CN115762798A - Method and device for determining hemodynamic parameters, storage medium and electronic equipment - Google Patents
Method and device for determining hemodynamic parameters, storage medium and electronic equipment Download PDFInfo
- Publication number
- CN115762798A CN115762798A CN202211357200.3A CN202211357200A CN115762798A CN 115762798 A CN115762798 A CN 115762798A CN 202211357200 A CN202211357200 A CN 202211357200A CN 115762798 A CN115762798 A CN 115762798A
- Authority
- CN
- China
- Prior art keywords
- blood vessel
- data
- determining
- dimensional simulation
- analyzed
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Abstract
The embodiment of the invention discloses a hemodynamic parameter determination method, a hemodynamic parameter determination device, a storage medium and electronic equipment. The method comprises the following steps: acquiring image data of a blood vessel to be analyzed, constructing a reduced-order model based on the image data of the blood vessel to be analyzed, and determining initial hemodynamic parameters of the blood vessel to be analyzed based on the reduced-order model; identifying a key area in the blood vessel to be analyzed, and performing three-dimensional simulation on the key area based on initial hemodynamic parameters corresponding to the key area to obtain a three-dimensional simulation result; post-processing the three-dimensional simulation result to a reduced dimension to obtain the coupling data of the key area; and updating the reduced-order model based on the coupling data of the key region, and determining the target hemodynamic parameters of the blood vessel to be analyzed based on the updated reduced-order model. Through three-dimensional simulation of the key area, the accuracy of a simulation result is improved while the calculation amount is considered, and the accuracy of the target hemodynamic parameters is further improved.
Description
Technical Field
The embodiment of the invention relates to a data processing technology, in particular to a hemodynamic parameter determination method, a hemodynamic parameter determination device, a storage medium and electronic equipment.
Background
With the improvement of living standard, vascular diseases are becoming more and more threats to human health, such as coronary artery related diseases, cerebrovascular diseases, aortic diseases, etc. Based on the above, the analysis of relevant studies on blood vessels, such as the kinetic parameters of blood vessels, has become the direction of research.
In the process of implementing the invention, at least the following technical problems are found in the prior art: the invasive inspection method is limited in its application range due to its high economic cost and damage to human body.
Disclosure of Invention
The invention provides a method and a device for determining hemodynamic parameters, a storage medium and electronic equipment, which are used for realizing noninvasive analysis of the hemodynamic parameters and improving the accuracy of the hemodynamic parameters.
According to an aspect of the present invention, there is provided a hemodynamic parameter determination method, comprising:
acquiring image data of a blood vessel to be analyzed, constructing a reduced-order model based on the image data of the blood vessel to be analyzed, and determining initial hemodynamic parameters of the blood vessel to be analyzed based on the reduced-order model;
identifying a key area in the blood vessel to be analyzed, and performing three-dimensional simulation on the key area based on initial hemodynamic parameters corresponding to the key area to obtain a three-dimensional simulation result;
post-processing the three-dimensional simulation result to a reduced dimension to obtain the coupling data of the key area;
and updating the reduced-order model based on the coupling data of the key region, and determining the target hemodynamic parameters of the blood vessel to be analyzed based on the updated reduced-order model.
According to another aspect of the present invention, there is provided a hemodynamic parameter determination device, comprising:
the initial hemodynamic parameter determination module is used for acquiring image data of a blood vessel to be analyzed, constructing a reduced-order model based on the image data of the blood vessel to be analyzed, and determining initial hemodynamic parameters of the blood vessel to be analyzed based on the reduced-order model;
the three-dimensional simulation module is used for identifying a key area in the blood vessel to be analyzed, and performing three-dimensional simulation on the key area based on initial hemodynamic parameters corresponding to the key area to obtain a three-dimensional simulation result;
the coupling data determining module is used for post-processing the three-dimensional simulation result to a reduced dimension to obtain the coupling data of the key area;
and the target hemodynamic parameter determination module is used for updating the reduced-order model based on the coupling data of the key region and determining the target hemodynamic parameters of the blood vessel to be analyzed based on the updated reduced-order model.
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 first and the second end of the pipe are connected with each other,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the method for determining hemodynamic parameters of any embodiment of the invention.
According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to implement a method for determining a hemodynamic parameter according to any one of the embodiments of the present invention when executed.
According to the technical scheme of the embodiment of the invention, after the blood vessel to be analyzed is subjected to once reduced-order simulation through the reduced-order model, the three-dimensional simulation is carried out on the key area based on the reduced-order simulation result serving as the boundary parameter of the three-dimensional simulation of the key area, and the three-dimensional simulation result is obtained and is the high-precision hemodynamic parameter of the key area. The three-dimensional simulation result is converted into the coupling data with the same latitude as the reduced-order model, so that the coupling with the reduced-order model is realized, and the secondary reduced-order simulation is carried out on the reduced-order model, so that the accuracy of the obtained target hemodynamic parameters is high. Through three-dimensional simulation of the key area, calculation amount is considered, meanwhile, high-precision simulation is carried out on the area with the complex structure, accuracy of a simulation result is improved, and accuracy of target hemodynamic parameters is further improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a hemodynamic parameter determination method according to an embodiment of the present invention;
FIG. 2 is a schematic view of a stenosis provided in an embodiment of the present invention;
FIG. 3 is a schematic view of a bifurcation area provided by embodiments of the present invention;
FIG. 4 is a preferred embodiment of a hemodynamic parameter determination method provided by an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a hemodynamic parameter determination apparatus according to a second embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or 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.
The technical scheme related by the application can be used for acquiring, storing and/or processing the data, and the relevant regulations of national laws and regulations are met.
