CN114052764A - 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
Methods, apparatus, systems, and computer storage media for obtaining fractional flow reserve of a subject application include: acquiring image data and physiological parameters related to coronary arteries and hearts, and constructing a corresponding three-dimensional model by processing the image data; the image data includes an aorta image and a coronary artery image, the coronary artery image including a diastolic coronary image and a systolic coronary image; solving a hemodynamic control equation according to the three-dimensional model to obtain the hemodynamic parameter distribution of the coronary artery in the region expressed by the three-dimensional model; and calculating the fractional flow reserve of the blood vessel stenosis part by using the hemodynamic parameter distribution. According to the method for acquiring the fractional flow reserve, the diastolic image data and the systolic image data are processed, so that the three-dimensional model is more in line with the physiological structure, and the restoration degree of the three-dimensional model is improved; by solving the hemodynamic parameter distribution, the blood flow reserve fraction of the blood vessel stenosis part 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 was considered the "gold standard" for diagnosing coronary heart disease, but it only qualitatively assesses the extent of lesion stenosis, but does not quantitatively assess the effect of lesion stenosis on coronary physiological function, and thus may overestimate or underestimate the severity of the lesion, resulting in untreated or untreated lesions requiring treatment. NicoPijls et al, 1993, proposed a new indicator for the estimation of coronary Flow by blood pressure measurements-Fractional Flow Reserve (FFR). Through long-term basic and clinical research, FFR has become a recognized indicator for functional assessment of coronary artery stenosis.
Fractional Flow Reserve (FFR) is a parameter used in the medical diagnosis of coronary artery physiological function and refers to the maximum blood flow Q obtained from the myocardial region supplied by the vessel in the presence of a stenotic lesion in the coronary arterySMaximum blood flow Q that can be obtained theoretically normally in the same areaNThe ratio of the equivalent pressure is defined as the ratio of the pressure of the coronary artery at the proximal end of the stenosis and the heart aorta in the maximal hyperemia state, namely the fractional flow reserve.
FFR may be obtained by invasive tests, such as diagnostic cardiac catheterization, which may include performing Conventional Coronary Angiography (CCA) to visualize coronary lesions, while calculating the ratio of the pressure proximal to the coronary stenosis and the pressure of the heart aorta obtained by the pressure sensor under conditions induced by intravenous administration of adenosine (coronary arteries in a maximal hyperemic state) to obtain FFR. Invasive tests have the disadvantage of causing increased risk and more expense to the patient.
In the prior art, the method for non-invasively acquiring FFR reduces the risk and cost of diagnosis, and the method and system need to provide coronary artery-specific medical images of a patient and physiological information of the patient, wherein the images can be acquired by Computed Tomography Angiography (CTA), Rotational Angiography (RA), magnetic resonance imaging angiography (MRA), Digital Subtraction Angiography (DSA) and the like. In addition, the method and system can acquire FFR under the condition that direct measurement cannot be carried out (such as movement and physical allergy discomfort), and predict the result of medical treatment, interventional therapy and surgical treatment of coronary blood flow and cardiac perfusion.
In the technical scheme, the specific medical image of the patient is obtained through medical radiography, the three-dimensional model is constructed for the specific medical image, and the blood flow reserve fraction is calculated through hemodynamics simulation after the physiological information of the patient is combined. However, in the prior art, based on the above technical scheme, the three-dimensional model reduction degree is not accurate enough, and the acquisition of the fractional flow reserve is not accurate enough.
Disclosure of Invention
In order to solve the problem of inaccurate Fractional Flow reserve calculation in the prior art, the invention provides a method for obtaining Fractional Flow Reserve (FFR) by using hemodynamic modeling after obtaining a patient-specific coronary artery medical image and patient physiological information through medical radiography.
