Quantitative risk assessment method and system for head and neck artery stenosis

Abstract

The invention provides a quantitative risk assessment method and system for head and neck artery stenosis, and the method comprises the steps: collecting the medical image of the head and neck artery of a patient, the blood pressure of the upper arm artery, and the heart rate; a narrow local form, a cerebral artery network anatomical structure / geometric size and a microcirculation blood flow automatic regulation mechanism are comprehensively incorporated into hemodynamic analysis through a geometric multi-scale modeling technology, and information such as an arterial stenosis far-end and near-end pressure ratio, cerebral artery blood flow and hemodynamic parameters of a narrow part is output; quantitative information support is provided for risk assessment of head and neck artery stenosis; the method has the advantages that patient data are obtained only through noninvasive measurement, and the risk that complications are increased due to invasive measurement is avoided; according to the method, a geometric multi-scale modeling method is adopted to achieve integrated analysis of the three-dimensional flow field and the whole-brain circulation hemodynamic parameters of the stenosis part, the blood flow regulation effect of brain microcirculation is considered, and the physiological significance and clinical application value of the evaluation result can be remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of arterial stenosis risk assessment technology, and in particular to a quantitative risk assessment method and system for head and neck arterial stenosis. Background Technology

[0002] Stenosis of the head and neck arteries can lead to insufficient blood perfusion to the brain, resulting in ischemic stroke. It is noteworthy that although some patients with head and neck artery stenosis may not have obvious clinical symptoms, their risk of silent stroke, cognitive impairment, or a serious stroke event in the coming years is significantly increased. Therefore, accurately assessing the risk of abnormal cerebral blood flow and cerebral ischemia caused by head and neck artery stenosis is crucial for clinical diagnosis and intervention selection. Currently, the clinical diagnosis of arterial stenosis mainly relies on medical imaging techniques such as computed tomography angiography (CTA), digital subtraction angiography (DSA), or magnetic resonance angiography (MRA). Existing clinical guidelines recommend using the arterial stenosis rate as a basis, combined with other pathological indications and the patient's clinical presentation, to determine whether to perform surgical treatment or adopt a conservative treatment strategy. However, this decision-making approach is highly dependent on the physician's clinical experience, and suffers from problems such as strong subjectivity and poor consistency. There is an urgent need to develop more objective and accurate clinical decision support methods and quantitative risk assessment indicators.

[0003] Inspired by the widespread application of fractional flow reserve (FFR) in the functional assessment of coronary artery stenosis, researchers have proposed using the distance-to-proximal pressure ratio (dpPR, also referred to as fractional flow (FF) or fractional pressure ratio (FPR)) or transstenosis pressure drop to assess the functional impact of cerebral artery stenosis, and have conducted a series of clinical and technical studies. Technically, considering the invasiveness and high cost of in vivo measurement of the distance-to-proximal pressure of cerebral artery stenosis using pressure guidewires, researchers have proposed a method based on medical images to construct cerebral artery models and perform hydrodynamic calculations to estimate dpPR and transstenosis pressure drop, and have validated its clinical effectiveness. After searching the existing technologies, the following functional assessment methods for head and neck artery stenosis based on computational fluid dynamics were found: (1) Chinese Patent Document No. CN107491636A (publication date 2017-07-26) discloses a simulation system and method for cerebral vascular reserve capacity based on computational fluid dynamics. The method includes: acquiring computed tomography images of human cerebral blood vessels, reconstructing the geometric model of blood vessels based on computed tomography images, processing the images to extract the flow velocity and flow rate boundary conditions of the blood vessel inlet and outlet, calculating and solving the hemodynamic information at various points of the three-dimensional geometric model of cerebral blood vessels, and comparing the calculation results with the measurement results of the boundary condition processing module; (2) Chinese Patent Document No. CN116649925A (publication date 2023-08-29) discloses a method for functional assessment of intracranial artery stenosis. Method and apparatus, the method includes: measuring the proximal pressure of the blood vessel through a catheter, measuring the blood flow velocity at a specified location of the intracranial blood vessel through transcranial Doppler ultrasound, and obtaining blood flow information in combination with the lumen area, constructing a three-dimensional model of the intracranial blood vessel, performing hemodynamic simulation based on blood flow and blood flow distribution relationship, and completing the functional assessment of stenosis by combining the hemodynamic simulation results and proximal pressure; (3) Chinese Patent Document No. CN117379012A (publication date 2024-01-12) discloses a non-invasive functional assessment method for carotid artery, vertebral artery and cerebral artery stenosis, the method includes: collecting clinical data of arteries above the chest, establishing three-dimensional models of stenotic and non-stenotic arteries, establishing numerical models to calculate blood flow in stenotic and non-stenotic models, calculating blood flow reserve fraction values ​​at the outlet of each artery based on blood flow and guiding surgical planning accordingly.

[0004] The shortcomings of existing technologies: Compared with existing functional assessment methods and technologies for head and neck artery stenosis, the current technologies still have the following shortcomings or defects: (1) Some technologies rely on invasive blood pressure measurement using pressure guidewires or non-invasive blood flow measurement such as transcranial Doppler ultrasound and MRI to set the boundary conditions of the computer model; such technologies have high medical examination costs and are time-consuming, and may increase the risk of complications due to invasive measurement. (2) Most existing technologies focus only on the local blood vessels with stenosis when constructing computer models, without fully considering the compensatory regulation of blood flow through stenosis and blood pressure and blood flow downstream of stenosis by the anatomical structure of the cerebral arterial network represented by the Circle of Willis, which may cause the calculation results to deviate from the patient's individual in vivo blood flow status, thereby reducing the clinical application value of the calculation results. (3) There is no technology that incorporates the inherent blood flow autoregulation function of cerebral microcirculation into the assessment model, which significantly reduces the physiological significance of the assessment results. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a quantitative risk assessment method and system for head and neck artery stenosis, which solves the problems that the prior art may increase the risk of complications due to invasive measurement, may cause the calculation results to deviate from the patient's personalized in vivo blood flow status, and has no technology to incorporate the inherent blood flow autoregulation function of cerebral microcirculation into the assessment model.