Example one
Non-invasive assessment modalities for blood vessels may include: 1. performing hemodynamic parameter simulation through a three-dimensional geometric model; 2. end-to-end prediction processing by a machine learning model; 3. and (4) performing prediction processing through a reduced model. For the above manner, a specific implementation manner of the hemodynamic parameter simulation manner for the three-dimensional geometric model may be to reconstruct the three-dimensional geometric model according to a blood vessel medical image of the target object, perform mesh division on the three-dimensional geometric model, and introduce the three-dimensional geometric model into a CFD (Computational Fluid Dynamics) solver to perform solution, so as to obtain a hemodynamic parameter of a blood vessel of the target object. The hemodynamic parameters determined based on the method have high accuracy and strong interpretability, but the required computational power and time resources are large, and the cost is high. For the end-to-end prediction processing mode performed by the machine learning model, the specific implementation mode may be to simplify the hemodynamic simulation (such as dimension reduction) as a standard, and learn the simulation calculation result by the machine learning algorithm to obtain the machine learning model. After the training of the machine learning model is completed, the blood vessel image of the target object is received as input and processed to obtain the hemodynamic parameters of the blood vessel of the target object. The method has the advantages of high calculation speed and high efficiency, but the calculation accuracy and the model interpretability are relatively poor. For the prediction processing mode through the reduced-order model, wherein the one-dimensional (1 d) simulation method can be to calculate the three-dimensional calculation as a single-scale calculation only along the center line of the blood vessel, the method can obtain a calculation result with a certain precision, the calculation effect is high, but for key areas (such as stenosis, bifurcation and bending, and the coupling of the stenosis, bifurcation and bending), the reduced-order model is generally calculated through a coupling empirical formula or other AI models, the flow field of the parts is complex, not only is related to the geometric shape of the blood vessel, but also is related to the flow state of the blood vessel, and the coupling empirical formula or other AI models are difficult to cover all situations, so that the calculation precision of the reduced-order model is low.
In view of the above technical problems, an embodiment of the present invention provides a method for determining hemodynamic parameters, where fig. 1 is a flowchart of a method for determining hemodynamic parameters, where the embodiment of the present invention is applicable to a situation of performing kinetic parameter analysis on a blood vessel to be analyzed, the method may be executed by a device for determining hemodynamic parameters, which may be implemented by software and/or hardware, and the device for determining hemodynamic parameters may be configured on an electronic computing device, and specifically includes the following steps:
s110, obtaining image data of a blood vessel to be analyzed, constructing a reduced-order model based on the image data of the blood vessel to be analyzed, and determining initial hemodynamic parameters of the blood vessel to be analyzed based on the reduced-order model.
S120, identifying a key area in the blood vessel to be analyzed, and performing three-dimensional simulation on the key area based on initial hemodynamic parameters corresponding to the key area to obtain a three-dimensional simulation result.
S130, post-processing the three-dimensional simulation result to a reduced dimension to obtain the coupling data of the key area.
And S140, updating the reduced-order model based on the coupling data of the key area, and determining the target hemodynamic parameters of the blood vessel to be analyzed based on the updated reduced-order model.
In this embodiment, the blood vessel to be analyzed may include, but is not limited to, a cerebral blood vessel, a coronary blood vessel, an aortic blood vessel, a visceral blood vessel, and the like of a target object, wherein the target object may be a human body or an animal body, and the like, without being limited thereto. Scanning the blood vessel to be analyzed to obtain image data of the blood vessel to be analyzed, wherein the scanning mode of the blood vessel to be analyzed includes but is not limited to angiography, CT scanning and the like.
Optionally, the image data of the blood vessel to be analyzed may be three-dimensional image data, and a reduced-order model is created through the image data of the blood vessel to be analyzed, specifically, the constructing of the reduced-order model includes constructing a geometric model of the blood vessel to be analyzed and determining a boundary condition. Optionally, the geometric parameters of the blood vessel to be analyzed are read from the image data of the blood vessel to be analyzed, and the center line of the blood vessel and the lumen contour diameter corresponding to each point on the center line of the blood vessel are determined according to the geometric parameters of the blood vessel to be analyzed. Optionally, centerline extraction is performed on the image data of the blood vessel to be analyzed, and a blood vessel centerline corresponding to the blood vessel to be analyzed and the lumen contour diameter corresponding to each point on the blood vessel centerline are determined. And constructing a geometric model of the reduced-order model through the vessel central line and the lumen contour diameter corresponding to each point on the vessel central line.
Setting a boundary condition for the reduced order model, wherein the boundary condition comprises one or more items as follows: blood flow data, pressure or resistance, etc. at the entrance and exit of the vessel to be analyzed. In some embodiments, the boundary conditions may be blood flow data at the entrance of the vessel to be analyzed and pressure at the exit. The values in the boundary conditions can be obtained by receiving external input or determined according to basic physiological parameters of the target object. For example, a preset type of physiological parameter of the target object may be obtained, and the physiological parameter is input into a preset machine learning model, so as to obtain a boundary condition output by the machine learning model. Physiological parameters such as preset types of target subject include, but are not limited to: blood pressure value, body temperature value, blood sugar value, etc.
And performing first simulation treatment on the blood vessel to be analyzed through the reduced-order model to obtain initial hemodynamic parameters of the blood vessel to be analyzed. The reduced order model may be a 1d model or a 0d model. Wherein the solution equation of the 1d model is as follows:
where t is time, x is length along the centerline, A is vessel cross-sectional area, q is blood flow, p is vessel pressure, ρ is blood flow density, K is blood flow density R Alpha is a preset hyper-parameter, which is a blood flow mechanical energy loss coefficient.
Here, the initial hemodynamic parameters of the blood vessel to be analyzed include, but are not limited to, blood flow data, pressure data, etc. of the blood vessel to be analyzed at each position along the centerline of the blood vessel. It should be noted that the simulation rule of the reduced-order model is preset, and the simulation rule of the reduced-order model can be called to perform simulation processing on the reduced-order model.
