CN107978371B - Method and system for rapidly calculating micro-circulation resistance - Google Patents
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Abstract
The invention provides a method and a system for rapidly calculating microcirculation resistance, which are characterized in that image data of an interested blood vessel lumen are obtained from traditional coronary angiography data, a geometric model of a blood vessel section is established, geometric parameters are obtained, the acquisition of the maximum blood flow speed and the coronary blood flow volume of the blood vessel section when the myocardial microcirculation is fully expanded can be completed without fully expanding the myocardial microcirculation through conventional angiography data, and the coronary microcirculation resistance is quantitatively calculated according to a simplified mode by solving the pressure difference corresponding to the maximum blood flow speed, the proximal end pressure of coronary artery and the distal end pressure. The scheme provided by the invention does not need guide wire intervention, improves the accuracy, reduces the cost, obviously shortens the calculation time and makes the whole operation process simpler.
Description
Technical Field
The invention relates to the field of microcirculation calculation, in particular to a method and a system for quickly calculating microcirculation resistance based on image data.
Background
Microcirculation is the circulation of blood between the oligodynamic and the venules, where blood exchanges material with tissue cells. Coronary microcirculation refers to the microcirculation system consisting of arterioles, capillaries and venules. Although the coronary artery microvasculature cannot be directly visualized by imaging, in the prior art, the coronary artery microcirculation function can be reflected by specific parameters. These techniques include invasive coronary hemodynamic parameter assessment via catheter examination such as coronary flow reserve, microcirculation resistance index, noninvasive imaging such as transthoracic doppler echocardiography, magnetic resonance, nuclear imaging, and the like.
The methods used in the prior art are as follows:
transthoracic Doppler Echocardiography (TTDE) allows measurement of blood flow velocity in the epicardial coronary artery, and thus allows non-invasive assessment of coronary flow velocity stores, i.e., the doppler measured ratio of peak diastolic flow velocity at maximum hyperemia to peak diastolic flow velocity at rest. Several studies have shown that the coronary blood flow rate measured by thoracic Doppler echocardiography is well consistent with the coronary blood flow rate measured by intracoronary Doppler (Caiati C, Montaldo C, Zedda N, et al. variation of a new nonlinear method (coherent-enhanced transcritical second harmonic Doppler) for the evaluation of coronary flow reserve: Complex with interferometric and intracoronary Doppler flow [ J ]. Jam colloid Heart card, 1999,31(4): 1193-.
Positron Emission Tomography (PET) can draw a time-activity curve of the tracer in the left ventricle and the myocardium by continuously monitoring the radioactivity of the venous tracer in circulation and the myocardium, obtain the kinetic information of the tracer taken by the myocardium, and finally calculate the myocardial blood flow and perfusion. Positron emission tomography is used to assess coronary microcirculation with the advantage that reliable myocardial blood flow data are available, both in the resting state and in the maximal hyperemic state. (Kaufmann PA, Camci PG. Myocardial Blood Flow Measurement by PET: Technical Aspects and Clinical Applications [ J ]. J Nucl Med,2005,46(1): 75-88).
The microcirculation status of Cardiac Magnetic Resonance (CMR) evaluation is reflected in microcirculation obstruction, which appears as a low enhancement region on the high signal background in the infarct zone. Studies have shown that microcirculation obstruction as evidenced by cardiac magnetic resonance is an independent risk factor for a long-term poor prognosis (Larose E, Rodes-Cabau J, Pibarot P, et al. predicting late myocardial recovery and outer clocks in the early clocks of ST-elevation myocardial information: translational media is used to compare to a microvasculature perfusion, salved myocardial recovery, and neosis by myocardial magnetic recovery [ J ]. Jam L cardio, 2010,55(22): 9-2469.DOI:10.1016/J. jacc. 2010.2454.033).
Coronary Flow Reserve (CFR) is defined as the ratio of the maximum blood flow to the resting blood flow in the coronary arteries, and the measurement method comprises: (1) evaluating coronary blood flow reserve by using a Doppler guide wire in a coronary artery; (2) the intracoronary heat dilution curve evaluates coronary flow reserve and is considered to be abnormal in coronary microcirculation if CFR <2.0 (MaGinn AL, Wilson RF. Interstuck variability of coronary flow resistance of heart rate, coronary pressure, and venous pressure [ J ] Circulation,1990,81(4): 1319-.
In 2003, Fearon et al (Fearon WF, Balsam LB, Farouque HM, et al. novel index for evaluating the cardiovascular microcirculation "J. Circulation, 2003, 107 (25): 3129-3132.) proposed a relatively novel and simple indicator for quantitatively evaluating microcirculation function, namely microcirculation resistance Index (IMR), which requires the use of a temperature/pressure guidewire to obtain a coronary arterial thermodilution curve and a coronary arterial pressure at maximum hyperemia, which proved to have a good correlation with actual microvascular resistance in animal models.
