CN107491636B - Cerebrovascular reserve force simulation system and method based on computational fluid dynamics - Google Patents
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Abstract
The invention discloses a cerebral vascular reserve force simulation system and method based on computational fluid dynamics, which comprises the following steps: the image data acquisition module is used for acquiring a computed tomography image of a human brain blood vessel; the blood vessel three-dimensional reconstruction module is used for carrying out three-dimensional reconstruction on the computed tomography image; the boundary condition extraction module is used for carrying out post-processing on the cerebrovascular computed tomography image to obtain boundary information required by simulation; the CFD pretreatment module is used for carrying out pretreatment required by numerical simulation on the cerebrovascular three-dimensional geometric model; the CFD calculation module is used for solving the hemodynamic information at each position of the cerebrovascular three-dimensional geometric model; and the CFD post-processing module is used for comparing the simulation result with the result measured by the boundary condition extraction module. The invention adopts the characteristic parameter of the blood flow dynamics as the standard for evaluating the reserve capacity of the cerebral vessels, avoids the one-sidedness of the judgment by the geometric structure of the cerebral vessels, and can realize the noninvasive, quantitative and personalized evaluation of the reserve capacity of the cerebral vessels.
Description
Technical Field
The invention relates to the technology of medical instruments, in particular to a cerebral vascular reserve force simulation system and method based on computational fluid mechanics.
Background
Stroke (stroke), commonly known as stroke, is a generic name for a group of acute cerebrovascular diseases, manifested as acute and chronic cerebral hemorrhage or ischemic symptoms. The traditional Chinese medicine composition has the characteristics of high morbidity, high mortality and high disability rate, and is the leading factor for causing death and disability of adults in China. Relevant clinical studies find that the evaluation of cerebrovascular reserve capacity plays a very important role in the prevention, diagnosis and treatment of stroke based on imaging.
The traditional imaging technical means for assessing the reserve capacity of cerebral vessels after stroke mainly comprise PET imaging, SPECT imaging, Xenon-enhanced CT imaging, CT perfusion (Dynamic perfusion CT), MRI perfusion imaging (perfusion-weighted MRI), Arterial Spin Labeling MRI (MRI), Blood oxygen level dependent imaging (Blood oxygen level-dependent MRI) and the like. The Transcranial ultrasonic Doppler technology (Transcranial Doppler Ultrasound) and the magnetic resonance Phase enhancement technology (Phase-contrast MRI) can non-invasively realize the measurement of the flow velocity and the flow data of local blood vessels, but the Transcranial ultrasonic Doppler technology is easily influenced by the subjective operation of an operator, and the measurement of the two technologies can only provide information of a certain section of an interested blood vessel, and cannot provide the reserve force condition of the whole blood vessel and a blood vessel network. How to conveniently and accurately measure the hemodynamic data of local cerebral vessels has very important significance on the accurate assessment of the reserve capacity of the cerebral vessels.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a cerebrovascular reserve force simulation system and method based on computational fluid dynamics, aiming at the defects in the prior art, so that noninvasive and quantitative evaluation of cerebrovascular hemodynamic conditions meeting individual differences can be realized, and accurate and comprehensive information is provided for analysis of cerebrovascular reserve force conditions of users.
