CN117010299A - Brain tissue blood flow condition prediction system based on hemodynamic coupling model - Google Patents

Brain tissue blood flow condition prediction system based on hemodynamic coupling model Download PDF

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CN117010299A
CN117010299A CN202311000473.7A CN202311000473A CN117010299A CN 117010299 A CN117010299 A CN 117010299A CN 202311000473 A CN202311000473 A CN 202311000473A CN 117010299 A CN117010299 A CN 117010299A
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blood flow
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flow condition
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blood
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CN117010299B (en
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刘艺
张慧
贾俊青
曾凡海
蒋晓芸
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Shandong University
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Abstract

The invention relates to the technical field of hemodynamics, and discloses a brain tissue blood flow condition prediction system based on a hemodynamic coupling model, which comprises the following steps: the data acquisition module is used for acquiring the blood flow condition at the initial moment; the solving module is used for solving the discrete one-dimensional hemodynamic coupling model by means of a reduced order extrapolation method after carrying out numerical discrete by combining the one-dimensional hemodynamic coupling model and applying a Dragon-Gregorian tower method and a finite element method based on the blood flow condition at the initial moment to obtain the blood flow condition at a plurality of future moments. The prediction efficiency of the blood flow condition is improved.

Description

Brain tissue blood flow condition prediction system based on hemodynamic coupling model
Technical Field
The invention relates to the technical field of hemodynamics, in particular to a brain tissue blood flow condition prediction system based on a hemodynamic coupling model.
Background
The statements in this section merely relate to the background of the present disclosure and may not necessarily constitute prior art.
Hemodynamics is an important branch of biomedical engineering, and is mainly used for studying the flow rule of blood in organs such as heart, blood vessels and brain tissues and the interaction of blood with surrounding tissues. The subject field integrates the knowledge of multiple subjects such as fluid mechanics, biology, medicine, physics and the like, and discusses the physical property of blood flow through theoretical analysis and experimental research so as to reveal the rule of blood flow change in organisms, solve the physiological and pathological problems and further guide clinical treatment.
The application of hemodynamic coupling models to practical medical problems is the leading research direction in the field of hemodynamics. The model mainly considers the complex interactions between blood flow and tissues and organs. The flow state of blood, pressure distribution, and its effect on the tissue of the vessel wall, myocardium, etc. are all incorporated into this comprehensive model. The model has great guiding significance for understanding the occurrence mechanism of diseases such as arteriosclerosis and hypertension or for designing medical equipment such as artificial hearts.
The existing medical technology can be used for analyzing and treating diseases of patients, but the existing technology cannot completely reveal the change condition of blood flow and blood vessels due to the complexity of cerebral tissue blood vessels.
Moreover, the analysis process using the prior art is time consuming, does not meet the needs of real life, increases the costs of hospitalization for patients and hospitals, or is not the optimal technique for this purpose. In many cases, mathematical techniques have proven to be faithful to effectively solve medical problems. The comprehensive hemodynamic coupling model is constructed by organically combining a plurality of factors such as blood flow, blood pressure distribution, influence on tissues and the like by using an advanced mathematical method and a calculation technology. The model is unique in that it can not only delineate the physical processes of blood flow, but also predict the physiological effects of blood flow on organs and tissues, such as induced deformation of vessel walls, organ loading, etc. The model can also simulate the influence of various pathological conditions, such as arteriosclerosis, hypertension and the like, on blood flow, and help doctors understand the occurrence mechanism of the diseases, so that a more accurate treatment scheme is formulated. In addition, the model has important engineering application value. For example, when medical instruments such as artificial hearts, vascular substitutes, hemodynamic monitoring equipment and the like are designed, the model can be utilized for simulation, the design is optimized, and the safety and the effectiveness of the equipment are improved.
