CN114052764B - Method, apparatus, system and computer storage medium for obtaining fractional flow reserve - Google Patents
Method, apparatus, system and computer storage medium for obtaining fractional flow reserve Download PDFInfo
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Abstract
The application provides a method, a device, a system and a computer storage medium for obtaining fractional flow reserve, comprising the following steps: acquiring image data and physiological parameters related to coronary arteries and hearts, and constructing a corresponding three-dimensional model through processing the image data; the image data includes an aortic image and a coronary image, the coronary image including a diastolic coronary image and a systolic coronary image; solving a hemodynamic control equation according to the three-dimensional model to obtain hemodynamic parameter distribution of a coronary artery in an area expressed by the three-dimensional model; the hemodynamic parameter distribution is used to calculate fractional flow reserve at the site of the vascular stenosis. The method for obtaining the fractional flow reserve provided by the application has the advantages that the three-dimensional model is more in accordance with a physiological structure by processing the diastolic image data and the systolic image data, and the reduction degree of the three-dimensional model is improved; by solving the hemodynamic parameter distribution, the fractional flow reserve of the narrow part of the blood vessel is calculated, and the accuracy is improved.
Description
Technical Field
The present application relates to data processing of medical images, and more particularly, to a method, apparatus, system and computer storage medium for obtaining fractional flow reserve based on medical image data.
Background
Coronary angiography has been considered as a "gold standard" for diagnosing coronary heart disease, but it only qualitatively evaluates the extent of stenosis, but does not quantitatively evaluate the effect of stenosis on the physiological function of the coronary artery, and thus may overestimate or underestimate the severity of the lesion, resulting in untreated or oversreated lesions requiring treatment. A new index for estimating coronary blood flow by blood pressure measurement, fractional flow reserve (Fractional Flow Reserve, FFR), was proposed by NicoPijls et al in 1993. FFR has become a recognized index for functional assessment of coronary artery stenosis through long-term basic and clinical studies.
The Fractional Flow Reserve (FFR) is a parameter used for medical diagnosis of the physiological function of the coronary artery, and refers to the ratio of the maximum blood flow Q S obtained in the myocardial area supplied by the coronary artery to the maximum blood flow Q N obtained in the same area theoretically and normally, wherein the equivalent pressure ratio is defined as the ratio of the pressure of the proximal end of the coronary artery to the pressure of the heart aorta in the maximum hyperemia state, namely the fractional flow reserve.
FFR may be obtained by invasive testing, such as diagnostic cardiac catheterization, which may include performing a Conventional Coronary Angiography (CCA) to visualize coronary lesions while calculating the ratio of proximal coronary stenosis pressure and aortic heart pressure obtained by a pressure sensor under conditions induced by intravenous administration of adenosine (coronary artery in a maximally hyperemic state) to obtain FFR. Invasive tests have the disadvantage of causing increased risk and increased costs to the patient.
Methods and systems for non-invasively acquiring FFR to reduce diagnostic risk and cost require the provision of patient coronary specific medical images and patient physiological information, which can be acquired by Computed Tomography (CTA), rotational imaging (RA), magnetic resonance imaging (MRA), digital subtraction imaging (DSA), and the like. In addition, the method and system can also obtain FFR under conditions that cannot be directly measured (e.g., exercise, physical allergy discomfort), and predict the outcome of medical, interventional and surgical treatments of coronary blood flow and cardiac perfusion.
In the technical scheme, the specific medical image of the patient is acquired through medical radiography, a three-dimensional model is constructed on the specific medical image, and the fractional flow reserve is calculated through hemodynamic simulation after the physiological information of the patient is combined. However, in the prior art, based on the technical scheme, the three-dimensional model restoration degree is not accurate enough, and the blood flow reserve score is not accurately acquired.
Disclosure of Invention
In order to solve the problem that the calculation of fractional flow reserve is not accurate enough in the prior art, the invention provides a method for obtaining fractional flow reserve (Fractional Flow Reverse, FFR) by utilizing hemodynamic modeling after obtaining a patient-specific coronary artery medical image and patient physiological information through medical radiography.
