WO2017047822A1 - Dispositif et procédé de prédiction d'apparition/de croissance de lésion vasculaire - Google Patents

Dispositif et procédé de prédiction d'apparition/de croissance de lésion vasculaire Download PDF

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Publication number
WO2017047822A1
WO2017047822A1 PCT/JP2016/077757 JP2016077757W WO2017047822A1 WO 2017047822 A1 WO2017047822 A1 WO 2017047822A1 JP 2016077757 W JP2016077757 W JP 2016077757W WO 2017047822 A1 WO2017047822 A1 WO 2017047822A1
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blood flow
blood vessel
blood
vulnerability
malignancy
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PCT/JP2016/077757
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English (en)
Japanese (ja)
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高伸 八木
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イービーエム株式会社
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings

Definitions

  • the present invention relates to an apparatus for predicting the onset of a vascular lesion and its growth risk by computer simulation.
  • cerebral aneurysms and aortic aneurysms can be caused by stimulation of blood flow.
  • a cerebral aneurysm when a cerebral aneurysm is experimentally created, it is empirically known that a cerebral aneurysm occurs on the inner peripheral side of a branch portion of a specific blood vessel.
  • cerebral aneurysms In clinical practice, cerebral aneurysms have been pointed out frequently, and about 60% of them are concentrated in the internal carotid artery-rear traffic artery branch and middle cerebral artery first branch. It is known that cerebral aneurysms occur in such places, but on the other hand, since the onset of cerebral aneurysms increases with aging, there is malignant blood flow that caused the pre-occurrence stage. It is considered that cerebral aneurysm develops due to the synergistic effect of increasing blood vessel vulnerability with aging.
  • stenosis has been pointed out as a frequent blood vessel or site. It is unlikely that there is no blood flow from the pre-stenosis stage. In other words, it is difficult to predict the onset and growth of stenosis only with the malignancy of the blood flow. Therefore, it was found that it is important to include the fragility of blood vessels in addition to the malignancy of blood flow. That is, when the ratio between the malignancy of blood flow and the fragility of blood vessels exceeds a certain threshold, the risk of developing vascular stenosis increases.
  • stabilization means so-called stable plaque formation, and indicates that the plaque tissue has been converted to a state where the risk of plaque rupture is low due to fibrosis.
  • destabilization is so-called unstable plaque formation, and the plaque tissue is composed of rod-shaped tissue such as macrophages and foam cells, or a part of the tissue is composed of a risk of plaque rupture. It has been converted to a high state.
  • the present invention provides an onset / growth risk prediction apparatus for cerebral aneurysms and vascular stenosis based on the above medical insights.
  • the malignancy of blood flow is obtained by computer simulation, and the fragility of blood vessels is obtained by predicting vascular endothelial cell function. Further, it will become clear from the following description that the present invention also provides a standard for the onset, growth, or stabilization / instability conversion of arteriosclerosis.
  • a computer inputs a medical image including an analysis target blood vessel part and information on endothelial cell function, and the computer inputs Blood flow analysis unit that obtains blood vessel shape data from medical data input from the department and performs numerical fluid analysis to obtain blood flow attributes including pressure and velocity fields, and a computer acquired by the blood flow analysis unit
  • a blood flow malignancy calculation unit that determines the blood flow characteristics from the wall shear stress vector based on the blood flow attribute and quantifies the blood flow malignancy
  • the blood vessel vulnerability calculation unit that obtains the blood vessel vulnerability from the information, and the computer calculates the vascular lesion from the blood flow malignancy obtained by the blood flow malignancy calculation unit and the blood vessel vulnerability obtained by the blood vessel vulnerability calculation unit.
  • a risk calculation section that calculates a risk value for the disease, or growth, computer, vascular lesions onset and growth prospects device is provided with an output unit for outputting the calculated risk value.
  • the blood flow analysis unit extracts a blood vessel shape from a medical image, generates a calculation grid, and obtains a pressure field and a flow field while considering fluid properties and boundary conditions.
