CN117688865A - Microcirculation resistance index determination method, device, equipment and medium - Google Patents

Microcirculation resistance index determination method, device, equipment and medium Download PDF

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CN117688865A
CN117688865A CN202311699284.3A CN202311699284A CN117688865A CN 117688865 A CN117688865 A CN 117688865A CN 202311699284 A CN202311699284 A CN 202311699284A CN 117688865 A CN117688865 A CN 117688865A
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吴浩
杨帆
马骏
郑凌霄
兰宏志
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Shenzhen Raysight Intelligent Medical Technology Co Ltd
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Abstract

The invention discloses a method, a device, equipment and a medium for determining a microcirculation resistance index. By acquiring coronary angiography image data of a target object, constructing a coronary vessel three-dimensional model based on the coronary angiography image data; determining coronary inlet congestion flow based on body conformation data of the target subject, the coronary vessel three-dimensional model, and a contrast agent filling time; determining an outlet flow boundary condition based on the coronary inlet congestion flow and the size data of the coronary branch vessel; and determining an inlet pressure boundary condition based on the aortic pressure of the target object; performing simulation processing based on the inlet pressure boundary condition and the outlet flow boundary condition, and determining the tail end pressure of the coronary branch vessel and the contrast agent conduction time; based on the end pressure of the coronary artery branch vessel and the contrast agent conduction time, the microcirculation resistance index corresponding to the coronary artery contrast image data is determined, so that the microcirculation resistance indexes of a plurality of coronary artery branch vessels can be determined at the same time, and the acquisition difficulty and cost of the microcirculation resistance indexes are reduced.

Description

Microcirculation resistance index determination method, device, equipment and medium
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 index.
Background
The microcirculation resistance index is a new index for evaluating the microcirculation of the coronary artery, which has been proposed in recent years, and can specifically evaluate the microcirculation function of the distal end of the coronary artery stenosis, which is defined as the product of the coronary artery stenosis end pressure and the average conduction time in the maximum hyperemia state.
At present, the prior art is often based on computational fluid dynamics to simulate the coronary artery and simulate the real flow of blood to determine the end pressure so as to obtain the microcirculation resistance index. However, the prior art is limited to calculating the microcirculation resistance index of a coronary artery single branch blood vessel, the coronary artery itself is provided with branches, and part of fluid is separated from the branches in the blood flow, so that a great amount of assumptions are introduced in the method for calculating the microcirculation resistance index by adopting the coronary artery single branch blood vessel, and the calculation accuracy of the microcirculation resistance index is seriously affected.
Disclosure of Invention
The invention provides a method, a device, equipment and a medium for determining a microcirculation resistance index, which are used for solving the problem that the coronary multi-branch blood vessel accurately calculates the microcirculation resistance index, obtaining the microcirculation resistance indexes of a plurality of coronary branch blood vessels by one-time calculation and improving the accuracy of the microcirculation resistance index.
According to an aspect of the present invention, there is provided a microcirculation resistance index determination method including:
acquiring coronary angiography image data of a target object, and constructing a coronary vessel three-dimensional model based on the coronary angiography image data;
determining coronary inlet congestion flow based on body conformation data of the target subject, the coronary vessel three-dimensional model, and a contrast agent filling time; determining an outlet flow boundary condition based on the coronary inlet congestion flow and the size data of the coronary branch vessel; and determining an inlet pressure boundary condition based on the aortic pressure of the target object;
performing simulation processing based on the inlet pressure boundary condition and the outlet flow boundary condition, and determining the tail end pressure of the coronary branch vessel and the contrast agent conduction time; and determining a microcirculation resistance index corresponding to the coronary angiography image data based on the end pressure of the coronary branch vessel and the contrast agent conduction time.
According to another aspect of the present invention, there is provided a microcirculation resistance index determination device including:
the coronary artery three-dimensional model construction module is used for acquiring coronary artery contrast image data of the target object and constructing a coronary artery three-dimensional model based on the coronary artery contrast image data;
the boundary condition determining module is used for determining coronary inlet congestion flow based on the body type data of the target object, the coronary vessel three-dimensional model and the contrast agent filling time; determining an outlet flow boundary condition based on the coronary inlet congestion flow and the size data of the coronary branch vessel; and determining an inlet pressure boundary condition based on the aortic pressure of the target object;
The microcirculation resistance index determining module is used for performing simulation processing based on the inlet pressure boundary condition and the outlet flow boundary condition to determine the tail end pressure of the coronary branch vessel and the contrast agent conduction time; and determining a microcirculation resistance index corresponding to the coronary angiography image data based on the end pressure of the coronary branch vessel and the contrast agent conduction time.
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 method of determining the microcirculatory resistance index according to any one of the embodiments 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 index determination method of any embodiment of the present invention when executed.