Due to different types of blood vessels, the blood vessels are branched, bent and the like, meanwhile, due to the problems of blood vessel pathological changes and the like, stenosis and the like can exist in the blood vessels, and for example, referring to fig. 2, fig. 2 is a schematic diagram of the stenosis of the blood vessels. Aiming at the special conditions of bifurcation, stenosis, bending and the like, the problem of low accuracy exists in the simulation process of the reduced order model. In this embodiment, a critical area of the blood vessel to be analyzed is identified, that is, one or more areas with bifurcation, stenosis or bending are identified, the critical area is analyzed with high precision, and the accuracy of the dynamic parameters of the blood vessel in the critical area is improved.
Optionally, the critical region comprises at least one of a bifurcation region and a non-bifurcation region; wherein the non-bifurcation region includes a stenotic region, a curved region, and a region where both a curve and a stenosis exist. In some embodiments, the image data of the blood vessel to be analyzed may be processed through a preset key region identification model, so as to obtain a key region in the blood vessel to be analyzed. For example, the key area may be marked in the image data of the blood vessel to be analyzed, and specifically, the key area may be marked by setting a distinguishing color or setting a distinguishing identifier. The key area identification model can be obtained by training a blood vessel sample image and a corresponding key area label. In some embodiments, the region satisfying the bifurcation requirement and/or the region satisfying the bending requirement or the stenosis requirement with the variation trend can be obtained by performing edge identification on the image data of the blood vessel to be analyzed, identifying the edge of the blood vessel, performing bifurcation identification on the edge of the blood vessel, and analyzing the variation trend of the edge of the blood vessel, and is determined as the key region.
And establishing a three-dimensional geometric model of the key area by identifying the obtained geometric parameters of the key area, and performing three-dimensional simulation on the key area. The accuracy of the hemodynamic parameters of the key region can be improved by three-dimensional simulation of the key region with a complex structure. Meanwhile, the three-dimensional simulation is only carried out on the key area, and the three-dimensional simulation is not carried out on the non-key area outside the key area, so that the calculation amount and the calculation time length can be reduced, and the effect of reducing the cost is realized. Under the condition that the blood vessel to be analyzed comprises a plurality of key areas, the key areas can be processed in parallel, and the processing efficiency is improved.
Optionally, performing three-dimensional simulation on the key region based on the initial hemodynamic parameter corresponding to the key region to obtain a three-dimensional simulation result, including: determining the geometric parameters of the key area based on the image data of the blood vessel to be analyzed, and generating a three-dimensional geometric model based on the geometric parameters; and taking the initial hemodynamic parameters corresponding to the key region in the initial hemodynamic parameters of the blood vessel to be analyzed as simulation boundary parameters, and taking the initial hemodynamic parameters as the input of the three-dimensional geometric model for simulation processing to obtain a three-dimensional simulation result. In this embodiment, a three-dimensional geometric model of the key region is created, and simulation boundary parameters are set, where the boundary parameters include blood flow data and pressure data at the inlet and outlet of the key region, that is, the blood flow data and pressure data at the inlet and outlet of the key region are obtained through simulation by using a reduced order model.
And carrying out grid division on the three-dimensional geometric model of the key area, and guiding the three-dimensional geometric model subjected to grid division into a CFD solver for simulation processing to obtain a three-dimensional simulation result. The three-dimensional simulation result comprises blood flow data, pressure data, blood flow rate data and the like at each position point in the blood vessel section corresponding to the key area.
Here, it should be noted that the dimension of the simulation result of the reduced order model and the dimension of the simulation result of the three-dimensional geometric model are different, where the simulation result of the reduced order model, that is, the initial hemodynamic parameter, is a one-dimensional result, and the simulation result of the three-dimensional geometric model is a three-dimensional result, and there is a dimension difference between the two. Aiming at the problems, coupling processing is carried out on the three-dimensional simulation result of the key area to obtain coupling data which can be coupled with the simulation result of the reduced-order model. Accordingly, the dimensions of the coupled data are the same as the dimensions of the simulation results of the reduced order model.
In some embodiments, the coupling data corresponding to the critical region includes one or more of a vascular resistance and a kinetic energy parameter. Illustratively, the coupling data for the non-bifurcation region includes vascular resistance; the coupling data of the bifurcation region includes vascular resistance and kinetic energy parameters. And determining the coupling data corresponding to the key area according to the type of the key area. Specifically, the method may include determining mechanical energy loss data of a key region, extracting blood flow data of the key region from the three-dimensional simulation data, and determining coupling data of the key region based on the mechanical energy loss data of the key region and the blood flow data of the key region.
For a non-bifurcation area, post-processing the three-dimensional simulation result to a reduced dimension to obtain the coupling data of the key area, wherein the step of obtaining the coupling data comprises the following steps: performing segmentation processing on the non-bifurcation area to obtain a plurality of first blood vessel sections of the non-bifurcation area; for any first blood vessel segment, determining mechanical energy loss data of the first blood vessel segment based on a three-dimensional simulation result corresponding to the first blood vessel segment; determining a vascular resistance of the first vessel segment based on the mechanical energy loss data of the first vessel segment and the blood flow data of the first vessel segment.
The non-bifurcation region may be any one of a stenotic region, a curved region, or a region where both a curve and a stenosis exist, i.e., there is a difference in geometric data at each vessel within the non-bifurcation region, and accordingly, there is a difference in vascular resistance throughout. In this embodiment, the non-bifurcation region is segmented to obtain a plurality of first blood vessel segments of the non-bifurcation region, that is, the plurality of blood vessel segments obtained by segmenting the non-bifurcation region are all first blood vessel segments, each first blood vessel segment is processed to obtain the blood vessel resistance corresponding to each first blood vessel segment, and the calculation accuracy of the blood vessel resistance of the non-bifurcation region is improved by improving the processing fine granularity.
