CN110782988A - Intracranial aneurysm virtual support diagnosis and treatment system and diagnosis and treatment method thereof - Google Patents
Intracranial aneurysm virtual support diagnosis and treatment system and diagnosis and treatment method thereof Download PDFInfo
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Abstract
The invention provides a diagnosis and treatment system and a diagnosis and treatment method of a virtual support of intracranial aneurysm, wherein a preoperative blood vessel model and a virtual support initialization model acquisition module are provided; the method is used for reconstructing a three-dimensional model of the blood vessel, smoothing the model, automatically extracting a central line and initializing a stent model; the virtual stent algorithm execution module is used for simplex grid conversion, virtual stent deployment, vessel wall contact detection and force balance judgment of a virtual stent and a preoperative vessel model; and the operation scheme evaluation module is used for evaluating the effectiveness of the operation scheme made by the preoperative doctor by using the morphological parameters and the hemodynamic function parameters. The invention uses the virtual support technology based on Simplex mesh to simulate the expansion and implantation process of the support in the intracranial artery, ensures the expansion speed of the virtual support on the basis of ensuring the accuracy, builds the intracranial aneurysm virtual support diagnosis and treatment system with timeliness and accuracy, and provides guidance for the preoperative scheme planning of the intracranial aneurysm.
Description
Technical Field
The invention relates to the fields of biomedical engineering and computer science, in particular to a intracranial aneurysm virtual support diagnosis and treatment system and a diagnosis and treatment method thereof.
Background
Intracranial aneurysm is a disease of pathological expansion of arterial blood vessels caused by embrittlement of blood vessel walls, the incidence rate of the intracranial aneurysm is high, more than one aneurysm is suffered by more than 30% of patients, the treatment difficulty degree of the disease is extremely high, the rupture risk is high, and the life safety of the patients is threatened all the time. At present, intraluminal repair has become a more mainstream treatment for aneurysm. Intracranial stents are commonly used to occlude the ostium of an aneurysm and thereby reduce the perfusion of blood in the aneurysm, but the quality of the therapeutic effect depends to a large extent on the choice of stent, including in particular the size, shape and placement of the stent. Secondly, the therapeutic approach of stent intervention is not applicable to all cases, and clinical follow-up has found that patients have aneurysm reoccurrence and in-stent restenosis after intraluminal repair. Therefore, before interventional therapy, complete preoperative planning, stent implantation effect and complication risk prediction are necessary and significant, the virtual stent technology aims to simulate an operation scheme made by a doctor preoperatively, simulate and visualize the treatment effect of the operation scheme and provide a prediction basis of preoperative complications.
For the simulated deployment of the virtual stent, many researches focus on the interaction force between the stent and the vessel wall in the deployment process of the stent and the prediction of the blood flow in the vessel after the stent is placed, so most scholars choose to use a finite element analysis method, pay attention to the specific form change of the stent and the local tension of each metal mesh, and acquire the calculation information of the blood flow through the hemodynamic analysis.
Disclosure of Invention
In order to realize coexistence of timeliness and accuracy, the invention provides the intracranial aneurysm virtual support diagnosis and treatment system with timeliness and accuracy and the diagnosis and treatment method thereof, which can accurately simulate the treatment effect of an operation scheme, avoid the occurrence of adverse complications, realize optimization of the clinical treatment effect of the intracranial aneurysm, and relieve the pain of a patient and the national medical burden.
The specific technical scheme is as follows:
the virtual support diagnosis and treatment system for the intracranial aneurysm comprises the following modules:
(1) a preoperative blood vessel model and virtual stent initialization model obtaining module; the method is used for reconstructing a three-dimensional model of the blood vessel, smoothing the model, automatically extracting a central line and initializing a stent model;
(2) the virtual stent algorithm execution module is used for simplex grid conversion, virtual stent deployment, vessel wall contact detection and force balance judgment of a virtual stent and a preoperative vessel model;
(3) and the operation scheme evaluation module is used for evaluating the effectiveness of the operation scheme made by the preoperative doctor by using the morphological parameters and the hemodynamic function parameters.