The technical scheme adopted by the invention for solving the technical problems is as follows: a computational fluid dynamics-based cerebrovascular reserve force simulation system, comprising:
the image data acquisition module is used for acquiring computed tomography images required by reconstructing a three-dimensional geometrical structure of the cerebral vessels, and comprises CTA (computed tomography angiography), MRA (computed tomography angiography), a 3D (3D-computed tomography angiography) fast spin echo sequence and 3D-DSA (three-dimensional-DSA) data; the image data acquisition module is also used for acquiring images required for calculating the boundary data of the three-dimensional structure of the cerebral vessels;
the cerebrovascular three-dimensional reconstruction module is used for carrying out three-dimensional reconstruction on the computed tomography image acquired by the image data acquisition module to obtain a three-dimensional geometric structure of the cerebrovascular;
the boundary condition extraction module is used for extracting boundary condition information required by fluid simulation by carrying out post-processing on the acquired original phase enhanced image data, wherein the boundary condition information comprises flow velocity and flow information of an inlet and an outlet of a cerebral blood vessel;
a CFD preprocessing module, which is used for preprocessing the three-dimensional geometric structure of the cerebral vessels obtained by the three-dimensional cerebral vessel reconstruction module before numerical simulation, wherein the preprocessing comprises: smoothing a three-dimensional geometric structure of a cerebral vessel, dividing a surface mesh and a body mesh, setting an inlet and an outlet required by simulation, and setting a vessel wall;
the CFD calculation module is used for constructing a model to set a solving method for the fluid mechanics simulation process and solving the blood flow dynamics information of each part of the three-dimensional geometric structure of the cerebral blood vessel; the models comprise an inlet model, an outlet model, a blood vessel wall model and a blood model; the system comprises an inlet model, an outlet model, a blood vessel wall model and a blood flow model, wherein the inlet model is used for setting inlet boundary conditions of fluid mechanics simulation, the outlet model is used for setting outlet boundary conditions of fluid mechanics simulation, the blood vessel wall model is used for setting boundary conditions of a blood vessel wall, and the blood model is used for setting a blood flow mechanics model according with blood flow characteristics;
and the CFD post-processing module is used for comparing the hemodynamic data obtained by the CFD calculation module through solution with actually measured data, guiding adjustment of a simulation mathematical model and solving parameters, and outputting final hemodynamic data when a simulation result is close to the actually measured result, wherein the final hemodynamic data comprises three-dimensional color cloud charts of intravascular pressure, vessel wall tangential stress and blood flow rate, and hemodynamic characteristic parameters for representing cerebral vascular reserve force, and the hemodynamic characteristic parameters comprise a pressure drop coefficient (pressure difference from an inlet to an interested vessel section), a vessel reserve fraction (pressure ratio from the inlet to a stenosis part), a blood flow rate mean value and peak value, a blood flow average flow rate and flow, blood vessel wall average tangential stress and the like, so that quantitative evaluation of the cerebral vascular reserve force is realized.
According to the scheme, the computer tomography image required by the reconstruction of the three-dimensional geometrical structure of the cerebral vessels in the image data acquisition module is specifically obtained by scanning the part of the cerebral vessels above the aortic brachium by using CTA, 3D-TOF MRA or 3D-DSA to obtain the computer tomography image of the human cerebral vessels.
The invention also provides a cerebral vascular reserve force simulation method based on computational fluid dynamics, which comprises the following steps:
1) acquiring a computed tomography image of a human brain blood vessel and an image for calculating boundary conditions;
2) post-processing the scanning image, obtaining a three-dimensional geometric structure of the cerebral vessels through reconstruction, processing phase enhancement data, and extracting boundary information required by simulation;
3) preprocessing the cerebrovascular three-dimensional geometric structure, including smoothing, mesh division and boundary condition setting;
4) setting a fluid simulation solving method, and solving the hemodynamic information of each part of the three-dimensional geometric structure of the cerebral blood vessel;
5) and comparing the simulation result with the measurement result, adjusting the simulation mathematical model and solving parameters, and outputting a color cloud picture of the hemodynamic information and hemodynamic characteristic parameters.
According to the scheme, the step 4) is as follows: the models comprise an inlet model, an outlet model, a blood vessel wall model and a blood model; the system comprises an inlet model, an outlet model, a blood vessel wall model and a blood flow model, wherein the inlet model is used for setting inlet boundary conditions of fluid mechanics simulation, the outlet model is used for setting outlet boundary conditions of fluid mechanics simulation, the blood vessel wall model is used for setting boundary conditions of a blood vessel wall, and the blood model is used for setting a blood flow mechanics model according with blood flow characteristics; after setting the parameters of the inlet, the outlet, the blood vessel wall and the blood flow model, setting a solving method and a convergence condition, and solving the hemodynamic information in each grid by solving a partial differential equation so as to obtain the hemodynamic information at each position.
The invention has the following beneficial effects:
1. the invention adopts the personalized flow velocity and flow data as the boundary conditions of the computational fluid dynamics simulation of the cerebral vessels, thereby avoiding the deviation of the simulation result caused by using the empirical value or the boundary conditions estimated by the model.
