CN113827207A - Pulmonary artery assessment method, device, computer equipment and storage medium - Google Patents
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Abstract
The present application relates to a method, an apparatus, a computer device and a storage medium for assessment of pulmonary arteries. The method comprises the steps of obtaining medical image data and pulmonary artery characteristic information of a patient, constructing a geometric model of a pulmonary artery structure according to the medical image data, and then carrying out fluid mechanics calculation on the geometric model of the pulmonary artery structure according to a preset boundary condition to obtain at least one piece of blood flow characteristic information of the pulmonary artery structure. The assessment method achieves non-invasive assessment of pulmonary hypertension, and reduces physical damage to a patient compared to traditional invasive assessment. Compared with the traditional invasive assessment method which can only provide single blood flow characteristic information corresponding to a fixed plurality of sites, the pulmonary artery assessment method provided by the application can more comprehensively assess pulmonary hypertension and can obtain an accurate assessment result.
Description
Technical Field
The present application relates to the field of medical detection technologies, and in particular, to a pulmonary artery assessment method, apparatus, computer device, and storage medium.
Background
Pulmonary hypertension is a hemodynamic and pathophysiological state in which the pulmonary arterial pressure rises above a certain threshold, and can lead to right heart failure. Pulmonary hypertension is a common disease and frequently encountered disease, and has high disability rate and high fatality rate. Three factors that affect pulmonary artery pressure are: pulmonary vascular impedance, cardiac output, and pulmonary capillary wedge pressure. Any disease that affects the above factors may lead to pulmonary hypertension.
At present, the diagnosis and evaluation of pulmonary hypertension, which are commonly used in clinical practice, are mainly realized through right atrial catheter examination, which is an invasive examination, that is, during examination, a doctor inserts a cardiac catheter to the right heart chamber and great vessels through veins under the guidance of X-ray fluoroscopy.
However, the method can only measure a single pressure index at a few sites such as the right atrium, the right ventricle, the pulmonary artery trunk and the like.
Disclosure of Invention
In view of the above, there is a need to provide a pulmonary artery assessment method, device, computer device and storage medium capable of measuring pulmonary hypertension comprehensively and non-invasively.
In a first aspect, a method of assessing a pulmonary artery, the method comprising:
acquiring medical image data and pulmonary artery characteristic information of a patient;
constructing a geometric model of the pulmonary artery structure from the medical image data;
and performing fluid mechanics calculation on the geometric model of the pulmonary artery structure according to a preset boundary condition to obtain at least one piece of blood flow characteristic information of the pulmonary artery structure.
In one embodiment, the constructing a geometric model of the pulmonary artery structure from the medical image data comprises:
segmenting and extracting pulmonary arteries in the medical image data by adopting a preset deep learning algorithm to obtain a first segmentation result;
extracting the pulmonary artery in the medical image data by adopting a preset blood vessel enhancement and/or blood vessel tracking method to obtain a second segmentation result;
and obtaining a geometric model containing the pulmonary artery structure according to the first segmentation result and the second segmentation result.
In one embodiment, after the constructing the geometric model of the pulmonary artery structure from the medical image data, the method further comprises:
performing mesh division on the model of the pulmonary artery structure to obtain a plurality of mesh models of the pulmonary artery structure;
loading preset boundary conditions on the geometric model of the pulmonary artery structure for hydrodynamics calculation to obtain at least one blood flow characteristic information of the pulmonary artery structure, wherein the method comprises the following steps:
and loading preset boundary conditions on each grid model to perform fluid mechanics calculation to obtain at least one piece of blood flow characteristic information of the pulmonary artery structure.
In one embodiment, the method further comprises:
receiving an instruction input by a user; the instruction comprises a target area or a target site;
acquiring at least one piece of blood flow characteristic information of a corresponding pulmonary artery structure according to the target area or the target site;
and displaying at least one piece of blood flow characteristic information of the pulmonary artery structure in a region to be displayed.
In one embodiment, the preset boundary conditions include: pulmonary artery entrance boundary conditions and pulmonary artery exit boundary conditions.
In one embodiment, the method for acquiring the pulmonary artery outlet boundary condition includes:
determining the vessel diameter of each pulmonary artery outlet according to the geometric model of the pulmonary artery structure;
and proportionally distributing the cardiac output to each pulmonary artery outlet according to the vessel diameter of each pulmonary artery outlet to obtain the flow or flow velocity corresponding to each pulmonary artery outlet.
In one embodiment, the pulmonary artery feature information includes: at least one of blood pressure, pulmonary artery pressure, non-capillary wedge pressure, pulmonary artery resistance, cardiac output, body surface area, cardiac index of the patient.
In a second aspect, an apparatus for evaluating a pulmonary artery, the apparatus comprising:
the loading module is used for acquiring medical image data and pulmonary artery characteristic information of a patient;
a construction module for constructing a geometric model of the pulmonary artery structure from the medical image data;
and the evaluation module is used for performing fluid mechanics calculation on the geometric model of the pulmonary artery structure according to a preset boundary condition to obtain at least one piece of blood flow characteristic information of the pulmonary artery structure.
In a third aspect, a computer device comprises a memory storing a computer program and a processor implementing the method of the first aspect when the processor executes the computer program.
