CN110742688B - Blood vessel model establishing method and device and readable storage medium - Google Patents

Blood vessel model establishing method and device and readable storage medium Download PDF

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CN110742688B
CN110742688B CN201911051033.8A CN201911051033A CN110742688B CN 110742688 B CN110742688 B CN 110742688B CN 201911051033 A CN201911051033 A CN 201911051033A CN 110742688 B CN110742688 B CN 110742688B
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陈端端
李振锋
梅玉倩
梁世超
石悦
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Abstract

The application provides a method and a device for establishing a blood vessel model and a readable storage medium, which relate to the technical field of medical imaging, and the method comprises the following steps: determining an initial blood vessel model according to a nuclear magnetic resonance scanning result of the artery blood vessel of the detection target; calculating first vessel wall displacement data of the initial vessel model according to the pulse pressure of the detection target and the nuclear magnetic resonance scanning result, and determining second vessel wall displacement data of the arterial vessel according to the nuclear magnetic resonance scanning result; and if the difference value between the first vascular wall displacement data and the second vascular wall displacement data is within a preset range, determining a vascular model of the detection target according to the first vascular wall displacement data and the initial vascular model. Because the second vascular wall displacement data represents the actual vascular wall displacement condition of the detected target artery blood vessel, the blood vessel model of the detected target can be accurately determined according to the first vascular wall displacement data which is not much different from the second vascular wall displacement data and the initial blood vessel model.

Description

Blood vessel model establishing method and device and readable storage medium
Technical Field
The application relates to the technical field of medical imaging, in particular to a method and a device for establishing a blood vessel model and a readable storage medium.
Background
The aorta is one of the very important great vessels in the human body, the internal hemodynamic analysis is also very important, the calculation fluid for the arterial vessel can reflect the blood flowing state in the vessel, but when the elastic movement of the vessel wall is considered to model the vessel, the flow-solid coupling hemodynamic analysis and calculation method is usually adopted, but the method cannot establish a model capable of accurately simulating the arterial vessel of a detection target.
Disclosure of Invention
An object of the embodiments of the present application is to provide a method and an apparatus for building a blood vessel model, and a readable storage medium, so as to solve the problem that a model capable of accurately simulating an artery of a detection target cannot be built in the prior art.
In a first aspect, an embodiment of the present application provides a method for building a blood vessel model, where the method includes: determining an initial blood vessel model according to a nuclear magnetic resonance scanning result of the artery blood vessel of the detection target; calculating first blood vessel wall displacement data of the initial blood vessel model according to the pulse pressure of the detection target and the nuclear magnetic resonance scanning result, and determining second blood vessel wall displacement data of the arterial blood vessel according to the nuclear magnetic resonance scanning result; and if the difference value between the first vascular wall displacement data and the second vascular wall displacement data is within a preset range, determining a vascular model of the detection target according to the first vascular wall displacement data and the initial vascular model.
The method determines that an initial blood vessel model is closer to the actual situation of the detected target artery blood vessel according to the nuclear magnetic resonance scanning result of the detected target artery blood vessel, then calculates first blood vessel wall displacement data of the initial blood vessel model according to the pulse pressure of the detected target and the nuclear magnetic resonance scanning result, because the first blood vessel wall displacement data is simulation data calculated according to the nuclear magnetic resonance scanning result, the blood vessel elasticity is considered, the actual situation of the artery blood vessel wall of the detected target artery blood vessel is better met, and second blood vessel wall displacement data determined according to the nuclear magnetic resonance scanning result of the detected target artery blood vessel can approximately represent the actual blood vessel wall displacement situation of the detected target artery blood vessel, therefore, when the difference value between the first blood vessel wall displacement data and the second blood vessel wall displacement data in the nuclear magnetic resonance scanning result is within a preset range, and determining a blood vessel model of the detection target according to the first blood vessel wall displacement data and the initial blood vessel model, so that the established blood vessel model can accurately simulate the actual condition of the detected target artery blood vessel.
Optionally, the nuclear magnetic resonance scan result comprises a nuclear magnetic resonance image; the determining second vessel wall displacement data of the arterial vessel according to the nuclear magnetic resonance scanning result comprises: determining at least one treatment section of the arterial vessel from the magnetic resonance image; and acquiring cross-section area data of each processing cross section in the at least one processing cross section along with the time period according to the nuclear magnetic resonance image, and determining the second blood vessel wall displacement data according to the cross-section area data.
In the implementation process, at least one processing section of the arterial vessel is determined according to the nuclear magnetic resonance image, and then the section area data of each processing section in the at least one processing section, which changes along with the time period, is obtained according to the nuclear magnetic resonance image.
Optionally, the nuclear magnetic resonance scan result further comprises blood flow data of the arterial vessel; the determining of the initial blood vessel model according to the nuclear magnetic resonance scanning result of the artery blood vessel of the detection target comprises the following steps: determining an inlet and outlet target flow value of the arterial blood vessel according to the blood flow data and the at least one processing section; determining a nuclear magnetic resonance image in diastole, and segmenting the nuclear magnetic resonance image in diastole to obtain an initial vascular structure; performing mesh division on the initial vascular structure to obtain a mesh and nodes of the initial vascular structure, and performing mesh independence test on the initial vascular structure according to the mesh and the nodes; if the initial vascular structure passes the grid independence test, setting the inlet and outlet target flow value and a plurality of zero-dimensional models as boundary conditions of the initial vascular structure, wherein each zero-dimensional model is correspondingly coupled with each port of the initial vascular structure; an initial vessel model is determined based on the boundary conditions of the initial vessel structure.