According to the technical scheme, the coronary angiography image data of the target object is obtained, and a coronary vessel three-dimensional model is built based on the coronary angiography image data; determining coronary inlet congestion flow based on body conformation data of the target subject, the coronary vessel three-dimensional model, and a contrast agent filling time; determining an outlet flow boundary condition based on the coronary inlet congestion flow and the size data of the coronary branch vessel; and determining an inlet pressure boundary condition based on the aortic pressure of the target object; performing simulation processing based on the inlet pressure boundary condition and the outlet flow boundary condition, and determining the tail end pressure of the coronary branch vessel and the contrast agent conduction time; the microcirculation resistance indexes corresponding to the coronary angiography image data are determined based on the tail end pressure of the coronary branched blood vessels and the contrast agent conduction time, so that the microcirculation resistance indexes of a plurality of coronary branched blood vessels can be obtained through one-time calculation, the problem that the microcirculation resistance indexes are accurately calculated by the coronary branched blood vessels is solved, the accuracy of the microcirculation resistance indexes is improved, a temperature/pressure guide wire is not needed, the wounds suffered by a patient are reduced, and the acquisition difficulty and cost of the microcirculation resistance indexes 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 a microcirculatory resistance index according to an embodiment of the invention;
FIG. 2 is a schematic illustration of coronary angiography image data provided according to an embodiment of the invention;
FIG. 3 is a schematic diagram of a coronary vessel segmentation result provided by an embodiment of the present invention;
FIG. 4 is a schematic diagram of a three-dimensional model of a coronary vessel constructed in accordance with an embodiment of the present invention;
FIG. 5 is a schematic diagram of a hemodynamic simulation visualization result provided by an embodiment of the present invention;
FIG. 6 is a flowchart of a method for determining a microcirculatory resistance index according to a second embodiment of the invention;
Fig. 7 is a schematic structural diagram of a microcirculatory resistance index determination device according to a third embodiment of the invention;
fig. 8 is a schematic structural diagram of an electronic device implementing a microcirculation resistance index determination method according to an 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 micro-circulation resistance index according to an embodiment of the present invention, where the method may be performed by a micro-circulation resistance index determining device, and the micro-circulation resistance index determining device may be implemented in hardware and/or software, and the micro-circulation resistance index determining device may be configured in electronic devices such as a computer and a server. As shown in fig. 1, the method includes:
s110, acquiring coronary angiography image data of a target object, and constructing a coronary vessel three-dimensional model based on the coronary angiography image data.
In this embodiment, the target object is an object to be subjected to coronary angiography to calculate the coronary micro resistance index, for example, the target object is a human or animal body. The coronary image data is a time series of images obtained by coronary imaging, for example, the coronary image data may be a real-time acquisition image or may be externally imported or read from a database.
Specifically, by performing coronary angiography imaging operation on a target object, a multi-angle time sequence image is acquired in real time, wherein the difference between two adjacent angles is greater than 25 degrees, one-time coronary angiography imaging of the same target object is performed, and the acquired multi-angle time sequence image is determined as coronary angiography image data. Fig. 2 is a schematic diagram of coronary angiography image data according to an embodiment of the present invention, where (a) in fig. 2 is a time-series image frame at a first angle, and (b) in fig. 2 is a time-series image frame at a second angle, and (a) in fig. 2 and (b) in fig. 2 are time-series image frames acquired at the same time, as shown in fig. 2. The coronary angiography image data may be stored to a database, e.g., a storage system image archiving and communication system (Picture Archiving and Communication Systems, PACS), from which the coronary angiography image data is read while the microcirculatory resistance index determination operation is performed; the microcirculation resistance index determination operation may also be directly performed on the coronary angiography image data, which is not limited in this embodiment.
Determining an image frame of which the complete coronary artery appears in the coronary angiography image data as a target image frame, extracting the coronary vessel lumen of the target image frame of each 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. Fig. 3 is a schematic diagram of a coronary blood vessel segmentation result provided by the embodiment of the present invention, where (a) in fig. 3 is a coronary blood vessel segmentation result at a first angle, and (b) in fig. 3 is a coronary blood vessel segmentation result at a second angle, as shown in fig. 3.
And processing the coronary vessel segmentation result based on a central line extraction algorithm to obtain a vessel central line corresponding to the target image frame. Illustratively, the centerline extraction algorithm is a skeletonizing algorithm. And processing the coronary vessel segmentation result based on a contour extraction algorithm to obtain a vessel contour corresponding to the target image frame. The contour extraction algorithm may be a gaussian difference algorithm, or a deep neural network algorithm, for example. And constructing a three-dimensional geometric model of the coronary blood vessel based on the coronary blood vessel outline and the coronary blood vessel central line corresponding to the multi-angle target image frame. Fig. 4 is a schematic diagram of constructing a three-dimensional model of a coronary blood vessel according to an embodiment of the present invention, and as shown in fig. 4, the three-dimensional model of the coronary blood vessel is constructed based on three-dimensional geometric relationships between the outline of the coronary blood vessel and the central line of the coronary blood vessel in two-angle target image frames.
It should be noted that, the coronary angiography image data includes three coronary main blood vessels, and each coronary main blood vessel includes a plurality of coronary branch blood vessels. Wherein the three coronary main vessels are left anterior descending branch (Left Anterior Descending artery, LAD) vessel, left circumflex branch (Left Circumflex artery, LCX) vessel and right coronary artery (Right Coronary Artery, RCA) vessel, respectively. The coronary branch vessels are vessels corresponding to the outlet of each coronary branch in the coronary vessels.
S120, determining coronary inlet congestion flow based on the body type data of the target object, the coronary three-dimensional model and the contrast agent filling time.
In the present embodiment, the body type data is data representing the body type of the target object, including the height and weight of the target object. Contrast agent filling time is the time from the start of contrast agent injection to the time when the coronary intravascular contrast agent filling reaches its peak. Coronary inlet hyperemic flow is the total amount of blood flowing in per unit time at the coronary inlet (i.e., at the intersection of the coronary artery and the aorta) in a hyperemic state.
Specifically, by measuring the body shape of the target object, body shape data is obtained, including but not limited to height and weight. The method comprises the steps of determining the lengths and the radiuses of a plurality of blood vessel sections of the coronary blood vessel based on the three-dimensional model of the coronary blood vessel, and calculating the volumes of the blood vessel sections based on the lengths and the radiuses. By scanning the coronary angiography image data according to time sequence frames, the time required for the contrast agent to flow from the coronary vessel inlet to the end of the coronary vessel is calculated, and the filling time of the contrast agent is obtained. Based on the relationship between the body volume data, the volume of the vessel segment, and the contrast agent filling time and the coronary inlet congestion flow, a predicted coronary inlet congestion flow is calculated. Optionally, determining the myocardial mass based on the coronary vessel three-dimensional model; determining body type surface parameters of the target object based on the body type data of the target object; determining a total coronary vessel volume based on the coronary vessel three-dimensional model; determining coronary inlet resting flow based on myocardial mass, body type surface parameters of the target object, total coronary vessel volume, and contrast agent filling time; the coronary inlet hyperemia flow is calculated based on the correlation coefficient of the resting state and the hyperemia state and the coronary inlet resting flow.