2. The invention is completely based on the image means, can not need to inject contrast medium, is convenient and has no wound.
3. The invention adopts a plurality of characteristic parameters including the cerebrovascular reserve fraction as the reference values for evaluating the cerebrovascular reserve capacity condition, and can more comprehensively evaluate the cerebrovascular reserve capacity condition.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a block diagram of a computational fluid dynamics-based cerebrovascular reserve force simulation system according to the present invention;
FIG. 2 is a computerized three-dimensional tomographic mechanism of cerebrovascular geometry;
FIG. 3 is a schematic representation of the three-dimensional reconstruction of a cerebral vessel according to the present invention;
FIG. 4 is a schematic diagram of the cerebrovascular mesh and portal partition of the present invention;
FIG. 5 is a color cloud of a pressure profile obtained in an embodiment of the present invention;
FIG. 6 is a flow chart of simulation method steps in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, the cerebral vascular reserve force simulation system based on computational fluid dynamics provided by the present embodiment includes,
and the image data acquisition module is used for acquiring a computed tomography image of the cerebrovascular geometric structure. In this embodiment, a computed tomography image of the head and neck vessels is acquired at 3.0T magnetic resonance using a magnetic resonance TOF-MRA MRI sequence, as shown in fig. 2. The image data acquisition module also acquires Phase-enhanced images of the entrance, exit and internal cross-section of the blood vessel within one cardiac cycle using a magnetic resonance Phase-contrast MRI (PC-MRI) Phase enhancement sequence.
After an original image is acquired, a three-dimensional reconstruction module of a cerebral vessel carries out three-dimensional reconstruction on a 3D TOF-MRA image, a three-dimensional geometric structure of a user cerebral vessel Willis ring is extracted by using a boundary recognition algorithm, plaque components possibly existing in the vessel are removed, and small branch vessels with low resolution and unclear display are removed, as shown in figure 3.
Meanwhile, the boundary condition extraction module is used for processing the magnetic resonance phase enhanced image data to obtain the flow velocity and flow information of the needed cerebrovascular entrance section.
And the CFD preprocessing module is used for smoothing the cerebrovascular Willis loop structure obtained by the cerebrovascular three-dimensional reconstruction module, so that the influence of possible abnormal structural mutation and image artifact on a simulation result, such as spikes on the surface of a blood vessel, is avoided. And then carrying out surface mesh division on the smoothed Willis ring, wherein triangular meshes or quadrilateral meshes can be used for surface mesh division, and the surface of the blood vessel with a complex geometric shape is also subjected to encryption processing when the surface meshes are divided. After the entrance and exit sections of the cerebral blood vessel and the wall surface of the blood vessel are defined, finally, the geometrical structure of the Willis ring is divided into a body mesh by using tetrahedrons of a non-structural network, the body mesh is required to be not more than 0.2mm, and the mesh division schematic diagram is shown in FIG. 4.
The CFD calculation module firstly models and defines parameters of an inlet model, an outlet model, a blood vessel wall model and a blood model. Wherein the inlet model is used for setting inlet boundary conditions of the fluid mechanics simulation. The outlet model is used to set outlet boundary conditions for the fluid mechanics simulation. The vessel wall model is used to set the boundary conditions of the vessel wall. The blood model is established in order to set up a hemodynamic model that conforms to the characteristics of blood flow. After setting the parameters of the inlet, the outlet, the blood vessel wall and the blood flow model, setting a solving method and a convergence condition, and solving the hemodynamic information in each grid by solving a partial differential equation so as to obtain the hemodynamic information at each position.
In this embodiment, the entrance model selects a flow entrance model, and simultaneously inputs entrance average flow information of internal carotid arteries and vertebral arteries on both sides, and the flow information is obtained by analyzing a magnetic resonance phase enhanced image in one cardiac cycle through the boundary condition extraction module. The portal model may also select a blood flow velocity model, and a vascular pressure portal model.
The outlet model can select one or more of an analog circuit outlet boundary model, a pressure outlet model and a flow outlet model. In the present embodiment, the outlet model selects the pressure model, and the outlet pressure is set to 0 Pa.