In a fourth aspect, a computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements the method of the first aspect described above.
According to the pulmonary artery assessment method, the pulmonary artery assessment device, the computer equipment and the storage medium, at least one piece of blood flow characteristic information of the pulmonary artery structure is obtained by acquiring medical image data and pulmonary artery characteristic information of a patient, constructing a geometric model of the pulmonary artery structure according to the medical image data, and then performing fluid mechanics calculation on the geometric model of the pulmonary artery structure according to preset boundary conditions. The assessment method achieves non-invasive assessment of pulmonary hypertension, and reduces physical damage to a patient compared to traditional invasive assessment. Compared with the traditional invasive assessment method which can only provide single blood flow characteristic information corresponding to a fixed plurality of sites, the pulmonary artery assessment method provided by the application can more comprehensively assess pulmonary hypertension, so that an accurate assessment result can be obtained when the pulmonary hypertension of a patient is effectively assessed based on a plurality of blood flow characteristic information on each site obtained by the assessment method.
Drawings
FIG. 1 is a diagram of an environment in which a method for evaluating pulmonary arteries is used in one embodiment;
FIG. 2 is a schematic flow chart diagram of a method for pulmonary artery assessment in one embodiment;
FIG. 3 is a flowchart illustrating an implementation manner of S102 in the embodiment of FIG. 2;
FIG. 3A is a diagram of a geometric model of a pulmonary artery structure in one embodiment;
FIG. 4 is a schematic flow chart diagram of a method for pulmonary artery assessment in one embodiment;
FIG. 5 is a schematic flow chart diagram of a method for pulmonary artery assessment in one embodiment;
FIG. 6 is a schematic flow chart diagram illustrating a method for pulmonary artery assessment in one embodiment;
FIG. 7 is a schematic flow chart diagram illustrating a method for pulmonary artery assessment in one embodiment;
FIG. 8 is a block diagram of an apparatus for evaluating a pulmonary artery according to an embodiment;
FIG. 9 is a block diagram of an apparatus for evaluating a pulmonary artery according to an embodiment;
FIG. 10 is a block diagram showing the structure of an apparatus for evaluating a pulmonary artery according to an embodiment;
FIG. 11 is a block diagram of an apparatus for evaluating a pulmonary artery according to an embodiment;
FIG. 12 is a block diagram showing the structure of an apparatus for evaluating a pulmonary artery according to an embodiment;
FIG. 13 is a block diagram showing the structure of an apparatus for evaluating a pulmonary artery according to an embodiment;
FIG. 14 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The pulmonary artery assessment method provided by the application can be applied to the application environment shown in fig. 1. The imaging device 102 is connected to the terminal 104 through a network, and the imaging device 102 is configured to scan a pulmonary artery structure of a patient and transmit scan data to the terminal 104, so that the terminal 104 performs imaging processing based on the scan data to obtain an imaging image. The imaging device 102 may be various types of imaging devices, such as a Computed Tomography (CT) imaging device and a magnetic resonance imaging device. The terminal 102 may be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices.
Those skilled in the art will appreciate that the application environment shown in fig. 1 is only a block diagram of a portion of the structure associated with the present application and does not constitute a limitation on the application environment to which the present application is applied, and that a particular application environment may include more or less components than those shown in the drawings, or may combine certain components, or have a different arrangement of components.
In one embodiment, as shown in fig. 2, a method for evaluating a pulmonary artery is provided, which is illustrated by applying the method to the terminal in fig. 1, and includes the following steps:
s101, medical image data and pulmonary artery characteristic information of a patient are obtained.
The medical image corresponding to the medical image data includes a pulmonary artery structure of the patient, which may be a medical image such as CT angiography (CTA), Magnetic Resonance Angiography (MRA), or the like. The pulmonary artery characteristic information is pulmonary artery characteristic information of a pulmonary artery hypertension patient, and the pulmonary artery characteristic information comprises: at least one of a blood pressure, a pulmonary artery pressure, a non-capillary wedge pressure, a pulmonary artery resistance, a cardiac output, a body surface area, and a cardiac index of the patient. It should be noted that the medical image includes a pulmonary artery structure of a patient, which may be a pulmonary hypertension patient, that is, the pulmonary artery structure corresponding to the patient is a specific diseased pulmonary artery structure, so that the diseased pulmonary artery structure evaluates the pulmonary hypertension of the patient.
In this embodiment, the pulmonary artery structure of the patient may be scanned and imaged by the imaging device to obtain medical image data of the patient, and then the medical image data of the patient is transmitted to the terminal, so that the terminal can evaluate the pulmonary artery based on the medical image data. In practical applications, some pulmonary artery characteristic information of the patient can be acquired through related acquisition equipment, for example, the blood pressure of the patient is measured by using a blood pressure monitor, the cardiac index of the patient is measured by using an electrocardiograph, and the like. For pulmonary artery characteristic information which cannot be measured by some common instruments, the terminal can obtain the pulmonary artery characteristic information according to historical pathological data of the patient, for example, a patient has performed invasive surgery related to a pulmonary artery before, and a large amount of pulmonary artery characteristic information can be recorded in the process of the surgery. Therefore, the terminal can obtain the pulmonary artery characteristic information of the patient from the historical pathological record of the corresponding acquisition device or the relevant patient. It is understood that the medical image data and the pulmonary artery characteristic information acquired in this step belong to data and information of the same patient.
