CN116227374A - Simulation method and device for blood flow force - Google Patents

Simulation method and device for blood flow force Download PDF

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CN116227374A
CN116227374A CN202211655782.3A CN202211655782A CN116227374A CN 116227374 A CN116227374 A CN 116227374A CN 202211655782 A CN202211655782 A CN 202211655782A CN 116227374 A CN116227374 A CN 116227374A
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blood vessel
target
arterial
vessel
pressure value
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张超
赵清华
毛益进
刘伟
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Beijing Yueying Technology Co ltd
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Abstract

The invention discloses a simulation method and device of blood flow dynamics. Wherein the method comprises the following steps: acquiring a blood vessel image of an arterial blood vessel of a target object preset area; performing level set segmentation on an initial blood vessel model corresponding to the blood vessel image to obtain a segmented blood vessel model; obtaining boundary conditions for simulating a target blood vessel corresponding to a blood vessel model; calculating the central arterial pressure of the target blood vessel to obtain an inlet pressure value of the target blood vessel; and carrying out blood flow dynamic simulation on the target blood vessel by using the boundary condition and the inlet pressure value. The invention solves the technical problem of lower reliability of blood flow dynamic simulation caused by potential safety hazard existing in the prior art by adopting invasive fractional flow reserve measurement.

Description

Simulation method and device for blood flow force
The application is the application date: 2021, 03, 26, application number: 202110328652.8, the entire contents of which are incorporated herein by reference, are incorporated by reference into the present application in its divisional application of the chinese patent application entitled "simulation method and apparatus of blood flow force".
Technical Field
The invention relates to the technical field of blood flow power simulation, in particular to a blood flow power simulation method and device.
Background
Intracranial atherosclerosis (Intracranial Atherosclerotic, abbreviated as ICAS) disease is the leading cause of stroke. SAMMPRIS test shows that even with optimal medical conditions, the recurrence rate of stroke in severe intracranial stenosis patients is as high as 12%. The visit test also does not show the benefits of intracranial stent surgery. However, one is that the use of devices and stent placement procedures is relatively inexperienced, leading to complications; another important factor is that patients with the highest risk of recurrent stroke are not selected. It is not sufficient to rely on optimal medical conditions alone, and many patients may benefit from vascular interventions. ICAS has traditionally treated patients based on the extent of lumen stenosis. Hemodynamics, as well as plaque characteristics, are important factors to consider when assessing risk of recurrence of a stroke. In coronary studies it was shown that functional stenosis assessed by fractional flow reserve (Fractional Flow Reserve, FFR for short) is superior to anatomical stenosis at risk of ischemia. The accuracy of the cut-off value of 0.8 exceeds 90% in determining whether the stenosis is functionally ischemic. Compared with percutaneous coronary intervention under the guidance of coronary angiography, the percutaneous coronary intervention under the guidance of FFR can reduce the mortality, the myocardial infarction rate and the repeated blood circulation reconstruction rate. Currently, coronary FFR is widely used as a clinical indicator for determining stent insertion in coronary artery disease. In the cerebrovascular system, a neurovascular fractional flow reserve (Cerebrovascular Reserve, abbreviated CVR) is used. Although FFR measurement is of great use for clinical diagnostics, it has several drawbacks: first, invasive measurement of pressure guidewires and second, the need for vasodilators to induce hyperemic conditions may lead to side effects of vasodilator administration such as adenosine. For neurovascular systems, invasive measurements are generally not recommended, as the procedure itself is very dangerous, and invasive measurements also present problems of radiation and CO2 side effects.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the invention provides a simulation method and a simulation device of blood flow force, which at least solve the technical problem of lower reliability of blood flow force simulation caused by potential safety hazard existing in the prior art by adopting invasive fractional flow reserve measurement.
According to an aspect of an embodiment of the present invention, there is provided a method for simulating blood flow dynamics, including: acquiring a blood vessel image of an arterial blood vessel of a target object preset area; performing level set segmentation on an initial blood vessel model corresponding to the blood vessel image to obtain a segmented blood vessel model; obtaining boundary conditions for simulating a target blood vessel corresponding to the blood vessel model; calculating the central arterial pressure of the target blood vessel to obtain an inlet pressure value of the target blood vessel; and carrying out blood flow dynamic simulation on the target blood vessel by using the boundary condition and the inlet pressure value.
Optionally, acquiring a vessel image of an arterial vessel of the target object in the predetermined region includes at least one of: scanning an arterial vessel in the preset area by using a time-of-flight method TOF-MRA to obtain a vessel image of the arterial vessel; scanning the arterial blood vessel of the preset area through contrast enhancement CE-MRA to obtain a blood vessel image of the arterial blood vessel; scanning an arterial vessel in the preset area by using a black blood method MRA to obtain a vessel image of the arterial vessel; and scanning the arterial blood vessel of the preset area through phase contrast PC-MRA to obtain a blood vessel image of the arterial blood vessel.
Optionally, before the level set segmentation is performed on the initial blood vessel model corresponding to the blood vessel image, the simulation method of blood flow dynamics further includes: generating an initial center line of a target blood vessel corresponding to the blood vessel image, and simultaneously establishing an initial radius function of the target blood vessel; and modeling based on the initial center line and the initial radius function to obtain the initial blood vessel model.
Optionally, performing a central arterial pressure calculation on the target blood vessel to obtain an inlet pressure value of the target blood vessel, including at least one of: acquiring a pressure waveform set of the target blood vessel, and calculating elements in the pressure waveform set by utilizing a general transfer function to obtain an inlet pressure value of the target blood vessel, wherein the general transfer function is a transfer function from a monitoring artery to a target artery, which is pre-constructed by using an autoregressive exogenous model; acquiring an inlet pressure value of the target blood vessel in a one-dimensional blood flow dynamics mode; and acquiring the inlet pressure value of the target blood vessel in a tube-load mode.
Optionally, obtaining the inlet pressure value of the target blood vessel by a one-dimensional hemodynamic mode includes: generating an arterial network structure of the target object; generating a one-dimensional hemodynamic control equation, and solving the one-dimensional hemodynamic control equation in a numerical mode after setting boundary conditions; optimizing relevant parameters corresponding to the one-dimensional hemodynamic control equation through an optimization algorithm; and solving the one-dimensional hemodynamic control equation according to the optimized related parameters to obtain an inlet pressure value of the target blood vessel.
Optionally, acquiring the inlet pressure value of the target blood vessel through a tube-load mode includes: acquiring a pulse wave reflection coefficient of the target blood vessel and a propagation time of a pulse wave propagating from a central artery inlet to a detection artery measurement point, and generating a data pair corresponding to the pulse wave reflection coefficient and the propagation time; calculating an arterial pressure waveform of the target blood vessel according to the data pair, and carrying out filtering processing and smoothing processing on the arterial pressure waveform; obtaining fitting errors of the data pairs through linear regression fitting straight lines, filtering treatment and arterial pressure waveform after smoothing treatment; and acquiring a target arterial pressure waveform with fitting error smaller than a preset error threshold value, and obtaining an inlet pressure value of the target blood vessel based on the target arterial pressure waveform.