[0006] To achieve the above and other related objectives, the present invention provides the following technical solution:

[0007] A quantitative risk assessment method for head and neck artery stenosis includes the following steps: acquiring tomographic images of the patient's head and neck from a medical imaging device, and acquiring blood pressure and heart rate of the patient's upper arm arteries at rest, measured by a sphygmomanometer; reconstructing a 3D geometric model of the head and neck arteries based on the tomographic images, determining the geometric information of each artery in the head and neck and the anatomical structure information of the circle of Willis based on the 3D geometric model of the head and neck arteries, and establishing a 3D hemodynamic model of the stenosis based on the 3D geometric model of the head and neck arteries;

[0008] A reduced-order hemodynamic model of the head and neck artery network is constructed based on the geometric information of each artery in the head and neck. A 0-dimensional hemodynamic model of the cerebral microcirculation and other parts of the cardiovascular system is also constructed. The 3-dimensional hemodynamic model of the stenosis, the reduced-order hemodynamic model of the head and neck artery network, and the 0-dimensional hemodynamic model of the cerebral microcirculation and other parts of the cardiovascular system are coupled to obtain a geometric multi-scale model based on the coupling result. An automatic regulation model of cerebral microcirculation blood flow is constructed, and the cardiac cycle is set according to the heart rate of the upper arm artery. Then, hemodynamic calculations are continuously performed on the geometric multi-scale model for multiple cardiac cycles based on the automatic regulation model of cerebral microcirculation blood flow. The risk assessment of head and neck artery stenosis in patients is based on the calculation results.

[0009] A quantitative risk assessment system for head and neck artery stenosis includes: a data acquisition module for acquiring tomographic images of the patient's head and neck from medical imaging equipment, and acquiring blood pressure and heart rate of the patient's upper arm arteries at rest, measured by a sphygmomanometer; a geometric model construction module for reconstructing a 3D geometric model of the head and neck arteries based on the tomographic images, determining the geometric information of each artery in the head and neck and the anatomical structure information of the circle of Willis based on the 3D geometric model of the head and neck arteries, and establishing a 3D hemodynamic model of the stenosis based on the 3D geometric model of the head and neck arteries; and also for constructing a reduced-order hemodynamic model of the head and neck artery network based on the geometric information of each artery in the head and neck, and constructing a 0D hemodynamic model of the cerebral microcirculation and other parts of the cardiovascular system.

[0010] The model coupling module is used to couple the 3D hemodynamic model of the stenosis, the reduced-order hemodynamic model of the head and neck arterial network, and the 0D hemodynamic model of the cerebral microcirculation and other parts of the cardiovascular system, and obtain a geometric multi-scale model based on the coupling result. The hemodynamic calculation module is used to construct an automatic regulation model of cerebral microcirculation blood flow, set the cardiac cycle according to the heart rate of the upper arm artery, and then continuously perform hemodynamic calculations on the geometric multi-scale model for multiple cardiac cycles based on the automatic regulation model of cerebral microcirculation blood flow, and conduct risk assessment of the patient's head and neck arterial stenosis based on the calculation results.

[0011] An electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a quantitative risk assessment method for cervical artery stenosis as described above.

[0012] In one embodiment of the present invention, the reconstructing of a 3D geometric model of the head and neck arteries based on the tomographic scan image includes: using medical image processing software to segment regions such as the arterial lumen, bones, and other soft tissues based on the grayscale values ​​of the tomographic scan image; constructing a 3D model of the artery using methods such as region growth and splitting / merging for the arterial lumen; and performing surface smoothing processing on the 3D model of the artery to obtain a 3D geometric model of the head and neck arteries. The reconstruction range of the 3D geometric model of the head and neck arteries includes the common carotid artery, internal carotid artery, external carotid artery, vertebral artery, basilar artery, and segments of the anterior cerebral artery I, anterior communicating artery I, posterior communicating artery I, posterior communicating artery, middle cerebral artery, anterior cerebral artery II, and posterior cerebral artery II constituting the circle of Willis.

[0013] In one embodiment of the present invention, determining the geometric information of each artery in the head and neck and the anatomical information of the circle of Willis based on the 3D geometric model of the head and neck arteries includes: extracting the centerline of each artery based on the 3D geometric model of the head and neck arteries, calculating the length of each artery and extracting the diameter information along the length direction based on the centerline, and determining the anatomical information of the circle of Willis based on the diameter information.

[0014] In one embodiment of the present invention, the step of constructing a reduced-order hemodynamic model of the head and neck artery network based on the geometric information of each artery in the head and neck includes: using a three-unit Windkessel model to characterize the hemodynamic characteristics of each artery, wherein the three-unit Windkessel model includes viscous impedance, blood inertia coefficient, and vascular compliance; and connecting the three-unit Windkessel models of each artery in series or parallel to obtain a reduced-order hemodynamic model of the head and neck artery network.

[0015] In one embodiment of the present invention, the coupling between the reduced-order hemodynamic model of the head and neck arterial network and the 0-dimensional hemodynamic model of other parts of the cerebral microcirculation and cardiovascular system is achieved by direct connection, while the coupling between the 3-dimensional hemodynamic model of the stenosis and the reduced-order hemodynamic model of the head and neck arterial network is achieved by geometric multi-scale modeling.

[0016] In one embodiment of the present invention, the step of continuously performing hemodynamic calculations for multiple cardiac cycles on the geometric multi-scale model based on the automatic regulation model of cerebral microcirculation blood flow, and assessing the risk of cerebral cerebral artery stenosis based on the calculation results, includes: in the hemodynamic calculation, adjusting the total peripheral vascular impedance of the cardiovascular system using a negative feedback method with cardiac cycles as the interval, and then adjusting the cerebral microcirculation impedance using the automatic regulation model of cerebral microcirculation blood flow; continuously performing hemodynamic calculations until the convergence condition is met, then outputting the hemodynamic calculation results of the last cardiac cycle, and generating a risk assessment report based on the hemodynamic calculation results of the last cardiac cycle.

