ* The calculation method is as follows: (Formula 2);

[0091] In the formula, A0 is the ratio of the total area of ​​all vessel wall units with an inclination angle greater than 0 to the total surface area of ​​the vessel wall; θ * max is the maximum effective shear tilt angle of the blood vessel wall unit; C is the formula fitting coefficient, which describes the unit angle distribution;

[0092] Optionally, the effective shear tilt angle The calculation method is as follows: ;(Formula 1)

[0093] In the formula, , , The effective tilt angle of the vessel wall micro-element. denoted as , where is the angle between the inclination of the vessel wall and the vessel centerline; t is the direction vector of the vessel centerline; n is the external normal vector of the element; n0 is the external normal vector of the blood flow direction plane; and n1 is the projection vector of the blood flow direction onto the blood flow plane.

[0094] S104, based on the effective shear tilt angle The fitting coefficient C is used to calculate the average wall inclination angle for a specified blood flow direction at different sampling intervals. The relationship between the average inclination angle of the wall and the corresponding sampling interval is quantified to obtain the fractal dimension D; the roughness of the blood vessel is quantified based on the fractal dimension D.

[0095] In some embodiments, the average inclination angle of the wall is calculated as follows: .

[0096] In some embodiments, the fractal dimension D is calculated as follows: (Equation 6); where, The actual contact joint roughness parameter when the sampling interval is δ. ; For an index with a measurement scale of 1 D is the fractal dimension;

[0097] Optionally, the calculation method for the fractal dimension D further includes: (Equation 7);

[0098] Optional sampling intervals include: 0.01mm, 0.1mm, 0.25mm, 0.5mm, and 1mm.

[0099] In some embodiments, the method further includes S105, calculating a roughness index based on the fractal dimension D and the average inclination angle of the wall when the sampling interval is 1;

[0100] Optionally, the roughness index is calculated as the product of the fractal dimension D and the average inclination angle of the wall when the sampling interval is 1.

[0101] Optionally, the method further includes: assessing the local wall shear force of the blood vessel based on a roughness index; the larger the roughness index, the greater the wall shear force.

[0102] Figure 3 This is a schematic diagram of a computer device provided in an embodiment of the present invention, such as... Figure 3 As shown, the device 2000 may include: one or more processors 2010 and one or more memories 2020; wherein the memories store computer-readable code that, when run by the one or more processors, can perform the methods described above.

[0103] The processor in this embodiment can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, operations, and logic block diagrams disclosed in this embodiment. The general-purpose processor can be a microprocessor or any conventional processor, and can be based on an x86 or ARM architecture.

[0104] In general, the various exemplary embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, firmware, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device. When aspects of embodiments of this disclosure are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it will be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as non-limiting examples in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0105] For example, the method or apparatus according to embodiments of this disclosure can also be used by means of Figure 4 The architecture of the computing device 3000 shown is used for implementation. For example... Figure 4 As shown, the computing device 3000 may include a bus 3010, one or more CPUs 3020, a read-only memory (ROM) 3030, a random access memory (RAM) 3040, a communication port 3050 connected to a network, an input / output component 3060, a hard disk 3070, etc. The storage devices in the computing device 3000, such as the ROM 3030 or the hard disk 3070, may store various data or files used for processing and / or communication of the methods provided in this disclosure, as well as program instructions executed by the CPU. The computing device 3000 may also include a user interface 3080. Of course, Figure 4 The architecture shown is merely exemplary and can be omitted as needed when implementing different devices. Figure 4 One or more components in the computing device shown.

[0106] This invention also includes a computer-readable storage medium, such as... Figure 5The diagram illustrates a storage medium 4000 provided in an embodiment of the present invention. The computer storage medium 4020 stores computer-readable instructions 4010. When the computer-readable instructions 4010 are executed by a processor, the method described above according to embodiments of the present disclosure can be performed. The computer-readable storage medium in the embodiments of the present disclosure may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Synchronous Link Dynamic Random Access Memory (SLDRAM), and Direct Memory Bus Random Access Memory (DR RAM). It should be noted that the memory used in the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0107] This disclosure also provides a computer program product or system, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0108] In some embodiments, this embodiment also discloses a vascular roughness quantification system based on the distribution characteristics and fractal laws of triangular micro-elements, such as... Figure 2 As shown, the system includes:

[0109] The geometric model acquisition module 201 is used or configured to acquire the geometric model of blood vessels;

[0110] The wall morphology model processing module 202 is used or configured to discretize the blood vessel wall of the geometric model into continuous triangular units using different sampling intervals, so as to obtain a wall morphology model displaying continuous triangular units;

[0111] The triangular element parameter calculation module 203 is used or configured to calculate the effective shear dip angle of a single triangular element based on the wall topography model. And the effective tilt angle; calculate the ratio A of the area of ​​the triangular unit whose effective tilt angle of the vessel wall is greater than the effective shear tilt angle to the total area. θ* According to A θ*Calculate the fitting coefficient C;

[0112] Fractal dimension calculation module 204, used or configured to calculate based on effective shear tilt angle The fitting coefficient C is used to calculate the average wall inclination angle for a specified blood flow direction at different sampling intervals. The relationship between the average inclination angle of the wall and the corresponding sampling interval is quantified to obtain the fractal dimension D; the roughness of the blood vessel is quantified based on the fractal dimension D.

[0113] In some embodiments, the system further includes, between the geometry model acquisition module and the wall topography model processing module:

[0114] The centerline extraction module is used or configured to extract the centerline of the target blood vessel based on a geometric model; the centerline is the main trunk centerline after removing irrelevant branches.

[0115] And / or, a target blood vessel region extraction module, used or configured to extract the target blood vessel region in the geometric model, discretizing the blood vessel wall of the target blood vessel region into continuous triangular units using different sampling intervals.

[0116] In some embodiments, after the fractal dimension calculation module, the system further includes:

[0117] A roughness index calculation module is used or configured to calculate the roughness index based on the fractal dimension D and the average inclination angle of the wall when the sampling interval is 1.

[0118] And / or, a vascular roughness quantification module, used or configured to evaluate the local wall shear force of a vascular vessel based on a roughness index; the larger the roughness index, the greater the wall shear force. Specific implementation examples:

[0120] Step 1: Acquisition of medical image data and construction of vascular geometric models (taking the aorta as an example)

[0121] 1. Acquisition of medical image data:

[0122] High-resolution medical imaging equipment, such as computed tomography (CT), magnetic resonance imaging (MRI), angiography, and ultrasound, is used to acquire three-dimensional medical images of the human aorta to be evaluated. These medical imaging devices provide accurate vascular imaging, ensuring the accuracy of subsequent analysis. The acquired medical image data must be stored in DICOM format (a standard format for digital imaging and communications medicine) to ensure standardization and high quality of the image data.

[0123] 2. Construction of the vascular geometric model:

[0124] In MIMICS software, thresholding techniques are used to extract a mask of the aorta from medical image data. This process separates the aortic region from the surrounding tissue in the image data, laying the foundation for subsequent segmentation and modeling. A series of segmentation algorithms (such as random walk algorithms based on image intensity and gradients along the tracking path, region growing algorithms, interval binary segmentation algorithms, thresholding algorithms, voxel growing algorithms, or deep learning segmentation algorithms) are used to generate the aortic vascular path. These algorithms achieve accurate segmentation of the aortic vascular region by analyzing the features of different regions in the image.

[0125] 3. Remove small branch vessels while preserving the main trunk:

[0126] In the segmented vascular model, some small branch vessels, such as the three branches of the aortic arch, the renal artery, the celiac trunk, and the superior mesenteric artery, are removed. Only the ascending aorta, the aortic arch, and the descending aorta leading to the iliac branches are retained to obtain a simpler geometric model of the aortic trunk. This process helps reduce interference from details, allowing subsequent vessel wall roughness quantification to focus on the analysis of the aortic trunk.

[0127] 4. Generation and denoising of 3D reconstructed geometry:

[0128] The extracted vascular model was used to generate the geometry of the aorta through 3D reconstruction technology. This 3D model reflects the morphology and structure of the blood vessel, providing a basis for subsequent accurate analysis. To improve model quality and reduce the impact of image noise, a Gaussian kernel local polynomial filter was used to denoise the 3D reconstructed geometry. This denoising process effectively removes high-frequency noise from the vascular model, making it smoother and more stable. The denoised 3D model of the aortic trunk provides a high-quality geometric data foundation for subsequent vascular wall roughness measurement. At this point, the model not only has an accurate morphological description but also provides a clearer and more stable vascular geometry for subsequent analysis. Figure 6 As shown, Figure 6 A is the 3D model before denoising. Figure 6 B represents the denoised and smoothed 3D model, clearly showing the model's geometry and the smoothing effect after denoising, preparing for the next step of quantitative analysis of blood vessel wall roughness. Through this series of steps, this patent ensures the high quality and accuracy of the blood vessel geometric model, providing a solid foundation for the quantitative analysis of blood vessel wall roughness.

