WO2019242161A1 - Method and device for acquiring blood flow characteristic values on the basis of medical history information - Google Patents

Method and device for acquiring blood flow characteristic values on the basis of medical history information Download PDF

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WO2019242161A1
WO2019242161A1 PCT/CN2018/109080 CN2018109080W WO2019242161A1 WO 2019242161 A1 WO2019242161 A1 WO 2019242161A1 CN 2018109080 W CN2018109080 W CN 2018109080W WO 2019242161 A1 WO2019242161 A1 WO 2019242161A1
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blood flow
model
blood
history information
medical history
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PCT/CN2018/109080
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French (fr)
Chinese (zh)
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涂圣贤
张素
徐波
常云霄
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博动医学影像科技（上海）有限公司
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Priority to DE112018007631.8T priority Critical patent/DE112018007631T5/en
Publication of WO2019242161A1 publication Critical patent/WO2019242161A1/en

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Definitions

  • the invention relates to a method and a device for obtaining blood flow characteristic values based on medical history information, and belongs to the technical field of medical equipment.
  • lipids and carbohydrates in human blood on the vessel wall will form plaques on the vessel wall, which will lead to vascular stenosis; especially the vascular stenosis that occurs near the coronary artery of the heart will cause insufficient blood supply to the heart muscle and induce coronary heart disease And angina pectoris, which pose a serious threat to human health.
  • vascular stenosis especially the vascular stenosis that occurs near the coronary artery of the heart will cause insufficient blood supply to the heart muscle and induce coronary heart disease And angina pectoris, which pose a serious threat to human health.
  • the blood flow reserve fraction usually refers to the myocardial blood flow reserve fraction, which is defined as the ratio of the maximum blood flow that the diseased coronary artery can provide to the heart muscle to the maximum blood supply flow when the coronary artery is completely normal.
  • the blood flow ratio can be replaced by the pressure value. That is to say, the measurement of FFR value can be calculated by measuring the pressure of the distal stenosis of the coronary artery and the pressure of the proximal stenosis of the coronary artery with a pressure sensor under the condition of maximum congestion of the coronary artery.
  • An object of the present invention is to provide a method and a device for obtaining blood flow characteristic values based on medical history information, so as to solve at least one of the technical problems existing in the prior art.
  • the method for obtaining blood flow characteristic values based on medical history information provided by the present invention introduces medical history information to correct various types of data in the calculation process of blood flow characteristic values, and clarifies the influence of different medical history information on the calculation of blood flow characteristic values. Improve the accuracy of blood flow characteristic value calculation.
  • the present invention provides a method for obtaining a blood flow characteristic value based on medical history information.
  • the method for obtaining a blood flow characteristic value based on medical history information includes:
  • the blood flow velocity V of the region of interest is obtained; combined with the blood flow velocity V and hemodynamics, the blood flow pressure Pa at the proximal end of the region of interest at the blood flow velocity V is obtained ; And the pressure difference value ⁇ P at the proximal end and the distal end of the region of interest in the corresponding state;
  • the blood flow characteristic value includes the blood flow pressure Pa, a pressure difference value ⁇ P, and a value reflecting a blood flow characteristic calculated based on the blood flow pressure Pa and the pressure difference value ⁇ P.
  • the second geometric model includes a cross-sectional morphological model at various positions between the proximal end point and the distal end point of the region of interest and at least one blood vessel tree or the vascular system of region of interest At least one single vessel segment; the vessel tree includes at least one aorta, or at least one aorta and multiple coronary arteries emanating from the aorta.
  • the establishment of the cross-sectional morphological model includes:
  • the cross section at the proximal end of the blood vessel segment of the region of interest is defined as a reference plane, and the center line of the geometric model is obtained by extracting the center line of the geometric model;
  • the cross-sectional morphological model includes the presence or absence of plaques on each cross-section, the location of the plaques, the size of the plaques, the angle at which the plaques are formed, the composition of the plaques, and changes in the composition of the plaques , The shape of the plaque and the change in the shape of the plaque.
  • the second blood flow model includes a fixed blood flow model and a personalized blood flow model
  • the personalized blood flow model includes a resting-state blood flow model and a load-state blood flow model.
  • the blood flow velocity V may pass through a rate of fluid filling in a blood vessel. Calculated; or obtained through morphological calculation of the vessel tree.
  • the morphology of the blood vessel tree includes at least one or more of an area, a volume, and a lumen diameter of a blood vessel segment in the blood vessel tree; the blood flow velocity V passes through the
  • the geometric parameters of the second geometric model further include one or more of the length, perfusion area, and branch angle of the blood vessel segment in the blood vessel tree.
  • the medical history information includes a circulatory system disease, a respiratory system disease, a neurological system disease, a skeletal disease, a digestive system disease, a metabolic disease, a tumor disease, and a family medical history that affect blood flow velocity or blood viscosity.
  • the present invention further provides a device for obtaining a blood flow reserve score based on medical history information.
  • the device for obtaining a blood flow reserve score based on medical history information includes:
  • a data collector for acquiring and storing geometric parameters and individual specific data of a region of interest in an anatomical model of the coronary system
  • a correction processor which is configured to receive one or more medical history information of an individual, and process the medical history information to generate correction parameters;
  • a blood flow characteristic processor based on the geometric parameters and individual specific data, the blood flow characteristic processor is used to establish a geometric model and a blood flow model of a region of interest;
  • the blood flow characteristic processor is further configured to modify the geometric model and the blood flow model based on the medical history information transmitted by the correction processor, and obtain the blood flow velocity V of the region of interest; at the same time, according to the blood
  • the flow velocity V is combined with hemodynamics to calculate and obtain the blood flow reserve fraction FFR.
  • the geometric model is that the blood flow characteristic processor measures the geometric parameters of the anatomical model transmitted by the data collector, and combines the correction parameter parameters transmitted by the correction processor with Obtained by fitting calibration;
  • the geometric model includes a cross-sectional morphological model at various positions between the proximal end and the distal end of the region of interest and at least one vascular tree of the vascular system of the region of interest or at least a single branch of the vascular system of the region of interest A blood vessel segment;
  • the blood vessel tree includes at least one aorta, or includes at least one aorta and a plurality of coronary arteries emanating from the aorta.
  • the cross-sectional morphological model is obtained directly / indirectly by the blood flow feature processor through the geometric model; the cross-sectional morphological model includes the presence or absence of plaques on each cross-section, and plaques. Location, plaque size, plaque formation angle, plaque composition and plaque composition change, plaque shape and plaque shape change.
  • the blood flow feature processor is further configured to establish a morphological difference function f (x) of a vessel lumen of a region of interest based on the cross-sectional morphological model, and the morphological difference function f (x) A function used to represent the change in cross-sectional morphology at different locations of the target vessel as a function of the distance x from that location to the proximal endpoint.
  • the device for obtaining a blood flow reserve score based on the medical history information further includes a speed collector, which is used to obtain the blood flow velocity V of the region of interest;
  • the speed collector includes a speed calculation module and a speed extraction module; the speed extraction module directly obtains blood flow velocity information through the data collector, or directly extracts the blood flow velocity V through a blood flow model;
  • the speed calculation module further includes a speed conversion module and a speed measurement module; the blood flow velocity V can be obtained by converting the speed of fluid filling in the blood vessel through the speed conversion module, and also can be obtained by the shape of the blood vessel tree in the geometric model through the speed measurement module Calculated.
  • the present invention further provides a device for obtaining an individual blood flow reserve score, the device having a processor, wherein the processor is configured to cause the device to perform the following steps:
  • the blood flow reserve fraction of the blood vessel to be tested is determined based on the modified blood vessel model and the blood flow reserve fraction calculation model.
  • the medical history information includes circulatory system diseases, respiratory system diseases, nervous system diseases, bone diseases, digestive system diseases, metabolic diseases, tumor diseases, and family medical history that affect blood flow velocity or blood viscosity. one or more.
  • the calculation model of the vascular pressure difference value is established based on a multi-scale calculation method.
  • the beneficial effect of the present invention is that the method for obtaining blood flow characteristic values based on the medical history information of the present invention introduces the medical history information into the blood flow characteristic value calculation to timely calculate the geometric model and / or the blood flow characteristic value during the calculation.
  • the blood flow model guarantees the accuracy of the geometric model and the establishment of the blood flow model, and further ensures that the relevant parameters obtained through the geometric model and the blood flow model are accurate, so that the method for obtaining blood flow characteristic values based on the medical history information of the present invention
  • the calculated blood flow characteristic value can accurately reflect the characteristics of the region of interest.
  • FIG. 1 is a schematic diagram of a geometric model of a target blood vessel in one form of the present invention.
  • FIG. 2 is a schematic structural diagram of a cross-sectional morphological model at a position D 1 in FIG. 1.
  • FIG. 3 is a schematic structural diagram of a cross-sectional morphological model at a position D 2 in FIG. 1.
  • FIG. 4 is a schematic structural diagram of the cross-sectional morphological model at positions D 1 and D 2 in FIG. 2 and FIG. 3 after fitting.
  • FIG. 5 is a schematic diagram of a geometric model in another form of the target blood vessel according to the present invention.
  • FIG. 6 is a schematic structural diagram of a cross-sectional morphological model at a position D 1 in FIG. 5.
  • FIG. 7 is a schematic structural diagram of a cross-sectional morphological model at a position D 2 in FIG. 5.
  • FIG. 8 is a schematic structural diagram of the cross-sectional morphological model at positions D 1 and D 2 in FIG. 6 and FIG. 7 after fitting.
  • FIG. 9 is a structural block diagram of a device for obtaining blood flow characteristic values based on medical history information according to the present invention.
  • the present invention provides a method for obtaining blood flow characteristic values based on medical history information.
  • the method for obtaining blood flow characteristic values based on medical history information includes:
  • the blood flow velocity V of the region of interest is obtained; combined with the blood flow velocity V and hemodynamics, the blood flow pressure Pa at the proximal end of the region of interest at the blood flow velocity V is obtained ; And the pressure difference value ⁇ P at the proximal end and the distal end of the region of interest in the corresponding state;
  • the blood flow characteristic value includes the blood flow pressure Pa, a pressure difference value ⁇ P, and a value reflecting a blood flow characteristic calculated based on the blood flow pressure Pa and the pressure difference value ⁇ P.
  • the first geometric model is a three-dimensional model reflecting the geometry of the individual coronary system.
  • the first geometric model is obtained by modeling the geometric parameters of the region of interest, and the geometric parameters are obtained through the individual coronary system.
  • the anatomical data of the venous system is obtained. Further, the anatomical data of the individual coronary system can be obtained through common image generating equipment such as CT equipment, OCT equipment, IVUS equipment and angiography equipment.
  • the first blood flow model is a model obtained by using empirical values or big data to characterize the blood flow characteristics of an individual in a normal state.
  • the second geometric model and the second blood flow model are obtained by modifying the one or more medical history information of the first geometric model and the first blood flow model, wherein the medical history information is The modification of the first geometric model and the first blood flow model may be performed separately or simultaneously.
  • the individual's medical history information may affect the shape of the vascular lumen of the region of interest and / or the viscosity of blood in the vascular lumen. , Flow velocity, etc.
  • the medical history information includes a circulatory system disease, a respiratory system disease, a neurological system disease, a skeletal disease, a digestive system disease, a metabolic disease, a tumor disease, and a family medical history that affect blood flow velocity or blood viscosity.
  • the second geometric model includes a cross-sectional morphological model at various positions between the proximal end and the distal end of the region of interest and at least one vascular tree of the vascular system of the region of interest or at least one section of the vascular system of the region of interest.
  • a single vessel segment; the vessel tree includes at least one aorta, or includes at least one aorta and multiple coronary arteries emanating from the aorta, and each of the vessel segments and / or vessel trees includes the Geometric parameters such as the shape, diameter, and area of the region of interest. Further, the geometric parameters include parameters such as the bending angle of a blood vessel segment that can reflect the actual shape of the region of interest.
  • the second geometric model further includes a cross-sectional morphology model at various positions between the proximal end and the distal end of the region of interest.
  • the cross-sectional morphological model is obtained directly / indirectly through the second geometric model, and the establishment of the cross-sectional morphological model includes the following steps:
  • a cross section at a proximal end of a target blood vessel is defined as a reference plane, and a center line of the geometric model is obtained by extracting a center line of the geometric model;
  • the cross-sectional morphological model includes plaque information at each cross-sectional position, and the plaque information is lesion information of the region of interest, and a large amount of data indicates that when the length of the plaque (that is, the lesion) is> 20 mm , It will lead to an increase in the target blood vessel pressure difference value ⁇ P, further leading to errors in the calculation of blood flow characteristic values such as the blood flow reserve fraction FFR; and when the composition of the plaque at the same cross-section is complex or the size is too large, the target blood vessel's A high stenosis rate will further increase the target vascular pressure difference value ⁇ P, and then affect the calculation of the characteristic value of the blood flow.
  • the plaque information also needs to include the presence or absence of plaque, the location of the plaque, the size of the plaque, the angle at which the plaque forms, the composition of the plaque, and the composition of the plaque. Changes in the shape of the plaque and the shape of the plaque, and in the present invention, the planar geometric image of the lumen cross section at each position needs to be referenced to the coordinate system established in step S2 to clarify each cross section Plaque location to facilitate subsequent fitting of the cross-section morphological model.
  • the cross-sectional morphological model when the anatomical data is obtained by using CT, OCT, IVUS and other detection methods, the cross-sectional morphological model can be directly obtained through the geometric model. It is only necessary to ensure that the origin and coordinate direction of each of the cross-sectional morphological models are consistent; when the anatomical data is acquired using detection means such as X-rays, since the geometric model is a three-dimensional model extending in the direction of blood flow, Then, when the cross-sectional morphology model is established by using the geometric model, coordinate transformation of the geometric model is required to accurately reflect the cross-sectional morphology of each cross-section.
  • the method of obtaining blood flow characteristic values based on medical history information further includes simulating the cross-sectional morphological models at different scales. Then, the morphological difference function f (x) of the lumen of the target vessel is calculated.
  • the morphological difference function f (x) is a function that represents a change in the cross-sectional morphological change at different positions of the target blood vessel as a function of the distance x from the position to a reference point; and the acquisition of the morphological difference function f (x) include:
  • the change rate of the lumen morphology with the distance x from the reference point is obtained according to the difference change function, and the position parameters of the target vessel from the proximal end to the distal end are normalized. Processing to finally obtain the morphological difference function f (x).
  • the morphological function includes an area function, a diameter function, or an edge distance function, that is, in the present invention, the difference between two adjacent cross-sections at different scales can be obtained by fitting between the cross-sectional area, diameter, or edge distance functions.
  • the change function further, the change rate of the lumen morphology with the distance x to the reference point is obtained through the difference change function to obtain the morphological difference function f (x).
  • the morphological function is an area function
  • the two cross-sectional morphological models at D 1 and D 2 positions are fitted, and the cross-sectional morphological positions at D 1 and D 2 positions are fitted.
  • the area with increased vascular plaque was A 1 and the corresponding area S 1 ;
  • the area with reduced vascular lumen was A 2 and the corresponding area S 2 .
  • the difference change function It is the ratio of the area between the non-overlapping areas (S 1 , S 2 ) and the overlapping area (S 3 ) in the vascular lumen, or the area (S 1 , S 2 ) and the total area (S 1 , S 1 ) of the non-overlapping area. S 2 , S 3 ); and at this time, the morphological difference function f (x)> 0, that is, there is a pressure difference between the cross sections D 1 and D 2 .
  • the morphological function is a distance function
  • the correspondence between each point on the selected first lumen boundary and each point on the second lumen boundary is established, and then each of the points on the first lumen boundary is obtained.
  • the blood flow velocity V and the blood pressure Pa are both obtained through a blood flow model.
  • the blood flow model includes a first blood flow model and a second blood flow model.
  • the first blood flow model and the second blood flow model may be both a data calculation model and a three-dimensional fluid flow model; further, in the present invention, the blood flow velocity V and the blood flow pressure Pa pass through the second blood.
  • the second blood flow model includes a fixed blood flow model and a personalized blood flow model; wherein the fixed blood flow model is an empirical blood flow model, and is based on clinical experience and is collected and simulated through big data. The method is directly established; the personalized blood flow model includes a resting state blood flow model and a load state blood flow model.
  • the blood flow velocity V when the second blood flow model is a fixed blood flow model, the blood flow velocity V may be directly obtained from the fixed blood flow model; when the first When the two blood flow model is a resting state blood flow model, the blood flow velocity V can be obtained by calculating the velocity of fluid filling in a blood vessel; in an embodiment of the present invention, the resting state blood flow model is a contrast agent A blood flow model, where the blood flow velocity V is the average flow velocity of the contrast agent during the imaging process of the region of interest obtained by using the gray-time fitting function; or the region of interest obtained by calculating using the TIMI frame method The average flow velocity of the contrast agent during the imaging process.
  • the blood flow velocity V may be obtained by calculating a shape of a blood vessel tree in a geometric model, and when the blood flow velocity is obtained by calculating a shape of a blood vessel tree
  • the geometric parameter further includes a length of a blood vessel segment in the blood vessel tree.
  • the blood flow velocity V at this time is the blood flow velocity V after the adenosine-injected blood vessel is sufficiently expanded, and at this time, the blood flow velocity V Is the maximum blood flow velocity Vmax.
  • the blood flow velocity V includes the blood flow velocity Vmax in the region of maximum congestion and the blood flow velocity Vqc in the resting state.
  • the blood flow velocity V is the blood flow velocity Vmax in the state of maximum congestion, and the further blood flow velocity Vmax may be directly obtained through the second blood flow model, or may be obtained by converting the blood flow velocity V calculated by the second blood flow model; when When the region of interest is located in the peripheral vascular system, the blood flow velocity V is the blood flow velocity Vqc in a resting state.
  • the pressure difference value ⁇ P in the blood flow characteristic value described in the present invention is calculated by using the morphological difference function f (x) at different scales and the blood flow velocity V of the region of interest, and the pressure difference value ⁇ P is The calculation formulas at different scales are:
  • ⁇ P (c 1 V + c 2 V 2 +... + c m V m )
  • c 1 , c 2 ,..., C m respectively represent parameter coefficients of the blood flow velocity V
  • the parameter coefficients include a plurality of parameter coefficients such as blood viscosity influence factors, blood turbulence influence factors, and viscosity coefficients;
  • m It is a natural number greater than or equal to 1, in order to represent the influence of different parameter coefficients on the blood flow velocity V, in order to modify the pressure difference value ⁇ P to ensure the accuracy of the pressure difference value ⁇ P calculation.
  • the value of m is 2, and when m is 2, c 1 is a parameter coefficient due to blood flow friction, and c 2 is a parameter coefficient due to blood turbulence.
  • ⁇ 1 , ⁇ 2 , ..., ⁇ n are the weighting coefficients of the morphological difference functions f 1 (x), f 2 (x), ..., f n (x) of the vascular lumen at different scales, where n is the scale It is a natural number greater than or equal to 1. Further, the increase of the weighting coefficient can further modify the morphological difference function f (x) to ensure the accuracy of the morphological difference fitting calculation between the two cross sections.
  • the different scales include a first scale, a second scale, ..., an n-th scale;
  • the first-scale morphological difference function f 1 (x) is used to detect a geometric morphological difference corresponding to two adjacent cross-sectional morphological models caused by a first lesion feature;
  • the second-scale morphological difference function f 2 (x) is used to detect a geometric morphological difference corresponding to two adjacent cross-sectional morphological models caused by a second lesion feature;
  • the n-th scale morphological difference function f n (x) is used to detect the geometric morphological differences corresponding to the morphological models of two adjacent cross-sections caused by the n-th lesion feature.
  • the calculation of the blood flow characteristic value may be independent of the blood flow velocity V of the region of interest, and the blood flow characteristic value is a pressure difference value ⁇ P as an example.
  • the calculation formulas of the ⁇ P at different scales are:
  • ⁇ P k * [ ⁇ 1 * ⁇ f 1 (x) dx + ⁇ 2 * ⁇ f 2 (x) dx +... + ⁇ n * ⁇ f n (x) dx)
  • k is a correction parameter and k is a constant; further, the correction parameter k is a value obtained directly / indirectly based on individual information;
  • ⁇ 1 , ⁇ 2 , ..., ⁇ n are weighting coefficients of morphological difference functions f 1 (x), f 2 (x), ..., f n (x) of blood vessel lumen at different scales, respectively;
  • the different scales include a first scale, a second scale, ..., an n-th scale;
  • the first-scale morphological difference function f 1 (x) is used to detect a geometric morphological difference corresponding to two adjacent cross-sectional morphological models caused by a first lesion feature;
  • the second-scale morphological difference function f 2 (x) is used to detect a geometric morphological difference corresponding to two adjacent cross-sectional morphological models caused by a second lesion feature;
  • the n-th scale morphological difference function f n (x) is used to detect the geometric morphological difference between two adjacent cross-sectional morphological models caused by the n-th lesion feature; wherein n is a natural number greater than or equal to 1.
  • the blood flow characteristic value further includes a blood flow reserve fraction FFR of the region of interest, and the blood flow reserve fraction passes a morphological difference function f (x) of a blood vessel lumen of the region of interest at different scales,
  • the pressure difference value ⁇ P and the blood flow pressure Pa at the proximal end position of the region of interest are calculated, and the blood flow reserve fraction FFR is calculated by the following formula:
  • the medical history information is a history of myocardial infarction.
  • the heart of an individual who has suffered from myocardial infarction has reduced myocardium.
  • the blood flow through the vascular segment of the region of interest is reduced, that is, lost by stenosis
  • the energy decreases.
  • the blood flow velocity V of the region of interest decreases.
  • the blood flow characteristic values calculated based on the blood flow velocity V will cause errors.
  • the blood flow velocity V of the region of interest should be corrected.
  • the blood flow velocity V includes a direct acquisition based on the second blood flow model.
  • the first blood flow velocity V 0 and the second blood flow velocity V 1 are obtained after modification, and the relationship between the first blood flow velocity V 0 and the second blood flow velocity V 1 is:
  • S is the total area of the individual's myocardium
  • S 0 is the area of myocardial infarction.
  • ⁇ P (c 1 V 1 + c 2 V 1 2 +... + c m V 1 m )
  • the medical history information is hyperlipidemia or a history of smoking. Specifically, when an individual has symptoms of hyperlipidemia, the blood viscosity of the individual increases, and the increase in blood viscosity will cause a sense of flow. The pressure difference value ⁇ P between the proximal end and the distal end of the vascular segment of the region of interest is biased.
  • the pressure difference value ⁇ P of the region of interest should be corrected.
  • the pressure difference value ⁇ P includes a value based on the second blood.
  • the first pressure difference value ⁇ P 0 obtained by the flow model and the second pressure difference value ⁇ P 1 obtained after the correction, and the relationship between the first pressure difference value ⁇ P 0 and the second pressure difference value ⁇ P 1 satisfy:
  • k is a constant related to blood fluidity
  • ⁇ is the blood viscosity of hyperlipidemia or smoking individuals
  • ⁇ 0 is the blood viscosity of the individual under normal conditions.
  • the medical history information is diabetes.
  • the vascular endothelial function of individuals with diabetes changes, and the blood flow velocity V of blood vessels in blood vessels is generally smaller than that of normal people, that is, the blood flow velocity V of the region of interest decreases.
  • the blood flow characteristic values calculated based on the blood flow velocity V will cause errors.
  • the blood flow velocity V of the region of interest should be corrected.
  • the blood flow velocity V includes a first blood flow velocity directly obtained based on the second blood flow model.
  • a blood flow velocity V 0 and a second blood flow velocity V 1 are obtained after correction, and the relationship between the first blood flow velocity V 0 and the second blood flow velocity V 1 is:
  • V 1 ⁇ ⁇ V 0 ;
  • ⁇ is a correction parameter related to an individual's blood glucose.
  • ⁇ P (c 1 V 1 + c 2 V 1 2 +... + c m V 1 m )
  • the correction parameter ⁇ is an empirical value obtained through a method of large data collection and simulation according to clinical practical experience.
  • the present invention also provides a device for obtaining a blood flow reserve score based on medical history information.
  • the device for obtaining a blood flow reserve score based on medical history information includes:
  • a data collector for acquiring and storing geometric parameters and individual specific data of a region of interest in an anatomical model of the coronary system
  • a correction processor which is configured to receive one or more medical history information of an individual, and process the medical history information to generate correction parameters;
  • a blood flow characteristic processor based on the geometric parameters and individual specific data, the blood flow characteristic processor is used to establish a second geometric model and a blood flow model of a region of interest;
  • the blood flow characteristic processor is further configured to correct the geometric model and the blood flow model based on the medical history information and the corresponding correction parameters transmitted by the correction processor, and obtain the blood flow velocity V of the region of interest.
  • the blood flow reserve fraction FFR is calculated and obtained according to the blood flow velocity V in combination with hemodynamics.
  • the geometric model is obtained by the blood flow characteristic processor by measuring and calculating the geometric parameters of the anatomical model transmitted by the data collector, and combining the correction parameters transmitted by the correction processor and fitting and calibrating; specifically, In other words, when the geometric parameters of the region of interest are obtained through CT, OCT, and IVUS, the data collector can directly collect the geometric parameters and pass them to the blood flow feature processor for simulation.
  • a geometric model is established together; and when the geometric parameters of the region of interest are acquired by a contrast method, when the data collector collects the geometric parameters, the image data is not less than two groups, any two There is a difference in acquisition angle between the image data, and the acquisition angle difference is not less than 20 degrees. With this setting, the geometric model obtained by the blood flow feature processor can ensure that the geometric model is established accurately.
  • the geometric model includes at least one blood vessel tree, and the blood vessel tree includes at least one section of the aorta or includes at least one section of the aorta and a plurality of coronary arteries emitted by the aorta; the geometric model may further include: Is at least a single vessel segment; and a cross-sectional morphological model at various locations between the proximal end and the distal end of the region of interest.
  • the cross-sectional morphological model is directly / indirectly obtained by the blood flow feature processor through the geometric model; the cross-sectional morphological model includes the presence or absence of plaques on each cross-section, the position of the plaques, and the plaques.
  • the blood flow characteristic processor is further configured to establish a morphological difference function f (x) of a blood vessel lumen of a region of interest based on the cross-sectional morphological model, and the morphological difference function f (x) is used for Represents a function of the change in cross-sectional morphology at different locations of a vessel segment of a region of interest as a function of the distance x from that location to the proximal endpoint.
  • the blood flow model established by the blood flow feature processor includes a fixed blood flow model and a personalized blood flow model; the personalized blood flow model includes a resting blood flow model and a load blood flow model.
  • the blood flow velocity V may be obtained by calculating the velocity of fluid filling in a blood vessel; or obtained by calculating the shape of a blood vessel tree; the shape of the blood vessel tree includes at least all One or more of the area, volume, and lumen diameter of a blood vessel segment in the blood vessel tree; and when the blood flow velocity V is obtained by morphological calculation of the blood vessel tree, the geometric parameters further include One or more of a length, a perfusion area, and a branch angle of a blood vessel segment in the blood vessel tree.
  • the apparatus for obtaining a blood pressure difference further includes a speed collector, which is used to obtain a blood flow velocity V of the region of interest, and the blood flow velocity V is used to estimate a proximal end point of the region of interest.
  • the calculation formula of the pressure difference value ⁇ P is:
  • ⁇ P (c 1 V + c 2 V 2 +... + c m V m )
  • c 1 , c 2 ,..., C m respectively represent parameter coefficients of blood flow velocity
  • the parameter coefficients include a plurality of parameter coefficients such as a blood viscosity influence factor, a blood turbulence influence factor, and a viscosity coefficient
  • m is Natural numbers greater than or equal to 1 are used to represent the influence of different parameter coefficients on blood flow velocity, respectively, to correct the pressure difference value ⁇ P to ensure the accuracy of the pressure difference value ⁇ P calculation.
  • the value of m is 2, and when m is 2, c 1 is a parameter coefficient generated by blood flow friction, and c 2 is a parameter coefficient generated by blood turbulence.
  • the ⁇ 1 , ⁇ 2 , ..., ⁇ n are weighting coefficients of morphological difference functions f 1 (x), f 2 (x), ..., f n (x) of blood vessel lumen at different scales, respectively, where n Is a natural number with a scale of 1 or more; further, the increase of the weighting coefficient can further modify the morphological difference function f (x) to ensure the accuracy of the morphological difference fitting calculation between the two cross sections.
  • the blood flow characteristic value further includes a blood flow reserve fraction FFR of the region of interest, and the blood flow reserve fraction passes the morphological difference function f (x), blood
  • the flow velocity V and the blood pressure Pa at the proximal end position of the region of interest are calculated, and the blood flow reserve fraction FFR is calculated by the following formula:
  • the present invention also provides a device for obtaining an individual blood flow reserve score, the device having a processor, wherein the processor is configured to cause the device to perform the following steps:
  • the blood flow reserve fraction of the blood vessel to be tested is determined based on the modified blood vessel model and the blood flow reserve fraction calculation model.
  • the "processor” includes any device that receives and / or generates a signal, and the data processed by the processor may be a text message, an instruction of an object / fluid movement, an input of an application program, or some other information;
  • the blood vessel to be tested Alternative terms may be target blood vessels or blood vessels of interest; and the blood vessels to be tested include coronary blood vessels, branch blood vessels issued by coronary blood vessels, blood vessel trees, and single vessel segments, such as blood vessel tissue at any location;
  • the blood vessel model includes at least one of the second geometric model and the second blood flow model, and alternative terms of the blood vessel model may also be a lumen model, a fluid flow model, and the like, which may reflect an individual's blood vessel to be examined A model of morphology and fluid flow in a blood vessel.
  • the blood vessel model includes the length, diameter, and bending angle of the blood vessel to be tested, and the existence of branch blood vessels in the blood vessel to be tested, the angle of the branch blood vessels, and the number of branch blood vessels. The data about the geometry of the blood vessel to be examined are described.
  • the alternative term of the luminal morphology model may also be a cross-sectional morphological model, and the luminal morphological model includes the presence or absence of plaque, the location of the plaque, the size of the plaque, and the plaque.
  • the angle formed, the composition of the plaque and the change in the composition of the plaque, the shape of the plaque and the change in the shape of the plaque; further establishing the luminal morphology model includes the following steps:
  • the cross section at the proximal end of the object to be examined is defined as a reference plane, and a center line of the blood vessel model is established and obtained by a center line extraction method;
  • the planar geometric image of the lumen morphology at each position needs to use the coordinate system established in step S2 as a reference to determine the position of the plaque on each lumen cross section to facilitate subsequent fitting of the lumen morphology model. .
  • the luminal morphological model when the anatomical data is obtained by using CT, OCT, IVUS and other detection methods, the luminal morphological model can be directly obtained through the vascular model. It is only necessary to ensure that the origin and coordinate directions of each of the luminal morphological models are consistent; when the anatomical data is acquired using detection methods such as X-rays, since the blood vessel model is a three-dimensional model extending in the direction of blood flow, Then, when the luminal morphology model is established through the vascular model, coordinate conversion is required on the vascular model to accurately reflect the cross-sectional morphology of each cross-section.
  • the processor is further configured to determine, based on a preset morphological difference function, a vascular pressure difference between any two positions of a blood vessel to be detected through the luminal morphological model and the blood vessel model.
  • the morphological difference function is obtained by fitting and establishing the luminal morphological model, and is used to represent the function of the luminal morphological change at different positions of the blood vessel to be tested as a function of the distance x from the position to the reference point; and
  • the morphological difference function includes a difference function related to the area, volume, edge position and edge morphology of the blood vessel to be examined, which can reflect the morphological difference between any two positions of the blood vessel to be examined, and the difference function can be obtained directly / indirectly through the luminal morphology model .
  • the anatomical data may also be defined as parameters that can reflect the shape of the lumen directly and / or indirectly from the image acquisition device, such as anatomical data.
  • the processor, the blood vessel to be examined, the anatomical data, the luminal morphology model, and the blood vessel model may be different names having the same meaning.
  • the scale is a distance between two adjacent cross sections; the different scales include a first scale, a second scale, ..., an n-th scale;
  • the morphological difference function f 1 (x) at the first scale is used to detect the geometric morphological differences corresponding to the morphological models of two adjacent cross-sections caused by the first lesion feature;
  • the morphological difference function f 2 (x) at the second scale is used to detect a geometric morphological difference corresponding to two adjacent cross-sectional morphological models caused by a second lesion feature;
  • the morphological difference function f n (x) on the n-th scale is used to detect the geometric morphological differences corresponding to the morphological models of two adjacent cross-sections caused by the n-th lesion feature.
  • the method of establishing the blood vessel model is basically the same as the method of establishing the second blood flow model and the second geometric model.
  • the blood vessel model can include the blood vessels to be examined at the same time. Segment morphology and blood flow information, so in this embodiment, the specific establishment method of the vascular model is not described in detail here.
  • the factors affecting the vascular pressure difference described in this device include medical history information and / or physiological parameters; the medical history information includes circulatory system diseases, respiratory system diseases, neurological system diseases that affect blood flow velocity or blood viscosity, One or more of bone disease, digestive system disease, metabolic disease, tumor disease, and family history.
  • the processor may be further configured to run the following formula to calculate and obtain the vascular pressure difference ⁇ P:
  • ⁇ P (c 1 V + c 2 V 2 +... + c m V m )
  • V is a blood flow velocity and is obtained directly / indirectly through the second blood flow model
  • c 1 , c 2 ,..., c m respectively represent the parameter coefficients of the blood flow velocity V, and the parameter coefficients include a plurality of parameter coefficients such as blood viscosity influence factors, blood turbulence influence factors, and viscosity coefficients; further, m is greater than A natural number equal to 1 to represent the influence of different parameter coefficients on the blood flow velocity V, to correct the pressure difference value ⁇ P, and to ensure the accuracy of the calculation of the vascular pressure difference ⁇ P.
  • the value of m is 2, and when m is 2, c 1 is a parameter coefficient due to blood flow friction, and c 2 is a parameter coefficient due to blood turbulence.
  • the ⁇ 1 , ⁇ 2 , ..., ⁇ n are weighting coefficients of morphological difference functions f 1 (x), f 2 (x), ..., f n (x) of blood vessel lumen at different scales, respectively, where n Is a natural number with a scale of 1 or more; further, the increase of the weighting coefficient can further modify the morphological difference function f (x) to ensure the accuracy of the morphological difference fitting calculation between the two cross sections.
  • the method for obtaining blood flow characteristic values based on medical history information of the present invention introduces medical history information in the process of calculating blood flow characteristic values, and timely calculates the geometric model and / or blood flow in the process of calculating blood flow characteristic values.
  • Model to ensure the accuracy of the establishment of the geometric model and the blood flow model, and further ensure that the relevant parameters obtained through the geometric model and the blood flow model are accurate, so that the method can be obtained by the method of obtaining blood flow characteristic values based on the medical history information of the present invention.
  • the characteristic value of the blood flow can accurately reflect the characteristics of the region of interest; at the same time, the method for obtaining the characteristic value of the blood flow based on the medical history information of the present invention introduces morphological factors in the calculation of the characteristic value of the blood flow, so that the method based on the medical history of the present invention
  • the method and device for obtaining the blood flow characteristic value by information is more accurate and suitable for use.

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Abstract

Disclosed is a method and device for acquiring blood flow characteristic values on the basis of medical history information. The method for acquiring blood flow characteristic values on the basis of medical history information comprises: collecting anatomical data of the coronary system to establish a first geometric model and a first blood flow model of a region of interest; correcting the first geometric model and/or the first blood flow model on the basis of one or more medical history information items to obtain a second geometric model and a second blood flow model of the region of interest; and acquiring a pressure difference value ΔP between a proximal endpoint and a distal endpoint of the region of interest. In the method for acquiring blood flow characteristic values on the basis of medical history information, medical history information is introduced to correct various types of data in the process of calculating blood flow characteristic values, and to clarify the impact of different medical history information on the calculation of blood flow characteristic values, thereby increasing accuracy when calculating blood flow characteristic values.

Description

基于病史信息获取血流特征值的方法及装置Method and device for obtaining blood flow characteristic value based on medical history information 技术领域Technical field
本发明涉及一种基于病史信息获取血流特征值的方法及装置，属于医疗器械技术领域。The invention relates to a method and a device for obtaining blood flow characteristic values based on medical history information, and belongs to the technical field of medical equipment.
背景技术Background technique
人体血液中的脂类及糖类物质在血管壁上的沉积将在血管壁上形成斑块，继而导致血管狭窄；特别是发生在心脏冠脉附近的血管狭窄将导致心肌供血不足，诱发冠心病、心绞痛等病症，对人类的健康造成严重威胁。据统计，我国现有冠心病个体约1100万人，心血管介入手术治疗个体数量每年增长大于10％。The deposition of lipids and carbohydrates in human blood on the vessel wall will form plaques on the vessel wall, which will lead to vascular stenosis; especially the vascular stenosis that occurs near the coronary artery of the heart will cause insufficient blood supply to the heart muscle and induce coronary heart disease And angina pectoris, which pose a serious threat to human health. According to statistics, there are about 11 million individuals with coronary heart disease in China, and the number of individuals undergoing cardiovascular intervention surgery has increased by more than 10% each year.
冠脉造影、CT等常规医用检测手段虽然可以显示心脏冠脉血管狭窄的严重程度，但是并不能准确评价冠脉的缺血情况。为提高冠脉血管功能评价的准确性，1993年Pijls提出了通过压力测定推算冠脉血管功能的新指标——血流储备分数(Fractional Flow Reserve，FFR)，经过长期的基础与临床研究，FFR已成为冠脉狭窄功能性评价的金标准。Although conventional medical detection methods such as coronary angiography and CT can show the severity of coronary stenosis of the heart, they cannot accurately evaluate the ischemia of the coronary arteries. In order to improve the accuracy of coronary vascular function evaluation, in 1993, Pijls proposed a new index to estimate coronary vascular function through pressure measurement-Fractional Flow Reserve (FFR). After long-term basic and clinical research, FFR Has become the gold standard for functional evaluation of coronary stenosis.
血流储备分数(FFR)通常是指心肌血流储备分数，定义为病变冠脉能为心肌提供的最大血流与该冠脉完全正常时最大供血流量之比，研究表明，在冠脉最大充血状态下，血流量的比值可以用压力值来代替。即FFR值的测量可在冠脉最大充血状态下，通过压力传感器对冠脉远端狭窄处的压力和冠脉狭窄近端压力进行测定继而计算得出。近年来，基于压力导丝测量FFR值的方法逐渐进入临床应用，成为冠心病个体获得精准诊断的有效方法；然而，由于压力导丝在介入过程中易对病人的血管造成损伤；同时，通过压力导丝对FFR值进行测定需要注射腺苷/ATP等药物保证冠脉达到最大充血状态， 部分病人会因药物的注射感到不适，使得基于压力导丝测量FFR值的方法存在较大的局限性。此外，虽然基于压力导丝引导的FFR的测定是冠脉狭窄血液动力学的重要指标，但是由于压力导丝的造价高，介入血管过程操作困难，因此严重限制了基于压力导丝测量FFR值的方法的推广及使用。The blood flow reserve fraction (FFR) usually refers to the myocardial blood flow reserve fraction, which is defined as the ratio of the maximum blood flow that the diseased coronary artery can provide to the heart muscle to the maximum blood supply flow when the coronary artery is completely normal. Studies show that the maximum congestion in the coronary artery In the state, the blood flow ratio can be replaced by the pressure value. That is to say, the measurement of FFR value can be calculated by measuring the pressure of the distal stenosis of the coronary artery and the pressure of the proximal stenosis of the coronary artery with a pressure sensor under the condition of maximum congestion of the coronary artery. In recent years, the method of measuring FFR value based on pressure guidewires has gradually entered clinical applications and has become an effective method for obtaining accurate diagnosis of coronary heart disease individuals; however, because pressure guidewires are likely to cause damage to patients' blood vessels during the intervention process, meanwhile, pressure The determination of FFR by a guidewire requires the injection of adenosine / ATP and other drugs to ensure that the coronary artery reaches the maximum congestion state. Some patients may feel uncomfortable due to the injection of the drug, which makes the method of measuring the FFR value based on the pressure guidewire have greater limitations. In addition, although the measurement of FFR based on pressure guidewires is an important indicator of hemodynamics of coronary stenosis, the high cost of pressure guidewires and the difficult operation of interventional vascular procedures have severely limited the measurement of FFR values based on pressure guidewires. Promotion and use of methods.
随着CT与三维造影重建技术的发展及3D冠状动脉几何重建技术在血液力学研究领域的推广应用，同时，为减少FFR值测量过程中对人体带来的伤害及测量成本，基于医疗影像学的FFR计算技术已成为研究重点。With the development of CT and 3D angiography reconstruction technology and the popularization and application of 3D coronary artery geometric reconstruction technology in the field of hemodynamics, at the same time, in order to reduce the harm to the human body and the measurement cost during the FFR value measurement process, based on medical imaging FFR calculation technology has become the focus of research.
现有技术中，Taylor等人将计算机流体力学应用于计算机断层扫描冠状动脉造影(CTA)中，利用CTA得到冠脉解剖数据，包括血管供应心肌的体积和质量等，估算出最大冠脉血流量，模拟出血管下游微循环阻力，作为计算流体力学仿真的边界条件进行流体方程求解，得到计算FFR的非侵入式方法FFR CT。 In the prior art, Taylor et al. Applied computer fluid dynamics to computed tomography coronary angiography (CTA) and used CTA to obtain coronary anatomy data, including the volume and mass of blood vessels supplying myocardium, etc., to estimate the maximum coronary blood flow To simulate the microcirculation resistance downstream of the blood vessel, as a boundary condition of computational fluid dynamics simulation, the fluid equation is solved, and the non-invasive method FFR CT for calculating FFR is obtained.
事实上，现有技术虽然从不同角度、不同方法中给出了确定血流储备分数(FFR)的方法，但其实质均是通过目标血管近端终点处的血流压力P a和目标血管近端终点处和远端终点处的血流压力的差值ΔP来计算FFR。而在血液流动的实际过程中，即血流压力的差值ΔP的实际计算过程中，病变的位置、大小和类型等因素均会对血流压力的差值ΔP的计算产生影响；特别地，不同的病史也会对血管的形态、血液流速等造成影响，从而影响血流压力的差值ΔP的计算，进一步导致现有技术中，通过血流压力的差值ΔP计算获得的FFR偏离实际值，致使通过FFR评价冠脉狭窄功能的结果存在误差。 In fact, although the prior art provides methods to determine the blood flow reserve fraction (FFR) from different angles and different methods, its essence is through the blood pressure P a at the proximal end of the target blood vessel and the target blood vessel near The difference ΔP of the blood pressure at the end point and the end point was used to calculate the FFR. In the actual process of blood flow, that is, the actual calculation of the difference ΔP of blood pressure, factors such as the position, size, and type of the lesion will affect the calculation of the difference ΔP of blood pressure; in particular, Different medical histories will also affect the morphology of blood vessels, blood flow velocity, etc., and thus affect the calculation of the difference ΔP in blood flow pressure, which further causes the FFR calculated from the difference ΔP in blood flow pressure in the prior art to deviate from the actual value , Leading to errors in the results of FFR evaluation of coronary stenosis.
有鉴于此，确有必要提供一种基于病史信息获取血流特征值的方法，以解决上述问题。In view of this, it is indeed necessary to provide a method for obtaining blood flow characteristic values based on medical history information to solve the above problems.
发明内容Summary of the Invention
本发明的目的在于提供一种基于病史信息获取血流特征值的方法及装置，以至少解决现有技术中存在的技术问题之一。本发明提供的基于病史信息获取血流特征值的方法，通过引入病史信息，对血流特征值计算过程中的 各类数据进行修正，明确不同的病史信息等对血流特征值计算的影响，提高血流特征值计算的准确性。An object of the present invention is to provide a method and a device for obtaining blood flow characteristic values based on medical history information, so as to solve at least one of the technical problems existing in the prior art. The method for obtaining blood flow characteristic values based on medical history information provided by the present invention introduces medical history information to correct various types of data in the calculation process of blood flow characteristic values, and clarifies the influence of different medical history information on the calculation of blood flow characteristic values. Improve the accuracy of blood flow characteristic value calculation.
为实现上述发明目的，本发明提供了一种基于病史信息获取血流特征值的方法，所述基于病史信息获取血流特征值的方法包括：To achieve the above-mentioned object of the present invention, the present invention provides a method for obtaining a blood flow characteristic value based on medical history information. The method for obtaining a blood flow characteristic value based on medical history information includes:
采集至少一部分的冠脉系统的解剖数据，并根据所述解剖数据获取感兴趣区域的几何参数，建立感兴趣区域的第一几何模型；Collecting at least a part of the anatomical data of the coronary system, and acquiring geometric parameters of the region of interest according to the anatomical data, and establishing a first geometric model of the region of interest;
根据感兴趣区域的第一几何模型和/或个体特异性数据建立感兴趣区域的第一血流模型；Establishing a first blood flow model of the region of interest according to the first geometric model of the region of interest and / or individual specific data;
基于一个或多个病史信息，对所述第一几何模型和/或所述第一血流模型进行修正，以获取感兴趣区域的第二几何模型及第二血流模型；Modifying the first geometric model and / or the first blood flow model based on one or more medical history information to obtain a second geometric model and a second blood flow model of the region of interest;
根据所述第二血流模型，获取感兴趣区域的血流速度V；结合所述血流速度V及血流动力学，获取血流速度V下感兴趣区域近端终点处的血流压力Pa；及相应状态下感兴趣区域近端终点处与远端终点处的压力差数值ΔP；According to the second blood flow model, the blood flow velocity V of the region of interest is obtained; combined with the blood flow velocity V and hemodynamics, the blood flow pressure Pa at the proximal end of the region of interest at the blood flow velocity V is obtained ; And the pressure difference value ΔP at the proximal end and the distal end of the region of interest in the corresponding state;
所述血流特性值包括所述血流压力Pa、压力差数值ΔP以及基于所述血流压力Pa、压力差数值ΔP计算的反映血流特征的数值。The blood flow characteristic value includes the blood flow pressure Pa, a pressure difference value ΔP, and a value reflecting a blood flow characteristic calculated based on the blood flow pressure Pa and the pressure difference value ΔP.
作为本发明的进一步改进，所述第二几何模型包括感兴趣区域近端终点和远端终点之间各个位置处的横截面形态模型及感兴趣区域血管系统至少一个血管树或者感兴趣区域血管系统至少一段单支血管段；所述血管树包括至少一段主动脉，或者包括至少一段主动脉和由所述主动脉发出的多个冠状动脉。As a further improvement of the present invention, the second geometric model includes a cross-sectional morphological model at various positions between the proximal end point and the distal end point of the region of interest and at least one blood vessel tree or the vascular system of region of interest At least one single vessel segment; the vessel tree includes at least one aorta, or at least one aorta and multiple coronary arteries emanating from the aorta.
作为本发明的进一步改进，所述横截面形态模型的建立包括：As a further improvement of the present invention, the establishment of the cross-sectional morphological model includes:
S1、定义感兴趣区域血管段近端终点处的横截面为参考面，通过对几何模型的中心线进行提取，以获取所述几何模型的中心径线；S1. The cross section at the proximal end of the blood vessel segment of the region of interest is defined as a reference plane, and the center line of the geometric model is obtained by extracting the center line of the geometric model;
S2、以所述参考面的中心点为原点建立坐标系，沿垂直所述中心径线的方向对所述感兴趣区域血管段进行分割，将各横截面内外边缘投影在所述坐标系中，以获取目标血管在各个位置处管腔横截面的平面几何图像，横截面形态模型建立结束。S2. Establish a coordinate system with the center point of the reference plane as the origin, divide the blood vessel segment of the region of interest in a direction perpendicular to the central radial line, and project the inner and outer edges of each cross section in the coordinate system. In order to obtain a planar geometric image of the lumen cross section of the target blood vessel at various positions, the establishment of the cross section morphological model is completed.
作为本发明的进一步改进，所述横截面形态模型包括各横截面上斑块的有无、斑块的位置、斑块的大小、斑块形成的角度、斑块的组成及斑块组成的变化、斑块的形状及斑块形状的变化。As a further improvement of the present invention, the cross-sectional morphological model includes the presence or absence of plaques on each cross-section, the location of the plaques, the size of the plaques, the angle at which the plaques are formed, the composition of the plaques, and changes in the composition of the plaques , The shape of the plaque and the change in the shape of the plaque.
作为本发明的进一步改进，所述第二血流模型包括固定血流模型及个性化血流模型；As a further improvement of the present invention, the second blood flow model includes a fixed blood flow model and a personalized blood flow model;
所述个性化血流模型包括静息态血流模型和负荷态血流模型；当所述血流模型为静息态血流模型时，所述血流速度V可通过血管中流体充盈的速度计算获得；或者通过血管树的形态计算获得。The personalized blood flow model includes a resting-state blood flow model and a load-state blood flow model. When the blood-flow model is a resting-state blood flow model, the blood flow velocity V may pass through a rate of fluid filling in a blood vessel. Calculated; or obtained through morphological calculation of the vessel tree.
作为本发明的进一步改进，所述血管树的形态至少包括所述血管树的面积、体积和血管树中血管段的管腔直径中的一种或几种；所述血流速度V通过所述血管树的形态计算获得时，所述第二几何模型的几何参数还包括所述血管树中血管段的长度、灌注面积及分支角度中的一种或几种。As a further improvement of the present invention, the morphology of the blood vessel tree includes at least one or more of an area, a volume, and a lumen diameter of a blood vessel segment in the blood vessel tree; the blood flow velocity V passes through the When the morphological calculation of the blood vessel tree is obtained, the geometric parameters of the second geometric model further include one or more of the length, perfusion area, and branch angle of the blood vessel segment in the blood vessel tree.
作为本发明的进一步改进，所述病史信息包括影响血流速度或血液黏度的循环系统疾病、呼吸系统疾病、神经系统疾病、骨骼疾病、消化系统疾病、代谢性疾病、肿瘤疾病及家族病史。As a further improvement of the present invention, the medical history information includes a circulatory system disease, a respiratory system disease, a neurological system disease, a skeletal disease, a digestive system disease, a metabolic disease, a tumor disease, and a family medical history that affect blood flow velocity or blood viscosity.
为实现上述发明目的，本发明还提供了一种基于病史信息获取血流储备分数的装置，所述基于病史信息获取血流储备分数的装置包括：In order to achieve the above-mentioned object of the present invention, the present invention further provides a device for obtaining a blood flow reserve score based on medical history information. The device for obtaining a blood flow reserve score based on medical history information includes:
数据采集器，所述数据采集器用于获取及存储冠脉系统的解剖模型中感兴趣区域的几何参数和个体特异性数据；A data collector for acquiring and storing geometric parameters and individual specific data of a region of interest in an anatomical model of the coronary system;
纠偏处理器，所述纠偏处理器用于接收个体的一个或多个病史信息，并对所述病史信息进行处理生成纠偏参数；A correction processor, which is configured to receive one or more medical history information of an individual, and process the medical history information to generate correction parameters;
血流特征处理器，基于所述几何参数和个体特异性数据，所述血流特征处理器用于建立感兴趣区域的几何模型及血流模型；A blood flow characteristic processor, based on the geometric parameters and individual specific data, the blood flow characteristic processor is used to establish a geometric model and a blood flow model of a region of interest;
所述血流特征处理器还用于，基于所述纠偏处理器传递的病史信息，对所述几何模型及血流模型进行修正，并获取感兴趣区域血流速度V；同时，根据所述血流速度V并结合血流动力学，计算获取血流储备分数FFR。The blood flow characteristic processor is further configured to modify the geometric model and the blood flow model based on the medical history information transmitted by the correction processor, and obtain the blood flow velocity V of the region of interest; at the same time, according to the blood The flow velocity V is combined with hemodynamics to calculate and obtain the blood flow reserve fraction FFR.
作为本发明的进一步改进，所述几何模型为所述血流特征处理器通过对 所述数据采集器传递的所述解剖模型的几何参数进行测算，并结合所述纠偏处理器传递的纠偏参数经拟合校准获得；所述几何模型包括感兴趣区域近端终点和远端终点之间各个位置处的横截面形态模型及感兴趣区域血管系统至少一个血管树或者感兴趣区域血管系统至少一段单支血管段；所述血管树包括至少一段主动脉，或者包括至少一段主动脉和由所述主动脉发出的多个冠状动脉。As a further improvement of the present invention, the geometric model is that the blood flow characteristic processor measures the geometric parameters of the anatomical model transmitted by the data collector, and combines the correction parameter parameters transmitted by the correction processor with Obtained by fitting calibration; the geometric model includes a cross-sectional morphological model at various positions between the proximal end and the distal end of the region of interest and at least one vascular tree of the vascular system of the region of interest or at least a single branch of the vascular system of the region of interest A blood vessel segment; the blood vessel tree includes at least one aorta, or includes at least one aorta and a plurality of coronary arteries emanating from the aorta.
作为本发明的进一步改进，所述横截面形态模型为所述血流特征处理器通过所述几何模型直接/间接获得；所述横截面形态模型包括各横截面上斑块的有无、斑块的位置、斑块的大小、斑块形成的角度、斑块的组成及斑块组成的变化、斑块的形状及斑块形状的变化。As a further improvement of the present invention, the cross-sectional morphological model is obtained directly / indirectly by the blood flow feature processor through the geometric model; the cross-sectional morphological model includes the presence or absence of plaques on each cross-section, and plaques. Location, plaque size, plaque formation angle, plaque composition and plaque composition change, plaque shape and plaque shape change.
作为本发明的进一步改进，所述血流特征处理器还用于基于所述横截面形态模型，建立感兴趣区域血管管腔的形态差异函数f(x)，所述形态差异函数f(x)用于表示目标血管不同位置处的横截面形态变化随着该位置到近端终点之间的距离x变化的函数。As a further improvement of the present invention, the blood flow feature processor is further configured to establish a morphological difference function f (x) of a vessel lumen of a region of interest based on the cross-sectional morphological model, and the morphological difference function f (x) A function used to represent the change in cross-sectional morphology at different locations of the target vessel as a function of the distance x from that location to the proximal endpoint.
作为本发明的进一步改进，所述基于病史信息获取血流储备分数的装置还包括速度采集器，所述速度采集器用于获取感兴趣区域的血流速度V；As a further improvement of the present invention, the device for obtaining a blood flow reserve score based on the medical history information further includes a speed collector, which is used to obtain the blood flow velocity V of the region of interest;
所述速度采集器包括速度计算模块及速度提取模块；所述速度提取模块通过所述数据采集器直接获取血流速度信息，或通过血流模型直接提取血流速度V；The speed collector includes a speed calculation module and a speed extraction module; the speed extraction module directly obtains blood flow velocity information through the data collector, or directly extracts the blood flow velocity V through a blood flow model;
所述速度计算模块还包括速度转换模块及速度测算模块；所述血流速度V可通过血管中流体充盈的速度经速度转换模块转换获得，还可通过几何模型中血管树的形态经速度测算模块测算获得。The speed calculation module further includes a speed conversion module and a speed measurement module; the blood flow velocity V can be obtained by converting the speed of fluid filling in the blood vessel through the speed conversion module, and also can be obtained by the shape of the blood vessel tree in the geometric model through the speed measurement module Calculated.
为实现上述发明目的，本发明还提供了一种用于获取个体血流储备分数的设备，所述设备具有处理器，其中，所述处理器被设置为使得所述设备执行以下步骤：To achieve the above-mentioned object of the present invention, the present invention further provides a device for obtaining an individual blood flow reserve score, the device having a processor, wherein the processor is configured to cause the device to perform the following steps:
收集个体特定病史信息和待检血管的几何参数；Collect individual specific medical history information and geometric parameters of blood vessels to be tested;
根据所述待检血管的几何参数建立个体的血管模型；Establishing an individual blood vessel model according to the geometric parameters of the blood vessel to be examined;
根据个体特定病史信息修正所述血管模型；Revising the vascular model according to individual specific medical history information;
提供至少一种血流储备分数的计算模型；Providing at least one calculation model of blood flow reserve fraction;
基于修正后的所述血管模型与所述血流储备分数的计算模型确定待检血管的血流储备分数。The blood flow reserve fraction of the blood vessel to be tested is determined based on the modified blood vessel model and the blood flow reserve fraction calculation model.
作为本发明的进一步改进，所述病史信息包括影响血流速度或血液黏度的循环系统疾病、呼吸系统疾病、神经系统疾病、骨骼疾病、消化系统疾病、代谢性疾病、肿瘤疾病及家族病史中的一个或多个。As a further improvement of the present invention, the medical history information includes circulatory system diseases, respiratory system diseases, nervous system diseases, bone diseases, digestive system diseases, metabolic diseases, tumor diseases, and family medical history that affect blood flow velocity or blood viscosity. one or more.
作为本发明的进一步改进，所述血管压力差数值的计算模型是基于多尺度计算方法而建立的。As a further improvement of the present invention, the calculation model of the vascular pressure difference value is established based on a multi-scale calculation method.
本发明的有益效果是：本发明的基于病史信息获取血流特征值的方法，通过在血流特征值计算的过程中引入病史信息，及时对血流特征值计算过程中的几何模型和/或血流模型，保证几何模型和血流模型建立的准确性，进一步保证通过所述几何模型和所述血流模型获取的相关参数准确，使得通过本发明的基于病史信息获取血流特征值的方法计算得到的血流特征值可准确反映感兴趣区域的特征。The beneficial effect of the present invention is that the method for obtaining blood flow characteristic values based on the medical history information of the present invention introduces the medical history information into the blood flow characteristic value calculation to timely calculate the geometric model and / or the blood flow characteristic value during the calculation. The blood flow model guarantees the accuracy of the geometric model and the establishment of the blood flow model, and further ensures that the relevant parameters obtained through the geometric model and the blood flow model are accurate, so that the method for obtaining blood flow characteristic values based on the medical history information of the present invention The calculated blood flow characteristic value can accurately reflect the characteristics of the region of interest.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1是本发明目标血管的一种形态下的几何模型的示意图。FIG. 1 is a schematic diagram of a geometric model of a target blood vessel in one form of the present invention.
图2是图1中D 1位置处横截面形态模型的结构示意图。 FIG. 2 is a schematic structural diagram of a cross-sectional morphological model at a position D 1 in FIG. 1.
图3是图1中D 2位置处横截面形态模型的结构示意图。 FIG. 3 is a schematic structural diagram of a cross-sectional morphological model at a position D 2 in FIG. 1.
图4是图2和图3中D 1和D 2位置处横截面形态模型拟合后的结构示意图。 FIG. 4 is a schematic structural diagram of the cross-sectional morphological model at positions D 1 and D 2 in FIG. 2 and FIG. 3 after fitting.
图5是本发明目标血管的另一种形态下的几何模型的示意图。FIG. 5 is a schematic diagram of a geometric model in another form of the target blood vessel according to the present invention.
图6是图5中D 1位置处横截面形态模型的结构示意图。 6 is a schematic structural diagram of a cross-sectional morphological model at a position D 1 in FIG. 5.
图7是图5中D 2位置处横截面形态模型的结构示意图。 FIG. 7 is a schematic structural diagram of a cross-sectional morphological model at a position D 2 in FIG. 5.
图8是图6和图7中D 1和D 2位置处横截面形态模型拟合后的结构示意图。 FIG. 8 is a schematic structural diagram of the cross-sectional morphological model at positions D 1 and D 2 in FIG. 6 and FIG. 7 after fitting.
图9是本发明基于病史信息获取血流特征值的装置的结构框图。FIG. 9 is a structural block diagram of a device for obtaining blood flow characteristic values based on medical history information according to the present invention.
具体实施方式detailed description
为了使本发明的目的、技术方案和优点更加清楚，下面结合附图和具体实施例对本发明进行详细描述。In order to make the objectives, technical solutions, and advantages of the present invention clearer, the following describes the present invention in detail with reference to the accompanying drawings and specific embodiments.
本发明提供了一种基于病史信息获取血流特征值的方法，所述基于病史信息获取血流特征值的方法包括：The present invention provides a method for obtaining blood flow characteristic values based on medical history information. The method for obtaining blood flow characteristic values based on medical history information includes:
采集至少一部分的冠脉系统的解剖数据，并根据所述解剖数据获取感兴趣区域的几何参数，建立感兴趣区域的第一几何模型；Collecting at least a part of the anatomical data of the coronary system, and acquiring geometric parameters of the region of interest according to the anatomical data, and establishing a first geometric model of the region of interest;
根据感兴趣区域的第一几何模型和/或个体特异性数据建立感兴趣区域的第一血流模型；Establishing a first blood flow model of the region of interest according to the first geometric model of the region of interest and / or individual specific data;
基于一个或多个病史信息，对所述第一几何模型和/或所述第一血流模型进行修正，以获取感兴趣区域的第二几何模型及第二血流模型；Modifying the first geometric model and / or the first blood flow model based on one or more medical history information to obtain a second geometric model and a second blood flow model of the region of interest;
根据所述第二血流模型，获取感兴趣区域的血流速度V；结合所述血流速度V及血流动力学，获取血流速度V下感兴趣区域近端终点处的血流压力Pa；及相应状态下感兴趣区域近端终点处与远端终点处的压力差数值ΔP；According to the second blood flow model, the blood flow velocity V of the region of interest is obtained; combined with the blood flow velocity V and hemodynamics, the blood flow pressure Pa at the proximal end of the region of interest at the blood flow velocity V is obtained ; And the pressure difference value ΔP at the proximal end and the distal end of the region of interest in the corresponding state;
所述血流特性值包括所述血流压力Pa、压力差数值ΔP以及基于所述血流压力Pa、压力差数值ΔP计算的反映血流特征的数值。The blood flow characteristic value includes the blood flow pressure Pa, a pressure difference value ΔP, and a value reflecting a blood flow characteristic calculated based on the blood flow pressure Pa and the pressure difference value ΔP.
所述第一几何模型为反映个体冠脉系统几何形状的三维模型，在本发明中所述第一几何模型通过感兴趣区域的几何参数经建模获得，所述几何参数为通过所述个体冠脉系统的解剖数据获得到，进一步的，所述个体冠脉系统的解剖数据，可通过CT设备、OCT设备、IVUS设备和造影设备等常用图像生成设备生成获取。所述第一血流模型为通过经验值或者大数据手段获取的用于表征个体在正常状态下的血流特征的模型。The first geometric model is a three-dimensional model reflecting the geometry of the individual coronary system. In the present invention, the first geometric model is obtained by modeling the geometric parameters of the region of interest, and the geometric parameters are obtained through the individual coronary system. The anatomical data of the venous system is obtained. Further, the anatomical data of the individual coronary system can be obtained through common image generating equipment such as CT equipment, OCT equipment, IVUS equipment and angiography equipment. The first blood flow model is a model obtained by using empirical values or big data to characterize the blood flow characteristics of an individual in a normal state.
所述第二几何模型和所述第二血流模型，为所述第一几何模型和所述第一血流模型在一种或多种病史信息的修正下获得，其中，所述病史信息对所述第一几何模型和所述第一血流模型的修正可单独或同时进行，具体来讲， 个体的病史信息可对感兴趣区域血管管腔的形态和/或血管管腔中血液的粘度、流速等在成影响，因此通过病史信息对第一几何模型和/或第一血流模型进行修正，可保证第二几何模型和第二血流模型的准确性；进一步的，在本发明中所述病史信息包括影响血流速度或血液黏度的循环系统疾病、呼吸系统疾病、神经系统疾病、骨骼疾病、消化系统疾病、代谢性疾病、肿瘤疾病、及家族病史。The second geometric model and the second blood flow model are obtained by modifying the one or more medical history information of the first geometric model and the first blood flow model, wherein the medical history information is The modification of the first geometric model and the first blood flow model may be performed separately or simultaneously. Specifically, the individual's medical history information may affect the shape of the vascular lumen of the region of interest and / or the viscosity of blood in the vascular lumen. , Flow velocity, etc. are being affected, so correcting the first geometric model and / or the first blood flow model through the medical history information can ensure the accuracy of the second geometric model and the second blood flow model; further, in the present invention The medical history information includes a circulatory system disease, a respiratory system disease, a neurological system disease, a skeletal disease, a digestive system disease, a metabolic disease, a tumor disease, and a family medical history that affect blood flow velocity or blood viscosity.
在本发明中，所述第二几何模型包括感兴趣区域近端终点和远端终点之间各个位置处的横截面形态模型及感兴趣区域血管系统至少一个血管树或者感兴趣区域血管系统至少一段单支血管段；所述血管树包括至少一段主动脉，或者包括至少一段主动脉和由所述主动脉发出的多个冠状动脉，且每个所述血管段和/或血管树均包括所述感兴趣区域的形状、直径和面积等几何参数，进一步的，所述几何参数还包括血管段的弯曲角度等可以反映感兴趣区域实际形态的参数。In the present invention, the second geometric model includes a cross-sectional morphological model at various positions between the proximal end and the distal end of the region of interest and at least one vascular tree of the vascular system of the region of interest or at least one section of the vascular system of the region of interest. A single vessel segment; the vessel tree includes at least one aorta, or includes at least one aorta and multiple coronary arteries emanating from the aorta, and each of the vessel segments and / or vessel trees includes the Geometric parameters such as the shape, diameter, and area of the region of interest. Further, the geometric parameters include parameters such as the bending angle of a blood vessel segment that can reflect the actual shape of the region of interest.
进一步的，所述第二几何模型还包括感兴趣区域近端终点和远端终点之间各个位置处的横截面形态模型。Further, the second geometric model further includes a cross-sectional morphology model at various positions between the proximal end and the distal end of the region of interest.
所述横截面形态模型为通过所述第二几何模型直接/间接获得，且所述横截面形态模型的建立包括以下步骤：The cross-sectional morphological model is obtained directly / indirectly through the second geometric model, and the establishment of the cross-sectional morphological model includes the following steps:
S1、定义目标血管近端终点处的横截面为参考面，通过对几何模型的中心线进行提取，以获取所述几何模型的中心径线；S1. A cross section at a proximal end of a target blood vessel is defined as a reference plane, and a center line of the geometric model is obtained by extracting a center line of the geometric model;
S2、以所述参考面的中心点为原点建立坐标系，沿垂直所述中心径线的方向对所述目标血管进行分割，将各横截面内外边缘投影在所述坐标系中，以获取目标血管在各个位置处管腔横截面的平面几何图像，横截面形态模型建立结束。S2. Establish a coordinate system with the center point of the reference plane as the origin, divide the target blood vessel in a direction perpendicular to the central radial line, and project the inner and outer edges of each cross section in the coordinate system to obtain the target. The plane geometric image of the lumen cross section of the blood vessel at various positions, and the cross-sectional morphological model is completed.
其中，所述横截面形态模型包括各横截面位置处的斑块信息，所述斑块信息即为感兴趣区域的病变信息，且大量数据表明：当斑块(即为病变)的长度＞20mm时，将导致目标血管压力差数值ΔP的升高，进一步导致血流特征值如血流储备分数FFR的计算出现误差；而当同一横截面处斑块的组成复 杂或尺寸过大致使目标血管的狭窄率高，则会进一步导致目标血管压力差数值ΔP的升高，继而影响血流特征值的计算；同时当所述斑块处于不同的位置处时，不同的心肌面积区域将导致病变位置与非病变位置处的比例发生变化，进一步影响血流速度V，从而导致感兴趣区域血流特征值的计算发生偏差。The cross-sectional morphological model includes plaque information at each cross-sectional position, and the plaque information is lesion information of the region of interest, and a large amount of data indicates that when the length of the plaque (that is, the lesion) is> 20 mm , It will lead to an increase in the target blood vessel pressure difference value ΔP, further leading to errors in the calculation of blood flow characteristic values such as the blood flow reserve fraction FFR; and when the composition of the plaque at the same cross-section is complex or the size is too large, the target blood vessel's A high stenosis rate will further increase the target vascular pressure difference value ΔP, and then affect the calculation of the characteristic value of the blood flow. At the same time, when the plaque is at different positions, different areas of the myocardial area will cause the lesion location and The proportion at the non-lesion location changes, which further affects the blood flow velocity V, which leads to deviations in the calculation of blood flow characteristic values in the region of interest.
因此，在建立所述横截面形态模型时，所述斑块信息还需包括斑块的有无、斑块的位置、斑块的大小、斑块形成的角度、斑块的组成及斑块组成的变化、斑块的形状及斑块形状的变化，且在本发明中，各个位置处的管腔横截面的平面几何图像均需以步骤S2中建立的坐标系为参考，明确各横截面上斑块的位置，以方便横截面形态模型的后续拟合。Therefore, when establishing the cross-sectional morphological model, the plaque information also needs to include the presence or absence of plaque, the location of the plaque, the size of the plaque, the angle at which the plaque forms, the composition of the plaque, and the composition of the plaque. Changes in the shape of the plaque and the shape of the plaque, and in the present invention, the planar geometric image of the lumen cross section at each position needs to be referenced to the coordinate system established in step S2 to clarify each cross section Plaque location to facilitate subsequent fitting of the cross-section morphological model.
需要说明的是，在所述横截面形态模型的建立过程中，当所述解剖数据为采用CT、OCT、IVUS等检测手段获取时，所述横截面形态模型可通过所述几何模型直接获取，只需保证每个所述横截面形态模型的原点及坐标方向一致即可；当所述解剖数据为采用X射线等检测手段获取时，由于所述几何模型为沿血流方向延伸的立体模型，则在通过所述几何模型建立所述横截面形态模型时，需对所述几何模型进行坐标转换，以准确反应各个横截面的截面形态。It should be noted that, during the establishment of the cross-sectional morphological model, when the anatomical data is obtained by using CT, OCT, IVUS and other detection methods, the cross-sectional morphological model can be directly obtained through the geometric model. It is only necessary to ensure that the origin and coordinate direction of each of the cross-sectional morphological models are consistent; when the anatomical data is acquired using detection means such as X-rays, since the geometric model is a three-dimensional model extending in the direction of blood flow, Then, when the cross-sectional morphology model is established by using the geometric model, coordinate transformation of the geometric model is required to accurately reflect the cross-sectional morphology of each cross-section.
为进一步保证所述基于病史信息获取血流特征值的方法获取的血流特征值准确，所述基于病史信息获取血流特征值的方法还包括对不同尺度下的所述横截面形态模型进行拟合，计算目标血管管腔的形态差异函数f(x)。其中，所述形态差异函数f(x)用于表示目标血管不同位置处的横截面形态变化随着该位置到参考点的距离x变化的函数；且所述形态差异函数f(x)的获取包括：In order to further ensure that the blood flow characteristic value obtained by the method of obtaining blood flow characteristic values based on medical history information is accurate, the method of obtaining blood flow characteristic values based on medical history information further includes simulating the cross-sectional morphological models at different scales. Then, the morphological difference function f (x) of the lumen of the target vessel is calculated. Wherein, the morphological difference function f (x) is a function that represents a change in the cross-sectional morphological change at different positions of the target blood vessel as a function of the distance x from the position to a reference point; and the acquisition of the morphological difference function f (x) include:
基于横截面形态模型，建立各横截面的形态函数；Based on the cross-sectional morphological model, the morphological function of each cross-section is established;
对相邻两横截面的形态函数进行拟合，并获取相邻两横截面在不同尺度下的差异变化函数；Fit the morphological functions of two adjacent cross-sections and obtain the difference change functions of two adjacent cross-sections at different scales;
以目标血管的近端终点为参考点，根据差异变化函数获取管腔形态随着 到参考点的距离x的变化率，对目标血管从近端终点到远端终点范围内的位置参数进行归一化处理，以最终获取形态差异函数f(x)。Taking the proximal end of the target vessel as the reference point, the change rate of the lumen morphology with the distance x from the reference point is obtained according to the difference change function, and the position parameters of the target vessel from the proximal end to the distal end are normalized. Processing to finally obtain the morphological difference function f (x).
所述形态函数包括面积函数、直径函数或边缘距离函数，即在本发明中可通过各横截面面积、直径或边缘距离函数之间的拟合，获取相邻两横截面在不同尺度下的差异变化函数；进一步的，通过差异变化函数获取管腔形态随着到参考点的距离x的变化率，获得形态差异函数f(x)。The morphological function includes an area function, a diameter function, or an edge distance function, that is, in the present invention, the difference between two adjacent cross-sections at different scales can be obtained by fitting between the cross-sectional area, diameter, or edge distance functions. The change function; further, the change rate of the lumen morphology with the distance x to the reference point is obtained through the difference change function to obtain the morphological difference function f (x).
具体来讲，当所述形态函数为面积函数时，如图1至图4，对D 1和D 2位置处的两横截面形态模型进行拟合，D 1、D 2位置处的横截面形态模型拟合后，有血管管腔斑块增加的区域为A 1，对应的面积S 1；血管管腔减少的区域为A 2，对应的面积S 2。由于所述D 1和D 2位置处的血管管腔(斑块)不重叠，因此当血流经D 1处流向D 2处时，血流压力将随之发生变化；此时，差异变化函数即为血管管腔中非重叠区域(S 1、S 2)与重叠区域之间面积(S 3)的比值，或者为非重叠区域的面积(S 1、S 2)与总面积(S 1、S 2、S 3)的比值；且此时，所述形态差异函数f(x)＞0，即横截面D 1和D 2之间存在压力差。进一步的，当所述D 1和D 2位置处的血管管腔(斑块)完全重叠时，如图5至图8，所述区域A 1与A 2完全重叠，即非重叠区域A 1与A 2的面积S 1＝S 2＝0，此时，差异变化函数为0，即所述形态差异函数f(x)＝0，此时，横截面D 1和D 2之间不存在压力差。 Specifically, when the morphological function is an area function, as shown in FIGS. 1 to 4, the two cross-sectional morphological models at D 1 and D 2 positions are fitted, and the cross-sectional morphological positions at D 1 and D 2 positions are fitted. After the model was fitted, the area with increased vascular plaque was A 1 and the corresponding area S 1 ; the area with reduced vascular lumen was A 2 and the corresponding area S 2 . Because the blood vessel lumen (plaque) at the D 1 and D 2 positions does not overlap, when the blood flows through D 1 to D 2 , the blood pressure will change accordingly; at this time, the difference change function It is the ratio of the area between the non-overlapping areas (S 1 , S 2 ) and the overlapping area (S 3 ) in the vascular lumen, or the area (S 1 , S 2 ) and the total area (S 1 , S 1 ) of the non-overlapping area. S 2 , S 3 ); and at this time, the morphological difference function f (x)> 0, that is, there is a pressure difference between the cross sections D 1 and D 2 . Further, when the blood vessel lumen (plaque) at the positions D 1 and D 2 completely overlap, as shown in FIGS. 5 to 8, the regions A 1 and A 2 completely overlap, that is, the non-overlapping regions A 1 and The area of A 2 S 1 = S 2 = 0. At this time, the difference change function is 0, that is, the morphological difference function f (x) = 0. At this time, there is no pressure difference between the cross sections D 1 and D 2 .
当所述形态函数为距离函数时，此时，确立选取的第一管腔边界上每个点与第二管腔边界上每个点的对应关系，然后求出第一管腔边界上的每个点与第二管腔边界上的每个点所对应的距离，减去沿着血管中心径线的距离，并获取所有点的距离之和或者是平均距离。具体来讲，若第一管腔边界与第二管腔边界的对应点到中心经线的距离均为y，则第一管腔与第二管腔的形态完全一致，即所述形态差异函数f(x)＝0；若第一管腔边界与第二管腔边界的对应点到中心经线的距离不同，则第一管腔与第二管腔的形态不完全一致，即所述形态差异函数f(x)＞0。When the morphological function is a distance function, at this time, the correspondence between each point on the selected first lumen boundary and each point on the second lumen boundary is established, and then each of the points on the first lumen boundary is obtained The distance between each point and each point on the boundary of the second lumen, minus the distance along the central line of the blood vessel, and obtain the sum or average distance of all points. Specifically, if the distance from the corresponding point of the first lumen boundary and the second lumen boundary to the central meridian is y, the morphology of the first lumen and the second lumen are completely consistent, that is, the morphological difference function f (x) = 0; if the distance from the corresponding point of the first lumen boundary and the second lumen boundary to the central meridian is different, the morphology of the first lumen and the second lumen are not completely consistent, that is, the morphological difference function f (x)> 0.
在本发明中，所述血流速度V和所述血流压力Pa均通过血流模型获取，具体来讲，所述血流模型包括第一血流模型和第二血流模型，所述第一血流模型和第二血流模型既可为数据计算模型也可为三维流体流动模型；进一步的，在本发明中所述血流速度V和所述血流压力Pa通过所述第二血流模型直接/间接获取。具体来讲，所述第二血流模型包括固定血流模型及个性化血流模型；其中所述固定血流模型即为经验值血流模型，为根据临床实际经验，通过大数据采集及模拟的方法直接建立；所述个性化血流模型包括静息态血流模型和负荷态血流模型。In the present invention, the blood flow velocity V and the blood pressure Pa are both obtained through a blood flow model. Specifically, the blood flow model includes a first blood flow model and a second blood flow model. The first blood flow model and the second blood flow model may be both a data calculation model and a three-dimensional fluid flow model; further, in the present invention, the blood flow velocity V and the blood flow pressure Pa pass through the second blood. Direct / indirect acquisition of the stream model. Specifically, the second blood flow model includes a fixed blood flow model and a personalized blood flow model; wherein the fixed blood flow model is an empirical blood flow model, and is based on clinical experience and is collected and simulated through big data. The method is directly established; the personalized blood flow model includes a resting state blood flow model and a load state blood flow model.
当所述第二血流模型为固定血流模型或静息态血流模型时，所述血流压力Pa可通过获取的个体的收缩压混合舒张压计算获得，且此时所述血流压力Pa＝1/3收缩压+2/3舒张压；当所述血流模型为负荷态血流模型，此时所述血流压力Pa可通过所述负荷态血流模型直接测量获得。When the second blood flow model is a fixed blood flow model or a resting blood flow model, the blood flow pressure Pa can be obtained by calculating the individual systolic pressure and diastolic blood pressure, and the blood flow pressure at this time Pa = 1/3 systolic blood pressure + 2/3 diastolic blood pressure; when the blood flow model is a load state blood flow model, the blood flow pressure Pa can be directly measured by the load state blood flow model.
进一步的，在所述血流速度V的获取过程中，当所述第二血流模型为固定血流模型时，所述血流速度V可从固定血流模型中直接获取；当所述第二血流模型为静息态血流模型时，所述血流速度V可通过血管中流体充盈的速度计算获得；在本发明的一个实施例中，所述静息态血流模型为造影剂血流模型，此时所述血流速度V为利用灰度时间拟合函数获得的感兴趣区域在造影过程中造影剂的平均流动速度；或者利用TIMI数帧法计算获得的所述感兴趣区域在造影过程中造影剂的平均流动速度。Further, in the obtaining process of the blood flow velocity V, when the second blood flow model is a fixed blood flow model, the blood flow velocity V may be directly obtained from the fixed blood flow model; when the first When the two blood flow model is a resting state blood flow model, the blood flow velocity V can be obtained by calculating the velocity of fluid filling in a blood vessel; in an embodiment of the present invention, the resting state blood flow model is a contrast agent A blood flow model, where the blood flow velocity V is the average flow velocity of the contrast agent during the imaging process of the region of interest obtained by using the gray-time fitting function; or the region of interest obtained by calculating using the TIMI frame method The average flow velocity of the contrast agent during the imaging process.
当所述静息态血流模型为CT血流模型时，所述血流速度V可通过几何模型中血管树的形态计算获得，且当所述血流速度通过所述血管树的形态计算获得时，所述几何参数还包括所述血管树中血管段的长度。When the resting blood flow model is a CT blood flow model, the blood flow velocity V may be obtained by calculating a shape of a blood vessel tree in a geometric model, and when the blood flow velocity is obtained by calculating a shape of a blood vessel tree The geometric parameter further includes a length of a blood vessel segment in the blood vessel tree.
进一步的，当所述第二血流模型为负荷态血流模型，此时所述血流速度V为注射腺苷血管充分扩张后的血流速度V，且此时，所述血流速度V为最大血流速度Vmax。Further, when the second blood flow model is a load state blood flow model, the blood flow velocity V at this time is the blood flow velocity V after the adenosine-injected blood vessel is sufficiently expanded, and at this time, the blood flow velocity V Is the maximum blood flow velocity Vmax.
特别地，在本发明中所述血流速度V包括感兴趣区域处于最大充血状态下的血流速度Vmax和静息状态下的血流速度Vqc，当感兴趣区域位于冠脉区域时，所述血流速度V为最大充血状态下的血流速度Vmax，进一步的血流速度 Vmax可直接通过第二血流模型获取，或通过所述第二血流模型计算的血流速度V转换获得；当感兴趣区域位于外周血管系统时，所述血流速度V为静息态下的血流速度Vqc。In particular, in the present invention, the blood flow velocity V includes the blood flow velocity Vmax in the region of maximum congestion and the blood flow velocity Vqc in the resting state. When the region of interest is located in the coronary region, The blood flow velocity V is the blood flow velocity Vmax in the state of maximum congestion, and the further blood flow velocity Vmax may be directly obtained through the second blood flow model, or may be obtained by converting the blood flow velocity V calculated by the second blood flow model; when When the region of interest is located in the peripheral vascular system, the blood flow velocity V is the blood flow velocity Vqc in a resting state.
在本明中所述血流特征值中压力差数值ΔP为通过不同尺度下的形态差异函数f(x)和感兴趣区域的所述血流速度V计算获得，且所述压力差数值ΔP在不同尺度下的计算公式为：The pressure difference value ΔP in the blood flow characteristic value described in the present invention is calculated by using the morphological difference function f (x) at different scales and the blood flow velocity V of the region of interest, and the pressure difference value ΔP is The calculation formulas at different scales are:
ΔP＝(c 1V+c 2V 2+…+c mV m) ΔP = (c 1 V + c 2 V 2 +… + c m V m )
        *[α 1*∫f 1(x)dx+α 2*∫f 2(x)dx+…+α n*∫f n(x)dx] * [α 1 * ∫f 1 (x) dx + α 2 * ∫f 2 (x) dx +… + α n * ∫f n (x) dx)
其中，c 1、c 2、…、c m分别代表血流速度V的参数系数，所述参数系数包括血液粘度影响因素、血液湍流影响因素及粘滞系数等多个参数系数；进一步的，m为大于等于1的自然数，以分别代表不同参数系数对血流速度V的影响，以对压力差数值ΔP进行修正，保证压力差数值ΔP计算的准确性。优选的，在本发明中m的取值为2，且当m为2时，c 1为因血液流动摩擦产生的参数系数，c 2为血液湍流产生的参数系数。 Among them, c 1 , c 2 ,..., C m respectively represent parameter coefficients of the blood flow velocity V, and the parameter coefficients include a plurality of parameter coefficients such as blood viscosity influence factors, blood turbulence influence factors, and viscosity coefficients; further, m It is a natural number greater than or equal to 1, in order to represent the influence of different parameter coefficients on the blood flow velocity V, in order to modify the pressure difference value ΔP to ensure the accuracy of the pressure difference value ΔP calculation. Preferably, in the present invention, the value of m is 2, and when m is 2, c 1 is a parameter coefficient due to blood flow friction, and c 2 is a parameter coefficient due to blood turbulence.
α 1、α 2、…、α n分别为不同尺度下血管管腔的形态差异函数f 1(x)、f 2(x)、…、f n(x)的加权系数，其中，n为尺度为大于等于1的自然数；进一步的，所述加权系数的增加可进一步对形态差异函数f(x)进行修正，保证两横截面之间形态差异拟合计算的准确性。 α 1 , α 2 , ..., α n are the weighting coefficients of the morphological difference functions f 1 (x), f 2 (x), ..., f n (x) of the vascular lumen at different scales, where n is the scale It is a natural number greater than or equal to 1. Further, the increase of the weighting coefficient can further modify the morphological difference function f (x) to ensure the accuracy of the morphological difference fitting calculation between the two cross sections.
其中，所述不同尺度包括第一尺度、第二尺度、……、第n尺度；The different scales include a first scale, a second scale, ..., an n-th scale;
所述第一尺度形态差异函数f 1(x)用于检测第一种病变特征所引起的相邻两横截面形态模型所对应的几何形态差异； The first-scale morphological difference function f 1 (x) is used to detect a geometric morphological difference corresponding to two adjacent cross-sectional morphological models caused by a first lesion feature;
所述第二尺度形态差异函数f 2(x)用于检测第二种病变特征所引起的相邻两横截面形态模型所对应的几何形态差异； The second-scale morphological difference function f 2 (x) is used to detect a geometric morphological difference corresponding to two adjacent cross-sectional morphological models caused by a second lesion feature;
……...
所述第n尺度形态差异函数f n(x)用于检测第n种病变特征所引起的相邻两横截面形态模型所对应的几何形态差异。 The n-th scale morphological difference function f n (x) is used to detect the geometric morphological differences corresponding to the morphological models of two adjacent cross-sections caused by the n-th lesion feature.
进一步的，在本发明的另一实施例中，所述血流特征值的计算还可与所述感兴趣区域的血流速度V无关，以所述血流特征值为压力差数值ΔP为例，当所述血流特征值的计算与所述血流速度V无关时，所述ΔP在不同尺度下的计算公式为：Further, in another embodiment of the present invention, the calculation of the blood flow characteristic value may be independent of the blood flow velocity V of the region of interest, and the blood flow characteristic value is a pressure difference value ΔP as an example. When the calculation of the blood flow characteristic value has nothing to do with the blood flow velocity V, the calculation formulas of the ΔP at different scales are:
ΔP＝k*[α 1*∫f 1(x)dx+α 2*∫f 2(x)dx+…+α n*∫f n(x)dx] ΔP = k * [α 1 * ∫f 1 (x) dx + α 2 * ∫f 2 (x) dx +… + α n * ∫f n (x) dx)
其中，k为修正参数，且k为常数；进一步的，所述修正参数k为基于个体信息直接/间接获取的数值；Where k is a correction parameter and k is a constant; further, the correction parameter k is a value obtained directly / indirectly based on individual information;
α 1、α 2、…、α n分别为不同尺度下血管管腔的形态差异函数f 1(x)、f 2(x)、…、f n(x)的加权系数； α 1 , α 2 , ..., α n are weighting coefficients of morphological difference functions f 1 (x), f 2 (x), ..., f n (x) of blood vessel lumen at different scales, respectively;
优选的，所述不同尺度包括第一尺度、第二尺度、……、第n尺度；Preferably, the different scales include a first scale, a second scale, ..., an n-th scale;
所述第一尺度形态差异函数f 1(x)用于检测第一种病变特征所引起的相邻两横截面形态模型所对应的几何形态差异； The first-scale morphological difference function f 1 (x) is used to detect a geometric morphological difference corresponding to two adjacent cross-sectional morphological models caused by a first lesion feature;
所述第二尺度形态差异函数f 2(x)用于检测第二种病变特征所引起的相邻两横截面形态模型所对应的几何形态差异； The second-scale morphological difference function f 2 (x) is used to detect a geometric morphological difference corresponding to two adjacent cross-sectional morphological models caused by a second lesion feature;
……...
所述第n尺度形态差异函数f n(x)用于检测第n种病变特征所引起的相邻两横截面形态模型所对应的几何形态差异；其中，所述n为大于等于1的自然数。 The n-th scale morphological difference function f n (x) is used to detect the geometric morphological difference between two adjacent cross-sectional morphological models caused by the n-th lesion feature; wherein n is a natural number greater than or equal to 1.
在本发明中所述血流特征值还包括所述感兴趣区域的血流储备分数FFR，所述血流储备分数通过感兴趣区域血管管腔在不同尺度下的形态差异函数f(x)、压力差数值ΔP和感兴趣区域近端终点位置处的血流压力Pa计算获得，且所述血流储备分数FFR通过如下公式计算获得：In the present invention, the blood flow characteristic value further includes a blood flow reserve fraction FFR of the region of interest, and the blood flow reserve fraction passes a morphological difference function f (x) of a blood vessel lumen of the region of interest at different scales, The pressure difference value ΔP and the blood flow pressure Pa at the proximal end position of the region of interest are calculated, and the blood flow reserve fraction FFR is calculated by the following formula:
Figure PCTCN2018109080-appb-000001
Figure PCTCN2018109080-appb-000001
以下说明书部分将以具体的基于病史信息为例，对本发明的基于病史信息获取血流特征值的方法进行详细阐述。In the following description, a specific method based on medical history information will be used as an example to describe the method for obtaining blood flow characteristic values based on medical history information in the present invention in detail.
实施例1：Example 1:
在本实施例中，所述病史信息为心梗史，曾患有心梗的个体心脏存活心肌减少，单一心动周期内，流经感兴趣区域血管段的血流量减少，即通过狭窄所损失的能量减少，此时感兴趣区域的血流速度V减小，此时基于所述血流速度V计算的血流特征值均将产生误差。In this embodiment, the medical history information is a history of myocardial infarction. The heart of an individual who has suffered from myocardial infarction has reduced myocardium. In a single cardiac cycle, the blood flow through the vascular segment of the region of interest is reduced, that is, lost by stenosis The energy decreases. At this time, the blood flow velocity V of the region of interest decreases. At this time, the blood flow characteristic values calculated based on the blood flow velocity V will cause errors.
因此，当个体曾患有心梗时，应对感兴趣区域的血流速度V进行修正，具体来讲，在本实施例中，所述血流速度V包括基于所述第二血流模型直接获取的第一血流速度V 0及经修正后获得第二血流速度V 1，且所述第一血流速度V 0和所述第二血流速度V 1之间满足关系式： Therefore, when an individual has suffered a myocardial infarction, the blood flow velocity V of the region of interest should be corrected. Specifically, in this embodiment, the blood flow velocity V includes a direct acquisition based on the second blood flow model. The first blood flow velocity V 0 and the second blood flow velocity V 1 are obtained after modification, and the relationship between the first blood flow velocity V 0 and the second blood flow velocity V 1 is:
Figure PCTCN2018109080-appb-000002
Figure PCTCN2018109080-appb-000002
其中，S为个体的心肌总面积；Where S is the total area of the individual's myocardium;
S 0为个体的心梗面积。 S 0 is the area of myocardial infarction.
且此时所述压力差数值ΔP在不同尺度下的计算公式为：And the calculation formula of the pressure difference value ΔP at different scales is:
ΔP＝(c 1V 1+c 2V 1 2+…+c mV 1 m) ΔP = (c 1 V 1 + c 2 V 1 2 +… + c m V 1 m )
        *[α 1*∫f 1(x)dx+α 2*∫f 2(x)dx+…+α n*∫f n(x)dx] * [α 1 * ∫f 1 (x) dx + α 2 * ∫f 2 (x) dx +… + α n * ∫f n (x) dx)
实施例2：Example 2:
在本实施例中，所述病史信息为高血脂或有吸烟史，具体来讲，当个体有高血脂症状时，个体的血液粘稠度升高，血液粘稠度的上升将导致流经感兴趣区域血管段近端终点和远端终点之间的压力差数值ΔP发生偏差。In this embodiment, the medical history information is hyperlipidemia or a history of smoking. Specifically, when an individual has symptoms of hyperlipidemia, the blood viscosity of the individual increases, and the increase in blood viscosity will cause a sense of flow. The pressure difference value ΔP between the proximal end and the distal end of the vascular segment of the region of interest is biased.
而吸烟的个体，受到烟草燃烧产生的烟雾中毒素的影响，将导致血液中血小板的增加，而血小板聚集增强，将导致纤维蛋白原增加进一步使得血液粘度增加，从而导致感兴趣区域血管段近端终点和远端终点之间的压力差数值ΔP发生偏差，继而导致基于所述压力差数值ΔP计算的血流特征值产生误差。And smoking individuals, affected by the toxins in the smoke produced by the burning of tobacco, will lead to an increase in platelets in the blood, and enhanced platelet aggregation will result in an increase in fibrinogen and further increase the blood viscosity, leading to the proximal end of the vascular segment in the region of interest A deviation in the pressure difference value ΔP between the end point and the distal end point causes an error in the blood flow characteristic value calculated based on the pressure difference value ΔP.
因此，当个体患有高血脂或者有吸烟的习惯时，应对感兴趣区域的压力差数值ΔP进行修正，具体来讲，在本实施例中，所述压力差数值ΔP包括基于所述第二血流模型获取的第一压力差数值ΔP 0及经修正后获得第二压力差 数值ΔP 1，且所述第一压力差数值ΔP 0和所述第二压力差数值ΔP 1之间满足关系式： Therefore, when the individual has hyperlipidemia or has a habit of smoking, the pressure difference value ΔP of the region of interest should be corrected. Specifically, in this embodiment, the pressure difference value ΔP includes a value based on the second blood. The first pressure difference value ΔP 0 obtained by the flow model and the second pressure difference value ΔP 1 obtained after the correction, and the relationship between the first pressure difference value ΔP 0 and the second pressure difference value ΔP 1 satisfy:
Figure PCTCN2018109080-appb-000003
Figure PCTCN2018109080-appb-000003
其中，k为与血液流动性相关的常数；Where k is a constant related to blood fluidity;
μ为高血脂或吸烟个体的血液黏度；μ is the blood viscosity of hyperlipidemia or smoking individuals;
μ 0为正常状态下个体的血液黏度。 μ 0 is the blood viscosity of the individual under normal conditions.
此时，所述个体血流储备分数FFR的计算公式为：At this time, the calculation formula of the individual's blood flow reserve fraction FFR is:
Figure PCTCN2018109080-appb-000004
Figure PCTCN2018109080-appb-000004
实施例3：Example 3:
在本实施例中，所述病史信息为糖尿病，患有糖尿病的个体血管内皮功能发生变化，血管内充血血流速度V一般会比正常人小，即感兴趣区域的血流速度V减小，此时基于所述血流速度V计算的血流特征值均将产生误差。In this embodiment, the medical history information is diabetes. The vascular endothelial function of individuals with diabetes changes, and the blood flow velocity V of blood vessels in blood vessels is generally smaller than that of normal people, that is, the blood flow velocity V of the region of interest decreases. At this time, the blood flow characteristic values calculated based on the blood flow velocity V will cause errors.
因此，当个体患有糖尿病时，应对感兴趣区域的血流速度V进行修正，具体来讲，在本实施例中，所述血流速度V包括基于所述第二血流模型直接获取的第一血流速度V 0及经修正后获得第二血流速度V 1，且所述第一血流速度V 0和所述第二血流速度V 1之间满足关系式： Therefore, when an individual has diabetes, the blood flow velocity V of the region of interest should be corrected. Specifically, in this embodiment, the blood flow velocity V includes a first blood flow velocity directly obtained based on the second blood flow model. A blood flow velocity V 0 and a second blood flow velocity V 1 are obtained after correction, and the relationship between the first blood flow velocity V 0 and the second blood flow velocity V 1 is:
V 1＝ω×V 0； V 1 = ω × V 0 ;
其中，ω为与个体的血糖相关的纠偏参数。Among them, ω is a correction parameter related to an individual's blood glucose.
此时，所述压力差数值ΔP在不同尺度下的计算公式为：At this time, the calculation formula of the pressure difference value ΔP at different scales is:
ΔP＝(c 1V 1+c 2V 1 2+…+c mV 1 m) ΔP = (c 1 V 1 + c 2 V 1 2 +… + c m V 1 m )
        *[α 1*∫f 1(x)dx+α 2*∫f 2(x)dx+…+α n*∫f n(x)dx] * [α 1 * ∫f 1 (x) dx + α 2 * ∫f 2 (x) dx +… + α n * ∫f n (x) dx)
需要说明的是，在本发明的实施例中，所述纠偏参数ω是根据临床实际经验，通过大数据采集及模拟的方法获取的经验值。It should be noted that, in the embodiment of the present invention, the correction parameter ω is an empirical value obtained through a method of large data collection and simulation according to clinical practical experience.
请参参阅图9所示，本发明还提供了一种基于病史信息获取血流储备分数的装置，所述基于病史信息获取血流储备分数的装置包括：Please refer to FIG. 9, the present invention also provides a device for obtaining a blood flow reserve score based on medical history information. The device for obtaining a blood flow reserve score based on medical history information includes:
数据采集器，所述数据采集器用于获取及存储冠脉系统的解剖模型中感 兴趣区域的几何参数和个体特异性数据；A data collector for acquiring and storing geometric parameters and individual specific data of a region of interest in an anatomical model of the coronary system;
纠偏处理器，所述纠偏处理器用于接收个体的一个或多个病史信息，并对所述病史信息进行处理生成纠偏参数；A correction processor, which is configured to receive one or more medical history information of an individual, and process the medical history information to generate correction parameters;
血流特征处理器，基于所述几何参数和个体特异性数据，所述血流特征处理器用于建立感兴趣区域的第二几何模型及血流模型；A blood flow characteristic processor, based on the geometric parameters and individual specific data, the blood flow characteristic processor is used to establish a second geometric model and a blood flow model of a region of interest;
所述血流特征处理器还用于，基于所述纠偏处理器传递的病史信息及相对应的纠偏参数，对所述几何模型及血流模型进行修正，并获取感兴趣区域的血流速度V；同时，根据所述血流速度V并结合血流动力学，计算获取血流储备分数FFR。The blood flow characteristic processor is further configured to correct the geometric model and the blood flow model based on the medical history information and the corresponding correction parameters transmitted by the correction processor, and obtain the blood flow velocity V of the region of interest. At the same time, the blood flow reserve fraction FFR is calculated and obtained according to the blood flow velocity V in combination with hemodynamics.
所述几何模型为所述血流特征处理器通过对所述数据采集器传递的所述解剖模型的几何参数进行测算，并结合所述纠偏处理器传递的纠偏参数经拟合校准获得；具体来讲，当所述感兴趣区域的几何参数为通过CT、OCT和IVUS等设备获取时，所述数据采集器可直接对所述几何参数进行收集，并传递至所述血流特征处理器进行拟合建立几何模型；而当所述感兴趣区域的几何参数为通过造影的方法获取时，所述数据采集器在对所述几何参数进行采集时，所述图像数据不少于两组，任意两组所述图像数据之间存在采集角度差，且所述采集角度差不小于20度，如此设置，所述血流特征处理器获取的几何模型时，可保证几何模型的建立准确。The geometric model is obtained by the blood flow characteristic processor by measuring and calculating the geometric parameters of the anatomical model transmitted by the data collector, and combining the correction parameters transmitted by the correction processor and fitting and calibrating; specifically, In other words, when the geometric parameters of the region of interest are obtained through CT, OCT, and IVUS, the data collector can directly collect the geometric parameters and pass them to the blood flow feature processor for simulation. A geometric model is established together; and when the geometric parameters of the region of interest are acquired by a contrast method, when the data collector collects the geometric parameters, the image data is not less than two groups, any two There is a difference in acquisition angle between the image data, and the acquisition angle difference is not less than 20 degrees. With this setting, the geometric model obtained by the blood flow feature processor can ensure that the geometric model is established accurately.
且在本发明中，所述几何模型包括至少一个血管树，所述血管树包括至少一段主动脉或者包括至少一段主动脉和由所述主动脉发出的多个冠状动脉；所述几何模型还可以为至少一段单支血管段；以及感兴趣区域近端终点和远端终点之间各个位置处的横截面形态模型。In the present invention, the geometric model includes at least one blood vessel tree, and the blood vessel tree includes at least one section of the aorta or includes at least one section of the aorta and a plurality of coronary arteries emitted by the aorta; the geometric model may further include: Is at least a single vessel segment; and a cross-sectional morphological model at various locations between the proximal end and the distal end of the region of interest.
进一步的，所述横截面形态模型为所述血流特征处理器通过所述几何模型直接/间接获得；所述横截面形态模型包括各横截面上斑块的有无、斑块的位置、斑块的大小、斑块形成的角度、斑块的组成及斑块组成的变化、斑块的形状及斑块形状的变化。Further, the cross-sectional morphological model is directly / indirectly obtained by the blood flow feature processor through the geometric model; the cross-sectional morphological model includes the presence or absence of plaques on each cross-section, the position of the plaques, and the plaques. The size of the patch, the angle of plaque formation, the composition of the plaque and the change in the composition of the plaque, the shape of the plaque and the change in the shape of the plaque.
在本发明中，所述血流特征处理器还用于基于所述横截面形态模型，建 立感兴趣区域血管管腔的形态差异函数f(x)，所述形态差异函数f(x)用于表示感兴趣区域血管段不同位置处的横截面形态变化随着该位置到近端终点之间的距离x变化的函数。In the present invention, the blood flow characteristic processor is further configured to establish a morphological difference function f (x) of a blood vessel lumen of a region of interest based on the cross-sectional morphological model, and the morphological difference function f (x) is used for Represents a function of the change in cross-sectional morphology at different locations of a vessel segment of a region of interest as a function of the distance x from that location to the proximal endpoint.
所述血流特征处理器建立的血流模型包括固定血流模型及个性化血流模型；所述个性化血流模型包括静息态血流模型和负荷态血流模型。The blood flow model established by the blood flow feature processor includes a fixed blood flow model and a personalized blood flow model; the personalized blood flow model includes a resting blood flow model and a load blood flow model.
当所述血流模型为静息态血流模型时，所述血流速度V可通过血管中流体充盈的速度计算获得；或者通过血管树的形态计算获得；所述血管树的形态至少包括所述血管树的面积、体积和血管树中血管段的管腔直径中的一种或几种；且当所述血流速度V通过所述血管树的形态计算获得时，所述几何参数还包括所述血管树中血管段的长度、灌注面积及分支角度中的一种或几种。When the blood flow model is a resting blood flow model, the blood flow velocity V may be obtained by calculating the velocity of fluid filling in a blood vessel; or obtained by calculating the shape of a blood vessel tree; the shape of the blood vessel tree includes at least all One or more of the area, volume, and lumen diameter of a blood vessel segment in the blood vessel tree; and when the blood flow velocity V is obtained by morphological calculation of the blood vessel tree, the geometric parameters further include One or more of a length, a perfusion area, and a branch angle of a blood vessel segment in the blood vessel tree.
进一步的，所述获取血管压力差的装置还包括速度采集器，所述速度采集器用于获取感兴趣区域的血流速度V，所述血流速度V用以推算所述感兴趣区域近端终点处的血流压力Pa及感兴趣区域近端终点与远端终点之间的压力差数值ΔP。Further, the apparatus for obtaining a blood pressure difference further includes a speed collector, which is used to obtain a blood flow velocity V of the region of interest, and the blood flow velocity V is used to estimate a proximal end point of the region of interest. The blood pressure Pa and the pressure difference ΔP between the proximal end and the distal end of the region of interest.
优选的，所述压力差数值ΔP的计算公式为：Preferably, the calculation formula of the pressure difference value ΔP is:
ΔP＝(c 1V+c 2V 2+…+c mV m) ΔP = (c 1 V + c 2 V 2 +… + c m V m )
        *[α 1*∫f 1(x)dx+α 2*∫f 2(x)dx+…+α n*∫f n(x)dx] * [α 1 * ∫f 1 (x) dx + α 2 * ∫f 2 (x) dx +… + α n * ∫f n (x) dx)
其中，c 1、c 2、…、c m分别代表血流速度的参数系数，所述参数系数包括血液粘度影响因数、血液湍流影响因数及粘滞系数等多个参数系数；进一步的，m为大于等于1的自然数，以分别代表不同参数系数对血流速度的影响，以对压力差数值ΔP进行修正，保证压力差数值ΔP计算的准确性。优选的，在本发明中所述m的取值为2，且当所述m为2时，c 1为因血液流动摩擦产生的参数系数，c 2为血液湍流产生的参数系数。 Among them, c 1 , c 2 ,..., C m respectively represent parameter coefficients of blood flow velocity, and the parameter coefficients include a plurality of parameter coefficients such as a blood viscosity influence factor, a blood turbulence influence factor, and a viscosity coefficient; further, m is Natural numbers greater than or equal to 1 are used to represent the influence of different parameter coefficients on blood flow velocity, respectively, to correct the pressure difference value ΔP to ensure the accuracy of the pressure difference value ΔP calculation. Preferably, in the present invention, the value of m is 2, and when m is 2, c 1 is a parameter coefficient generated by blood flow friction, and c 2 is a parameter coefficient generated by blood turbulence.
所述α 1、α 2、…、α n分别为不同尺度下血管管腔的形态差异函数f 1(x)、f 2(x)、…、f n(x)的加权系数，其中，n为尺度为大于等于1的自然数；进一步的，所述加权系数的增加可进一步对形态差异函数f(x)进行修正，保 证两横截面之间形态差异拟合计算的准确性。 The α 1 , α 2 , ..., α n are weighting coefficients of morphological difference functions f 1 (x), f 2 (x), ..., f n (x) of blood vessel lumen at different scales, respectively, where n Is a natural number with a scale of 1 or more; further, the increase of the weighting coefficient can further modify the morphological difference function f (x) to ensure the accuracy of the morphological difference fitting calculation between the two cross sections.
进一步的，所述血流特征值还包括所述感兴趣区域的血流储备分数FFR，所述血流储备分数通过感兴趣区域血管管腔在不同尺度下的形态差异函数f(x)、血流速度V和感兴趣区域近端终点位置处的血流压力Pa计算获得，且所述血流储备分数FFR通过如下公式计算获得：Further, the blood flow characteristic value further includes a blood flow reserve fraction FFR of the region of interest, and the blood flow reserve fraction passes the morphological difference function f (x), blood The flow velocity V and the blood pressure Pa at the proximal end position of the region of interest are calculated, and the blood flow reserve fraction FFR is calculated by the following formula:
Figure PCTCN2018109080-appb-000005
Figure PCTCN2018109080-appb-000005
进一步的，本发明还提供一种用于获取个体血流储备分数的设备，所述设备具有处理器，其中，所述处理器被设置为使得所述设备执行以下步骤：Further, the present invention also provides a device for obtaining an individual blood flow reserve score, the device having a processor, wherein the processor is configured to cause the device to perform the following steps:
收集个体特定病史信息和待检血管的几何参数；Collect individual specific medical history information and geometric parameters of blood vessels to be tested;
根据所述待检血管的几何参数建立个体的血管模型；Establishing an individual blood vessel model according to the geometric parameters of the blood vessel to be examined;
根据个体特定病史信息修正所述血管模型；Revising the vascular model according to individual specific medical history information;
提供至少一种血流储备分数的计算模型；Providing at least one calculation model of blood flow reserve fraction;
基于修正后的所述血管模型与所述血流储备分数的计算模型确定待检血管的血流储备分数。The blood flow reserve fraction of the blood vessel to be tested is determined based on the modified blood vessel model and the blood flow reserve fraction calculation model.
所述“处理器”包括接收和/或生成信号的任意装置，所述处理器处理的数据可以是文本消息、物体/流体运动的指令、应用程序的输入或一些其它信息；所述待检血管的备选术语可以为目标血管或感兴趣血管；且所述待检血管包括冠脉血管、由冠脉血管发出的分支血管、血管树和单支血管段等个体任意位置处的血管组织；所述血管模型至少包括所述第二几何模型和所述第二血流模型中的一种，且所述血管模型的备选术语还可为管腔模型、流体流动模型等可反映个体待检血管形态和血管内流体流动情况的模型，进一步的，所述血管模型包括待检血管的长度、直径、弯曲角度及待检血管中分支血管的存在、分支血管的角度、分支血管的数量等与所述待检血管的几何形貌有关的数据。The "processor" includes any device that receives and / or generates a signal, and the data processed by the processor may be a text message, an instruction of an object / fluid movement, an input of an application program, or some other information; the blood vessel to be tested Alternative terms may be target blood vessels or blood vessels of interest; and the blood vessels to be tested include coronary blood vessels, branch blood vessels issued by coronary blood vessels, blood vessel trees, and single vessel segments, such as blood vessel tissue at any location; The blood vessel model includes at least one of the second geometric model and the second blood flow model, and alternative terms of the blood vessel model may also be a lumen model, a fluid flow model, and the like, which may reflect an individual's blood vessel to be examined A model of morphology and fluid flow in a blood vessel. Further, the blood vessel model includes the length, diameter, and bending angle of the blood vessel to be tested, and the existence of branch blood vessels in the blood vessel to be tested, the angle of the branch blood vessels, and the number of branch blood vessels. The data about the geometry of the blood vessel to be examined are described.
在本实施例中，所述管腔形态模型的备选术语还可为横截面形态模型，且所述管腔形态模型包括斑块的有无、斑块的位置、斑块的大小、斑块形成 的角度、斑块的组成及斑块组成的变化、斑块的形状及斑块形状的变化；进一步的所述管腔形态模型的建立包括以下步骤：In this embodiment, the alternative term of the luminal morphology model may also be a cross-sectional morphological model, and the luminal morphological model includes the presence or absence of plaque, the location of the plaque, the size of the plaque, and the plaque. The angle formed, the composition of the plaque and the change in the composition of the plaque, the shape of the plaque and the change in the shape of the plaque; further establishing the luminal morphology model includes the following steps:
S1、定义待检近端终点处的横截面为参考面，通过中心线提取方法，建立获取所述血管模型的中心径线；S1. The cross section at the proximal end of the object to be examined is defined as a reference plane, and a center line of the blood vessel model is established and obtained by a center line extraction method;
S2、以所述参考面的中心点为原点建立坐标系，沿垂直所述中心径线的方向对所述待检血管进行分割，将各横截面内外边缘投影在所述坐标系中，以获取待检血管在各个位置处管腔形态的平面几何图像，管腔形态模型建立结束。S2. Establish a coordinate system with the center point of the reference plane as the origin, divide the blood vessel to be inspected in a direction perpendicular to the central radial line, and project the inner and outer edges of each cross section in the coordinate system to obtain The planar geometric images of the luminal morphology of the blood vessels to be inspected at various positions, and the luminal morphological model has been established.
在本发明中，各个位置处的管腔形态的平面几何图像均需以步骤S2中建立的坐标系为参考，明确各管腔截面上斑块的位置，以方便管腔形态模型的后续拟合。In the present invention, the planar geometric image of the lumen morphology at each position needs to use the coordinate system established in step S2 as a reference to determine the position of the plaque on each lumen cross section to facilitate subsequent fitting of the lumen morphology model. .
需要说明的是，在所述管腔形态模型的建立过程中，当所述解剖数据为采用CT、OCT、IVUS等检测手段获取时，所述管腔形态模型可通过所述血管模型直接获取，只需保证每个所述管腔形态模型的原点及坐标方向一致即可；当所述解剖数据为采用X射线等检测手段获取时，由于所述血管模型为沿血流方向延伸的立体模型，则在通过所述血管模型建立所述管腔形态模型时，需对所述血管模型进行坐标转换，以准确反映各个截面的截面形态。It should be noted that, during the establishment of the luminal morphological model, when the anatomical data is obtained by using CT, OCT, IVUS and other detection methods, the luminal morphological model can be directly obtained through the vascular model. It is only necessary to ensure that the origin and coordinate directions of each of the luminal morphological models are consistent; when the anatomical data is acquired using detection methods such as X-rays, since the blood vessel model is a three-dimensional model extending in the direction of blood flow, Then, when the luminal morphology model is established through the vascular model, coordinate conversion is required on the vascular model to accurately reflect the cross-sectional morphology of each cross-section.
所述处理器还用于基于预设的形态差异函数，通过所述管腔形态模型以及所述血管模型确定待检血管任意两位置间的血管压力差。其中，所述形态差异函数通过所述管腔形态模型拟合建立获取，用于表示待检血管不同位置处的管腔形态变化随着该位置到参考点的距离x变化的函数；且所述形态差异函数包括与待检血管的面积、体积、边缘位置和边缘形态有关的可以体现待检血管任意两位置间形态差异的差异函数，且所述差异函数可通过管腔形态模型直接/间接获取。The processor is further configured to determine, based on a preset morphological difference function, a vascular pressure difference between any two positions of a blood vessel to be detected through the luminal morphological model and the blood vessel model. Wherein, the morphological difference function is obtained by fitting and establishing the luminal morphological model, and is used to represent the function of the luminal morphological change at different positions of the blood vessel to be tested as a function of the distance x from the position to the reference point; and The morphological difference function includes a difference function related to the area, volume, edge position and edge morphology of the blood vessel to be examined, which can reflect the morphological difference between any two positions of the blood vessel to be examined, and the difference function can be obtained directly / indirectly through the luminal morphology model .
所述解剖数据在其他实施例中还可定义为解剖数据等可从图像获取装置直接和/或间接获取的可反映管腔形态的参数。In other embodiments, the anatomical data may also be defined as parameters that can reflect the shape of the lumen directly and / or indirectly from the image acquisition device, such as anatomical data.
即在另一上下文中，所述处理器、待检血管、解剖数据、管腔形态模型 和血管模型可以为具有相同含义的不同名称。That is, in another context, the processor, the blood vessel to be examined, the anatomical data, the luminal morphology model, and the blood vessel model may be different names having the same meaning.
所述尺度为所述尺度为相邻两横截面之间的距离；所述不同尺度包括第一尺度、第二尺度、……、第n尺度；The scale is a distance between two adjacent cross sections; the different scales include a first scale, a second scale, ..., an n-th scale;
所述第一尺度下的形态差异函数f 1(x)用于检测第一种病变特征所引起的相邻两横截面形态模型所对应的几何形态差异； The morphological difference function f 1 (x) at the first scale is used to detect the geometric morphological differences corresponding to the morphological models of two adjacent cross-sections caused by the first lesion feature;
所述第二尺度下的形态差异函数f 2(x)用于检测第二种病变特征所引起的相邻两横截面形态模型所对应的几何形态差异； The morphological difference function f 2 (x) at the second scale is used to detect a geometric morphological difference corresponding to two adjacent cross-sectional morphological models caused by a second lesion feature;
……...
所述第n尺度下的形态差异函数f n(x)用于检测第n种病变特征所引起的相邻两横截面形态模型所对应的几何形态差异。 The morphological difference function f n (x) on the n-th scale is used to detect the geometric morphological differences corresponding to the morphological models of two adjacent cross-sections caused by the n-th lesion feature.
进一步的，在本发明中所述血管模型的建立方式与所述第二血流模型和所述第二几何模型的建立方式基本相同，其差别点仅在于所述血管模型可同时包括待检血管段的形态和血流信息，故在本实施方式中，所述血管模型的具体建立方式与此不在赘述。Further, in the present invention, the method of establishing the blood vessel model is basically the same as the method of establishing the second blood flow model and the second geometric model. The only difference is that the blood vessel model can include the blood vessels to be examined at the same time. Segment morphology and blood flow information, so in this embodiment, the specific establishment method of the vascular model is not described in detail here.
当然，在本设备中所述影响所述血管压力差的因素包括病史信息和/或生理参数；所述病史信息包括影响血流速度或血液黏度的循环系统疾病、呼吸系统疾病、神经系统疾病、骨骼疾病、消化系统疾病、代谢性疾病、肿瘤疾病及家族病史中的一个或多个。Of course, the factors affecting the vascular pressure difference described in this device include medical history information and / or physiological parameters; the medical history information includes circulatory system diseases, respiratory system diseases, neurological system diseases that affect blood flow velocity or blood viscosity, One or more of bone disease, digestive system disease, metabolic disease, tumor disease, and family history.
进一步的，在本发明中所述处理器还可用于运行如下公式以计算获得所述血管压力差ΔP：Further, in the present invention, the processor may be further configured to run the following formula to calculate and obtain the vascular pressure difference ΔP:
ΔP＝(c 1V+c 2V 2+…+c mV m) ΔP = (c 1 V + c 2 V 2 +… + c m V m )
        *[α 1*∫f 1(x)dx+α 2*∫f 2(x)dx+…+α n*∫f n(x)dx] * [α 1 * ∫f 1 (x) dx + α 2 * ∫f 2 (x) dx +… + α n * ∫f n (x) dx)
其中，c 1V+c 2V 2+…+c mV m可为常数； Where c 1 V + c 2 V 2 +… + c m V m can be constant;
V为血流速度，为通过所述第二血流模型直接/间接获取；V is a blood flow velocity and is obtained directly / indirectly through the second blood flow model;
c 1、c 2、…、c m分别代表血流速度V的参数系数，所述参数系数包括血液粘度影响因素、血液湍流影响因素及粘滞系数等多个参数系数；进一步的， m为大于等于1的自然数，以分别代表不同参数系数对血流速度V的影响，以对压力差数值ΔP进行修正，保证血管压力差ΔP计算的准确性。优选的，在本发明中m的取值为2，且当m为2时，c 1为因血液流动摩擦产生的参数系数，c 2为血液湍流产生的参数系数。 c 1 , c 2 ,..., c m respectively represent the parameter coefficients of the blood flow velocity V, and the parameter coefficients include a plurality of parameter coefficients such as blood viscosity influence factors, blood turbulence influence factors, and viscosity coefficients; further, m is greater than A natural number equal to 1 to represent the influence of different parameter coefficients on the blood flow velocity V, to correct the pressure difference value ΔP, and to ensure the accuracy of the calculation of the vascular pressure difference ΔP. Preferably, in the present invention, the value of m is 2, and when m is 2, c 1 is a parameter coefficient due to blood flow friction, and c 2 is a parameter coefficient due to blood turbulence.
所述α 1、α 2、…、α n分别为不同尺度下血管管腔的形态差异函数f 1(x)、f 2(x)、…、f n(x)的加权系数，其中，n为尺度为大于等于1的自然数；进一步的，所述加权系数的增加可进一步对形态差异函数f(x)进行修正，保证两横截面之间形态差异拟合计算的准确性。 The α 1 , α 2 , ..., α n are weighting coefficients of morphological difference functions f 1 (x), f 2 (x), ..., f n (x) of blood vessel lumen at different scales, respectively, where n Is a natural number with a scale of 1 or more; further, the increase of the weighting coefficient can further modify the morphological difference function f (x) to ensure the accuracy of the morphological difference fitting calculation between the two cross sections.
需要指出的是，本说明书正文中所述的装置及功能模块仅仅为示例性的给出实现该技术方案的基本结构，而非唯一结构。It should be pointed out that the devices and functional modules described in the text of the present specification are merely exemplary basic structures for implementing the technical solution, and are not unique structures.
综上所述，本发明的基于病史信息获取血流特征值的方法，通过在血流特征值计算的过程中引入病史信息，及时对血流特征值计算过程中的几何模型和/或血流模型，保证几何模型和血流模型建立的准确性，进一步保证通过所述几何模型和所述血流模型获取的相关参数准确，使得通过本发明的基于病史信息获取血流特征值的方法计算得到的血流特征值可准确反映感兴趣区域的特征；同时，本发明的基于病史信息获取血流特征值的方法通过在血流特征值计算的过程中引入形态因素，使得通过本发明的基于病史信息获取血流特征值的方法和装置的计算的血流特征值更加准确并适于使用。In summary, the method for obtaining blood flow characteristic values based on medical history information of the present invention introduces medical history information in the process of calculating blood flow characteristic values, and timely calculates the geometric model and / or blood flow in the process of calculating blood flow characteristic values. Model to ensure the accuracy of the establishment of the geometric model and the blood flow model, and further ensure that the relevant parameters obtained through the geometric model and the blood flow model are accurate, so that the method can be obtained by the method of obtaining blood flow characteristic values based on the medical history information of the present invention. The characteristic value of the blood flow can accurately reflect the characteristics of the region of interest; at the same time, the method for obtaining the characteristic value of the blood flow based on the medical history information of the present invention introduces morphological factors in the calculation of the characteristic value of the blood flow, so that the method based on the medical history of the present invention The method and device for obtaining the blood flow characteristic value by information is more accurate and suitable for use.
以上实施例仅用以说明本发明的技术方案而非限制，尽管参照较佳实施例对本发明进行了详细说明，本领域的普通技术人员应当理解，可以对本发明的技术方案进行修改或者等同替换，而不脱离本发明技术方案的精神和范围。The above embodiments are only used to illustrate the technical solution of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or equivalently replaced. Without departing from the spirit and scope of the technical solution of the present invention.

Claims (15)

  1. 一种基于病史信息获取血流特征值的方法，其特征在于，包括：A method for obtaining blood flow characteristic values based on medical history information, which is characterized in that it includes:
    采集至少一部分的冠脉系统的解剖数据，并根据所述解剖数据获取感兴趣区域的几何参数，建立感兴趣区域的第一几何模型；Collecting at least a part of the anatomical data of the coronary system, and acquiring geometric parameters of the region of interest according to the anatomical data, and establishing a first geometric model of the region of interest;
    根据感兴趣区域的解剖数据和/或个体特异性数据建立感兴趣区域的第一血流模型；Establishing a first blood flow model of the region of interest according to the anatomical data and / or individual-specific data of the region of interest;
    基于一个或多个病史信息，对所述第一几何模型和/或所述第一血流模型进行修正，以获取感兴趣区域的第二几何模型及第二血流模型；Modifying the first geometric model and / or the first blood flow model based on one or more medical history information to obtain a second geometric model and a second blood flow model of the region of interest;
    根据所述第二血流模型，获取感兴趣区域的血流速度V；结合所述血流速度V及血流动力学，获取血流速度V下感兴趣区域近端终点处的血流压力Pa；及相应状态下感兴趣区域近端终点处与远端终点处的压力差数值ΔP；According to the second blood flow model, the blood flow velocity V of the region of interest is obtained; combined with the blood flow velocity V and hemodynamics, the blood flow pressure Pa at the proximal end of the region of interest at the blood flow velocity V is obtained ; And the pressure difference value ΔP at the proximal end and the distal end of the region of interest in the corresponding state;
    所述血流特性值包括所述血流压力Pa、压力差数值ΔP以及基于所述血流压力Pa、压力差数值ΔP计算的反映血流特征的数值。The blood flow characteristic value includes the blood flow pressure Pa, a pressure difference value ΔP, and a value reflecting a blood flow characteristic calculated based on the blood flow pressure Pa and the pressure difference value ΔP.
  2. 根据权利要求1所述的基于病史信息获取血流特征值的方法，其特征在于：所述第二几何模型包括感兴趣区域近端终点和远端终点之间各个位置处的横截面形态模型及感兴趣区域血管系统至少一个血管树或者感兴趣区域血管系统至少一段单支血管段；所述血管树包括至少一段主动脉，或者包括至少一段主动脉和由所述主动脉发出的多个冠状动脉。The method for obtaining blood flow characteristic values based on medical history information according to claim 1, wherein the second geometric model comprises a cross-sectional morphological model at each position between a proximal end and a distal end of the region of interest, and At least one vascular tree of the vascular system of the region of interest or at least a single vascular segment of the vascular system of the region of interest; the vascular tree includes at least one aorta, or at least one aorta and multiple coronary arteries emanating from the aorta .
  3. 根据权利要求2所述的基于病史信息获取血流特征值的方法，其特征在于，所述横截面形态模型的建立包括：The method for obtaining blood flow characteristic values based on medical history information according to claim 2, wherein the establishment of the cross-sectional morphological model comprises:
    S1、定义感兴趣区域血管段近端终点处的横截面为参考面，通过对几何模型的中心线进行提取，以获取所述几何模型的中心径线；S1. The cross section at the proximal end of the blood vessel segment of the region of interest is defined as a reference plane, and the center line of the geometric model is obtained by extracting the center line of the geometric model;
    S2、以所述参考面的中心点为原点建立坐标系，沿垂直所述中心径线的方向对所述感兴趣区域血管段进行分割，将各横截面内外边缘投影在所述坐标系中，以获取目标血管在各个位置处管腔横截面的平面几何图像，横截面形态模型建立结束。S2. Establish a coordinate system with the center point of the reference plane as the origin, divide the blood vessel segment of the region of interest in a direction perpendicular to the central radial line, and project the inner and outer edges of each cross section in the coordinate system. In order to obtain a planar geometric image of the lumen cross section of the target blood vessel at various positions, the establishment of the cross section morphological model is completed.
  4. 根据权利要求3所述的基于病史信息获取血流特征值的方法，其特征在 于：所述横截面形态模型包括各横截面上斑块的有无、斑块的位置、斑块的大小、斑块形成的角度、斑块的组成及斑块组成的变化、斑块的形状及斑块形状的变化。The method for obtaining blood flow characteristic values based on medical history information according to claim 3, wherein the cross-sectional morphological model includes the presence or absence of plaques on each cross-section, the location of the plaques, the size of the plaques, and the plaques. The angle of the plaque formation, the composition of the plaque and the change in the plaque composition, the shape of the plaque and the change in the shape of the plaque.
  5. 根据权利要求3所述的基于病史信息获取血流特征值的方法，其特征在于：所述第二血流模型包括固定血流模型及个性化血流模型；所述个性化血流模型包括静息态血流模型和负荷态血流模型；当所述血流模型为静息态血流模型时，所述血流速度V可通过血管中流体充盈的速度计算获得；或者通过血管树的形态计算获得。The method for obtaining blood flow characteristic values based on medical history information according to claim 3, wherein the second blood flow model includes a fixed blood flow model and a personalized blood flow model; and the personalized blood flow model includes a static Resting state blood flow model and load state blood flow model; when the blood flow model is a resting state blood flow model, the blood flow velocity V can be obtained by calculating the velocity of fluid filling in a blood vessel; or by the shape of a blood vessel tree Calculated.
  6. 根据权利要求5所述的基于病史信息获取血流特征值的方法，其特征在于：所述血管树的形态至少包括所述血管树的面积、体积和血管树中血管段的管腔直径中的一种或几种；所述血流速度V通过所述血管树的形态计算获得时，所述第二几何模型的几何参数还包括所述血管树中血管段的长度、灌注面积及分支角度中的一种或几种。The method for obtaining blood flow characteristic values based on medical history information according to claim 5, wherein the morphology of the blood vessel tree includes at least the area and volume of the blood vessel tree and the lumen diameter of the blood vessel segment in the blood vessel tree. One or more types; when the blood flow velocity V is obtained by calculating the shape of the blood vessel tree, the geometric parameters of the second geometric model further include the length of the blood vessel segment, the perfusion area, and the branch angle in the blood vessel tree. One or more of them.
  7. 根据权利要求1～6中任一项所述的基于病史信息获取血流特征值的方法，其特征在于：所述病史信息包括影响血流速度或血液黏度的循环系统疾病、呼吸系统疾病、神经系统疾病、骨骼疾病、消化系统疾病、代谢性疾病、肿瘤疾病、及家族病史。The method for obtaining blood flow characteristic values based on medical history information according to any one of claims 1 to 6, characterized in that the medical history information includes circulatory system diseases, respiratory system diseases, nerves that affect blood flow velocity or blood viscosity Systemic, skeletal, digestive, metabolic, tumor, and family history.
  8. 一种基于病史信息获取血流储备分数的装置，其特征在于，所述基于病史信息获取血流储备分数的装置包括：A device for obtaining a blood flow reserve score based on medical history information, wherein the device for obtaining a blood flow reserve score based on medical history information includes:
    数据采集器，所述数据采集器用于获取及存储冠脉系统的解剖模型中感兴趣区域的几何参数和个体特异性数据；A data collector for acquiring and storing geometric parameters and individual specific data of a region of interest in an anatomical model of the coronary system;
    纠偏处理器，所述纠偏处理器用于接收个体的一个或多个病史信息，并对所述病史信息进行处理生成纠偏参数；A correction processor, which is configured to receive one or more medical history information of an individual, and process the medical history information to generate correction parameters;
    血流特征处理器，基于所述几何参数和个体特异性数据，所述血流特征处理器用于建立感兴趣区域的几何模型及血流模型；A blood flow characteristic processor, based on the geometric parameters and individual specific data, the blood flow characteristic processor is used to establish a geometric model and a blood flow model of a region of interest;
    所述血流特征处理器还用于，基于所述纠偏处理器传递的病史信息，对所述几何模型及血流模型进行修正，并获取感兴趣区域血流速度V；同时，根据所述血流速度V并结合血流动力学，计算获取血流储备分数FFR。The blood flow characteristic processor is further configured to modify the geometric model and the blood flow model based on the medical history information transmitted by the correction processor, and obtain the blood flow velocity V of the region of interest; at the same time, according to the blood The flow velocity V is combined with hemodynamics to calculate and obtain the blood flow reserve fraction FFR.
  9. 根据权利要求8所述的基于病史信息获取血流储备分数的装置，其特征在于：所述几何模型为所述血流特征处理器通过对所述数据采集器传递的所述解剖模型的几何参数进行测算，并结合所述纠偏处理器传递的纠偏参数经拟合校准获得；所述几何模型包括感兴趣区域近端终点和远端终点之间各个位置处的横截面形态模型及感兴趣区域血管系统至少一个血管树或者感兴趣区域血管系统至少一段单支血管段；所述血管树包括至少一段主动脉，或者包括至少一段主动脉和由所述主动脉发出的多个冠状动脉。The device according to claim 8 for obtaining a blood flow reserve score based on medical history information, wherein the geometric model is a geometric parameter of the anatomical model transmitted by the blood flow characteristic processor to the data collector. Performing calculation, and combining and correcting parameters passed by the correction processor to obtain a fitting calibration; the geometric model includes a cross-sectional morphological model at various positions between a proximal end point and a distal end point of the region of interest and a blood vessel of the region of interest At least one blood vessel tree of the system or at least one single vessel segment of the vascular system of the region of interest; the blood vessel tree includes at least one aorta, or at least one aorta and a plurality of coronary arteries emanating from the aorta.
  10. 根据权利要求9所述的基于病史信息获取血流储备分数的装置，其特征在于：所述横截面形态模型为所述血流特征处理器通过所述几何模型直接/间接获得；所述横截面形态模型包括各横截面上斑块的有无、斑块的位置、斑块的大小、斑块形成的角度、斑块的组成及斑块组成的变化、斑块的形状及斑块形状的变化。The device for obtaining a blood flow reserve score based on medical history information according to claim 9, wherein the cross-sectional morphology model is obtained directly / indirectly by the blood flow feature processor through the geometric model; the cross-section The morphological model includes the presence or absence of plaque on each cross section, the location of the plaque, the size of the plaque, the angle at which the plaque is formed, the composition of the plaque and the change in the plaque composition, the shape of the plaque, and the change in the shape of the plaque .
  11. 根据权利要求9所述的基于病史信息获取血流储备分数的装置，其特征在于：所述血流特征处理器还用于基于所述横截面形态模型，建立感兴趣区域血管管腔的形态差异函数f(x)，所述形态差异函数f(x)用于表示目标血管不同位置处的横截面形态变化随着该位置到近端终点之间的距离x变化的函数。The device for obtaining a blood flow reserve score based on medical history information according to claim 9, wherein the blood flow feature processor is further configured to establish a morphological difference of a blood vessel lumen of a region of interest based on the cross-sectional morphological model. Function f (x), which is a function that represents the change in cross-sectional morphology at different locations of the target blood vessel as a function of the distance x between the location and the proximal endpoint.
  12. 根据权利要求8所述的基于病史信息获取血流储备分数的装置，其特征在于：所述基于病史信息获取血流储备分数的装置还包括速度采集器，所述速度采集器用于获取感兴趣区域的血流速度V；The device for obtaining a blood flow reserve score based on medical history information according to claim 8, wherein the device for obtaining the blood flow reserve score based on medical history information further comprises a speed collector, the speed collector is used to obtain a region of interest Blood flow velocity V;
    所述速度采集器包括速度计算模块及速度提取模块；所述速度提取模块通过所述数据采集器直接获取血流速度信息，或通过血流模型直接提取血流速度V；The speed collector includes a speed calculation module and a speed extraction module; the speed extraction module directly obtains blood flow velocity information through the data collector, or directly extracts the blood flow velocity V through a blood flow model;
    所述速度计算模块还包括速度转换模块及速度测算模块；所述血流速度V可通过血管中流体充盈的速度经速度转换模块转换获得，还可通过几何模型中血管树的形态经速度测算模块测算获得。The speed calculation module further includes a speed conversion module and a speed measurement module; the blood flow velocity V can be obtained by converting the speed of fluid filling in the blood vessel through the speed conversion module, and also can be obtained by the shape of the blood vessel tree in the geometric model through the speed measurement module Calculated.
  13. 一种用于获取个体血流储备分数的设备，所述设备具有处理器，其特征在于：所述处理器被设置为使得所述设备执行以下步骤：A device for obtaining an individual blood flow reserve score, the device having a processor, wherein the processor is configured to cause the device to perform the following steps:
    收集个体特定病史信息和待检血管的几何参数；Collect individual specific medical history information and geometric parameters of blood vessels to be tested;
    根据所述待检血管的几何参数建立个体的血管模型；Establishing an individual blood vessel model according to the geometric parameters of the blood vessel to be examined;
    根据个体特定病史信息修正所述血管模型；Revising the vascular model according to individual specific medical history information;
    提供至少一种血流储备分数的计算模型；Providing at least one calculation model of blood flow reserve fraction;
    基于修正后的所述血管模型与所述血流储备分数的计算模型确定待检血管的血流储备分数。The blood flow reserve fraction of the blood vessel to be tested is determined based on the modified blood vessel model and the blood flow reserve fraction calculation model.
  14. 根据权利要求13所述的基于病史信息获取血流储备分数的装置，其特征在于：所述病史信息包括影响血流速度或血液黏度的循环系统疾病、呼吸系统疾病、神经系统疾病、骨骼疾病、消化系统疾病、代谢性疾病、肿瘤疾病及家族病史中的一个或多个。The device for obtaining a blood flow reserve score based on medical history information according to claim 13, wherein the medical history information includes a circulatory system disease, a respiratory system disease, a neurological system disease, a skeletal disease, a blood circulation speed or a blood viscosity that affect blood flow velocity or blood viscosity. Digestive system disease, metabolic disease, tumor disease and family history.
  15. 根据权利要求13所述的基于病史信息获取血流储备分数的装置，其特征在于：所述血管压力差数值的计算模型是基于多尺度计算方法而建立的。The apparatus according to claim 13 for obtaining a blood flow reserve score based on medical history information, wherein the calculation model of the vascular pressure difference value is established based on a multi-scale calculation method.
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