[0017] In one embodiment of the present invention, the convergence condition is: the difference between the calculated system arterial mean blood pressure and the measured mean blood pressure of the patient's upper arm artery is less than a first threshold, and the rate of change of the mean blood flow of each peripheral cerebral artery during adjacent cardiac cycles is less than a second threshold.

[0018] In one embodiment of the present invention, generating a risk assessment report based on the hemodynamic calculation results of the last cardiac cycle includes: post-processing the hemodynamic calculation results to extract the average blood pressure at the proximal and distal ends of the arterial stenosis, and determining the proximal-distal pressure ratio based on the average blood pressure at the proximal and distal ends of the arterial stenosis; obtaining detailed hemodynamic parameters of the stenosis and blood flow of each peripheral cerebral artery, comparing the blood flow of each peripheral cerebral artery with statistical values ​​of healthy individuals, and assessing the degree of ischemia in downstream brain tissue based on the comparison results; and generating a patient risk assessment report based on the proximal-distal pressure ratio, the detailed hemodynamic parameters of the stenosis, and the assessed degree of ischemia in downstream brain tissue.

[0019] As described above, the quantitative risk assessment method and system for head and neck artery stenosis of the present invention has the following beneficial effects: Based on the acquisition of medical images of the patient's head and neck arteries, upper arm artery blood pressure, and heart rate, the present invention integrates the local morphology of the stenosis, the anatomical structure / geometric dimensions of the cerebral artery network, and the microcirculation blood flow autoregulation mechanism into hemodynamic analysis through geometric multi-scale modeling technology. It outputs information such as the pressure ratio of the proximal and distal ends of the arterial stenosis, cerebral artery blood flow, and 3D hemodynamic parameters of the stenotic portion, providing quantitative information support for risk assessment and clinical decision-making for head and neck artery stenosis. Therefore, the present invention obtains patient data only through non-invasive measurement, which not only has low examination costs but also effectively avoids the risks associated with invasive procedures. This invention mitigates the risk of complications that may arise from invasive measurement methods. Furthermore, it employs a geometric multi-scale modeling approach to achieve integrated analysis of the 3D flow field of the stenosis and hemodynamic parameters of the whole cerebral circulation, taking into account the hemodynamic regulation of the cerebral microcirculation. This significantly enhances the physiological significance and clinical application value of the assessment results. The invention also provides hemodynamic information covering both the stenosis site and the whole cerebral circulation. This information can not only quantitatively evaluate the functional impacts of stenosis, such as local pressure drop, the pressure ratio between the proximal and distal ends of the arterial stenosis, and the degree of ischemia in downstream brain tissue, but also provide detailed hemodynamic information such as wall shear stress distribution and near-wall flow field characteristics for assessing plaque stability and plaque progression risk, thus supporting clinical decision-making from multiple information dimensions. Attached Figure Description

[0020] Figure 1 This is an overall flowchart of the quantitative risk assessment method for cervical artery stenosis in the first embodiment of the present invention;

[0021] Figure 2 This is a flowchart illustrating the implementation of the quantitative risk assessment method for cervical artery stenosis in the first embodiment of the present invention.

[0022] Figure 3 This is a schematic diagram of the quantitative risk assessment system for cervical artery stenosis according to the second embodiment of the present invention;

[0023] Figure 4 This is a schematic diagram of an electronic device according to the third embodiment of the present invention;

[0024] Figure 5 This is a schematic diagram of a 0-3D geometric multi-scale model of the central cerebrovascular system of the present invention;

[0025] Figure 6 This is a schematic diagram of the automatic regulation curve of cerebral blood flow in this invention;

[0026] Figure 7 This is a schematic diagram of the risk assessment report obtained based on hemodynamic calculations in this invention;

[0027] Figure 8This is a Bland-Altman plot showing the difference between the model-calculated value and the clinically measured value of dpPR in this invention;

[0028] Figure 9 This is a correlation analysis graph between the model-calculated value and the clinically measured value of dpPR in this invention. Detailed Implementation

[0029] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. It should be noted that, unless otherwise specified, the following embodiments and features described herein can be combined with each other.

[0030] The first embodiment of the present invention relates to a quantitative risk assessment method for head and neck artery stenosis, the process of which is as follows: Figure 1 As shown, the details are as follows:

[0031] Step 101: Acquire tomographic images of the patient's head and neck from medical imaging equipment, and acquire blood pressure and heart rate of the patient's upper arm artery at rest, measured by a sphygmomanometer.

[0032] Specifically, medical imaging equipment is used to acquire tomographic images of the patient's head and neck, and a sphygmomanometer is used to measure the patient's blood pressure (including systolic, diastolic, and mean pressure) and heart rate in the upper arm arteries at rest.

[0033] Step 102: Reconstruct a 3D geometric model of the head and neck arteries based on the tomographic scan images, determine the geometric information of each artery in the head and neck and the anatomical structure information of the circle of Willis based on the 3D geometric model of the head and neck arteries, and establish a 3D hemodynamic model of the stenosis based on the 3D geometric model of the head and neck arteries.

[0034] Specifically, the reconstruction process of the 3D geometric model of the head and neck arteries is as follows: 1) Import the tomographic scan image into medical image processing software, such as Mimics or 3D Slicer; 2) Segment the arterial lumen, bones, and other soft tissues based on the image grayscale values, and construct the 3D model of the artery using methods such as region growth and splitting and merging for the arterial lumen; 3) Perform surface smoothing on the 3D model of the artery to obtain the 3D geometric model of the head and neck arteries. The model reconstruction scope includes the common carotid artery, internal carotid artery, external carotid artery, vertebral artery, basilar artery, and major intracranial arteries that constitute the Circle of Willis, such as the anterior cerebral artery segment I, anterior cerebral communicating artery segment I, posterior cerebral communicating artery, middle cerebral artery, anterior cerebral artery segment II, and posterior cerebral artery segment II.