The blood vessel wall model can be set to be one or more of a non-slip model and a slip model, a rigid wall model and a one-way or two-way fluid-solid coupling model. In this embodiment, the vessel wall model is selected to be a non-slip, rigid wall model. The blood model may use laminar or turbulent flow models, compressible and incompressible fluids, newtonian and non-newtonian fluids. Blood flow is laminar in most cases and turbulent flow patterns may occur only in diseased vessels and vessel bifurcations. In this example, the blood model was selected from incompressible Newtonian liquids with a density of 1057kg/m3The viscosity was 0.0035 Pa.s. The blood flow satisfies the three-dimensional fluid motion control equation:
wherein equation (1) is a conservation equation of fluid mass, and equation (2) is a conservation equation of fluid momentum.
After the mathematical model and the model parameters are defined, a solving method and a convergence condition are required to be set, and when the calculation converges to a specified threshold, the simulation is ended, so that a hemodynamics simulation result can be obtained.
The CFD post-processing module firstly compares the blood vessel section flow velocity and flow information obtained by simulation with the blood vessel section flow velocity and flow information obtained by acquisition of the magnetic resonance phase enhanced sequence, and finally makes the simulation result close to the actual measurement result by modifying the simulation model and model parameters and a grid division method when the simulation result is greatly different from the actual measurement result. And finally, the CFD post-processing module maps the hemodynamic data including pressure, flow speed and wall shear force to a cerebral blood vessel Willis ring three-dimensional geometric structure, performs color cloud picture display, and outputs various hemodynamic characteristic parameters capable of evaluating cerebral blood vessel reserve force, such as pressure drop coefficient, blood flow reserve fraction, blood vessel average flow speed, blood vessel wall average shear stress and the like, so as to realize quantitative evaluation of the cerebral blood vessel reserve force. The color cloud is shown in fig. 5.
By utilizing the cerebral vessel computational fluid mechanics simulation system, the cerebral vessel fluid mechanics simulation method can be obtained. On the basis of the system, the invention also provides a cerebral vascular reserve force simulation method based on computational fluid dynamics, as shown in figure 6,
the method comprises the following steps:
s101: a computed tomography image of a human brain blood vessel is acquired.
In specific implementation, CTA, 3D-TOF MRA or 3D-DSA is used for scanning the part of the cerebral artery above the aortic brachium to obtain a computed tomography image of the human cerebral artery. In particular, it is preferred to acquire computed tomography images of human brain vessels using magnetic resonance 3D TOF-MRA sequences, enabling non-invasive extraction of cerebrovascular geometries.
S201: and post-processing the computed tomography image, obtaining a three-dimensional geometric structure of the cerebral vessels through reconstruction, processing the magnetic resonance phase enhanced image, and extracting boundary information required by fluid simulation.
In specific implementation, the three-dimensional geometric structure of the cerebral vessels is preferably accurately reconstructed by using image segmentation algorithms such as threshold, erosion, region growth and the like, plaque components, aneurysm clips and small branch vessels which may exist in the images are removed, and only main cerebral artery vessel structures including anterior cerebral artery, middle cerebral artery, posterior cerebral artery, internal/external carotid artery, vertebra/basilar artery, superficial temporal artery and anterior/posterior traffic artery are reserved. In specific implementation, it is preferable to perform hemodynamic data measurement on the main entrance and exit of the cerebral blood vessel by using the magnetic resonance phase enhanced image, the cross-sectional position is based on the bifurcation position of the blood vessel, the magnetic resonance phase enhanced image is measured at the positions 1cm, 2cm, 3cm and 5cm above and below the bifurcation position, and the proper blood vessel cross section is selected as the position of the entrance and exit of the cerebral blood vessel. Meanwhile, the flow velocity and flow information of the entrance and the exit of the cerebral blood vessel based on one cardiac cycle can be obtained through processing the magnetic resonance phase enhanced image.
S301: and preprocessing the three-dimensional geometric structure of the cerebral vessels, including smoothing, mesh division and boundary setting.