The above prior art, although presenting a method for determining microcirculatory dysfunction from different perspectives, different computational methods, has at least one or more of the following technical drawbacks:
(1) transthoracic doppler echocardiography measurement accuracy is related to different operator levels and it is difficult to distinguish between epicardial vascular stenosis and the effects of microcirculatory disturbance on myocardial blood flow;
(2) the positron tomography examination is expensive and long in operation time, and the spatial resolution of the positron tomography examination is still lower than that of an ideal state, so that the myocardial blood flow abnormality in a micro area is difficult to evaluate by applying the technology;
(3) motion artifacts easily occur in the cardiac magnetic resonance loading imaging process, and a large dose of contrast agent needs to be applied; meanwhile, the time resolution of the scanning device is still lower than that of the ideal state, and the scanning operation time is longer;
(4) coronary artery blood flow reserve has great variation with different ages, sexes and weights and is influenced by various conditions such as heart rate, blood pressure, myocardial metabolism, collateral circulation and the like;
(5) measuring the microcirculation resistance index is an invasive examination technique and requires that the maximum hyperemia state is reached, otherwise maximum reduction of the microvascular resistance cannot be achieved, resulting in an overestimation of the microcirculation resistance index value, and the position of the pressure guide wire placed in the blood vessel may also affect the measured microcirculation resistance index value.
Disclosure of Invention
The invention provides a method and a system for rapidly calculating microcirculation resistance, which are characterized by acquiring image data of an interested blood vessel lumen from traditional coronary angiography data, establishing a geometric model of the blood vessel section and acquiring geometric parameters, calculating and acquiring the maximum blood flow velocity and coronary blood flow of the blood vessel section under the maximum hyperemia state, namely the full expansion of the myocardial microcirculation, through resting state conventional angiography data which does not need the full expansion of the myocardial microcirculation, solving the numerical values of pressure difference corresponding to the maximum blood flow velocity, the proximal end point pressure and the distal end point pressure of the coronary artery, and finally quantitatively calculating the coronary microcirculation resistance according to the numerical values.
Specifically, the invention provides the following technical scheme:
in one aspect, the present invention provides a method for rapidly calculating a resistance to microcirculation based on contrast data, the method comprising:
step 1, receiving image data of a blood vessel of interest, and determining a starting point and an end point of a blood vessel section to be analyzed; wherein the image data may alternatively be conventional image data in which a conventional X-ray contrast imaging procedure is performed, without special contrast image data for a special apparatus, and more preferably, the contrast image data may be contrast image data acquired with the aid of a contrast medium injection.
Step 2, establishing a geometric model of the blood vessel section to be analyzed, wherein the geometric model comprises an actual blood vessel lumen geometric model and an ideal blood vessel lumen geometric model;
step 3, acquiring the geometric parameters of the vessel section to be analyzed by using the geometric model in the step 2;
step 4, obtaining the average blood flow velocity V of the blood vessel section in a resting state;
step 5, obtaining the maximum blood flow velocity V by using the average blood flow velocity V obtained in the step 4maxAnd coronary blood flow Qmax；
Step 6, based on the maximum blood flow velocity VmaxCalculating the pressure difference Δ Pmax(ii) a A value P based on the geometrical parameter and the resting state proximal blood flow pressurea (resting state)Calculating the proximal coronary pressure Pa(ii) a And based on said Δ PmaxAnd PaCalculating a remote end point pressure value Pd；
Step 7, according to the formula
And calculating the coronary microcirculation resistance MR.
More preferably, the coronary microcirculation resistance MR can be obtained by the following simplification:
simplified microcirculation resistance calculation formula based on coronary artery microcirculation resistance model
In the above simplification, P is assumedv0, wherein: paProximal pressure of coronary stenosis, PdDistal pressure to coronary stenosis, i.e. pressure before microcirculation; pvTo obtain coronary venous pressure after microcirculation, QmaxIs the maximum coronary blood flow, Δ PmaxThe pressure, Δ P, at which the blood flow decreases from the proximal to the distal end of the coronary artery at maximum hyperemiamrIs the pressure drop in the blood flow as it passes through the microcirculation. Thus, we can perform the resistance calculation in the simplified manner described above.
Preferably, the step 2 further comprises:
step 201, obtaining a geometric difference function based on the actual vessel lumen geometric model and the ideal vessel lumen geometric model;
step 202, based on the geometric difference function andmaximum blood flow velocity VmaxCalculating said pressure difference Δ Pmax。
More preferably, the geometric difference function and the maximum blood flow velocity V when the myocardial microcirculation is fully expanded are usedmaxAnd the square of the maximum blood flow velocity, Vmax 2Calculating to obtain the pressure difference delta P between the near-end pressure and the far-end pressure when the microcirculation is fully expandedmax。
Preferably, the geometric parameters in step 3 include: the area or diameter of the proximal cross-section of the vessel segment, the area or diameter of the distal cross-section of the vessel segment, the cross-sectional area or diameter of the lesion site between the proximal and distal endpoints of the vessel segment, lesion length, stenosis rate, etc. It should be understood by those skilled in the art that the geometric parameters mentioned above are only exemplary, and the geometric parameters can be adjusted according to specific calculation requirements, and the conventional adjustment mentioned above should be considered as falling within the protection scope of the present invention.