[0129] Step 2: Extract the aortic centerline from the three-dimensional aortic anatomy model

[0130] Based on the aforementioned three-dimensional aortic anatomical model corresponding to the human aorta to be evaluated, the aortic centerline is extracted from the three-dimensional aortic anatomical model (using any one of the following methods: manual calibration, topology refinement, distance transformation, or minimum cost path algorithm to extract the aortic trunk centerline from the smooth aortic trunk model), such as... Figure 7 , Figure 7 A represents the result without removing the centerlines of irrelevant branches. Figure 7 B represents the result after removing irrelevant branch centerlines.

[0131] Step 3: Obtaining the wall morphology of continuous triangular units at different sampling intervals

[0132] 1. Generation of continuous triangular wall morphology:

[0133] The sampled vessel wall geometry is discretized into continuous triangular units. This discretization method transforms complex vessel geometry models into a series of small triangular patches, facilitating subsequent mathematical analysis and numerical simulation.

[0134] Triangular elements possess excellent geometric properties, effectively representing surface variations and making them suitable for the quantitative analysis of blood vessel wall roughness. Using these elements, the roughness of each small element can be calculated more precisely, ultimately yielding the overall roughness distribution of the blood vessel wall.

[0135] 2. Generate models according to different sampling intervals:

[0136] A small segment of a blood vessel model was selected, and the geometric morphology of the vessel wall surface was sampled at 0.01mm, 0.1mm, 0.25mm, 0.5mm, and 1mm, generating a model as continuous triangular units. (See figure) Figure 8 The sampling intervals from left to right are 0.01mm, 0.1mm, 0.25mm, 0.5mm, and 1mm, respectively.

[0137] Depend on Figure 8 It is evident that as the sampling interval increases, the details of the vessel wall surface are gradually simplified, making it difficult to capture minute geometric features (such as subtle protrusions or depressions), resulting in a coarser and less refined overall model. This is because a larger sampling interval cannot accurately depict the microscopic details of the vessel wall surface, leading to the smoothing of small-scale features and the loss of some information. Smaller sampling intervals can more accurately capture changes in the microscopic roughness of the vessel wall surface, while larger sampling intervals are more suitable for describing macroscopic geometric features. However, this simplification may miss some potential minute lesions or localized features, indicating that the choice of sampling interval has a significant impact on the quantification of vessel wall surface roughness.

[0138] This step, using linear interpolation and triangular element discretization, accurately obtained the vessel wall morphology at different sampling intervals. The choice of sampling interval directly affects the detail of the morphology. This provides necessary data support for the quantitative analysis of vessel wall roughness and lays the foundation for subsequent biomechanical analysis.

[0139] Step 4: Obtain three-dimensional topographic parameter lines along the blood flow direction at different sampling intervals.

[0140] The morphology of the blood vessel wall can be approximated by triangular mesh elements. The geometric relationship between the triangular elements and the blood vessel centerline is shown in [reference needed]. Figure 9 Calculate the effective shear tilt angle The algorithm is shown in equation (1).

[0141] (1)

[0142] In the formula: tanθ is obtained by converting cosθ; , The angle of inclination of a micro-element of the blood vessel wall. denoted as , where is the angle between the inclination of the vessel wall and the vessel centerline; t is the direction vector of the vessel centerline; n is the external normal vector of the element; n0 is the external normal vector of the blood flow direction plane; and n1 is the projection vector of the blood flow direction onto the blood flow plane.

[0143] During shearing, only wall elements facing the shear direction and exceeding a certain critical angle can resist shear. The ratio of the area of ​​all contacting micro-elements to the total area of ​​the wall surface is... The effective tilt angle is greater than The ratio of the area of ​​all infinitesimal elements to the total area. and The relationship between them conforms to the relationship of a higher-order parabolic function, as shown in equation (2).