In particular, the convergence of computational fluid dynamics simulation is affected due to the possible unevenness of the three-dimensional geometry of the cerebral vessels during three-dimensional reconstruction. When in specific implementation, firstly, the three-dimensional geometric structure of the cerebral vessels is smoothed to smooth the possible nail-shaped objects and the geometric structure with larger deformation; and then dividing the smooth three-dimensional geometric structure of the cerebral vessels into a surface mesh and a body mesh. Preferably, the surface mesh division uses triangles, the body mesh division uses regular tetrahedrons, mesh encryption processing is carried out on the bifurcation part of the blood vessel, and finally the positions of the inlet, the outlet cross section and the blood vessel wall of the blood vessel are defined.
S401, a fluid simulation solving method is set, and the hemodynamic information of each position of the three-dimensional geometric structure of the cerebral blood vessel is solved.
Specifically, parameters of a cerebrovascular entrance model, an exit model, a vascular wall model, and a blood model are first modeled and defined. Preferably, the cerebrovascular entrance model selects a flow entrance model, and average flow information of the entrances of the internal carotid artery and the vertebral artery on both sides in one cardiac cycle is input. The inlet model may also select a flow rate model, a pressure model, and an analog circuit model. Preferably, the cerebrovascular outlet model is a pressure outlet model for facilitating simulation convergence, and a flow model, a flow rate model and a simulation circuit model can be selected. Preferably, the blood vessel wall is selected from a non-slip rigid wall model, the influence of blood vessel deformation on hemodynamic data is neglected, and the calculation amount is reduced. When diseases such as aneurysm, arteriovenous malformation and the like are studied, the vessel wall can be selected to be coupled in a one-way or two-way mode by considering the deformation of the vessel. Preferably, the blood model selects a laminar flow model or a turbulent flow model, and the fluid dynamics information characteristics of the blood are captured.
S501: and comparing the simulation result with the measurement result, adjusting the simulation mathematical model and solving parameters, and outputting a color cloud picture of the hemodynamic information and hemodynamic characteristic parameters. Firstly, comparing the blood vessel section flow velocity and flow information obtained by simulation with the blood vessel section flow velocity and flow information obtained by magnetic resonance phase enhanced sequence acquisition, and finally enabling the simulation result to be close to the actual measurement result by modifying the simulation model and model parameters and a grid division method when the simulation result is greatly different from the actual measurement result. Finally, mapping the hemodynamic information including pressure, flow rate and wall shear force to a cerebral blood vessel Willis ring three-dimensional geometric structure, displaying a color cloud chart, and outputting various hemodynamic characteristic parameters capable of evaluating cerebral blood vessel reserve capacity, such as pressure drop coefficient, fractional flow reserve, mean and peak values of blood flow velocity, mean flow velocity and flow of blood flow, mean shear stress of blood vessel wall and the like, so as to realize quantitative evaluation of cerebral blood vessel reserve capacity.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.
Claims (2)
1. A computational fluid dynamics-based cerebrovascular reserve force simulation system, comprising:
the image data acquisition module is used for acquiring a computed tomography image required by reconstructing a three-dimensional geometric structure of the cerebral vessels; the image data acquisition module is also used for acquiring images required for calculating the boundary data of the three-dimensional structure of the cerebral vessels;
the computer tomography image used for acquiring and reconstructing the three-dimensional geometrical structure of the cerebral vessels in the image data acquisition module is specifically to scan the part of the cerebral vessels above the aortic brachium by using CTA, 3D-TOF MRA, 3D-PC MRA, 3D fast spin echo sequence or 3D-DSA to obtain a computer tomography image of the human cerebral vessels;
the cerebrovascular three-dimensional reconstruction module is used for carrying out three-dimensional reconstruction on the computed tomography image acquired by the image data acquisition module to obtain a three-dimensional geometric structure of the cerebrovascular;
the boundary condition extraction module is used for extracting boundary condition information required by fluid simulation by carrying out post-processing on the acquired original phase enhanced image data, wherein the boundary condition information comprises flow velocity and flow information of an inlet and an outlet of a cerebral blood vessel;
a CFD preprocessing module, which is used for preprocessing the three-dimensional geometric structure of the cerebral vessels obtained by the three-dimensional cerebral vessel reconstruction module before numerical simulation, wherein the preprocessing comprises: smoothing a three-dimensional geometric structure of a cerebral vessel, dividing a surface mesh and a body mesh, setting an inlet and an outlet required by simulation, and setting a vessel wall;
the CFD calculation module is used for constructing a model to set a solving method for the fluid mechanics simulation process and solving the blood flow dynamics information of each part of the three-dimensional geometric structure of the cerebral blood vessel; the models comprise an inlet model, an outlet model, a blood vessel wall model and a blood model; the system comprises an inlet model, an outlet model, a blood vessel wall model and a blood flow model, wherein the inlet model is used for setting inlet boundary conditions of fluid mechanics simulation, the outlet model is used for setting outlet boundary conditions of fluid mechanics simulation, the blood vessel wall model is used for setting boundary conditions of a blood vessel wall, and the blood model is used for setting a blood flow mechanics model according with blood flow characteristics;
and the CFD post-processing module is used for comparing the hemodynamic data obtained by the CFD calculation module and the actually measured data, guiding adjustment of the simulation mathematical model and solving parameters, and outputting final hemodynamic data when the simulation result is close to the actually measured result, wherein the final hemodynamic data comprises three-dimensional color cloud pictures of intravascular pressure, vessel wall tangential stress and blood flow rate, and hemodynamic characteristic parameters for representing cerebral vascular reserve force, such as pressure drop coefficient, vessel reserve fraction, mean and peak values of blood flow rate and mean tangential stress of vessel wall, so as to realize quantitative evaluation of the cerebral vascular reserve force.
2. A cerebral vascular reserve force simulation method based on computational fluid dynamics comprises the following steps:
1) acquiring a computed tomography image of a human brain blood vessel and an image for calculating boundary conditions; the computed tomography image is specifically a computed tomography image of a human brain blood vessel obtained by scanning a part of a cerebral blood vessel artery above an aortic brachium by using CTA, 3D-TOF MRA, 3D-PC MRA, 3D fast spin echo sequence or 3D-DSA;
2) reconstructing the scanning image to obtain a three-dimensional geometric structure of the cerebral vessels through reconstruction, processing phase enhancement data and extracting boundary information required by simulation;
3) preprocessing the cerebrovascular three-dimensional geometric structure, including smoothing the cerebrovascular three-dimensional geometric structure, dividing a planar mesh and a volume mesh of the smoothed cerebrovascular three-dimensional geometric structure, and finally defining the positions of a blood vessel inlet, an outlet section and a blood vessel wall;
4) constructing a model and setting a fluid simulation solving method to solve the hemodynamic information of each part of the three-dimensional geometric structure of the cerebral vessel; the step 4) is as follows: the models comprise an inlet model, an outlet model, a blood vessel wall model and a blood model; the system comprises an inlet model, an outlet model, a blood vessel wall model and a blood flow model, wherein the inlet model is used for setting inlet boundary conditions of fluid mechanics simulation, the outlet model is used for setting outlet boundary conditions of fluid mechanics simulation, the blood vessel wall model is used for setting boundary conditions of a blood vessel wall, and the blood model is used for setting a blood flow mechanics model according with blood flow characteristics; after setting parameters of an inlet, an outlet, a blood vessel wall and a blood flow model, setting a solving method and a convergence condition, and solving the hemodynamic information in each grid by solving a partial differential equation so as to obtain the hemodynamic information at each position;
5) comparing the simulation result with the measurement result, adjusting a simulation mathematical model and solving parameters, and outputting a color cloud picture of hemodynamic information and hemodynamic characteristic parameters; firstly, comparing the blood vessel section flow velocity and flow information obtained by simulation with the blood vessel section flow velocity and flow information obtained by acquisition of a magnetic resonance phase enhanced sequence, finally enabling the simulation result to be close to the actual measurement result by modifying a simulation model and model parameters and a grid division method, finally mapping the hemodynamic information including pressure, flow velocity and wall shear force to a cerebral vessel Willis ring three-dimensional geometric structure, carrying out color cloud picture display, and outputting various hemodynamic characteristic parameters capable of evaluating cerebral vessel reserve force, including pressure drop coefficients, blood vessel reserve fractions, blood vessel flow velocity mean values and peak values and blood vessel wall mean shear stress, thereby realizing quantitative evaluation of the cerebral vessel reserve force.
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