The application discloses a method for obtaining fractional flow reserve, which comprises the following steps:
step S1, acquiring image data and physiological parameters related to coronary artery and heart, and constructing a corresponding three-dimensional model by processing the image data;
the image data includes an aorta image and a coronary artery image, the coronary artery image including a diastolic coronary image and a systolic coronary image;
step S2, solving a hemodynamic control equation according to the three-dimensional model to obtain the hemodynamic parameter distribution of the coronary artery in the region expressed by the three-dimensional model;
and step S3, calculating the fractional flow reserve of the blood vessel stenosis part by using the hemodynamic parameter distribution.
Optionally, in step S1, the processing of the image data at least includes performing image segmentation and image addition on the diastolic coronary image, the systolic coronary image, and the aorta image.
Optionally, 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 coronary image in a systolic state and a right coronary image in a systolic state;
the processing of the image data at least comprises image segmentation and image addition of the left coronary image in the diastolic state, the right coronary image in the systolic state and the aorta image.
Optionally, in step S2, step S2 is performed to solve a hemodynamic control equation according to the three-dimensional model to obtain a hemodynamic parameter distribution of a coronary artery in a region expressed by the three-dimensional model, specifically including;
simulating a boundary control equation by using physiological parameters;
calculating a hemodynamic parameter distribution of a boundary portion of the three-dimensional model using a boundary control equation.
Optionally, the method for obtaining fractional flow reserve, which calculates the hemodynamic parameter distribution of the boundary portion of the three-dimensional model by using a boundary control equation, specifically includes:
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 the processes until the processes are converged finally;
and solving the converged coupled operation to obtain the hemodynamic parameter distribution of the boundary part of the three-dimensional model.
Optionally, the method for obtaining fractional flow reserve, the physiological parameter includes individual characteristics and/or statistical data, and the physiological parameter is solved for the hemodynamic parameter by a fluid control equation.
Optionally, the method for obtaining fractional flow reserve, wherein the three-dimensional model comprises a three-dimensional model of a lesion and a three-dimensional model of a normal lesion;
in step S1, a three-dimensional model of a lesion is constructed by processing the image data;
step S1 further comprises the steps of constructing a normal three-dimensional model without lesion according to the three-dimensional model with lesion;
in step S2, solving a hemodynamic control equation according to the three-dimensional model of the lesion and the normal three-dimensional model without the lesion, respectively, to obtain hemodynamic parameter distribution of the coronary artery in the region expressed by the corresponding three-dimensional model;
in step S3, the fractional flow reserve at the narrowed region of the blood vessel 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 having stored thereon a computer program which, when executed by a computer processor, implements the method of obtaining fractional flow reserve described herein.
The present application further provides an apparatus for fractional flow reserve acquisition, comprising a computer memory, a computer processor, and a computer program stored in the computer memory and executable on the computer processor, which when executed by the computer processor, implements the method for fractional flow reserve acquisition described herein.
The present application also provides a system for obtaining fractional flow reserve, comprising a terminal and a 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 computer processor, when executing the computer program, implements the method for obtaining fractional flow reserve described herein.
The method for acquiring the fractional flow reserve has at least one of the following effects:
according to the method for acquiring the fractional flow reserve, the diastolic image data and the systolic image data are processed, and the three-dimensional model is enabled to better accord with a physiological structure through image processing in different periods (diastole and systole), so that the degree of restoration of the three-dimensional model is improved; the hemodynamic parameter distribution is solved through the three-dimensional model with high reduction degree, and then the blood flow reserve fraction of the blood vessel stenosis part is calculated, so that the accuracy is improved.
Drawings
Fig. 1 is a schematic flow chart of a method for obtaining fractional flow reserve according to an embodiment of the present application;
fig. 2 is a schematic diagram illustrating an image data acquisition result in step S1 shown in fig. 1;
FIG. 3 is a schematic diagram of the construction of the three-dimensional model in step S1 shown in FIG. 1;
FIG. 4 is a schematic diagram illustrating the FFR cloud distribution calculation result in step S3 shown in FIG. 1;
FIG. 5 is a schematic diagram of the local calculation result of the FFR cloud distribution in the stenosis region in step S3 shown in FIG. 1;
fig. 6 is a block diagram illustrating a structure of a fractional flow reserve acquiring device according to an embodiment of the present application.