Preferably, the average blood flow velocity V in step 4 is obtained by:
obtaining the average flowing speed of the contrast agent of the blood vessel section in the coronary angiography process by utilizing a gray scale time fitting function, or calculating the average flowing speed of the contrast agent of the blood vessel section in the coronary angiography process by utilizing a TIMI frame method;
the average flow velocity is the average blood flow velocity V.
Preferably, the step 5 further comprises:
step 501, obtaining the maximum blood flow velocity V by using the average blood flow velocity V obtained in the step 4 and a table look-up methodmax；
Step 502, utilizing the cross section area S of the vessel section of the vessel geometric parameter obtained in step 3 and the maximum blood flow velocity V obtained in step 501maxCalculating coronary blood flow Qmax＝Vmax*S。
Preferably, in the step 6, the value P based on the geometric parameter and the resting state near-end blood flow pressurea (resting state)Calculating the proximal coronary pressure PaThe method specifically comprises the following steps: computing the P by a deep learning methodaWith said geometric parameters and Pa (resting state)As network input, with said PaIs an output;
in learning training, based on Pa＝α*Pa (resting state)And taking alpha as a regular term to participate in the weight updating of the network.
Preferably, in the step 6, the P is based ond＝Pa-ΔPmaxCalculating the value P of the remote end point pressured。
Specifically, the method further comprises the steps of receiving the blood flow pressure at the near end point in the resting state and the geometric parameters of the blood vessels obtained in the step 3, and estimating the blood flow pressure P at the near end point when the myocardial microcirculation is fully expanded by using a deep learning methoda(ii) a Preferably, the deep learning method includes, but is not limited to, an artificial neural network algorithm, inputting parameters such as a resting state proximal end pressure value, a lesion length, a blood vessel cross-sectional area, a diameter, a stenosis rate, a blood flow velocity, an anatomical position and the like, and outputting a blood flow pressure P at a proximal end point when the myocardial microcirculation is fully expanded by learning and adjusting weights of the parametersa(ii) a The method for deep learning further comprises the step of obtaining the value P of the resting state near-end blood flow pressure through accurate measurement of an angiography cathetera (resting state)And according to formula Pa＝α*Pa (resting state)Calculating a proximal pressure value in a maximum hyperemia state, wherein alpha is 85% -90%, and alpha is regarded as prior knowledge in a training process; the method for deep learning further comprises the step of using the prior knowledge alpha as a regular term to participate in weight updating of the neural network until the artificial neural network achieves the optimal value on the existing test set.
In addition, the present invention provides a system for rapidly calculating the resistance to microcirculation based on contrast data, the system comprising:
the image acquisition module is used for receiving image data of the interested blood vessel lumen;
the geometric model establishing module is used for establishing an actual vessel lumen model and an ideal vessel lumen model of the interested vessel and transmitting the results to the geometric parameter acquiring module;
the geometric parameter acquisition module is used for acquiring geometric parameters of the interested blood vessel section, wherein the geometric parameters comprise the area or the diameter of the proximal cross section of the blood vessel section, the area or the diameter of the distal cross section of the blood vessel section, the cross section area or the diameter of a lesion part of the blood vessel section between a proximal end point and a distal end point, lesion length, stenosis rate and the like; the geometric parameter acquisition module also comprises a speed acquisition module which is used for acquiring the average blood flow speed of the blood vessel section and the coronary blood flow speed in the maximum hyperemia state;
and the result calculation module is used for receiving the geometric parameters and calculating the microcirculation resistance.
Preferably, the result calculation module further comprises: a far-end pressure acquisition module for calculating the far-end pressure value P of the blood vesseld(ii) a A maximum blood flow obtaining module for calculating the coronary blood flow Q in the maximum hyperemia statemax(ii) a A microcirculation resistance calculation module for calculating resistance based on Pd、QmaxThe microcirculation resistance in the maximal hyperemic state is calculated.
Preferably, the system further comprises a result display module for displaying all the calculation results.
Preferably, the result calculation module further comprises: a blood vessel pressure difference calculation module for calculating the blood vessel pressure difference delta P corresponding to the coronary blood flow velocity in the maximum hyperemia statemaxFast calculation of (2).