S102, constructing a geometric model of the pulmonary artery structure according to the medical image data.
Since the medical image data includes the pulmonary artery structure of the patient, the medical image data is image data corresponding to the pulmonary artery structure of the patient. After the terminal acquires the medical image data of the patient, a corresponding geometric model construction algorithm can be adopted, the geometric model construction algorithm comprises a multilayer convolution neural network deep learning algorithm used for obtaining a pulmonary artery main trunk result and a pulmonary artery fine branch rough segmentation result, a deep learning image enhancement algorithm used for obtaining an enhancement result related to the medical image data of the patient and used for further segmenting the pulmonary artery fine branch, and algorithms such as a level set and graph segmentation used for improving the segmentation effect of the pulmonary artery fine branch. Finally, a geometric model of the complete pulmonary artery structure is obtained. In practical application, the terminal can be provided with corresponding modeling simulation software, medical image data is directly input into the modeling simulation software after being acquired, corresponding calculation parameters are set, and the modeling simulation software is used for constructing a geometric model of the pulmonary artery structure. It will be appreciated that the medical image data may be pre-processed, e.g. de-noised, or background data processed, etc., before the geometric model of the pulmonary artery structure is constructed based on the medical image data.
S103, performing fluid mechanics calculation on the geometric model of the pulmonary artery structure according to a preset boundary condition to obtain at least one piece of blood flow characteristic information of the pulmonary artery structure.
The preset boundary condition is determined according to the geometric model of the pulmonary artery structure and the pulmonary artery characteristic information. The preset boundary conditions include: pulmonary artery entrance boundary conditions and pulmonary artery exit boundary conditions; the boundary condition of the pulmonary artery inlet is the pulmonary artery pressure of the patient, and the boundary condition of the pulmonary artery outlet is the corresponding blood flow volume or the blood flow speed of the pulmonary artery outlet. Pulmonary artery pressure refers to the lateral pressure that blood flows through the pulmonary circulation creating a blood vessel in the pulmonary artery. The pulmonary artery characteristic information can be obtained from clinical parameters of the patient, so that the boundary condition is obtained from the clinical parameters of the patient. At least one piece of blood flow characteristic information of the pulmonary artery structure is a quantitative description of the pulmonary artery high pressure, in particular an evaluation value of the pulmonary artery high pressure.
Optionally, when the terminal acquires the at least one piece of blood flow characteristic information of the pulmonary artery structure, the at least one piece of blood flow characteristic information of the pulmonary artery structure obtained by calculation may be stored in the database, so that the user can view the relevant blood flow characteristic information of the pulmonary artery structure at any position on the pulmonary artery structure later by accessing the database.
In this embodiment, after the geometric model of the pulmonary artery structure is constructed at the terminal, the geometric model of the pulmonary artery structure may be loaded with the preset boundary conditions and then subjected to the hydrodynamics calculation, and specifically, a computational hydrodynamics algorithm may be used to solve a pulmonary artery model control equation Navier-Stokes (Navier-Stokes) equation to obtain the blood flow characteristic information of each site on the geometric model of the pulmonary artery structure, for example, a pulmonary artery high pressure value. Then, the doctor can quickly determine the corresponding blood flow characteristic information based on the position of each position point on the geometric model of the pulmonary artery structure. It should be noted that, the geometric model of the pulmonary artery structure in this embodiment may be a patient-specific lesion model, that is, a geometric model of the pulmonary artery structure of the pulmonary hypertension patient, and boundary conditions corresponding to clinical parameters of the patient are loaded, so that blood flow characteristic information of the pulmonary hypertension of the patient can be obtained through calculation. Furthermore, the blood flow characteristic information at a certain point position obtained finally can be one or a plurality of, and the provided evaluation value is richer.
According to the assessment method of the pulmonary artery, at least one blood flow characteristic information of the pulmonary artery structure is obtained by obtaining medical image data and pulmonary artery characteristic information of a patient, constructing a geometric model of the pulmonary artery structure according to the medical image data, and then performing fluid mechanics calculation on the geometric model of the pulmonary artery structure according to a preset boundary condition. The assessment method achieves non-invasive assessment of pulmonary hypertension, and reduces physical damage to a patient compared to traditional invasive assessment. Compared with the traditional invasive assessment method which can only provide single blood flow characteristic information corresponding to a fixed plurality of sites, the pulmonary artery assessment method provided by the application can more comprehensively assess pulmonary hypertension, so that an accurate assessment result can be obtained when the pulmonary hypertension of a patient is effectively assessed based on a plurality of blood flow characteristic information on each site obtained by the assessment method.
Optionally, an implementation manner of the foregoing S102 is provided, which specifically includes: and (3) segmenting and extracting the pulmonary artery main trunk and branch blood vessels in the medical image data by adopting a preset deep learning algorithm to obtain a geometric model of the pulmonary artery structure.
