CN117688867A

CN117688867A - Method, device, equipment and medium for determining microcirculation resistance reserve fraction

Info

Publication number: CN117688867A
Application number: CN202311704039.7A
Authority: CN
Inventors: 吴浩; 杨帆; 马骏; 郑凌霄; 兰宏志
Original assignee: Shenzhen Raysight Intelligent Medical Technology Co Ltd
Current assignee: Shenzhen Raysight Intelligent Medical Technology Co Ltd
Priority date: 2023-12-12
Filing date: 2023-12-12
Publication date: 2024-03-12

Abstract

The invention discloses a method, a device, equipment and a medium for determining a microcirculation resistance reserve fraction. Constructing a coronary vessel three-dimensional model by acquiring coronary angiography image data; determining coronary inlet resting flow and coronary inlet hyperemia flow based on the contrast agent filling time in the resting state and the volume of the three-dimensional model of the coronary blood vessel; determining an inlet pressure boundary condition based on the resting aortic pressure in the resting state; determining resting outlet flow boundary conditions and hyperemic outlet flow boundary conditions based on the coronary inlet resting flow and coronary inlet hyperemic flow; simulating the numerical calculation model based on the inlet pressure boundary condition, the resting outlet flow boundary condition and the congestion outlet flow boundary condition to obtain a branch prediction result of the coronary branch vessel; the microcirculation resistance reserve fraction corresponding to the coronary angiography image data is determined based on the branch prediction result and the resting aortic pressure, and the microcirculation resistance reserve fraction of the multi-branch coronary blood vessel can be determined.

Description

Method, device, equipment and medium for determining microcirculation resistance reserve fraction

Technical Field

The present invention relates to the field of image processing technologies, and in particular, to a method, an apparatus, a device, and a medium for determining a microcirculation resistance reserve fraction.

Background

The microvascular resistance reserve fraction, defined as the ratio of microvascular resistance in resting (rest) to hyperemic state (hyper), can be used to assess the severity of myocardial vascular microcirculation disease. Microvascular resistance reserve scores are suitable indicators for distinguishing angina from non-obstructive coronary artery disease target subjects for the presence of coronary microvascular dysfunction (Coronary Microvascular Dysfunction, CMD).

At present, the microvascular resistance reserve fraction is clinically measured mainly by a pressure guide wire by adopting a thermal dilution method, the microcirculation resistance of a target object in a resting state and a hyperemic state is measured by adopting the thermal dilution method, and the microvascular resistance reserve fraction is calculated by the ratio of the resting state to the hyperemic microcirculation resistance. However, the pressure guide wire is required to be inserted into the tail end of a blood vessel for measuring the capillary resistance reserve fraction, the determination difficulty is high, a certain risk exists for a target body, and meanwhile, the large-scale application of the pressure guide wire is limited by the high price.

Disclosure of Invention

The invention provides a microcirculation resistance reserve fraction determining method, device, equipment and medium, which are used for solving the problem that coronary artery multi-branch blood vessels accurately calculate the microvascular resistance reserve fraction, obtaining the microvascular resistance reserve fractions of a plurality of coronary artery branch blood vessels through one-time calculation, and improving the accuracy of the microvascular resistance reserve fraction.

According to an aspect of the present invention, there is provided a microcirculation resistance reserve fraction determining method including:

acquiring coronary angiography image data, and constructing a coronary vessel three-dimensional model based on the coronary angiography image data; determining coronary inlet resting flow and coronary inlet hyperemia flow based on the contrast agent filling time in the resting state and the volume of the three-dimensional model of the coronary blood vessel;

determining an inlet pressure boundary condition based on the resting aortic pressure in the resting state; determining resting outlet flow boundary conditions and hyperemic outlet flow boundary conditions based on the coronary inlet resting flow and coronary inlet hyperemic flow; wherein the resting outlet flow boundary condition comprises an outlet resting flow of the coronary branch vessel and the hyperemic outlet flow boundary condition comprises an outlet hyperemic flow of the coronary branch vessel;

simulating the numerical calculation model based on the inlet pressure boundary condition, the resting outlet flow boundary condition and the congestion outlet flow boundary condition to obtain a branch prediction result of the coronary branch vessel; a fractional reserve of microcirculation resistance corresponding to the coronary angiography image data is determined based on the branch prediction result and the resting aortic pressure.

According to another aspect of the present invention, there is provided a microcirculation resistance reserve fraction determining device including:

The inlet flow determining module is used for acquiring coronary angiography image data and constructing a coronary vessel three-dimensional model based on the coronary angiography image data; determining coronary inlet resting flow and coronary inlet hyperemia flow based on the contrast agent filling time in the resting state and the volume of the three-dimensional model of the coronary blood vessel;

a boundary condition determining module for determining an inlet pressure boundary condition based on the resting aortic pressure in the resting state; determining resting outlet flow boundary conditions and hyperemic outlet flow boundary conditions based on the coronary inlet resting flow and coronary inlet hyperemic flow; wherein the resting outlet flow boundary condition comprises an outlet resting flow of the coronary branch vessel and the hyperemic outlet flow boundary condition comprises an outlet hyperemic flow of the coronary branch vessel;

the microcirculation resistance reserve fraction determining module is used for carrying out simulation processing on the numerical calculation model based on the inlet pressure boundary condition, the resting outlet flow boundary condition and the congestion outlet flow boundary condition to obtain a branch prediction result of the coronary branch vessel; a fractional reserve of microcirculation resistance corresponding to the coronary angiography image data is determined based on the branch prediction result and the resting aortic pressure.

According to another aspect of the present invention, there is provided an electronic apparatus including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the fractional determination of the fractional reserve of microcirculation of any embodiment of the invention.

According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the microcirculation resistance reserve fraction determination method of any embodiment of the present invention when executed.

According to the technical scheme, coronary angiography image data are acquired, and a coronary vessel three-dimensional model is built based on the coronary angiography image data; determining coronary inlet resting flow and coronary inlet hyperemia flow based on the contrast agent filling time in the resting state and the volume of the three-dimensional model of the coronary blood vessel; determining an inlet pressure boundary condition based on the resting aortic pressure in the resting state; determining resting outlet flow boundary conditions and hyperemic outlet flow boundary conditions based on the coronary inlet resting flow and coronary inlet hyperemic flow; wherein the resting outlet flow boundary condition comprises an outlet resting flow of the coronary branch vessel and the hyperemic outlet flow boundary condition comprises an outlet hyperemic flow of the coronary branch vessel; simulating the numerical calculation model based on the inlet pressure boundary condition, the resting outlet flow boundary condition and the congestion outlet flow boundary condition to obtain a branch prediction result of the coronary branch vessel; the microcirculation resistance reserve fraction corresponding to the coronary angiography image data is determined based on the branch prediction result and the resting aortic pressure, the microcirculation resistance reserve fractions of a plurality of coronary branch blood vessels can be obtained through one-time calculation, the problem that the coronary multi-branch blood vessels accurately calculate the microcirculation resistance reserve fraction is solved, the accuracy of the microcirculation resistance reserve fraction is improved, a temperature/pressure guide wire is not needed, wounds suffered by a patient are reduced, and the acquisition difficulty and cost of the microcirculation resistance reserve fraction are reduced.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method for determining fractional reserve of microcirculation resistance according to an embodiment of the invention;

FIG. 2 is a flow chart of a method for determining fractional reserve of microcirculation resistance according to a second embodiment of the present invention;

FIG. 3 is a schematic structural diagram of a fractional reserve fraction determining device for determining a resistance to microcirculation according to a third embodiment of the present invention;

fig. 4 is a schematic structural diagram of an electronic device implementing the microcirculation resistance reserve fraction determination method according to the embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a detailed description of embodiments of the present invention will be provided below, with reference to the accompanying drawings, wherein it is apparent that the described embodiments are only some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above-described drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

Fig. 1 is a flowchart of a method for determining a fractional reserve of micro-circulation resistance according to an embodiment of the present invention, where the method may be performed by a fractional reserve of micro-circulation resistance determining device, which may be implemented in hardware and/or software, and may be configured in electronic devices such as a computer and a server, where the fractional reserve of micro-circulation resistance determining device is calculated based on coronary angiographic image data.