In this embodiment, the myocardial mass is a predicted myocardial mass calculated based on the coronary main vessel volume of any coronary main vessel. Based on the conversion relation between the coronary main blood vessel volume and the myocardial mass of the coronary main blood vessel, the coronary main blood vessel volume is converted into the myocardial mass. The volumes of the LAD blood vessel, LCX blood vessel and RCA blood vessel were identical to the conversion relation structure of the myocardial mass, but the conversion relation parameters were different. Optionally, determining any coronary main vessel volume based on the coronary vessel three-dimensional model; determining a first and a second heart volume coefficient based on the coronary main vessel volume; the myocardial mass is calculated based on the coronary main vessel volume, the vessel volume abnormal growth rate correction coefficient, the first heart volume coefficient, the second heart volume coefficient, and the myocardial density.
Specifically, based on the three-dimensional model of the coronary artery, determining a vessel segment corresponding to any coronary artery main vessel, wherein the volume of the vessel segment is the volume of the coronary artery main vessel. Based on the coronary main blood vessel volume, a prestored coronary-cardiac muscle volume relation comparison table is called, wherein a plurality of coronary main blood vessel volume-cardiac muscle volume conversion relation parameters of each coronary main blood vessel are stored in the coronary-cardiac muscle volume relation comparison table, and the conversion relation parameters comprise a first cardiac organism volume coefficient and a second cardiac organism volume coefficient. The conversion relation parameters are obtained by carrying out mathematical statistics operation on the conversion relation between a large number of coronary artery main blood vessel volumes and myocardial volumes, and the conversion relation parameters and the corresponding coronary artery main blood vessel volumes are stored in a coronary artery-myocardial volume relation comparison table. Based on the main coronary vessel volume, corresponding conversion relation parameters are searched in a coronary artery-myocardial volume relation comparison table to obtain a first heart organism volume coefficient and a second heart organism volume coefficient.
In some embodiments, the coronary-myocardial volume relationship lookup table is optionally a dictionary keyed to the coronary main vessel volume range of each coronary main vessel and values of the transformation relationship parameters. By comparing the coronary main vessel volume with a plurality of keys of the coronary main vessel in the dictionary, the key is determined as the searched conversion relation parameter for a corresponding value in case the coronary main vessel volume is within the coronary main vessel volume range.
The coronary main vessel volume of the vascular volume abnormal growth rate correction coefficient to the power of the number is multiplied by a first heart body volume coefficient and then added with a second heart body volume coefficient to obtain the myocardial volumeV c Characterizing any coronary main vessel volume, α characterizing the first heart volume coefficient, γ characterizing the first heart volume coefficient, β characterizing the vessel volume abnormal growth rate correction coefficient, and β taking an exemplary value between 0.75 and 1. The myocardial mass m=ρ is obtained by multiplying the myocardial volume and the myocardial density h X V, where ρ h Characterization of myocardial density, exemplary, myocardial density ρ h =1.055 g/cc.
According to the technical scheme, based on the volume of any coronary main blood vessel, the myocardial mass is obtained, the myocardial mass can be obtained rapidly, and the speed of determining the microcirculation resistance index is improved.
Determining body type surface parameters of the target object based on the body type data of the target object; a total coronary vessel volume is determined based on the coronary vessel three-dimensional model.
In the present embodiment, the body type surface parameter is a parameter characterizing the body surface area of the target object. The total coronary vessel volume is the volume of all vessel segments in the three-dimensional model of coronary vessels.
Specifically, based on the body type data of the target object, the body surface area bsa= 0.007184 ×w of the target object is calculated 0.425 ×H 0.725 Wherein W represents body weight and H represents height. Based on the coronary vessel three-dimensional model, all vessel segments corresponding to the coronary vessel three-dimensional model are determined, and the sum of the volumes of all vessel segments is determined as the total coronary vessel volume. By way of example, the volume of the LAD vessel in the three-dimensional model of coronary vessels is 80 milliliters, the volume of the LCX vessel is 30 milliliters,the RCA vessel volume was 50 ml, and the total coronary vessel volume was 160 ml.
Determining coronary inlet resting flow based on myocardial mass, body type surface parameters of the target object, total coronary vessel volume, and contrast agent filling time; the coronary inlet hyperemia flow is calculated based on the correlation coefficient of the resting state and the hyperemia state and the coronary inlet resting flow.
In this embodiment, the coronary inlet resting flow is the total amount of blood flowing in per unit time at the coronary inlet (i.e., at the intersection of the coronary and the aorta) in a resting state. The coronary inlet resting flow is calculated by substituting the body type surface parameters, the total coronary vessel volume and the contrast agent filling time into a coronary inlet resting flow equation. The coronary inlet resting flow equation isWherein ε represents the first coronary inlet resting flow coefficient and δ represents the second coronary inlet resting flow coefficient, V all Total coronary vessel volume is characterized, t characterizes contrast agent filling time.
The correlation coefficient is a coefficient for converting coronary inlet resting flow to coronary inlet hyperemic flow. The correlation coefficient is specific data of the target subject, and is influenced by physical factors of the target subject, such as the type and severity of heart disease, blood pressure, and coronary stenosis degree. There may be differences in the association coefficients between different target objects. The association coefficient may be an association coefficient of a specific target object group to which the target object belongs, where sign factors of a plurality of target objects in the specific target object group are similar; the correlation coefficient of the target object may be used, and this embodiment is not limited thereto. The coronary inlet congestion flow Q is calculated by multiplying the coronary inlet resting flow and the associated coefficient hyper ＝τ×Q rest Wherein τ characterizes the correlation coefficient. Illustratively, τ takes a value between 3 and 5.