The method for obtaining fractional flow reserve of the application comprises the following steps:
Step S1, acquiring image data and physiological parameters related to coronary arteries and hearts, and constructing a corresponding three-dimensional model through processing the image data;
The image data includes an aortic image and a coronary image, the coronary image including a diastolic coronary image and a systolic coronary image;
s2, solving a hemodynamic control equation according to the three-dimensional model to obtain hemodynamic parameter distribution of a coronary artery in an area expressed by the three-dimensional model;
And step S3, calculating the fractional flow reserve of the vascular stenosis part by using the hemodynamic parameter distribution.
Optionally, in the method for obtaining fractional flow reserve, in step S1, the processing of the image data at least includes image segmentation and image addition of the diastolic coronary image, the systolic coronary image, and the aortic image.
Optionally, in the method for obtaining fractional flow reserve, in step S1:
The diastolic coronary image comprises a left coronary image in a diastolic state and a right coronary image in a diastolic state;
The systolic coronary image comprises a left crown image in a contracted state and a right crown image in a contracted state;
the processing of the image data at least comprises image segmentation and image addition of the left crown image in the diastole state, the right crown image in the systole state and the aortic image.
Optionally, in the method for obtaining fractional flow reserve, in step S2, step S2 solves a hemodynamic control equation according to the three-dimensional model to obtain hemodynamic parameter distribution of a coronary artery in an area expressed by the three-dimensional model, including;
simulating a boundary control equation by using physiological parameters;
The hemodynamic parameter distribution of the boundary portion of the three-dimensional model is calculated using a boundary control equation.
Optionally, the method for obtaining fractional flow reserve calculates hemodynamic parameter distribution of boundary portions of the three-dimensional model by using a boundary control equation, specifically including:
coupling operation is carried out by utilizing a boundary control equation and a fluid control equation;
the coupling operation comprises mutual interactive iteration of all equations until final convergence;
And obtaining hemodynamic parameter distribution of the boundary part of the three-dimensional model after the coupling operation solution is converged.
Optionally, the method for obtaining fractional flow reserve, the physiological parameter comprising individual characteristics and/or statistical data, the physiological parameter solving the hemodynamic parameter by a fluid control equation.
Optionally, the method for obtaining fractional flow reserve includes a three-dimensional model of a lesion and a normal three-dimensional model without a lesion;
in step S1, a three-dimensional model of a lesion is constructed by processing image data;
Step S1, constructing a normal three-dimensional model without lesions according to the three-dimensional model without lesions;
In the step S2, a hemodynamic control equation is solved according to the pathological change three-dimensional model and the normal pathological change-free three-dimensional model respectively so as to obtain hemodynamic parameter distribution of a coronary artery in an area expressed by the corresponding three-dimensional model;
in step S3, the fractional flow reserve at the stenotic vascular site is calculated based on the hemodynamic parameters obtained from the two three-dimensional models, respectively.
The present application also provides a computer readable storage medium storing a computer program which when executed by a computer processor implements the method of obtaining fractional flow reserve of the present application.
The application also provides a device for obtaining fractional flow reserve, which comprises a computer memory, a computer processor and a computer program stored in the computer memory and executable on the computer processor, wherein the method for obtaining fractional flow reserve is realized when the computer processor executes the computer program.
The present application also provides a system for obtaining fractional flow reserve comprising a terminal and a server, the server comprising a computer memory, a computer processor, and a computer program stored in the computer memory and executable on the computer processor, the server obtaining image data and physiological parameters related to coronary arteries and heart from the terminal; the method for obtaining fractional flow reserve according to the application is implemented when the computer processor executes the computer program.
The method for obtaining fractional flow reserve of the application has at least one of the following effects:
According to the method for obtaining the fractional flow reserve, the diastolic image data and the systolic image data are processed, and the three-dimensional model is enabled to be more in accordance with a physiological structure through image processing in different periods (diastole and systole), so that the reduction degree of the three-dimensional model is improved; the hemodynamic parameter distribution is solved through a three-dimensional model with high reduction degree, so that the fractional flow reserve of the narrow part of the blood vessel is calculated, and the accuracy is improved.
Drawings
FIG. 1 is a flow chart of a method for obtaining fractional flow reserve according to an embodiment of the application;
FIG. 2 is a schematic diagram of the image data acquisition result in the step S1 shown in FIG. 1;
FIG. 3 is a schematic diagram of constructing a three-dimensional model in the step S1 shown in FIG. 1;
FIG. 4 is a schematic diagram of the FFR cloud pattern distribution calculation result in step S3 shown in FIG. 1;
FIG. 5 is a schematic diagram of a local calculation result of FFR cloud patterns distributed in a narrow area in step S3 shown in FIG. 1;
fig. 6 is a block diagram of a fractional flow reserve acquisition device according to an embodiment of the application.