  • Device the blood flow analysis unit is configured as a device characterized by inputting a blood vessel shape, blood physical properties, boundary conditions and calculation conditions, and outputting a four-dimensional velocity field and pressure field including time. May be.
  • the determination of the blood flow property is performed by obtaining a wall shear stress vector at each position of the blood vessel wall surface of the blood vessel part to be analyzed from the state amount of the blood flow obtained by the blood flow analysis unit, The relative relationship between the direction of the wall shear stress vector at a specific wall position and the direction of the wall shear stress vector at the surrounding wall positions is obtained, and the blood flow characteristics at the wall position are determined from the form, and the determination result is output. It is a device that does.
  • the blood flow property determination unit calculates the rotation amount rot ⁇ and the divergence div ⁇ that are scalar quantities of the vector field ⁇ . It is determined by comparing these values with the threshold value as the degree of randomness to determine whether it is “parallel”, “confluence”, “rotation”, or “divergence”. When the value is negative or positive outside the threshold range, it is determined to be “rotation”, and when the divergence div ⁇ value of the randomness is a negative value outside the predetermined threshold range, it is determined to be “join”, and the randomness is determined.
  • the information on the endothelial cell function is obtained by calculating a diameter change from a blood vessel diameter measurement value at rest and at the time of release of blood transfusion.
  • the risk calculation unit determines whether the blood flow property is “Rotation” or “Merging” and the blood vessel vulnerability is high, and the blood flow property is “collision” and the blood vessel vulnerability is high. If it is determined that there is a risk of onset / growth of a lesion, the apparatus may be used.
  • the blood flow characteristics are determined to be “rotation” or “confluence”, and the vascular vulnerability is determined to be high, the onset / growth of arteriosclerosis
  • the blood flow property is judged as “collision” and the vascular vulnerability is judged to be high, it is judged that there is a risk of onset / growth of cerebral aneurysm Also good.
  • the apparatus is characterized by determining that there is a risk when the ratio of the malignancy of blood flow to the vascular vulnerability exceeds a threshold value.
  • a computer inputs information about a medical image including an analysis target blood vessel region and endothelial cell function;
  • the computer obtains blood vessel shape data from the medical data input in the input process and performs numerical fluid analysis to determine blood flow attributes including pressure and velocity fields, and the computer performs blood flow analysis.
  • the blood flow malignancy calculation step that determines the blood flow malignancy by quantifying the blood flow malignancy from the wall shear stress vector based on the blood flow attributes obtained in the process, and the endothelium input in the input process
  • the computer performs the blood flow malignancy calculated in the blood flow malignancy calculation process and the blood vessel vulnerability calculation process.
  • a blood vessel lesion onset / growth prediction method comprising a risk calculating step for calculating a risk value for the onset or growth of a vascular lesion from the determined vascular vulnerability and an output step for a computer to output the calculated risk value. is there.
  • FIG. 1 is a configuration diagram of an endovascular treatment simulation apparatus according to an embodiment of the present invention.
  • FIG. 2 is a diagram conceptually showing functions and processes of the vascular lesion onset / growth prediction apparatus according to an embodiment of the present invention.
  • FIG. 3 is a diagram showing a flow of blood flow analysis in one embodiment of the present invention.
  • FIG. 4 is a schematic diagram illustrating fluid shear stress.
  • FIG. 5 is a schematic diagram illustrating fluid shear stress.
  • FIG. 6 is a diagram showing a global coordinate system related to calculation of wall shear stress.
  • FIG. 7 is a diagram illustrating a local coordinate system related to the calculation of the wall shear stress.
  • FIG. 8 is a diagram graphically showing the shear stress vector superimposed on the three-dimensional shape of the blood vessel.
  • FIG. 4 is a schematic diagram illustrating fluid shear stress.
  • FIG. 5 is a schematic diagram illustrating fluid shear stress.
  • FIG. 6 is a diagram showing a global coordinate system related to calculation
  • FIG. 9 is a diagram graphically showing the shear stress vector and the pressure superimposed on the three-dimensional shape of the blood vessel.
  • FIG. 10 is a diagram for explaining calculation of randomness according to an embodiment of the present invention.