Because the nuclear magnetic resonance scanning result also comprises the blood flow data of the artery vessel, the inlet and outlet target flow value of the artery vessel can be determined according to the blood flow data and at least one processing section, the blood pressure of the vessel wall in the diastole is minimum, the segmentation is carried out according to the magnetic resonance image in the diastole so as to obtain the initial vascular structure which is closer to the actual situation of the wall of the artery to be detected, then, the initial blood vessel structure is gridded and divided into the grids and the nodes of the initial blood vessel structure, the grid independence test is carried out on the initial blood vessel structure according to the grids and the nodes, when the initial blood vessel structure passes the grid independence test, the inlet and outlet target flow value and the plurality of zero-dimensional models are set as boundary conditions of the initial blood vessel structure, so that a more accurate initial blood vessel model is determined according to the set boundary conditions.
Optionally, the calculating first blood vessel wall displacement data of the initial blood vessel model according to the pulse pressure of the detection target and the nuclear magnetic resonance scanning result includes: obtaining diastolic pressure and pulse pressure of the detection target, and calculating vasodilatation parameters of each processing section in the at least one processing section according to the pulse pressure and the second blood vessel wall displacement data; calculating the rigidity coefficient of each node according to the area of the section where each node is located in the second vascular wall displacement data in the diastole and the vasodilation parameter of the processing section closest to each node; acquiring a pressure value of each node according to the grids and nodes of the initial vascular structure; and calculating the displacement of each node according to the diastolic pressure, the rigidity coefficient and the pressure value of each node, and determining first vascular wall displacement data according to the displacement of each node.
In order to consider the elastic movement of the actual condition of the blood vessel wall, the vasodilation property parameter of each processing section in at least one processing section can be calculated according to the pulse pressure of a detection target and the second blood vessel wall displacement data, the rigidity coefficient of each node is calculated according to the section area of the section where each node is located in the diastole and the vasodilation property parameter of the processing section closest to each node in the second blood vessel wall displacement data and is used for representing the elasticity of the blood vessel wall, and finally the displacement of each node is calculated according to the diastolic pressure, the rigidity coefficient of each node and the pressure value, so that the first blood vessel wall displacement data capable of accurately simulating the detection target arterial blood vessel wall displacement data is determined according to the displacement of each node.
Optionally, the acquiring diastolic pressure and pulse pressure of the detection target, and calculating a vasodilatation parameter of each of the at least one processing section according to the pulse pressure and the second vascular wall displacement data includes: calculating the vasodilation parameter according to the formula:
Figure BDA0002253470250000041
wherein D represents the vasodilation parameter, Δ A represents a difference between maximum cross-sectional area data and minimum cross-sectional area data for each of the at least one treatment sections in the second vessel wall displacement data over a period, A0And data representing the minimum cross-sectional area of each of the at least one treatment cross-section in one cycle in the second vascular-wall-displacement data, and Δ P represents the pulse pressure of the detection target.
Optionally, the calculating a stiffness coefficient of each node according to a cross-sectional area of a cross section where each node is located in the second vascular-wall displacement data in the diastolic period and a vasodilation parameter of a processing cross section closest to each node includes: the stiffness coefficient at each node is calculated according to the following formula:
Figure BDA0002253470250000042
wherein i represents the ith node, i is a positive integer, KiRepresenting the stiffness coefficient of the i-th node, D representing the vasodilatation parameter,
Figure BDA0002253470250000043
presentation instrumentThe cross section of the ith node in the second vessel wall displacement data is the cross section area in the diastole.
Optionally, the calculating a displacement of each node according to the diastolic pressure and the stiffness coefficient and the pressure value of each node, and determining first vascular wall displacement data according to the displacement of each node includes: calculating first vessel wall displacement data according to:
Figure BDA0002253470250000044
wherein i represents the ith node, i is a positive integer, and deltaiFirst vascular wall displacement data, K, representing the ith nodeiRepresenting the stiffness coefficient, p, at the ith nodeiIndicating the pressure value, p, of the ith nodeextA diastolic pressure, n, representing the detection targetiAnd a normal vector representing the ith node and a tangent plane determined by the initial blood vessel model surface.
Optionally, after determining the blood vessel model of the detection target according to the first blood vessel wall displacement data and the initial blood vessel model, the method further includes: performing hemodynamic simulation analysis on the blood vessel model of the detection target to obtain analysis data of the blood vessel model of the detection target in a stable period; and obtaining a blood flow simulation result in the artery vessel of the detection target according to the analysis data.
In a second aspect, an embodiment of the present application provides a blood vessel model building apparatus, including: the initial blood vessel model determining module is used for determining an initial blood vessel model according to a nuclear magnetic resonance scanning result of the artery blood vessel of the detection target; a vessel wall displacement data determining module, configured to calculate first vessel wall displacement data of the initial vessel model according to the pulse pressure of the detection target and the nuclear magnetic resonance scanning result, and determine second vessel wall displacement data of the arterial vessel according to the nuclear magnetic resonance scanning result; and the blood vessel model determining module is used for determining the blood vessel model of the detection target according to the first blood vessel wall displacement data and the initial blood vessel model when the difference value between the first blood vessel wall displacement data and the second blood vessel wall displacement data is within a preset range.
Optionally, the nuclear magnetic resonance scan result includes a nuclear magnetic resonance image, and the vessel wall displacement data determining module includes: a processing section determining unit for determining at least one processing section of the arterial vessel from the magnetic resonance image; and the second blood vessel wall displacement data determining unit is used for acquiring cross section area data of each processing cross section in the at least one processing cross section along with time period according to the nuclear magnetic resonance image and determining the second blood vessel wall displacement data according to the cross section area data.