Optionally, before the blood flow dynamics simulation of the target blood vessel using the boundary condition and the inlet pressure value, the simulation method of blood flow dynamics further comprises: and meshing the blood vessel model.
Optionally, performing a hemodynamic simulation of the target vessel using the boundary condition and the inlet pressure value includes: generating a simulation solver for simulating the target blood vessel; and solving the simulation solver by utilizing the boundary condition and the inlet pressure value to perform hemodynamic simulation on the target blood vessel.
Optionally, after performing a hemodynamic simulation on the target blood vessel using the boundary condition and the inlet pressure value, the hemodynamic simulation method further includes: displaying the simulation result of the target blood vessel, wherein the simulation result at least comprises: fractional flow reserve FFR, blood flow velocity, vessel wall shear stress, blood flow pressure.
According to another aspect of the embodiment of the present invention, there is also provided a blood flow dynamic simulation apparatus, including: a first acquisition unit for acquiring a blood vessel image of an arterial blood vessel in a predetermined region of a target object; the level set segmentation unit is used for carrying out level set segmentation on the initial blood vessel model corresponding to the blood vessel image to obtain a segmented blood vessel model; the second acquisition unit is used for acquiring boundary conditions for simulating a target blood vessel corresponding to the blood vessel model; the third acquisition unit is used for calculating the central arterial pressure of the target blood vessel so as to acquire an inlet pressure value of the target blood vessel; and the simulation unit is used for carrying out blood flow dynamic simulation on the target blood vessel by utilizing the boundary condition and the inlet pressure value.
Optionally, the first acquisition unit includes at least one of: the first acquisition module is used for scanning the arterial blood vessel of the preset area through a time-of-flight method TOF-MRA to obtain a blood vessel image of the arterial blood vessel; the second acquisition module is used for scanning the arterial blood vessel of the preset area through contrast enhancement CE-MRA to obtain a blood vessel image of the arterial blood vessel; the third acquisition module is used for scanning the arterial blood vessel of the preset area through a black blood method MRA to obtain a blood vessel image of the arterial blood vessel; and the fourth acquisition module is used for scanning the arterial blood vessel of the preset area through phase contrast PC-MRA to obtain a blood vessel image of the arterial blood vessel.
Optionally, the blood flow dynamic simulation device further includes: the generating unit is used for generating an initial center line of a target blood vessel corresponding to the blood vessel image and simultaneously establishing an initial radius function of the target blood vessel before carrying out level set segmentation on an initial blood vessel model corresponding to the blood vessel image; and a fourth obtaining unit, configured to perform modeling based on the initial center line and the initial radius function, and obtain the initial vessel model.
Optionally, the third obtaining unit includes at least one of: a fifth obtaining module, configured to obtain a pressure waveform set of the target blood vessel, and calculate elements in the pressure waveform set by using a general transfer function to obtain an inlet pressure value of the target blood vessel, where the general transfer function is a transfer function from a monitoring artery to a target artery that is pre-configured by using an autoregressive exogenous model; a sixth acquisition module, configured to acquire an inlet pressure value of the target blood vessel in a one-dimensional hemodynamic manner; and a seventh acquisition module, configured to acquire an inlet pressure value of the target blood vessel in a tube-load manner.
Optionally, the sixth acquisition module includes: the generation submodule is used for generating an arterial network structure of the target object; the solving sub-module is used for generating a one-dimensional hemodynamic control equation and solving the one-dimensional hemodynamic control equation in a numerical mode after setting boundary conditions; the optimization sub-module is used for optimizing the relevant parameters corresponding to the one-dimensional hemodynamic control equation through an optimization algorithm; the first acquisition submodule is used for solving the one-dimensional hemodynamic control equation according to the optimized related parameters to obtain an inlet pressure value of the target blood vessel.
Optionally, the seventh acquisition module includes: the second acquisition submodule is used for acquiring the pulse wave reflection coefficient of the target blood vessel and the propagation time of the pulse wave from the central artery inlet to the detection artery measurement point and generating a data pair of the pulse wave reflection coefficient corresponding to the propagation time; the processing sub-module is used for calculating the arterial pressure waveform of the target blood vessel according to the data pair, and carrying out filtering processing and smoothing processing on the arterial pressure waveform; the third acquisition submodule is used for obtaining the fitting error of the data pair through linear regression fitting straight lines, filtering treatment and arterial pressure waveform after the smoothing treatment; and the fourth acquisition sub-module is used for acquiring a target arterial pressure waveform with fitting error smaller than a preset error threshold value and obtaining an inlet pressure value of the target blood vessel based on the target arterial pressure waveform.
Optionally, the blood flow dynamic simulation device further includes: and the meshing module is used for meshing the blood vessel model before carrying out blood flow dynamic simulation on the target blood vessel by utilizing the boundary condition and the inlet pressure value.
Optionally, the simulation unit includes: the generation module is used for generating a simulation solver for simulating the target blood vessel; and the placement module is used for solving the simulation solver by utilizing the boundary condition and the inlet pressure value so as to simulate the blood flow dynamics of the target blood vessel.
Optionally, the apparatus further includes: the display module is configured to display a simulation result of the target blood vessel after performing a hemodynamic simulation on the target blood vessel by using the boundary condition and the inlet pressure value, where the simulation result at least includes: fractional flow reserve FFR, blood flow velocity, vessel wall shear stress, blood flow pressure.
According to another aspect of the embodiments of the present invention, there is provided a computer readable storage medium including a stored computer program, wherein the computer readable storage medium, when executed by a processor, controls a device in which the computer readable storage medium is located to perform the method of simulating blood flow dynamics according to any one of the above.
According to another aspect of an embodiment of the present invention, there is provided a processor for executing a computer program, where the computer program when executed performs the method for simulating blood flow dynamics according to any one of the above.