Detailed Description
In the prior art, a patient-specific medical image is acquired through medical radiography, a three-dimensional model is constructed on the specific medical image, and the fractional flow reserve is calculated through hemodynamic simulation after the physiological information of the patient is combined. However, the three-dimensional model is not accurate enough in restoration degree, and the fractional flow reserve is not accurately acquired.
In order to solve the above-mentioned problems, referring to fig. 1, in an embodiment of the present application, a method for obtaining fractional flow reserve is provided, which includes:
Step S1, acquiring image data and physiological parameters related to coronary arteries and hearts, and constructing a corresponding three-dimensional model through processing the image data;
the image data includes an aortic image and a coronary image, the coronary image including a diastolic coronary image and a systolic coronary image;
S2, solving a hemodynamic control equation according to the three-dimensional model to obtain hemodynamic parameter distribution of a coronary artery in an area expressed by the three-dimensional model;
And step S3, calculating the fractional flow reserve of the vascular stenosis part by using the hemodynamic parameter distribution.
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In reality, the left coronary artery is in a vasodilation state in diastole, and the right coronary artery blood vessel is extruded by cardiac muscle, so that the imaging quality is generally poor due to the blood vessel pulsation; in contrast, the right coronary vessel is in optimal diastole and imaging quality is also better during systole. In the prior art, the image data in the diastole is purified only, so that the reconstruction quality of coronary vessels is limited due to the image quality problem; on the other hand, the coronary artery in a certain phase is reconstructed immediately, and the blood vessel is not guaranteed to be in a diastole state, so that the restored coronary artery blood vessel model is not accurate enough; the three-dimensional model is used as an intermediate parameter of the acquisition process, and influences the accuracy of acquiring the fractional flow reserve.
In step S1, the image data includes image data of the subject in diastole and systole, and the image data of the subject in diastole and systole includes coronary artery and heart. Unless explicitly stated otherwise, the aortic images described below are collectively referred to as aortic images at different times.
In step S1, the processing of the image data at least includes image segmentation and image addition of the diastolic coronary image, the systolic coronary image, and the aortic image. Specifically, the diastolic coronary image includes a left crown image of the diastolic state and a right crown image of the diastolic state; the systolic coronary image comprises a left coronary image in a contracted state and a right coronary image in a contracted state; the processing of the image data at least comprises image segmentation and image addition of the left crown image in the diastolic state, the right crown image in the systolic state and the aortic image.
In step S1, the specific step of constructing the corresponding three-dimensional model is to reconstruct a geometric model of at least a part of the heart of the patient according to the image data after the image data is acquired, and create a three-dimensional model with a coronary artery mesh structure based on the geometric model. To make the image data clearer, the three-dimensional model preferably includes at least a portion of the ascending aorta of the heart and coronary vessels having a diameter greater than 1 mm.
The method comprises the following specific steps: the coronary artery digital information of the patient is acquired and subjected to coronary artery radiography and reconstruction, such as the coronary artery radiography in which the image data is acquired in a CTA mode in fig. 2.
When reconstructing image data, structural and morphological parameters related to coronary arteries need to be calculated, such as: ascending aorta and descending aorta; left Coronary Artery (LCA): left trunk, left anterior descending branch (left anterior ventricular branch, right anterior ventricular branch, anterior septal branch), left gyratory branch (sinus node branch, left Fang Zhi, left anterior ventricular branch, blunt edge branch, left posterior ventricular branch); right Coronary Artery (RCA): right trunk, right conical branch, sinus node branch, right anterior branch, sharp edge branch, posterior descending branch, left posterior branch, right atrial branch.
The micro-arterial blood vessel and the capillary blood vessel of the coronary artery are not in the reconstruction model because of the limitation of the definition of the image data. Also based on this, the subsequent steps of the present invention use a lumped model to simulate the tiny arterial and capillary vessels of the coronary arteries to obtain hemodynamic parameters, such as blood flow or blood pressure, at the boundary of the vessels in the reconstructed model to improve the accuracy of FFR calculation.