Optionally, the nuclear magnetic resonance scan result further includes blood flow data of the artery vessel, and the initial vessel model determining module includes: an inlet and outlet target flow value determination unit, configured to determine an inlet and outlet target flow value of the arterial blood vessel according to the blood flow data and the at least one processing cross section; an initial vascular structure acquisition unit, configured to determine a nuclear magnetic resonance image in a diastolic phase, and segment the nuclear magnetic resonance image in the diastolic phase to obtain an initial vascular structure; the independence test unit is used for carrying out grid division on the initial blood vessel structure to obtain a grid and nodes of the initial blood vessel structure, and carrying out grid independence test on the initial blood vessel structure according to the grid and the nodes; a boundary condition setting unit, configured to set the inlet and outlet target flow value and a plurality of zero-dimensional models as boundary conditions of the initial vascular structure if the initial vascular structure passes a mesh independence test, where each zero-dimensional model is correspondingly coupled to each port of the initial vascular structure; an initial vessel model determination unit for determining an initial vessel model based on boundary conditions of the initial vessel structure.
Optionally, the vessel wall displacement data determination module comprises: a vasodilatation parameter calculation unit, configured to obtain diastolic pressure and pulse pressure of the detection target, and calculate a vasodilatation parameter of each processing section of the at least one processing section according to the pulse pressure and the second vascular wall displacement data; the rigidity coefficient calculation unit is used for calculating the rigidity coefficient of each node according to the area of the section, in the diastole, of the section where each node is located in the second vascular wall displacement data and the vasodilation parameter of the processing section closest to each node; a node pressure value calculation unit, configured to obtain a pressure value of each node according to the mesh and the node of the initial vascular structure; and the first vascular wall displacement data calculation unit is used for calculating the displacement of each node according to the diastolic pressure, the rigidity coefficient and the pressure value of each node, and determining first vascular wall displacement data according to the displacement of each node.
Optionally, the vasodilation parameter calculating unit includes: a vasodilation parameter calculating subunit for calculating the vasodilation parameter according to:
Figure BDA0002253470250000061
wherein D represents the vasodilation parameter, Δ A represents a difference between maximum cross-sectional area data and minimum cross-sectional area data for each of the at least one treatment sections in the second vessel wall displacement data over a period, A0And data representing the minimum cross-sectional area of each of the at least one treatment cross-section in one cycle in the second vascular-wall-displacement data, and Δ P represents the pulse pressure of the detection target.
Optionally, the stiffness coefficient calculation unit includes: a stiffness coefficient calculating subunit configured to calculate a stiffness coefficient at each node according to the following equation:
Figure BDA0002253470250000062
wherein i represents the ith node, i is a positive integer, KiRepresenting the stiffness coefficient of the i-th node, D representing the vasodilatation parameter,
Figure BDA0002253470250000063
and the cross section area of the cross section where the ith node is located in the second blood vessel wall displacement data is in the diastole.
Optionally, the first blood-vessel-wall-displacement-data calculating unit includes: a first vascular wall displacement data calculation subunit for rootCalculating first vessel wall displacement data according to:
Figure BDA0002253470250000071
wherein i represents the ith node, i is a positive integer, and deltaiFirst vascular wall displacement data, K, representing the ith nodeiRepresenting the stiffness coefficient, p, at the ith nodeiIndicating the pressure value, p, of the ith nodeextA diastolic pressure, n, representing the detection targetiAnd a normal vector representing the ith node and a tangent plane determined by the initial blood vessel model surface.
Optionally, the apparatus further comprises: the blood vessel model analysis module is used for carrying out hemodynamic simulation analysis on the blood vessel model of the detection target so as to obtain analysis data of the blood vessel model of the detection target in a stable period; and the blood flow simulation result acquisition module is used for acquiring a blood flow simulation result in the artery of the detection target according to the analysis data.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor and a memory, where the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the electronic device executes the method provided in the first aspect.
In a fourth aspect, embodiments of the present application provide a readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, performs the method as provided in the first aspect.
Additional features and advantages of the present application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the present application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a block diagram of an electronic device according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a method for building a blood vessel model according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of a model reconstructed from an arterial blood vessel mri provided in an embodiment of the present application;
fig. 4 is a flowchart of an initial blood vessel model determination method according to an embodiment of the present application;
FIG. 5 is a schematic diagram of arterial blood flow data provided in an embodiment of the present application;
fig. 6 is a block diagram of a blood vessel model building apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, where the electronic device may include: at least one processor 110, such as a CPU, at least one communication interface 120, at least one memory 130, and at least one communication bus 140. Wherein the communication bus 140 is used for realizing direct connection communication of these components. The communication interface 120 of the device in the embodiment of the present application is used for performing signaling or data communication with other node devices. The memory 130 may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. Memory 130 may optionally be at least one memory device located remotely from the aforementioned processor. The memory 130 stores computer readable instructions that, when executed by the processor 110, cause the electronic device to perform the method processes described below with reference to fig. 2.
It will be appreciated that the configuration shown in FIG. 1 is merely illustrative and that electronic device 100 may include more or fewer components than shown in FIG. 1 or have a different configuration than shown in FIG. 1. The components shown in fig. 1 may be implemented in hardware, software, or a combination thereof. In this embodiment, the electronic device 100 may be, but is not limited to, a dedicated detection device, a desktop, a laptop, a smart phone, an intelligent wearable device, a vehicle-mounted device, or other physical devices, and may also be a virtual device such as a virtual machine. In addition, the electronic device 100 is not necessarily a single device, but may also be a combination of multiple devices, such as a server cluster, and the like.
The method has the advantages that the haemodynamic analysis of the artery plays an important role in the current medical research, the modeling is carried out on the artery of the detection target, the obtained blood vessel model is analyzed, the condition of the artery of the detection target can be well reflected, when the blood vessel model is established, the elastic movement condition of the blood vessel wall and the cost of establishing the blood vessel model are difficult to be considered simultaneously, and the blood vessel model establishing method is provided for the embodiment of the application below.
Fig. 2 provides a method for establishing a blood vessel model according to an embodiment of the present application, where the method includes the following steps:
step S110: and determining an initial blood vessel model according to the nuclear magnetic resonance scanning result of the artery blood vessel of the detection target.