[0035] The information extraction process is as follows: 1) Use medical image processing software such as Mimics or 3D Slicer to extract the centerline of each artery in the head and neck, and calculate the length of the centerline (i.e., the length of the blood vessel); 2) Generate a normal plane along the centerline, calculate the equivalent diameter based on the blood vessel contour obtained by the intersection of the normal plane and the blood vessel wall, and record it as the diameter of the blood vessel (i.e., the diameter along the vessel); 3) Determine the anatomical structure type of the circle of Willis based on the presence or absence of arteries constituting the circle of Willis (if there is no specific artery in the model, it is determined that the artery is missing) and the diameter along the vessel (if the diameter of a specific artery is too small, it is determined that the artery is underdeveloped).

[0036] The process of constructing the 3D hemodynamic model of the stenosis is as follows: the 3D geometric model of the head and neck arteries is clipped and segmented using image processing or CAD software such as Mimics or Solidworks to separate the geometric model of the stenotic segment. Then, it is imported into mesh generation software such as Ansys ICEM or Fluent meshing to generate a high-precision 3D mesh model of the stenosis. The control equations for blood flow and the boundary conditions of the model are set to form the 3D hemodynamic model of the stenosis.

[0037] Step 103: Construct a reduced-order hemodynamic model of the head and neck artery network based on the geometric information of each artery in the head and neck, and construct a 0-dimensional hemodynamic model of the cerebral microcirculation and other parts of the cardiovascular system.

[0038] Specifically, the reduced-order hemodynamic model of the head and neck arterial network is a reduced-order (0-dimensional or 1-dimensional) hemodynamic model of the head and neck arterial network (including the Willis ring); taking the construction of a 0-dimensional hemodynamic model of the head and neck arterial network as an example, firstly, a model based on viscous impedance (R... 0D ), blood inertia coefficient (L) 0D ) and vascular compliance (C 0D A three-unit Windkessel model composed of three arteries is used to characterize the hemodynamic characteristics of each artery; further, the Windkessel models of each artery are connected in series or parallel to form a 0-dimensional model representing the entire arterial network; wherein, the R of each artery is... 0D L 0D and C 0D Based on the vessel length (l) and the vessel area along the length direction (A0), combined with the blood density (ρ), blood viscosity coefficient (μ), and pulse wave velocity (c0), the following formula is used for integration:

[0039] The construction of 0-dimensional models of cerebral microcirculation and other parts of the cardiovascular system employs a similar approach, with the notable difference being that R... 0D L 0D and C 0DIt can characterize not only a specific blood vessel (such as the aorta or cerebral artery), but also the overall hemodynamic characteristics of a cluster of blood vessels (such as the cerebral microcirculation downstream of each peripheral cerebral artery, the peripheral microcirculation of the systemic circulation, etc.).

[0040] Step 104: Couple the 3D hemodynamic model of the stenosis, the reduced-order hemodynamic model of the head and neck arterial network, and the 0D hemodynamic model of the cerebral microcirculation and other parts of the cardiovascular system, and obtain a geometric multiscale model based on the coupling results.

[0041] Specifically, the 3D hemodynamic model of the stenosis, the reduced-order (0D or 1D) hemodynamic model of the head and neck arterial network, and the 0D hemodynamic model of the cerebral microcirculation and other parts of the cardiovascular system are coupled to form a geometric multi-scale model characterizing the hemodynamics of the whole cerebral circulation and its bidirectional coupling with the system's blood flow. Among them, the coupling between the reduced-order model of the head and neck arterial network and the 0D model of the cerebral microcirculation and cardiovascular system is achieved by direct connection, while the coupling between the 3D hemodynamic model of the stenosis and the reduced-order model of the head and neck arterial network is achieved by geometric multi-scale modeling. The specific operation is divided into two steps: 1) Use the 3D hemodynamic model of the stenosis to replace the stenotic segment in the reduced-order model of the head and neck arterial network; 2) Set interface conditions at the interface between the two models to ensure that the flow is conserved and the pressure is continuous on both sides of the interface.

[0042] Step 105: Construct an automatic regulation model of cerebral microcirculation blood flow, set the cardiac cycle according to the heart rate of the upper arm artery, and then perform hemodynamic calculations for multiple cardiac cycles on the geometric multi-scale model based on the automatic regulation model of cerebral microcirculation blood flow. Based on the calculation results, assess the risk of cerebral microcirculation blood flow stenosis in the patient's head and neck arteries.

[0043] Specifically, a mathematical model characterizing the automatic regulation mechanism of cerebral microcirculation blood flow is constructed, namely, a cerebral microcirculation blood flow automatic regulation model, and coupled with a hemodynamic model to achieve automatic regulation of cerebral microcirculation impedance based on blood pressure and blood flow information of various peripheral cerebral arteries (such as the middle cerebral artery, segment II of the anterior cerebral artery, and segment II of the posterior cerebral artery). The specific implementation process is as follows: 1) Obtain the relationship curve between cerebral artery perfusion pressure and blood flow through polynomial function fitting based on experimental data reported in the literature (such as...). Figure 6 As shown), this invention refers to the relationship curve as the cerebral blood flow autoregulation curve; 2) Using cardiac cycles as intervals, a negative feedback algorithm is used to adjust the cerebral microcirculation impedance downstream of each peripheral cerebral artery. Through iterative calculation, the relationship between peripheral cerebral artery blood flow and blood pressure calculated by the model is matched with the cerebral blood flow autoregulation curve. The expression of the negative feedback algorithm is as follows: In this formula, R cmThis refers to cerebral microcirculatory impedance, where n and n-1 represent the current and previous cardiac cycles, respectively, α is the sustained-release factor (0 < α < 1), and Q is the mean arterial blood flow. T The target blood flow is calculated from the mean arterial blood pressure and the cerebral blood flow autoregulation curve of the previous cardiac cycle.