According to the technical scheme, the coronary inlet resting flow is calculated by calculating the myocardial mass, the body type surface parameters and the total coronary blood vessel volume and based on the myocardial mass, the body type surface parameters and the total coronary blood vessel volume, and the coronary inlet hyperemia flow is obtained by converting the coronary inlet resting flow, so that the coronary inlet hyperemia flow can be obtained rapidly, and the speed of determining the microcirculation resistance index is improved.
S130, determining an outlet flow boundary condition based on the coronary inlet congestion flow and the size data of the coronary branch vessel.
In the present embodiment, the size data of the coronary branch vessel is data characterizing the size of the coronary branch vessel. By way of example, the size data may be any of the radius, diameter, area, etc. of the coronary branch vessel.
Specifically, based on the size data of each coronary branch vessel, the coronary inlet congestion flow is distributed to the corresponding coronary branch vessel, so as to obtain the coronary outlet congestion flow of each coronary branch vessel, and the sum of the coronary outlet congestion flow and the coronary inlet congestion flow is ensured to be equal. Wherein, the coronary outlet congestion flow is the total blood volume flowing out per unit time at the outlet of the coronary branch vessel (i.e., the end of the coronary branch vessel) in the congestion state. The outlet flow boundary condition is a condition set at the outlet of the coronary branch vessel when simulating the blood flow in the three-dimensional model of the coronary blood vessel, and includes coronary outlet congestion flows of the plurality of coronary branch vessels. Optionally, acquiring size data of each coronary branch vessel, and determining a size ratio of each coronary branch vessel based on the size data of each coronary branch vessel; distributing coronary inlet congestion flow based on the size ratio of each coronary branch vessel, and determining coronary outlet congestion flow of each coronary branch vessel; coronary outlet hyperemic flow rates of the plurality of coronary branch vessels are determined as outlet flow boundary conditions.
Specifically, a plurality of size data of the blood vessel segment corresponding to each coronary branch blood vessel is determined based on the three-dimensional model of the coronary branch blood vessel, and the size data may be, for example, the radius of the coronary branch blood vessel, the volume of the coronary branch blood vessel, or other data capable of characterizing the relationship between the coronary outlet congestion flow and the coronary inlet congestion flow distributed to each coronary branch blood vessel, which is not the case in this embodimentAnd limiting. Determining an average value of a plurality of size data as size data f of each coronary branch 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. Calculating the size ratio 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 size ratio of each coronary branch vessel, distributing coronary inlet congestion flow to each coronary branch vessel to obtain +.about.of each coronary branch vessel>
Taking the example that the size data is the radius of the coronary branch vessels, the size data of each coronary branch vesselWhere ω is a coronary flow distribution coefficient for distributing more coronary inlet congestion flow to the large radius coronary branch vessel and less coronary inlet congestion flow to the small radius coronary branch vessel. Illustratively, ω is a value between 2.5 and 3.
According to the technical scheme, the size ratio of each coronary branch vessel is used for distributing the coronary inlet congestion flow, 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.
S140, determining an inlet pressure boundary condition based on the aortic pressure of the target object.
In the present embodiment, the inlet pressure boundary condition is a condition set at the coronary inlet when simulating the blood flow in the coronary vessel three-dimensional model, including the aortic pressure. Where aortic pressure is specific data of the target subject, and is affected by physical factors of the target subject, such as height, weight, sex and age of the target subject, aortic pressure may vary from target subject to target subject. The aortic pressure 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 of the target object, which is not limited in this embodiment, and the 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.
S150, performing simulation processing based on the inlet pressure boundary condition and the outlet flow boundary condition, and determining the tail end pressure of the coronary branch vessel and the contrast agent conduction time.
In this embodiment, the tip pressure is the pressure distributed at the outlet of the coronary branch vessel, and the contrast agent conduction time is the time from the start of contrast agent injection to the peak of contrast agent filling in the coronary branch vessel.
Specifically, an inlet pressure boundary condition is set at the coronary inlet of the coronary three-dimensional model of the coronary blood vessel and used for representing cardiac pumping, and an outlet flow boundary condition is set at the outlet of the coronary branch blood vessel of the coronary three-dimensional model of the coronary blood vessel and used for representing the outflow of blood from the coronary branch blood vessel. Simulating blood flow in the three-dimensional model of coronary vessels, the three-dimensional model of coronary vessels may be imported into simulation software, such as ANSYS Fluent, openFOAM, and COMSOL Multiphysics; numerical analysis may also be performed on the coronary vessel three-dimensional model, for example, a finite element method, a finite difference method, and a finite volume method, to construct a hemodynamic simulation model, which is not limited in this embodiment. The hemodynamic simulation model is a model for simulating blood flow in a coronary vessel three-dimensional model. And (3) setting an inlet pressure boundary condition and an outlet flow boundary condition for the hemodynamic simulation model, and simulating blood flow in the hemodynamic simulation model to obtain the end pressure of the coronary branch vessel and the contrast agent conduction time.
S160, determining a microcirculation resistance index corresponding to the coronary angiography image data based on the tail end pressure of the coronary branch blood vessel and the contrast agent conduction time.
In the present embodiment, the microcirculation of each coronary branch vesselThe resistance index is the product of the end pressure of the coronary branch vessel and the contrast agent conduction time, namely the microcirculation resistance index of the ith coronary branch vessel is IMR i ＝P i ×Tmn i Wherein P is i Characterization of the end pressure of the ith coronary branch vessel, tmn i Contrast agent transit times for the ith coronary branch vessel are characterized.