The initial blood vessel model can be determined according to the nuclear magnetic resonance scanning result of the artery blood vessel of the detection target by medical image processing software, the initial blood vessel model obtained by the medical image processing software is relatively close to the artery blood vessel structure of the detection target, for example, the nuclear magnetic resonance scanning result of the artery blood vessel of the detection target is input into medical image processing software Mimics developed by Materialise company in Belgium, after the nuclear magnetic resonance image in the nuclear magnetic resonance scanning result is subjected to three-dimensional simulation and reconstruction by the medical image processing software, the initial blood vessel model can be obtained, and after the initial blood vessel model is further processed and set, the initial blood vessel model of the artery blood vessel structure of the detection target can be obtained.
Step S120: and calculating first vessel wall displacement data of the initial vessel model according to the pulse pressure of the detection target and the nuclear magnetic resonance scanning result, and determining second vessel wall displacement data of the arterial vessel according to the nuclear magnetic resonance scanning result.
And calculating first blood vessel wall displacement data of the initial blood vessel model according to the pulse pressure of the detection target and the nuclear magnetic resonance scanning result to be simulation data for simulating the displacement data of the artery blood vessel wall of the detection target, and determining second blood vessel wall displacement data of the artery blood vessel according to the nuclear magnetic resonance scanning result to be regarded as real data of the artery blood vessel wall of the detection target.
As an embodiment, if the nuclear magnetic resonance scan result includes a nuclear magnetic resonance image, the second vessel wall displacement data of the arterial vessel may be determined according to the nuclear magnetic resonance scan result, and the specific steps include:
at least one treatment section of the arterial vessel is determined from the magnetic resonance image.
And acquiring cross-section area data of each processing cross section in the at least one processing cross section along with the time period according to the nuclear magnetic resonance image, and determining second blood vessel wall displacement data according to the cross-section area data.
It should be noted that the artery vessels in the embodiments of the present application refer to an aorta vessel, a light-loaded artery vessel, an intracranial artery vessel, and the like, and the artery vessels in the embodiments of the present application are all described by taking an ascending and descending aorta as an example, please refer to fig. 3, where fig. 3 is a schematic model diagram of the nuclear magnetic resonance image reconstruction of the artery vessels provided in the embodiments of the present application, and in the drawing, the selection of the processing section is described by taking the ascending and descending aorta as an example. It can be seen that a processing section a is selected at the ascending aorta, four processing sections b, c, d and e are respectively selected at equal intervals at the descending aorta, then the section area data of the arterial blood vessel at each moment at the five processing sections are respectively obtained, and the second blood vessel wall displacement data is determined according to the section area data. Wherein the plurality of processing sections may be unequally spaced.
Specifically, the nuclear magnetic resonance image in the nuclear magnetic resonance scanning result may be imported into image processing software, such as GTFlow software, and then diameter data of the artery at each time of the processing cross section may be obtained, where the diameter of the artery at the processing cross section may be selected manually, or the diameter of the artery at the processing cross section may be selected by the image processing software. The diameter of the artery at the processing section can be selected in image processing software, and then adjusted manually to reduce errors.
In the implementation process, at least one processing section of the arterial vessel is determined according to the nuclear magnetic resonance image, and then the section area data of each processing section in the at least one processing section, which changes along with the time period, is obtained according to the nuclear magnetic resonance image.
Step S130: and judging whether the difference value between the first vascular wall displacement data and the second vascular wall displacement data is within a preset range.
If yes, go to step S140: and determining a blood vessel model of the detection target according to the first blood vessel wall displacement data and the initial blood vessel model.
Because the second vascular wall displacement data and the real displacement data of the arterial vessel wall of the detection target, the first vascular wall displacement data and the second vascular wall displacement data can be compared, and when the difference value between the first vascular wall displacement data and the second vascular wall displacement data is within a preset range, the blood vessel model of the detection target is determined according to the first vascular wall displacement data and the initial blood vessel model.
In the method for establishing the blood vessel model, the initial blood vessel model determined according to the nuclear magnetic resonance scanning result of the artery blood vessel of the detection target is closer to the actual situation of the artery blood vessel of the detection target, then the first blood vessel wall displacement data of the initial blood vessel model is calculated according to the pulse pressure of the detection target and the nuclear magnetic resonance scanning result, because the first blood vessel wall displacement data is the simulation data calculated according to the nuclear magnetic resonance scanning result, the blood vessel elasticity is considered, the actual situation of the artery blood vessel wall of the detection target is better met, the second blood vessel wall displacement data determined according to the nuclear magnetic resonance scanning result of the artery blood vessel of the detection target can approximately represent the actual blood vessel wall displacement situation of the artery blood vessel of the detection target, therefore, when the difference value between the first blood vessel wall displacement data and the second blood vessel wall displacement data in the nuclear magnetic resonance scanning result is within the preset, and determining a blood vessel model of the detection target according to the first blood vessel wall displacement data and the initial blood vessel model, so that the established blood vessel model can accurately simulate the actual condition of the detected target artery blood vessel.
The accurate determination of the initial blood vessel model can ensure that the accurate blood vessel model is finally established, and the process of determining the initial blood vessel model is explained below.
The magnetic resonance scan also includes blood flow data of the arterial vessel. Fig. 4 shows a flow chart of an initial vessel model determination method, which includes the following steps:
step S111: and determining the target flow value of the inlet and outlet of the arterial vessel according to the blood flow data and the at least one processing section.