  • FIG. 11 is an explanatory diagram relating to the interpretation of the degree of randomness in one embodiment of the present invention.
  • FIG. 12 is a diagram representing the relationship between the degree of randomness and various risks as a map in one embodiment of the present invention.
  • FIG. 13 is a visual representation of the relationship between the vascular malignancy calculated in step I, the vascular vulnerability calculated in step II, and the vascular lesion onset / growth risk determined in step III in an embodiment of the present invention.
  • FIG. 1 shows a configuration diagram of a vascular lesion onset / growth prediction apparatus 10 according to an embodiment of the present invention.
  • a program storage unit 60 and a data storage unit 70 are connected to a bus 50 to which a CPU 20, a memory 30 and an input / output unit 40 are connected.
  • the program storage unit 60 includes a blood flow analysis unit 11, a blood flow property determination unit 12, a blood flow malignancy degree calculation unit 13, a vasodilation response measurement unit 14, a blood vessel vulnerability calculation unit 15, a risk calculation unit 16, and a result output unit 17. Etc.
  • the above configuration requirements (blood flow analysis unit 11, blood flow property determination unit 12, blood flow malignancy calculation unit 13, vasodilation response measurement unit 14, vascular vulnerability calculation unit 15, risk calculation unit 16, result output unit 17, etc.) Is actually constituted by computer software stored in a hard disk, and is read out by the CPU, developed on the memory 30 and executed, thereby functioning as each component of the present invention. .
  • the data storage unit 70 stores 24 calculation condition templates including blood vessel shape information 21, fluid physical properties 22, arterial diameter change information 23, various coefficients and multipliers, and the like.
  • the contents stored in the program storage unit 60 and the data storage unit 70 can be updated as appropriate by inputting from the outside of the apparatus via the input / output unit 40.
  • Various data stored in this manner can be used for later-described numerical fluid analysis (CFD) and vascular dilation analysis (FMD) as an endothelial cell test.
  • the input / output unit 40 is an interface with various devices and communication means.
  • a display means such as a display
  • an operation means such as a keyboard and a mouse
  • an external storage device all not shown
  • FIG. 2 is a diagram conceptually illustrating the functions and processes of the vascular lesion onset / growth prediction apparatus according to an embodiment of the present invention.
  • the result obtained from the numerical fluid analysis (CFD) and the vasodilator analysis (FMD) as the endothelial cell function test is input.
  • CFD in Step I is a blood flow simulation, and obtains a velocity field and a pressure field of a blood flow by calculation using a medical image as described later.
  • blood flow malignancy is calculated and output from the velocity field and pressure field of CFD.
  • the blood flow malignancy is based on the form evaluation of the wall shear stress vector. That is, the shape of the wall shear stress vector is classified into (1) parallel, (2) rotation, (3) merging, and (4) collision (divergence).
  • step II the vascular vulnerability is calculated and output based on the FMD results, such as numerical values for vascular endothelial cell function measured using ultrasound.
  • step III the vascular lesion onset / growth risk calculation unit calculates and outputs a risk related to vascular lesion onset / growth based on the vascular malignancy and vascular vulnerability calculated in steps I and II.
  • the blood flow analysis unit 11 acquires a pressure field / flow velocity field based on a medical image input via the input / output unit 40.
  • a medical image a vascular tomographic image obtained by MRA (magnetic resonance angiography), CTA (computerized tomography), DSA (digital subtraction angiography), or the like may be used.
  • the blood flow analysis unit 11 first receives a medical image (a).
  • a blood vessel shape is extracted based on the received medical image (b)
  • a calculation grid (volume mesh) is generated (c), while taking into account the fluid physical properties of blood and boundary conditions (wall surface).
  • the fluid physical properties of blood considered at this time are density and viscosity.
  • the boundary condition is a flow rate designated for each blood vessel, and an actual measurement value, a statistical value, or an estimated value can be used.
  • the calculation condition is a condition used for the simulation, and a pulsatile flow may be used, or a steady flow that can reduce the load on the computing unit may be used. Based on this set flow rate and pressure, the equation is iteratively calculated (f) to obtain the pressure field / velocity field (g). If it solves it, it will become the pressure field and the velocity field in space and time.