Fig. 2 is merely illustrative of the acquisition of image data, and in the implementation, image data of diastole and systole are acquired respectively. The specific mode for processing the image data comprises the following steps:
and reconstructing an aorta model through an image segmentation algorithm.
Finding left and right coronary artery ports Aorta-L1, aorta-L2, and right coronary artery ports Aorta-R1 and Aorta-L2 of the diastolic and systolic Aorta, respectively. And (3) selecting an Aorta in diastole or systole as an aortic model, respectively identifying a left coronary vessel center line in the diastole image and a right coronary vessel center line in the systole image, segmenting vessels through an image segmentation algorithm, finally forcing a left coronary segmentation image to correspond to two coronary artery positions of the aortic model based on Aorta-L1 and a right coronary segmentation image based on Aorta-R2, and forming a new image data result from a left coronary image segmentation result, a right coronary image segmentation result and an aortic segmentation result through an image addition or other image merging algorithm to construct a corresponding three-dimensional model. And finally, reconstructing the model through a three-dimensional reconstruction algorithm to obtain a reconstruction result shown in fig. 3. The reconstruction algorithm is preferably a Maring cube algorithm; the image segmentation algorithm can be LEVEL SETS, graph Cuts, CNN neural network model, deep learning model, edge learning model and the like.
In step S2, the hemodynamic parameter distribution of the boundary portion of the three-dimensional model is obtained by using physiological parameters and solving by a coupling algorithm, and specifically includes:
simulating a boundary control equation by using physiological parameters; the physiological parameter may be, for example, a lumped model;
coupling operation is carried out by utilizing a boundary control equation and a fluid control equation;
the coupling operation comprises mutual interactive iteration of all equations until final convergence;
And after the coupling operation is converged, solving to obtain the hemodynamic parameter distribution of the boundary part of the three-dimensional model.
It will be appreciated that the distribution of hemodynamic parameters in the non-boundary portion is prior art and will not be described in detail herein.
In step S2, the fluid control equation is:
Wherein:
v is the blood flow velocity;
ρ is the blood density;
t is time;
P is blood pressure;
mu is the blood viscosity;
F is a volumetric force, such as gravity.
The resistance boundary control equation corresponding to the fluid control equation is:
ΔP＝RQ
Q＝Qo(D/Do)^a
R＝Po/(Qo(D/Do)^a)
Wherein:
Δp is the pressure drop of a segment of blood vessel, (e.g., an upstream blood vessel or a downstream blood vessel);
Q is the blood flow at the boundary of the blood vessel in the maximum hyperemia state;
R is the total resistance represented by the boundary of the blood vessel;
d is the diameter of the blood vessel;
do is the vessel diameter of the vessel at a reference site;
po is the blood pressure value of the aortic inlet;
qo is the blood flow at the aortic inlet in the maximum hyperemic state;
a is a coefficient of 2 to 3.
In step S2, the method further comprises solving hemodynamic parameters through a fluid control equation using the physiological parameters of step S1, the physiological parameters comprising individual characteristics and/or statistical data.
Individual characteristics include physiological and clinical data of the subject including, but not limited to, height, weight, blood density, blood viscosity, hematocrit, platelets, brachial artery systolic and diastolic pressure curves, heart rate, aortic blood pressure waveforms, aortic and main coronary flow as measured by doppler ultrasound, and waveforms, medical history, etc. The statistical data includes medical statistical big data, cardiac lumped model and coronary lumped model. In particular, the statistical data is understood in the application manner of the statistical data in each embodiment.
The FFR calculation formula/equation is:
for relevant parameters in the calculation formula, see the explanation of the embodiments of the present application.
The blood vessel stenosis position can be obtained by the image data and physiological parameters of the step S1, and can be obtained by measuring a three-dimensional model (such as a reconstruction model) constructed in the step S1;
It will be appreciated that this resulting FFR equation is a computational equation introduced to solve for the exact solution of the FFR equation.
In step S2, the total coronary flow, the resting aortic flow and the movement aortic flow are calculated by a flow calculation equation.