Fig. 5 is a schematic diagram illustrating a hemodynamic simulation visualization result according to an embodiment of the present invention. As shown in fig. 5, the microcirculation resistance indexes of the 4 coronary branch vessels are 10.29, 8.28, 8.56 and 7.12, respectively.
According to the technical scheme, coronary angiography image data of a target object are acquired, and a coronary vessel three-dimensional model is built based on the coronary angiography image data; determining coronary inlet congestion flow based on body conformation data of the target subject, the coronary vessel three-dimensional model, and a contrast agent filling time; determining an outlet flow boundary condition based on the coronary inlet congestion flow and the size data of the coronary branch vessel; and determining an inlet pressure boundary condition based on the aortic pressure of the target object; performing simulation processing based on the inlet pressure boundary condition and the outlet flow boundary condition, and determining the tail end pressure of the coronary branch vessel and the contrast agent conduction time; the microcirculation resistance indexes corresponding to the coronary angiography image data are determined based on the tail end pressure of the coronary branched blood vessels and the contrast agent conduction time, so that the microcirculation resistance indexes of a plurality of coronary branched blood vessels can be obtained through one-time calculation, the problem that the microcirculation resistance indexes are accurately calculated by the coronary branched blood vessels is solved, the accuracy of the microcirculation resistance indexes is improved, a temperature/pressure guide wire is not needed, the wounds suffered by a patient are reduced, and the acquisition difficulty and cost of the microcirculation resistance indexes are reduced.
Example two
Fig. 6 is a flowchart of a method for determining a microcirculatory resistance index according to a second embodiment of the present invention, where the technical solution of the second embodiment of the present invention is further optimized based on any of the foregoing embodiments. As shown in fig. 6, the method includes:
s210, acquiring coronary angiography image data of a target object, and constructing a coronary vessel three-dimensional model based on the coronary angiography image data.
S220, determining the quality of cardiac muscle based on the coronary blood vessel three-dimensional model; determining coronary inlet hyperemia flow based on myocardial mass and body type data of the target subject; determining an outlet flow boundary condition based on the coronary inlet congestion flow and the size data of the coronary branch vessel; an inlet pressure boundary condition is determined based on the aortic pressure of the target object.
S230, constructing a hemodynamic simulation model.
In this embodiment, the hemodynamic simulation model may be a reduced-order hydrodynamic simulation model or a full-order hydrodynamic simulation model, which is not limited in this embodiment.
In some embodiments, optionally, a reduced order hemodynamic simulation model is constructed based on the neural network.
Specifically, a neural network model for hemodynamic simulation is constructed based on learning algorithms (e.g., machine learning algorithms and deep learning algorithms). In the training process of the neural network model, a training data set which is stored in a local or server in advance is called, and the neural network model is trained based on the training data set, wherein the training data set comprises training data of a large number of target objects, and each training data comprises but is not limited to an inlet pressure boundary condition and an outlet flow boundary condition.
In one implementation, the neural network model is a data-driven model, each training data comprises an inlet pressure boundary condition, an outlet flow boundary condition and a simulation result label, wherein the simulation result label comprises real pressure distribution and real blood flow velocity of a coronary branch vessel, the inlet pressure boundary condition and the outlet flow boundary condition are input into the neural network model to obtain a simulation prediction result, and the simulation prediction result comprises predicted pressure distribution and predicted blood flow velocity of the coronary branch vessel. Constructing a loss function based on the simulation prediction result and the corresponding simulation result label, and restricting the training process by using the loss function as a penalty term.
In one implementation, the neural network model isBased on a model of embedded physical knowledge, each training data includes an inlet pressure boundary condition and an outlet flow boundary condition, a fluid control equation is integrated into the neural network model, and a loss function is constructed based on a residual term of the fluid control equation, which is used as a penalty term to limit the space of feasible solutions. The fluid control equation solution is obtained by converting the problem of directly solving the fluid control equation into an optimization problem of the loss function. Exemplary fluid control equations include the Navier-Stokes equation And a continuity equation, u=0, where u represents a fluid (blood) velocity vector, t represents time, p represents pressure, ρ represents blood density, and μ represents the dynamic viscosity of blood. And determining the trained neural network model as a reduced-order hemodynamic simulation model.
According to the technical scheme, the relation between the computing resources and the simulation precision can be balanced to a certain extent by constructing the reduced-order hemodynamic simulation model based on the neural network.
In some embodiments, optionally, constructing a plurality of coronary vessel mesh models based on the coronary vessel three-dimensional model; and constructing a full-order hemodynamic simulation model based on the fluid control equation and the multiple coronary artery grid models.
In the present embodiment, the plurality of coronary vessel mesh models are mesh models including a plurality of coronary branch vessel discrete meshes, each mesh being one calculation unit of the plurality of coronary vessel mesh models. The coronary vessel three-dimensional model is subjected to grid division based on a grid division algorithm, so that discretization processing of the coronary vessel three-dimensional model is realized, and the grid division algorithm can be any one of a Cartesian grid algorithm, a quadtree grid algorithm, an octree grid algorithm and the like by way of example. And obtaining a plurality of coronary artery blood vessel grid models by setting a fluid control equation for each grid. It can be appreciated that the accuracy of the simulation prediction result can be ensured by manually or automatically adjusting the fineness and the quality of the grid.
According to the technical scheme, the blood flow of the hemodynamic simulation model can be comprehensively analyzed by constructing the full-order hemodynamic simulation model, and an accurate simulation prediction result is obtained.
S240, inputting the inlet pressure boundary condition and the outlet flow boundary condition into a hemodynamic simulation model for simulation processing, and determining the blood flow velocity and the pressure distribution of the coronary branch blood vessel.