In the embodiment of the invention, a blood vessel image of an arterial blood vessel in a preset area of a target object is acquired; performing level set segmentation on an initial blood vessel model corresponding to the blood vessel image to obtain a segmented blood vessel model; obtaining boundary conditions for simulating a target blood vessel corresponding to a blood vessel model; calculating the central arterial pressure of the target blood vessel to obtain an inlet pressure value of the target blood vessel; the boundary condition and the inlet pressure value are utilized to simulate the blood flow of the target blood vessel, and the blood flow simulation method provided by the embodiment of the invention realizes the purposes of obtaining the inlet pressure value of the blood vessel based on noninvasive measurement monitoring of the arterial pressure waveform after the three-dimensional nuclear magnetic image models the blood vessel, and carrying out the blood flow simulation of the target blood vessel by combining the inlet pressure value of the blood vessel and the boundary condition, namely, carrying out the blood flow simulation in a non-invasive mode, thereby achieving the technical effects of improving the reliability and the safety of the blood flow reserve value measurement, and further solving the technical problems of lower reliability of the blood flow dynamic simulation caused by the potential safety hazard existing in the adoption of invasive blood flow reserve value measurement in the related technology.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart of a method of simulating blood flow force according to an embodiment of the invention;
FIG. 2 (a) is a schematic diagram of a time-fly-by nuclear magnetic resonance image (bright blood method) according to an embodiment of the present invention;
FIG. 2 (b) is a schematic illustration of a black blood nuclear magnetic resonance image according to an embodiment of the present invention;
FIG. 3 (a) is a schematic diagram of a measurement position selection for two-dimensional phase contrast magnetic resonance imaging in accordance with an embodiment of the present invention;
FIG. 3 (b) is a schematic illustration of a measurement plane of two-dimensional phase-contrast magnetic resonance imaging according to an embodiment of the present invention;
FIG. 4 (a) is a schematic diagram illustrating a measurement position selection of a three-dimensional phase contrast MRI according to an embodiment of the present invention;
FIG. 4 (b) is a schematic diagram of a three-dimensional phase contrast MRI slice measurement according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a human arterial network according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a tube-load model according to an embodiment of the invention;
FIG. 7 is a schematic diagram of a brain vascular system computing grid according to an embodiment of the present invention;
FIG. 8 is a schematic representation of a digitized display of fractional color codes of cerebral vascular system blood flow reserve in accordance with an embodiment of the present invention;
FIG. 9 is a flow chart of a method for simulating a cerebral vascular hemodynamic system based on nuclear magnetic resonance imaging according to an embodiment of the present invention;
fig. 10 is a schematic diagram of a blood flow force simulation device according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
According to an embodiment of the present invention, there is provided a method embodiment of a method of simulating blood flow dynamics, it being noted that the steps shown in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in an order different from that shown or described herein.
Fig. 1 is a flowchart of a simulation method of blood flow force according to an embodiment of the present invention, as shown in fig. 1, the simulation method of blood flow force includes the steps of:
step S102, acquiring a blood vessel image of an arterial blood vessel in a target object preset area.
In this embodiment, the target object may be a human body, and the predetermined region may be a region including an artery such as a head, a neck, or the like. The vessel image is a nuclear magnetic resonance image.
Specifically, in step S102, a blood vessel image of an arterial blood vessel of a predetermined region of the target object is acquired, which may include at least one of: scanning an arterial vessel in a preset area by using a time-of-flight method TOF-MRA to obtain a vessel image of the arterial vessel; scanning an arterial vessel in a preset area through contrast enhancement CE-MRA to obtain a vessel image of the arterial vessel; scanning an arterial vessel in a preset area by using a black blood method MRA to obtain a vessel image of the arterial vessel; and scanning the arterial blood vessel in the preset area through phase contrast PC-MRA to obtain a blood vessel image of the arterial blood vessel.
That is, in this step, the method of modeling the blood vessel of the sensing region ROI by using the time-of-flight nuclear magnetic resonance image (TOF MRA) and performing the overlapping measurement with the phase contrast nuclear magnetic resonance image (PC MRA) of the same region of the same object can be used to perform the instantaneous or time-averaged quantitative measurement of the blood flow rate of the arterial blood vessel ROI region, simulate the flow condition of the blood in the blood vessel by the algorithm of the hemodynamic simulation, and display or output the relevant hemodynamic simulation result.
Among them, the time fly-by method is based on the inflow enhancement effect of blood. The short TR rapid phase disturbing GRE T1W1 sequence is collected, static tissues in an imaging volume or a layer are repeatedly excited to be in a saturated state, and magnetization vectors are small, so that static background tissues are restrained, blood outside imaging is not saturated by radio frequency pulses, and when the blood flows into the imaging volume or the layer, the blood has a high signal, and a good contrast is formed between the blood and the static tissues. Specifically, it can be classified into 2D TOF and 3D TOF. Fig. 2 (a) is a schematic diagram of a time-fly-by-magnetic resonance image (bright blood method) according to an embodiment of the present invention, where, as shown in fig. 2 (a), stationary tissue in an imaging volume or layer is repeatedly excited to be in a saturated state, and a magnetization vector is small, so that stationary background tissue is suppressed, and blood outside the imaging is not saturated by radio frequency pulses. The mode has the following advantages: (1) the imaging speed is high; (2) the blood flow velocity need not be selected; (3) less affected by the direction of blood flow and better shows the branches of blood vessels.
The imaging principle of the contrast enhancement nuclear magnetic resonance image is as follows: the CE-MRA is formed by injecting paramagnetic contrast agent intravenously, forming a phenomenon of obviously shortening the relaxation time of blood T1 by using a relatively short high concentration state of the contrast agent in blood vessels, simultaneously effectively inhibiting signals of surrounding background tissues by matching with a short TR effect of a rapid gradient echo MR scanning technology, forming a strong contrast effect imaging with obviously increased blood vessel signals and obviously inhibited surrounding static tissue signals, and obtaining various forms of blood vessel imaging by performing computer post-processing on the obtained original image. It has the following advantages: (1) the display of the vessel lumen is more reliable than without contrast agent MRA; (2) the occurrence of signal loss is less, and the blood vessel stenosis artifact is reduced; (3) multiple imaging segments can be performed with one injection of contrast agent, while arteries and veins can be imaged separately.
The black blood nuclear magnetic resonance image is mainly based on the flow void effect, and the blood flow presents a low signal (black), or the blood flow presents a low signal by adopting a method of spatial presaturation band, inversion pulse or out-of-phase gradient, and the like, and simultaneously, selecting appropriate parameters to cause surrounding background tissues to present a bright signal, and fig. 2 (b) is a schematic diagram of the black blood nuclear magnetic resonance image according to an embodiment of the present invention, as shown in fig. 2 (b), the blood flow presents a low signal. This has the advantage that there is no concern about turbulence-induced signal degradation, and the resulting signal attenuation no longer overestimates the extent of stenosis as in the bright blood technique. It should be noted that, the black blood nmr image may be reconstructed by using techniques such as minimum intensity projection to display the blood vessel, and the main purpose is to display the vessel wall.
The phase contrast nuclear magnetic resonance image is mainly a method for suppressing background and highlighting blood flow signals by utilizing the phase change of macroscopic transverse magnetization vector (Mxy) caused by flow. It encodes the flow using bipolar gradient fields, i.e. after excitation by a radio frequency pulse, two gradient fields of exactly the same magnitude and duration are applied, in opposite directions. For proton populations of stationary tissue, the phase change of Mxy caused by the first gradient field is completely corrected by the second gradient field, with Mxy phase change equal to zero. Whereas the mobile mass group is preserved with Mxy phase change due to position change. The pixel intensity represents the phase change of the magnetization vector, not the tissue magnetization, which is related to the flow velocity of the proton population, the faster the flow the more pronounced the phase change. The imaging of the mode is related to the blood flow speed and the blood flow direction, and different flow velocity codes (Velocity Encoding, VENC for short) are selected for the imaging of blood vessels at different positions. The advantages are that: (1) due to the application of the subtraction technique, the background tissue is well inhibited, which is helpful for the display of the blood vessel outline; (2) quantitative analysis of blood flow can be performed using different flow rates.