[0144] The ratio of the area of ​​all infinitesimal elements with an effective inclination angle greater than θ* to the total area of ​​the blood vessel wall is A. θ* The relationship between θ* and θ is shown in equation (2).

[0145] (2)

[0146] In the formula: A0 is the ratio of the total area of ​​all vessel wall micro-elements with an inclination angle greater than 0 to the total surface area of ​​the vessel wall; θ * max is the maximum effective shear tilt angle of the vessel wall micro-element; C is the formula fitting coefficient, which describes the distribution of the micro-element angle.

[0147] Using the obtained data and the theoretical part, the average wall inclination angle for a specific blood flow direction under different sampling intervals is obtained as shown in equation (3):

[0148] (3)

[0149] The average wall inclination angle for a specific blood flow direction at different sampling intervals was obtained as follows: Figure 10 The distribution function of the downsight tilt angle in a specific blood flow direction is obtained. and The relationship.

[0150] Step 5: Eliminate the influence of sampling interval on wall roughness using fractal theory.

[0151] As the sampling interval increases, some minute topographic features on the vessel wall are lost, resulting in different surface roughness values ​​obtained at different sampling intervals. For the three-dimensional topography of the vessel wall, as the sampling interval (i.e., the measurement scale δ) increases, more and more minute topographic features on the wall are ignored, causing the wall to gradually become smoother. Therefore, the surface roughness decreases as the measurement scale increases. This phenomenon can be described by a power law relationship.

[0152] For a three-dimensional wall, the wall roughness index is obtained at different measurement scales δ. There is a power law relationship between it and the measurement scale δ, that is:

[0153] (6)

[0154] In the formula: The actual contact joint roughness parameter when the sampling interval is δ. ; For an index with a measurement scale of 1 D is the fractal dimension. Equation (6) reveals the power law relationship between the roughness of the blood vessel wall and the measurement scale δ, which is controlled by the fractal dimension D of the blood vessel wall. Taking the logarithm of both sides of equation (6), we can obtain the fractal dimension D of the joint surface roughness, as shown in equation (7):

[0155] (7)

[0156] Parameters obtained at several different sampling intervals By plotting the data points in a rectangular coordinate system and performing least-squares fitting on the data using a straight line, the fractal dimension D can be obtained. The intersection of the line with the vertical axis is the fractal intercept. The fitting results using equation (7) are shown below. Figure 11 It can be known that and The relationship is shown on the vertical axis, where the vertical axis represents the average value at different sampling intervals.

[0157] Step Six: Considering the wall inclination angle distribution characteristics and sampling interval, the roughness index of fractal theory is as follows:

[0158] According to the specific D, there is also , obtain ( The roughness index takes into account the distribution characteristics of triangular micro-elements and the fractal characteristics under different sampling intervals.

[0159] Results Display:

[0160] This example considers information at different scales, discretizing the blood vessel into triangular units to ensure comprehensive information coverage (scanning at each scale to obtain higher-dimensional information), demonstrating the relationship between vessel wall roughness and wall shear force. Greater roughness results in greater local wall shear force, which may accelerate the progression of vascular lesions. Therefore, quantifying vessel wall roughness can provide physicians with more accurate hemodynamic simulation data, helping to assess the risk of vascular lesions and optimize treatment plans. Figure 12 As shown, the blood vessel was divided into 10 parts to calculate the roughness index, and the roughness and shear force values ​​of the 10 blood vessel segments were calculated (the calculation results are shown in Table 1).

[0161] Table 1. Roughness and shear force values ​​of different vessel segments under the same boundary conditions.

[0162] More specifically, such as Figure 13 As shown, different roughness treatments can be selectively extracted from segment W3 to calculate the roughness. Figure 14 As can be seen, the WSS value varies under different roughness conditions, and the rougher the part, the higher the WSS value.

[0163] In some embodiments, the correlation between roughness indices and hemodynamic parameters of different segments is also calculated, specifically as follows: Figure 15 As shown.

[0164] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0165] In general, the various exemplary embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, firmware, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device. When aspects of embodiments of this disclosure are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it will be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as non-limiting examples in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0166] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0167] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0168] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0169] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0170] The exemplary embodiments of this disclosure described in detail above are merely illustrative and not restrictive. Those skilled in the art will understand that various modifications and combinations can be made to these embodiments or their features without departing from the principles and spirit of this disclosure, and such modifications should fall within the scope of this disclosure.

