CN112185551B - System and method for predicting coronary artery stenosis resistance based on deep learning - Google Patents
System and method for predicting coronary artery stenosis resistance based on deep learning Download PDFInfo
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Abstract
A system and method for predicting coronary stenosis resistance based on deep learning. Belongs to the field of artificial intelligence, and comprises the following steps: constructing a blood vessel model based on the real coronary artery parameters and recording the parameters (entrance area, stenosis length and the like) of the blood vessel model; performing model gridding pretreatment based on the blood vessel model; calculating a preprocessing model based on geometric multi-scale hemodynamics and extracting stenosis resistance of a blood vessel model; establishing a stenosis resistance training set and a prediction set based on data extraction and blood vessel model parameters; establishing a neural network framework based on the BP neural network; training a narrow resistance training set and performing prediction verification on a prediction set based on deep learning.
Description
Technical Field
The invention belongs to the field of artificial intelligence, and relates to a system and a method for predicting coronary artery stenosis resistance based on deep learning.
Background
FFR is a reliable indicator directly describing the degree of functional myocardial ischemia, and can be obtained by numerical simulation in related studies, and the determination of coronary artery stenosis resistance is particularly important for numerical calculation of FFR. The coronary artery stenosis resistance determination method is intended to provide a calculation basis for non-invasively obtaining the accuracy of the FFR (defined as the ratio of the mean pressure at the distal end of the stenosis (Pd) to the mean pressure at the root of the aorta (Pa)) value.
Machine learning is a multi-field interdiscipline, deep learning is a new research direction in the field of machine learning, is an internal rule and a representation level of learning sample data, and finally aims to enable a machine to have the analysis and learning capacity like a human and to recognize data such as characters, images and shapes. The method combines deep learning and numerical simulation to provide an algorithm for predicting coronary artery stenosis resistance based on deep learning, so that the stenosis resistance is determined based on the coronary artery geometric parameters, and the accuracy of the FFR numerical simulation result is improved in noninvasive calculation. Has certain theoretical value for the research of diagnosis and treatment strategies of personalized coronary artery physiological diseases.
Disclosure of Invention
The invention provides an algorithm for predicting coronary artery stenosis resistance based on deep learning, which is quicker and more accurate than numerical simulation and is generally suitable for determining stenosis resistance calculation generated by vascular stenosis. The algorithm for predicting coronary artery stenosis resistance based on deep learning comprises the following steps: the method comprises the steps of construction of a blood vessel model, model grid preprocessing, target parameter extraction, database establishment, neural network establishment and prediction verification.
In order to achieve the purpose, the invention is realized by the following technical scheme:
a system for predicting coronary artery stenosis resistance based on deep learning is characterized in that the system is used for predicting the coronary artery stenosis resistance by the following method, and comprises the following steps:
step A1: constructing a coronary vessel model based on the real coronary anatomical structure parameters and recording the parameters (such as entrance area, narrow length, narrow area and the like) of the coronary vessel model;
step A2: performing model gridding pretreatment based on the blood vessel model;
step A3: calculating a preprocessing model based on geometric multi-scale hemodynamics and extracting stenosis resistance of a blood vessel model;
a4, establishing a stenosis resistance training set and a prediction set based on data extraction and blood vessel model parameters;
step A5: establishing a neural network framework based on the BP neural network;
step A6: training a narrow resistance training set and performing prediction verification on a prediction set based on deep learning.