In the embodiment of the invention, the head-foot direction, the left-right direction and the front-back direction are selected as the directions of phase contrast radio frequency excitation, the VENC is generally set to be between 100cm/s and 150cm/s, and for the object with high blood vessel stenosis degree in the region of interest, a higher flow velocity code can be selected.
It should be noted that the above-mentioned phase contrast nmr image may include, but is not limited to: two-dimensional phase contrast nuclear magnetic resonance imaging, three-dimensional phase contrast nuclear magnetic resonance imaging, and four-dimensional blood flow analysis nuclear magnetic resonance imaging.
Step S104, carrying out level set segmentation on the initial blood vessel model corresponding to the blood vessel image to obtain a segmented blood vessel model.
Before the step S014, that is, before the level set segmentation of the initial blood vessel model corresponding to the blood vessel image, the method for simulating blood flow may further include: generating an initial central line of a target blood vessel corresponding to the blood vessel image, and simultaneously establishing an initial radius function of the target blood vessel; modeling is performed based on the initial center line and the initial radius function, and an initial blood vessel model is obtained.
In the embodiment of the present invention, the initial blood vessel model is determined based on the center line of the blood vessel corresponding to the initial blood vessel model and a radius function, where the radius function is used to describe the blood vessel region.
Alternatively, the level set segmentation of the initial vessel model may be performed on the basis of a local lumen statistical model, where the local lumen statistical model comprises lumen thresholds of the vessel along a cross-section on the centerline.
The aim of carrying out self-adaptive accurate modeling on the blood vessel by carrying out iterative processing on the central line of the blood vessel, the blood vessel model and the local lumen statistical value of the blood vessel by using the level set segmentation mode is achieved, and the technical effect of improving the accurate modeling on the blood vessel is achieved.
Step S106, obtaining boundary conditions for simulating the target blood vessel corresponding to the blood vessel model.
In this embodiment, the boundary condition information required for the fluid simulation of the blood vessel model is mainly extracted, for example, information such as the blood flow rate at the blood vessel inlet and the blood flow rate at the blood vessel outlet.
For example, the method can be realized by adopting a two-dimensional phase contrast nuclear magnetic resonance image 2D PC MRA, the 2DPC MRA under time domain analysis is a two-dimensional phase contrast magnetic resonance imaging with time resolution, the speed information along the plane normal direction in any plane can be measured, the speed coding is mainly carried out by utilizing the direct relation between the phase change of MR signals and the blood flow speed along the measured plane normal direction, and the measurement of the blood flow speed normal vector is realized. Specifically, the position measured by the 2D PC MRA may be placed at the position shown by 4 boxes (vertical segments of the carotid artery and the vertebral artery) in fig. 3 (a) (fig. 3 (a) is a schematic view of the measurement position selection of the two-dimensional phase contrast magnetic resonance imaging according to an embodiment of the present invention), and the measurement plane is ensured to be perpendicular to the blood flow direction as much as possible (the angle between the measurement plane and the normal plane of the blood flow direction is <20 degrees), so that the two-dimensional phase contrast magnetic resonance imaging measurement blood vessel flow/flow rate error is ensured to be smaller, and particularly as shown in fig. 3 (b) (fig. 3 (b) is a schematic view of the measurement plane of the two-dimensional phase contrast magnetic resonance imaging according to an embodiment of the present invention).
For example, the method can also be realized by adopting a three-dimensional phase contrast nuclear magnetic resonance image 3D PC MRA, wherein a three-dimensional nuclear magnetic phase contrast image (3D PC MRA) under time domain average statistics is three-dimensional phase contrast magnetic resonance imaging without time resolution, and blood velocity information of any one space point in three directions can be measured; as shown in fig. 2 (b), the end-truncated region of the hemodynamic simulation is generally far away from the region of interest of the blood vessel (as shown by the vessel-truncated boundary in fig. 4 (a), fig. 4 (a) is a schematic view of the measurement location selection of the three-dimensional phase-contrast nmr image according to an embodiment of the present invention), so as to ensure that the influence of the calculated boundary on the result is minimal (as shown in fig. 4 (b), fig. 4 (b) is a schematic view of the measurement of a certain slice of the three-dimensional phase-contrast nmr image according to an embodiment of the present invention).
For another example, the method can be implemented by using a four-dimensional phase contrast nuclear magnetic resonance image 4D PC MRA, and the three-dimensional nuclear magnetic phase contrast image (Time-Resolved 3-Dimensional Magnetic Resonance Phase Contrast Imaging, abbreviated as 4D Flow MRI) under Time domain analysis is also called four-dimensional blood Flow analysis. The 4D Flow MRI is three-dimensional phase contrast magnetic resonance imaging with time resolution, three mutually perpendicular dimensions can be simultaneously subjected to phase encoding, blood Flow data are acquired in multiple directions, and complex three-dimensional dynamic parameters are obtained. The 4D Flow MRI technique can be used to calculate various Flow related parameters of intracranial arterial blood Flow and abdominal vascular blood Flow, such as pulse wave velocity (Pulse Wave Velocity, abbreviated as PWV), wall shear stress (Wall Shear Stress, abbreviated as WSS), and the like. Unlike 3D PC MRA, 4D Flow MRI techniques can make instantaneous measurements over the heart cycle of the Flow rate/volume of the blood vessels in the scanned area through ECG gating techniques. Likewise, a scan region using 4D Flow MRI would be far from the vessel region of interest to obtain the import-export boundary conditions required for the hemodynamic simulation (as shown by the vessel cutoff boundary in fig. 2 (b)).
In addition, in order to construct the computational fluid dynamics CFD model, the 3D geometry of the arterial vessel wall may be first extracted from the CT or MRI image using the method described in step S102 above, as shown in fig. 2 (a). While the distal end of each artery remote from the region of interest is truncated, the relationship of vessel end resistance coefficient, blood flow and blood pressure is characterized by using a circuit model, and the boundary at which the vessel end is truncated is generally represented as a simple circuit formulated by a lumped parameter model (Lumped Parameter Model, abbreviated as LPM). In LPM, the hemodynamic elements of the cardiovascular system are represented as a series of equivalent electrical elements, namely resistance and capacitance. By solving the equation of the circuitry, we can obtain a time-dependent solution of the pressure and flow of the LPM node (e.g., coronary capillaries, coronary veins). At the outlet of the CFD model, the pressure and flow calculated from the LPM affects the solution of the CFD model and vice versa. Iterative computations are required until convergence to obtain consistent solutions in the LPM and CFD domains. FFR or CVR may then be calculated from the calculated pressure distribution of the CFD model.
Generally, for the hemodynamic simulation of intracranial arteries, the entrance boundary of the computational region of CFD will be chosen from the vertical segments of the vertebral artery and carotid artery (as shown in fig. 2 (a)), and the cut-off position of the vessel end may be chosen arbitrarily, typically at a distance from the region of interest (more than 2cm from the region of interest). The blood flow measuring method can accurately measure the blood flow at the cut-off boundary of the blood vessel. Simple circuits that may be formulated using the lumped parameter model LPM include, but are not limited to, the following models: a One-unit circuit simulation Model (One-elements lumped parameter Model), a Two-unit circuit simulation Model (Two-elements lumped parameter Model), a three-unit circuit simulation Model (Windkessel Model), other multi-unit lumped parameter circuit models, and the like.