Detailed Description
In the prior art, a specific medical image of a patient is obtained through medical radiography, a three-dimensional model is constructed for the specific medical image, and the fractional flow reserve is calculated through hemodynamic simulation after physiological information of the patient is combined. But the three-dimensional model reduction degree is not accurate enough, and the acquisition of the blood flow reserve fraction is not accurate enough.
To solve the above technical problem, referring to fig. 1, an embodiment of the present application provides a method for obtaining fractional flow reserve, including:
step S1, acquiring image data and physiological parameters related to coronary artery and heart, and constructing a corresponding three-dimensional model by processing the image data;
the image data includes an aorta image and a coronary artery image, the coronary artery image includes a diastolic coronary image and a systolic coronary image;
step S2, solving a hemodynamic control equation according to the three-dimensional model to obtain the hemodynamic parameter distribution of the coronary artery in the region expressed by the three-dimensional model;
in step S3, the hemodynamic parameter distribution is used to calculate the fractional flow reserve at the stenotic site of the blood vessel.
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In the practical situation, the left coronary artery in diastole is in a vasodilation state, and the right coronary artery blood vessel is extruded by the myocardium at the moment, so that the imaging quality is poor due to the blood vessel pulsation; the opposite right coronary vessel is in the systolic phase, is in the optimal diastolic state and has better imaging quality. In the prior art, image data in a diastole period is purified, so that the reconstruction quality of coronary artery blood vessels is limited due to the problem of image quality; on the other hand, the coronary artery in a certain phase is reconstructed in real time, and the blood vessel cannot be ensured to be in a diastolic state, so that the reduced coronary artery blood vessel model is not accurate enough; the three-dimensional model is used as an intermediate parameter of the acquisition process, and the accuracy of acquiring the fractional flow reserve is influenced.
In step S1, the image data includes diastolic and systolic image data of the subject, and each of the diastolic and systolic image data includes a coronary artery and a heart. In the case where no specific description is given, the aorta image described below is a generic term of the aorta image at different times.
In step S1, the processing of the image data includes at least image segmentation and image addition for the diastolic coronary image, the systolic coronary image, and the aortic image. Specifically, the diastolic coronary image includes a left crown image in a diastolic state and a right crown image in a diastolic state; the systolic coronary image comprises a left coronary image in a systolic state and a right coronary image in a systolic state; the processing of the image data includes at least image segmentation and image addition of the left coronary image in the diastolic state, the right coronary image in the systolic state, and the aorta image.
In step S1, the step of constructing the corresponding three-dimensional model includes, after acquiring the image data, reconstructing a geometric model of at least a portion of the patient' S heart from the image data, and creating 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 comprises at least a portion of the ascending cardiac artery and coronary vessels having a diameter greater than 1 mm.
The method comprises the following specific steps: digital information of the coronary arteries of the patient is acquired and a coronary angiogram and reconstruction is performed, such as the coronary angiogram in fig. 2 where the image data is obtained in CTA mode.
When reconstructing image data, structural and morphological parameters related to coronary arteries need to be calculated, such as: ascending aorta, descending aorta; left Coronary Artery (LCA): left trunk, left anterior descending branch (left anterior ventricular branch, right anterior ventricular branch, anterior septal branch), left circumflex branch (sinus node branch, left atrial branch, left anterior ventricular branch, blunt limbic branch, left posterior ventricular branch); right Coronary Artery (RCA): right trunk, right branch of the cone, sinus node, right anterior ventricular branch, acute limbus branch, posterior descending branch, left posterior ventricular branch, and right atrial branch.