The deep learning algorithm may be a segmentation algorithm, a modeling algorithm, or both. The embodiment relates to a method for constructing a geometric model of a pulmonary artery structure by adopting a deep learning algorithm, namely, a corresponding segmentation algorithm is adopted to segment and extract a pulmonary artery main trunk and branch vessels in medical image data to remove the medical image data of other tissue structures except the pulmonary artery in the medical image data, so that the medical image data corresponding to the pulmonary artery structure is obtained, and then the corresponding model construction algorithm is adopted to construct the geometric model of the pulmonary artery structure based on the medical image data corresponding to the pulmonary artery structure. In practical application, the terminal can be provided with corresponding modeling simulation software, after medical image data corresponding to the pulmonary artery structure is obtained, the medical image data is directly input into the modeling simulation software, corresponding calculation parameters are set, and the modeling simulation software is used for constructing a geometric model of the pulmonary artery structure.
Further, as shown in fig. 3, the implementation manner of S102 specifically includes:
s201, a preset deep learning algorithm is adopted to conduct segmentation and extraction on pulmonary arteries in medical image data, and a first segmentation result is obtained.
The first segmentation result comprises medical image data corresponding to the main artery and the branch vessels of the pulmonary artery.
When the terminal adopts a deep learning algorithm to perform segmentation modeling on the pulmonary artery structure, a deep learning algorithm with a segmentation function can be adopted to segment and extract the pulmonary artery main trunk and the branch blood vessels from the medical image data to obtain the medical image data corresponding to the main trunk and the branch blood vessels of the pulmonary artery, namely a first segmentation result.
S202, extracting the pulmonary artery in the medical image data by adopting a preset blood vessel enhancement and/or blood vessel tracking method to obtain a second segmentation result.
Wherein the second segmentation result comprises medical image data corresponding to a distal branch vessel of the pulmonary artery.
Since the pulmonary artery structure includes not only the pulmonary artery main trunk and the branch vessels, but also some far-end branch vessels at the far ends of the branch vessels, in order to more accurately construct a geometric model of the pulmonary artery structure, after the terminal segments and extracts the pulmonary artery main trunk and the branch vessels from the medical image data based on the step S201, the far-end branch vessels of the branch vessels can be further extracted based on the medical image data corresponding to the segmented and extracted pulmonary artery main trunk and branch vessels by using a preset vessel enhancement and/or vessel tracking method, so as to achieve the segmentation and extraction of the far-end branch vessels, and further obtain the medical image data corresponding to the far-end branch vessels of the pulmonary artery, i.e., the second segmentation result. It can be understood that, in actual operation, when the terminal extracts the distal branch blood vessel of the branch blood vessel based on the medical image data corresponding to the pulmonary artery trunk and the branch blood vessel extracted by segmentation, the medical image data corresponding to the branch blood vessel may be obtained from the medical image data corresponding to the pulmonary artery trunk and the branch blood vessel extracted by segmentation, and then the distal branch blood vessel of the branch blood vessel is tracked based on the medical image data corresponding to the branch blood vessel, and then further extracted.
And S203, obtaining a geometric model of the pulmonary artery structure according to the first segmentation result and the second segmentation result.
After the terminal obtains the first segmentation result based on the steps, the terminal can start to adopt a deep learning algorithm of a corresponding constructed model to construct a geometric model of the pulmonary artery trunk and the branch vessel based on the medical image data corresponding to the pulmonary artery trunk and the branch vessel because the first segmentation result is the medical image data corresponding to the pulmonary artery trunk and the branch vessel. After the terminal obtains the second segmentation result based on the steps, the terminal can start to adopt a deep learning algorithm of a corresponding construction model because the second segmentation result is medical image data corresponding to the far-end branch blood vessel, construct a geometric model of the far-end branch blood vessel based on the medical image data corresponding to the far-end branch blood vessel, and then splice the geometric models of the trunk and branch blood vessels of the pulmonary artery and the geometric model of the far-end branch blood vessel to obtain the geometric model of the pulmonary artery structure. Optionally, when the terminal obtains the first segmentation result and the second segmentation result based on the foregoing steps, a deep learning algorithm of a corresponding constructed model may be started to be adopted, and a geometric model of the pulmonary artery structure is constructed based on medical image data corresponding to the pulmonary artery trunk and the branch vessels and medical image data corresponding to the distal branch vessels.
Referring to fig. 3A, a diagram (a) is a medical image corresponding to a pulmonary artery structure of a normal person, a diagram (b) is a geometric model of a pulmonary artery structure corresponding to the diagram (a), a diagram (c) is a medical image corresponding to a pulmonary artery structure of a pulmonary hypertension patient, and a diagram (d) is a geometric model of a pulmonary artery structure corresponding to the diagram (c).
In the process of constructing the geometric model of the pulmonary artery structure, not only the pulmonary artery main trunk and the branch vessels in the pulmonary artery structure are considered, but also the far-end branch vessels on the branch vessels in the pulmonary artery structure are considered, so that the accuracy of constructing the geometric model of the pulmonary artery structure is increased, the matching degree of the geometric model of the constructed pulmonary artery structure and the real pulmonary artery structure is improved, the accuracy of performing fluid mechanics calculation on the geometric model of the pulmonary artery structure at the later stage can be further improved, and the accuracy of pulmonary artery high pressure assessment is further improved.