The flow calculation equation is:
mmyo＝ρmyo×VLV
QCOrest＝Qcoronary/β
QCOhyperemia＝QCOrest×χ
V LV is left ventricular volume calculated by left ventricular reconstruction;
ρ myo is the myocardial density;
m myo is the myocardial mass, calculated;
the blood flow consumption rate of unit mass of cardiac muscle is the medical statistics big data;
Q coronary is the coronary blood flow obtained by a medical empirical formula;
q COrest is aortic blood flow in resting state;
Alpha, beta and χ are all parameter coefficients, and belong to medical statistics big data, and are correspondingly adjusted according to individual characteristics of patients. In the exercise state, the cardiac cycle is shortened and the heart rate is accelerated, and the aortic blood flow Q COhyperemia is increased to several times that at rest due to the exercise intensity and the specificity of individual characteristics of the patient.
In step S2, the method further comprises calculating an aortic movement reference average pressure according to a pressure calculation equation. The pressure calculation equation is:
Pdias-hyperemia＝Pdias-rest×(1+γdias)
Psys-hyperemia＝Psys-rest×(1+γsys)
in the motion state, the cardiac cycle is shortened, the heart rate is accelerated, the systolic pressure change rate gamma sys, the diastolic pressure change rate gamma dias, the aortic motion systolic pressure P sys-hyperemia and the diastolic pressure P dias-hyperemia, the diastolic phase duty ratio S d and the systolic phase duty ratio S t are adopted to obtain the aortic motion reference average pressure Gamma sys,γdias, these parameter coefficients, and the parameter S d,St are all medical statistics big data, and are adjusted accordingly according to the individual characteristics of the patient.
In the step S2, the resistance calculation method further comprises the step of calculating the resistance of the inlet and outlet of the aorta and the resistance of the inlet and outlet of the coronary artery through a resistance calculation equation; and solving according to Poiseuille Law, a boundary control equation and lumped model coupling.
The total resistance of the blood vessel branches is calculated by the formulaCalculation, wherein:
r i represents the total resistance of the ith branch vessel;
represents the average value of the coronary pressure in the hyperemic state;
Q i represents the flow of the ith vessel in the maximum hyperemic state.
In step S2, the resistance model is used as a boundary condition of a boundary control equation to couple with a fluid control equation, and the hemodynamic parameters of the boundary portion of the three-dimensional model are obtained by solving.
Hemodynamic parameters include fluid parameters within the coronary arteries, including flow, flow rate, and pressure.
And S2, obtaining an accurate solution of the target FFR equation by carrying out coupling solution on the fluid control equation and the boundary control equation, including steady state and transient state. The boundary control equation is simulated by adopting a lumped parameter model, and the lumped parameter model is not limited to a resistance model, an RCR model and the like. And the parameters solved by the fluid control equation and the parameters output by the boundary control equation are mutually exchanged and iterated until final fluid parameters, pressure, flow and flow velocity are obtained by final convergence.
In the specific implementation process, each calculation equation (including a boundary control equation, a fluid control equation, a flow calculation equation, a pressure calculation equation, and a resistance calculation equation) is added to the flow of step S2, and each calculation equation processes each parameter generated in the flow through a corresponding working unit (setter, coupler), including:
The boundary conditions are set by the boundary condition setter, and the physiological parameters are set by the characteristic parameter setter. Comprising the following steps: results of physiological medical tests (cardiac cycle, blood pressure, blood flow, hemoglobin, platelets, electrocardiogram, genes, family history, etc.), image data/segmentation data/reconstruction geometry data (heart size, coronary artery branches and topology, stenosis location, stenosis length, stenosis cross-section, calcified plaque, etc.).
The boundary condition setter and feature parameter setter provide the CFD lumped model coupler and FFR processor with sufficient and necessary parameters for computation. Boundary conditions include, but are not limited to, traffic boundaries, such as Murray's Law traffic boundaries; pressure boundaries, such as Windkessel pressure correction boundaries; flow and pressure blend boundaries, such as cardiac lumped model boundaries, and Murray's Law and Windkessel blend boundaries.
Summarizing step S2: in step S2, the physiological and clinical detection data, the medical statistics big data and the heart and coronary artery lumped model of the patient are synthesized, and the optimal solution is obtained through coupling iterative operation, so that the boundary conditions required by accurate CFD simulation calculation are optimized. From the obtained patient individual specific data and medical statistics big data, the parameters needed for calculating FFR are derived. On the premise that the three-dimensional model with high reduction degree is ensured in the step S1, parameters (hemodynamic parameter distribution) required by FFR obtained based on the three-dimensional model are more accurately obtained.