The arterioles and capillaries of the coronary arteries are not included in the reconstructed model because of the limitation of the degree of resolution of the image data itself. Also based on this, the subsequent steps of the present invention are to simulate the tiny artery and capillary vessels of the coronary artery by using the lumped model, and obtain the hemodynamic parameters, such as blood flow or blood pressure, at the boundary part of the blood vessel in the reconstructed model, so as to improve the accuracy of the FFR calculation.
Fig. 2 is merely an illustration of the acquisition of image data, and in an implementation, image data for diastole and systole are acquired separately. The specific processing mode of the image data comprises the following steps:
and reconstructing an aorta model through an image segmentation algorithm.
The left coronary ostia-L1, Aorta-L2 and right coronary ostia-R1 and right coronary ostia-L2 of the diastolic Aorta and the systolic Aorta, respectively, are found. Selecting Aorta in diastole or systole as an Aorta model, respectively identifying a left coronary artery blood vessel center line in a diastole image and a right coronary artery blood vessel center line in a systole image, segmenting blood vessels by an image segmentation algorithm, forcing a left coronary segmentation image to correspond to two coronary ostium positions of the Aorta model based on Aorta-L1 and a right coronary segmentation image based on Aorta-R2, and forming a new image data result by an image addition or other image combination algorithm according to the left coronary image segmentation result, the right coronary image segmentation result and the Aorta segmentation result so as to construct a corresponding three-dimensional model. And finally, reconstructing the model through a three-dimensional reconstruction algorithm, namely obtaining a reconstruction result shown in figure 3. The reconstruction algorithm is preferably a Marching cubes algorithm; the image segmentation algorithm can be Level Sets, Graph Cuts, CNN neural network models, deep learning models, edge learning models and the like.
In step S2, the hemodynamic parameter distribution at the boundary portion of the three-dimensional model is obtained by solving the physiological parameters through 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 the processes until the processes are converged finally;
and solving after the coupling operation is converged to obtain the hemodynamic parameter distribution of the boundary part of the three-dimensional model.
It is understood that the distribution of hemodynamic parameters in non-boundary portions is prior art and will not be described in detail herein.
In step S2, the fluid control equation is:
wherein:
v is blood flow velocity;
ρ is the blood density;
t is time;
p is blood pressure;
μ 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:
Δ P is the pressure drop across a segment of a blood vessel, (e.g., an upstream or downstream blood vessel);
q is blood flow at the blood vessel boundary under the maximum hyperemia state;
r is the total resistance represented by the vessel boundary;
d is the diameter of the blood vessel;
do is the vessel diameter of the vessel at a reference;
po is the blood pressure value at the aortic inlet;
qo is the blood flow at the aortic inlet in the maximal hyperemic state;
a is a coefficient of 2 to 3.
In step S2, the method further includes solving hemodynamic parameters through a fluid control equation using the physiological parameters in step S1, the physiological parameters including individual characteristics and/or statistical data.
The 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 curves, heart rate, aortic blood pressure oscillograms, aortic and primary coronary flow and oscillograms as measured by doppler ultrasound, medical history, etc. The statistical data includes medical statistical big data, a heart lumped model and a coronary artery lumped model. The understanding of the statistical data in particular is subject to the application mode of the statistical data in each embodiment.
The FFR calculation formula/equation is:
The position of the stenosis in the blood vessel can be known from the image data and the physiological parameters in step S1, and can be obtained by measuring the three-dimensional model (e.g., reconstructed model) constructed in step S1;
it will be appreciated that this resulting FFR equation is a computational equation introduced to solve the FFR equation accurately.
In step S2, the method further includes calculating total coronary flow, resting aortic flow, and moving aortic flow by using a flow calculation equation.
The flow calculation equation is:
mmyo＝ρmyo×VLV
QCOrest＝Qcoronary/β
QCOhyperemia＝QCOrest×χ
VLVcalculating by left ventricle reconstruction for left ventricle volume;
ρmyois the myocardial density;
mmyois the myocardial mass and is obtained by calculation;
Qcoronarycoronary artery blood flow obtained by medical empirical formula;
QCOrestthe aortic blood flow in a resting state;
alpha, beta and chi are parameter coefficients, all belong to medical statistics big data, and are correspondingly adjusted according to individual characteristics of patients. In exercise state, the cardiac cycle is shortened, the heart rate is accelerated, and the blood flow Q of the aorta is obtained due to the exercise intensity and the specificity of individual characteristics of patientsCOhyperemiaIncreasing to several times at rest.