Claims

1. A quantitative method for measuring vascular roughness based on the distribution characteristics and fractal laws of triangular micro-elements, characterized in that, The method includes: S101, Obtain the geometric model of the blood vessel; S102, the blood vessel wall of the geometric model is discretized into continuous triangular units by using different sampling intervals, and a wall morphology model displaying continuous triangular units is obtained; S103, Calculate the effective shear dip angle of a single triangular element based on the wall topography model. And the effective tilt angle; calculate the ratio A of the area of ​​the triangular unit whose effective tilt angle of the vessel wall is greater than the effective shear tilt angle to the total area. θ* According to A θ* Calculate the fitting coefficient C; S104, based on the effective shear tilt angle The fitting coefficient C is used to calculate the average wall inclination angle for a specified blood flow direction at different sampling intervals. The relationship between the average inclination angle of the wall and the corresponding sampling interval is quantified to obtain the fractal dimension D; the roughness of the blood vessel is quantified based on the fractal dimension D.

2. The method for quantifying vascular roughness based on the distribution characteristics and fractal laws of triangular micro-elements according to claim 1, characterized in that, The A θ* The calculation method is as follows: ; In the formula, A0 is the ratio of the total area of ​​all vessel wall units with an inclination angle greater than 0 to the total surface area of ​​the vessel wall; θ * max is the maximum effective shear tilt angle of the blood vessel wall unit; C is the formula fitting coefficient, which describes the unit angle distribution; Optionally, the effective shear tilt angle The calculation method is as follows: ; In the formula, tanθ is obtained by converting cosθ; , The effective tilt angle of the vessel wall micro-element. t is the angle between the inclination of the vessel wall and the centerline of the vessel, n is the direction vector of the centerline of the vessel, n0 is the out-of-plane normal vector of the blood flow direction, and n1 is the projection vector of the blood flow direction onto the blood flow plane. Optionally, the average inclination angle of the wall surface The calculation method is as follows: .

3. The method for quantifying vascular roughness based on the distribution characteristics and fractal laws of triangular micro-elements according to claim 2, characterized in that, The fractal dimension D is calculated as follows: In the formula, The actual contact joint roughness parameter when the sampling interval is δ. ; For an index with a measurement scale of 1 D is the fractal dimension; Optionally, the calculation method for the fractal dimension D further includes: ; Optional sampling intervals include: 0.01mm, 0.1mm, 0.25mm, 0.5mm, and 1mm.

4. The method for quantifying vascular roughness based on the distribution characteristics and fractal laws of triangular micro-elements according to claim 1, characterized in that, Between S101 and S102, the method further includes: extracting the centerline of the target blood vessel based on a geometric model; the centerline is the main trunk centerline after removing irrelevant branches; Optionally, the methods for extracting the centerline include any one or more of the following: manual calibration method, topology refinement method, distance transformation method, and minimum cost path algorithm; Optionally, between S101 and S102, the method further includes: denoising the geometric model to effectively remove high-frequency noise from the blood vessel model, making the model smoother and more stable.

5. The method for quantifying vascular roughness based on the distribution characteristics and fractal laws of triangular micro-elements according to claim 1, characterized in that, Between S101 and S102, the method further includes: extracting the target blood vessel region from the geometric model, and discretizing the blood vessel wall of the target blood vessel region into continuous triangular units using different sampling intervals.

6. The method for quantifying vascular roughness based on the distribution characteristics and fractal laws of triangular micro-elements according to any one of claims 1-5, characterized in that, The method further includes S105, calculating the roughness index based on the fractal dimension D and the average inclination angle of the wall when the sampling interval is 1; Optionally, the roughness index is calculated as the product of the fractal dimension D and the average inclination angle of the wall when the sampling interval is 1. Optionally, the method further includes: assessing the local wall shear force of the blood vessel based on a roughness index; The greater the roughness index, the greater the shear force on the wall.

7. The method for quantifying vascular roughness based on the distribution characteristics and fractal laws of triangular micro-elements according to any one of claims 1-5, characterized in that, The target blood vessels include: the aorta, the superior vena cava, and the inferior vena cava; Optionally, the geometric model includes a three-dimensional model.

8. A computer device, characterized in that, The device includes: a memory and a processor; the memory is used to store a computer program; the processor executes the computer program to implement the steps of the method according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any one of claims 1-7.

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