Specifically, by setting an inlet pressure boundary condition and an outlet flow boundary condition for the hemodynamic simulation model, applying the aortic pressure in the inlet pressure boundary condition at the aortic inlet, and applying the congestion outlet flow corresponding to each coronary branch vessel in the outlet flow boundary condition at the outlet of each coronary branch vessel, the simulation of the blood flow in the coronary three-dimensional model is realized, and the simulation prediction result of each coronary branch vessel is obtained. And determining the predicted pressure distribution of the coronary branch vessel in the simulation prediction result as the pressure distribution of the coronary branch vessel, and determining the predicted blood flow velocity of the coronary branch vessel in the simulation prediction result as the blood flow velocity of the coronary branch vessel. Optionally, inputting an inlet pressure boundary condition and an outlet flow boundary condition into a full-order hemodynamic simulation model, performing finite element simulation on the hemodynamic simulation model, and determining the blood flow speed and pressure of each grid in the coronary artery grid model; based on each grid of coronary branch vessels, a blood flow velocity and pressure distribution of the coronary branch vessels are determined.
Specifically, the outlet flow boundary condition and the inlet pressure boundary condition are input to a full-order numerical calculation model to perform finite element (for example, finite element method, finite difference method and finite volume method) simulation processing, and the finite element simulation processing may be based on the finite element method, the finite difference method or the finite volume method, which is not limited in this embodiment, so as to obtain the blood flow velocity and pressure of each grid in the coronary artery grid model. The blood flow velocity of the coronary branch vessel is obtained by integrating the blood flow velocity of a plurality of grids corresponding to each coronary branch vessel in the multiple coronary vessel grid model, and the pressure distribution of the coronary branch vessel is obtained by integrating the pressure of a plurality of grids corresponding to each coronary branch vessel in the multiple coronary vessel grid model.
For example, as shown in fig. 5, the pressure distribution of the coronary branch vessel is visually displayed based on the color mark corresponding to the pressure of each grid, wherein the color mark may be a gray value or an RGB value.
According to the technical scheme, based on the full-order hemodynamic simulation model, finite element simulation is carried out on the hemodynamic simulation model, so that the accurate blood flow velocity and pressure distribution of coronary branch vessels can be obtained, and the accuracy of the microcirculation resistance index is ensured.
S250, calculating the contrast agent conduction time of the coronary branch vessel based on the length of the vessel section and the blood flow velocity of the coronary branch vessel.
In this embodiment, the length of the vessel segment is the length of the vessel centerline of the vessel segment in the coronary branch vessel. Determining a plurality of blood vessel segments corresponding to each coronary branch blood vessel based on a coronary three-dimensional model, determining the length of the blood vessel segments of each blood vessel segment based on the blood vessel center line of each blood vessel segment, determining the blood flow velocity of each blood vessel segment based on the blood flow velocity of the coronary branch blood vessel obtained by simulation, obtaining the contrast agent conduction time of each blood vessel segment by performing a quotient processing on the blood vessel segment length and the blood flow velocity of each blood vessel segment, obtaining the contrast agent conduction time of the coronary branch blood vessel by performing a summation processing on the contrast agent conduction time of the plurality of blood vessel segments corresponding to each coronary branch blood vesselWherein L is i,j A vessel centerline length of a jth vessel segment representing an ith coronary branch vessel, +.>Mean value of blood flow velocity in the jth vessel segment representing the ith coronary branch vessel, exemplary,/-for example>The jth vessel segment which is the ith coronary branch vesselM represents the average value of the blood flow velocity at the inlet and the blood flow velocity at the outlet, the ith coronary branch vessel comprises m vessel segments, it being understood that m is related to the vessel centerline length of the vessel segment, the longer the vessel centerline length of the vessel segment, the smaller m.
S260, determining the tail end pressure at the outlet of the coronary branch vessel based on the pressure distribution of the coronary branch vessel.
Specifically, based on the pressure distribution of the coronary branch vessel obtained by the simulation, the pressure distributed at the outlet of the coronary branch vessel is determined as the tip pressure. Illustratively, as shown in fig. 5, the end pressures of the 4 coronary branch vessels are pressures distributed at the outlets of the 4 coronary branch vessels, respectively.
S270, determining a microcirculation resistance index corresponding to the coronary angiography image data based on the end pressure of the coronary branch blood vessel and the contrast agent conduction time.
According to the technical scheme, coronary angiography image data of a target object are acquired, and a coronary vessel three-dimensional model is built based on the coronary angiography image data; determining myocardial mass based on the coronary vessel three-dimensional model; determining coronary inlet hyperemia flow based on myocardial mass and body type data of the target subject; determining an outlet flow boundary condition based on the coronary inlet congestion flow and the size data of the coronary branch vessel; determining an inlet pressure boundary condition based on an aortic pressure of the target object; constructing a hemodynamic simulation model; inputting the inlet pressure boundary condition and the outlet flow boundary condition into a hemodynamic simulation model for simulation treatment, and determining the blood flow velocity and pressure distribution of the coronary branch vessel; calculating contrast agent conduction time of the coronary branch vessel based on the vessel segment length and the blood flow velocity of the coronary branch vessel; determining an end pressure at an outlet of the coronary branch vessel based on the pressure distribution of the coronary branch vessel; based on the end pressure of the coronary artery branch vessel and the contrast agent conduction time, the microcirculation resistance index corresponding to the coronary artery contrast image data is determined, so that the blood flow in the coronary artery three-dimensional model can be simulated, the accurate end pressure of each coronary artery branch vessel and the accurate contrast agent conduction time are obtained, and the accuracy of the microcirculation resistance index is improved.