In step S2, the method further includes calculating the reference mean pressure of the aorta movement by using the pressure calculation equation. The pressure calculation equation is:
Pdias-hyperemia＝Pdias-rest×(1+γdias)
Psys-hyperemia＝Psys-rest×(1+γsys)
in motion state, the cardiac cycle is shortened, the heart rate is accelerated, and the change rate gamma of the systolic pressuresysDiastolic pressure change rate γdiasAortic systolic pressure Psys-hyperemiaAnd diastolic pressure Pdias-hyperemiaThe diastolic phase SdHeart contraction period ratio StTo obtain the reference average pressure of the aorta movement
γsys，γdiasCoefficient of these parameters, and parameter Sd，StAll the data are medical statistic big data, and are adjusted correspondingly according to individual characteristics of patients.
Step S2, calculating aorta entrance and exit resistance and coronary artery entrance and exit resistance through a resistance calculation equation; solving according to Poiseuille Law, a boundary control equation and a lumped model coupling.
The total resistance value of the blood vessel branch is calculated by the formula
And calculating to obtain, wherein:
Rirepresenting the total resistance of the ith blood vessel branch;
Qirepresenting the flow in the ith branch of the vessel in the maximal hyperemic state.
In step S2, the resistance value model is used as a boundary condition of the boundary control equation to be coupled with the 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 artery, including flow, flow rate, and pressure.
Step S2, an accurate solution of the target FFR equation is obtained by performing coupled solution on the fluid control equation and the boundary control equation, including steady state and transient state. The boundary control equation needs to be simulated by using a lumped parameter model, and the lumped parameter model is not limited to a resistance value model, an RCR model and the like. And mutually exchanging and iterating parameters solved by the fluid control equation and parameters output by the boundary control equation until final convergence to obtain final fluid parameters, pressure, flow and flow rate.
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 process of step S2, and each calculation equation processes each parameter generated in the process through a corresponding working unit (setter, coupler), including:
the boundary condition is set by a boundary condition setter, and the physiological parameter is set by a characteristic parameter setter. The method comprises the following steps: biomedical test results (cardiac cycle, blood pressure, blood flow, hemoglobin, platelets, electrocardiogram, genes, family history, etc.), image data/segmentation data/reconstruction geometry data (heart size, coronary branches and topology, stenosis location, stenosis length, stenosis cross-section, calcified plaque, etc.).
The boundary condition setter and the characteristic parameter setter provide sufficient and necessary parameters for the CFD lumped model coupler and the FFR processor to calculate. 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 mixing boundaries, such as cardiac lumped model boundaries, and Murray's Law and Windkessel mixing boundaries.
Summarizing step S2: in step S2, the physiological and clinical examination data of the patient, the medical statistical data, and the heart and coronary artery lumped model are integrated, and through coupling iterative operation, the optimal solution is obtained, and the boundary conditions required for accurate CFD simulation calculation are optimized. And deducing parameters required for calculating the FFR according to the obtained individual patient specific data and the medical statistic big data. On the premise that the three-dimensional model with high reduction degree is ensured in step S1, the FFR required parameter (hemodynamic parameter distribution) obtained based on the three-dimensional model is acquired more accurately.
The conventional calculation of fractional flow reserve is equivalent by the ratio of the pressure in the coronary artery at the maximum hyperemia, proximal to the stenosis and in the aorta of the heart. As known in the art, 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 in the presence of a stenotic lesion to the maximum blood flow that can be obtained theoretically normally in the same region. However, in the prior art, both the conventional pressure guide wire and the FFR calculated from the CT image are used to perform the equivalent fractional flow reserve by using the ratio of the pressure of the proximal end of the stenosis to the pressure of the cardiac aorta, which is based on a series of assumptions, and this equivalent method may affect the accuracy of the final fractional flow reserve due to the complexity of the physiological structure of the human body.