Preferably, the remote pressure obtaining module uses formula Pd＝Pa-ΔPmaxCalculating a far-end pressure value of the blood vessel;
preferably, the maximum blood flow obtaining module uses the obtained geometric parameters of the blood vessel, namely the area S of the cross section of the blood vessel section and the maximum blood flow velocity V when the myocardial microcirculation is fully expandedmaxObtaining the coronary blood flow Q in the maximum hyperemia statemax＝Vmax*S。
Preferably, the microcirculation resistance obtaining module obtains the pressure value P in the maximal hyperemia state of the distal end point according to the distal pressure obtaining moduledAnd maximum hyperemia status obtained by blood flow obtaining moduleLower coronary blood flow QmaxCalculating the quantitative value of the microcirculation resistance
Preferably, the geometric model building module further comprises the following sub-modules: and the actual lumen and ideal lumen model establishing module is used for establishing a real blood vessel lumen model and an ideal blood vessel lumen model based on the image data received by the image acquisition module.
The invention has the beneficial effects that:
the invention is improved on the basis of the existing method and system for calculating the blood vessel pressure difference and the blood flow reserve fraction, adopts conventional contrast data, does not need the full expansion of microcirculation, and can realize the remote pressure value P of the blood vessel section when the microcirculation is fully expandeddAnd coronary blood flow QmaxAccording to a formula
The rapid and quantitative calculation of the coronary microcirculation resistance provides a new method for rapidly calculating the microcirculation resistance. Meanwhile, the invention rapidly evaluates the blood flow pressure at the near-end endpoint when the myocardial microcirculation is fully expanded by utilizing deep learning. In the microcirculation resistance calculation process, guide wire intervention is not needed, the accuracy is improved, meanwhile, the cost is reduced, the time is saved, and the whole operation process is simpler.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method for rapidly calculating resistance to microcirculation based on contrast data according to the present invention;
FIG. 2 is a flow chart of a system for rapidly calculating resistance to microcirculation based on contrast data according to the present invention;
FIG. 3 is a schematic representation of the resistance of the coronary microcirculation according to the invention;
Detailed Description
An application program recommendation method and apparatus according to an embodiment of the present invention are described in detail below with reference to the accompanying drawings. It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be appreciated by those of skill in the art that the following specific examples or embodiments are a series of presently preferred arrangements of the invention to further explain the principles of the invention, and that such arrangements may be used in conjunction or association with one another, unless it is expressly stated that some or all of the specific examples or embodiments are not in association or association with other examples or embodiments. Meanwhile, the following specific examples or embodiments are only provided as an optimized arrangement mode and are not to be understood as limiting the protection scope of the present invention.
Example 1
For a further understanding of the invention, reference will now be made to the following examples which illustrate and describe the invention. FIG. 1 is a flow chart of the method of the present invention, taken in conjunction with the cross-sectional view of the blood vessel of FIG. 3, which, in one embodiment, may be summarized as: acquiring image data of an interested blood vessel lumen from traditional coronary angiography data, establishing a geometric model of the blood vessel section and acquiring geometric parameters, and evaluating the maximum blood flow velocity V of the blood vessel section under the condition of full expansion of myocardial microcirculation, namely the maximum hyperemia state through the conventional angiography data in the resting state without full expansion of the myocardial microcirculationmaxAnd coronary blood flow QmaxSolving the pressure difference delta P corresponding to the maximum blood flow velocitymaxCoronary artery proximal end pressure PaAnd a remote end point pressure value PdFinally according to the formula
And quantitatively calculating the coronary microcirculation resistance.
Specifically, in a specific embodiment, the method may be configured to:
in one aspect, the present invention provides a method for rapidly calculating a resistance to microcirculation based on contrast data, the method comprising:
step 1, receiving image data of a blood vessel of interest, and determining a starting point and an end point of a blood vessel section to be analyzed; wherein the image data may alternatively be conventional image data in which a conventional X-ray contrast imaging procedure is performed, without special contrast image data for a special apparatus, and more preferably, the contrast image data may be contrast image data acquired with the aid of a contrast medium injection.
Step 2, establishing a geometric model of the blood vessel section to be analyzed, wherein the geometric model comprises an actual blood vessel lumen geometric model and an ideal blood vessel lumen geometric model;
step 3, acquiring the geometric parameters of the vessel section to be analyzed by using the geometric model in the step 2;
step 4, obtaining the average blood flow velocity V of the blood vessel section in a resting state;
step 5, obtaining the maximum blood flow velocity V by using the average blood flow velocity V obtained in the step 4maxAnd coronary blood flow Qmax；
Step 6, based on the maximum blood flow velocity VmaxCalculating the pressure difference Δ Pmax(ii) a A value P based on the geometrical parameter and the resting state proximal blood flow pressurea (resting state)Calculating the proximal coronary pressure Pa(ii) a And based on said Δ PmaxAnd PaCalculating a remote end point pressure value Pd；
Step 7, according to the formula
And calculating the coronary microcirculation resistance MR.
More preferably, the coronary microcirculation resistance MR can be obtained by the following simplification:
simplified microcirculation resistance calculation formula based on coronary artery microcirculation resistance model
In the above simplification, P is assumedv0, wherein: paProximal pressure of coronary stenosis, PdDistal pressure to coronary stenosis, i.e. pressure before microcirculation; pvTo obtain coronary venous pressure after microcirculation, QmaxIs the maximum coronary blood flow, Δ PmaxThe pressure, Δ P, at which the blood flow decreases from the proximal to the distal end of the coronary artery at maximum hyperemiamrIs the pressure drop in the blood flow as it passes through the microcirculation. Thus, we can perform the resistance calculation in the simplified manner described above.