It should be noted that, in addition to the above-mentioned lumped parameter model combining the vertebral artery/carotid artery inlet pressure and intracranial artery outlet, the boundary condition of CFD calculation may also be a conventional given pressure flow boundary or a mixed boundary of pressure flow and lumped model. For example, if all intracranial arterial vessel flow and pressure at the vertebral artery/carotid artery or pressure at the outlet of a vessel have been measured in the calculation, the CFD boundary can be reduced to a boundary condition of known inlet pressure and outlet flow (or known inlet flow and outlet pressure); if it is known to calculate the pressure at a certain reference point in the domain and the intracranial arterial blood vessel flow, instead of using the above-mentioned blood vessel outlet lumped model and inlet pressure calibration boundary, a mixed boundary of outlet model and lumped model for a given flow may be used: that is, part of the blood vessels adopts a lumped model, the rest part adopts a flow model, and the inlet is a calibration pressure. Thus, based on the measurements of blood vessel pressure and flow as described in embodiments of the present invention, the boundary conditions of the CFD may be a combination of different known data.
Step S108, central arterial pressure calculation is carried out on the target blood vessel so as to acquire an inlet pressure value of the target blood vessel.
In this step, a central arterial pressure calculation is performed on the target vessel to obtain an inlet pressure value of the target vessel, including at least one of: acquiring a pressure waveform set of a target blood vessel, and calculating elements in the pressure waveform set by utilizing a general transfer function to obtain an inlet pressure value of the target blood vessel, wherein the general transfer function is a transfer function from a monitoring artery to a target artery, which is pre-constructed by using an autoregressive exogenous model; acquiring an inlet pressure value of a target blood vessel in a one-dimensional hemodynamic mode; and obtaining the inlet pressure value of the target blood vessel through a tube-load mode.
Specifically, calculating the elements in the pressure waveform set using the universal transfer function to obtain the inlet pressure value of the target vessel may include the steps of: 1) Collecting and monitoring an arterial (brachial/radial) pressure waveform set; 2) Construction of personal transfer function y (t) +a from monitored artery to target artery based on autoregressive exogenous model 1 y(t-1)+L+a na y(t-na)＝b 1 u(t-nk)+L+b nb u (t-nb-nk+1) +e (t), where na, nb is the order of the model, n k Is the time delay of the model, e (t) is white noise disturbance, u (t) is the input monitoring arterial pressure, and y (t) is the output target arterial pressure; 3) And (3) averaging the personal transfer functions in all measured data sets to finally obtain a general transfer function (generalized transfer function), and applying the general transfer function to the monitored arterial blood pressure waveform of clinical measurement to obtain the target arterial pressure waveform.
Optionally, obtaining the inlet pressure value of the target blood vessel by a one-dimensional hemodynamic mode includes: generating an arterial network structure of the target object; generating a one-dimensional hemodynamic control equation, and solving the one-dimensional hemodynamic control equation in a numerical mode after setting boundary conditions; optimizing relevant parameters corresponding to the one-dimensional hemodynamic control equation through an optimization algorithm; and solving a one-dimensional hemodynamic control equation according to the optimized related parameters to obtain an inlet pressure value of the target blood vessel.
In particular, obtaining the inlet pressure value of the target vessel by means of one-dimensional hemodynamics may comprise the steps of: 1) Establishing a human body artery network structure shown in fig. 5, wherein fig. 5 is a schematic diagram of a human body artery network according to an embodiment of the invention, and 1 to 55 are artery distribution areas; 2) A one-dimensional hemodynamic control equation is constructed,
Figure BDA0004012713370000111
wherein A is the cross-sectional area of the blood vessel, q is the blood flow, v is the kinematic viscosity, delta boundary layer thickness; pressure p is expressed by the equation of state->
Figure BDA0004012713370000112
Calculation, p 0 ,A 0 The pressure and cross-sectional area of the vessel when undeformed, respectively; 3) Given the boundary conditions of an inlet and an outlet, solving a one-dimensional hemodynamic force control equation by a numerical method (finite difference, finite volume, finite element and the like); 4) Measuring the pressure of a monitoring artery (brachial artery/radial artery), defining the pressure difference between the monitoring artery calculated by the model and the measured pressure difference of the monitoring artery as an objective function, and continuously adjusting related parameters related to a control equation through an optimization algorithm until the objective function value is smaller than a given threshold value; 5) And (5) solving a control equation based on the parameter set obtained after the optimization, and calculating to obtain the target arterial pressure.
Optionally, obtaining the inlet pressure value of the target blood vessel through a tube-load mode includes: acquiring a pulse wave reflection coefficient of a target blood vessel and propagation time of the pulse wave from a central artery inlet to a detection artery measurement point, and generating a data pair corresponding to the pulse wave reflection coefficient and the propagation time; calculating an arterial pressure waveform of a target blood vessel according to the data, and performing filtering treatment and smoothing treatment on the arterial pressure waveform; fitting a straight line through linear regression, and obtaining fitting errors of the data pairs through filtering and smoothing arterial pressure waveforms; and obtaining a target arterial pressure waveform with fitting error smaller than a preset error threshold value, and obtaining an inlet pressure value of the target blood vessel based on the target arterial pressure waveform.
Specifically, the obtaining the inlet pressure value of the target blood vessel through the tube-load mode may include the following steps: establishing a Tube-Load model as shown in FIG. 6, wherein FIG. 6 is a schematic diagram of the Tube-Load model according to an embodiment of the present invention, wherein p c (T) is the pressure of the target arterial pressure over time, T d Is the propagation time of the pulse wave from the central arterial access to the monitoring arterial measurement point (brachial/radial artery), Z c Is the characteristic impedance of the artery, R is the peripheral resistance; 2) According to the formula
Figure BDA0004012713370000121
Calculating the pulse wave reflection coefficient; 3) According to T d Physiological range of Γ, i.e. T d ∈[0,0.15](units: seconds), Γ ε [0,1 ]]At an interval of delta T d ＝5×10 -3 ,ΔΓ＝5×10 -2 Generation (T) d Γ) pairs; 4) Measuring pressure waveform p of monitoring artery over time r (t); 5) By the formula T-0.4 (1-e -2T ) Calculating a diastole interval corresponding to the central arterial pressure waveform, wherein T=60/HR, and HR is the number of beats per minute; 6) Each (T) d Γ) pairs according to the formula
Figure BDA0004012713370000122
Calculating a corresponding target arterial pressure waveform and smoothing the waveform through a low-pass filter; 7) For each pair (T) d The smoothed target arterial pressure waveform is calculated by Γ), the corresponding pressure in diastole interval is logarithmically transformed, a straight line is fitted through linear regression, and all (T) are recorded d Gamma) fitting error for the pair; 8) The target arterial pressure waveform with the minimum fitting error is the final calculated waveform.