According to the method, the three-dimensional model of the lesion in the same region and the normal three-dimensional model without the lesion are simulated and reconstructed, the theoretical numerical value of the fractional flow reserve is directly obtained, equivalent substitution in the prior art is replaced, and interference to equivalent uncertain influence factors in the prior art is avoided. Namely, the blood flow reserve fraction is evaluated by obtaining the blood flow in the blood vessel through a coronary artery reconstruction process and simulation.
The three-dimensional model comprises a three-dimensional model of a lesion and a three-dimensional model of a normal lesion-free model;
step S3, calculating the fractional flow reserve of the blood vessel stenosis part by using the distribution of the hemodynamic parameters; the method specifically comprises the following steps:
constructing a normal three-dimensional model without lesion according to the three-dimensional model of the lesion; for example, the morphology of healthy blood vessels without lesions can be estimated based on the centerline of the coronary artery and the area of the blood vessel in which the section of the centerline is located. The specific mode can be that the blood vessel area or the blood vessel diameter of a section where all the central points on one branch are located is counted, as the branch diameter of the coronary blood vessel is gradually reduced from the mouth to the tail end of the coronary artery, a curve representing the descending trend of the blood vessel area/diameter is fitted, and 70% -80% of the points are under the fitted straight line, the fitted curve can be considered as the reference blood vessel of the branch blood vessel, namely the blood vessel without pathological changes. The diseased vessel can also be repaired by connecting the upstream of the diseased vessel and the downstream of the diseased vessel through the difference distribution of the maximum inscribed sphere in a mode of positioning the diseased vessel section.
Repeating the operation of step S2 according to the normal three-dimensional model without pathological changes to obtain the corresponding hemodynamic parameter distribution of the normal three-dimensional model without pathological changes. The boundary parameters used for the calculations are consistent with the three-dimensional model of the lesion, and may therefore be considered fixed because the patient's resistance to microcirculation does not change as the stenosis of the vessel changes.
The flow for calculating the fractional flow reserve of the blood vessel stenosis part according to the three-dimensional model of the lesion and the normal three-dimensional model without the lesion comprises the following steps: firstly, calculating the blood flow Qs of a vascular stenosis part in a three-dimensional model of a lesion; then, the angiostenosis part in the three-dimensional model of the lesion is reduced to a normal three-dimensional model without lesion, and the step S2 is repeated on the normal three-dimensional model without lesion to calculate the flow Q flowing through the same position under the conditionN。
In general, in step S3, the fractional flow reserve at the stenosis site of the vessel is calculated using the hemodynamic parameter distribution (derived from the three-dimensional model of the lesion). And (4) establishing and solving three-dimensional CFD simulation through the related working units to obtain the FFR. According to the above steps, the flow rate in the stenosis and the flow rate in the normal blood vessel are calculated, and finally FFR is equal to Qs/QN(all formulae Qs, Q in this textNUsing standard format, S, N is subscript), and FFR is calculated using the formula described in step S2. The obtained FFR cloud map distribution calculated based on the present application is shown in fig. 4, and the local map of the FFR cloud map distribution in the narrow region is shown in fig. 5.
In particular toFirstly, extracting a three-dimensional model central line of a pathological change, and mapping the calculated blood flow parameters to points of the central line to obtain Qs; on the other hand, extracting the normal three-dimensional model central line without pathological changes, and mapping the calculated blood flow parameters to the points of the central line, namely QNThe two center lines are the same in length, and points on the center lines are dispersed at the same interval, so that the points on the two center lines are in one-to-one correspondence. Finally according to
And calculating the FFR value of each point, storing the FFR value on the point of the central line of the three-dimensional model of the lesion, and finally mapping the parameters of each point on the central line to all grid units of the three-dimensional model through an algorithm to display the FFR cloud picture.