The outlet and inlet target flow values include an inlet target flow value of the ascending aorta and an outlet target flow value of the descending aorta. The blood flow data a at different times of the cardiac cycle can be acquired at a processing section a at the ascending aorta as shown in fig. 3 and taken as the inlet target flow value. In obtaining the exit target flow rate value of the descending aorta, a plurality of processing sections may be selected at the descending aorta, a plurality of blood flow data of the plurality of processing sections may be obtained as an average, and the average may be used as the exit target flow rate value to avoid errors, for example, blood flow data B, C, D and E at four processing sections b, c, d, and E in fig. 3 may be obtained respectively, and then an average F of the blood flow data B, C, D and E may be obtained, and the blood flow data F may be used as the exit target flow rate value of the descending aorta. The blood flow data A, B, C, D, E and F may be time-dependent function values or flow values obtained by integrating a time-varying flow curve. Fig. 5 is a graph of blood flow data of an arterial vessel with time on the abscissa and flow value on the ordinate, and the curve shown in fig. 5 is the change in blood flow at the aortic inlet for one cardiac cycle.
In addition, three blood vessel branches are arranged on the aortic arch of the ascending and descending aorta, the diameters of the three blood vessel branches are small, the image resolution is low, and a large error is generated when the target flow rate values of the outlets of the three branches are determined by setting a processing section to determine blood flow data, so that the difference value between the blood flow data of the ascending aorta and the blood flow data of the descending aorta can be obtained, and the difference value is distributed to each blood vessel branch according to the proportion of the diameter of each blood vessel branch in the total diameter of the three blood vessel branches to serve as the blood flow data of each blood vessel branch, namely the target flow rate value of the outlet of each blood vessel branch. For example, if the average of the descending aorta blood flow data B, C, D and E is F and the ascending aorta blood flow data a is, the difference G may be calculated as a-F, and if the proportions of the first blood vessel branch diameter, the second blood vessel branch diameter, and the third blood vessel branch diameter, which are sequentially viewed from left to right in fig. 3, in the sum of the three blood vessel branch diameters are 35%, 25%, and 40%, respectively, the blood flow data of the first blood vessel branch is 0.35G, the blood flow data of the second blood vessel branch is 0.25G, and the blood flow data of the third blood vessel branch is 0.4G.
Step S112: and determining a nuclear magnetic resonance image in diastole, and segmenting the nuclear magnetic resonance image in diastole to obtain an initial vascular structure.
The blood pressure of the blood vessel wall in the diastole is the minimum, so that the nuclear magnetic resonance image in the diastole can be determined, the initial blood vessel structure can be obtained according to the nuclear magnetic resonance image in the diastole, and in the medical image processing, the nuclear magnetic resonance image can be input into medical image processing software, for example, the software named as mics, and the initial blood vessel structure can be obtained by segmentation.
In addition, in order to obtain a more accurate initial vascular structure, the initial vascular structure obtained by the processing of the medical image processing software Mimics may be input into the geographic Studio processing software, and the initial vascular structure may be smoothed and cut to obtain a processed initial vascular model.
Step S113: and performing meshing on the initial blood vessel structure to obtain a mesh and nodes of the initial blood vessel structure.
For further processing of the initial vessel structure, the initial vessel structure may be gridded to obtain a grid and nodes of the initial vessel structure, e.g. the initial vessel structure may be processed by gridding the initial vessel structure by ANSYS ICEM-CFD software, wherein the initial vessel structure may be divided into tetrahedral grids when gridding.
Step S114: and carrying out grid independence test on the initial blood vessel structure according to the grids and the nodes.
If the initial vascular structure passes the mesh independence test, it indicates that the reliability of the result of the mesh partition is high, and then the step S115 may be continued: and setting the inlet and outlet target flow value and the plurality of zero-dimensional models as boundary conditions of the initial vascular structure.
Wherein each zero-dimensional model is coupled to each ostium of the initial vascular structure. In this embodiment, the inlet and outlet target flow value and the plurality of zero-dimensional models are set as boundary conditions of the initial vascular structure, and one or more of the plurality of conditions including the inlet and outlet target flow value and the plurality of zero-dimensional models may also be set as boundary conditions of the initial vascular structure according to actual needs.
The blood can be regarded here as an incompressible Newtonian fluid with a density of 1056kg/m3The kinematic viscosity was 4 cP.
Step S116: an initial vessel model is determined based on boundary conditions of the initial vessel structure.
Because the nuclear magnetic resonance scanning result also comprises the blood flow data of the artery vessel, the inlet and outlet target flow value of the artery vessel can be determined according to the blood flow data and at least one processing section, the blood pressure of the vessel wall in the diastole is minimum, the segmentation is carried out according to the magnetic resonance image in the diastole so as to obtain the initial vascular structure which is closer to the actual situation of the wall of the artery to be detected, then, the initial blood vessel structure is gridded and divided into the grids and the nodes of the initial blood vessel structure, the grid independence test is carried out on the initial blood vessel structure according to the grids and the nodes, when the initial blood vessel structure passes the grid independence test, the inlet and outlet target flow value and the plurality of zero-dimensional models are set as boundary conditions of the initial blood vessel structure, so that a more accurate initial blood vessel model is determined according to the set boundary conditions.
Optionally, calculating the first blood vessel wall displacement data of the initial blood vessel model according to the pulse pressure of the detection target and the nuclear magnetic resonance scanning result includes the following four steps:
and acquiring diastolic pressure and pulse pressure of a detection target, and calculating the vasodilatation parameter of each processing section in at least one processing section according to the pulse pressure and the second blood vessel wall displacement data.
The diastolic pressure and the pulse pressure of the detection target can be measured by a sphygmomanometer, and then the vasodilation parameter is calculated according to the following formula:
Figure BDA0002253470250000141
wherein D represents a vasodilatation parameter, Δ A represents a difference between maximum cross-sectional area data and minimum cross-sectional area data for each of at least one treatment section in the second vessel wall displacement data over a period, and A0And data representing the minimum cross-sectional area of each of the at least one treatment cross-section in the second vascular wall displacement data within one period, wherein Δ P represents the pulse pressure of the detection target.