1. As the technical scheme of the invention, the characteristics in the step A1 are that firstly, a coronary blood vessel model is constructed based on the real coronary anatomical structure parameters, and the coronary blood vessel model is constructed by using the Sold Work software. The requirements for constructing the model are as follows: the shape parameters such as the entrance area, the blood vessel length, the stenosis rate and the like are in accordance with the parameters of the real blood vessel. For example: vessel diameter D (2-6 mm), stenosis length ls = (2-40 mm), stenosis rate DS% (20-90%), stenosis diameter DS = D (1-DS%), vessel length L = ls + D8 + D12);
as a further technical solution of the present invention, in the feature described in step A2, model gridding preprocessing is performed based on the blood vessel model, and the input form of the mesh file is supported by the computing software, so that the following preprocessing steps are performed before gridding the model:
step B1: deriving an x _ t format for the constructed blood vessel model through Sold Works software;
and step B2: and performing pre-calculation meshing on the constructed blood vessel model through an Ansys workbench 15.0 meshing sub-function module, wherein the step requires sensitivity analysis on meshes, and eliminates the influence of mesh factors on calculation results.
As a further technical scheme of the invention, the characteristics in the step A3 are used for extracting the stenosis resistance of the blood vessel model based on a multi-scale haemodynamics calculation preprocessing model. The specific implementation process comprises the following steps:
step C1: giving the same resting flow to determine a corresponding zero-dimensional afterload model, wherein the zero-dimensional afterload model consists of basic electronic component inductors and resistors, and the equivalent relation between the hemodynamic parameters and the physical electrical parameters is shown in table 1.
TABLE 1 equivalent relationship between hemodynamic parameters and physical electrical parameters
Assuming that the length of a section of blood vessel is l, the cross-sectional area of the blood vessel is a, and the zero-dimensional and three-dimensional corresponding parameter relationship of a section of blood vessel is shown in fig. 5, according to the elastic cavity theory established by Frank, the quantitative relationship among the flow, pressure and related resistance on the section of blood vessel can be expressed as follows:
Δp＝Q*R′#(1)
in the formula, Δ P represents the pressure drop generated in the section of blood vessel, Q represents the blood flow of the branch blood vessel, and R' represents the viscous resistance generated by the section of blood vessel; mu is the blood viscosity value under normal physiology, and the value is 3.5e-3 Pa.s; l represents the length of the section of the blood vessel, and A represents the cross-sectional area of the section of the blood vessel.
The self-inductivity of the inductance is equivalent to the flow inertia of blood flow, and the resistance effect of the resistance is equivalent to the microcirculation resistance of each branch of the coronary artery. The values of the resistance R and the inductance L in the zero-dimensional model are:
in the formula P Powder Represents the pressure at the end of the section of the blood vessel, and rho represents the blood flow density value and is defined as 1060kg/m 3 。
And C2: introducing the gridding model into Ansys-CFX fluid calculation software, and giving fluid calculation parameters including blood flow density and blood flow dynamic viscosity value (namely the blood viscosity value under normal physiology); (ii) a
And C3: determining boundary conditions and calculating the pressure-flow-afterload relation of three-dimensional model, defining average arterial pressure at the blood vessel inlet, defining virtual flow boundary at the outlet, and coupling zero-dimensional afterload model
And C4: in the numerical simulation calculation, the setting time is 1.2s, the setting time step length is 0.01s, and the simulation calculation process is executed.
Step C5: after the calculation is finished, the blood vessel pressure is extracted at the position 30mm behind the stenosis, the pressure difference of the pretreatment model is obtained through comparison with the blood vessel inlet pressure, and the stenosis resistance is obtained through dividing the pressure difference by the flow. And extracting blood flow at the stenotic inlet.
As a further technical scheme of the invention, the characteristics in the step A4 are based on data extraction and blood vessel model parameters to establish a stenosis resistance training set and a prediction set; 340 different stenosis parameter blood vessels are calculated through the steps A1, A2 and A3, a deeply learned data set is constructed by the extracted stenosis resistance and blood flow of the blood vessel model and the geometric parameters of the blood vessel model, and the data set is divided into a training set and a testing set.
As a further technical solution of the present invention, the characteristic described in step A5, establishing a neural network framework based on a BP neural network, specifically includes the following steps:
step D1: establishing a deep learning hidden layer, and setting the number of hidden layers, the number of nodes of the hidden layer, an activation function and the like;
step D2: and selecting a proper optimizer, a Loss function and other hyper-parameters.