Because of the coupling property, multiple physical properties, high dimension and singularity of the hemodynamic coupling model, the accurate solution of the model in predicting the blood flow condition of brain tissue is generally difficult to obtain by an analytic method, while the prior art provides a hemodynamic coupling model calculation method based on partial differential equation, but in the same wide range, the problems of long calculation time and low calculation efficiency exist.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a brain tissue blood flow condition prediction system based on a hemodynamic coupling model, which utilizes a Dragon library tower method and a finite element method to solve a one-dimensional hemodynamic coupling model on an irregular area, and improves the optimization and calculation efficiency of the one-dimensional hemodynamic coupling model and the prediction efficiency of the blood flow condition by means of a reduced-order extrapolation method in the realization process of a rapid algorithm.
In a first aspect, the present invention provides a brain tissue blood flow condition prediction system based on a hemodynamic coupling model;
a brain tissue blood flow condition prediction system based on a hemodynamic coupling model, comprising:
the data acquisition module is used for acquiring the blood flow condition at the initial moment;
the solving module is used for solving the discrete one-dimensional hemodynamic coupling model by means of a reduced order extrapolation method after carrying out numerical discrete by combining the one-dimensional hemodynamic coupling model and applying a Dragon-Gregorian tower method and a finite element method based on the blood flow condition at the initial moment to obtain the blood flow condition at a plurality of future moments.
Further, the one-dimensional hemodynamic coupling model is discretized by using a Dragon-Gregory tower method in the time direction and a finite element method in the space direction, so that a full-discrete format and a matrix calculation form are obtained.
Further, the blood flow condition includes: vascular cross-sectional area, vascular volumetric flow, and vascular pulsatile blood pressure distribution in the brain tissue blood flow system.
Further, the brain tissue blood flow system includes a blood flow channel having a plurality of bifurcation points.
Further, the one-dimensional hemodynamic coupling model is:
wherein t is a time variable, z is a space variable, satisfying (z, t) ∈Ω× (0, T)]T represents the total number of future moments, and omega is a one-dimensional irregular area; a (z, t) is the cross-sectional area of the blood vessel, Q (z, t) is the volumetric flow of the blood vessel, and P (z, t) is the pulsatile blood pressure of the blood vessel; alpha is the Coriolis coefficient, ρ is the blood flow density, v is the kinematic viscosity coefficient of blood, P ext Is extravascular pressure, A 0 Is the reference cross-sectional area of the blood vessel.
Further, the initial conditions of the one-dimensional hemodynamic coupling model are:
A(z，0)＝A h0 (z)，Q(z，0)＝Q h0 (z)，P(z，0)＝P h0 (z)
the boundary conditions of the one-dimensional hemodynamic coupling model are as follows:
A(0，t)＝A 0 (t)，Q(0，t)＝Q 0 (t)，P(0，t)＝P 0 (t)
wherein A is 0 (t)、Q 0 (t) and P 0 (t) the cross-sectional area of the vessel, the volumetric flow of the vessel and the pulsatile blood pressure of the vessel at the boundary point, respectively; a is that h0 (z)、Q h0 (z) and P h0 (z) the vessel cross-sectional area, the vessel volume flow and the vessel pulsatile blood pressure at the initial moment, respectively.
In a second aspect, the present invention also provides an electronic device, including:
a memory for non-transitory storage of computer readable instructions; and
a processor for executing the computer-readable instructions,
wherein the computer readable instructions, when executed by the processor, perform the steps of:
acquiring the blood flow condition at the initial moment;
based on the blood flow condition at the initial moment, combining with a one-dimensional hemodynamic coupling model, performing numerical value dispersion by using a Dragon-Gregory tower method and a finite element method, and solving the dispersed one-dimensional hemodynamic coupling model by means of a reduced-order extrapolation method to obtain the blood flow condition at a plurality of future moments.
In a third aspect, the present invention also provides a storage medium storing non-transitory computer readable instructions, wherein the non-transitory computer readable instructions, when executed by a computer, perform the steps of:
acquiring the blood flow condition at the initial moment;
based on the blood flow condition at the initial moment, combining with a one-dimensional hemodynamic coupling model, performing numerical value dispersion by using a Dragon-Gregory tower method and a finite element method, and solving the dispersed one-dimensional hemodynamic coupling model by means of a reduced-order extrapolation method to obtain the blood flow condition at a plurality of future moments.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the one-dimensional hemodynamic coupling model on the irregular area is solved by using the Dragon's tower method and the finite element method, in the realization process of the rapid algorithm, the existing algorithm is greatly simplified from the algorithm realization by means of the reduced-order extrapolation method, the optimization and calculation efficiency of the one-dimensional hemodynamic coupling model are improved, the calculation amount of the hemodynamic coupling model in the solving process can be reduced, the analysis efficiency of ischemic brain tissue blood flow problems, the prediction efficiency of blood flow conditions and the judgment efficiency of brain tissue vascular diseases are improved, the diagnosis time of a patient is saved, and the medical pressure is reduced.