In practical applications, after the terminal performs the step of S102, as shown in fig. 4, the terminal further performs the steps of:
and S104, performing grid division on the model of the pulmonary artery structure to obtain a plurality of grid models of the pulmonary artery structure.
Before the terminal carries out fluid mechanics calculation based on a geometric model of a pulmonary artery structure, the terminal also needs to carry out meshing on the model of the pulmonary artery structure for simulation calculation, automatic meshing can be adopted during meshing, the automatic meshing comprises the division of a structural mesh and an unstructured mesh, the unstructured mesh can be provided with a plurality of layers of boundary layers (or is not provided) on the wall surface of a blood vessel, and the mesh with smaller size can be adopted in a region with smaller pipe diameter of the model. Therefore, when the model of the pulmonary artery structure is subjected to grid division, uniform grid division can be adopted, non-uniform grid division can also be adopted, or some parts are subjected to uniform grid division and some parts are subjected to non-uniform grid division, and a plurality of grid models of the pulmonary artery structure can be obtained after grid division is finished, so that the division of the geometric model of the pulmonary artery structure into small calculation units is realized, and the subsequent simulation calculation is facilitated.
Correspondingly, when the terminal executes the above S103, the terminal specifically executes the steps of: and performing fluid mechanics calculation on each network model according to a preset boundary condition to obtain at least one piece of blood flow characteristic information of the pulmonary artery structure.
In this embodiment, after the terminal constructs a geometric model of the pulmonary artery structure and performs mesh division on the model of the pulmonary artery structure, a preset boundary condition may be loaded on each mesh model of the pulmonary artery structure to perform a hydrodynamics calculation, and specifically, a computational hydrodynamics algorithm may be used to solve a Navier-Stokes equation of each mesh model to obtain blood flow characteristic information corresponding to the mesh model, for example, pulmonary artery pressure. During specific calculation, a user can adjust grid division parameters and blood vessel boundary regions (including an inlet/outlet section and a vessel wall region) to calculate so as to flexibly adjust the divided grid model, realize fluid mechanics calculation of any position on the pulmonary artery structure and achieve the function of evaluating any position on the pulmonary artery structure.
The pulmonary artery outlet boundary condition is a blood flow volume or a blood flow rate corresponding to the pulmonary artery outlet, and optionally, the present application further provides a method for obtaining the pulmonary artery outlet boundary condition, as shown in fig. 5, where the method includes:
s301, determining the vessel diameter of each pulmonary artery outlet according to the geometric model of the pulmonary artery structure.
S302, proportionally distributing the cardiac output to each pulmonary artery outlet according to the blood vessel diameter of each pulmonary artery outlet to obtain the flow or flow velocity corresponding to each pulmonary artery outlet.
Different vessel diameters correspond to different blood vessel blood flow volumes, and according to the corresponding relation between the vessel diameters and the blood vessel blood flow volumes: q&dkWherein Q is the blood flow through the vessel, d is the vessel diameter, and k is a constant. The heart discharge volume is proportionally distributed to each pulmonary artery outlet, and correspondingly, the outlet flow velocity or flow corresponding to each blood vessel outlet can be calculated. In this embodiment, the vessel diameter may be obtained by a geometric model of the pulmonary artery structure, the cardiac output may be obtained by the pulmonary artery feature information obtained before, and then the terminal may calculate the flow rate or velocity corresponding to each pulmonary artery outlet based on the vessel diameter of each pulmonary artery outlet and the corresponding cardiac output, that is, the boundary condition of the pulmonary artery outlet.
Solving a Navier-Stokes (Navier-Stokes) equation of each grid model by adopting a computational fluid dynamics algorithm, wherein the Navier-Stokes equation can be expressed by the following relation (1):
where u represents the velocity vector, ρ represents the viscosity coefficient of blood, and t represents time. According to the geometric model of the pulmonary artery, the characteristic information of the pulmonary artery and the boundary conditions, a method of finite volume can be adopted to solve a Navier-Stokes equation and obtain the distribution of the parameters (such as velocity vector, pressure and stress tensor) in a flow field, so that the blood flow characteristic information corresponding to the grid model, such as the pulmonary artery pressure, is obtained.
In practical applications, after the terminal performs the step of S103, as shown in fig. 6, at least one blood flow characteristic information of the pulmonary artery structure may be displayed, and the specific steps are performed as follows:
s105, receiving an instruction input by a user; the instructions include obtaining a target region or site.
The instruction is used for acquiring blood flow characteristic information corresponding to the target area or the target site. The target region is a specific region on a geometric model of a pulmonary artery structure which is desired to be calculated by a user, and when the instruction is used for acquiring blood flow characteristic information corresponding to the target region, the blood flow characteristic information at each position included in the range of the target region can be obtained. The position point is a specific position point on a geometric model of the pulmonary artery structure which is desired to be calculated by the user, and when the instruction is used for acquiring blood flow characteristic information corresponding to the position point, the blood flow characteristic information on the position point corresponding to the position point can be obtained. It should be noted that the target region may include one or more calculable sites.