Optionally, the non-bifurcation region may be segmented based on a preset segmentation length, that is, the blood vessel segment of the non-bifurcation region is divided into a plurality of first blood vessel segments with the same length. Optionally, the non-bifurcation region may be segmented, or the segmentation may be performed based on geometric data of the blood vessel segment of the non-bifurcation region, for example, if a variation of the geometric data (for example, one or more of a cross-sectional diameter, a normal slope, and the like) at any blood vessel in the blood vessel segment of the non-bifurcation region is greater than a preset threshold, the segmentation is performed at the blood vessel. Accordingly, the vessel lengths resulting in the plurality of first vessel segments may be different. Illustratively, for vessel segments in the region of the curve, the segmentation process is performed at the maximum curve. Illustratively, the vessel segment in the stenosis region may be segmented at the narrowest point. Optionally, the non-bifurcation region may be divided into at least two intermediate blood vessel segments based on geometric data of any blood vessel in the blood vessel segments of the non-bifurcation region, and the at least two intermediate blood vessel segments are respectively segmented again based on a preset segmentation length to obtain a plurality of first blood vessel segments.
And for any first blood vessel section, determining the mechanical energy loss data of the first blood vessel section based on the three-dimensional simulation result corresponding to the first blood vessel section. Wherein the mechanical energy loss data of the first vessel segment may be determined based on a difference of the mechanical energy data at the inlet and the mechanical energy data at the outlet of the first vessel segment. In particular, the mechanical energy data for any location on the first vessel segment may be determined based on the blood flow energy, which may be determined based on the blood flow data and the blood flow rate data, and the static pressure energy, which may be determined based on the pressure data, for example, as the mechanical energy data. The blood flow volume data, the blood flow velocity data and the pressure data can be extracted from the three-dimensional simulation result. Specifically, the calculation formula of the blood flow energy and the static pressure energy may be called. The method further comprises determining mechanical energy data at the outlet of the first vessel segment based on the above calculation formula and the blood flow data, the blood flow rate data and the pressure data at the outlet, and determining mechanical energy data at the inlet of the first vessel segment based on the above calculation formula and the blood flow data, the blood flow rate data and the pressure data at the inlet. Further, mechanical energy loss data of the first vessel segment is determined.
Determining the vascular resistance of the first blood vessel section based on the mechanical energy loss data of the first blood vessel section and the blood flow data of the first blood vessel section, specifically, the vascular resistance of the first blood vessel section may be calculated based on the following formula: r =Δp/q, where Δ P is mechanical energy loss data for the first vessel segment and q is blood flow data for the first vessel segment.
For the bifurcation area, post-processing the three-dimensional simulation result to a reduced dimension to obtain the coupling data of the key area, wherein the coupling data comprises the following steps: performing segmentation treatment on the bifurcation area to obtain a plurality of second blood vessel sections of the bifurcation area, wherein the plurality of second blood vessel sections comprise an inlet blood vessel section; for any second blood vessel segment except the inlet blood vessel segment, determining mechanical energy data of the inlet blood vessel segment of the bifurcation area and mechanical energy data of the second blood vessel segment based on the three-dimensional simulation result of the bifurcation area, determining mechanical energy loss data corresponding to the second blood vessel segment based on the mechanical energy data of the inlet blood vessel segment of the bifurcation area and the mechanical energy data of the second blood vessel segment, and determining the blood vessel resistance of the second blood vessel segment based on the mechanical energy loss data corresponding to the second blood vessel segment and the blood flow data of the second blood vessel segment; and determining kinetic energy data of a second blood vessel section based on the three-dimensional simulation result of the bifurcation area, and determining kinetic energy parameters of the second blood vessel section based on the kinetic energy data, the section area of the second blood vessel section and blood flow data.
Optionally, the split area is segmented, which may be segmented by a preset segment length. Optionally, the segmentation processing may be performed on the branch region, where the branch point of the branch region is identified, and the segmentation processing is performed based on the branch point, and specifically, each branch may be respectively segmented according to the branch point. Exemplarily, referring to fig. 3, fig. 3 is a schematic diagram of a bifurcation area provided by an embodiment of the present invention. The bifurcation area in fig. 5 includes an inflow branch and two outflow branches, and each branch is segmented based on a bifurcation point to obtain four vessel segments, including a vessel segment between an inlet cross section 1 and a first bifurcation point cross section 2, a vessel segment between the first bifurcation point cross section 2 and a second bifurcation point cross section 3, a vessel segment between the second bifurcation point cross section 3 and an outlet cross section 1, and a vessel segment between a bifurcation cross section 5 and an outlet cross section 6.
For each second blood vessel segment, determining the mechanical energy loss data corresponding to the second blood vessel segment, specifically, determining the mechanical energy data of the inlet blood vessel segment of the bifurcation area and the mechanical energy data of the second blood vessel segment, where the inlet blood vessel segment may be the blood vessel segment including the inlet cross section, i.e., the first second blood vessel segment through which blood passes at the bifurcation area according to the blood flow direction, and the mechanical energy data of the inlet blood vessel segment may be the mechanical energy data at the inlet of the inlet blood vessel segment. The mechanical energy data of any second vessel segment may be the mechanical energy data at the outlet of the second vessel segment. Here, the determination of the mechanical energy data is not described in detail.