As a further technical scheme of the invention, the characteristics in the step A5 are based on deep learning to train the narrow resistance training set and to predict and verify the prediction set. The specific implementation process comprises the following steps:
step E1: importing a training set and a testing set into a program through a pandas module;
step E2: the influence of the geometric parameters of the blood vessel on the stenosis resistance of the blood vessel model is analyzed through a correlation coefficient matrix, multiple collinearity among variables is analyzed and avoided, and finally seven variables with strong correlation are obtained, wherein the variables are respectively the entrance area, the stenosis entrance length, the stenosis outlet length, the minimum stenosis length, the stenosis rate and the entrance blood flow.
And E3: and importing the processed data into a neural network framework to adjust the hyper-parameters.
Step E4: and saving the training model and executing the prediction of the stenosis resistance of the blood vessel model.
The system of the invention can predict coronary artery stenosis resistance accurately with small error.
Description of the drawings:
FIG. 1 is a flow chart of the method of the present invention
FIG. 2: blood vessel model diagram
FIG. 3: zero-dimensional three-dimensional coupling model diagram
FIG. 4 is a schematic view of: parameter matrix correlation analysis
FIG. 5: neural network architecture
FIG. 6: comparison graph of predicted value and calculated value
Detailed Description
The present invention will be further illustrated with reference to the following examples, but the present invention is not limited to the following examples.
Example 1
A system for predicting coronary artery stenosis resistance based on deep learning is used for predicting coronary artery stenosis resistance by the following method, and comprises the following steps:
step A1: constructing a coronary vessel model based on the real coronary anatomical structure parameters and recording the coronary vessel model parameters (entrance area, stenosis entrance length, stenosis exit length, minimum stenosis length, stenosis rate);
step A2: performing model gridding pretreatment based on the blood vessel model;
step A3: calculating a preprocessing model based on geometric multi-scale hemodynamics and extracting stenosis resistance of a blood vessel model;
a4, establishing a stenosis resistance training set and a prediction set based on data extraction and blood vessel model parameters;
step A5: establishing a neural network framework based on the BP neural network;
step A6: training a narrow resistance training set and performing prediction verification on a prediction set based on deep learning.
As the technical scheme of the invention, the characteristics in the step A1 are that firstly, a coronary vessel model is constructed based on the real coronary anatomical structure parameters, and the coronary vessel model is constructed by Sold Work software. The requirements for constructing the model are as follows: the shape parameters such as entrance area, blood vessel length, stenosis rate, etc. should conform to the parameters of the real blood vessel. For example: blood vessel diameter D (2-6 mm), stenosis length ls = (2-40 mm), stenosis rate DS% (20-90%), stenosis diameter Ds = D (1-DS%), blood vessel length L = ls + D8 + D12)
As a further technical solution of the present invention, the feature in step A2 is to perform model gridding preprocessing based on the blood vessel model, and calculate the input form of the software-supported grid file, so the following preprocessing steps are performed before gridding the model:
step B1: deriving an x _ t format for the constructed blood vessel model through Sold Works software;
and step B2: and performing pre-calculation meshing on the constructed blood vessel model through an Ansys workbench 15.0 meshing sub-function module, wherein the step requires sensitivity analysis on the meshes and eliminates the influence of mesh factors on a calculation result.
As a further technical scheme of the invention, the characteristics in the step A3 are used for extracting the stenosis resistance of the blood vessel model based on a multi-scale haemodynamics calculation preprocessing model. The specific implementation process comprises the following steps:
step C1: giving the same resting flow to determine a corresponding zero-dimensional afterload model, wherein the zero-dimensional afterload model consists of basic electronic component inductors and resistors, and the equivalent relation between the hemodynamic parameters and the physical electrical parameters is shown in table 1.