  • blood flow analysis inputs blood vessel shape, blood physical properties, boundary conditions and calculation conditions, and outputs a four-dimensional velocity field and pressure field including time.
  • the blood flow property determination unit 12 is installed with a program that causes a computer to function as the following means. That is, as shown in FIG. 1, the blood flow property determination unit 12 determines the fluid shear stress acting on the blood vessel wall surface by the blood flow and its vector from the pressure field / velocity field of each mesh obtained by the blood flow analysis unit 11. (Hereinafter, simply referred to as “wall shear stress vector”) for each mesh, and a numerical index (disturbance) for determining blood flow properties from the wall shear stress vector calculation unit 121 and the wall shear stress vector.
  • (Wall shear stress vector calculation section) 4 and 5 are schematic diagrams showing a method of determining the shear stress vector ⁇ (x, y, z) based on the flow velocity U and the pressure P determined for each mesh in the wall surface shear stress vector calculation unit 121.
  • FIG. is there.
  • the wall shear stress is a viscous force of a fluid acting in a parallel direction with respect to the minute elements forming the blood vessel lumen
  • the wall shear stress vector is a vector view of the numerical value.
  • the wall shear stress vector and the pressure are orthogonal to each other, and the pressure is a fluid force acting in the surface normal direction with respect to the center of gravity of the microelement.
  • the shear stress acting on the position of the blood vessel wall surface is tangent to the wall surface. It is necessary to convert the pressure and velocity into a local coordinate system based on the blood vessel wall surface in order to obtain the size.
  • the global coordinate system is a single coordinate system for universally indicating the positions of the nodes of the mesh constituting the blood vessel surface and the inside thereof in this system.
  • a calculation target is composed of a set of minute elements (triangle, tetrahedron, hexahedron, etc.). Each element has a vertex called a node, and the position information of each element is (X1g, Y1g, Z1g), (X2g, Y2g, Z2g), (X3g, Y3g, Z3g) using the global coordinate system Hold on.
  • the local coordinate system is a local coordinate system defined for each minute triangle element (polygon) constituting the blood vessel surface.
  • the center of gravity of the minute triangle element is defined as the origin.
  • the surface normal vector as one axis (Z axis).
  • the velocity and pressure at each node are acquired in the global coordinate system from the output of the blood flow analysis unit 11 (i-CFD).
  • the triangular element for which the wall shear stress vector is to be obtained is designated.
  • a local coordinate system is set for the triangular element.
  • the position G where the wall shear stress vector is to be calculated is determined (usually, the distance from the wall is made constant for each triangular element, for example, a point entering 0.1 mm from the wall).
  • the flow velocity at this position G is 0 because it is on the wall surface as shown in FIG.
  • the wall shear stress vector is obtained by calculating the rate of change in the normal direction of the velocity vector parallel to the minute element and multiplying it by the viscosity coefficient of the fluid.
  • the speed at each candidate point can be obtained by installing a plurality of candidate points on the Zl axis and interpolating the speed vector from the surrounding speed vector group.
  • interpolation is performed by setting a weight function for the distance. Since the ambient velocity vector is described in the global coordinate system, the velocity component in the plane parallel direction at each candidate point is calculated by coordinate-transforming the interpolated velocity vector into the local coordinate system.
  • the rate of change in the normal direction it may be calculated as a primary approximation using one candidate point near the wall, or a polynomial approximation is performed using a plurality of candidate points near the wall, Thereafter, a higher-order differentiation process of mathematical differentiation may be performed.
  • ⁇ (Xl) ⁇ ⁇ dUt (Xl) / dZ
  • ⁇ (Yl) ⁇ ⁇ dUt (Yl) / dZ
  • a vector value ⁇ (Xl, Yl) obtained by combining the local coordinate axes becomes a wall shear stress vector.
  • the wall shear stress vector is a vector having an x-direction component and a y-direction component with respect to the surface within the surface in contact with the blood vessel wall.
  • FIG. 8 is a diagram in which the shear stress vector along the blood vessel wall thus obtained is superimposed on the three-dimensional shape model.