And determining the data difference of the mechanical energy data of the inlet vessel section and the mechanical energy data of the second vessel section as the mechanical energy loss data of the second vessel section. And determining the ratio of the mechanical energy loss data of the second blood vessel section to the blood flow data of the second blood vessel section as the blood vessel resistance of the second blood vessel section. Wherein the blood flow data of the second vessel segment may be read from the three-dimensional simulation result of the bifurcation region. In some embodiments, the blood flow data of the second vessel segment may be blood flow data at the outlet of the branch in which the second vessel segment is located. Illustratively, in fig. 3, the blood flow data of the blood vessel segment between the inlet cross section 1 and the first bifurcation cross section 2, and the blood vessel segment between the first bifurcation cross section 2 and the second bifurcation cross section 3, may be set to be the same as the blood flow data at the outlet 1 (i.e., cross section 4). I.e. q 2 ＝q 4 ，q 3 ＝q 4 。
Accordingly, the vascular resistance of the second vessel segment can be calculated by the following formula:
r = (P1-Pi)/out _ q, where P1 is mechanical energy data of the inlet vessel segment, pi is mechanical energy data of the second vessel segment being measured, and out _ q is blood flow data at the outlet of the branch where the second vessel segment being measured is located.
In addition, the kinetic energy data of the second blood vessel segment is determined based on the three-dimensional simulation result of the bifurcation area, specifically, the kinetic energy data of the second blood vessel segment may be determined based on the blood flow volume data and the blood flow velocity data of the second blood vessel segment in the three-dimensional simulation result of the bifurcation area, which is not described herein again.
Furthermore, the cross-sectional area, the blood flow data and the kinetic energy parameter of the second vessel segment may determine kinetic energy data of the second vessel segment, wherein the kinetic energy data may be characterized by a square of the ratio of the blood flow data to the cross-sectional area, the product of the square and the kinetic energy parameter, the blood density, the hyper-parameter (1/2). The kinetic energy data of the second blood vessel section determined by the two modes can be analyzed to obtain the kinetic energy parameters of the second blood vessel section.
Correspondingly, the kinetic energy parameters of the second blood vessel section can be analyzed based on the kinetic energy data, the section area of the second blood vessel section and the blood flow data. In some embodiments, the blood flow data of the second vessel segment may be set to the blood flow data at the exit of the branch. See the following equation:
E k ＝C*ρ*(out_q/area) 2 /2 wherein, E k The kinetic energy data of the second blood vessel section, C the kinetic energy parameter to be analyzed, rho the blood density, can be extracted from the three-dimensional simulation result, area is the cross-sectional area of the second blood vessel section, and out _ q the blood flow data at the outlet of the branch where the second blood vessel section to be measured is located.
Accordingly, the analyzable kinetic energy parameter is C = Pi 2 /out_q 2 。
In summary, for the bifurcation area and the non-bifurcation area, the coupling data with reduced dimension can be obtained through the three-dimensional simulation result.
In some embodiments, post-processing the three-dimensional simulation result to a reduced dimension to obtain the coupling data of the key region includes: and calling a coupling data conversion model, and inputting the blood flow data of the key area in the three-dimensional simulation result into the coupling data conversion model to obtain the coupling data of the key area.
By presetting the coupling data conversion model, the determination process of the coupling data can be simplified, the calculated amount and the calculation time length are reduced, and the cost is reduced. It should be noted that different coupling data conversion models may be set for the bifurcation region and the non-bifurcation region, and further, according to the type of the critical region, the coupling data conversion model of the corresponding type is called to realize the conversion from the three-dimensional simulation data to the coupling data.
Optionally, the method for generating the coupled data conversion model includes: creating an initial coupling data conversion model, wherein the initial coupling data conversion model comprises parameters to be determined; and determining data pairs of the coupling data and the blood flow data of the key area based on a three-dimensional simulation result obtained by three-dimensional simulation of the key area for at least two times, and determining parameter values of parameters to be determined in the initial coupling data conversion model based on at least two data pairs to obtain a coupling data conversion model.
In this embodiment, the coupling data conversion model may be a machine learning model, or an association relationship model between the coupling data and the blood flow data. The coupling data conversion models are all provided with undetermined parameters at the initial establishing moment, wherein the undetermined parameters in the machine learning model can be network parameters, and the undetermined parameters in the incidence relation model between the coupling data and the blood flow data can be hyper-parameters between the data.
And performing multiple three-dimensional simulations on the same type of key area in advance to obtain multiple different three-dimensional simulation results, wherein the blood flow data in the different three-dimensional simulations are different. And determining the coupling data and the blood flow data of the key area to form a data pair aiming at the key area of each simulation. The determining method of the coupling data of the key region may refer to the determining method of the coupling data of the non-bifurcation region and the bifurcation region in the above embodiments, and details are not repeated here.
And training the initial coupling data conversion model through a plurality of data pairs to obtain parameter values of parameters to be determined in the initial coupling data conversion model so as to obtain the coupling data conversion model.
Taking a machine learning model as an example, the coupling data conversion model of the non-bifurcation region may be represented as R = f (q), and the coupling data conversion model of the bifurcation region may be represented as R = f1 (q); c = f2 (q). The network parameters in R = f (q) are trained on multiple three-dimensional simulation result-determined data pairs (vascular resistance versus blood flow data pairs) for non-bifurcation regions until a coupled data transformation model is obtained for vascular resistance prediction for non-bifurcation regions. The data pairs (data pairs of vascular resistance and blood flow volume data, data pairs of kinetic energy parameters and blood flow volume data) determined by a plurality of three-dimensional simulation results of the bifurcation area are respectively R = f1 (q); the network parameters in C = f2 (q) are trained until a coupled data transformation model is obtained for vessel resistance prediction for non-bifurcated regions.