TABLE 1 equivalent relationship between hemodynamic parameters and physical electrical parameters
Assuming that the length of a section of blood vessel is l, the cross-sectional area of the blood vessel is a, and the zero-dimensional and three-dimensional corresponding parameter relationship of a section of blood vessel is shown in fig. 3, according to the elastic cavity theory established by Frank, the quantitative relationship among the flow, pressure and related resistance on the section of blood vessel can be expressed as follows:
Δp＝Q*R′#(1)
in the formula, Δ P represents the pressure drop generated in the section of blood vessel, Q represents the blood flow of the branch blood vessel, and R' represents the viscous resistance generated by the section of blood vessel; mu is a blood viscosity value under normal physiology, and the value is 3.5e-3 Pa.s; l represents the length of the section of the blood vessel, and A represents the cross-sectional area of the section of the blood vessel.
The self-inductivity of the inductor is equivalent to the flow inertia of blood flow, and the resistance effect of the resistor is equivalent to the microcirculation resistance of each branch of the coronary artery. The values of the resistance R and the inductance L in the zero-dimensional model are:
in the formula P Powder The pressure at the end of the vessel is expressed, and rho is the density value of blood flow and is defined as 1060kg/m 3 。
And step C2: introducing the gridding model into Ansys-CFX fluid calculation software, and giving fluid calculation parameters including blood flow density and blood flow dynamic viscosity values;
and C3: determining boundary conditions and calculating the pressure-flow-afterload relation of the three-dimensional model, giving mean arterial pressure at a fluid inlet, defining a virtual flow boundary at an outlet, and coupling the zero-dimensional afterload model.
And C4: in the numerical simulation calculation, the setting time is 1.2s, the setting time step length is 0.01s, and the simulation calculation process is executed.
And C5: after the calculation is finished, the blood vessel pressure is extracted at the position 30mm behind the stenosis, the pressure difference of the pretreatment model is obtained through comparison with the inlet pressure, and the stenosis resistance is obtained through dividing the pressure difference by the flow. And extracting blood flow at the stenotic inlet.
As a further technical scheme of the invention, the characteristics in the step A4 are based on data extraction and blood vessel model parameters to establish a stenosis resistance training set and a prediction set; 340 different stenosis parameter blood vessels are calculated through the steps A1, A2 and A3, a deeply learned data set is constructed by the extracted stenosis resistance and blood flow of the blood vessel model and the geometric parameters of the blood vessel model, and the data set is divided into a training set and a testing set.
As a further technical solution of the present invention, the characteristic described in step A5, establishing a neural network framework based on a BP neural network, specifically includes the following steps:
step D1: and D3: establishing a deep learning hidden layer and selecting a Dense layer of Keras, setting an input layer to be 7 variables and an output layer to be 1 narrow resistance, setting the hidden layer to be 7 layers, and using an activation function tanh function
Step D2: selecting an adam optimizer to adjust the hyper-parameters such as lr, beta _1, beta _2, epsilon and the like, and selecting an MSE (mean square error) as a Loss function:
as a further technical scheme of the invention, the characteristics in the step A5 are based on deep learning to train the narrow resistance training set and to predict and verify the prediction set. The specific implementation process comprises the following steps:
step D1: importing a training set and a testing set into a program through a pandas module;
step D2: the influence of the geometric parameters of the blood vessel on the stenosis resistance of the blood vessel model is analyzed through a correlation coefficient matrix, multiple collinearity among variables is analyzed and avoided, and finally seven variables with strong correlation are obtained, wherein the variables are respectively the entrance area, the stenosis entrance length, the stenosis outlet length, the minimum stenosis length, the stenosis rate and the entrance blood flow.
And D3: importing the processed data into a neural network framework to adjust parameters such as batch _ size, epochs, estimation _ split, estimation _ freq and the like.
Step D5: and saving the training model and executing the prediction of the stenosis resistance of the blood vessel model.