[0044] More specifically, the cardiac cycle is set based on the patient's heart rate measured at rest, and hemodynamic calculations for multiple cardiac cycles are performed continuously based on a geometric multi-scale model. The specific calculation process is as follows: 1) The hemodynamic control equations of the 3D model of the stenosis are numerically solved using commercial, open-source, or self-developed computational fluid dynamics (CFD) software such as ANSYS Fluent or OpenFORM; 2) The computer solution program (self-developed) for the reduced-order model of the head and neck arterial network and the 0D model of the cerebral microcirculation and cardiovascular system is embedded into the 3D CFD software through user-defined functions. Pressure and blood flow information are exchanged at the interface between the 3D model and the reduced-order model to achieve 3D hemodynamic calculations. 3) The coupled solution of the dimensional model and the reduced-order model; 4) The total peripheral vascular impedance of the cardiovascular system is adjusted by using a negative feedback method with cardiac cycles as the interval, so as to reduce the difference between the calculated mean blood pressure of the system arteries and the mean blood pressure of the patient's upper arm arteries measured in step 101. At the same time, the mathematical model of the automatic regulation mechanism of cerebral microcirculation blood flow is run to adjust the cerebral microcirculation impedance; 5) Hemodynamic calculations are continuously carried out. When the difference between the calculated mean blood pressure of the system arteries and the measured mean blood pressure of the upper arm arteries is less than 0.5%, and the rate of change of the mean blood flow of each peripheral cerebral artery during adjacent cardiac cycles is less than 0.1%, the calculation is considered to have converged, and the calculation results of the last cardiac cycle are saved for subsequent analysis.

[0045] In more detail, the report generation process involves: post-processing the calculation results of the last cardiac cycle to extract blood pressure from the proximal and distal ends of the arterial stenosis and calculate the dpPR; outputting detailed hemodynamic parameters of the stenosis (including but not limited to blood flow velocity, blood pressure, time-mean wall shear stress (TAWSS), oscillating shear index (OSI), relative residence time (RRT), and transverse wall shear stress (TransWSS), etc.), and visualizing the data using third-party software; outputting the blood flow of each peripheral cerebral artery and comparing it with statistical values ​​(mean ± standard deviation) of healthy individuals to assess the degree of ischemia in downstream brain tissue. Specifically, if the ratio of the model-calculated blood flow of a certain peripheral cerebral artery to the average blood flow of that artery in the population is less than 0.8 or 0.75, then the brain tissue downstream of that artery is considered to have a risk of ischemia; the smaller the ratio, the higher the degree of ischemia; and generating a risk assessment report based on the above results to assist in clinical diagnosis and decision-making. Please refer to the overall process described above. Figure 2 .

[0046] In practical applications, this embodiment uses carotid artery stenosis as the evaluation object to demonstrate the specific application of the present invention; however, those skilled in the art will understand that the technical solution of the present invention is also applicable to other arterial stenosis in the head and neck (such as vertebral artery stenosis, basilar artery stenosis, intracranial artery stenosis, etc.), because these arteries are all part of the cerebral arterial network, and stenotic lesions will affect brain tissue perfusion and may cause ischemic stroke; in addition, the medical images used in the present invention are not limited to CTA images, but may also include images obtained by other imaging methods such as DSA (digital subtraction angiography), MRA (magnetic resonance angiography), etc., all of which can This invention provides information on the morphology of the arterial lumen to meet the requirements of geometric model reconstruction. Therefore, the technical solution of this invention has wide applicability and can flexibly select the target artery and imaging method according to specific clinical needs. The head and neck arterial network reduction model constructed in this embodiment is a 0-dimensional (i.e., lumped parameter or lumped parameter) model. However, those skilled in the art will understand that 1-dimensional models or other types of reduction models are also applicable to the representation of head and neck arterial networks, because these models have the ability to describe hemodynamic phenomena in large-scale vascular networks with low computational cost and can be coupled with 3-dimensional models of stenotic localities to form geometric multi-scale models, which have a similar role in this invention.

[0047] Step 1: Patient Data Acquisition: 1.1 Acquire CTA images of the head and neck arteries of patients with carotid artery stenosis, ensuring that the image quality and resolution meet the requirements of geometric model reconstruction; 1.2 Measure the patient's upper arm artery systolic blood pressure, diastolic blood pressure, mean blood pressure, and heart rate at rest using a blood pressure monitor;

[0048] Step 2: Geometric Model Reconstruction and Information Extraction: 2.1 Using medical image processing software, CTA images were segmented to construct a high-precision 3D geometric model of the head and neck arteries. The model reconstruction included the common carotid artery, internal carotid artery, external carotid artery, vertebral artery, basilar artery, and major intracranial arteries forming the Circle of Willis, such as the anterior cerebral artery segment I, anterior communicating artery, posterior communicating artery segment I, posterior communicating artery, middle cerebral artery, anterior cerebral artery segment II, and posterior cerebral artery segment II. 2.2 Based on the 3D geometric model of the head and neck arteries reconstructed in 2.1, the centerline of each artery was extracted. The length of each artery was calculated based on the centerline, and the diameter information along the length direction was extracted. The specific steps to determine the anatomical structure type of the Circle of Willis include: 1) using medical image processing software such as Mimics or 3DSlicer to extract the centerline of each artery and calculate the length of the centerline (i.e., the length of the vessel); 2) generating a normal plane along the centerline, calculating the equivalent diameter based on the vessel contour obtained by the intersection of the normal plane and the vessel wall, and recording it as the vessel diameter (i.e., the diameter along the vessel); 3) determining the anatomical structure type of the Circle of Willis based on the presence or absence of the arteries constituting the Circle of Willis (e.g., if there is no specific artery in the model, it is determined that the artery is absent) and the diameter along the vessel (e.g., if the diameter of a specific artery is too small, it is determined that the artery is underdeveloped).