The invention provides a brand-new way to understand and process the interaction problem of blood flow and surrounding tissues, and through a one-dimensional hemodynamic coupling model, the change conditions of volume flow, blood vessel cross-sectional area and blood vessel pressure in the blood flow process are researched, the blood flow mechanism in brain tissues is revealed, the hemodynamic behavior is better explained, the hemodynamic correlation technique is promoted to be applied to the actual medical problem more quickly, important support is provided for the practice of clinical medicine and biomedical engineering, the diagnosis accuracy and treatment effect of diseases are effectively improved, and the design and performance of medical equipment are further improved.
Additional aspects of the invention will be set forth in part in the description which follows, or may be learned by practice of the invention.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a block diagram of a brain tissue flow condition prediction system based on a hemodynamic coupling model according to a first embodiment;
FIG. 2 is a schematic diagram of a brain tissue blood vessel according to a first embodiment;
FIG. 3 is a comparison of the fast algorithm and direct method calculation time for a family of spatial subdivision for the first embodiment;
fig. 4 is a graph comparing the fast algorithm and direct method calculation time for the multi-family spatial subdivision of the first embodiment.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
Embodiments of the invention and features of the embodiments may be combined with each other without conflict.
All data acquisition in the embodiment is legal application of the data on the basis of meeting laws and regulations and agreements of users.
Example 1
The embodiment provides a brain tissue blood flow condition prediction system based on a hemodynamic coupling model.
The brain tissue blood flow condition prediction system based on the hemodynamic coupling model is applicable to ischemic brain tissue.
The brain tissue blood flow condition prediction system based on the hemodynamic coupling model provided by the embodiment is based on the one-dimensional hemodynamic coupling model, so that the calculation time required by the ischemic brain tissue blood flow condition prediction can be effectively reduced, the judging efficiency of brain diseases is improved, and a reasonable and effective solution is provided.
As shown in fig. 1, the brain tissue blood flow condition prediction system based on the hemodynamic coupling model includes a controller through which the blood flow condition in an ischemic brain tissue blood flow system including a blood flow channel having a plurality of bifurcation points is studied. Wherein the controller includes: and the data acquisition module and the solving module. As shown in fig. 2, the present example investigated the flow of blood in a vessel having three bifurcation points, one inlet and four outlets.
And the data acquisition module is used for acquiring the blood flow condition at the initial moment.
The solving module is used for combining the one-dimensional hemodynamic coupling model in the human brain tissue blood vessel based on the blood flow condition at the initial moment, carrying out numerical value dispersion by using a Dragon-Grave tower method and a finite element method, and solving the dispersed one-dimensional hemodynamic coupling model by means of a reduced extrapolation method to obtain the blood flow condition at a plurality of future moments.
Among these, blood flow conditions include blood vessel cross-sectional area, blood vessel volume flow and blood vessel pressure distribution in the ischemic brain tissue blood flow system. Based on the obtained cross-sectional area of blood flow, volume flow and blood vessel pressure, ischemic brain tissue blood flow condition is analyzed, and corresponding therapeutic measures are taken for the patient.
As an embodiment, the blood flow situation at several future moments of a certain cardiac cycle is predicted based on the blood flow situation at the initial moments of the cardiac cycle.
In the embodiment, based on the blood flow condition of human brain tissue, an ischemic brain tissue blood flow model based on a one-dimensional hemodynamic coupling model is established, wherein the one-dimensional hemodynamic coupling model comprises a cross-sectional area equation, a volume flow equation and a blood pressure equation:
wherein Ω is a one-dimensional irregular region (i.e., brain tissue vascular region, region shape is brain tissue vascular shape, as shown in fig. 2), t is time variable, z is space variable, satisfying (z, t) ∈Ω× (0, t)]T represents the total number of future time instants. A (z, t) is the cross-sectional area of the vessel, Q (z, t) is the volumetric flow of the vessel, and P (z, t) is the pulsatile blood pressure of the cerebral vessel. Alpha is the Coriolis coefficient (or the momentum flux correction coeffcient, see Mathematical modeling ofthe vascular system), ρ is the blood flow density, vIs the kinematic viscosity coefficient of blood (kinematic viscosity means the ratio of the dynamic viscosity of a fluid to the density of the fluid), P ext Is extravascular pressure, A 0 Is the reference cross-sectional area of the blood vessel, and can be measured.
Defining initial conditions and boundary conditions of a one-dimensional hemodynamic coupling model:
A(0，t)＝A 0 (t)，Q(0，t)＝Q 0 (t)，P(0，t)＝P 0 (t) (4)
A(z，0)＝A h0 (z)，Q(z，0)＝Q h0 (z)，P(z，0)＝P h0 (z) (5)
wherein A is h0 (z)、Q h0 (z) and P h0 (z) the cross-sectional area of the blood vessel, the volumetric flow of the blood vessel and the pulsatile blood pressure of the blood vessel at the initial moment, respectively; a is that 0 (t)、Q 0 (t) and P 0 (t) is the vessel cross-sectional area, the vessel volume flow and the vessel pulsatile blood pressure at the boundary points (the values at the inlet and outlet of the vessel with branching), respectively. At the boundary points are the values that need to be acquired at all times.
In this embodiment, considering the complexity of cerebral tissue vessels, for a one-dimensional hemodynamic coupling model, the model is numerically discretized using the longger-kutta method and the finite element method.
In this embodiment, the equations (1) - (3) are subjected to numerical value dispersion, the dispersion is performed in the time direction by using the longgrid tower method, the dispersion is performed in the space direction by using the finite element method, and finally the full dispersion format and the matrix calculation format are obtained.
For time discretized intervalsCan be divided into time steps->Is->The number of equal sub-intervals is equal,
let A n (z)＝A(z，t n )，Q n (z)＝Q(z，t n )，P n (z)＝P(z，t n )，The time semi-discrete format is:
wherein A is n (z) or A n 、Q n (z) or Q n 、P n (z) or P n Respectively the nth time t n The cross-sectional area of the blood vessel, the volume flow of the blood vessel, the pulsatile blood pressure of the cerebral blood vessel.And->Representing source itemsAt the nth time t n The value of (1), the source term f 1 (z，t)、f 2 (z, t) and f 3 (z, t) is a known continuous function, and is introduced for researching more general condition and providing numerical method with more general applicability, when researching original model, f is made in numerical experiment 1 (z，t)＝f 2 (z，t)＝f 3 (z, t).
Due to the complexity of the solving area, the finite element method is adopted to solve in the space direction. Is provided withFor a family subdivision of the region Ω +.>Is a split node, N h To split the total number of nodes, z 0 And->Is the boundary point of the region Ω. The subdivision can be chosen arbitrarily, typically an equidistant subdivision over longer vessels, the subdivision being encrypted at the node. Taking h as->Maximum value of element dimension, i.e.)>Suppose V h Is a finite element space (approximate space), defined as:
wherein v is h Representing the approximate space V h The above expression represents the approximate space V h The function in (a) satisfies the condition behind the colon;representation->The upper order is not more than->S is an arbitrary number. Order theFor the model at t=t n Finite element numerical solution of time, then the fully discrete format is: ask for->So that for any u ε V h The method comprises the following steps:
wherein,and->Respectively the nth time t n Group h space division->Cross-sectional area of blood vessel, volume flow of blood vessel, pulsatile blood pressure of cerebral blood vessel.
For a family of split points of region ΩTake { phi } j Is a basis function meeting phi j (z i )＝δ ij ，i，.j＝0，1，…，N h Wherein delta when i+.j ij =0, when i=0, 1, …, N h Time delta ii =1. Then finite element value solution->Can be expressed as:
in equation (7)Taking v=Φ l (z), the following matrix form can be obtained:
wherein,respectively represent source function +.>And->Is a basis function discrete vector of (1);is an unknown numerical solution vector. />Representing a matrix of basis functions, the elements of which are basis functionsInner product (phi) i ，φ l ) Calculation was performed using the Gauss product equation:
wherein G is E Is a set of Gauss points, ω, on Unit cell E k To correspond to Gauss point z k Is a weight of (2).
In this embodiment, a discrete hemodynamic coupling model is solved by using a fast algorithm based on a reduced-order extrapolation method, so as to obtain a numerical solution of the coupling model. The numerical solution obtained by solving the model comprises: cross-sectional area of blood vessels, volumetric flow and vascular pulsatile blood pressure distribution during blood flow in brain tissue.
Analyzing the blood flow condition and the change condition of blood vessels in brain tissue blood vessels of a patient based on the obtained cross-sectional area, volume flow and blood vessel pressure of the blood flow, and taking corresponding treatment measures for the patient; simulating the influence of various pathological conditions, such as arteriosclerosis, hypertension and the like, on blood flow, helping doctors understand the occurrence mechanism of the diseases, thereby developing more accurate treatment schemes.
Specifically, considering a fast algorithm based on a reduced-order extrapolation method, for a matrix format of a general differential equation, a positive integer L is first given, and a previous L iteration numerical solution is calculated according to a direct methodAnd store the solution in a matrixIs a kind of medium. By matrix M U Singular value decomposition is performed to obtain:
wherein the method comprises the steps ofTo correspond to matrix M U Diagonal matrix, eta i (i=1, 2, …, l) is a positive singular value. />Respectively represent matrix->And->Is described. Due to L N h And matrix->Sum matrix->Is equivalent, the matrix +.>Positive singular value η i And feature vector matrix>Then the relationship is utilized:
calculating to obtain matrixIs a characteristic value of (a).
Based on the result, a positive integer L is given, and a solution of the previous L layer number value is calculated When n is greater than or equal to L+1, the drugs are used in the formula +.>To approximately replace A n ，Q n ，P n ThenThe matrix form can be written as:
wherein,can be obtained by calculating the value of the front L layers>And->Is the unknown quantity to be solved. When n is 1.ltoreq.L, there is +.>Finally, a numerical solution of the coupling model can be obtained.
In this embodiment, the fast algorithm used may reduce the matrix dimension used in the calculation process. The calculation efficiency can be improved, the numerical solution of the model can be accurately solved, and further, the change conditions of the cross-sectional area, the volume flow and the pulsating blood pressure of the blood vessel in the process of solving the obtained brain tissue blood flow are utilized for analysis.
FIG. 3 shows a comparison of the fast algorithm and direct method calculation times for a family of spatial subdivision in this embodiment; FIG. 4 shows a comparison of the fast algorithm and direct method calculation times for the multi-family spatial subdivision in this embodiment; it can be seen that the calculation time of the fast algorithm in this embodiment is significantly shorter than that of the direct method, and the calculation efficiency of the fast algorithm gradually increases as the space subdivision is gradually encrypted. The results show that the fast algorithm of the embodiment is indeed superior to the direct method in terms of efficiency and saving of computational cost.
The ischemic brain tissue blood flow system based on the one-dimensional hemodynamic coupling model can simulate and predict the flow condition of blood in brain tissue blood vessels with high accuracy, and simultaneously considers the physiological influence of blood flow on surrounding tissues such as blood vessel walls.
The brain tissue blood flow condition prediction system based on the hemodynamic coupling model provided by the embodiment is not only beneficial to promoting the academic research of hemodynamics, but also provides important support for the practice of clinical medicine and biomedical engineering.
The brain tissue blood flow condition prediction system based on the hemodynamic coupling model provided by the embodiment performs mechanism analysis on the one-dimensional hemodynamic coupling model to study the change condition of volume flow, blood vessel cross-sectional area and blood vessel pressure in the blood flow process in one cardiac cycle; revealing blood flow mechanism in brain tissue, better explaining hemodynamic behavior, promoting relevant hemodynamic technology to be applied to actual medical problem faster, providing important support for clinical medicine and biomedical engineering practice; the novel way is provided for understanding and treating the interaction problem of blood flow and surrounding tissues, so that the diagnosis accuracy and treatment effect of diseases are effectively improved, and the design and performance of medical equipment are further improved.
According to the brain tissue blood flow condition prediction system based on the hemodynamic coupling model, the coupling model on the irregular area is solved by using the Dragon library tower method and the finite element method, in the implementation process of a rapid algorithm, the existing algorithm is greatly simplified from the algorithm by means of a reduced order extrapolation method, the optimization and calculation efficiency of the one-dimensional hemodynamic coupling model are improved, the calculation amount of the hemodynamic coupling model in the solving process can be reduced, the analysis efficiency of ischemic brain tissue blood flow problems, the prediction efficiency of blood flow conditions and the judgment efficiency of brain tissue vascular diseases are improved, the treatment time of patients is saved, and the medical pressure is reduced.
The brain tissue blood flow condition prediction system based on the hemodynamic coupling model is applied to medical research, can effectively improve the judging efficiency of brain diseases, and provides a reasonable and effective solution; simulating the influence of various pathological conditions, such as arteriosclerosis, hypertension and the like, on blood flow, helping doctors understand the occurrence mechanism of the diseases and making a more accurate treatment scheme.
Example two
The embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein the processor is coupled to the memory, the one or more computer programs being stored in the memory, the processor executing the one or more computer programs stored in the memory when the electronic device is running, to cause the electronic device to perform the steps of:
acquiring the blood flow condition at the initial moment;
based on the blood flow condition at the initial moment, combining with a one-dimensional hemodynamic coupling model, performing numerical value dispersion by using a Dragon-Gregory tower method and a finite element method, and solving the dispersed one-dimensional hemodynamic coupling model by means of a reduced-order extrapolation method to obtain the blood flow condition at a plurality of future moments.
The one-dimensional hemodynamic coupling model is discretized by using a Dragon-Gregory tower method in the time direction and a finite element method in the space direction, so that a full-discretization format and a matrix calculation form are obtained.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic device, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include read only memory and random access memory and provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store information of the device type.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software.
The method in the first embodiment may be directly implemented as a hardware processor executing or implemented by a combination of hardware and software modules in the processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method. To avoid repetition, a detailed description is not provided herein.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
Example III
The present embodiment also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, perform the steps of:
acquiring the blood flow condition at the initial moment;
based on the blood flow condition at the initial moment, combining with a one-dimensional hemodynamic coupling model, performing numerical value dispersion by using a Dragon-Gregory tower method and a finite element method, and solving the dispersed one-dimensional hemodynamic coupling model by means of a reduced-order extrapolation method to obtain the blood flow condition at a plurality of future moments.
The one-dimensional hemodynamic coupling model is discretized by using a Dragon-Gregory tower method in the time direction and a finite element method in the space direction, so that a full-discretization format and a matrix calculation form are obtained.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. The brain tissue blood flow condition prediction system based on the hemodynamic coupling model is characterized by comprising:
the data acquisition module is used for acquiring the blood flow condition at the initial moment;
the solving module is used for solving the discrete one-dimensional hemodynamic coupling model by means of a reduced order extrapolation method after carrying out numerical discrete by combining the one-dimensional hemodynamic coupling model and applying a Dragon-Gregorian tower method and a finite element method based on the blood flow condition at the initial moment to obtain the blood flow condition at a plurality of future moments.
2. The system for predicting the blood flow of brain tissue based on the hemodynamic coupling model of claim 1, wherein the one-dimensional hemodynamic coupling model is discretized in a time direction by using a longgnus-base method and is discretized in a space direction by using a finite element method, so as to obtain a full-discretization format and a matrix calculation format.
3. The hemodynamic coupling model-based brain tissue blood flow situation prediction system of claim 1, wherein the blood flow situation comprises: vascular cross-sectional area, vascular volumetric flow, and vascular pulsatile blood pressure distribution in the brain tissue blood flow system.
4. The hemodynamic coupling model-based brain tissue blood flow situation prediction system of claim 1, wherein the brain tissue blood flow system comprises a blood flow channel having a plurality of bifurcation points.