The invention aims to realize prediction of stenosis resistance of a stenosis blood vessel, numerical simulation calculation is carried out by using an ideal model when a data set is established to obtain the stenosis resistance, model reconstruction is carried out by using real case CTA data when the data set is actually applied to carry out extraction of blood vessel length, inlet area, stenosis inlet length, stenosis rate, minimum length of stenosis, stenosis outlet length and blood vessel flow parameters on the stenosis blood vessel, and then the stenosis resistance of the stenosis blood vessel is predicted by a neural network model to obtain a non-invasive FFR value. The method carries out MSE (mean square error) analysis and verification on the narrow resistance obtained by numerical simulation and the narrow resistance predicted by the neural network. The final MSE result is 3.06%, and the application standard is met below 5%.
Claims (6)
1. A system for predicting coronary artery stenosis resistance based on deep learning is characterized in that the system is used for predicting the coronary artery stenosis resistance by the following method, and comprises the following steps:
step A1: constructing a coronary artery model based on the real coronary artery anatomical structure parameters and recording the coronary artery model parameters, wherein the parameters comprise an entrance area, a narrow area and a narrow length;
step A2: performing model gridding pretreatment based on the coronary artery blood vessel model;
step A3: calculating a preprocessing model based on multi-scale hemodynamics and extracting the stenosis resistance of the coronary artery blood vessel model;
a4, establishing a stenosis resistance training set and a prediction set based on data extraction and coronary artery blood vessel model parameters; calculating 340 different stenosis parameter blood vessels through the steps A1, A2 and A3, constructing a deep learning data set by the extracted stenosis resistance and blood flow of the coronary artery blood vessel model and the geometric parameters of the coronary artery blood vessel model, and dividing the data set into a training set and a testing set;
step A5: establishing a neural network framework based on the BP neural network;
step A6: training a narrow resistance training set and performing prediction verification on a prediction set based on deep learning;
wherein the characteristics in the step A3 are used for extracting the stenosis resistance of the coronary artery blood vessel model based on a multi-scale haemodynamics calculation preprocessing model; the specific implementation process comprises the following steps:
step C1: giving the same resting flow and then determining a corresponding zero-dimensional after-load model, wherein the zero-dimensional after-load model consists of basic electronic component inductance and resistance;
assuming a segment of a blood vessel with a length l and a cross-sectional area a, the quantitative relationship between flow, pressure and associated resistance over the segment of the blood vessel can be expressed as:
Δp＝Q*R′#(1)
in the formula, Δ P represents the pressure drop generated in the section of blood vessel, Q represents the blood flow of the branch blood vessel, and R' represents the viscous resistance generated by the section of blood vessel; mu is the blood viscosity value under normal physiology, and the value is 3.5e-3 Pa.s; l represents the length of the section of the blood vessel, and A represents the cross-sectional area of the section of the blood vessel;
the self-inductivity of the inductor is equivalent to the flow inertia of blood flow, the resistance effect of the resistor is equivalent to the microcirculation resistance of each branch of the coronary artery, and the values of the resistor R and the inductor L in the zero-dimensional model are as follows:
in the formula P Powder The pressure at the end of the vessel is expressed, and rho is the density value of blood flow and is defined as 1060kg/m 3 ；
And step C2: introducing the gridding model into Ansys-CFX fluid calculation software, and giving fluid calculation parameters including blood flow density and blood flow dynamic viscosity value, namely the blood viscosity value under normal physiology;
step C3: determining boundary conditions and calculating the pressure-flow-afterload relation of the three-dimensional model, giving mean arterial pressure at the inlet of a blood vessel, defining a virtual flow boundary at the outlet of the blood vessel, and coupling a zero-dimensional afterload model;
and C4: in the numerical simulation calculation, setting time to be 1.2s and setting time step length to be 0.01s, and executing a simulation calculation process;
step C5: after calculation, extracting blood vessel pressure at a position 30mm behind the stenosis, obtaining the pressure difference of the pretreatment model through comparison with the blood vessel inlet pressure, and obtaining the stenosis resistance through dividing the pressure difference by the flow; and extracting blood flow at the stenotic inlet.