Preferably, the step 2 further comprises:
step 201, obtaining a geometric difference function based on the actual vessel lumen geometric model and the ideal vessel lumen geometric model;
step 202, based on the geometric difference function and the maximum blood flow velocity VmaxCalculating said pressure difference Δ Pmax。
More preferably, the geometric difference function and the maximum blood flow velocity V when the myocardial microcirculation is fully expanded are usedmaxAnd the square of the maximum blood flow velocity, Vmax 2Calculating to obtain the pressure difference delta P between the near-end pressure and the far-end pressure when the microcirculation is fully expandedmax。
Preferably, the geometric parameters in step 3 include: the area or diameter of the proximal cross-section of the vessel segment, the area or diameter of the distal cross-section of the vessel segment, the cross-sectional area or diameter of the lesion site between the proximal and distal endpoints of the vessel segment, lesion length, stenosis rate, etc. It should be understood by those skilled in the art that the geometric parameters mentioned above are only exemplary, and the geometric parameters can be adjusted according to specific calculation requirements, and the conventional adjustment mentioned above should be considered as falling within the protection scope of the present invention.
Preferably, the average blood flow velocity V in step 4 is obtained by:
obtaining the average flowing speed of the contrast agent of the blood vessel section in the coronary angiography process by utilizing a gray scale time fitting function, or calculating the average flowing speed of the contrast agent of the blood vessel section in the coronary angiography process by utilizing a TIMI frame method;
the average flow velocity is the average blood flow velocity V.
Preferably, the step 5 further comprises:
step 501, obtaining the maximum blood flow velocity V by using the average blood flow velocity V obtained in the step 4 and a table look-up methodmaxThe table is a list of average blood flow velocity of coronary artery and maximum blood flow velocity corresponding to the condition that the myocardial microcirculation is fully expanded in the resting state of the patient;
step 502, utilizing the cross section area S of the vessel section of the vessel geometric parameter obtained in step 3 and the maximum blood flow velocity V obtained in step 501maxCalculating coronary blood flow Qmax＝Vmax*S。
Preferably, in the step 6, the value P based on the geometric parameter and the resting state near-end blood flow pressurea (resting state)Calculating the proximal coronary pressure PaThe method specifically comprises the following steps: computing the P by a deep learning methodaWith said geometric parameters and Pa (resting state)As network input, with said PaIs an output;
in learning training, based on Pa＝α*Pa (resting state)And taking alpha as a regular term to participate in the weight updating of the network.
Preferably, in the step 6, the P is based ond＝Pa-ΔPmaxCalculating the value P of the remote end point pressured。
Specifically, the method further comprises the steps of receiving the blood flow pressure at the resting state near-end point and the blood vessel geometric parameters obtained in the step 3, and evaluating the near-end point when the myocardial microcirculation is fully expanded by using a deep learning methodPressure P of blood flowa(ii) a Preferably, the deep learning method includes, but is not limited to, an artificial neural network algorithm, inputting parameters such as a resting state proximal end pressure value, a lesion length, a blood vessel cross-sectional area, a diameter, a stenosis rate, a blood flow velocity, an anatomical position and the like, and outputting a blood flow pressure P at a proximal end point when the muscle microcirculation is fully dilated by learning and adjusting weights of the parametersa(ii) a The method for deep learning further comprises the step of obtaining the value P of the resting state near-end blood flow pressure through accurate measurement of an angiography cathetera (resting state)And according to formula Pa＝α*Pa (resting state)Calculating a proximal pressure value in a maximum hyperemia state, wherein alpha is 85% -90%, and alpha is regarded as prior knowledge in a training process; the method for deep learning further comprises the step of using the prior knowledge alpha as a regular term to participate in weight updating of the neural network until the artificial neural network achieves the optimal value on the existing test set.
In a specific embodiment, the geometric difference function is obtained based on the actual vessel lumen geometric model and the ideal vessel lumen geometric model.
In one particular embodiment, the geometric difference function may be calculated using the following formula:
wherein (x, y, z) and (x)0，y0，z0) Respectively representing the position coordinates of any point on the lumen boundary of the real blood vessel and the position coordinates, s, of the corresponding point on the lumen boundary of the ideal blood vessel with the same cross section0And s represents the ideal and real lumen area, ω, respectively, of the cross-section at that location1And ω2Weighting coefficients respectively representing the above parameters, where ω1+ω2＝1。
Preferably, ω is1＝0.45-0.65、ω2＝0.35-0.55。
In a specific embodiment, in the step 6, the blood flow velocity is based on the maximum blood flow velocity VmaxCalculating the pressure difference Δ PmaxIt is determined by means of a geometric difference function. The geometric difference function is at n scales.