And calculating the rigidity coefficient of each node according to the section area of the section where each node is located in the second blood vessel wall displacement data in the diastole and the vasodilation parameter of the processing section closest to each node.
The stiffness coefficient at each node is calculated according to the following formula:
Figure BDA0002253470250000151
wherein i represents the ith node, i is a positive integer, KiRepresenting the stiffness coefficient of the ith node, D representing the vasodilation parameter,
Figure BDA0002253470250000152
and the section area of the section where the ith node is located in the second blood vessel wall displacement data in the diastole period is shown.
And acquiring the pressure value of each node according to the grid and the nodes of the initial vascular structure.
After the initial vascular structure is subjected to meshing, and the mesh and the nodes of the initial vascular structure are obtained, fluid mechanics analysis can be further performed to analyze and obtain the pressure value of each node, so that the first vascular wall displacement data can be conveniently determined. For example, the mesh and nodes of the original vessel structure after meshing can be hydrodynamically analyzed using the hydrodynamics analysis software CFX, FLUENT, AUTODESK CFD, and the like.
And calculating the displacement of each node according to the diastolic pressure, the rigidity coefficient and the pressure value of each node, and determining first vascular wall displacement data according to the displacement of each node.
Calculating first vessel wall displacement data according to:
Figure BDA0002253470250000153
wherein i represents the ith node, i is a positive integer, and deltaiFirst vascular wall displacement data, K, representing the ith nodeiRepresenting the stiffness coefficient, p, at the ith nodeiIndicating the pressure value, p, of the ith nodeextDiastolic pressure, n, representing the object of detectioniAnd a normal vector representing the ith node and a tangent plane determined by the surface of the initial blood vessel model.
In order to consider the elastic movement of the actual condition of the blood vessel wall, the vasodilation property parameter of each processing section in at least one processing section can be calculated according to the pulse pressure of a detection target and the second blood vessel wall displacement data, the rigidity coefficient of each node is calculated according to the section area of the section where each node is located in the diastole and the vasodilation property parameter of the processing section closest to each node in the second blood vessel wall displacement data and is used for representing the elasticity of the blood vessel wall, and finally the displacement of each node is calculated according to the diastolic pressure, the rigidity coefficient of each node and the pressure value, so that the first blood vessel wall displacement data capable of accurately simulating the detection target arterial blood vessel wall displacement data is determined according to the displacement of each node.
At this time, explanation is continued to step S130: and judging whether the difference value between the first vascular wall displacement data and the second vascular wall displacement data is within a preset range. At this time, since the first vascular-wall displacement data δ has been calculated and acquirediMay be further based on δiIn acquiring first vessel wall displacement data
Figure BDA0002253470250000161
And in the second vessel wall displacement data
Figure BDA0002253470250000162
May also be acquired, thus comparing in the first vessel wall displacement data
Figure BDA0002253470250000163
And second vessel wall displacement data
Figure BDA0002253470250000164
The effectiveness of the calculated first vascular wall displacement data can be determined, and therefore the accurate establishment of the blood vessel model according to the first vascular wall displacement data can be guaranteed.
It is understood that, after the blood vessel model is established, the blood flow dynamics simulation analysis may be performed on the blood vessel model of the detection target to obtain analysis data of the blood vessel model of the detection target in a stable period, and then a blood flow simulation result in the artery blood vessel of the detection target may be obtained according to the analysis data. For example, a blood vessel model of the detection target can be analyzed by ANSYS-CFX software using the navier-stokes equation.
Based on the same concept, the embodiment of the present application further provides a structural block diagram of a blood vessel model building apparatus 200 as shown in fig. 6. The apparatus may be a module, a program segment, or code on an electronic device. It should be understood that the blood vessel modeling apparatus 200 corresponds to the above-mentioned embodiment of the method of fig. 2, and can perform the steps related to the embodiment of the method of fig. 2, and the specific functions of the blood vessel modeling apparatus 200 can be referred to the above description, and the detailed description is appropriately omitted here to avoid redundancy.
Optionally, the blood vessel model building apparatus 200 includes:
an initial blood vessel model determining module 210, configured to determine an initial blood vessel model according to a nuclear magnetic resonance scan result of an artery blood vessel of a detection target.
The blood vessel wall displacement data determining module 220 is configured to calculate first blood vessel wall displacement data of the initial blood vessel model according to the pulse pressure of the detection target and the nuclear magnetic resonance scanning result, and determine second blood vessel wall displacement data of the arterial blood vessel according to the nuclear magnetic resonance scanning result.
And a blood vessel model determining module 230, configured to determine a blood vessel model of the detection target according to the first blood vessel wall displacement data and the initial blood vessel model when the difference between the first blood vessel wall displacement data and the second blood vessel wall displacement data is within a preset range.
Optionally, the nuclear magnetic resonance scan result includes a nuclear magnetic resonance image, and the blood vessel wall displacement data determining module 220 includes:
a processing section determination unit for determining at least one processing section of the arterial vessel from the magnetic resonance image.
And the second vascular wall displacement data determining unit is used for acquiring cross section area data of each processing cross section in the at least one processing cross section along with time period according to the nuclear magnetic resonance image and determining second vascular wall displacement data according to the cross section area data.
Optionally, the nuclear magnetic resonance scan result further includes blood flow data of the artery vessel, and the initial vessel model determining module 210 includes:
and the inlet and outlet target flow value determining unit is used for determining the inlet and outlet target flow value of the arterial blood vessel according to the blood flow data and the at least one processing section.
And the initial vascular structure acquisition unit is used for determining the nuclear magnetic resonance image in the diastole and segmenting the nuclear magnetic resonance image in the diastole to obtain the initial vascular structure.
And the independence test unit is used for carrying out grid division on the initial blood vessel structure to obtain grids and nodes of the initial blood vessel structure, and carrying out grid independence test on the initial blood vessel structure according to the grids and the nodes.