From the above, in the embodiment of the present invention, the target vessel (carotid artery/vertebral artery) inlet pressure can be obtained by measuring the noninvasive pressure waveform of the monitoring artery (brachial artery/radial artery) and calculating the central cardiac pulse pressure by setting the patient-specific parameters; the calculation of the central cardiac pulse pressure mainly comprises, but is not limited to, the following three embodiments, namely a general transfer function method, a one-dimensional hemodynamic method and a Tube-Load method.
Step S110, performing a hemodynamic simulation on the target blood vessel using the boundary condition and the inlet pressure value.
As can be seen from the above, in the embodiment of the present invention, a blood vessel image of an arterial blood vessel in a predetermined area of a target object can be obtained; performing level set segmentation on an initial blood vessel model corresponding to the blood vessel image to obtain a segmented blood vessel model; obtaining boundary conditions for simulating a target blood vessel corresponding to a blood vessel model; calculating the central arterial pressure of the target blood vessel to obtain an inlet pressure value of the target blood vessel; the boundary condition and the inlet pressure value are utilized to carry out blood flow dynamic simulation on the target blood vessel, so that the purpose of carrying out blood flow dynamic simulation on the target blood vessel by combining the blood vessel inlet pressure value and the boundary condition is achieved, namely, the blood flow dynamic simulation is carried out in a non-invasive mode, and the technical effects of improving the reliability and the safety of blood flow reserve score measurement are achieved after the blood vessel is modeled by the three-dimensional nuclear magnetic image and the arterial pressure waveform is monitored based on noninvasive measurement.
Therefore, by adopting the simulation method of the blood flow dynamics, the technical problem of low reliability of the blood flow dynamics simulation caused by potential safety hazard existing in the prior art by adopting invasive fractional flow reserve measurement is solved.
In an alternative embodiment, the method of simulating blood flow dynamics further comprises, prior to simulating blood flow dynamics of the target vessel using the boundary conditions and the inlet pressure values: and meshing the blood vessel model.
In particular, meshing here is the division of a particular region of investigation into a number of very small sub-regions (elements) to meet certain specific requirements. In an ideal case, the shape and distribution of each element in the grid can be determined by an automatic grid generation algorithm. For example, the generated geometric model of the intracranial artery requires that the space enclosed by the geometric model be divided into hundreds of thousands to tens of millions of computational grids by an automated grid distribution algorithm (as shown in fig. 7, fig. 7 is a schematic diagram of a cerebrovascular system computational grid according to an embodiment of the present invention).
The connection relation of the grids is used for distinguishing the grids, and two main categories are as follows: structured grids and unstructured grids. The structured grid generation algorithm mainly comprises an infinite interpolation method and a partial differential equation grid generation method; the unstructured grid generation algorithm mainly comprises a node connecting element method, a mapping method and a Delaunay triangulation method.
In an alternative embodiment, the hemodynamic simulation of the target vessel using the boundary conditions and the inlet pressure values includes: generating a simulation solver for simulating the target vessel; and solving the simulation solver by using the boundary conditions and the inlet pressure value to simulate the blood flow dynamics of the target blood vessel.
In particular, local blood flow in 3D arterial geometry is simulated by Computational Fluid Dynamics (CFD). The objective of CFD is to model continuous blood flow with Partial Differential Equations (PDEs), i.e., navier-Stokes equations, and discretize the PDEs into algebraic problems (i.e., matrix equations) that are solved to restore the true hemodynamic conditions in the patient's blood vessels.
Wherein, navier-Stokes equation is as follows:
Figure BDA0004012713370000131
(2) The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Figure BDA0004012713370000132
Is the velocity vector of blood flow, p is the intravascular pressure, ρ is the blood density, and v is the kinematic viscosity coefficient of blood.
The method for solving the Navier-Stokes equation includes, but is not limited to, a finite volume method, a finite difference method, a finite element method, a spectral method, a lattice Boltzmann method, and various numerical algorithms, and variations of these methods.
In an alternative embodiment, after the blood flow dynamics simulation of the target vessel using the boundary conditions and the inlet pressure values, the blood flow dynamics simulation method may further include: displaying a simulation result of the target blood vessel, wherein the simulation result at least comprises: fractional flow reserve FFR, blood flow velocity, vessel wall shear stress, blood flow pressure.
Specifically, the hemodynamic simulation results may digitally display common clinical data such as blood pressure (fractional flow reserve), blood flow velocity, wall shear stress, etc. through an image display and processing algorithm, digitally display blood vessels in a region of interest through a rendering algorithm and a three-dimensional stereo display algorithm, and output corresponding quantitative results (as shown in fig. 8, fig. 8 is a schematic diagram of a digital display of fractional flow reserve color codes of the cerebrovascular system according to an embodiment of the present invention).
FIG. 9 is a flowchart of a simulation method of a cerebral vascular hemodynamic system based on nuclear magnetic resonance images according to an embodiment of the present invention, as shown in FIG. 9, the inner wall of a Blood vessel can be segmented and modeled accurately by using a modeling method of three-dimensional nuclear magnetic imaging (3D TOF/Dark Blood/CE-MRA/PC MRA), accurate Blood Flow measurement is given to the inlet and outlet boundaries of a hemodynamic simulation model by using a phase contrast nuclear magnetic resonance method (including but not limited to 2D PC MRA,3D PC MRA and 4D Flow), simultaneously, a digital model image of the three-dimensional time-flight nuclear magnetic image and the Blood vessel is spatially registered (if necessary) with a phase contrast nuclear magnetic image by using a registration method, finally, the magnitude of Blood Flow at the boundary of the Blood vessel model in the phase contrast nuclear magnetic image after automatic statistics registration is directly input into the hemodynamic simulation system or input into a lumped parameter circuit model LPM as a simulation boundary condition and the measured Blood Flow is matched; meanwhile, a pressure waveform curve of a target blood vessel is obtained by using a noninvasive measurement and calculation (a general transfer function method/a one-dimensional hemodynamic method/a Tube-Load method) mode of the target arterial pressure, and the pressure waveform curve is calculated together with three-dimensional CFD simulation until convergence; and finally, carrying out three-dimensional digital display on the result of the calculation simulation and outputting a corresponding result.