Taking the model of the correlation between the coupling data and the blood flow data as an example, the coupling data conversion model of the non-bifurcation area can be expressed as R = a + bq; and determining the undetermined parameters a and b by using the data pairs determined by the three-dimensional simulation results of the non-bifurcation area, so as to obtain a coupling data conversion model of the non-bifurcation area. The coupled data conversion model of the bifurcation region may be represented as R = a1+ b1q, C = a2+ b2q; and determining the undetermined parameters a1, b1, a2 and b2 by using the data pairs determined by the three-dimensional simulation results of the non-bifurcation area, so as to obtain a coupling data conversion model of the bifurcation area.
In the analysis process of the blood vessel to be analyzed, the corresponding coupling data conversion model can be called according to the type of the key area obtained through identification, blood flow volume data in the three-dimensional simulation data are extracted, and the coupling data of the key area can be obtained through prediction of the coupling data conversion model.
Through the processing, the reduced order model is updated based on the coupling data of the key area, and the target hemodynamic parameters of the blood vessel to be analyzed are determined based on the updated reduced order model. Namely, the updated reduced-order model is subjected to simulation again to obtain the target hemodynamic parameters of the blood vessel to be analyzed.
According to the technical scheme, after the blood vessel to be analyzed is subjected to one-time reduced simulation through the reduced-order model, three-dimensional simulation is carried out on a key region based on a reduced-order simulation result serving as a boundary parameter for carrying out three-dimensional simulation on the key region, and a three-dimensional simulation result is obtained, wherein the three-dimensional simulation result is a high-precision hemodynamic parameter of the key region. The three-dimensional simulation result is converted into coupling data with the same latitude as the reduced-order model, so that the coupling with the reduced-order model is realized, and the secondary reduced-order simulation is carried out on the reduced-order model, so that the accuracy of the obtained target hemodynamic parameters is high. Through three-dimensional simulation of the key area, the calculated amount is considered, and meanwhile, high-precision simulation is carried out on the area with the complex structure, so that the accuracy of a simulation result is improved, and the accuracy of target hemodynamic parameters is further improved.
Referring to fig. 4, fig. 4 is a preferred example of a hemodynamic parameter determination method provided by an embodiment of the present invention. The method comprises the following steps:
(1) Obtaining input of a reduced order model, wherein the input of the reduced order model comprises a geometric model and boundary conditions. The geometric parameters required by the geometric model are input into the center line of the blood vessel and the equivalent lumen contour diameter corresponding to the tangent plane of the center line. A method for obtaining the calculation model is to directly extract the central line of the blood vessel and the corresponding lumen contour diameter by an AI algorithm from medical images. The other method is that starting from medical images, a 3d geometric model is restored through an image recognition technology, and then lumen contour areas corresponding to a central line and a central line are extracted from the 3d geometric model. The boundary conditions are the flow, pressure or resistance, etc. of the inlet and outlet. The boundary condition may be obtained by empirical formula or AI
(2) And solving the hemodynamic parameters by the reduced order model.
(3) Critical flow area 3dCFD simulation. Three-dimensional CFD simulations were performed for areas with complex flow, such as stenosis, bends, bifurcations, with the aim of obtaining mechanical energy loss data Δ p _ s for the complex flow area.
(4) Non-bifurcated critical area resistive coupling.
(5) The resistance of the bifurcation area is calculated.
(6) The reduced order model calculates hemodynamic parameters. And replacing the vascular resistance R adopted in the first reduced-order model calculation with the vascular resistance R and the momentum parameter C obtained through three-dimensional simulation conversion, and repeating the calculation process of the reduced-order model to obtain a final simulation calculation result.
Example two
Fig. 5 is a schematic structural diagram of a hemodynamic parameter determination device according to a second embodiment of the present disclosure. The device specifically includes:
the initial hemodynamic parameter determination module 210 is configured to obtain image data of a blood vessel to be analyzed, construct a reduced-order model based on the image data of the blood vessel to be analyzed, and determine an initial hemodynamic parameter of the blood vessel to be analyzed based on the reduced-order model;
the three-dimensional simulation module 220 is configured to identify a key region in the blood vessel to be analyzed, and perform three-dimensional simulation on the key region based on an initial hemodynamic parameter corresponding to the key region to obtain a three-dimensional simulation result;
a coupling data determining module 230, configured to post-process the three-dimensional simulation result to a reduced dimension, so as to obtain coupling data of the key area;
a target hemodynamic parameter determination module 240, configured to update the reduced order model based on the coupling data of the key region, and determine a target hemodynamic parameter of the blood vessel to be analyzed based on the updated reduced order model.
On the basis of the above embodiment, optionally, the key region includes at least one of a bifurcation region and a non-bifurcation region; the non-bifurcated region includes a narrow region, a bent region, and a region where both bending and narrowing are present.
On the basis of the above embodiment, optionally, the three-dimensional simulation module 220 is configured to:
determining the geometric parameters of the key area based on the image data of the blood vessel to be analyzed, and generating a three-dimensional geometric model based on the geometric parameters;
and taking the initial hemodynamic parameters corresponding to the key region in the initial hemodynamic parameters of the blood vessel to be analyzed as simulation boundary parameters, and taking the initial hemodynamic parameters as the input of the three-dimensional geometric model for simulation processing to obtain a three-dimensional simulation result.
On the basis of the above embodiment, optionally, the coupling data of the non-bifurcation region includes vascular resistance;
the coupling data determination module 230 is configured to: performing segmentation processing on the non-bifurcation area to obtain a plurality of first blood vessel sections of the non-bifurcation area;
for any first blood vessel segment, determining mechanical energy loss data of the first blood vessel segment based on a three-dimensional simulation result corresponding to the first blood vessel segment; determining a vascular resistance of the first vessel segment based on the mechanical energy loss data of the first vessel segment and the blood flow data of the first vessel segment.