  • the force acting on the blood vessel wall acts as a pressure P not only in the direction along the blood vessel wall but also in the direction of collision with the blood vessel wall.
  • This pressure is obtained by applying the pressure at the point G obtained in the global coordinate system to the local coordinate system. It can be obtained as the pressure value in the Zl-axis direction when converted.
  • FIG. 9 shows the pressure values acting on the wall surface in a superimposed manner on FIG. The lighter the color, the higher the pressure.
  • the vector calculation unit 121 obtains the wall shear stress and the vector obtained for each polygon.
  • the randomness calculation unit 122 obtains the randomness as an index obtained by quantifying the shape of the wall shear stress vector group in each mesh.
  • This randomness is a numerical index indicating the degree of whether or not the wall shear stress vector of a certain mesh is aligned in the same direction as compared with the surrounding wall shear stress vector group. That is, each angle formed between the wall shear stress vector of a mesh for which the degree of randomness is obtained (hereinafter referred to as “target mesh”) and the wall shear stress vector of each mesh adjacent to the target mesh.
  • target mesh each angle formed between the wall shear stress vector of a mesh for which the degree of randomness is obtained
  • the degree of randomness is obtained by calculating ⁇ .
  • FIG. 10 shows the relationship between the shear stress vector in the microelement G (approximate to a point for explanation) used in the system of this embodiment and the shear stress vector in the eight microelements surrounding the element G in a grid pattern. It is a thing. In this example, it is only necessary to extract not the magnitude of the shear stress but only the direction, so that the wall shear stress vector is handled as a unit vector. Strictly speaking, each microelement is in a three-dimensional configuration, but adjacent elements are sufficiently close to each other and are handled in two dimensions. That is, the processing is performed in such a way that each wall shear stress vector is projected onto a two-dimensional plane.
  • FIG. 10 shows a state in which the minute element G and surrounding minute elements are mapped onto a two-dimensional orthogonal coordinate system.
  • the form of the wall shear stress vector group is quantified by calculating ⁇ divergence (div) '' and ⁇ rotation (rotation) '' by the vector analysis with respect to the target mesh.
  • a component display at a point G (x, y) obtained by mapping a vector field ⁇ (shear stress vector) of a mesh surrounding a space to the two-dimensional orthogonal coordinate system (x, y) is represented by the following expression.
  • ⁇ (G) ( ⁇ x (x, y), ⁇ y (x, y)) It is expressed.
  • FIG. 11 shows the relationship between the form of the wall shear stress vector group and the values of the “divergence (div)” and “rotation (rot)”.
  • the discriminating unit 123 categorizes the wall shear stress vector group into 1) parallel type, 2) confluence type, 3) rotation type, and 4) collision type.
  • Fig. 12 shows the div and rot values mapped. That is, in this figure, the randomness (div, rot) is obtained for a typical example of the shear stress vector.
  • a typical example is an ideal pattern that can be described mathematically, not experimental data.
  • the degree of randomness has already been standardized, which enables comparison between patients. That is, according to this embodiment, the degree of randomness can be obtained as an index that can be evaluated as an absolute value.
  • the pressure of the target mesh is combined as a weighting factor so that the damage determination to the blood vessel given when the blood flow collides with the blood vessel wall is performed with higher accuracy.
  • a standardized pressure that is, a pressure index is used.
  • a value obtained by dividing each pressure by the average pressure is used as the pressure index after being calculated (multiplied in this example).
  • the combination of the shape of the shear stress vector group and the pressure is effective in improving the accuracy, particularly in predicting the thinned portion of the cerebral aneurysm. That is, there are a plurality of ways of indexing the pressure, and a method of superimposing the pressure on the randomness calculated from the shear stress vector may be a multiplication or a power law, or may be a plurality.
  • the determination unit 123 determines the state of each mesh from the randomness value of each mesh obtained by the randomness calculation unit 122.
  • the wall shear stress vector states here include a “parallel state” that is parallel to the surrounding wall shear stress vector, a “merging state” that extends in a direction approaching the surrounding wall shear stress vector, and a surrounding wall surface. It can be defined as a “rotation state” that rotates with the shear stress vector and a “collision state” in which the direction is radial with respect to the surrounding wall shear stress vector.
  • the blood flow malignancy calculated by the blood flow malignancy calculation unit 13 is based on the form evaluation of the wall shear stress vector performed by the determination unit 123. That is, the form of the wall shear stress vector is divided into the above states and indexed and classified. It should be noted that a coefficient and a multiplier can be added when designing the index.
  • the coefficient and multiplier may be information derived from the wall shear stress or wall pressure, and are, for example, numerical values of the time instability of the wall shear stress and the degree of unevenness of the wall pressure.
  • the merging state may be divided and identified as “low merging type” or “high merging type” according to the threshold according to the magnitude of the curing risk. The same applies to the rotation state and the collision state. The above is the content of the process I in FIG.
  • the vasodilation reaction analysis unit 14 for examining the endothelial cell function processes the result of measuring the change in the vascular diameter of the brachial artery using ultrasound. Specifically, the blood vessel diameter at rest and the change in the brachial artery blood vessel diameter after a predetermined time of blood transfusion are measured in real time by an ultrasonic image, and the result is output to the blood vessel vulnerability calculation unit 15. .
  • the evaluation object in vascular vulnerability is a vascular endothelial cell. It is known that vascular endothelial cells regulate physiological functions by sensing wall shear stress of blood flow. This degree of expansion is known to represent endothelial cell function.
  • Drest indicates the diameter of the blood vessel at rest
  • Dmax indicates the maximum blood vessel diameter after the blood is released. The stronger the degree of vasodilation, the more the endothelial cell function is maintained. The above is the detail of the process II.
  • the vascular lesion onset / growth risk calculator calculates the vascular lesion onset / growth risk based on the blood flow malignancy and vascular vulnerability calculated in steps I and II.
  • the risk may be output as having a risk when the ratio between the blood flow malignancy and the vascular vulnerability exceeds a threshold.
  • the risk of developing a vascular lesion / growth risk is determined and digitized based on the following. That is, when it is determined that the blood flow malignancy is a rotation type or a confluence type and the vascular vulnerability is a high value, it is determined that there is an onset / growth risk of arteriosclerosis, and the risk value is output.
  • the result output unit 17 visualizes the result, transmits it to the input / output unit 40, and displays it on a display (not shown) or the like.
  • the present invention predicts and outputs the onset and growth risk of vascular lesions by computer simulation, including those related to devices, methods, etc., it does not fall under the so-called medical practice or treatment method and is highly industrial With the availability of

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Abstract

L'invention concerne un dispositif, permettant de prédire l'apparition et la croissance de lésions dans un vaisseau sanguin. Le dispositif acquiert des données de forme d'un vaisseau sanguin en entrant des images médicales dans le dispositif, et réalise une analyse numérique de fluide ; le dispositif calcule en outre les propriétés d'écoulement sanguin en quantifiant la qualité de la circulation sanguine, distinguant les écoulements parallèles, les confluences, les rotations et les collisions ; le dispositif calcule en outre la fragilité du vaisseau sanguin à partir des informations concernant la fonction cellulaire endothéliale, et, sur la base de ce qui précède, calcule une valeur de risque pour l'apparition ou la croissance d'une lésion vasculaire.
PCT/JP2016/077757 2015-09-18 2016-09-20 Dispositif et procédé de prédiction d'apparition/de croissance de lésion vasculaire WO2017047822A1 (fr)

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JP2020518362A (ja) * 2017-05-04 2020-06-25 コーニンクレッカ フィリップス エヌ ヴェＫｏｎｉｎｋｌｉｊｋｅ Ｐｈｉｌｉｐｓ Ｎ．Ｖ． 血管内の壁せん断応力の同時視覚化及び定量化のためのシステム及び方法
JP7232195B2 (ja) 2017-05-04 2023-03-02 コーニンクレッカ フィリップス エヌ ヴェ 血管内の壁せん断応力の同時視覚化及び定量化のためのシステム及び方法

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