In a particular real-time manner, the solution Δ P is obtained by using a geometric difference functionmaxThe method of (1) is as follows:
using the function f of geometric differences at n scales1(x，y，z)、……、fn(x, y, z) integral, and the maximum blood flow velocity V at which sufficient expansion of the myocardial microcirculation is achievedmaxAnd the square of the maximum blood flow velocity, Vmax 2The pressure difference Δ P between the proximal and distal pressures at which sufficient expansion of the microcirculation is achieved can be calculatedmax. The scale refers to the resolution, i.e., the distance between two adjacent points when the derivative is numerically calculated. The n scales are a first scale, a second scale, … … and an nth scale which have different scales; wherein the first scale difference derivative function f1(x, y, z) for detecting a geometric parameter difference between the actual lumen diameter and the reference lumen diameter caused by the first lesion feature, ignoring geometric parameter differences caused by other lesions; … …, the nth scale difference derivative function fn(x, y, z) for detecting a geometric parameter difference between the real lumen diameter and the reference lumen diameter caused by the nth lesion feature; wherein n is a natural number greater than 1.
In a specific embodiment, the pressure difference Δ Ρ between the proximal pressure and the distal pressure of the blood vessel when the myocardial microcirculation is sufficiently dilatedmaxThe calculation formula of (2) is as follows:
ΔPmax＝α1[C1Vmax+C2Vmax 2]∫∫∫f1(x，y，z)dxdydz+α2[C1Vmax+C2Vmax 2]∫∫∫f2(x，y，z)dxdydz+…+αn[C1Vmax+C2Vmax 2]∫∫∫fn(x，y，z)dxdydz
wherein, C1、C2Respectively representing the cardiac microcirculationMaximum blood flow velocity V at dilationmaxAnd the maximum blood flow velocity squared Vmax 2Parameter coefficient of (a)1、α2...αnDifference derivative functions f of different scales1(x，y，z)，f2(x，y，z)...fn(x, y, z).
Example 2
In another specific embodiment, the method of the present invention is used to analyze the resistance of microcirculation in specific coronary vessels, and the specific anterior descending branch of the left coronary artery of the heart is taken as an example to illustrate the specific embodiment of the present invention. In particular, the method may be implemented by:
(1) a conventional radiographic imaging procedure is performed. For example, may be conventional image data taken by combining with a contrast agent;
(2) selecting the anterior descending branch of the left coronary heart as an interested blood vessel, and determining the starting point and the end point of the blood vessel; namely the starting point and the end point of a section of blood vessel which is focused;
(3) establishing a geometric model of the anterior descending branch of the left coronary artery, wherein the geometric model comprises an actual vascular lumen geometric model and an ideal vascular lumen geometric model; and the subsequent pressure difference Δ P can be calculated by a multi-step geometric difference function in the manner as in embodiment 1 on the basis of the actual vessel lumen geometric model and the ideal vessel lumen geometric modelmax；
(4) Acquiring geometric parameters of the anterior descending branch of the left crown by using the geometric model in the step 3, such as the cross-sectional area, the diameter and the like of the blood vessel of the anterior descending branch;
(5) obtaining the average blood flow velocity V of the anterior descending branch of the left coronary artery in a resting state, and obtaining the average blood flow velocity V of the blood vessel section by a TIMI number frame method;
(6) obtaining the maximum blood flow velocity V when the myocardial microcirculation is fully expanded by using the average blood flow velocity V of the anterior descending branch of the left crown in the resting state obtained in the step (5)maxAnd coronary blood flow Qmax；
(7) Solving the pressure difference delta P corresponding to the blood flow from the proximal end to the distal end of the anterior descending branch under the maximum hyperemia statemax(ii) a Proximal pressure P of anterior descending branchaThe solution can be obtained by deep learning, and can be obtained by other calculation methods, such as PaThe value P of the proximal blood flow pressure of the anterior descending branch of the left coronary artery in the resting statea (resting state)The functional relationship can be obtained by fitting the previous data, and is not described herein again.
In a specific embodiment, the deep learning method includes, but is not limited to, artificial neural network algorithm, inputting parameters such as resting state proximal pressure value, lesion length, blood vessel cross-sectional area, diameter, stenosis rate, blood flow velocity, anatomical position and the like, and outputting blood flow pressure P at the proximal end point when the muscle microcirculation is fully expanded by adjusting the weight of each parameter through learninga(ii) a The method for deep learning further comprises the step of obtaining the value P of the resting state near-end blood flow pressure through accurate measurement of an angiography cathetera (resting state)And according to formula Pa＝α*Pa (resting state)Calculating a proximal pressure value in a maximum hyperemia state, wherein alpha is 85% -90%, and alpha is regarded as prior knowledge in a training process; the method for deep learning further comprises the step of using the prior knowledge alpha as a regular term to participate in weight updating of the neural network until the artificial neural network achieves the optimal value on the existing test set.