On the basis of the above embodiment, optionally, the coupling data of the bifurcation region includes parameters of vascular resistance and kinetic energy;
the coupling data determination module 230 is configured to: performing segmentation processing on the bifurcation area to obtain a plurality of second blood vessel sections of the bifurcation area;
for any second blood vessel segment, determining mechanical energy data of an inlet blood vessel segment of the bifurcation region and mechanical energy data of the second blood vessel segment based on the three-dimensional simulation result of the bifurcation region, determining mechanical energy loss data corresponding to the second blood vessel segment based on the mechanical energy data of the inlet blood vessel segment of the bifurcation region and the mechanical energy data of the second blood vessel segment, and determining vascular resistance of the second blood vessel segment based on the mechanical energy loss data corresponding to the second blood vessel segment and blood flow data of the second blood vessel segment;
and determining kinetic energy data of a second blood vessel section based on the three-dimensional simulation result of the bifurcation area, and determining kinetic energy parameters of the second blood vessel section based on the kinetic energy data, the section area of the second blood vessel section and blood flow data.
On the basis of the above embodiment, optionally, the coupling data determining module 230 is configured to: and calling a coupling data conversion model, and inputting the blood flow data of the key area in the three-dimensional simulation result into the coupling data conversion model to obtain the coupling data of the key area.
Optionally, the apparatus further comprises: a model generation module;
the model generation module is used for creating an initial coupling data conversion model, wherein the initial coupling data conversion model comprises parameters to be determined;
and determining data pairs of the coupling data and the blood flow data of the key area based on a three-dimensional simulation result obtained by three-dimensional simulation of the key area for at least two times, and determining parameter values of undetermined parameters in the initial coupling data conversion model based on at least two data pairs to obtain a coupling data conversion model.
Optionally, the coupling data conversion model is a machine learning model; alternatively, the coupling data conversion model is an association model between the coupling data and the blood flow data.
The hemodynamic parameter determination device provided by the embodiment of the invention can execute the hemodynamic parameter determination method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
EXAMPLE III
FIG. 6 illustrates a block diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 6, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various suitable 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 necessary for the operation of the electronic apparatus 10 can 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.
A number of 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, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, 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, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as the hemodynamic parameter determination method.
In some embodiments, the hemodynamic parameter determination method may be implemented as a computer program tangibly embodied in a computer readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the hemodynamic parameter determination method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the hemodynamic parameter determination method by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a 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 that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may 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 performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
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. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, 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 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) by 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 can 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, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (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 back-end, middleware, or front-end components. The components of the system can 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. A client and server are generally 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 host and VPS service are overcome.
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (11)
1. A method for determining hemodynamic parameters, comprising:
acquiring image data of a blood vessel to be analyzed, constructing a reduced-order model based on the image data of the blood vessel to be analyzed, and determining initial hemodynamic parameters of the blood vessel to be analyzed based on the reduced-order model;
identifying a key area in the blood vessel to be analyzed, and performing three-dimensional simulation on the key area based on initial hemodynamic parameters corresponding to the key area to obtain a three-dimensional simulation result;
post-processing the three-dimensional simulation result to a reduced dimensionality to obtain coupling data of the key area;
and updating the reduced-order model based on the coupling data of the key region, and determining the target hemodynamic parameters of the blood vessel to be analyzed based on the updated reduced-order model.
2. The method of claim 1, wherein the critical regions comprise at least one of bifurcation regions and non-bifurcation regions; the non-bifurcation region includes a narrow region, a bent region, and a region where both bending and narrowing exist.
3. The method according to claim 1 or 2, wherein three-dimensional simulation is performed on the critical region based on the initial hemodynamic parameters corresponding to the critical region, and a three-dimensional simulation result is obtained, including:
determining the geometric parameters of the key area based on the image data of the blood vessel to be analyzed, and generating a three-dimensional geometric model based on the geometric parameters;
and taking the initial hemodynamic parameters corresponding to the key region in the initial hemodynamic parameters of the blood vessel to be analyzed as simulation boundary parameters, and taking the simulation boundary parameters as the input of the three-dimensional geometric model for simulation processing to obtain a three-dimensional simulation result.
4. The method of claim 2, wherein the coupling data of the non-bifurcation region comprises vascular resistance;
the post-processing the three-dimensional simulation result to a reduced dimension to obtain the coupling data of the key area comprises:
performing segmentation processing on the non-bifurcation area to obtain a plurality of first blood vessel sections of the non-bifurcation area;
for any one of the first blood vessel segments, determining mechanical energy loss data of the first blood vessel segment based on a three-dimensional simulation result corresponding to the first blood vessel segment; determining a vascular resistance of the first vessel segment based on the mechanical energy loss data of the first vessel segment and the blood flow data of the first vessel segment.
5. The method of claim 2, wherein the coupling data of the bifurcation region includes vascular resistance and kinetic energy parameters;
the post-processing the three-dimensional simulation result to a reduced dimension to obtain the coupling data of the key area comprises:
performing segmentation treatment on the bifurcation area to obtain a plurality of second blood vessel sections of the bifurcation area, wherein the plurality of second blood vessel sections comprise an inlet blood vessel section;
for any second blood vessel segment except the inlet blood vessel segment, determining mechanical energy data of the inlet blood vessel segment of the bifurcation area and mechanical energy data of the second blood vessel segment based on a three-dimensional simulation result of the bifurcation area, determining mechanical energy loss data corresponding to the second blood vessel segment based on the mechanical energy data of the inlet blood vessel segment of the bifurcation area and the mechanical energy data of the second blood vessel segment, and determining vascular resistance of the second blood vessel segment based on the mechanical energy loss data corresponding to the second blood vessel segment and blood flow data of the second blood vessel segment;
and determining kinetic energy data of a second blood vessel section based on a three-dimensional simulation result of the bifurcation area, and determining kinetic energy parameters of the second blood vessel section based on the kinetic energy data, the cross-sectional area of the second blood vessel section and blood flow data.