By adopting the simulation method of the blood flow force provided by the embodiment of the invention and adopting the computer simulation method of the non-invasive cerebral blood vessel blood dynamic system, an anatomically accurate blood vessel three-dimensional model based on images can be reconstructed from the patient-specific nuclear magnetic resonance image; the method for simulating hemodynamic parameters during rest and congestion by combining physiological boundary conditions of the circulatory system of each patient and performing hemodynamic calculation to obtain all hemodynamic information of the neurovascular system has the following advantages: using three-dimensional nuclear magnetic images (including but not limited to three-dimensional time-fly nuclear magnetic images, three-dimensional phase contrast nuclear magnetic images, etc.) to conduct accurate modeling of blood vessels; measuring the blood flow velocity and the blood flow of the blood vessel by using the three-dimensional phase contrast nuclear magnetic image; if necessary, using a three-dimensional image registration method to register different images of the same patient; monitoring the pressure waveform of an artery (such as a brachial artery/radial artery) through noninvasive measurement, and performing calculation correction based on patient specific parameterization on the pressure waveform of a target artery (such as a carotid artery/vertebral artery); performing hemodynamic simulation on the generated blood vessel by using a computational fluid dynamics CFD solver, wherein a non-slip boundary condition (No slip BC) is used on the inner wall surface of the blood vessel, and a direct flow boundary condition or a lumped parameter circuit model LPM is used for the inlet and outlet boundary conditions so as to realize accurate reduction of the measured flow; in addition, the blood flow dynamic simulation can be performed in an artificial intelligent simulation mode; blood pressure (fractional flow reserve) is applied to the carotid artery, vertebral artery and intracranial artery by using a three-dimensional post-processing display technique, and three-dimensional color code results such as blood flow speed/flow rate are displayed or output.
Example 2
According to another aspect of the embodiment of the present invention, there is provided a blood flow power simulation apparatus, and fig. 10 is a schematic diagram of the blood flow power simulation apparatus according to the embodiment of the present invention, and as shown in fig. 10, the blood flow power simulation apparatus includes: a first acquisition unit 1001, a level set division unit 1003, a second acquisition unit 1005, a third acquisition unit 1007, and a simulation unit 1009. The following describes the blood flow power simulation device in detail.
A first acquisition unit 1001 is configured to acquire a blood vessel image of an arterial blood vessel in a predetermined region of a target object.
The level set segmentation unit 1003 is configured to perform level set segmentation on an initial vessel model corresponding to the vessel image, and obtain a segmented vessel model.
A second acquiring unit 1005 is configured to acquire a boundary condition for simulating a target blood vessel corresponding to the blood vessel model.
A third obtaining unit 1007 is configured to perform central arterial pressure calculation on the target blood vessel to obtain an inlet pressure value of the target blood vessel.
A simulation unit 1009 is configured to perform a hemodynamic simulation on the target blood vessel using the boundary condition and the inlet pressure value.
Here, the first obtaining unit 1001, the level set dividing unit 1003, the second obtaining unit 1005, the third obtaining unit 1007, and the simulation unit 1009 correspond to steps S102 to S110 in embodiment 1, and the modules are the same as examples and application scenarios implemented by the corresponding steps, but are not limited to those disclosed in embodiment 1. It should be noted that the modules described above may be implemented as part of an apparatus in a computer system, such as a set of computer-executable instructions.
As can be seen from the above, in the embodiment of the present invention, the first acquiring unit may be used to acquire the blood vessel image of the arterial blood vessel in the predetermined area of the target object; then, carrying out level set segmentation on an initial blood vessel model corresponding to the blood vessel image by using a level set segmentation unit to obtain a segmented blood vessel model; then, a second acquisition unit is utilized to acquire boundary conditions for simulating a target blood vessel corresponding to the blood vessel model; calculating the central arterial pressure of the target blood vessel by using a third acquisition unit to acquire an inlet pressure value of the target blood vessel; and performing hemodynamic simulation on the target vessel by using the boundary condition and the inlet pressure value by using the simulation unit. By means of the blood flow dynamic simulation device provided by the embodiment of the invention, the purposes of blood flow dynamic simulation on the target blood vessel by combining the blood vessel inlet pressure value and the boundary condition after the blood vessel is modeled by the three-dimensional nuclear magnetic image and based on noninvasive measurement monitoring of the arterial pressure waveform are achieved, namely, the blood flow dynamic simulation is carried out in a noninvasive mode, the technical effects of improving the reliability and the safety of blood flow reserve score measurement are achieved, and the technical problem that potential safety hazards exist in invasive blood flow reserve score measurement in the related art, and the reliability of the blood flow dynamic simulation is low is solved.
In an alternative embodiment, the first acquisition unit comprises at least one of: the first acquisition module is used for scanning the arterial blood vessel of the preset area through a time-of-flight method TOF-MRA to obtain a blood vessel image of the arterial blood vessel; the second acquisition module is used for scanning the arterial blood vessel in the preset area through contrast enhancement CE-MRA to obtain a blood vessel image of the arterial blood vessel; the third acquisition module is used for scanning the arterial blood vessel in the preset area through a black blood method MRA to obtain a blood vessel image of the arterial blood vessel; and the fourth acquisition module is used for scanning the arterial blood vessel in the preset area through phase contrast PC-MRA to obtain a blood vessel image of the arterial blood vessel.
In an alternative embodiment, the hemodynamic simulation apparatus further includes: the generating unit is used for generating an initial central line of a target blood vessel corresponding to the blood vessel image and simultaneously establishing an initial radius function of the target blood vessel before the initial blood vessel model corresponding to the blood vessel image is subjected to level set segmentation; and the fourth acquisition unit is used for modeling based on the initial center line and the initial radius function to obtain an initial blood vessel model.
In an alternative embodiment, the third acquisition unit comprises at least one of: a fifth obtaining module, configured to obtain a pressure waveform set of the target blood vessel, and calculate elements in the pressure waveform set by using a general transfer function to obtain an inlet pressure value of the target blood vessel, where the general transfer function is a transfer function from a monitoring artery to a target artery that is pre-configured by using an autoregressive exogenous model; the sixth acquisition module is used for acquiring an inlet pressure value of the target blood vessel in a one-dimensional hemodynamic mode; and a seventh acquisition module, configured to acquire an inlet pressure value of the target blood vessel through a tube-load mode.
In an alternative embodiment, the sixth acquisition module includes: the generation submodule is used for generating an arterial network structure of the target object; the solving sub-module is used for generating a one-dimensional hemodynamic control equation and solving the one-dimensional hemodynamic control equation in a numerical mode after setting boundary conditions; the optimization sub-module is used for optimizing relevant parameters corresponding to the one-dimensional hemodynamic control equation through an optimization algorithm; the first acquisition submodule is used for solving a one-dimensional hemodynamic control equation according to the optimized related parameters to obtain an inlet pressure value of the target blood vessel.
In an alternative embodiment, the seventh acquisition module includes: the second acquisition submodule is used for acquiring the pulse wave reflection coefficient of the target blood vessel and the propagation time of the pulse wave from the central artery inlet to the detection artery measuring point and generating a data pair corresponding to the pulse wave reflection coefficient and the propagation time; the processing sub-module is used for calculating the arterial pressure waveform of the target blood vessel according to the data, and carrying out filtering processing and smoothing processing on the arterial pressure waveform; the third acquisition submodule is used for obtaining fitting errors of the data pairs through linear regression fitting straight lines, filtering treatment and arterial pressure waveform after the smoothing treatment; and the fourth acquisition sub-module is used for acquiring a target arterial pressure waveform with fitting error smaller than a preset error threshold value and obtaining an inlet pressure value of a target blood vessel based on the target arterial pressure waveform.