Example III
Fig. 7 is a schematic structural diagram of a microcirculatory resistance index determining device according to a third embodiment of the invention. As shown in fig. 7, the apparatus includes:
a coronary vessel three-dimensional model construction module 310, configured to acquire coronary angiography image data of a target object, and construct a coronary vessel three-dimensional model based on the coronary angiography image data;
a boundary condition determination module 320 for determining coronary inlet congestion flow based on body type data of the target object, the three-dimensional model of the coronary vessel, and the contrast filling time; determining an outlet flow boundary condition based on the coronary inlet congestion flow and the size data of the coronary branch vessel; and determining an inlet pressure boundary condition based on the aortic pressure of the target object;
a microcirculation resistance index determination module 330 for performing simulation processing based on the inlet pressure boundary condition and the outlet flow boundary condition to determine the end pressure and contrast agent conduction time of the coronary branch vessel; and determining a microcirculation resistance index corresponding to the coronary angiography image data based on the end pressure of the coronary branch vessel and the contrast agent conduction time.
According to the technical scheme, coronary angiography image data of a target object are acquired, and a coronary vessel three-dimensional model is built based on the coronary angiography image data; determining coronary inlet hyperemia flow based on body conformation data based on the target subject, the three-dimensional model of the coronary vessel, and a contrast agent filling time; determining an outlet flow boundary condition based on the coronary inlet congestion flow and the size data of the coronary branch vessel; and determining an inlet pressure boundary condition based on the aortic pressure of the target object; performing simulation processing based on the inlet pressure boundary condition and the outlet flow boundary condition, and determining the tail end pressure of the coronary branch vessel and the contrast agent conduction time; the microcirculation resistance indexes corresponding to the coronary angiography image data are determined based on the tail end pressure of the coronary branched blood vessels and the contrast agent conduction time, so that the microcirculation resistance indexes of a plurality of coronary branched blood vessels can be obtained through one-time calculation, the problem that the microcirculation resistance indexes are accurately calculated by the coronary branched blood vessels is solved, the accuracy of the microcirculation resistance indexes is improved, a temperature/pressure guide wire is not needed, the wounds suffered by a patient are reduced, and the acquisition difficulty and cost of the microcirculation resistance indexes are reduced.
Based on the above embodiment, the boundary condition determining module 320 is optionally specifically configured to: determining myocardial mass based on the coronary vessel three-dimensional model; determining body type surface parameters of the target object based on the body type data of the target object; determining a total coronary vessel volume based on the coronary vessel three-dimensional model; determining coronary inlet resting flow based on myocardial mass, body type surface parameters of the target object, total coronary vessel volume, and contrast agent filling time; the coronary inlet hyperemia flow is calculated based on the correlation coefficient of the resting state and the hyperemia state and the coronary inlet resting flow.
On the basis of the above embodiment, optionally, the boundary condition determining module 320 is further configured to:
determining the volume of any coronary artery main vessel based on the coronary artery three-dimensional model; determining a first and a second heart volume coefficient based on the coronary main vessel volume; the myocardial mass is calculated based on the coronary main vessel volume, the vessel volume abnormal growth rate correction coefficient, the first heart volume coefficient, the second heart volume coefficient, and the myocardial density.
Optionally, in addition to the above embodiment, the three-dimensional model of coronary vessels comprises a plurality of coronary branch vessels.
Based on the above embodiment, the boundary condition determining module 320 is optionally specifically configured to: acquiring size data of each coronary branch vessel, and determining the size ratio of each coronary branch vessel based on the size data of each coronary branch vessel; distributing coronary inlet congestion flow based on the size ratio of each coronary branch vessel, and determining coronary outlet congestion flow of each coronary branch vessel; coronary outlet hyperemic flow rates of the plurality of coronary branch vessels are determined as outlet flow boundary conditions.
Based on the above embodiments, the microcirculatory resistance index determination module 330 is optionally specifically configured to: constructing a hemodynamic simulation model; inputting the inlet pressure boundary condition and the outlet flow boundary condition into a hemodynamic simulation model for simulation treatment, and determining the blood flow velocity and pressure distribution of the coronary branch vessel; calculating contrast agent conduction time of the coronary branch vessel based on the vessel segment length and the blood flow velocity of the coronary branch vessel; based on the pressure distribution of the coronary branch vessel, the tip pressure at the outlet of the coronary branch vessel is determined.
Based on the above embodiments, the microcirculatory resistance index determination module 330 is further configured to:
And constructing a reduced-order hemodynamic simulation model based on the neural network.
Based on the above embodiments, the microcirculatory resistance index determination module 330 is further configured to:
constructing a plurality of coronary artery mesh models based on the coronary artery three-dimensional model; and constructing a full-order hemodynamic simulation model based on the fluid control equation and the multiple coronary artery grid models.
Based on the above embodiments, the microcirculatory resistance index determination module 330 is optionally specifically configured to: inputting an inlet pressure boundary condition and an outlet flow boundary condition into a full-order hemodynamic simulation model, performing finite element simulation on the hemodynamic simulation model, and determining the blood flow speed and pressure of each grid in the coronary artery grid model; based on each grid of coronary branch vessels, a blood flow velocity and pressure distribution of the coronary branch vessels are determined.
The microcirculation resistance index determining device provided by the embodiment of the invention can execute the microcirculation resistance index 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. 8 is a schematic structural diagram of an electronic device implementing a microcirculation resistance index determination method according to an 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 devices may also represent various forms of mobile equipment, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing equipment. 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. 8, 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 respective methods and processes described above, such as the microcirculation resistance index determination method.