2. The system for predicting coronary artery stenosis resistance based on deep learning of claim 1, wherein the features of step A1 are obtained by first constructing a coronary artery model based on the real coronary artery anatomical parameters, constructing the coronary artery model by using a Sold Works software, and constructing the model to require the shape parameters to be in accordance with the parameters of the real blood vessel.
3. The system for predicting coronary artery stenosis resistance based on deep learning as claimed in claim 1, wherein the features in step A2 are pre-processed by model meshing based on a coronary artery blood vessel model, the input form of the mesh file is supported by the computing software, and thus the following pre-processing steps are performed before the model meshing:
step B1: deriving an x _ t format for constructing a coronary artery model by Sold Works software;
and step B2: and (3) carrying out pre-calculation meshing on the constructed coronary artery blood vessel model through an Ansys workbench 15.0 meshing sub-functional module, wherein the step requires sensitivity analysis on a mesh, and the influence of a mesh factor on a calculation result is eliminated.
4. The system for predicting coronary artery stenosis resistance based on deep learning as claimed in claim 1, wherein the characteristics in step A5, the neural network framework is established based on a BP neural network, and the implementation comprises the following steps:
step D1: establishing a deep learning hidden layer, and setting the number of hidden layers, the number of nodes of the hidden layer and an activation function;
step D2: selecting a proper optimizer and a Loss function;
the characteristics in the step A6 are used for training a narrow resistance training set and carrying out prediction verification on a prediction set based on deep learning; the specific implementation process comprises the following steps:
step E1: importing a training set and a testing set into a program through a pandas module;
step E2: analyzing the influence of the geometric parameters of the blood vessels on the stenosis resistance of the coronary artery blood vessel model through a correlation coefficient matrix, analyzing and avoiding multiple collinearity among variables, and finally obtaining seven variables with strong correlation, namely an inlet area, a stenosis inlet length, a stenosis outlet length, a minimum stenosis length, a stenosis rate and an inlet blood flow;
step E3: importing the processed data into a neural network framework to adjust the hyper-parameters;
and E4: the training model is saved and the prediction of the stenosis resistance of the coronary vessel model is performed.
5. The system for predicting coronary artery stenosis resistance based on deep learning as claimed in claim 4, wherein the neural network framework is established based on a BP neural network, and the implementation comprises the following steps:
step D1: establishing a deep learning hidden layer and selecting a Dense layer of Keras, setting an input layer to be 7 variables and an output layer to be 1 stenosis resistance, setting the hidden layer to be 7 layers, and using an activation function tanh function
Step D2: selecting an adam optimizer to adjust lr, beta _1, beta _2, epsilon hyper-parameters and a Loss function, wherein the MSE (mean square error) is selected:
6. the system for predicting coronary artery stenosis resistance based on deep learning of claim 4, wherein the features of step A6 are trained on a stenosis resistance training set and predictive verification is performed on a prediction set based on deep learning; the specific implementation process comprises the following steps:
step D1: importing a training set and a testing set into a program through a pandas module;
step D2: analyzing the influence of the geometric parameters of the blood vessel on the stenosis resistance of the coronary artery blood vessel model through a correlation coefficient matrix, analyzing and avoiding multiple collinearity among variables, and finally obtaining seven variables with strong correlation as 7 variables of an input layer, wherein the seven variables are respectively the entrance area, the stenosis entrance length, the stenosis exit length, the minimum stenosis length, the stenosis rate and the entrance blood flow;
and D3: importing the processed data into a neural network framework to adjust batch _ size, epochs, estimation _ split and estimation _ freq parameters;
step D5: the training model is saved and the prediction of the stenosis resistance of the coronary vessel model is performed.
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