Specifically, the method comprises the following steps:
(1) constructing an initialization model acquisition module of a preoperative blood vessel and a virtual stent; the method mainly comprises the steps of reconstructing a three-dimensional model of a preoperative blood vessel, smoothing the model, automatically extracting a central line and initializing a virtual stent model;
(2) constructing a virtual stent algorithm execution module, which mainly comprises simplex grid conversion of a virtual stent and a preoperative blood vessel model, virtual stent deployment, blood vessel wall contact detection and force balance judgment;
(3) and constructing a surgical scheme evaluation module, and evaluating the effectiveness of the preoperative scheme by using the morphological parameters and the hemodynamic functional parameters.
Further, the step (1) specifically comprises the following steps:
(1.1) obtaining preoperative vascular tomography data from a hospital, reconstructing a preoperative vascular model by using a semi-threshold automatic segmentation reconstruction method through mimics software, and exporting the model in an STL format;
(1.2) the derived blood vessel model is a preoperative aneurysm blood vessel model, and the compression algorithm is used for compressing the aneurysm model in matlab to eliminate the influence of expanded aneurysm when a central line is extracted;
(1.3) reconstructing a preoperative aneurysm blood vessel model grid by using Geomagic Studio, aiming at reducing the number of grids and improving the execution efficiency of an intracranial aneurysm virtual support algorithm; (ii) a
(1.4) calculating the tangential direction of each point on the central line, making circles perpendicular to the tangential direction by taking the point of the central line as the center of the circle, setting the initial radius of each circle to be 0.1mm, and constructing the initial model grid of the support.
The step (2) specifically comprises the following steps:
(2.1) after obtaining a preoperative aneurysm model and a stent initialization model, calculating the position and the adjacent relation of a spatial point of the model and the adjacent relation of a triangular mesh;
(2.2) converting the triangular mesh forms of the preoperative blood vessel model and the virtual stent initialization model into a simplex mesh in matlab;
(2.3) after the grid is converted into a simplex grid, describing the movement of the grid point under the action of force by using a motion law; the specific details are that the vertex on each curved surface is regarded as a physical particle, and the motion of each physical particle follows Newton's motion law; at this time, the stent point receives the expansion force of the stent itself, which is called internal force, and the resistance of the vascular wall to the stent point, which is called external force, and the evolution equation of the physical particle in space under the combined action of the internal force and the external force is expressed as formula (1):
where m is the mass of the grid particles, Pi is the brace point, γ is the damping coefficient, and F
intIs the internal force, F, experienced by the node at time t
extIs the external force applied to the node at time t; the above equation is a continuity equation, and in order to serve an actual implementation, discretizing the above equation can yield equation (2):
P
i t＝P
i t+(1-γ)(P
i t-P
i t-1)+αF
int(P
i t)+βF
ext(P
i t) (2)
α and β in the Chinese formula are weight factors of internal and external forces borne by nodes respectively, in order to simulate the self expansion force of the stent and the resistance of a blood vessel wall more truly, the weight factors are obtained through a stent stretching experiment and an intracranial blood vessel tissue stretching experiment respectively, the stent is unfolded into two stages, the first stage is a wall-untouched stage, stent points are expanded outwards only under the action of internal force, the collision detection needs to be carried out by using an AABB bounding box algorithm because the blood vessel with aneurysm is locally expanded, the stent points are prevented from entering a tumor body, the situation which is inconsistent with the reality is avoided, the distance between the current point of each stent point and the point after the previous iteration is judged when the wall adhesion detection is carried out, and when the distance is smaller than a threshold value, for example 10
-2At this point, the stent is considered to be in contact with the vessel wall, a process known as wall detection. After the adherence takes place for the support point, the expansion of support point gets into the second stage, also opens the contact process of support and vascular wall promptly, and the support point not only can outwards expand under the effect of internal force, still can receive the resistance action of vascular wall, is called external force, and when the support expansion force that the support point received and the resistance number value of vascular wall equal, think that the support point has reached balanced position, this mass point stops the iteration this moment, also is that the motion of this physical mass point has reached the balance, and this process is called balanced detection. When all stent points reach the equilibrium position, the final stent deployment is achieved.