The fractional flow reserve is conventionally calculated by the ratio of the pressures of the proximal stenosis and the heart aorta in the maximum hyperemia state of the coronary arteries. It is known in the art that Fractional Flow Reserve (FFR) refers to the ratio of the maximum blood flow that can be obtained in the region of the myocardium supplied by a coronary artery to the maximum blood flow that can be obtained in the theoretically normal case of the same region. However, in the prior art, the FFR calculated from the CT image or the conventional pressure guide wire is used to perform the equivalent fractional flow reserve by using the ratio of the pressure of the proximal end of the stenosis and the pressure of the heart aorta, which is based on a series of assumptions, and the accuracy of the final fractional flow reserve may be affected by the complexity of the physiological structure of the human body.
According to the application, the theoretical value of the fractional flow reserve is directly obtained by simulating and reconstructing the three-dimensional model of the lesion and the normal three-dimensional model without the lesion in the same area, so that the equivalent substitution in the prior art is replaced, and the interference to the equivalent uncertain influence factors in the prior art is avoided. I.e. fractional flow reserve is estimated by coronary reconstruction procedures and by obtaining intravascular flow through simulation.
The three-dimensional model comprises a pathological three-dimensional model and a normal pathological three-dimensional model without pathological changes;
step S3, calculating fractional flow reserve of the vascular stenosis part by using hemodynamic parameter distribution; the method specifically comprises the following steps:
Constructing a normal lesion-free three-dimensional model according to the lesion three-dimensional model; for example, the morphology of a healthy blood vessel when no lesion is generated can be estimated based on the center line of the coronary artery and the area of the blood vessel where the tangential plane of the center line is located. The specific mode can count the area or the diameter of the blood vessel of the section where all the central points on one branch are located, and as the branch diameter of the coronary blood vessel gradually decreases from the coronary artery port to the tail end, a curve is fitted to represent the descending trend of the area/diameter of the blood vessel, and 70% -80% of points are below a fitting straight line, the fitting curve can be considered as a reference blood vessel of the branch blood vessel, namely the blood vessel when no lesion occurs. The lesion vessel can also be repaired by connecting the upstream of the lesion vessel with the downstream of the lesion vessel by the difference distribution of the maximum inscribed sphere by means of locating the lesion vessel segment.
Repeating the operation of the step S2 according to the normal non-pathological three-dimensional model to obtain the corresponding hemodynamic parameter distribution of the normal non-pathological three-dimensional model. The boundary parameters used for the calculation are consistent with the lesion three-dimensional model, since the patient's microcirculation resistance does not change with the change of the vessel stenosis and thus can be considered to be fixed.
The blood flow reserve score calculation flow for calculating the blood vessel stenosis part according to the lesion three-dimensional model and the normal lesion-free three-dimensional model comprises the following steps: firstly, calculating blood flow Qs of a blood vessel stenosis part in a pathological change three-dimensional model; and then reducing the vascular stenosis part in the lesion three-dimensional model into a normal lesion-free three-dimensional model, and repeating the step S2 on the normal lesion-free three-dimensional model to calculate the flow Q N flowing through the same position under the condition.
In general, in step S3, the fractional flow reserve at the site of the vascular stenosis is calculated using the hemodynamic parameter distribution (derived from the three-dimensional model of the lesion). And establishing and solving a three-dimensional CFD simulation through the related working units to obtain FFR. According to the above steps, the flow under the stenosis condition and the flow in the normal blood vessel are calculated, and finally FFR is calculated by ffr=qs/Q N (all formulas Qs, Q N are in standard format, S, N are subscripts), and the formula described in step S2. The obtained FFR cloud image distribution calculated based on the application is shown in fig. 4, and the partial image of the FFR cloud image distribution in a narrow area is shown in fig. 5.
Specifically, the method comprises the steps of firstly extracting a center line of a three-dimensional model of a lesion, and mapping calculated blood flow parameters to points of the center line, namely Qs; on the other hand, extracting the center line of the normal lesion-free three-dimensional model, mapping the calculated blood flow parameters to the points of the center line, namely Q N, wherein the lengths of the two center lines are the same, and dispersing the points on the center lines according to the same interval, so that the points on the two center lines are in one-to-one correspondence. Finally according toAnd calculating the FFR value of each point, storing the FFR value on the point of the central line of the lesion three-dimensional model, and finally mapping the parameters of each point on the central line to all grid cells of the three-dimensional model through an algorithm to display an FFR cloud picture.