[0049] Step 3: Hemodynamic Model Construction: 3.1 The carotid artery stenosis segment is isolated from the 3D geometric model reconstructed in 2.1, meshed, and a high-precision mesh model is generated. The governing equations for blood flow and boundary conditions are set to form a 3D hemodynamic model. 3.2 A 0D hemodynamic model of the head and neck artery network is constructed based on the geometric information of each artery extracted in 2.2. In this model, the viscous impedance (R) of each artery is... 0D ), blood inertia coefficient (L) 0D ) and pipe wall compliance (C 0D The following formula is used to integrate the following: blood vessel length (l), blood vessel area along the length (A0), blood density (ρ), blood viscosity coefficient (μ), and pulse wave velocity (c0). 3.3 Construct a 0-dimensional hemodynamic model of the cerebral microcirculation and other parts of the cardiovascular system, and couple it with a 0-dimensional model of the head and carotid artery network and a 3-dimensional model of local carotid artery stenosis to form a closed-loop 0-3-dimensional geometric multi-scale model of the cardiovascular system. The model structure is as follows: Figure 5 As shown;

[0050] Step 4: Construction of Systemic Blood Pressure and Cerebral Blood Flow Regulation Model: 4.1 Adjusting the Total Peripheral Vascular Impedance (Ri) of the Cardiovascular System Using a Negative Feedback Algorithm T This allows the model to calculate the system's average arterial blood pressure (i.e., the average blood pressure at the aorta, P). aThe mean brachial artery blood pressure (P) measured in step 1 was compared with that measured in step 1. b The algorithm's mathematical expression is consistent with the given information: Where n represents the current cardiac cycle, n-1 represents the previous cardiac cycle, and α is the sustained-release factor (0 < α < 1); 4.2 Based on experimental data reported in the literature, the relationship curve between cerebral artery perfusion pressure and blood flow was obtained by fitting a polynomial function (e.g. Figure 6 As shown), this invention refers to it as the cerebral blood flow autoregulation curve; this curve provides a reference benchmark for characterizing the cerebral microcirculation blood flow autoregulation mechanism by adjusting the blood flow and blood pressure calculated by regulating the blood flow of each peripheral cerebral artery through adjusting the cerebral microcirculation impedance.

[0051] Step 5: Hemodynamic Calculation: 5.1 Set the cardiac cycle based on the heart rate measured in Step 1, and run the geometric multiscale model constructed in Step 3 on the computer for multiple consecutive cardiac cycles. During this period, introduce the systemic blood pressure and cerebral blood flow regulation model established in Step 4, and adjust the peripheral vascular total impedance and cerebral microcirculation impedance at cardiac cycle intervals so that the systemic arterial mean blood pressure calculated by the model is consistent with the brachial artery mean blood pressure measured in Step 1 (criterion: difference less than 0.5%), and the calculated peripheral cerebral artery blood flow reaches a stable state (criterion: the difference in average blood flow between two adjacent cardiac cycles is less than 0.1%); 5.2 After the calculation reaches the periodic convergence state, save the calculation results of the last cardiac cycle on the computer, including but not limited to the 3D hemodynamic parameters of the stenosis and the blood pressure and blood flow information in each cerebral artery;

[0052] Step 6: Report Output: 6.1 Post-process the hemodynamic calculation results, such as... Figure 7 As shown in (a), the distal end of the carotid artery stenosis (P) was extracted. d ) and proximal (P a The average blood pressure was used to calculate the proximal-distal pressure ratio (dpPR) of the stenosis using the formula, and a pressure contour map was generated using third-party software to show the blood pressure variation characteristics along the stenosis. The formula for calculating dpPR is as follows: dpPR = P d / P a 6.2 Output wall shear stress (WSS) time-series data for one cardiac cycle, calculate wall shear stress-related parameters or indices such as TAWSS, OSI, RRT, and transWSS, and use third-party software for data visualization, outputting data such as... Figure 7 (b) shows the cloud map of wall shear stress parameters / indices, which intuitively displays the distribution characteristics of parameters related to endothelial cell damage or plaque occurrence, progression, and rupture risk, such as high TAWSS, low TAWSS, high OSI, and high transWSS; the calculation formulas for wall shear stress-related parameters / indices are as follows: Where T is the cardiac cycle, τ WLet τ be the time-varying wall shear stress vector. mean n is the periodically averaged wall shear stress vector. f 6.3 Output the average blood flow of each peripheral cerebral artery and compare it with the statistical values ​​(mean ± standard deviation) of healthy individuals (e.g., ...). Figure 7 (c) As shown, assess the degree of ischemia in downstream brain tissue; 6.4 Generate a risk assessment report based on the above results to assist in clinical diagnosis and decision-making.

[0053] Verification of the reliability of the dpPR assessment technology in this invention: Taking carotid artery stenosis as an example, the dpPR assessment technology of this invention was verified by clinical invasive pressure guidewire measurement as follows: (1) Patient recruitment and clinical data collection: Patients with carotid artery stenosis were recruited from the neurology and interventional cerebrovascular departments, and head and neck CTA images and upper arm artery blood pressure, heart rate and other clinical data were non-invasively collected according to the implementation plan of this invention; (2) Invasive measurement of dpPR of carotid artery stenosis by pressure guidewire: During DSA angiography, the pressure guidewire was advanced to 0.5-1 cm distal to the carotid artery stenosis, and distal blood pressure was measured. The average value of 5 cardiac cycles was recorded as the average blood pressure distal to the stenosis (P). d Subsequently, the pressure guidewire was withdrawn to 0.5-1 cm proximal to the stenosis, and the mean blood pressure (P) proximal to the stenosis was obtained using the same data acquisition method as that distal to the stenosis. a Finally, the clinically measured value of dpPR is calculated using the following formula: dpPR = P d / P a ;

[0054] (2) Consistency and correlation test between calculated results and measured values: Invasive dpPR measurement was performed on 27 cases of carotid artery stenosis. Hemodynamic modeling and numerical calculation were conducted according to the implementation scheme of this invention. The dpPR calculated by the model was statistically compared with the invasive measurement data. First, the consistency test between the model calculation results and the clinical measured values ​​of dpPR was performed, and the results were plotted as follows: Figure 8 As shown in the Bland-Altman plot, the mean difference in dpPR between the two groups was -0.00571, with a 95% confidence interval of -0.0475 to 0.0361. Almost all the difference data were within the 95% confidence interval. Further analysis of the error in the model-calculated dpPR, based on the clinically measured dpPR, revealed that the absolute values ​​of all errors were less than 5%, with a mean error of 0.64%. Finally, Pearson correlation analysis was performed on the model-calculated dpPR and the clinically measured values, and the results are as follows... Figure 9 As shown, the two sets of data are significantly correlated (r = 0.986, p < 0.00001, n = 27); the above comparative analysis shows that the dpPR calculated by the model has a high consistency and strong correlation with the clinical measured value.