In the step (3), the morphological parameters mainly comprise the diameter of the blood vessel model after the operation of using the virtual stent, particularly the diameter change of a tumor body, and the final embedded length of the stent after the virtual stent algorithm is observed to evaluate whether the stent can well block an aneurysm opening or not;
the hemodynamic parameters mainly include blood flow volume in tumor, blood flow velocity and wall shear stress, and particle retention time.
In the step (3), the hemodynamic parameter simulation is used, and the method comprises the following steps:
firstly, obtaining models before and after the execution of a virtual stent algorithm, namely an aneurysm model before an operation and a virtual post-operation model are used, wherein the reconstruction of the aneurysm model is realized by using a mix through a semi-threshold automatic segmentation reconstruction algorithm, the virtual stent post-operation model is generated by a virtual stent algorithm execution module, and a reconstructed blood vessel and a virtual stent model are derived into an STL; smoothing the preoperative vascular model and the postoperative vascular model of the virtual stent by using Geomagic Studio, and cutting an inlet and an outlet of the model;
performing grid division on the model by using ICEM (ANSYS Inc, Canonsburg, USA), wherein a triangular prism boundary layer (5 layers) is adopted near the wall surface, and quadrilateral grids are used in other areas, so as to realize fine calculation on a complex boundary layer;
solving the flow field state by using a finite volume solver CFD-ACE + (ESI Group, France); before solving, boundary attributes, fluid attributes and blood flowing states in a solving domain need to be set, and a blood vessel wall is set to be a non-slip rigid wall surface; setting the blood as an incompressible fluid, i.e. having a blood density of 1044kg/m
3Setting the blood viscosity at 0.00365kg m
-1s
-1(ii) a Because of the small diameter of intracranial arterial vessels, the blood flow therein can be considered as laminar flow; the inlet pulsation velocity boundary condition is given and the outlet constant pressure condition is given, here set to 0 Pa. After the condition setting is finished, solving the interested physical quantity by using a solver, dispersing 0.8s of one cardiac cycle into 55 points, solving for 5 cardiac cycles, and observing the result of the last cycle to ensure the accuracy and the stability of the result;
the formula of the calculation error is shown in formula (3), V
A prioriRepresenting the value obtained by measuring the prior knowledge, wherein the acquisition of the prior knowledge mainly depends on the clinical corresponding bracket brand, the real postoperative data determination of the physiological and pathological characteristics of similar patients, V
VirtualizationObtaining parameter values representing the measured virtual stent post-operation model and the stent model:
and if the operation scheme evaluation is not met, the existing operation scheme needs to be changed again, and new stent parameters are selected again to perform virtual stent test until the requirements are met.
According to the intracranial aneurysm virtual support diagnosis and treatment system and the diagnosis and treatment method thereof, the virtual support technology based on Simplexmesh is used for simulating the implantation and expansion process of a support in an intracranial artery, the expansion speed of the virtual support is guaranteed on the basis of ensuring the accuracy, the intracranial aneurysm virtual support diagnosis and treatment system is built, and guidance is provided for preoperative scheme planning of intracranial aneurysm.
Drawings
FIG. 1 is a general block diagram of a virtual support diagnosis system for intracranial aneurysm according to the present invention;
fig. 2 is a flow chart of blood vessel model acquisition and virtual stent model initialization before the virtual stent diagnosis and treatment system for intracranial aneurysm according to the present invention;
FIG. 3 is a flow chart of the virtual stent algorithm execution of the present invention;
FIG. 4 is a flow chart of a hemodynamic simulation of the present invention;
FIG. 5 is a flow chart of a surgical protocol evaluation module of the present invention;
FIG. 6(a) is a schematic view of a stent in a compressed state in an intracranial vessel;
FIG. 6(b) is a schematic view of the deployment process of a stent in an intracranial vessel;
fig. 6(c) simulates the final placement effect of the stent in the intracranial vessel.
Detailed Description
The specific technical scheme of the invention is explained by combining the attached drawings.