CFD calculation methods used in the present embodiment include, but are not limited to, finite Element Method (FEM), finite Volume Method (FVM), finite Difference Method (FDM), boundary Element Method (BEM), immersion Boundary Method (IBM), lattice Boltzmann Method (LBM), smooth particle method (SPH), semi-implicit moving particle Method (MPS), finite volume particle method (FVP), and the like. The CFD calculation method is used for calculating a three-dimensional flow dynamics equation of blood in coronary artery blood vessels, and outputting calculation results of flow velocity, flow, pressure and the like.
In this embodiment, a non-invasive method is adopted, which reduces the risk and cost of diagnosis, rapidly and conveniently acquires FFR, and can predict the results of medical treatment, interventional treatment and surgical treatment of coronary blood flow and myocardial perfusion. The method combines the advantages of CFD and simulation based on patient-specific boundary conditions, can accurately calculate FFR of normal and narrow regions of coronary arteries through CFD, and can also cover non-CFD simulation geometric regions such as micro blood vessels and capillaries by utilizing a lumped model to provide accurate calculated boundary conditions for CFD models.
The embodiment of the application also at least comprises a set of computer system which comprises a terminal and a server, wherein the server comprises a computer memory, a computer processor and a computer program which is stored in the computer memory and can be executed on the computer processor, and the server acquires image data and physiological parameters related to coronary arteries and hearts from the terminal; the above-described method of obtaining fractional flow reserve is implemented when the computer program is executed by a computer processor.
In various embodiments, the terminal is an autonomous development automation system, and each subsystem can be completed by one or more of five programming languages of C, C ++, java, python, HTM5 and the like.
The terminal may also be software or APP, including but not limited to Mac OS version, windows version, unix/Linux version, androids version, apple (ios) version. The terminal software can be used for a client user to upload the heart and coronary artery specific medical images of the patient and the physiological information of the patient, and can download corresponding coronary artery FFR calculation cloud images and reports, and on-line consulting and consulting including but not limited to reports, images and videos. The cloud server can realize data interaction and calculation by the aid of the terminals.
The patient coronary artery specific medical image uploaded to the terminal by the client should contain all the information of the heart vessels: the imaging of the heart aorta and coronary arteries, the system performs coronary reconstruction and fractional flow reserve calculation requiring simultaneous acquisition of Shu Zhanqi and systolic coronary medical images.
An embodiment of the application further provides an apparatus for obtaining fractional flow reserve, including a computer memory, a computer processor, and a computer program stored in the computer memory and executable on the computer processor, the computer processor implementing a method for obtaining fractional flow reserve as described above when executing the computer program.
The computer memory is used for storing image data, intermediate data generated by the FFR obtaining method in the process and calculation results of fractional flow reserve; the computer processor comprises the boundary condition setter, the characteristic parameter setter, the CFD lumped model coupler and the FFR processor; the computer program is used to refer to the corresponding computer processor required by each step flow, and execute each step of the method for obtaining fractional flow reserve.
In this embodiment, the server is a cloud server, and the computer memory, the computer processor, and the computer program stored in the computer memory and executable on the computer processor all belong to a cloud software system; the cloud system receives and stores the heart coronary artery specific medical image of the patient; by the method for obtaining fractional flow reserve according to each embodiment, the FFR is obtained by establishing and solving three-dimensional CFD simulation, and the FFR report is output.
In one embodiment, a computer device is provided, which may be the terminal described above, and the internal structure thereof may be as shown in fig. 6. The computer device includes a processor, a computer memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of obtaining fractional flow reserve. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
For the devices and/or systems for obtaining fractional flow reserve provided by embodiments of the present application; the device and/or system is used for acquiring image data and physiological parameters related to coronary arteries and hearts; for example, leading coronary angiography images of diastole and systole into a terminal; after undergoing the method of obtaining fractional flow reserve as described above, FFR reports are generated and output.
Summarizing, the method for obtaining fractional flow reserve described in the embodiments of the present application obtains patient-specific coronary medical images and patient physiological information by medical angiography; based on three-dimensional modeling of coronary artery medical images and boundary condition optimization model specific to patients; the blood flow reserve fraction is calculated by means of blood flow through hemodynamic simulation by using a vascular hemodynamic control equation simulation algorithm.