In the formation of PaAnd Δ PmaxThen, the remote end point pressure value P can be calculatedd；
(8) Finally, according to the formula
And quantitatively and rapidly calculating the anterior descending branch microcirculation resistance.
Example 3
In yet another specific embodiment, as shown in FIG. 2, the present invention also provides a system for rapidly calculating resistance to microcirculation based on contrast data, the system comprising:
the image acquisition module is used for receiving image data of the interested blood vessel lumen;
the geometric model establishing module is used for establishing an actual vessel lumen model and an ideal vessel lumen model of the interested vessel and transmitting the results to the geometric parameter acquiring module;
the geometric parameter acquisition module is used for acquiring geometric parameters of the interested blood vessel section, wherein the geometric parameters comprise the area or the diameter of the proximal cross section of the blood vessel section, the area or the diameter of the distal cross section of the blood vessel section, the cross section area or the diameter of a lesion part of the blood vessel section between a proximal end point and a distal end point, lesion length, stenosis rate and the like; the geometric parameter acquisition module also comprises a speed acquisition module which is used for acquiring the average blood flow speed of the blood vessel section and the coronary blood flow speed in the maximum hyperemia state;
the result calculation module is used for receiving the geometric parameters and calculating the microcirculation resistance;
and the result display module is used for displaying all the calculation results.
Preferably, the result calculation module further comprises: a far-end pressure acquisition module for calculating the far-end pressure value P of the blood vesseld(ii) a A maximum blood flow obtaining module for calculating the coronary blood flow Q in the maximum hyperemia statemax(ii) a A microcirculation resistance calculation module for calculating resistance based on Pd、QmaxThe microcirculation resistance in the maximal hyperemic state is calculated.
Preferably, the result calculation module further comprises: a blood vessel pressure difference calculation module for calculating the blood vessel pressure difference delta P corresponding to the coronary blood flow velocity in the maximum hyperemia statemaxFast calculation of (2).
Preferably, the remote pressure obtaining module uses formula Pd＝Pa-ΔPmaxCalculating a far-end pressure value of the blood vessel;
preferably, the maximum blood flow obtaining module uses the obtained geometric parameters of the blood vessel, namely the area S of the cross section of the blood vessel section and the maximum blood flow velocity V when the myocardial microcirculation is fully expandedmaxObtaining the coronary blood flow Q in the maximum hyperemia statemax＝Vmax*S。
Preferably, the microcirculation resistance obtaining module obtains the pressure of the maximal hyperemia state of the distal end point according to the distal pressure obtaining moduleForce value PdAnd the blood flow quantity obtaining module obtains the coronary blood flow Q under the maximum hyperemia statemaxCalculating the quantitative value of the microcirculation resistance
Preferably, the geometric model building module further comprises the following sub-modules: and the actual lumen and ideal lumen model establishing module is used for establishing a real blood vessel lumen model and an ideal blood vessel lumen model based on the image data received by the image acquisition module.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that: based on the existing method and system for calculating the blood vessel pressure difference and the fractional flow reserve, the far-end pressure value P of the blood vessel section can be realized when the microcirculation is fully expanded by adopting the conventional contrast data without fully expanding the microcirculationdAnd coronary blood flow QmaxAccording to a formula
The rapid and quantitative calculation of the coronary microcirculation resistance provides a new method for rapidly calculating the microcirculation resistance. Meanwhile, the invention provides a method for calculating the value of the blood vessel proximal pressure, which utilizes deep learning to quickly evaluate the blood flow pressure at the proximal end point when the myocardial microcirculation is fully expanded. In the microcirculation resistance calculation process, guide wire intervention is not needed, the accuracy is improved, meanwhile, the cost is reduced, the time is saved, and the whole operation process is simpler.
The calculation methods or manners used in the system can be implemented in any manner of embodiments 1 to 3.