As shown in fig. 1, the virtual stent diagnosis and treatment system for intracranial aneurysm comprises the following modules:
(1) a vessel and stent model acquisition module; the method is used for reconstructing a three-dimensional model of a preoperative blood vessel, smoothing the model, automatically extracting a central line and initializing a virtual stent model;
(2) the virtual stent algorithm execution module is used for simplex grid conversion, virtual stent expansion, adherence detection and balance state detection of a virtual stent and a preoperative blood vessel model;
(3) and the operation scheme evaluation module is used for evaluating the effectiveness of the treatment effect of the operation scheme formulated by the doctor before the operation by using the morphological parameters and the hemodynamic parameters.
The intracranial aneurysm virtual support diagnosis and treatment method comprises the following steps:
(1) constructing an initialization model acquisition module of a preoperative blood vessel and a virtual stent; the method mainly comprises the steps of reconstructing a three-dimensional model of a preoperative blood vessel, smoothing the model, automatically extracting a central line and initializing a virtual stent model;
as shown in fig. 2, tomographic vessel image data in the format of preoperative vessels CTA, 3DRA, and the like are obtained from a hospital, and a vessel model is obtained by a semi-threshold automatic segmentation reconstruction method using mimics software and is derived in the STL format. The derived blood vessel model is an aneurysm blood vessel model, the compression algorithm is used in matlab to compress the aneurysm blood vessel model, the influence of expanded aneurysm is eliminated when centerline extraction is carried out, and then the centerline of the blood vessel is extracted. The method is characterized in that a preoperative aneurysm blood vessel model grid is reconstructed by using Geomagic Studio, and aims to reduce the number of grids and improve the execution efficiency of an intracranial aneurysm virtual support algorithm. . And then calculating the tangential direction of each point on the central line, making a circle perpendicular to the tangential direction by taking the point of the central line as the center of the circle, setting the initial radius of each circle to be 0.1mm, and constructing a support initialization model grid.
(2) Constructing a virtual stent algorithm model module, as shown in fig. 3, which mainly comprises simplex mesh conversion of a stent and a blood vessel model, stent deployment, blood vessel wall contact detection and force balance judgment;
after obtaining the preoperative aneurysm model and the stent initialization model, calculating the spatial point position and the adjacent relation of the model and the adjacent relation of the triangular meshes. The triangular mesh form of the preoperative vessel model and the virtual stent model is converted into a simplex mesh in matlab. After the grid points are converted into the simplex grids, describing the motion of the grid points under the action of force by using a motion law; the specific details are that the vertex on each curved surface is regarded as a physical particle, and the motion of each physical particle follows Newton's motion law; at this time, the stent site receives an expanding force of the stent itself, which is called an internal force, and a resistance force of the vascular wall to this, which is called an external force. Under the combined action of the internal force and the external force, the evolution equation of the physical particles in space can be expressed as formula (1),
where m is the mass of the grid particles, gamma is the damping coefficient, F
intIs the internal force, F, experienced by the node at time t
extIs the external force applied to the node at time t. The above formula continuity equation, in order to serve the actual implementation, discretizing the above equation can yield formula (2):
P
i t＝P
i t+(1-γ)(P
i t-P
i t-1)+αF
int(P
i t)+βF
ext(P
i t) (2)
α and β in the Chinese formula are weight factors of internal and external forces borne by nodes respectively, in order to simulate the self expansion force of the stent and the resistance of a blood vessel wall more truly, the weight factors are obtained through a stent stretching experiment and an intracranial blood vessel tissue stretching experiment respectively, the stent is unfolded into two stages, the first stage is a wall-untouched stage, stent points are expanded outwards only under the action of internal force, the collision detection needs to be carried out by using an AABB bounding box algorithm because the blood vessel with aneurysm is locally expanded, the stent points are prevented from entering a tumor body, the situation which is inconsistent with the reality is avoided, the distance between the current point of each stent point and the point after the previous iteration is judged when the wall adhesion detection is carried out, and when the distance is smaller than a threshold value, for example 10
-2At this point, the stent is considered to be in contact with the vessel wall, a process known as wall detection. After the adherence takes place for the support point, the expansion of support point gets into the second stage, also opens the contact of support and vascular wall promptly, and the support point not only can outwards expand under the effect of internal force, still can receive the resistance effect of vascular wall, is called external force, support point when receivingWhen the expansion force and the resistance value of the blood vessel wall are equal, the stent point is considered to reach the equilibrium position, and the mass point stops iteration at the moment to realize equilibrium point detection, which is called equilibrium detection. When all stent points reach the equilibrium position, the final stent deployment is achieved.