5. The hemodynamic coupling model-based brain tissue blood flow situation prediction system of claim 1, wherein the one-dimensional hemodynamic coupling model is:
wherein t is a time variable, z is a space variable, satisfying (z, t) ∈Ω× (0, T)]T represents the total number of future moments, and omega is a one-dimensional irregular area; a (z, t) is the cross-sectional area of the blood vessel, Q (z, t) is the volumetric flow of the blood vessel, and P (z, t) is the pulsatile blood pressure of the blood vessel; alpha is the Coriolis coefficient, ρ is the blood flow density, v is the kinematic viscosity coefficient of blood, P ext Is extravascular pressure, A 0 Is the reference cross-sectional area of the blood vessel.
6. The hemodynamic coupling model-based brain tissue blood flow situation prediction system of claim 1, wherein the initial conditions of the one-dimensional hemodynamic coupling model are:
A(z,0)＝A h0 (z)，Q(z,0)＝Q h0 (z)，P(z,0)＝P h0 (z)
the boundary conditions of the one-dimensional hemodynamic coupling model are as follows:
A(0,t)＝A 0 (t),Q(0,t)＝Q 0 (t),P(0,t)＝P 0 (t)
wherein A is 0 (t)、Q 0 (t) and P 0 (t) the cross-sectional area of the vessel, the volumetric flow of the vessel and the pulsatile blood pressure of the vessel at the boundary point, respectively; a is that h0 (z)、Q h0 (z) and P h0 (z) the vessel cross-sectional area, the vessel volume flow and the vessel pulsatile blood pressure at the initial moment, respectively.
7. An electronic device, comprising:
a memory for non-transitory storage of computer readable instructions; and
a processor for executing the computer-readable instructions,
wherein the computer readable instructions, when executed by the processor, perform the steps of:
acquiring the blood flow condition at the initial moment;
based on the blood flow condition at the initial moment, combining with a one-dimensional hemodynamic coupling model, performing numerical value dispersion by using a Dragon-Gregory tower method and a finite element method, and solving the dispersed one-dimensional hemodynamic coupling model by means of a reduced-order extrapolation method to obtain the blood flow condition at a plurality of future moments.
8. The electronic device of claim 1, wherein the one-dimensional hemodynamic coupling model is discretized in a temporal direction using a longgnus-base tower method and in a spatial direction using a finite element method to obtain a fully discretized format and a matrix computing format.
9. A storage medium, characterized by non-transitory storing computer-readable instructions, wherein the non-transitory computer-readable instructions, when executed by a computer, perform the steps of:
acquiring the blood flow condition at the initial moment;
based on the blood flow condition at the initial moment, combining with a one-dimensional hemodynamic coupling model, performing numerical value dispersion by using a Dragon-Gregory tower method and a finite element method, and solving the dispersed one-dimensional hemodynamic coupling model by means of a reduced-order extrapolation method to obtain the blood flow condition at a plurality of future moments.
10. The storage medium of claim 9, wherein the one-dimensional hemodynamic coupling model is discretized in a temporal direction using a longgnus-base tower method and in a spatial direction using a finite element method, resulting in a fully discrete format and a matrix computation format.
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Citations (3)

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Publication number Priority date Publication date Assignee Title
CN110634572A (en) * 2019-09-24 2019-12-31 杭州阿特瑞科技有限公司 Blood vessel blood flow simulation method based on mechanical equation and related device
CN113160921A (en) * 2021-05-26 2021-07-23 南京大学 Construction method and application of digital human cardiovascular system based on hemodynamics
CN114530252A (en) * 2022-03-03 2022-05-24 北京理工大学 Coronary artery blood flow dynamics simulation method and device

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110634572A (en) * 2019-09-24 2019-12-31 杭州阿特瑞科技有限公司 Blood vessel blood flow simulation method based on mechanical equation and related device
CN113160921A (en) * 2021-05-26 2021-07-23 南京大学 Construction method and application of digital human cardiovascular system based on hemodynamics
CN114530252A (en) * 2022-03-03 2022-05-24 北京理工大学 Coronary artery blood flow dynamics simulation method and device

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