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 a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. A method for rapidly calculating resistance to microcirculation, the method comprising:
step 1, receiving image data of a blood vessel of interest, and determining a starting point and an end point of a blood vessel section to be analyzed;
step 2, establishing a geometric model of the blood vessel section to be analyzed, wherein the geometric model comprises an actual blood vessel lumen geometric model and an ideal blood vessel lumen geometric model;
step 3, acquiring the geometric parameters of the vessel section to be analyzed by using the geometric model in the step 2; the geometric parameters include: the area or diameter of the proximal cross section of the vessel section, the area or diameter of the distal cross section of the vessel section, the cross-sectional area or diameter of the lesion site between the proximal and distal endpoints of the vessel section, lesion length, stenosis rate;
step 4, obtaining the average blood flow velocity V of the blood vessel section in a resting state;
step 5, obtaining the maximum blood flow velocity V by using the average blood flow velocity V obtained in the step 4maxAnd coronary blood flow Qmax；
Step 6, based on the maximum blood flow velocity VmaxCalculating the pressure difference Δ Pmax(ii) a A value P based on the geometrical parameter and the resting state proximal blood flow pressurea (resting state)Calculating the proximal coronary pressure Pa(ii) a And based on said Δ PmaxAnd PaCalculating a remote end point pressure value Pd；
Step 7, according to the formula
Calculating coronary microcirculation resistance MR;
the average blood flow velocity V in step 4 is obtained by:
obtaining the average flowing speed of the contrast agent of the blood vessel section in the coronary angiography process by utilizing a gray-scale time fitting function, or calculating the average flowing speed of the contrast agent of the blood vessel section in the coronary angiography process by utilizing a TIMI frame method, wherein the average flowing speed is the average blood flow speed V;
the step 5 further comprises:
step 501, obtaining the maximum blood flow velocity V by using the average blood flow velocity V obtained in the step 4 and a table look-up methodmax；
Step 502, utilizing the cross section area S of the vessel section of the vessel geometric parameter obtained in step 3 and the maximum blood flow velocity V obtained in step 501maxCalculating coronary blood flow Qmax＝Vmax*S；
In step 6, a value P based on the geometric parameter and the resting state proximal blood flow pressurea (resting state)Calculating the proximal coronary pressure PaThe method specifically comprises the following steps:
computing the P by a deep learning methodaWith said geometric parameters and Pa (resting state)As network input, with said PaIs an output; in learning training, based on Pa＝α*Pa (resting state)And taking alpha as a regular term to participate in the weight updating of the network.
2. The method of claim 1, wherein the step 2 further comprises:
step 201, obtaining a geometric difference function based on the actual vessel lumen geometric model and the ideal vessel lumen geometric model;
step 202, calculating the pressure difference Δ P based on the geometric difference functionmax。
3. The method according to claim 1, wherein, in the step 6,
based on Pd＝Pa-ΔPmaxCalculating the value P of the remote end point pressured。
4. The method of claim 1, wherein α is 85% to 90%.
5. The method of claim 1, wherein the image data of step 1 is conventional radiographic imaging data.
6. A system for rapidly calculating resistance to microcirculation based on contrast data, said system comprising:
the image acquisition module is used for receiving image data of the interested blood vessel lumen;
the geometric model establishing module is used for establishing an actual vessel lumen model and an ideal vessel lumen model of the interested vessel and transmitting the results to the geometric parameter acquiring module;
the geometric parameter acquisition module is used for acquiring geometric parameters of the interested blood vessel section, wherein the geometric parameters comprise the area or the diameter of the proximal cross section of the blood vessel section, the area or the diameter of the distal cross section of the blood vessel section, the cross section area or the diameter of a lesion part of the blood vessel section between a proximal end point and a distal end point, lesion length and stenosis rate;
the result calculation module is used for receiving the geometric parameters and the average blood flow velocity V of the blood vessel section in the resting state and calculating the microcirculation resistance; the average blood flow velocity V is obtained by: obtaining the average flowing speed of the contrast agent of the blood vessel section in the coronary angiography process by utilizing a gray-scale time fitting function, or calculating the average flowing speed of the contrast agent of the blood vessel section in the coronary angiography process by utilizing a TIMI frame method, wherein the average flowing speed is the average blood flow speed V;
the result calculation module further comprises: a far-end pressure acquisition unit for calculating far-end pressure value P of blood vesseld(ii) a Maximum blood flow volume acquisition sheetElement for calculating the coronary flow Q in the maximum hyperemic statemax(ii) a A microcirculation resistance calculation unit for calculating resistance based on Pd、QmaxCalculating the microcirculation resistance in the maximum hyperemia state;
in the result calculation module, the maximum blood flow velocity V is obtained based on the average blood flow velocity V of the blood vessel section in the resting statemaxAnd coronary blood flow Qmax(ii) a Based on said maximum blood flow velocity VmaxCalculating the pressure difference Δ Pmax(ii) a A value P based on the geometrical parameter and the resting state proximal blood flow pressurea (resting state)Calculating the proximal coronary pressure PaThe method specifically comprises the following steps: computing the P by a deep learning methodaWith said geometric parameters and Pa (resting state)As network input, with said PaIs an output; in learning training, based on Pa＝α*Pa (resting state)Taking alpha as a regular term to participate in weight updating of the network;
based on the Δ PmaxAnd PaCalculating a remote end point pressure value Pd(ii) a According to the formula
Calculating coronary microcirculation resistance MR;
wherein the obtaining of the maximum blood flow velocity VmaxAnd coronary blood flow QmaxThe method comprises the following steps: obtaining the maximum blood flow velocity V by using the average blood flow velocity V of the blood vessel section in the resting state and a table look-up methodmax(ii) a Using the geometric parameters of blood vessel, the cross-section area S and the maximum blood flow velocity V of the blood vessel sectionmaxCalculating coronary blood flow Qmax＝Vmax*S。
7. The system of claim 6, further comprising a result display module for displaying all of the above calculated results.
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