(3) And constructing a surgical scheme evaluation module, and evaluating the effectiveness of the preoperative scheme by using the morphological parameters and the hemodynamic parameters.
The morphological parameters are mainly to measure the diameter of the blood vessel model after the operation using the virtual stent, particularly to pay attention to the diameter change at the tumor body, and to observe the final embedded length of the stent after the virtual stent algorithm so as to evaluate whether the stent can well block the aneurysm opening. The hemodynamic parameters used in the evaluation protocol include blood flow volume within the tumor, blood flow velocity and wall shear stress, and particle residence time. The method comprises the steps of firstly obtaining models before and after the execution of a virtual stent algorithm, namely, using a preoperative aneurysm model and a virtual postoperative model, reconstructing the aneurysm model by using a mix through a semi-threshold automatic segmentation reconstruction algorithm, generating the virtual stent postoperative model by using a virtual stent algorithm execution module, and deriving a reconstructed blood vessel and the virtual stent model into the STL. The pre-operative vascular model and the post-operative vascular model of the virtual stent were smoothed using a geographic Studio, and the incision model was used as an entrance. The model is subjected to grid division by using ICEM (ANSYS Inc, Canonsburg, USA), a triangular prism boundary layer (5 layers) is adopted near the wall surface, and quadrilateral grids are used in other areas, so that the purpose of realizing fine calculation on a complex boundary layer is achieved.
Solving the flow field state by using a finite volume solver CFD-ACE + (ESI Group, France); before solving, boundary attributes, fluid attributes and blood flowing states in a solving domain need to be set, and a blood vessel wall is set to be a non-slip rigid wall surface; setting the blood as an incompressible fluid, i.e. having a blood density of 1044kg/m
3Setting the blood viscosity at 0.00365kg m
-1s
-1(ii) a Because of the small diameter of intracranial arterial vessels, the blood flow therein can be considered as laminar flow; given inlet pulse velocity boundary conditionsThe outlet constant pressure condition is given, here set to 0 Pa. After the condition setting is completed, a solver is used for solving the interested physical quantity, 0.8s of one cardiac cycle is scattered into 55 points, the 5 cardiac cycles are solved, the result of the last cycle is taken for observation, the accuracy and the stability of the result are ensured, and the block diagram of the hemodynamic numerical simulation program is shown in fig. 4. In the operation scheme evaluation module, the prior knowledge obtained by a large number of cases is still used for evaluation, and in the quantitative parameter evaluation, the virtual stent result and the prior knowledge result are used for error calculation evaluation, and it needs to be explained that the acquisition of the prior knowledge mainly depends on the clinical corresponding stent brand and the real postoperative data determination of the similar patient physiological and pathological characteristics. The formula of the calculation error is shown in formula (3), V
A prioriRepresenting a value obtained by measuring a priori knowledge, V
VirtualizationAnd obtaining parameter values representing the measured virtual stent post-operation model and the measured stent model.
If the operation scheme evaluation is not satisfied, the existing operation scheme needs to be changed again, new stent parameters are reselected for virtual stent test until the requirements are satisfied, and a flow chart of the operation scheme evaluation of the intracranial aneurysm virtual stent diagnosis and treatment system is shown in fig. 5.
The stent deployment process and the stent implantation result during the initialization of the stent model and the algorithm execution process of the virtual stent applied to the intracranial aneurysm are schematically shown in fig. 6(a) in a state that the stent is compressed in the blood vessel, fig. 6(b) in a state that the stent is deployed in the blood vessel, and fig. 6(c) in which the final stent implantation effect is simulated.