As shown in fig. 1, the method includes:

s110, acquiring coronary angiography image data, and constructing a coronary vessel three-dimensional model based on the coronary angiography image data.

In this embodiment, the target object is an object subjected to coronary imaging with a fractional reserve of microcirculation resistance, for example, the target object is a human or animal body. The coronary image data is a time-series image obtained by coronary imaging of the target object, for example, the coronary image data may be a real-time acquisition image or may be externally imported or read from a database.

Specifically, coronary angiography image data are read, an image frame of the complete coronary artery appearing in the coronary angiography image data is determined as a target image frame, coronary vessels are extracted based on the target image frame, three-dimensional reconstruction is carried out on the extracted coronary vessels, and a three-dimensional model of the coronary vessels is obtained, wherein the three-dimensional model of the coronary vessels is a three-dimensional geometric model representing the shape and structure of the coronary vessels. Optionally, acquiring multi-view coronary angiography image data; under the condition that the coronary blood vessel is filled with the contrast agent, a blood vessel outline map is determined based on the multi-view coronary angiography image data, and a coronary blood vessel three-dimensional model is generated based on the space geometrical relationship between the blood vessel outline map and the coronary branch blood vessel.

In this embodiment, the coronary angiography image data includes time-series images of multiple views, where the image frames in the time-series images of multiple views correspond to each other, and it is understood that the image frames of each view have image frames of other views that correspond to each other, and the image frames that correspond to each other have the same timestamp. It should be noted that, the included angle between two adjacent view angles in the coronary angiography image data is greater than 25 degrees, so as to ensure that there is a significant difference between the target image frames of the plurality of view angles.

Specifically, the coronary angiography image data is read, the time stamp of the image frame of the complete coronary in the time sequence image is determined as the time stamp of the coronary vessel filled with the contrast agent based on the time sequence image of any view angle, and the image frames of a plurality of view angles in the coronary angiography image data are extracted based on the time stamp, so that the target image frame of a plurality of view angles is obtained. And (3) carrying out coronary vessel lumen extraction on the target image frame of each view angle in the coronary angiography image data based on a vessel lumen extraction algorithm to obtain a coronary vessel segmentation result of the target image frame, and removing a background part in the target image frame so as to reduce interference of the background part. Wherein the vessel lumen extraction algorithm may be a conventional vessel lumen extraction algorithm, for example, a franga (franki) filter algorithm; a deep learning-based vessel lumen extraction algorithm, such as U-Net, is also possible, which is not limited in this embodiment. Based on a blood vessel contour generation algorithm, extracting blood vessel contours of the coronary vessel segmentation results of each view angle to obtain a blood vessel contour diagram of the view angle, and removing blood vessel inner parts in the coronary vessel segmentation results to reduce interference of the blood vessel inner parts. Wherein, the vessel contour generation algorithm can be a traditional vessel lumen extraction algorithm, such as a Gaussian difference algorithm; the present embodiment does not limit the deep learning based vessel contour generation algorithm, e.g., U-Net.

Based on the vessel profile map for each view angle, a two-dimensional geometric relationship of each coronary branch vessel of the coronary angiography image data is determined, wherein the two-dimensional geometric relationship includes, but is not limited to, a two-dimensional position, a two-dimensional angle, and a two-dimensional length of the coronary branch vessel. Based on a projection reconstruction algorithm, performing projection conversion processing on the two-dimensional geometric relationship of each coronary artery branch vessel in the multi-view vessel profile graph to obtain the three-dimensional geometric relationship of each coronary artery branch vessel in the coronary angiography image data, and generating a coronary artery three-dimensional model based on the three-dimensional geometric relationship of the coronary artery branch vessel. The projection reconstruction algorithm may be a filtered back projection algorithm, an algebraic reconstruction algorithm, an iterative reconstruction algorithm, a statistical reconstruction algorithm, etc., which is not limited in this embodiment.

According to the technical scheme, the coronary vessel three-dimensional model is generated based on the multi-view coronary angiography image data, the two-dimensional geometric relationship of the coronary branch vessels with different view angles can be obtained, the accuracy of the three-dimensional geometric relationship of the coronary branch vessels is improved, and each coronary branch vessel in the coronary vessel three-dimensional model is completely reconstructed.

In some embodiments, the three-dimensional geometry is optionally refined, where the refinement includes, but is not limited to, removing noise, smoothing the vessel surface, and filling in voids to generate a more realistic and refined three-dimensional model of the coronary vessel.

S120, determining coronary inlet resting flow and coronary inlet hyperemia flow based on the contrast agent filling time and the volume of the coronary blood vessel three-dimensional model in the resting state.

In this embodiment, the contrast agent filling time is the time from the start of injection of the contrast agent to the time when the coronary intravascular contrast agent filling reaches the maximum in the resting state. The coronary inlet resting flow is the total blood volume flowing into a plurality of coronary branch vessels in the coronary vessel three-dimensional model in a unit time under a resting state. The coronary inlet congestion flow is the total blood volume flowing into a plurality of coronary branch vessels in the three-dimensional model of the coronary vessel in a unit time under the resting state.

Specifically, the coronary inlet resting flow of the target object is calculated by substituting the filling time of the contrast agent in the resting state and the volume of the coronary blood vessel three-dimensional model into a relation formula between the filling time and the coronary inlet resting flow. Optionally, determining a contrast agent filling time under resting conditions based on a TIMI framing method; calculating coronary inlet resting flow based on contrast agent filling time, volume of the coronary vessel three-dimensional model, resting flow correction coefficient and abnormal growth rate correction coefficient of vessel volume; coronary inlet hyperemia flow is calculated based on coronary inlet resting flow and resting-to-hyperemia flow conversion coefficients.

Specifically, based on a time-series image of any view angle under a resting condition, an image frame in which coronary artery appears for the first time in the time-series image is determined as a contrast starting image frame, each image frame in the time-series image is ordered based on a time stamp, and the number of image frame sequences of target image frames in the time-series image is determined as a contrast frame number. And carrying out a quotient processing on the contrast frame number and the time resolution of the time sequence image based on a TIMI frame method to obtain the filling time t=F/fps of the contrast agent, wherein F represents the contrast frame number and fps represents the time resolution of the time sequence image. By way of example, assuming a contrast frame number of 10 frames and a contrast frame rate of 15 seconds/frame, the contrast filling time is 0.667 seconds.

Based on the three-dimensional model of coronary vessels, the volumes of the plurality of vessel segments are determined, and it is understood that the vessel radius within each vessel segment is similar, and the volume of each vessel segment is obtained by multiplying the length of the vessel segment by the average radius. And obtaining the volume of the three-dimensional model of the coronary artery by summing the volumes of all the blood vessel sections in the three-dimensional model of the coronary artery.

By contrast agents of the target object Filling time and the volume of the three-dimensional model of the coronary blood vessel are substituted into the relation between the filling time of the contrast agent and the volume of the coronary blood vessel and the coronary inlet resting flow, and the coronary inlet resting flow Q of the target object is calculated _rest ＝k×V ^β T, wherein k represents a resting flow correction coefficient of the target object and β represents an abnormal growth rate correction coefficient of the vessel volume. Illustratively, β takes a value between 0.75 and 1. Obtaining coronary inlet congestion flow Q of the target object by multiplying coronary inlet resting flow and resting-to-congestion flow conversion coefficient of the target object _hyper ＝α×Q _rest Alpha characterizes a resting to hyperemic flow rate conversion coefficient for converting coronary inlet resting flow rate to coronary inlet hyperemic flow rate. Illustratively, α takes a value between 3 and 5.

It should be noted that, the resting flow correction coefficient, the abnormal growth rate correction coefficient of the blood vessel volume, and the resting-to-hyperemic flow rate conversion coefficient are specific data of the target subject, and are affected by physical factors of the target subject, such as the type and severity of heart disease, blood pressure, and coronary stenosis degree, and may be different among different target subjects. The resting flow rate correction coefficient (or, a differential rate growth rate correction coefficient of a blood vessel volume, or, a resting to hyperemic flow rate conversion coefficient) may be a specific resting flow rate correction coefficient (or, a differential rate growth rate correction coefficient of a blood vessel volume, or, a resting to hyperemic flow rate conversion coefficient) of a specific target object group to which the target object belongs, wherein the physical sign factors of a plurality of target objects in the specific target object group are similar; the specific resting flux correction coefficient (or the abnormal growth rate correction coefficient of the blood vessel volume, or the resting to hyperemic flux conversion coefficient) of the target object may be also used, and this embodiment is not limited.

According to the technical scheme, based on the volume of the three-dimensional coronary vessel model of the target object and the filling time of the contrast agent, the coronary inlet resting flow and the coronary inlet congestion flow can be rapidly calculated, and the coronary inlet resting flow and the coronary inlet congestion flow are guaranteed to be calculated and obtained through the specific resting flow correction coefficient, the abnormal growth rate correction coefficient of the blood vessel volume and the resting-to-congestion flow conversion coefficient, so that the real coronary inlet resting flow and the coronary inlet congestion flow of the target object are close to each other, the authenticity of the subsequently constructed numerical calculation model is guaranteed, and the accuracy of the microcirculation resistance reserve fraction is improved.

S130, determining an inlet pressure boundary condition based on the resting aortic pressure in a resting state.

The inlet pressure boundary condition is a boundary condition of resting aortic pressure of the coronary vessel three-dimensional model, wherein the aortic pressure is specific data of the target object, is influenced by physical factors of the target object, such as height, weight, sex and age of the target object, and may be different between different target objects. The resting aortic pressure in the resting state may be calculated based on the physical sign factor of the target object, or may be obtained based on real-time acquisition of the upper arm blood pressure in the resting state of the target object, which is not limited in this embodiment, and the resting aortic pressure (or the mean value of the aortic pressure) of the target object is taken as the inlet pressure boundary condition. Illustratively, assuming the target object is a human body, the aortic pressure is 120 mmhg.

S140, determining a resting outlet flow boundary condition and a hyperemia outlet flow boundary condition based on the coronary inlet resting flow and the coronary inlet hyperemia flow; wherein the resting outlet flow boundary condition comprises an outlet resting flow of the coronary branch vessel and the hyperemic outlet flow boundary condition comprises an outlet hyperemic flow of the coronary branch vessel.

In this embodiment, the resting outlet flow boundary condition is a boundary condition of outlet resting flow of each coronary branch vessel in the coronary vessel three-dimensional model, and the congestion outlet flow boundary condition is a boundary condition of outlet congestion flow of each coronary branch vessel in the coronary vessel three-dimensional model; the outlet resting flow is the blood quantity flowing out of the corresponding coronary branch vessel in unit time under the resting state; the blood volume flowing out of the corresponding coronary branch vessel in the three-dimensional model of the coronary blood vessel in the unit time under the congestion state is the blood volume of each outlet congestion flow.

Specifically, by distributing the coronary inlet resting flow to a plurality of coronary branch vessels, the outlet resting flow of the plurality of coronary branch vessels is obtained. Optionally, determining an outlet resting flow of the coronary branch vessel based on the blood flow allocation data of each coronary branch vessel and the coronary inlet resting flow; wherein the resting outlet flow boundary condition comprises outlet resting flows of the plurality of coronary branch vessels.

Specifically, the size data of the plurality of blood vessel segments corresponding to each coronary branch vessel is determined based on the three-dimensional model of the coronary blood vessel, and the exemplary size data may be a radius, a volume, or other data capable of characterizing the relationship between the blood flow distributed to the blood vessel segments in the resting state and the coronary inlet resting flow, which is not limited in this embodiment. Determining an average value of the size data of a plurality of blood vessel segments as the size data f of the coronary branch blood vessel _i I=1, 2, …, n, where i characterizes the i-th coronary branch vessel and n is the total number of coronary branch vessels in the three-dimensional model of coronary vessels. Calculating blood flow distribution data of each coronary branch vessel based on the sum of the size data of each coronary branch vessel and the size data of all coronary branch vesselsBased on the blood flow distribution data of each coronary branch vessel, distributing the coronary inlet resting flow to each coronary branch vessel to obtain the outlet resting flow of each coronary branch vessel>Combining the outlet resting flows of a plurality of coronary branch vessels into a resting outlet flow boundary condition { Q _rest,out,i |i＝1,2,…,n}。

Taking the example that the size data is the radius of the coronary branch vessels, the size data of each coronary branch vessel Wherein ω is the coronary flow distribution coefficient, usingMore coronary inlet congestion flow is allocated to the coronary branch vessel with the larger radius, and less coronary inlet congestion flow is allocated to the coronary branch vessel with the smaller radius. Illustratively, ω is a value between 2.5 and 3.

According to the technical scheme, the outlet resting flow of the coronary branch vessel is distributed based on the blood flow distribution data, so that the coronary outlet resting flow of the coronary branch vessel is closer to a true value, and the effectiveness of subsequent simulation processing is ensured.

By distributing coronary inlet congestion flow to a plurality of coronary branch vessels, outlet congestion flow is obtained for the plurality of coronary branch vessels. Optionally, determining an outlet congestion flow of the coronary branch vessel based on the blood flow allocation data of each coronary branch vessel and the coronary inlet congestion flow; wherein the hyperemic outlet flow boundary condition comprises outlet hyperemic flow of a plurality of coronary branch vessels.

In this embodiment, the blood flow distribution data of each coronary branch vessel is the same as the blood flow distribution data of the corresponding coronary branch vessel in the above-mentioned process of determining the resting outlet flow boundary condition.

Specifically, based on the blood flow distribution data of each coronary branch vessel, the coronary inlet congestion flow is distributed to each coronary branch vessel to obtain the outlet congestion flow of each coronary branch vessel Combining the outlet congestion flows of a plurality of coronary branch vessels into a congestion outlet flow boundary condition { Q ] _hyper,out,i |i＝1,2,…,n}。

According to the technical scheme, the outlet congestion flow of the coronary branch vessel is distributed based on the blood flow distribution data, so that the coronary outlet congestion flow of the coronary branch vessel is closer to a true value, and the effectiveness of subsequent simulation processing is ensured.

And S150, performing simulation processing on the numerical calculation model based on the inlet pressure boundary condition, the resting outlet flow boundary condition and the congestion outlet flow boundary condition to obtain a branch prediction result of the coronary branch vessel.

In the present embodiment, the numerical calculation model is a model for simulating the blood flow in the coronary blood vessel three-dimensional model, and the numerical calculation model may be a full-order numerical calculation model or a reduced-order numerical calculation model, which is not limited in this embodiment. The full-order numerical calculation model can carry out high-precision simulation treatment on blood flow in the coronary blood vessel three-dimensional model; the reduced numerical calculation model can balance the relation between calculation resources and simulation precision to a certain extent, and the simulation efficiency is improved. The branch prediction result is obtained by performing simulation processing on the blood flow in the three-dimensional model of the coronary artery. Branch predictions include a resting state branch prediction and a congestion state branch prediction.

Specifically, the inlet pressure boundary condition and the resting outlet flow boundary condition or the congestion outlet flow boundary condition are input into a numerical calculation model, the aortic pressure and the simulated resting outlet pressure or the simulated congestion outlet pressure of the numerical calculation model are subjected to parameter setting, and the numerical calculation model after parameter setting is subjected to simulation processing to obtain a branch prediction result in a coronary branch vessel resting state or a congestion state.

By way of example, assuming that the three-dimensional model of coronary vessel comprises three coronary branch vessels, it is understood that the three-dimensional model of coronary vessel comprises one blood flow inlet and three blood flow outlets, the resting aortic pressure in the inlet pressure boundary condition is set at the blood flow inlet, and the resting outlet flow boundary condition or the hyperemic outlet flow boundary condition is set at each blood flow outlet, the outlet resting flow or the outlet hyperemic flow of the corresponding coronary branch vessel.

S160, determining a microcirculation resistance reserve fraction corresponding to the coronary angiography image data based on the branch prediction result and the resting aortic pressure.

Specifically, the microcirculation resistance reserve fraction of each coronary branch vessel is determined based on the branch prediction result in the resting state of the coronary branch vessel, the branch prediction result in the congestion state and the resting aortic pressure, and the microcirculation resistance reserve fractions corresponding to the coronary angiography image data are obtained by combining the microcirculation resistance reserve fractions based on the microcirculation resistance reserve fractions of a plurality of coronary branch vessels. Optionally, determining a simulated resting outlet pressure, a simulated resting outlet flow, a simulated hyperemic outlet pressure, and a simulated hyperemic outlet flow based on the branch prediction results; determining a hyperemic aortic pressure in a hyperemic state based on the resting aortic pressure and the resting-hyperemic pressure correction factor; determining a ratio of the hyperemic aortic pressure to the simulated hyperemic outlet pressure as a first ratio coefficient; determining a ratio of the resting aortic pressure to the simulated resting outlet pressure as a second scaling factor; and obtaining the microcirculation resistance reserve fraction based on the ratio of the simulated hyperemia outlet flow to the simulated resting outlet flow, the first proportionality coefficient and the second proportionality coefficient.

In this embodiment, the simulated resting outlet pressure is the pressure distributed at the outlet of each coronary branch vessel in the branch prediction result in the resting state of the coronary branch vessel; the simulated resting outlet flow is the flow distributed at the outlet of each coronary branch vessel in the branch prediction result in the resting state of the coronary branch vessel; the simulated hyperemic outlet pressure is the pressure distributed at the outlet of each coronary branch vessel in the branch prediction result under the hyperemic state of the coronary branch vessel; the simulated hyperemic outlet flow rate is the flow rate distributed at the outlet of each coronary branch vessel in the branch prediction result in the hyperemic state of that branch vessel.

Specifically, based on the branch prediction result of each coronary branch vessel in the resting state, determining the pressure distributed at the outlet of the coronary branch vessel as the simulated resting outlet pressure of the coronary branch vessel, and determining the flow distributed at the outlet of the coronary branch vessel as the simulated resting outlet flow of the coronary branch vessel. Based on the branch prediction result of each coronary branch vessel under the congestion state, determining the pressure distributed at the outlet of the coronary branch vessel as the simulated congestion outlet pressure of the coronary branch vessel, and determining the flow distributed at the outlet of the coronary branch vessel as the simulated congestion outlet flow of the coronary branch vessel.

Rest-hyperemic pressure correction factorIs a coefficient for converting resting aortic pressure to hyperemic aortic pressure, including a first resting-hyperemic pressure correction coefficient and a second resting-hyperemic pressure correction coefficient. By multiplying resting aortic pressure by a first resting-hyperemic pressure correction factor and summing with a second resting-hyperemic pressure correction factor, hyperemic aortic pressure P is obtained _a,hyper ＝P _a,rest X x+y, where P _a,rest The resting aortic pressure is characterized, x by a first resting-hyperemic pressure correction factor, and y by a second resting-hyperemic pressure correction factor.

Obtaining a first ratio coefficient of the ith coronary branch vessel by performing a quotient processing on the hyperemic aortic pressure and the simulated hyperemic outlet pressure of the ith coronary branch vesselWherein P is _a,hyper Characterization of hyperemic aortic pressure, pd _hyper,out,i The simulated hyperemic outlet pressure of the ith coronary branch vessel is characterized. Obtaining a second proportionality coefficient tau of the ith coronary branch vessel by performing a quotient processing on the resting aortic pressure and the simulated resting outlet pressure of the ith coronary branch vessel _i ＝P _a,rest /Pd _rest,out,i Wherein P is _a,rest Characterization of resting aortic pressure, pd _rest,out,i The simulated resting outlet pressure of the ith coronary branch vessel is characterized. The simulated congestion outlet flow and the simulated resting outlet flow of the ith coronary branch vessel are subjected to a quotient treatment to obtain the ratio of the simulated congestion outlet flow to the simulated resting outlet flow, and the ratio is subjected to a multiplication treatment with a first proportionality coefficient and a second proportionality coefficient of the ith coronary branch vessel to obtain the microcirculation resistance reserve fraction (S) of the coronary branch vessel >Wherein (1)>Simulated hyperemia for characterizing the ith coronary branch vesselOral flow, ->And (5) representing the simulated resting outlet flow of the ith coronary branch vessel.

Example two

Fig. 2 is a flowchart of a method for determining a fractional storage of a microcirculation resistance according to a second embodiment of the present invention, where the technical solution of the embodiment of the present invention is further optimized based on any of the above embodiments. As shown in fig. 2, the method includes:

s210, acquiring coronary angiography image data, and constructing a coronary vessel three-dimensional model based on the coronary angiography image data.

S220, determining coronary inlet resting flow and coronary inlet hyperemia flow based on the contrast agent filling time and the volume of the coronary blood vessel three-dimensional model in the resting state.

S230, determining an inlet pressure boundary condition based on the resting aortic pressure in a resting state.

S240, determining a resting outlet flow boundary condition and a hyperemic outlet flow boundary condition based on the coronary inlet resting flow and the coronary inlet hyperemic flow; wherein the resting outlet flow boundary condition comprises an outlet resting flow of the coronary branch vessel and the hyperemic outlet flow boundary condition comprises an outlet hyperemic flow of the coronary branch vessel.

S250, constructing a numerical calculation model based on the continuity equation and the momentum equation.

In the present embodiment, the continuity equation is an equation that characterizes equality of the blood inflow velocity and the blood outflow velocity at any point in the blood vessel, and is exemplified by Where u represents the fluid (blood) velocity vector. The momentum equation is an equation characterizing the relationship between the change in momentum of a plurality of points in a blood vessel and the acceleration of blood, and is exemplified by +.>Where t represents time, p represents pressure, ρ represents blood density, and μ represents dynamic viscosity of blood.

Specifically, the three-dimensional model of the coronary blood vessel is subjected to grid division based on a grid division algorithm, so that discretization processing of the three-dimensional model of the coronary blood vessel is realized, and a plurality of grids are obtained. Illustratively, the meshing algorithm may be a Cartesian meshing algorithm, a quadtree meshing algorithm, an octree meshing algorithm, or the like. In some embodiments, optionally, the numerical calculation model is a full-order numerical calculation model, and each mesh of the coronary vessel three-dimensional model is provided with a corresponding continuity equation and momentum equation. In some embodiments, optionally, the numerical calculation model is a reduced-order numerical calculation model, the same continuity equation and momentum equation may be set for multiple grids of the coronary three-dimensional model, the grids of the coronary three-dimensional model may be simplified into two-dimensional grids, for example, a two-dimensional flow model, a quasi-two-dimensional flow model, a scaling model, and a neural network model based on data driving or embedding physical knowledge, where the physical knowledge is the continuity equation and the momentum equation, or the simplified continuity equation and momentum equation, which is not limited in this embodiment.

S260, inputting the inlet pressure boundary condition and the resting outlet flow boundary condition into a numerical calculation model for simulation processing, and obtaining resting outlet resistance of the coronary branch vessel in the simulation process.

Specifically, by inputting the inlet pressure boundary condition and the resting outlet flow boundary condition into a numerical calculation model, setting the aortic pressure in the numerical calculation model as the resting aortic pressure in the inlet pressure boundary condition, setting the simulated resting outlet flow of each coronary branch vessel as the outlet resting flow in the resting outlet flow boundary condition, and performing simulation processing, such as finite element analysis processing, finite element volume analysis processing and neural network reasoning processing, on the coronary vessel three-dimensional model with the aortic pressure and the simulated resting outlet flow set, the branch prediction result of each coronary branch vessel is obtained. Based on branch prediction results of coronary branch vessels, determining simulated resting outlet pressure and simulated resting outlet flow of the coronary branch vessels, and obtaining resting outlet resistance of the coronary branch vessels in the simulation process by performing a quotient treatment on the simulated resting outlet pressure and the simulated resting outlet flow

By way of example, assuming that the three-dimensional model of coronary vessel comprises three coronary branch vessels, the resting outlet resistance of the three coronary branch vessels can be obtained in one simulation procedure.

S270, inputting the inlet pressure boundary condition and the congestion outlet flow boundary condition into a numerical calculation model for simulation processing, and obtaining the congestion outlet resistance of the coronary branch vessel in the simulation process.

Specifically, by inputting the inlet pressure boundary condition and the congestion outlet flow boundary condition into a numerical calculation model, setting the aortic pressure in the numerical calculation model as the resting aortic pressure in the inlet pressure boundary condition, setting the simulated congestion outlet flow of each coronary branch vessel as the outlet congestion flow in the congestion outlet flow boundary condition, and performing simulation processing on the coronary vessel three-dimensional model provided with the aortic pressure and the simulated congestion outlet flow, it can be understood that the simulation processing manner in this embodiment is the same as that of S260. Determining simulated congestion outlet pressure and simulated congestion outlet flow of the coronary branch vessel based on branch prediction results of the coronary branch vessel, and obtaining congestion outlet resistance of the coronary branch vessel in the simulation process by performing a quotient treatment on the simulated congestion outlet pressure and the simulated congestion outlet flow

By way of example, assuming that the three-dimensional model of coronary vessels includes three coronary branch vessels, the hyperemic outlet resistance of the three coronary branch vessels can be obtained in a single simulation procedure.

S280, determining the simulated resting outlet flow and the simulated resting outlet pressure corresponding to the resting outlet resistance, and the simulated hyperemic outlet flow and the simulated hyperemic outlet pressure corresponding to the hyperemic outlet resistance as branch prediction results of coronary branch vessels under the condition that the resting outlet resistance in the simulation process meets the preset resting outlet resistance threshold and the hyperemic outlet resistance in the simulation process meets the preset hyperemic outlet resistance threshold.

In this embodiment, the preset resting outlet resistance threshold is a threshold value of a preset kth resting outlet resistance for determining to stop the simulation process in the resting state. Exemplary, the preset resting outlet resistance threshold is P _a,rest /4Q _rest,out,i . By combining a preset resting outlet resistance threshold value with the kth resting outlet resistance value for each coronary branch vesselPerforming comparison, wherein Pd _rest,out,i,k The kth simulated resting outlet pressure characterizing the ith coronary branch vessel, +.>And (3) representing the k-th simulated resting outlet flow of the ith coronary branch vessel, under the condition that the k-th resting outlet resistance of each coronary branch vessel is larger than or equal to a preset resting outlet resistance threshold, determining that the k-th resting outlet resistance of each coronary branch vessel meets the preset resting outlet resistance threshold, stopping performing iterative simulation processing on blood flow in a resting state in a three-dimensional model of the coronary branch vessel, and determining the simulated resting outlet flow and simulated resting outlet pressure of the coronary branch vessel obtained by the k-th iterative simulation processing as a branch prediction result of the coronary branch vessel in the resting state. Optionally, under the condition that the resting outlet resistance in the simulation process does not meet the preset resting outlet resistance threshold, the simulated resting outlet flow corresponding to the resting outlet resistance is adjusted, the next simulation processing is performed based on the adjusted simulated resting outlet flow, and the simulated resting outlet pressure is recalculated until the resting outlet resistance meets the preset resting outlet resistance threshold.

Specifically, when the k-th resting outlet resistance is smaller than the resting outlet resistance threshold, the simulated resting outlet flow arranged at the outlet of the coronary branch vessel is adjusted if the k-th resting outlet resistance is judged to not meet the resting outlet resistance threshold. Optionally, adding the first proportion data of the simulated resting outlet flow to the outlet resting flow to obtain the adjusted simulated resting outlet flow.

In the present embodiment, the first ratio data θ ₁ Is the proportion data of the simulated resting outlet flow and is used for adjusting the simulated resting outlet flow. The k-th simulated resting outlet flow is multiplied by the first proportion data and summed with the outlet resting flow to obtain the regulated simulated resting outlet flowIllustratively, θ ₁ The value is 0.01.

According to the technical scheme, the simulation congestion outlet flow is adjusted based on the first proportion data, the adjustment efficiency of the simulation congestion outlet flow can be controlled by controlling the size of the first proportion data, and the proper first proportion data can balance the relation between the iteration simulation times and the simulation precision in the resting state to a certain extent.

Based on the adjusted simulated resting outlet flow, the simulated treatment in the (k+1) th resting state is carried out to obtain the (k+1) th simulated resting outlet pressure Pd of the (i) th coronary branch vessel _{rest,out,i,k+1} And kth simulated resting outlet flowCalculating the k+1st resting outlet resistance of the ith coronary branch vessel>By comparing the preset resting outlet resistance threshold value of each coronary branch vessel with the k+1st resting outlet resistance, if the resting outlet resistance of each coronary branch vessel meets the preset resting outlet resistance threshold value, the simulation processing in the resting state is stopped, and the simulation resting outlet flow arranged at the outlet of the coronary branch vessel is not regulated any more.

According to the technical scheme, based on the fact that the iteration simulation is conducted by adjusting the simulated resting outlet flow, the branch prediction result in the resting state of the coronary branch blood vessel meeting the preset resting outlet resistance threshold is obtained, and the reliability of the branch prediction result in the resting state of the coronary branch blood vessel can be improved.

The preset hyperemic outlet resistance threshold is a threshold value of a preset kth hyperemic outlet resistance for determining a simulated process in a stopped hyperemic state. Exemplary, the preset hyperemic outlet resistance threshold is P _a,hyper /4Q _hyper,out,i . By combining a preset hyperemic outlet resistance threshold value with a kth hyperemic outlet resistance value for each coronary branch vesselPerforming comparison, wherein Pd _{hyper,out,i,k} The kth simulated hyperemic outlet pressure characterizing the ith coronary branch vessel,/th simulated hyperemic outlet pressure>And (3) representing the kth simulated congestion outlet flow of the ith coronary branch vessel, determining that the kth congestion outlet resistance of each coronary branch vessel meets the preset congestion outlet resistance threshold under the condition that the kth congestion outlet resistance of each coronary branch vessel is larger than or equal to the preset congestion outlet resistance threshold, stopping performing iterative simulation processing on blood flow in a congestion state in the coronary vessel three-dimensional model, and determining the simulated congestion outlet flow and the simulated congestion outlet pressure of the coronary branch vessel obtained by the kth iterative simulation processing as a branch prediction result of the coronary branch vessel in the congestion state. Optionally, under the condition that the congestion outlet resistance in the simulation process does not meet the preset congestion outlet resistance threshold, adjusting the simulated congestion outlet flow corresponding to the congestion outlet resistance, performing the next simulation processing based on the adjusted simulated congestion outlet flow, and recalculating the simulated congestion outlet pressure until the congestion outlet resistance meets the preset congestion outlet resistance threshold.

Specifically, when the kth hyperemia outlet resistance is smaller than the hyperemia outlet resistance threshold, the simulated hyperemia outlet flow arranged at the outlet of the coronary branch vessel is adjusted if the kth hyperemia outlet resistance is judged not to meet the hyperemia outlet resistance threshold. Optionally, adding the second ratio data of the simulated hyperemic outlet flow rate to the outlet hyperemic flow rate to obtain an adjusted simulated hyperemic outlet flow rate.

In the present embodiment, the second proportion data θ ₂ The second ratio data may be the same as the first ratio data or may be different from the first ratio data, and the present embodiment is not limited to this. By multiplying the kth simulated hyperemic outlet flow rate by the second ratio dataAnd then, carrying out summation treatment on the simulated hyperemia outlet flow and the outlet hyperemia flow to obtain the regulated simulated hyperemia outlet flowIllustratively, θ ₂ The value is 0.01.

According to the technical scheme, the simulated congestion outlet flow is adjusted based on the second proportion data, the adjustment efficiency of the simulated congestion outlet flow can be controlled by controlling the size of the second proportion data, and the proper second proportion data can balance the relation between the iteration simulation times and the simulation precision in the congestion state to a certain extent.

Performing simulation treatment in the kth+1st hyperemia state based on the adjusted simulated hyperemia outlet flow to obtain the kth+1st simulated hyperemia outlet pressure Pd of the ith coronary branch vessel _{hyper,out,i,k+1} And kth simulated hyperemic outlet flow rateCalculating the (k+1) th hyperemia outlet resistance of the ith coronary branch vessel By comparing the preset congestion outlet resistance threshold value of each coronary branch vessel with the k+1th congestion outlet resistance, if the congestion outlet resistance of each coronary branch vessel meets the preset congestion outlet resistance threshold value, the simulation processing in the congestion state is stopped, and the simulation congestion outlet flow rate arranged at the outlet of the coronary branch vessel is not regulated any more.

According to the technical scheme, based on the fact that the simulated congestion outlet flow is adjusted, iterative simulation is conducted, so that the branch prediction result in the congestion state of the coronary branch blood vessel meeting the preset congestion outlet resistance threshold is obtained, and the reliability of the branch prediction result in the congestion state of the coronary branch blood vessel can be improved.

S290, determining the microcirculation resistance reserve fraction corresponding to the coronary angiography image data based on the branch prediction result and the resting aortic pressure.

According to the technical scheme, coronary angiography image data are acquired, and a coronary vessel three-dimensional model is built based on the coronary angiography image data; determining coronary inlet resting flow and coronary inlet hyperemia flow based on the contrast agent filling time in the resting state and the volume of the three-dimensional model of the coronary blood vessel; determining an inlet pressure boundary condition based on the resting aortic pressure in the resting state; determining resting outlet flow boundary conditions and hyperemic outlet flow boundary conditions based on the coronary inlet resting flow and coronary inlet hyperemic flow; wherein the resting outlet flow boundary condition comprises an outlet resting flow of the coronary branch vessel and the hyperemic outlet flow boundary condition comprises an outlet hyperemic flow of the coronary branch vessel; constructing a numerical calculation model based on the continuity equation and the momentum equation; inputting the inlet pressure boundary condition and the resting outlet flow boundary condition into a numerical calculation model for simulation treatment to obtain resting outlet resistance of the coronary branch vessel in the simulation process; inputting the inlet pressure boundary condition and the congestion outlet flow boundary condition into a numerical calculation model for simulation treatment to obtain congestion outlet resistance of the coronary branch vessel in the simulation process; under the condition that the resting outlet resistance in the simulation process meets a preset resting outlet resistance threshold value and the congestion outlet resistance in the simulation process meets a preset congestion outlet resistance threshold value, determining the simulated resting outlet flow and the simulated resting outlet pressure corresponding to the resting outlet resistance, the simulated congestion outlet flow and the simulated congestion outlet pressure corresponding to the congestion outlet resistance as branch prediction results of coronary branch vessels; the accuracy of the microcirculation resistance reserve fraction can be further improved by determining the microcirculation resistance reserve fraction corresponding to the coronary angiography image data based on the branch prediction result and the resting aortic pressure.

Example III

Fig. 3 is a schematic structural diagram of a device for determining fractional reserve of microcirculation resistance according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes:

an inlet flow determination module 310 for acquiring coronary angiography image data, and constructing a coronary vessel three-dimensional model based on the coronary angiography image data; determining coronary inlet resting flow and coronary inlet hyperemia flow based on the contrast agent filling time in the resting state and the volume of the three-dimensional model of the coronary blood vessel;

a boundary condition determination module 320 for determining an inlet pressure boundary condition based on the resting aortic pressure in the resting state; determining resting outlet flow boundary conditions and hyperemic outlet flow boundary conditions based on the coronary inlet resting flow and coronary inlet hyperemic flow; wherein the resting outlet flow boundary condition comprises an outlet resting flow of the coronary branch vessel and the hyperemic outlet flow boundary condition comprises an outlet hyperemic flow of the coronary branch vessel;

the microcirculation resistance reserve fraction determining module 330 is configured to perform simulation processing on the numerical calculation model based on the inlet pressure boundary condition, the resting outlet flow boundary condition and the congestion outlet flow boundary condition to obtain a branch prediction result of the coronary branch vessel; a fractional reserve of microcirculation resistance corresponding to the coronary angiography image data is determined based on the branch prediction result and the resting aortic pressure.

Based on the above embodiments, optionally, the inlet flow determining module 310 is specifically configured to:

acquiring multi-view coronary angiography image data; under the condition that the coronary blood vessel is filled with the contrast agent, a blood vessel outline map is determined based on the multi-view coronary angiography image data, and a coronary blood vessel three-dimensional model is generated based on the space geometrical relationship between the blood vessel outline map and the coronary branch blood vessel.

On the basis of the above embodiment, optionally, the inlet flow determining module 310 is further configured to:

determining a contrast agent filling time under a resting condition based on a TIMI framing method; calculating coronary inlet resting flow based on contrast agent filling time, volume of the coronary vessel three-dimensional model, resting flow correction coefficient and abnormal growth rate correction coefficient of vessel volume; coronary inlet hyperemia flow is calculated based on coronary inlet resting flow and resting-to-hyperemia flow conversion coefficients.

Based on the above embodiment, the boundary condition determining module 320 is optionally specifically configured to: determining an outlet resting flow of the coronary branch vessel based on the blood flow distribution data of each coronary branch vessel and the coronary inlet resting flow; wherein the resting outlet flow boundary condition comprises outlet resting flows of the plurality of coronary branch vessels; determining an outlet congestion flow rate of the coronary branch vessel based on the blood flow allocation data of each coronary branch vessel and the coronary inlet congestion flow rate; wherein the hyperemic outlet flow boundary condition comprises outlet hyperemic flow of a plurality of coronary branch vessels.

Based on the above embodiments, the micro-circulation resistance reserve fraction determining module 330 is optionally specifically configured to:

constructing a numerical calculation model based on the continuity equation and the momentum equation; inputting the inlet pressure boundary condition and the resting outlet flow boundary condition into a numerical calculation model for simulation treatment to obtain resting outlet resistance of the coronary branch vessel in the simulation process; inputting the inlet pressure boundary condition and the congestion outlet flow boundary condition into a numerical calculation model for simulation treatment to obtain congestion outlet resistance of the coronary branch vessel in the simulation process; and under the condition that the resting outlet resistance in the simulation process meets the preset resting outlet resistance threshold and the congestion outlet resistance in the simulation process meets the preset congestion outlet resistance threshold, determining the simulated resting outlet flow and the simulated resting outlet pressure corresponding to the resting outlet resistance, the simulated congestion outlet flow and the simulated congestion outlet pressure corresponding to the congestion outlet resistance as branch prediction results of coronary branch vessels.

Based on the above embodiments, the micro-circulation resistance reserve fraction determining module 330 is optionally further configured to:

and under the condition that the resting outlet resistance in the simulation process does not meet the preset resting outlet resistance threshold, adjusting the simulated resting outlet flow corresponding to the resting outlet resistance, performing next simulation processing based on the adjusted simulated resting outlet flow, and recalculating the simulated resting outlet pressure until the resting outlet resistance meets the preset resting outlet resistance threshold.

and under the condition that the congestion outlet resistance in the simulation process does not meet the preset congestion outlet resistance threshold, regulating the simulated congestion outlet flow corresponding to the congestion outlet resistance, performing next simulation processing based on the regulated simulated congestion outlet flow, and recalculating the simulated congestion outlet pressure until the congestion outlet resistance meets the preset congestion outlet resistance threshold.

Based on the above embodiments, the micro-circulation resistance reserve fraction determining module 330 is optionally further configured to: and adding the first proportion data of the simulated resting outlet flow to the outlet resting flow to obtain the regulated simulated resting outlet flow.

Based on the above embodiments, the micro-circulation resistance reserve fraction determining module 330 is optionally further configured to: and adding the second proportion data of the simulated hyperemia outlet flow to the outlet hyperemia flow to obtain the regulated simulated hyperemia outlet flow.

determining a simulated resting outlet pressure, a simulated resting outlet flow, a simulated hyperemic outlet pressure, and a simulated hyperemic outlet flow based on the branch prediction results; determining a hyperemic aortic pressure in a hyperemic state based on the resting aortic pressure and the resting-hyperemic pressure correction factor; determining a ratio of the hyperemic aortic pressure to the simulated hyperemic outlet pressure as a first ratio coefficient; determining a ratio of the resting aortic pressure to the simulated resting outlet pressure as a second scaling factor; and obtaining the microcirculation resistance reserve fraction based on the ratio of the simulated hyperemia outlet flow to the simulated resting outlet flow, the first proportionality coefficient and the second proportionality coefficient.

The microcirculation resistance reserve fraction determining device provided by the embodiment of the invention can execute the microcirculation resistance reserve fraction determining method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the executing method.

Example IV

Fig. 4 is a schematic structural diagram of an electronic device implementing the microcirculation resistance reserve fraction determination method according to the embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the microcirculation resistance reserve fraction determination method.

In some embodiments, the microcirculation resistance reserve fraction determination method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the above-described method of determining the fractional reserve resistance reserve may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the microcirculation resistance reserve fraction determination method in any other suitable manner (e.g., by means of firmware).

Various implementations of the systems and techniques described here above can be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

The computer program for implementing the microcirculatory resistance reserve fraction determination method of the invention can be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

Example five

The fifth embodiment of the present invention also provides a computer readable storage medium storing computer instructions for causing a processor to execute a method for determining a fractional reserve of microcirculation resistance, the method comprising:

acquiring coronary angiography image data, and constructing a coronary vessel three-dimensional model based on the coronary angiography image data; determining coronary inlet resting flow and coronary inlet hyperemia flow based on the contrast agent filling time in the resting state and the volume of the three-dimensional model of the coronary blood vessel; determining an inlet pressure boundary condition based on the resting aortic pressure in the resting state; determining resting outlet flow boundary conditions and hyperemic outlet flow boundary conditions based on the coronary inlet resting flow and coronary inlet hyperemic flow; wherein the resting outlet flow boundary condition comprises an outlet resting flow of the coronary branch vessel and the hyperemic outlet flow boundary condition comprises an outlet hyperemic flow of the coronary branch vessel; simulating the numerical calculation model based on the inlet pressure boundary condition, the resting outlet flow boundary condition and the congestion outlet flow boundary condition to obtain a branch prediction result of the coronary branch vessel; a fractional reserve of microcirculation resistance corresponding to the coronary angiography image data is determined based on the branch prediction result and the resting aortic pressure.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background (e.g., as a data server), or that includes middleware (e.g., an application server), or that includes a front end (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front end. The systems may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. A method of determining a fractional reserve of microcirculatory resistance, comprising:

acquiring coronary angiography image data, and constructing a coronary vessel three-dimensional model based on the coronary angiography image data; determining coronary inlet resting flow and coronary inlet hyperemia flow based on the contrast agent filling time in the resting state and the volume of the three-dimensional model of coronary blood vessels;

determining an inlet pressure boundary condition based on the resting aortic pressure in the resting state; determining a resting outlet flow boundary condition and a hyperemic outlet flow boundary condition based on the coronary inlet resting flow and the coronary inlet hyperemic flow; wherein the resting outlet flow boundary condition comprises an outlet resting flow of a coronary branch vessel, and the hyperemic outlet flow boundary condition comprises an outlet hyperemic flow of the coronary branch vessel;

Simulating a numerical calculation model based on the inlet pressure boundary condition, the resting outlet flow boundary condition and the congestion outlet flow boundary condition to obtain a branch prediction result of the coronary branch vessel; a microcirculation resistance reserve fraction corresponding to the coronary angiography image data is determined based on the branch prediction result and the resting aortic pressure.

2. The method of claim 1, wherein the acquiring coronary angiography image data, constructing a three-dimensional model of a coronary vessel based on the coronary angiography image data, comprises:

acquiring multi-view coronary angiography image data;

in case of filling the coronary vessel with a contrast agent, determining a vessel contour map based on the multi-view coronary angiography image data, and generating the three-dimensional model of the coronary vessel based on a spatial geometrical relationship of the vessel contour map and coronary branch vessels.

3. The method of claim 1, wherein determining coronary inlet resting flow and coronary inlet hyperemia flow based on a time of contrast agent filling in a resting state and a volume of the three-dimensional model of coronary blood vessels comprises:

determining a contrast agent filling time under a resting condition based on a TIMI framing method;

Calculating the coronary inlet resting flow based on the contrast agent filling time, the volume of the coronary vessel three-dimensional model, a resting flow correction coefficient and a differential rate growth rate correction coefficient of vessel volume;

calculating the coronary inlet hyperemia flow based on the coronary inlet resting flow and a resting-to-hyperemia flow conversion coefficient.

4. The method of claim 1, wherein the determining a resting outlet flow boundary condition and a hyperemic outlet flow boundary condition based on the coronary inlet resting flow and the coronary inlet hyperemic flow comprises:

determining an outlet resting flow of each of the coronary branch vessels based on the blood flow allocation data of the coronary branch vessel and the coronary inlet resting flow; wherein the resting outlet flow boundary condition comprises the outlet resting flows of a plurality of the coronary branch vessels;

determining an outlet congestion flow of each of the coronary branch vessels based on the blood flow allocation data and the coronary inlet congestion flow of the coronary branch vessel; wherein said hyperemic outlet flow boundary condition comprises said outlet hyperemic flow of a plurality of said coronary branch vessels.

5. The method of claim 1, wherein simulating the numerical calculation model based on the inlet pressure boundary condition, the resting outlet flow boundary condition, and the hyperemic outlet flow boundary condition results in a branch prediction result for the coronary branch vessel, comprising:

constructing the numerical calculation model based on a continuity equation and a momentum equation;

inputting the inlet pressure boundary condition and the resting outlet flow boundary condition into the numerical calculation model for simulation treatment to obtain resting outlet resistance of the coronary branch vessel in the simulation process;

inputting the inlet pressure boundary condition and the congestion outlet flow boundary condition into the numerical calculation model for simulation treatment to obtain the congestion outlet resistance of the coronary branch vessel in the simulation process;

and under the condition that the resting outlet resistance in the simulation process meets a preset resting outlet resistance threshold value and the congestion outlet resistance in the simulation process meets a preset congestion outlet resistance threshold value, determining the simulated resting outlet flow and the simulated resting outlet pressure corresponding to the resting outlet resistance, the simulated congestion outlet flow and the simulated congestion outlet pressure corresponding to the congestion outlet resistance as branch prediction results of the coronary branch blood vessel.

6. The method of claim 5, wherein the method further comprises:

under the condition that the resting outlet resistance in the simulation process does not meet a preset resting outlet resistance threshold, adjusting the simulated resting outlet flow corresponding to the resting outlet resistance, and carrying out next simulation processing based on the adjusted simulated resting outlet flow, and recalculating the simulated resting outlet pressure until the resting outlet resistance meets the preset resting outlet resistance threshold;

and/or, under the condition that the congestion outlet resistance in the simulation process does not meet the preset congestion outlet resistance threshold, adjusting the simulated congestion outlet flow corresponding to the congestion outlet resistance, performing next simulation processing based on the adjusted simulated congestion outlet flow, and recalculating the simulated congestion outlet pressure until the congestion outlet resistance meets the preset congestion outlet resistance threshold.

7. The method of claim 6, wherein said adjusting said simulated resting outlet flow corresponding to said resting outlet resistance comprises:

adding the first proportion data of the simulated resting outlet flow to the outlet resting flow to obtain the regulated simulated resting outlet flow;

Correspondingly, the adjusting the simulated hyperemic outlet flow corresponding to the hyperemic outlet resistance includes:

and adding the second proportion data of the simulated hyperemia outlet flow to the outlet hyperemia flow to obtain the regulated simulated hyperemia outlet flow.

8. The method of claim 1, wherein the determining a microcirculation resistance reserve fraction for the coronary angiography image data based on the branch prediction result and the resting aortic pressure comprises:

determining a simulated resting outlet pressure, a simulated resting outlet flow, a simulated hyperemic outlet pressure, and a simulated hyperemic outlet flow based on the branch prediction results;

determining a hyperemic aortic pressure in a hyperemic state based on the resting aortic pressure and a resting-hyperemic pressure correction factor;

determining a ratio of the hyperemic aortic pressure to the simulated hyperemic outlet pressure as a first ratio coefficient; determining a ratio of the resting aortic pressure to the simulated resting outlet pressure as a second scaling factor;

and obtaining the microcirculation resistance reserve fraction based on the ratio of the simulated hyperemia outlet flow to the simulated resting outlet flow, the first scaling factor and the second scaling factor.

9. A microcirculation resistance reserve fraction determination device characterized by comprising:

the inlet flow determining module is used for acquiring coronary angiography image data and constructing a coronary vessel three-dimensional model based on the coronary angiography image data; determining coronary inlet resting flow and coronary inlet hyperemia flow based on the contrast agent filling time in the resting state and the volume of the three-dimensional model of coronary blood vessels;

a boundary condition determining module for determining an inlet pressure boundary condition based on the resting aortic pressure in the resting state; determining a resting outlet flow boundary condition and a hyperemic outlet flow boundary condition based on the coronary inlet resting flow and the coronary inlet hyperemic flow; wherein the resting outlet flow boundary condition comprises an outlet resting flow of a coronary branch vessel, and the hyperemic outlet flow boundary condition comprises an outlet hyperemic flow of the coronary branch vessel;

the microcirculation resistance reserve fraction determining module is used for carrying out simulation processing on a numerical calculation model based on the inlet pressure boundary condition, the resting outlet flow boundary condition and the congestion outlet flow boundary condition to obtain a branch prediction result of the coronary branch vessel; a microcirculation resistance reserve fraction corresponding to the coronary angiography image data is determined based on the branch prediction result and the resting aortic pressure.

10. An electronic device, the electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the fractional determination of the microcirculation resistance reserve method of any one of claims 1-8.

11. A computer readable storage medium storing computer instructions for causing a processor to perform the method of determining the fractional reserve of microcirculation according to any one of claims 1 to 8.