In an alternative embodiment, the hemodynamic simulation apparatus further includes: and the meshing module is used for meshing the blood vessel model before carrying out the hemodynamic simulation on the target blood vessel by utilizing the boundary condition and the inlet pressure value.
In an alternative embodiment, the simulation unit comprises: the generation module is used for generating a simulation solver for simulating the target blood vessel; and the placement module is used for solving the simulation solver by utilizing the boundary conditions and the inlet pressure value so as to simulate the blood flow dynamics of the target blood vessel.
In an alternative embodiment, the apparatus further comprises: the display module is used for displaying the simulation result of the target blood vessel after the target blood vessel is subjected to the blood flow dynamic simulation by utilizing the boundary condition and the inlet pressure value, wherein the simulation result at least comprises: fractional flow reserve FFR, blood flow velocity, vessel wall shear stress, blood flow pressure.
Example 3
According to another aspect of the embodiments of the present invention, there is provided a computer-readable storage medium including a stored computer program, wherein the computer-readable storage medium, when executed by a processor, controls a device in which the computer-readable storage medium is located to perform the method of simulating blood flow force according to any one of the above.
Example 4
According to another aspect of the embodiments of the present invention, there is also provided a processor for running a computer program, where the computer program when run performs the method for simulating blood flow force according to any one of the above.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.

Claims (9)

1. A method of simulating blood flow dynamics, comprising:
acquiring a blood vessel image of an arterial blood vessel of a target object preset area;
generating an initial center line of a target blood vessel corresponding to the blood vessel image, and simultaneously establishing an initial radius function of the target blood vessel, wherein the initial radius function is used for describing a blood vessel region of the arterial blood vessel;
modeling based on the initial center line and the initial radius function to obtain an initial vessel model;
performing level set segmentation on the initial blood vessel model based on a local lumen statistical model to obtain a segmented blood vessel model, wherein the local lumen statistical model comprises lumen thresholds of cross sections of the arterial blood vessel along a central line;
obtaining boundary conditions for simulating a target blood vessel corresponding to the blood vessel model;
calculating the central arterial pressure of the target blood vessel to obtain an inlet pressure value of the target blood vessel;
And carrying out blood flow dynamic simulation on the target blood vessel by using the boundary condition and the inlet pressure value.
2. The method of claim 1, wherein acquiring a vessel image of an arterial vessel of the predetermined region of the target object comprises at least one of:
scanning an arterial vessel in the preset area by using a time-of-flight method TOF-MRA to obtain a vessel image of the arterial vessel;
scanning the arterial blood vessel of the preset area through contrast enhancement CE-MRA to obtain a blood vessel image of the arterial blood vessel;
scanning an arterial vessel in the preset area by using a black blood method MRA to obtain a vessel image of the arterial vessel;
and scanning the arterial blood vessel of the preset area through phase contrast PC-MRA to obtain a blood vessel image of the arterial blood vessel.
3. The method of claim 1, wherein performing a central arterial pressure calculation on the target vessel to obtain an inlet pressure value for the target vessel comprises at least one of:
acquiring a pressure waveform set of the target blood vessel, and calculating elements in the pressure waveform set by utilizing a general transfer function to obtain an inlet pressure value of the target blood vessel, wherein the general transfer function is a transfer function from a monitoring artery to a target artery, which is pre-constructed by using an autoregressive exogenous model;
Acquiring an inlet pressure value of the target blood vessel in a one-dimensional blood flow dynamics mode;
and acquiring the inlet pressure value of the target blood vessel in a tube-load mode.
4. A method according to claim 3, wherein obtaining the inlet pressure value of the target vessel by one-dimensional hemodynamic means comprises:
generating an arterial network structure of the target object;
generating a one-dimensional hemodynamic control equation, and solving the one-dimensional hemodynamic control equation in a numerical mode after setting boundary conditions;
optimizing relevant parameters corresponding to the one-dimensional hemodynamic control equation through an optimization algorithm;
and solving the one-dimensional hemodynamic control equation according to the optimized related parameters to obtain an inlet pressure value of the target blood vessel.
5. A method according to claim 3, wherein obtaining the inlet pressure value of the target vessel by means of tube-load comprises:
acquiring a pulse wave reflection coefficient of the target blood vessel and a propagation time of a pulse wave propagating from a central artery inlet to a detection artery measurement point, and generating a data pair corresponding to the pulse wave reflection coefficient and the propagation time;
Calculating an arterial pressure waveform of the target blood vessel according to the data pair, and carrying out filtering processing and smoothing processing on the arterial pressure waveform;
obtaining fitting errors of the data pairs through linear regression fitting straight lines, filtering treatment and arterial pressure waveform after smoothing treatment;
and acquiring a target arterial pressure waveform with fitting error smaller than a preset error threshold value, and obtaining an inlet pressure value of the target blood vessel based on the target arterial pressure waveform.
6. The method of claim 1, wherein prior to performing a hemodynamic simulation on the target vessel using the boundary condition and the inlet pressure value, the method further comprises: and meshing the blood vessel model.
7. The method of claim 1, wherein performing a hemodynamic simulation of the target vessel using the boundary condition and the inlet pressure value comprises:
generating a simulation solver for simulating the target blood vessel;
and solving the simulation solver by utilizing the boundary condition and the inlet pressure value to perform hemodynamic simulation on the target blood vessel.
8. The method of any one of claims 1 to 7, wherein after the hemodynamic simulation of the target vessel using the boundary conditions and the inlet pressure value, the method further comprises:
Displaying the simulation result of the target blood vessel, wherein the simulation result at least comprises: fractional flow reserve FFR, blood flow velocity, vessel wall shear stress, blood flow pressure.
9. A blood flow dynamic simulation device, comprising:
a first acquisition unit for acquiring a blood vessel image of an arterial blood vessel in a predetermined region of a target object;
the generation unit is used for generating an initial center line of a target blood vessel corresponding to the blood vessel image before carrying out level set segmentation on an initial blood vessel model corresponding to the blood vessel image, and simultaneously establishing an initial radius function of the target blood vessel, wherein the initial radius function is used for describing a blood vessel region of the arterial blood vessel;
a fourth obtaining unit, configured to perform modeling based on the initial center line and the initial radius function, to obtain an initial vessel model;
the level set segmentation unit is used for carrying out level set segmentation on the initial blood vessel model based on a local lumen statistical model to obtain a segmented blood vessel model, wherein the local lumen statistical model comprises lumen threshold values of cross sections of the arterial blood vessel along a central line;
the second acquisition unit is used for acquiring boundary conditions for simulating a target blood vessel corresponding to the blood vessel model;
The third acquisition unit is used for calculating the central arterial pressure of the target blood vessel so as to acquire an inlet pressure value of the target blood vessel;
and the simulation unit is used for carrying out blood flow dynamic simulation on the target blood vessel by utilizing the boundary condition and the inlet pressure value.
CN202211655782.3A 2021-03-26 2021-03-26 Simulation method and device for blood flow force Pending CN116227374A (en)

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