In some embodiments, the microcirculatory resistance index determination method can be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the 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 microcirculation resistance index determination method may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the microcirculatory resistance index determination method by any other suitable means (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 index 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 microcirculatory resistance index, the method comprising:
acquiring coronary angiography image data of a target object, and constructing a coronary vessel three-dimensional model based on the coronary angiography image data; determining coronary inlet congestion flow based on body conformation data of the target subject, the coronary vessel three-dimensional model, and a contrast agent filling time; determining an outlet flow boundary condition based on the coronary inlet congestion flow and the size data of the coronary branch vessel; and determining an inlet pressure boundary condition based on the aortic pressure of the target object; performing simulation processing based on the inlet pressure boundary condition and the outlet flow boundary condition, and determining the tail end pressure of the coronary branch vessel and the contrast agent conduction time; and determining a microcirculation resistance index corresponding to the coronary angiography image data based on the end pressure of the coronary branch vessel and the contrast agent conduction time.
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 (10)

1. A method for determining a microcirculatory resistance index, comprising:
acquiring coronary angiography image data of a target object, and constructing a coronary vessel three-dimensional model based on the coronary angiography image data;
determining coronary inlet congestion flow based on the body type data of the target object, the three-dimensional model of coronary vessels, and contrast agent filling time; determining an outlet flow boundary condition based on the coronary inlet congestion flow and the size data of the coronary branch vessel; and determining an inlet pressure boundary condition based on an aortic pressure of the target object;
performing simulation processing based on the inlet pressure boundary condition and the outlet flow boundary condition to determine the end pressure and contrast agent conduction time of the coronary branch vessel; and determining a microcirculation resistance index corresponding to the coronary angiography image data based on the tail end pressure of the coronary branch blood vessel and the contrast agent conduction time.
2. The method of claim 1, wherein the determining coronary inlet congestion flow based on the body type data of the target object, the three-dimensional model of coronary vessels, and contrast filling time comprises:
determining myocardial mass based on the coronary vessel three-dimensional model;
Determining body type surface parameters of the target object based on the body type data of the target object; determining a total coronary vessel volume based on the coronary vessel three-dimensional model; determining a coronary inlet resting flow based on the myocardial mass, body type surface parameters of the target object, the total coronary vessel volume, and contrast filling time; the coronary inlet hyperemia flow is calculated based on the correlation coefficient of the resting state and the hyperemia state and the coronary inlet resting flow.
3. The method of claim 2, wherein the determining the myocardial mass based on the three-dimensional model of coronary vessels comprises:
determining any coronary main vessel volume based on the coronary vessel three-dimensional model;
determining a first and a second cardiomyocyte volume coefficient based on the coronary main vessel volume;
the myocardial mass is calculated based on the coronary main vessel volume, vessel volume differential rate growth rate correction coefficient, the first cardiac volume coefficient, the second cardiac volume coefficient, and myocardial density.
4. The method of claim 1, wherein the three-dimensional model of coronary vessels comprises a plurality of the coronary branch vessels;
Acquiring size data of each coronary branch vessel, and determining the size ratio of each coronary branch vessel based on the size data of each coronary branch vessel;
assigning the coronary inlet congestion flow based on the size ratio of each of the coronary branch vessels, determining a coronary outlet congestion flow for each of the coronary branch vessels;
determining the coronary outlet hyperemic flow rate of a plurality of said coronary branch vessels as an outlet flow rate boundary condition.
5. The method of claim 1, wherein the determining the end pressure and contrast agent conduction time of the coronary branch vessel based on the inlet pressure boundary condition and the outlet flow boundary condition comprises:
constructing a hemodynamic simulation model;
inputting the inlet pressure boundary condition and the outlet flow boundary condition into the hemodynamic simulation model for simulation processing, and determining the blood flow velocity and pressure distribution of the coronary branch blood vessel;
calculating the contrast agent conduction time of the coronary branch vessel based on a vessel segment length of the coronary branch vessel and the blood flow velocity;
The tip pressure at the outlet of the coronary branch vessel is determined based on the pressure distribution of the coronary branch vessel.
6. The method of claim 5, wherein said constructing a hemodynamic simulation model comprises:
constructing a reduced-order hemodynamic simulation model based on a neural network;
or constructing a plurality of coronary artery mesh models based on the coronary artery three-dimensional model; and constructing a full-order hemodynamic simulation model based on a fluid control equation and the multiple coronary artery grid models.
7. The method of claim 6, wherein said inputting the inlet pressure boundary condition and the outlet flow boundary condition into the hemodynamic simulation model for simulation processing determines a blood flow velocity and a pressure distribution of the coronary branch vessel, comprising:
inputting the inlet pressure boundary condition and the outlet flow boundary condition into a full-order hemodynamic simulation model, performing finite element simulation on the hemodynamic simulation model, and determining the blood flow speed and pressure of each grid in the coronary artery grid model;
based on each of the meshes of the coronary branch vessels, a blood flow velocity and a pressure distribution of the coronary branch vessels are determined.
8. A microcirculation resistance index determination device characterized by comprising:
the coronary artery three-dimensional model construction module is used for acquiring coronary artery contrast image data of a target object and constructing a coronary artery three-dimensional model based on the coronary artery contrast image data;
a boundary condition determination module for determining coronary inlet congestion flow based on body type data of the target object, the coronary three-dimensional model, and a contrast agent filling time; determining an outlet flow boundary condition based on the coronary inlet congestion flow and the size data of the coronary branch vessel; and determining an inlet pressure boundary condition based on an aortic pressure of the target object;
the microcirculation resistance index determining module is used for performing simulation processing based on the inlet pressure boundary condition and the outlet flow boundary condition to determine the tail end pressure and the contrast agent conduction time of the coronary branch vessel; and determining a microcirculation resistance index corresponding to the coronary angiography image data based on the tail end pressure of the coronary branch blood vessel and the contrast agent conduction time.
9. 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 microcirculation resistance index determination method of any one of claims 1-7.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores computer instructions for causing a processor to implement the microcirculation resistance index determination method according to any one of claims 1 to 7 when executed.
CN202311699284.3A 2023-12-12 2023-12-12 Microcirculation resistance index determination method, device, equipment and medium Pending CN117688865A (en)

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