Claims (6)
1. The virtual support diagnosis and treatment system for the intracranial aneurysm is characterized by comprising the following modules:
(1) a preoperative blood vessel model and virtual stent initialization model obtaining module; the method is used for reconstructing a three-dimensional model of the blood vessel, smoothing the model, automatically extracting a central line and initializing a stent model;
(2) the virtual stent algorithm execution module is used for simplex grid conversion, virtual stent deployment, vessel wall contact detection and force balance judgment of a virtual stent and a preoperative vessel model;
(3) and the operation scheme evaluation module is used for evaluating the effectiveness of the operation scheme made by the preoperative doctor by using the morphological parameters and the hemodynamic function parameters.
2. The diagnosis and treatment method of the intracranial aneurysm virtual stent diagnosis and treatment system according to claim 1, comprising the steps of:
(1) constructing an initialization model acquisition module of a preoperative blood vessel and a virtual stent; the method mainly comprises the steps of reconstructing a three-dimensional model of a preoperative blood vessel, smoothing the model, automatically extracting a central line and initializing a virtual stent model;
(2) constructing a virtual stent algorithm execution module, which mainly comprises simplex grid conversion of a virtual stent and a preoperative blood vessel model, virtual stent deployment, blood vessel wall contact detection and force balance judgment;
(3) and constructing a surgical scheme evaluation module, and evaluating the effectiveness of the preoperative scheme by using the morphological parameters and the hemodynamic functional parameters.
3. The diagnosis and treatment method of the intracranial aneurysm virtual stent diagnosis and treatment system according to claim 2, wherein the step (1) specifically comprises the following steps:
(1.1) obtaining preoperative vascular tomography data from a hospital, reconstructing a preoperative vascular model by using a semi-threshold automatic segmentation reconstruction method through mimics software, and exporting the model in an STL format;
(1.2) the derived blood vessel model is a preoperative aneurysm blood vessel model, and the compression algorithm is used for compressing the aneurysm model in matlab to eliminate the influence of expanded aneurysm when a central line is extracted;
(1.3) reconstructing a preoperative aneurysm blood vessel model grid by using Geomagic Studio, aiming at reducing the number of grids and improving the execution efficiency of an intracranial aneurysm virtual support algorithm; (ii) a
(1.4) calculating the tangential direction of each point on the central line, making circles perpendicular to the tangential direction by taking the point of the central line as the center of the circle, setting the initial radius of each circle to be 0.1mm, and constructing the initial model grid of the support.
4. The diagnosis and treatment method of the intracranial aneurysm virtual stent diagnosis and treatment system according to claim 2, wherein the step (2) specifically comprises the following steps:
(2.1) after obtaining a preoperative aneurysm model and a stent initialization model, calculating the position and the adjacent relation of a spatial point of the model and the adjacent relation of a triangular mesh;
(2.2) converting the triangular mesh forms of the preoperative blood vessel model and the virtual stent initialization model into a simplex mesh in matlab;
(2.3) after the grid is converted into a simplex grid, describing the movement of the grid point under the action of force by using a motion law; the specific details are that the vertex on each curved surface is regarded as a physical particle, and the motion of each physical particle follows Newton's motion law; at this time, the stent point receives the expansion force of the stent itself, which is called internal force, and the resistance of the vascular wall to the stent point, which is called external force, and the evolution equation of the physical particle in space under the combined action of the internal force and the external force is expressed as formula (1):
where m is the mass of the grid particles, Pi is the brace point, γ is the damping coefficient, and F
intIs the internal force, F, experienced by the node at time t
extIs the external force applied to the node at time t; the above equation is a continuity equation, and in order to serve an actual implementation, discretizing the above equation can yield equation (2):
P
i t＝P
i t+(1-γ)(P
i t-P
i t-1)+αF
int(P
i t)+βF
ext(P
i t) (2)
wherein α and β in the formula are weighting factors of internal and external forces applied to the node respectively, in order to make the model more trueThe self expansion force of the quasi-stent and the resistance of the blood vessel wall are obtained, and the weight factors are obtained through a stent stretching experiment and an intracranial vascular tissue stretching experiment respectively; the expansion of the stent is divided into two stages, the first stage is a wall-untouched stage, stent points are expanded outwards only under the action of internal force, and the collision detection needs to be carried out by using an AABB bounding box algorithm because the blood vessels with the aneurysm are locally expanded, so that the stent points are prevented from entering the aneurysm body, and the situation that the stent points do not conform to the reality is avoided; when detecting adherence, the distance between the current point of each stent point and the point after the end of the previous iteration needs to be judged, and when the distance is smaller than a threshold value, for example, 10
-2At this point, the stent is considered to be in contact with the vessel wall, a process known as wall detection. After the adherence takes place for the support point, the expansion of support point gets into the second stage, also opens the contact process of support and vascular wall promptly, and the support point not only can outwards expand under the effect of internal force, still can receive the resistance action of vascular wall, is called external force, and when the support expansion force that the support point received and the resistance number value of vascular wall equal, think that the support point has reached balanced position, this mass point stops the iteration this moment, also is that the motion of this physical mass point has reached the balance, and this process is called balanced detection. When all stent points reach the equilibrium position, the final stent deployment is achieved.