And the boundary condition setting unit is used for setting the inlet and outlet target flow value and a plurality of zero-dimensional models as the boundary conditions of the initial vascular structure if the initial vascular structure passes the grid independence test, wherein each zero-dimensional model is correspondingly coupled with each port of the initial vascular structure.
An initial vessel model determination unit for determining an initial vessel model based on boundary conditions of the initial vessel structure.
Optionally, the blood vessel wall displacement data determining module 220 comprises:
and the vasodilatation parameter calculating unit is used for acquiring diastolic pressure and pulse pressure of a detection target and calculating the vasodilatation parameter of each processing section in at least one processing section according to the pulse pressure and the second blood vessel wall displacement data.
And the rigidity coefficient calculation unit is used for calculating the rigidity coefficient of each node according to the section area of the section where each node is located in the diastole in the second vascular wall displacement data and the vasodilation parameter of the processing section closest to each node.
And the node pressure value calculation unit is used for acquiring the pressure value of each node according to the grid and the nodes of the initial vascular structure.
And the first vascular wall displacement data calculation unit is used for calculating the displacement of each node according to the diastolic pressure, the rigidity coefficient and the pressure value of each node, and determining first vascular wall displacement data according to the displacement of each node.
Optionally, the vasodilation parameter calculating unit includes:
a vasodilation parameter calculating subunit for calculating a vasodilation parameter according to the following formula:
Figure BDA0002253470250000181
wherein D represents a vasodilatation parameter, Δ A represents a difference between maximum cross-sectional area data and minimum cross-sectional area data for each of at least one treatment section in the second vessel wall displacement data over a period, and A0And data representing the minimum cross-sectional area of each of the at least one treatment cross-section in the second vascular wall displacement data within one period, wherein Δ P represents the pulse pressure of the detection target.
Optionally, the stiffness coefficient calculation unit includes:
a stiffness coefficient calculating subunit configured to calculate a stiffness coefficient at each node according to the following equation:
Figure BDA0002253470250000183
wherein i represents the ith node, i is a positive integer, KiRepresenting the stiffness coefficient of the ith node, D representing the vasodilation parameter,
Figure BDA0002253470250000182
and the section area of the section where the ith node is located in the second blood vessel wall displacement data in the diastole period is shown.
Optionally, the first blood-vessel-wall-displacement-data calculating unit includes:
a first vascular-wall-displacement-data calculating subunit operable to calculate first vascular-wall-displacement data according to:
Figure BDA0002253470250000191
wherein i represents the ith node, i is a positive integer, and deltaiFirst vascular wall displacement data, K, representing the ith nodeiRepresenting the stiffness coefficient, p, at the ith nodeiIndicating the pressure value, p, of the ith nodeextDiastolic pressure, n, representing the object of detectioniAnd a normal vector representing the ith node and a tangent plane determined by the surface of the initial blood vessel model.
Optionally, the apparatus further comprises:
and the blood vessel model analysis module is used for carrying out hemodynamic simulation analysis on the blood vessel model of the detection target so as to acquire analysis data of the blood vessel model of the detection target in a stable period.
And the blood flow simulation result acquisition module is used for acquiring a blood flow simulation result in the artery of the detection target according to the analysis data.
The embodiment of the present application provides a readable storage medium, and when being executed by a processor, a computer program performs the method processes performed by the electronic device in the method embodiment shown in fig. 2.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method, and will not be described in too much detail herein.
To sum up, the present application provides a method, an apparatus, and a readable storage medium for establishing a blood vessel model, wherein the method comprises determining an initial blood vessel model according to a nuclear magnetic resonance scan result of an artery vessel of a detection target, the initial blood vessel model being closer to an actual condition of the artery vessel of the detection target, and then calculating first blood vessel wall displacement data of the initial blood vessel model according to a pulse pressure of the detection target and the nuclear magnetic resonance scan result, because the first blood vessel wall displacement data is simulation data calculated according to the nuclear magnetic resonance scan result, not only is the elasticity of the blood vessel considered, but also the actual condition of the artery wall of the detection target is better met, and second blood vessel wall displacement data determined according to the nuclear magnetic resonance scan result of the artery vessel of the detection target can approximately represent the actual blood vessel wall displacement condition of the artery of the detection target, therefore, when a difference value between the first blood vessel wall displacement data and the second blood vessel wall displacement data in And determining a blood vessel model of the detection target according to the first blood vessel wall displacement data and the initial blood vessel model, so that the established blood vessel model can accurately simulate the actual situation of the detection target artery blood vessel.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (8)

1. A method of vessel modeling, the method comprising:
determining an initial blood vessel model according to a nuclear magnetic resonance scanning result of the artery blood vessel of the detection target;
calculating first blood vessel wall displacement data of the initial blood vessel model according to the pulse pressure of the detection target and the nuclear magnetic resonance scanning result, and determining second blood vessel wall displacement data of the arterial blood vessel according to the nuclear magnetic resonance scanning result;
if the difference value between the first vascular wall displacement data and the second vascular wall displacement data is within a preset range, determining a vascular model of the detection target according to the first vascular wall displacement data and the initial vascular model;
wherein the nuclear magnetic resonance scan result comprises a nuclear magnetic resonance image; the determining second vessel wall displacement data of the arterial vessel according to the nuclear magnetic resonance scanning result comprises: determining at least one treatment section of the arterial vessel from the magnetic resonance image; acquiring cross-section area data of each processing cross section in the at least one processing cross section along with time period according to the nuclear magnetic resonance image, and determining second blood vessel wall displacement data according to the cross-section area data;
the calculating first blood vessel wall displacement data of the initial blood vessel model according to the pulse pressure of the detection target and the nuclear magnetic resonance scanning result includes: obtaining diastolic pressure and pulse pressure of the detection target, and calculating vasodilatation parameters of each processing section in the at least one processing section according to the pulse pressure and the second blood vessel wall displacement data; calculating the rigidity coefficient of each node according to the area of the section where each node is located in the second vascular wall displacement data in the diastole and the vasodilation parameter of the processing section closest to each node; acquiring a pressure value of each node according to the grid and the nodes of the initial vascular structure; and calculating the displacement of each node according to the diastolic pressure, the rigidity coefficient and the pressure value of each node, and determining first vascular wall displacement data according to the displacement of each node.