[0055] The second embodiment of the present invention relates to a quantitative risk assessment system for head and neck artery stenosis. Please refer to [link to relevant documentation]. Figure 3 ,include:

[0056] The data acquisition module is used to acquire tomographic scan images of the patient's head and neck collected by medical imaging equipment, and to acquire blood pressure and heart rate of the patient's upper arm artery measured by a blood pressure monitor in a resting state.

[0057] The geometric model construction module is used to reconstruct a 3D geometric model of the head and neck arteries based on tomographic images, determine the geometric information of each artery in the head and neck and the anatomical structure information of the circle of Willis based on the 3D geometric model of the head and neck arteries, and establish a 3D hemodynamic model of the stenosis based on the 3D geometric model of the head and neck arteries; it is also used to construct a reduced-order hemodynamic model of the head and neck artery network based on the geometric information of each artery in the head and neck, and construct a 0D hemodynamic model of the cerebral microcirculation and other parts of the cardiovascular system;

[0058] The model coupling module is used to couple the 3D hemodynamic model of the stenosis, the reduced-order hemodynamic model of the head and neck arterial network, and the 0D hemodynamic model of the cerebral microcirculation and other parts of the cardiovascular system, and obtain a geometric multi-scale model based on the coupling results.

[0059] The hemodynamic calculation module is used to construct an automatic regulation model of cerebral microcirculation blood flow and set the cardiac cycle according to the heart rate of the upper arm artery. Then, based on the automatic regulation model of cerebral microcirculation blood flow, it continuously performs hemodynamic calculations on the geometric multi-scale model for multiple cardiac cycles and assesses the risk of cerebral and cervical artery stenosis based on the calculation results.

[0060] It is not difficult to see that this embodiment is a system implementation corresponding to the first embodiment, and this embodiment can be implemented in conjunction with the first embodiment. The relevant technical details mentioned in the first embodiment are still valid in this embodiment, and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the first embodiment.

[0061] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this invention, this embodiment does not introduce units that are not closely related to solving the technical problem proposed by this invention; however, this does not mean that other units are absent from this embodiment.

[0062] The third embodiment of the present invention relates to an electronic device; please refer to [link / reference]. Figure 4,include:

[0063] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the quantitative risk assessment method for cervical and head artery stenosis as described above.

[0064] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0065] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0066] The fourth embodiment of the present invention relates to a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method embodiments.

[0067] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0068] In summary, the present invention has the following advantages:

[0069] 1. Effectively reduces examination costs and avoids the risks of invasive measurements: This invention obtains patient data solely through non-invasive measurements, which not only reduces examination costs but also effectively avoids the risk of complications that may be caused by invasive measurements;

[0070] 2. The evaluation results have stronger physiological significance and clinical application value: This invention uses a geometric multi-scale modeling method to achieve integrated analysis of the 3D flow field of the stenosis and the hemodynamic parameters of the whole brain circulation, and considers the blood flow regulation effect of the cerebral microcirculation, which can significantly improve the physiological significance and clinical application value of the evaluation results.

[0071] 3. Provides richer information support for risk assessment of head and neck artery stenosis: This invention provides hemodynamic information covering the stenosis site and the whole cerebral circulation. It can not only quantitatively evaluate the functional effects of stenosis, such as local pressure drop, dpPR, and downstream cerebral ischemia, but also provide detailed hemodynamic information such as wall shear stress distribution and near-wall flow field characteristics for assessing plaque stability and plaque progression risk, thereby supporting clinical decision-making from multiple information dimensions.

[0072] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. All equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this invention should still be covered by the claims of this invention.

Claims

1. A quantitative risk assessment method for head and neck artery stenosis, characterized in that, Includes the following steps: Acquire tomographic images of the patient's head and neck from medical imaging equipment, and acquire blood pressure and heart rate of the patient's upper arm artery at rest, measured by a sphygmomanometer. A 3D geometric model of the head and neck arteries is reconstructed based on the tomographic images. The geometric information of each artery in the head and neck and the anatomical structure information of the circle of Willis are determined based on the 3D geometric model of the head and neck arteries. A 3D hemodynamic model of the stenosis is also established based on the 3D geometric model of the head and neck arteries. Based on the geometric information of the arteries in the head and neck, a reduced-order hemodynamic model of the head and neck artery network is constructed, and a 0-dimensional hemodynamic model of the cerebral microcirculation and other parts of the cardiovascular system is constructed. The 3D hemodynamic model of the stenosis, the reduced-order hemodynamic model of the head and neck arterial network, and the 0D hemodynamic model of the cerebral microcirculation and other parts of the cardiovascular system are coupled together to obtain a geometric multi-scale model based on the coupling results. An automatic regulation model of cerebral microcirculation blood flow was constructed, and the cardiac cycle was set according to the heart rate of the upper arm artery. Then, hemodynamic calculations were continuously performed on the geometric multi-scale model for multiple cardiac cycles based on the automatic regulation model of cerebral microcirculation blood flow. The risk assessment of cerebral microcirculation blood flow stenosis of the patient was performed based on the calculation results.