The CFD calculation method used in the present embodiment includes, but is not limited to, a Finite Element Method (FEM), a Finite Volume Method (FVM), a Finite Difference Method (FDM), a Boundary Element Method (BEM), an Immersion Boundary Method (IBM), a Lattice Boltzmann Method (LBM), a smooth particle method (SPH), a semi-implicit moving particle Method (MPS), a finite volume particle method (FVP), and the like. The CFD calculation method is used for calculating a three-dimensional flow kinetic equation of blood in the coronary artery vessel and outputting calculation results such as flow rate, flow, pressure and the like.
In the embodiment, a non-invasive method is adopted, so that the diagnosis risk and cost are reduced, the FFR is quickly and conveniently acquired, and the results of the medical treatment, interventional treatment and surgical treatment of coronary artery blood flow and myocardial perfusion can be predicted. The method integrates the advantages of CFD and patient-specific boundary condition simulation, can accurately calculate the FFR of normal and narrow coronary artery regions through CFD, and can also cover non-CFD simulation geometric regions such as capillary vessels and capillary vessels by utilizing a lumped model to provide accurately calculated boundary conditions for the CFD model.
The embodiments of the present application also include at least one computer system 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 acquiring image data and physiological parameters related to coronary arteries and heart from the terminal; the computer processor, when executing the computer program, implements the above-described method of obtaining fractional flow reserve.
In each embodiment, the terminal is an automatic system developed autonomously, and each subsystem can be implemented by one or more of five programming languages, such as C, C + +, Java, Python, and HTM 5.
The terminal may also be software or APP, including but not limited to Mac OS version, Windows version, Unix/Linux version, android version, apple (ios) version. The terminal software allows the client user to upload the specific medical image of the heart and coronary artery of the patient and the physiological information of the patient, and can download the corresponding FFR (coronary artery function) calculation cloud picture and report, and the online consultation and consultation comprises but is not limited to report, image and video. It can be understood that the terminals can realize data interaction and calculation through the cloud server.
The patient coronary artery specific medical image uploaded to the terminal by the client should contain all the information of the cardiac vessels: images of the heart aorta and the coronary artery, and coronary artery medical images in the diastole and the systole are required to be acquired simultaneously when the system carries out coronary artery reconstruction and fractional flow reserve calculation.
There is also provided in an embodiment of the application an 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, when executing the computer program, implementing the method for obtaining fractional flow reserve as described above.
The computer memory is used for storing image data, intermediate data generated in the process by the FFR acquisition method and a calculation result of the 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 operative to reference a corresponding computer processor required for the flow of steps to perform the steps 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 specific medical image of the coronary artery of the heart of the patient; by the method for acquiring fractional flow reserve described in each embodiment, the FFR is acquired by establishing and solving three-dimensional CFD simulation, and an FFR report is output.
In one embodiment, a computer device is provided, and the computer device may be the terminal described above, and its internal structure diagram 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 comprises a nonvolatile 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 an operating system and computer programs in the non-volatile storage medium. 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, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
The device and/or system for obtaining fractional flow reserve provided by the embodiments of the present application; the apparatus and/or system is used to acquire image data and physiological parameters related to coronary arteries and the heart; for example, coronary artery angiography images in diastole and systole are introduced into the terminal; after going through the method of obtaining fractional flow reserve as described above, an FFR report is generated and output.
In summary, the method for obtaining fractional flow reserve described in the embodiments of the present application obtains a patient-specific coronary medical image and patient physiological information through medical angiography; three-dimensional modeling based on coronary artery medical images and a patient-specific boundary condition optimization model; and calculating the fractional flow reserve by utilizing a blood flow mode through a blood flow dynamic simulation by utilizing a blood vessel blood flow dynamic control equation simulation algorithm.