2. The vessel modeling method of claim 1, wherein the nmr scan further comprises blood flow data of the arterial vessel;
the determining of the initial blood vessel model according to the nuclear magnetic resonance scanning result of the artery blood vessel of the detection target comprises the following steps:
determining an inlet and outlet target flow value of the arterial blood vessel according to the blood flow data and the at least one processing section;
determining a nuclear magnetic resonance image in diastole, and segmenting the nuclear magnetic resonance image in diastole to obtain an initial vascular structure;
performing mesh division on the initial vascular structure to obtain a mesh and nodes of the initial vascular structure, and performing mesh independence test on the initial vascular structure according to the mesh and the nodes;
if the initial vascular structure passes the grid independence test, setting the inlet and outlet target flow value and a plurality of zero-dimensional models as boundary conditions of the initial vascular structure, wherein each zero-dimensional model is correspondingly coupled with each port of the initial vascular structure;
an initial vessel model is determined based on the boundary conditions of the initial vessel structure.
3. The method of claim 1, wherein the obtaining diastolic pressure and pulse pressure of the detection target and calculating vasodilation parameters of each of the at least one treatment section according to the pulse pressure and the second vascular wall displacement data comprises:
calculating the vasodilation parameter according to the formula:
Figure FDA0002818277050000021
wherein D represents the vasodilation parameter, Δ A represents a difference between maximum cross-sectional area data and minimum cross-sectional area data for each of the at least one treatment sections in the second vessel wall displacement data over a period, A0And data representing the minimum cross-sectional area of each of the at least one treatment cross-section in one cycle in the second vascular-wall-displacement data, and Δ P represents the pulse pressure of the detection target.
4. The method for building a vascular model according to claim 3, wherein the calculating the stiffness coefficient of each node according to the cross-sectional area of the cross section where each node is located in the second vascular-wall displacement data in the diastolic phase and the vasodilation parameter of the processing cross section closest to each node includes:
the stiffness coefficient at each node is calculated according to the following formula:
Figure FDA0002818277050000031
wherein i represents the ith node, i is a positive integer, KiRepresents the stiffness coefficient of the i-th node,d represents the vasodilation parameter,
Figure FDA0002818277050000032
and the cross section area of the cross section where the ith node is located in the second blood vessel wall displacement data is in the diastole.
5. The method for building a vascular model according to claim 4, wherein the calculating the displacement of each node according to the diastolic pressure and the stiffness coefficient and pressure value of each node, and determining the first vascular wall displacement data according to the displacement of each node comprises:
calculating first vessel wall displacement data according to:
Figure FDA0002818277050000033
wherein i represents the ith node, i is a positive integer, and deltaiFirst vascular wall displacement data, K, representing the ith nodeiRepresenting the stiffness coefficient, p, at the ith nodeiIndicating the pressure value, p, of the ith nodeextA diastolic pressure, n, representing the detection targetiAnd a normal vector representing the ith node and a tangent plane determined by the initial blood vessel model surface.
6. The method of claim 1, wherein after determining the blood vessel model of the detection target according to the first blood vessel wall displacement data and the initial blood vessel model, the method further comprises:
performing hemodynamic simulation analysis on the blood vessel model of the detection target to obtain analysis data of the blood vessel model of the detection target in a stable period;
and obtaining a blood flow simulation result in the artery vessel of the detection target according to the analysis data.
7. A blood vessel modeling apparatus, the apparatus comprising:
the initial blood vessel model determining module is used for determining an initial blood vessel model according to a nuclear magnetic resonance scanning result of the artery blood vessel of the detection target;
a vessel wall displacement data determining module, configured to calculate first vessel wall displacement data of the initial vessel model according to the pulse pressure of the detection target and the nuclear magnetic resonance scanning result, and determine second vessel wall displacement data of the arterial vessel according to the nuclear magnetic resonance scanning result;
a blood vessel model determining module, configured to determine a blood vessel model of the detection target according to the first blood vessel wall displacement data and the initial blood vessel model when a difference value between the first blood vessel wall displacement data and the second blood vessel wall displacement data is within a preset range;
wherein the nuclear magnetic resonance scan result comprises a nuclear magnetic resonance image; the determining second vessel wall displacement data of the arterial vessel according to the nuclear magnetic resonance scanning result comprises: determining at least one treatment section of the arterial vessel from the magnetic resonance image; acquiring cross-section area data of each processing cross section in the at least one processing cross section along with time period according to the nuclear magnetic resonance image, and determining second blood vessel wall displacement data according to the cross-section area data;
the calculating first blood vessel wall displacement data of the initial blood vessel model according to the pulse pressure of the detection target and the nuclear magnetic resonance scanning result includes: obtaining diastolic pressure and pulse pressure of the detection target, and calculating vasodilatation parameters of each processing section in the at least one processing section according to the pulse pressure and the second blood vessel wall displacement data; calculating the rigidity coefficient of each node according to the area of the section where each node is located in the second vascular wall displacement data in the diastole and the vasodilation parameter of the processing section closest to each node; acquiring a pressure value of each node according to the grid and the nodes of the initial vascular structure; and calculating the displacement of each node according to the diastolic pressure, the rigidity coefficient and the pressure value of each node, and determining first vascular wall displacement data according to the displacement of each node.
8. A readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1 to 6.
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