2. The quantitative risk assessment method for head and neck artery stenosis according to claim 1, characterized in that: The reconstruction of the 3D geometric model of the head and neck arteries based on the tomographic scan image includes: Medical image processing software was used to segment regions such as arterial lumen, bone and other soft tissues based on the gray values ​​of tomographic scan images. For the arterial lumen, methods such as region growth and splitting and merging were used to construct a 3D model of the artery. The surface of the 3D model of the artery is smoothed to obtain a 3D geometric model of the head and neck arteries. The reconstruction range of the 3D geometric model of the head and neck arteries includes the common carotid artery, internal carotid artery, external carotid artery, vertebral artery, basilar artery, and segments of the anterior cerebral artery I, anterior cerebral communicating artery I, posterior cerebral communicating artery I, posterior cerebral communicating artery, middle cerebral artery, anterior cerebral artery II, and posterior cerebral artery II, which constitute the circle of Willis.

3. The quantitative risk assessment method for head and neck artery stenosis according to claim 1, characterized in that: The determination of the geometric information of each artery in the head and neck and the anatomical information of the circle of Willis based on the 3D geometric model of the head and neck arteries includes: The centerline of each artery is extracted based on the 3D geometric model of the head and neck arteries. The length of each artery is calculated based on the centerline, and the diameter information along the length direction is extracted. The anatomical structure information of the Circle of Willis is determined based on the diameter information.

4. The quantitative risk assessment method for head and neck artery stenosis according to claim 1, characterized in that: The process of constructing a reduced-order hemodynamic model of the head and neck arterial network based on the geometric information of each artery in the head and neck includes: A three-unit Windkessel model was used to characterize the hemodynamic properties of each artery, wherein the three-unit Windkessel model includes viscous resistance, blood inertia coefficient, and vascular compliance; A reduced-order hemodynamic model of the head and neck arterial network is obtained by connecting the three-unit Windkessel model of each artery in series or parallel.

5. The quantitative risk assessment method for head and neck artery stenosis according to claim 1, characterized in that: The coupling between the reduced-order hemodynamic model of the head and neck arterial network and the 0-dimensional hemodynamic model of other parts of the cerebral microcirculation and cardiovascular system is achieved by direct connection, while the coupling between the 3-dimensional hemodynamic model of the stenosis and the reduced-order hemodynamic model of the head and neck arterial network is achieved by geometric multi-scale modeling.

6. The quantitative risk assessment method for head and neck artery stenosis according to claim 1, characterized in that: The process involves continuously performing hemodynamic calculations for multiple cardiac cycles on the geometric multi-scale model based on the automatic regulation model of cerebral microcirculation blood flow, and assessing the risk of head and neck arterial stenosis based on the calculation results, including: In the hemodynamic calculation, the total peripheral vascular impedance of the cardiovascular system is adjusted using a negative feedback method with cardiac cycles as the interval, and then the cerebral microcirculation impedance is adjusted using an automatic regulation model of cerebral microcirculation blood flow. Hemodynamic calculations are continuously performed until the convergence condition is met. Then, the hemodynamic calculation results of the last cardiac cycle are output, and a risk assessment report is generated based on the hemodynamic calculation results of the last cardiac cycle.

7. The quantitative risk assessment method for head and neck artery stenosis according to claim 6, characterized in that: The convergence condition is as follows: the difference between the calculated average blood pressure of the system arteries and the measured average blood pressure of the patient's upper arm arteries is less than a first threshold, and the rate of change of the average blood flow of each peripheral cerebral artery during adjacent cardiac cycles is less than a second threshold.

8. The quantitative risk assessment method for head and neck artery stenosis according to claim 6, characterized in that: The risk assessment report generated based on the hemodynamic calculations of the last cardiac cycle includes: The hemodynamic calculation results are post-processed to extract the average blood pressure at the proximal and distal ends of the arterial stenosis, and the pressure ratio at the proximal and distal ends of the stenosis is determined based on the average blood pressure at the proximal and distal ends of the arterial stenosis. Detailed hemodynamic parameters of the stenosis were obtained, and the blood flow of each peripheral cerebral artery was obtained. The blood flow of each peripheral cerebral artery was compared with the statistical values ​​of healthy individuals, and the degree of ischemia in downstream brain tissue was assessed based on the comparison results. A risk assessment report for the patient is generated based on the proximal-distal pressure ratio of the stenosis, detailed hemodynamic parameters of the stenosis, and the assessed degree of ischemia in the downstream brain tissue.

9. A quantitative risk assessment system for cervical artery stenosis, characterized in that: include: The data acquisition module is used to acquire tomographic scan images of the patient's head and neck collected by medical imaging equipment, and to acquire blood pressure and heart rate of the patient's upper arm artery measured by a blood pressure monitor in a resting state. The geometric model construction module is used to reconstruct a 3D geometric model of the head and neck arteries based on the tomographic scan images, determine the geometric information of each artery in the head and neck and the anatomical structure information of the circle of Willis based on the 3D geometric model of the head and neck arteries, and establish a 3D hemodynamic model of the stenosis based on the 3D geometric model of the head and neck arteries; it is also used to construct a reduced-order hemodynamic model of the head and neck artery network based on the geometric information of each artery in the head and neck, and construct a 0D hemodynamic model of the cerebral microcirculation and other parts of the cardiovascular system; The model coupling module is used to couple the 3D hemodynamic model of the stenosis, the reduced-order hemodynamic model of the head and neck arterial network, and the 0D hemodynamic model of the cerebral microcirculation and other parts of the cardiovascular system, and obtain a geometric multi-scale model based on the coupling result. The hemodynamic calculation module is used to construct an automatic regulation model of cerebral microcirculation blood flow, and set the cardiac cycle according to the heart rate of the upper arm artery. Then, based on the automatic regulation model of cerebral microcirculation blood flow, it continuously performs hemodynamic calculations on the geometric multi-scale model for multiple cardiac cycles, and performs risk assessment of the patient's head and neck artery stenosis based on the calculation results.

10. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the quantitative risk assessment method for cervical artery stenosis as described in any one of claims 1 to 8.

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