In this embodiment, after the terminal constructs the geometric model of the pulmonary artery structure of the patient, the geometric model can be displayed in a graphical form on the display interface (see fig. 3A). When a user wants to acquire blood flow characteristic information corresponding to a target area, the user can input an instruction in a mode of using a preset block diagram to outline the target area on a geometric model, or can input an instruction in a mode of manually outlining the target area on the geometric model, and the method is not limited in the above step; when the user wants to acquire the blood flow characteristic information corresponding to the site, the user may input the instruction by clicking the position of the site on the geometric model, or the user may input the instruction by editing the position of the site in the edit box, which is not limited herein.
S106, acquiring at least one piece of blood flow characteristic information of the corresponding pulmonary artery structure according to the target area or the target site.
When the terminal receives an instruction input by a user, and the instruction comprises an acquisition target area, the terminal can further extract a position coordinate of the target area from the instruction, determine a position point included in the range of the target area according to the position coordinate, determine the position coordinate of the position point when the target area comprises one position point, determine the position coordinate of the position point as the position coordinate of the target area on a geometric model to be calculated and displayed, load a corresponding preset boundary condition on a grid model corresponding to the position coordinate to perform fluid mechanics calculation, and calculate to obtain at least one blood flow characteristic information of the grid model corresponding to the position coordinate, namely at least one blood flow characteristic information of a pulmonary artery structure corresponding to the target area; optionally, when the target area includes multiple sites, the position coordinates of each site may be determined, the position coordinates of the multiple sites are determined as the position coordinates of the target area on the geometric model to be calculated and displayed, then corresponding preset boundary conditions are loaded on the grid model corresponding to each position coordinate in sequence to perform the fluid mechanics calculation, and at least one piece of blood flow characteristic information of the network model corresponding to each position coordinate is obtained through calculation, the blood flow characteristic information corresponding to the position coordinates of the multiple sites in the target area range may be further subjected to mean calculation, weighted accumulation and calculation, or other processing calculations, and the calculation result is used as at least one piece of blood flow characteristic information of the pulmonary artery structure corresponding to the target area. Optionally, when the target region includes a plurality of sites, the plurality of sites in the target region may also be screened first, one of the sites is screened out and calculated according to the above method, so as to obtain at least one piece of blood flow characteristic information of the mesh model corresponding to the site, and the at least one piece of blood flow characteristic information of the mesh model corresponding to the site is determined as the at least one piece of blood flow characteristic information of the mesh model corresponding to the target region. It should be noted that, in the screening, the site on the central region of the target region may be specifically screened out, or other screening strategies may be adopted for screening, which is not limited herein.
Optionally, when the terminal extracts the position coordinate of the target region from the instruction and determines a locus included in the target region according to the position coordinate of the target region, if the locus is a locus, the terminal may find at least one piece of blood flow characteristic information of the corresponding pulmonary artery structure from the database according to the position coordinate of the locus as at least one piece of blood flow characteristic information of the pulmonary artery structure corresponding to the target region; if the target area contains a plurality of sites, the terminal can find blood flow characteristic information corresponding to the sites from the database according to the position coordinates of the sites, and then further perform mean value or weighted accumulation or other processing calculation on the blood flow characteristic information corresponding to the sites, wherein the calculation result is the blood flow characteristic information corresponding to the target area.
When the terminal receives an instruction input by a user, and the instruction comprises an acquisition site, the terminal can directly extract a position coordinate of the site from the instruction, determine the position coordinate as the position of the site on a geometric model to be calculated and displayed, load a corresponding preset boundary condition on a grid model corresponding to the position of the site, and calculate to obtain at least one piece of blood flow characteristic information of the grid model corresponding to the position of the site, namely at least one piece of blood flow characteristic information of a pulmonary artery structure corresponding to the site.
Optionally, when the terminal extracts the position coordinate of the location point from the instruction, at least one piece of blood flow characteristic information of the corresponding pulmonary artery structure may also be found from the database according to the position coordinate, that is, at least one piece of blood flow characteristic information of the pulmonary artery structure corresponding to the location point.
S107, displaying at least one piece of blood flow characteristic information of the pulmonary artery structure in the area to be displayed.
The region to be displayed may be a target region, a region where a site is located, or a region planned in advance on a display interface, and is used to display at least one piece of blood flow characteristic information of a pulmonary artery structure corresponding to the target region or the site to be acquired, which is specified by a user on a model of the pulmonary artery structure.
In this embodiment, when the terminal obtains the at least one piece of blood flow characteristic information of the pulmonary artery structure corresponding to the target region or the site through calculation, the at least one piece of blood flow characteristic information of the pulmonary artery structure may be displayed in the region to be displayed, so that the user can visually check the information. It should be noted that, this embodiment provides two ways of displaying the evaluation value, one way is to simultaneously calculate and display the blood flow characteristic information of the pulmonary artery structure corresponding to the target region or site (see the first way of acquiring the blood flow characteristic information of the corresponding pulmonary artery structure in S106), and the other way is to calculate the blood flow characteristic information of each site on the geometric model of the pulmonary artery structure first, and then display the blood flow characteristic information on the corresponding position coordinate according to the position coordinate of the target region or site (see the second way of acquiring the blood flow characteristic information of the corresponding pulmonary artery structure in S106).