In the embodiments of the application, the reduction degree of the three-dimensional model is improved through the image processing of the diastolic image and the systolic image; the accuracy of hemodynamic parameter distribution is improved through the coupling operation of each equation; the FFR value is directly obtained in a definition mode (instead of being calculated in a traditional pressure equivalent mode) through recovering the three-dimensional model of the lesion, so that the accuracy of FFR is improved.
There is also provided in an embodiment of the application a computer readable storage medium storing a computer program which when executed by a computer processor implements a method of obtaining fractional flow reserve as described above.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description. When technical features of different embodiments are embodied in the same drawing, the drawing can be regarded as a combination of the embodiments concerned also being disclosed at the same time.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (9)
1. A method of obtaining fractional flow reserve comprising:
Step S1, obtaining image data and physiological parameters related to coronary arteries and hearts, constructing a lesion three-dimensional model through processing the image data, and constructing a normal lesion-free three-dimensional model according to the lesion three-dimensional model, wherein the method for constructing the normal lesion-free three-dimensional model comprises the following steps: constructing according to the estimation of the blood vessel area where the central line and the central line tangent plane of the coronary artery are; or connecting the upstream of the lesion blood vessel and the downstream of the lesion blood vessel, and repairing the lesion blood vessel through the difference distribution of the maximum inscribed sphere;
The image data includes an aortic image and a coronary image, the coronary image including a diastolic coronary image and a systolic coronary image;
Step S2, respectively solving a hemodynamic control equation according to the pathological change three-dimensional model and the normal pathological change-free three-dimensional model to obtain hemodynamic parameter distribution of a coronary artery in an area expressed by the corresponding three-dimensional model;
And S3, calculating the fractional flow reserve of the stenosis part of the blood vessel according to the hemodynamic parameters obtained by the two three-dimensional models.
2. The method of claim 1, wherein in step S1, the processing of the image data includes at least image segmentation and image addition of the diastolic coronary image, the systolic coronary image, and the aortic image.
3. The method of obtaining fractional flow reserve according to claim 1, wherein in step S1:
The diastolic coronary image comprises a left coronary image in a diastolic state and a right coronary image in a diastolic state;
The systolic coronary image comprises a left crown image in a contracted state and a right crown image in a contracted state;
the processing of the image data at least comprises image segmentation and image addition of the left crown image in the diastole state, the right crown image in the systole state and the aortic image.
4. The method of obtaining fractional flow reserve according to claim 1,
Step S2, respectively solving a hemodynamic control equation according to a pathological change three-dimensional model and a normal pathological change-free three-dimensional model according to the three-dimensional model so as to obtain hemodynamic parameter distribution of coronary arteries in an area expressed by the corresponding three-dimensional model, wherein the method specifically comprises the following steps of;
simulating a boundary control equation by using physiological parameters;
The hemodynamic parameter distribution of the boundary portion of the three-dimensional model is calculated using a boundary control equation.
5. The method of obtaining fractional flow reserve according to claim 4, wherein calculating the hemodynamic parameter distribution of the boundary portion of the three-dimensional model using a boundary control equation, in particular comprises:
coupling operation is carried out by utilizing a boundary control equation and a fluid control equation;
the coupling operation comprises mutual interactive iteration of all equations until final convergence;
And after the coupling operation is converged, solving to obtain the hemodynamic parameter distribution of the boundary part of the three-dimensional model.
6. The method of deriving fractional flow reserve according to claim 5, wherein the physiological parameter comprises individual characteristics and/or statistical data, the physiological parameter solving the hemodynamic parameter by means of a fluid control equation.
7. A computer readable storage medium storing a computer program, wherein the computer program when executed by a computer processor implements the method of obtaining fractional flow reserve according to any one of claims 1 to 6.
8. Apparatus for obtaining fractional flow reserve comprising a computer memory, a computer processor, and a computer program stored in the computer memory and executable on the computer processor, wherein the computer processor, when executing the computer program, implements the method for obtaining fractional flow reserve as claimed in any one of claims 1 to 6.
9. A system for obtaining fractional flow reserve comprising a terminal and a server, the server comprising a computer memory, a computer processor, and a computer program stored in the computer memory and executable on the computer processor, characterized in that the server obtains image data and physiological parameters related to coronary arteries and heart from the terminal; the computer processor, when executing the computer program, implements a method of obtaining fractional flow reserve as claimed in any one of claims 1 to 6.
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