5. The diagnosis and treatment method of the intracranial aneurysm virtual stent diagnosis and treatment system according to claim 2, wherein in the step (3), the morphological parameters are mainly to measure the diameter of the vascular model after the operation using the virtual stent, especially the diameter change at the aneurysm, and observe the final embedded length of the stent after the virtual stent algorithm to evaluate whether the stent can well block the aneurysm mouth;
the hemodynamic parameters mainly include blood flow volume in tumor, blood flow velocity and wall shear stress, and particle retention time.
6. The diagnosis and treatment method of the intracranial aneurysm virtual stent diagnosis and treatment system according to claim 2 or 5, wherein in the step (3), the simulation by using the hemodynamic parameters comprises the following steps:
firstly, obtaining models before and after the execution of a virtual stent algorithm, namely an aneurysm model before an operation and a virtual post-operation model are used, wherein the reconstruction of the aneurysm model is realized by using a mix through a semi-threshold automatic segmentation reconstruction algorithm, the virtual stent post-operation model is generated by a virtual stent algorithm execution module, and a reconstructed blood vessel and a virtual stent model are derived into an STL; smoothing the preoperative vascular model and the postoperative vascular model of the virtual stent by using Geomagic Studio, and cutting an inlet and an outlet of the model;
performing grid division on the model by using ICEM (ANSYS Inc, Canonsburg, USA), wherein a triangular prism boundary layer (5 layers) is adopted near the wall surface, and quadrilateral grids are used in other areas, so as to realize fine calculation on a complex boundary layer;
solving the flow field state by using a finite volume solver CFD-ACE + (ESI Group, France); before solving, boundary attributes, fluid attributes and blood flowing states in a solving domain need to be set, and a blood vessel wall is set to be a non-slip rigid wall surface; setting the blood as an incompressible fluid, i.e. having a blood density of 1044kg/m
3Setting the blood viscosity at 0.00365kg m
-1s
-1(ii) a Because of the small diameter of intracranial arterial vessels, the blood flow therein can be considered as laminar flow; the inlet pulsation velocity boundary condition is given and the outlet constant pressure condition is given, here set to 0 Pa. After the condition setting is finished, solving the interested physical quantity by using a solver, dispersing 0.8s of one cardiac cycle into 55 points, solving for 5 cardiac cycles, and observing the result of the last cycle to ensure the accuracy and the stability of the result;
the formula of the calculation error is shown in formula (3), V
A prioriRepresenting the value obtained by measuring the prior knowledge, wherein the acquisition of the prior knowledge mainly depends on the clinical corresponding bracket brand, the real postoperative data determination of the physiological and pathological characteristics of similar patients, V
VirtualizationObtaining parameter values representing the measured virtual stent post-operation model and the stent model:
and if the operation scheme evaluation is not met, the existing operation scheme needs to be changed again, and new stent parameters are selected again to perform virtual stent test until the requirements are met.
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Application publication date: 20200211 Assignee: Shanghai Lianying Medical Technology Co.,Ltd. Assignor: BEIJING INSTITUTE OF TECHNOLOGY Contract record no.: X2024110000024 Denomination of invention: Virtual stent simulation method for intracranial aneurysms Granted publication date: 20221101 License type: Common License Record date: 20240408 |