The embodiment provides a method for viewing blood flow characteristic information of a pulmonary artery structure at any position by a user, which not only realizes the assessment of any position on the pulmonary artery structure, but also realizes the viewing of any position, and provides a convenient and efficient assessment means for a doctor to assess the pulmonary artery hypertension of a patient.
In combination with all the above embodiments, there is provided a method for evaluating a pulmonary artery, as shown in fig. 7, the method comprising:
s401, medical image data and pulmonary artery characteristic information of a patient are obtained.
S402, a preset deep learning algorithm is adopted to conduct segmentation and extraction on the pulmonary artery main trunk and the branch blood vessels in the medical image data, and a first segmentation result is obtained.
And S403, extracting the far-end branch blood vessel in the medical image data by adopting a preset blood vessel enhancement and/or blood vessel tracking method to obtain a second segmentation result.
S404, obtaining a geometric model of the pulmonary artery structure according to the first segmentation result and the second segmentation result.
S405, carrying out grid division on the model of the pulmonary artery structure to obtain a plurality of grid models of the pulmonary artery structure.
S406, determining a pulmonary artery inlet boundary condition and a pulmonary artery outlet boundary condition according to the geometric model of the pulmonary artery structure and the pulmonary artery characteristic information; the boundary condition of the pulmonary artery inlet is the pulmonary artery pressure of the patient, and the boundary condition of the pulmonary artery outlet is the corresponding blood flow volume or the blood flow speed of the pulmonary artery outlet.
S407, loading the boundary condition of the pulmonary artery inlet and the boundary condition of the pulmonary artery outlet on each grid model to perform fluid mechanics calculation to obtain at least one piece of blood flow characteristic information of the pulmonary artery structure, and storing the at least one piece of blood flow characteristic information of the pulmonary artery structure in a database.
S408, receiving an instruction input by a user; the instructions include obtaining a target region or site.
S409, acquiring at least one piece of blood flow characteristic information of the pulmonary artery structure on the corresponding position coordinate according to the position coordinate of the target area or the position point.
And S410, displaying at least one piece of blood flow characteristic information of the pulmonary artery structure in the area to be displayed.
The above steps are all explained in the foregoing, and please refer to the foregoing for details, which are not described herein.
The assessment method for the pulmonary artery provided by the embodiment realizes non-invasive assessment of the pulmonary artery hypertension, and reduces physical damage of a patient compared with traditional invasive assessment. Compared with the traditional invasive assessment method which can only provide blood flow characteristic information of a fixed few points, the pulmonary artery assessment method provided by the embodiment can more comprehensively assess pulmonary hypertension, so that an accurate assessment result can be obtained when the pulmonary hypertension of a patient is effectively assessed based on the blood flow characteristic information of each point obtained by the assessment method.
It should be understood that although the various steps in the flow charts of fig. 2-7 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-7 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 8, there is provided a pulmonary artery assessment apparatus including:
the loading module 11 is used for acquiring medical image data and pulmonary artery characteristic information of a patient;
a construction module 12 for constructing a geometric model of the pulmonary artery structure from the medical image data;
the evaluation module 13 is configured to perform fluid mechanics calculation on the geometric model of the pulmonary artery structure according to a preset boundary condition, so as to obtain at least one piece of blood flow characteristic information of the pulmonary artery structure.
In one embodiment, as shown in fig. 9, the building block 12 includes:
the construction unit 121 is configured to perform segmentation and extraction on the pulmonary artery main trunk and the branch vessels in the medical image data by using a preset deep learning algorithm, so as to obtain a geometric model including the pulmonary artery structure.
In one embodiment, as shown in fig. 10, the building unit 121 includes:
a first segmentation subunit 1211, configured to perform segmentation and extraction on a pulmonary artery in the medical image data by using a preset deep learning algorithm, so as to obtain a first segmentation result;
a second segmentation subunit 1212, configured to extract a pulmonary artery in the medical image data by using a preset blood vessel enhancement and/or blood vessel tracking method, so as to obtain a second segmentation result;
a construction subunit 1213 for deriving a geometric model comprising the pulmonary artery structure from the first and second segmentation results.
In one embodiment, as shown in fig. 11, the pulmonary artery assessment apparatus further includes:
a dividing module 14, configured to perform mesh division on the model of the pulmonary artery structure to obtain multiple mesh models of the pulmonary artery structure;
correspondingly, the evaluation module 13 is specifically configured to perform fluid mechanics calculation on each of the mesh models according to a preset boundary condition to obtain at least one piece of blood flow characteristic information of the pulmonary artery structure.
In one embodiment, as shown in fig. 12, the pulmonary artery assessment apparatus further includes:
a receiving module 15, configured to receive an instruction input by a user; the instruction comprises a target area or a target site;
an obtaining module 16, configured to obtain at least one piece of blood flow characteristic information of a corresponding pulmonary artery structure according to the target region or site;
a display module 17, configured to display at least one piece of blood flow characteristic information of the pulmonary artery structure in a region to be displayed.
In one embodiment, the preset boundary conditions include: pulmonary artery entrance boundary conditions and pulmonary artery exit boundary conditions.
In one embodiment, the pulmonary artery feature information includes: at least one of blood pressure, pulmonary artery pressure, non-capillary wedge pressure, pulmonary artery resistance, cardiac output, body surface area, cardiac index of the patient.
In one embodiment, as shown in fig. 13, the pulmonary artery assessment apparatus further includes: an acquire boundary conditions module 18, the acquire boundary conditions module 18 comprising: a first determining unit 181 and a second determining unit 182,
a first determining unit 181, configured to determine a vessel diameter of each pulmonary artery outlet according to the geometric model of the pulmonary artery structure;
a second determining unit 182, configured to proportionally distribute the cardiac output to each pulmonary artery outlet according to a blood vessel diameter of each pulmonary artery outlet, so as to obtain a flow rate or a flow velocity corresponding to each pulmonary artery outlet.
For specific definition of the pulmonary artery assessment device, reference may be made to the above definition of the pulmonary artery assessment method, which is not described herein again. The modules in the pulmonary artery assessment device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
The pulmonary artery assessment method provided by the present application can be applied to a computer device shown in fig. 14, where the computer device can be a server, the computer device can also be a terminal, and its internal structure diagram can be shown in fig. 14. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of assessing a pulmonary artery. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 14 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring medical image data and pulmonary artery characteristic information of a patient; the medical image data contains a pulmonary artery structure of the patient;
constructing a geometric model of the pulmonary artery structure from the medical image data;
and performing fluid mechanics calculation on the geometric model of the pulmonary artery structure according to a preset boundary condition to obtain at least one piece of blood flow characteristic information of the pulmonary artery structure.
The implementation principle and technical effect of the computer device provided by the above embodiment are similar to those of the above method embodiment, and are not described herein again.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring medical image data and pulmonary artery characteristic information of a patient; the medical image data contains a pulmonary artery structure of the patient;
constructing a geometric model of the pulmonary artery structure from the medical image data;
and performing fluid mechanics calculation on the geometric model of the pulmonary artery structure according to a preset boundary condition to obtain at least one piece of blood flow characteristic information of the pulmonary artery structure.
The implementation principle and technical effect of the computer-readable storage medium provided by the above embodiments are similar to those of the above method embodiments, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of assessing a pulmonary artery, the method comprising:
acquiring medical image data and pulmonary artery characteristic information of a patient;
constructing a geometric model of the pulmonary artery structure from the medical image data;
and performing fluid mechanics calculation on the geometric model of the pulmonary artery structure according to a preset boundary condition to obtain at least one piece of blood flow characteristic information of the pulmonary artery structure.
2. The method of claim 1, wherein said constructing a geometric model of said pulmonary artery structure from said medical image data comprises:
segmenting and extracting pulmonary arteries in the medical image data by adopting a preset deep learning algorithm to obtain a first segmentation result;
extracting the pulmonary artery in the medical image data by adopting a preset blood vessel enhancement and/or blood vessel tracking method to obtain a second segmentation result;
and obtaining a geometric model containing the pulmonary artery structure according to the first segmentation result and the second segmentation result.
3. The method according to claim 1 or 2, wherein after said constructing a geometric model of the pulmonary artery structure from the medical image data, the method further comprises:
performing mesh division on the model of the pulmonary artery structure to obtain a plurality of mesh models of the pulmonary artery structure;
performing fluid mechanics calculation on the geometric model of the pulmonary artery structure according to a preset boundary condition to obtain at least one blood flow characteristic information of the pulmonary artery structure, including:
and performing fluid mechanics calculation on each grid model according to a preset boundary condition to obtain at least one piece of blood flow characteristic information of the pulmonary artery structure.
4. The method of claim 1, further comprising:
receiving an instruction input by a user; the instruction comprises a target area or a target site;
acquiring at least one piece of blood flow characteristic information of a corresponding pulmonary artery structure according to the target area or the target site;
and displaying at least one piece of blood flow characteristic information of the pulmonary artery structure in a region to be displayed.
5. The method of claim 1, wherein the preset boundary conditions comprise: pulmonary artery entrance boundary conditions and pulmonary artery exit boundary conditions.
6. The method of claim 5, wherein the obtaining of the pulmonary artery exit boundary condition comprises:
determining the vessel diameter of each pulmonary artery outlet according to the geometric model of the pulmonary artery structure;
and proportionally distributing the cardiac output to each pulmonary artery outlet according to the vessel diameter of each pulmonary artery outlet to obtain the flow or flow velocity corresponding to each pulmonary artery outlet.
7. The method of claim 1, wherein the pulmonary artery characterization information comprises: at least one of blood pressure, pulmonary artery pressure, non-capillary wedge pressure, pulmonary artery resistance, cardiac output, body surface area, cardiac index of the patient.
8. An assessment device of a pulmonary artery, characterized in that it comprises:
the loading module is used for acquiring medical image data and pulmonary artery characteristic information of a patient;
a construction module for constructing a geometric model of the pulmonary artery structure from the medical image data;
and the evaluation module is used for performing fluid mechanics calculation on the geometric model of the pulmonary artery structure according to a preset boundary condition to obtain at least one piece of blood flow characteristic information of the pulmonary artery structure.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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