In the embodiments of the application, the reduction degree of the three-dimensional model is improved through image processing of the diastole image and the systole image; the accuracy of the distribution of the hemodynamic parameters is improved through coupling operation of each equation; the FFR value is directly calculated in a defined mode (instead of being calculated in a traditional pressure equivalent mode) through restoring a three-dimensional model of the lesion, so that the accuracy of the FFR is improved.
An embodiment of the application further provides a computer readable storage medium, in which a computer program is stored, which when executed by a computer processor implements the method for obtaining fractional flow reserve as described above.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile 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 DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features. When technical features in different embodiments are represented in the same drawing, it can be seen that the drawing also discloses a combination of the embodiments concerned.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of obtaining fractional flow reserve, comprising:
step S1, acquiring image data and physiological parameters related to coronary artery and heart, and constructing a corresponding three-dimensional model by processing the image data;
the image data includes an aorta image and a coronary artery image, the coronary artery image including a diastolic coronary image and a systolic coronary image;
step S2, solving a hemodynamic control equation according to the three-dimensional model to obtain the hemodynamic parameter distribution of the coronary artery in the region expressed by the three-dimensional model;
and step S3, calculating the fractional flow reserve of the blood vessel stenosis part by using the hemodynamic parameter distribution.
2. The method of claim 1, wherein the processing of the image data in step S1 at least comprises image segmentation and image addition of the diastolic coronary image, the systolic coronary image and the aorta image.
3. The method for 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 coronary image in a systolic state and a right coronary image in a systolic state;
the processing of the image data at least comprises image segmentation and image addition of the left coronary image in the diastolic state, the right coronary image in the systolic state and the aorta image.
4. The method of obtaining fractional flow reserve according to claim 1,
step S2, solving a hemodynamic control equation according to the three-dimensional model to obtain the hemodynamic parameter distribution of coronary artery in the region expressed by the three-dimensional model, which specifically comprises the following steps;
simulating a boundary control equation by using physiological parameters;
calculating a hemodynamic parameter distribution of a boundary portion of the three-dimensional model using a boundary control equation.
5. The method for obtaining fractional flow reserve according to claim 4, wherein the calculating the hemodynamic parameter distribution of the boundary portion of the three-dimensional model using a boundary control equation 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 the processes until the processes are converged finally;
and solving after the coupling operation is converged to obtain the hemodynamic parameter distribution of the boundary part of the three-dimensional model.
6. The method of obtaining fractional flow reserve according to claim 5, wherein the physiological parameters comprise individual features and/or statistical data, and the physiological parameters are solved for the hemodynamic parameters by a fluid control equation.
7. The method of obtaining fractional flow reserve of claim 1, wherein the three-dimensional models comprise a three-dimensional model of a lesion and a three-dimensional model of a normal lesion-free model;
in step S1, a three-dimensional model of a lesion is constructed by processing the image data;
step S1 further comprises the steps of constructing a normal three-dimensional model without lesion according to the three-dimensional model with lesion;
in step S2, solving a hemodynamic control equation according to the three-dimensional model of the lesion and the normal three-dimensional model without the lesion, respectively, to obtain hemodynamic parameter distribution of the coronary artery in the region expressed by the corresponding three-dimensional model;
in step S3, the fractional flow reserve at the narrowed region of the blood vessel is calculated based on the hemodynamic parameters obtained from the two three-dimensional models, respectively.
8. Computer readable storage medium, in which a computer program is stored which, when being executed by a computer processor, carries out the method of obtaining fractional flow reserve according to any one of claims 1 to 7.
9. Apparatus for fractional flow reserve acquisition, 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 a method for fractional flow reserve acquisition according to any of claims 1 to 7.
10. A system for obtaining fractional flow reserve, comprising a terminal and a server comprising a computer memory, a computer processor, and a computer program stored in and executable on the computer memory, wherein the server obtains image data and physiological parameters relating to coronary arteries and the heart from the terminal; the computer processor, when executing the computer program, implements the method of obtaining fractional flow reserve of any of claims 1-7.
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