6. The method of claim 1 or 2, wherein post-processing the three-dimensional simulation result to a reduced dimension to obtain the coupling data of the key region comprises:
and calling a coupling data conversion model, and inputting the blood flow data of the key area in the three-dimensional simulation result into the coupling data conversion model to obtain the coupling data of the key area.
7. The method of claim 6, wherein the method of generating the coupled data transformation model comprises:
creating an initial coupling data conversion model, wherein the initial coupling data conversion model comprises parameters to be determined;
and determining data pairs of the coupling data and the blood flow data of the key area based on a three-dimensional simulation result obtained by three-dimensional simulation of the key area for at least two times, and determining parameter values of parameters to be determined in the initial coupling data conversion model based on at least two data pairs to obtain a coupling data conversion model.
8. The method of claim 6, wherein the coupled data conversion model is a machine learning model; alternatively, the coupling data conversion model is an association model between the coupling data and the blood flow data.
9. A hemodynamic parameter determination device, comprising:
the initial hemodynamic parameter determination module is used for acquiring image data of a blood vessel to be analyzed, constructing a reduced-order model based on the image data of the blood vessel to be analyzed, and determining initial hemodynamic parameters of the blood vessel to be analyzed based on the reduced-order model;
the three-dimensional simulation module is used for identifying a key region in the blood vessel to be analyzed and carrying out three-dimensional simulation on the key region based on initial hemodynamic parameters corresponding to the key region to obtain a three-dimensional simulation result;
the coupling data determining module is used for post-processing the three-dimensional simulation result to a reduced dimensionality to obtain coupling data of the key area;
and the target hemodynamic parameter determination module is used for updating the reduced-order model based on the coupling data of the key region and determining the target hemodynamic parameters of the blood vessel to be analyzed based on the updated reduced-order model.
10. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the hemodynamic parameter determination method of any of claims 1 to 8.
11. A computer-readable storage medium, having stored thereon computer instructions for causing a processor to execute the method for determining hemodynamic parameters of any of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211357200.3A CN115762798A (en) | 2022-11-01 | 2022-11-01 | Method and device for determining hemodynamic parameters, storage medium and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211357200.3A CN115762798A (en) | 2022-11-01 | 2022-11-01 | Method and device for determining hemodynamic parameters, storage medium and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115762798A true CN115762798A (en) | 2023-03-07 |
Family
ID=85355082
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211357200.3A Pending CN115762798A (en) | 2022-11-01 | 2022-11-01 | Method and device for determining hemodynamic parameters, storage medium and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115762798A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117476241A (en) * | 2023-12-28 | 2024-01-30 | 柏意慧心(杭州)网络科技有限公司 | Method, computing device and medium for determining a blood flow of a blood vessel |
-
2022
- 2022-11-01 CN CN202211357200.3A patent/CN115762798A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117476241A (en) * | 2023-12-28 | 2024-01-30 | 柏意慧心(杭州)网络科技有限公司 | Method, computing device and medium for determining a blood flow of a blood vessel |
| CN117476241B (en) * | 2023-12-28 | 2024-04-19 | 柏意慧心(杭州)网络科技有限公司 | Method, computing device and medium for determining a blood flow of a blood vessel |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109559326B (en) | Hemodynamic parameter calculation method and system and electronic device | |
| US11030765B2 (en) | Prediction method for healthy radius of blood vessel path, prediction method for candidate stenosis of blood vessel path, and blood vessel stenosis degree prediction device | |
| CN112560874B (en) | Training method, device, equipment and medium for image recognition model | |
| CN112785582B (en) | Training method and device for thermodynamic diagram generation model, electronic equipment and storage medium | |
| CN114565763B (en) | Image segmentation method, device, apparatus, medium and program product | |
| CN115762798A (en) | Method and device for determining hemodynamic parameters, storage medium and electronic equipment | |
| Gharleghi et al. | Deep learning for time averaged wall shear stress prediction in left main coronary bifurcations | |
| CN114004793A (en) | Blood flow characteristic acquisition method and device, electronic equipment and storage medium | |
| US20150278976A1 (en) | Systems and methods for using geometry sensitivity information for guiding workflow | |
| CN116091702A (en) | Vascular structure reconstruction method, device, equipment and storage medium | |
| CN115861189A (en) | Image registration method and device, electronic equipment and storage medium | |
| CN114664410B (en) | Video-based focus classification method and device, electronic equipment and medium | |
| CN115482261A (en) | Blood vessel registration method, device, electronic equipment and storage medium | |
| CN115526882A (en) | Medical image classification method, device, equipment and storage medium | |
| CN115482358A (en) | Method, device and equipment for generating triangular mesh curved surface and storage medium | |
| CN117496055A (en) | Vascular reconstruction method, vascular reconstruction device, electronic equipment and storage medium | |
| Alromema et al. | Temporal database models validation and verification using mapping methodology | |
| CN116245853A (en) | Fractional flow reserve determination method, fractional flow reserve determination device, electronic equipment and storage medium | |
| CN117372399A (en) | Postoperative detection method, device and equipment for aortic dissection and storage medium | |
| CN117422687A (en) | Image processing method and device, electronic equipment and storage medium | |
| CN117454804A (en) | Hemodynamic parameter prediction method and device, electronic equipment and storage medium | |
| CN117746109A (en) | Pathological grade determining method, pathological grade determining device, pathological grade determining equipment and storage medium | |
| CN115984294A (en) | Multi-mode three-dimensional image segmentation method, device, equipment and storage medium | |
| CN117688866A (en) | Microcirculation resistance reserve fraction obtaining method, device, equipment and medium | |
| CN117611905A (en) | Updating method, device, equipment and medium of machine learning model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |