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WO2014163334A1 - Procédé permettant de modéliser et d'analyser une mécanique des fluides numérique sur la base de propriétés matérielles - Google Patents

Procédé permettant de modéliser et d'analyser une mécanique des fluides numérique sur la base de propriétés matérielles Download PDF

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WO2014163334A1
WO2014163334A1 PCT/KR2014/002654 KR2014002654W WO2014163334A1 WO 2014163334 A1 WO2014163334 A1 WO 2014163334A1 KR 2014002654 W KR2014002654 W KR 2014002654W WO 2014163334 A1 WO2014163334 A1 WO 2014163334A1
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model
medical image
region
mesh
interest
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Korean (ko)
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김영학
양동현
김남국
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Asan Foundation
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Asan Foundation
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/10—Numerical modelling
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular
    • G06T2207/30104—Vascular flow; Blood flow; Perfusion
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2210/00—Indexing scheme for image generation or computer graphics
    • G06T2210/24—Fluid dynamics

Definitions

  • the present disclosure relates to a computational fluid dynamics modeling and analysis method based on material properties as a whole, and to a computational fluid dynamics modeling and analysis method based on material properties for analyzing perfusion based on material properties of blood vessel walls and plaques.
  • vascular stenosis due to plaque formed in blood vessels such as the carotid and coronary arteries is an important risk factor such as stroke and myocardial ischemia.
  • the severity of the stenosis determines the method of treatment, for example, intervention, stent placement, or drug treatment.
  • An indicator called Myocardial Fractional Flow Reserve (FFR) is used to assess the severity of stenosis or the likelihood that plaque will break from the vessel.
  • FFR refers to the ratio of blood pressure at a particular location in the coronary artery to blood pressure in the aorta in myocardial perfusion analysis.
  • a method is used in which a catheter is inserted into a vessel and moved to the FFR measurement location.
  • this method is invasive, making the patient uncomfortable and at risk of damaging the body. Therefore, recently, a method of diagnosing and evaluating lesions of blood vessels by a non-invasive method has attracted attention.
  • 3D modeling of cardiovascular vessels by means of computational fluid dynamics (CFD), and analysis of myocardial perfusion are used to evaluate FFR.
  • FFR_CT the FFR calculated by the CFD model
  • U.S. Patent No. 8,315,812 reflects input blood pressure, blood flow rate, mass of myocardium fed by blood vessels, and material properties of plaque.
  • a region of interest included in a medical image is modeled by a finite element method to determine the region of interest.
  • Generating a 3D model Mapping an intensity of a medical image corresponding to each finite element of the 3D model of the ROI to material properties of each finite element; And analyzing the flow associated with the 3D model of the region of interest by means of Computational Fluid Dynamics (CFD) means. do.
  • CFD Computational Fluid Dynamics
  • 1 is a view showing an example of a 3D cardiac image generated by the heart CT
  • FIG. 2 is a diagram illustrating an example of coronary artery and plaque divided based on a heart image
  • FIG. 3 shows an example of lumens of blood vessels in segmented coronary arteries
  • FIG. 4 is a view showing that the CT density is different depending on the type of plaque in the medical image separated lumen
  • FIG. 5 is a diagram illustrating an example of a blood vessel wall model and a plaque model modeled by a finite element method
  • FIG. 6 is a view showing the vessel wall and the plaque modeled by the tetrahedral volume mesh
  • FIG. 7 is a view for explaining a method of mapping intensity values of a heart image to each mesh constituting a blood vessel wall model and a plaque model;
  • FIG. 8 is a view for explaining an example of a method for setting boundary conditions of a computational fluid dynamics modeling and analysis method based on material properties according to the present disclosure
  • FIG. 9 is a diagram illustrating that a computer-related flow of a 3D model of a region of interest is computed by computational fluid dynamics means
  • FIG. 1 is a diagram illustrating an example of a 3D heart image generated by a heart CT.
  • a region of interest included in a medical image as shown in FIG. 1 is modeled by a finite element method and thus a region of interest.
  • a 3D model of is created.
  • the intensity of the medical image corresponding to each finite element of the 3D model of the ROI is mapped to the material properties of each finite element.
  • the flow associated with the 3D model of the region of interest is analyzed by means of Computational Fluid Dynamics (CFD).
  • CFD Computational Fluid Dynamics
  • a process of generating a 3D model of the ROI is described.
  • a cardiac image as shown in FIG. 1 is generated by 64 slice coronary CT angiography.
  • Cardiac images include coronary artery (region of interest). Coronary artery imaging alone does not provide information about the hemodynamic significance of coronary lesions (flags). Therefore, coronary arteries and plaques are modeled to enable the analysis of computational fluid dynamics.
  • FIG. 2 is a diagram illustrating an example of a coronary artery 10 and a plaque 11 segmented based on a heart image.
  • the heart image is a collection of voxels with gray scale.
  • the cardiac image is binarized to segment a region of interest or other region for computational purposes.
  • the cardiovascular can be segmented using an adaptive threshold method, and a coronary tree can be obtained.
  • FIG. 3 shows an example of lumens of blood vessels in segmented coronary arteries.
  • the walls of blood vessels are very thin, there is no noticeable difference between the surrounding tissues and the intensity, and due to the partial volume effect, they are hardly visible on the cardiac image.
  • the blood vessel wall is a boundary portion in contact with the lumen, and since the plaque also contacts the lumen, as shown in FIG. 3, the surface of the lumen needs to be clearly distinguished first.
  • FIG. 4 is a view showing that the CT density is different according to the type of plaque in the medical image divided lumen.
  • Atherosclerotic plaques can be classified into three types in CT. Pointed arrows indicate calcium, non-calcified, and mixed plaques, respectively. Small boxes each represent a cross section of the plaque (orthogonal plane to the vessel axis). Calcium-type plaques are hard, and non-calcium-type plaques are softer than calcium-type plaques, but are believed to be somewhat hard. The plaque is in contact with the lumen and has a different intensity than the vessel wall, for example a different value of Hounsfield unit (HU). For example, the intensities of calcium-type plaques, fibrous tissue plaques and lipid plaques are on the order of 657-416 HU, 88-18 HU and 25-19 HU, respectively.
  • HU Hounsfield unit
  • FIG. 5 is a diagram illustrating an example of a blood vessel wall model and a plaque model modeled by a finite element method.
  • the boundary that contacts the lumen is modeled as a blood vessel wall by a 3D triangular mesh. Plaques are also modeled by 3D triangular mesh.
  • FIG. 5 (a) shows the fluid mesh
  • FIG. 5 (b) shows the solid mesh
  • FIG. 5 (c) shows the vessel wall 20 and the plaque 21 modeled by the large coarse mesh
  • FIG. 5 (d) shows that the number of meshes of the plaque 21 is significantly increased by FIG. 5 (c) by the fine mesh.
  • FIG. 6 is a diagram showing blood vessel walls and plaques modeled by a tetrahedral volume mesh.
  • FIG. 6 shows modeled vessel wall 33, lumen 30, lipid plaque 31, fibrous plaque 32.
  • FIG. 6 (d) shows that the lumen 30 is surrounded by the vessel wall 33.
  • Various types of meshes can be mixed and used to model complex vascular walls and plaques.
  • the meshes that make up the plaques may be generated more finely.
  • the fluid domain is modeled by the fluid mesh, and hundreds of thousands of tetrahedral volume meshes can be used.
  • FIG. 7 is a view for explaining a method of mapping intensity values of a heart image to each mesh constituting the blood vessel wall model and the plaque model.
  • the vessel wall model and the plaque model were generated by modeling a region of interest.
  • the intensity of the heart image corresponding to each mesh of the vessel wall model and the plaque model is mapped to the material properties on each mesh.
  • the cardiac image is a collection of voxels, each voxel having an intensity, for example a CT value. As shown in the upper part of FIG. 7, the intensity of the voxel may be obtained as a pixel value of the heart image.
  • the pixels 40 corresponding to the plaques are displayed in a different color from the surroundings.
  • each mesh constituting the plaque model does not match one-to-one with the voxels of the heart image
  • the voxels closest to each mesh may be found.
  • the distance from the node of each mesh to the voxel can be obtained by computer calculation.
  • the CT values of the voxels closest to the nodes of each mesh constituting the plaque are mapped to the properties of the meshes.
  • the intensity of the voxels corresponding to the vessel wall can be obtained. Since the vessel wall is so thin, the CT values of the voxels closest to the nodes of the mesh at the interface with the lumens are mapped to the material properties of the mesh. For example, because the vessel wall is in contact with the lumen, the CT value of the voxel closest to the node outside of the lumen for the node that is in contact with or closest to the lumen is mapped to the mesh as a material property.
  • the material property may be density. Since the CT value reflects the density of the material, it may be used as it is or as a value indicating the density of the plaque through a separate conversion.
  • the vessel wall model and the plaque model are based on the coronary artery image included in the heart image, the shape is close to reality, and the intensity of the coronary image is mapped to the density of each mesh constituting the vessel wall model and the plaque model. .
  • blood vessels and plaques are not homogenized, but are modeled by reflecting density, which may be different depending on the position of one vessel or plaque.
  • density mapped to each mesh is based on the intensity of the cardiac image, which is very close to the physical reality of blood vessels and plaques.
  • FIG. 8 is a view for explaining an example of a method of setting the boundary conditions of the computational fluid dynamics modeling and analysis method based on the material properties according to the present disclosure.
  • the reliability of the results of analysis or interpretation by means of computational fluid dynamics for the flow or perfusion associated with the vessel wall model and the plaque model requires the accuracy of the boundary conditions as well as the modeling reflecting the material properties as described above. It is desirable to be a patient specipic boundary condition.
  • Boundary conditions include input conditions, output conditions, and so on.
  • the input condition is preferably patient-specific blood flow input (blood pressure, blood flow rate, etc.), and the output condition may be calculated in consideration of other conditions, for example, the mass of the myocardium of the patient, based on the input condition.
  • FIG. 8 shows the blood flow rate that changes with time at the input boundary measured by MRI venc.
  • Patient-specific blood flow input can be obtained based on patient-specific blood input measurements using clinical data, MRI venc and cardiac muscle segmentation, and Left Ventricle Volume.
  • venc velocity encoding
  • the myocardial splitting method can be used to measure the amount of myocardium that the coronary arteries feed and, as a result, obtain the output boundary condition of the CFD model.
  • boundary conditions of the CFD model can be obtained from clinical data.
  • boundary conditions such as sex, age, pulse rate, blood pressure, hematocrit values can be obtained from clinical data.
  • the CFD model based on the setting of patient-specific boundary conditions and the material properties improves the reliability of FFR_CT.
  • FIG. 9 is a diagram illustrating the computation of the flow associated with the 3D model of the ROI by computational fluid dynamics means.
  • FIG. 10 shows FFR_CT of coronary arteries obtained by CFD means.
  • vessel walls and plaques were modeled using meshes, with CT values mapped to material properties on each mesh.
  • patient-specific boundary conditions were set using MRI venc. The flow associated with the 3D model of the region of interest is then analyzed by CFD means.
  • the FFR (CT Fractional Flow Reserve) is computerized for blood flow before and after plaque at a specific location in the vessel wall model to obtain FFR_CT.
  • the stability of the plaque is evaluated based on FFR_CT.
  • the computational fluid dynamics model including the vascular wall model and the plaque model is trimmed and the boundary conditions are defined as described above.
  • FFR is Equation (1) Is defined as:
  • Pd distal coronary pressure
  • distal blood pressure from the center of the body Pa is distal coronary pressure
  • Pa is the central coronary pressure
  • arterial blood pressure may be used.
  • Pd is the pressure of the blood flow through the plaque.
  • CFD hemodynamic analysis
  • Equation (1) Even if only a few percent of Pd is changed, the FFR_CT can cross the boundary between normal and abnormal. It is therefore important that the model more accurately reflect the physical reality.
  • the blood vessel wall and the plaque may vary in density depending on the position of the mesh, not a homogeneous material. Morphological features of blood vessel walls and plaques are also modeled closer to reality based on heart images. In addition, the outer boundary condition was found to be patient-specific boundary condition, especially using MRI venc. Therefore, in the hemodynamic analysis, the analysis is closer to the physical reality of the patient.
  • mapping of material properties, eg, density, to each mesh constituting the vessel wall model and the plaque model affects the solution of several flow equations used in hemodynamic analysis. For example, there may be a difference in the solution of the flow equations when modeling blood vessel walls and plaques as homogeneous and when material properties are mapped to each mesh according to the present disclosure.
  • ⁇ f and ⁇ s are fluid density and solid density, respectively, p is fluid pressure and ⁇ is Newtonian fluid viscosity.
  • ⁇ is the Cauchy stress tensor
  • f B is the body force the solid experiences.
  • the equations include solid density, mesh motion and displacement. Therefore, according to the present disclosure, when density is mapped to the blood vessel wall and the plaque mesh, the stress-strain analysis may be more accurate than the case where the material properties of the blood vessel wall and the plaque are uniformly modeled. As a result, the FFR_CT becomes more accurate, and the stability of the plaque or the possibility of falling from the blood vessel, etc. can be more accurately evaluated.
  • Calcium-type plaques may have a very small or negligible strain due to stress.
  • fibrous and lipidic plaques must take into account the magnitude of strain due to blood pressure, in particular lipidic plaques are relatively softer. Therefore, simply considering the morphological features of the plaque or modeling it as a homogeneous material is less reliable in stress-strain analysis for the plaque.
  • the stress-strain CFD calculation results for the vessel wall and the plaque are very accurate.
  • the calculated blood pressure values before and after the plaque are also very close to the measured value, and the difference between the FFR_CT and the measured FFR is within the tolerance range.
  • the stress applied to the lipidic plaque is calculated by reflecting the morphological deformation of the lipidic plaque, so that more accurate information can be obtained in evaluating the possibility of dropping from the blood vessel of the lipidic plaque by FFR_CT.
  • Computational fluid dynamics modeling and analysis methods based on the material properties described in FIGS. 1-10 may be performed automatically by one or more software or in combination with a user interface.
  • (1) generating the 3D model of the region of interest includes a process of modeling the region of interest by a finite element mesh; wherein the intensity of the medical image is mapped to the material properties of each finite element. Mapping the intensity of a medical image corresponding to each mesh to material properties of each mesh; and analyzing a flow associated with a 3D model of a region of interest may include identifying a 3D model of the region of interest.
  • Computational fluid dynamics modeling and analysis method characterized in that it comprises a; computer-calculated FFR (Fractional Flow Reserve) at the location.
  • (2) generating the 3D model of the ROI may include generating a segmented medical image by segmenting the ROI or another region into a set of voxels based on the medical image; A region of interest based on the image is modeled by a finite element method to generate a 3D model of the region of interest, and the intensity of the medical image is mapped to the material properties of each finite element. Comprising the step of mapping the intensity of the voxel closest to each finite element to the material properties of each finite element; Computational fluid dynamics modeling and analysis method, characterized in that it comprises a.
  • (3) generating the 3D model of the ROI may include generating a segmented medical image by segmenting the ROI or another region into a set of voxels based on the medical image;
  • the region of interest is modeled by a finite element mesh based on the image.
  • the step of mapping the intensity of the medical image to the material properties of each finite element includes: Computational fluid dynamics modeling and analysis method, characterized in that it comprises a; process of mapping the intensity of the voxel closest to the node of the material to each mesh.
  • (4) generating the 3D model of the ROI may include generating a medical image including blood vessels; A process of segmenting lumens of blood vessels based on a medical image; A region of interest in contact with the lumen is modeled by a finite element mesh to generate a blood vessel wall model; Computational fluid dynamics modeling based on material properties, comprising: a region of interest in contact with the lumen and having an intensity different from that of the vessel wall is modeled by a finite element mesh to generate a lesion model. And analytical method.
  • the step of mapping the intensity of the medical image to the material properties of each finite element is such that the pixel value of the medical image corresponding to each mesh of the blood vessel wall model is a material property of each mesh of the blood vessel wall model.
  • (6) generating the 3D model of the ROI may include generating a medical image by contrast-enhanced heart CT; A process in which a cardiovascular is segmented into a set of voxels based on a medical image; A process of generating a blood vessel wall model by modeling a region of interest of the soft tissue in contact with the lumen of the cardiovascular vessel by a 3D triangular mesh; And a process of generating a plaque model by modeling a region of interest in contact with the cardiovascular lumen and having different intensities from the soft tissues by a 3D triangular mesh.
  • the step of mapping the intensity of the medical image to the material properties of each finite element is such that the CT density of the voxel closest to each 3D triangular mesh of the vessel wall model outside the lumen is determined by the CT density of the 3D triangular mesh.
  • Computational fluid dynamics modeling and analysis method based on the material properties characterized in that it comprises a; mapping to density (density).
  • the step of mapping the intensity of the medical image to the material properties of each finite element is such that the CT values of the voxels closest to each 3D triangular mesh of the plaque model outside the lumen are mapped to the density of the corresponding 3D triangular mesh.
  • Computational fluid dynamics modeling and analysis method based on the material characteristics, characterized in that it comprises a; process.
  • the analysis of the flow associated with the 3D model of the region of interest may include computerized calculation of the Fractional Flow Reserve (FFR) for perfusion before and after the passage of plaque in the cardiovascular system; And Computational fluid dynamics modeling and analysis method based on the material characteristics, characterized in that it comprises a; process based on the calculated FFR stability (stability) is evaluated.
  • FFR Fractional Flow Reserve
  • the setting of the condition may include taking a blood flow input of the cardiovascular vessel using an MRI Venc image; And setting a patient-specific input boundary condition in the CFD means based on the photographed blood flow input.
  • (11) establishing patient-specific boundary conditions prior to the step of analyzing the flow associated with the 3D model of the region of interest by means of Computational Fluid Dynamics (CFD) means;
  • the step of setting the condition is a process in which the amount of myocardium that is fed by the cardiovascular system is measured using a myocardial splitting method;
  • setting a patient-specific output boundary condition on the CFD means based on the measured amount of myocardium.
  • a computer-readable recording medium having recorded thereon a computer program for performing computational fluid dynamics modeling and analysis method based on material properties.
  • the reliability of the method of evaluating the severity of vascular lesions in a non-invasive manner is improved.

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Abstract

L'invention concerne un procédé permettant de modéliser et d'analyser une mécanique des fluides numérique (CFD) sur la base de propriétés matérielles, comprenant les étapes suivantes : générer un modèle 3D d'une région d'intérêt incluse dans une image médicale en modélisant la région d'intérêt à l'aide d'une méthode des éléments finis ; mapper l'intensité de l'image médicale, qui correspond à chaque élément fini du modèle 3D de la région d'intérêt, aux propriétés matérielles de chaque élément fini ; et analyser un flux associé au modèle 3D de la région d'intérêt par des moyens CFD.
PCT/KR2014/002654 2013-04-02 2014-03-28 Procédé permettant de modéliser et d'analyser une mécanique des fluides numérique sur la base de propriétés matérielles Ceased WO2014163334A1 (fr)

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KR10-2013-0035761 2013-04-02
KR20130035763 2013-04-02
KR10-2013-0035763 2013-04-02
KR20130035761 2013-04-02
KR1020130080429A KR101530352B1 (ko) 2013-04-02 2013-07-09 물질특성에 기반한 전산유체역학 모델링 및 분석 방법
KR10-2013-0080429 2013-07-09

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CN108846192A (zh) * 2018-06-08 2018-11-20 中国船舶科学研究中心（中国船舶重工集团公司第七0二研究所） 一种结构任意阻尼处理的船舶三维声弹性分析方法
CN109363661A (zh) * 2018-09-25 2019-02-22 杭州晟视科技有限公司 血流储备分数确定系统、方法、终端及存储介质
EP3525744A4 (fr) * 2016-10-14 2020-03-25 Di Martino, Elena Procédés, systèmes et supports lisibles par ordinateur permettant d'évaluer des risques associés à des pathologies vasculaires
US11395597B2 (en) 2018-06-26 2022-07-26 General Electric Company System and method for evaluating blood flow in a vessel

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US20090318802A1 (en) * 2007-12-18 2009-12-24 Searete Llc, A Limited Liability Corporation Of The State Of Delaware System, devices, and methods for detecting occlusions in a biological subject
WO2012021307A2 (fr) * 2010-08-12 2012-02-16 Heartflow, Inc. Procédé et système de modélisation du débit sanguin spécifique d'un patient

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3525744A4 (fr) * 2016-10-14 2020-03-25 Di Martino, Elena Procédés, systèmes et supports lisibles par ordinateur permettant d'évaluer des risques associés à des pathologies vasculaires
EP4094742A1 (fr) * 2016-10-14 2022-11-30 Di Martino, Elena Procédé d'évaluation de risques associés à des pathologies vasculaires
US11521741B2 (en) 2016-10-14 2022-12-06 Elena Di Martino Methods, systems, and computer readable media for evaluating risks associated with vascular pathologies
CN108846192A (zh) * 2018-06-08 2018-11-20 中国船舶科学研究中心（中国船舶重工集团公司第七0二研究所） 一种结构任意阻尼处理的船舶三维声弹性分析方法
US11395597B2 (en) 2018-06-26 2022-07-26 General Electric Company System and method for evaluating blood flow in a vessel
CN109363661A (zh) * 2018-09-25 2019-02-22 杭州晟视科技有限公司 血流储备分数确定系统、方法、终端及存储介质

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