WO2019242159A1 - 获取血管压力差的方法及装置 - Google Patents

获取血管压力差的方法及装置 Download PDF

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WO2019242159A1
WO2019242159A1 PCT/CN2018/109077 CN2018109077W WO2019242159A1 WO 2019242159 A1 WO2019242159 A1 WO 2019242159A1 CN 2018109077 W CN2018109077 W CN 2018109077W WO 2019242159 A1 WO2019242159 A1 WO 2019242159A1
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model
blood vessel
blood flow
morphological
blood
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PCT/CN2018/109077
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English (en)
French (fr)
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涂圣贤
余炜
陈树湛
李莹光
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博动医学影像科技（上海）有限公司
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Priority to US17/254,255 priority Critical patent/US20210259559A1/en
Priority to JP2020570780A priority patent/JP7118464B2/ja
Publication of WO2019242159A1 publication Critical patent/WO2019242159A1/zh

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/02007—Evaluating blood vessel condition, e.g. elasticity, compliance
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/021—Measuring pressure in heart or blood vessels
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/026—Measuring blood flow
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/026—Measuring blood flow
    • A61B5/0285—Measuring or recording phase velocity of blood waves
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4887—Locating particular structures in or on the body
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A61B5/0062—Arrangements for scanning
    • A61B5/0066—Optical coherence imaging
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
    • A61B5/1076—Measuring physical dimensions, e.g. size of the entire body or parts thereof for measuring dimensions inside body cavities, e.g. using catheters
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4887—Locating particular structures in or on the body
    • A61B5/489—Blood vessels
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
    • A61B6/03—Computed tomography [CT]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
    • A61B6/504—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of blood vessels, e.g. by angiography

Definitions

  • the invention relates to a method and a device for obtaining a blood pressure difference, and belongs to the field of medical technology.
  • 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 for obtaining a blood pressure difference, so as to solve at least one of the technical problems existing in the prior art.
  • the method for obtaining the vascular pressure difference provided by the present invention introduces the concept of morphology, clarifies the influence of plaque information and the like on the calculation of the vascular pressure difference, and improves the accuracy of the vascular pressure difference calculation.
  • the present invention provides a method for obtaining a blood pressure difference.
  • the method for obtaining a blood pressure difference includes:
  • the cross-sectional morphological model at different scales is fitted to calculate the morphological difference function f (x) of the lumen of the target blood vessel, and the scale is to calculate the morphological difference function f ( x) the distance between two adjacent cross sections;
  • a pressure difference value ⁇ P at any two positions of the target blood vessel is calculated and obtained.
  • the blood vessel includes a coronary blood vessel, a branched blood vessel, a blood vessel tree, and a single vessel segment emitted by the coronary blood vessel;
  • the individual data includes an individual universal parameter and an individual specific parameter;
  • the blood flow The model includes at least the blood flow velocity V of the target blood vessel.
  • the pressure difference value ⁇ P is obtained by calculating the morphological difference function f (x) of the target vessel lumen at different scales and the blood flow velocity V of the target vessel, and the ⁇ P is calculated at different scales.
  • the formula is:
  • ⁇ P (c 1 V + c 2 V 2 +... + c m V m )
  • V is the blood flow velocity and is obtained directly / indirectly through the blood flow model
  • c 1 , c 2 , ..., c m respectively represent parameter coefficients of the blood flow velocity V;
  • ⁇ 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;
  • n is a natural number greater than or equal to 1;
  • n is a natural number with a scale of 1 or more
  • 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 establishment of the cross-sectional morphological model includes:
  • the cross section at the proximal end of the target blood vessel is defined as a reference plane, and the central radius of the geometric model is obtained by a method of centerline extraction and establishment;
  • 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 morphological difference function f (x) is a function used to represent a change in cross-sectional morphology at different positions of a target blood vessel as a function of a distance x from the position to a reference point;
  • the obtaining of the morphological difference function f (x) includes:
  • a morphological function of each cross-section is established;
  • the morphological function includes an area function, a diameter function, and an edge position function;
  • 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 obtain the morphological difference function f (x).
  • the obtaining of the blood flow velocity V further includes modifying the blood flow model through medical history information and / or physiological parameter information, and obtaining the blood flow model through the modified blood flow model; the blood flow model Including fixed blood flow model and personalized blood flow model;
  • 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 can be calculated by a velocity of fluid filling in a blood vessel Obtained; or obtained by morphological calculation of the vascular 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 is obtained by calculating the morphology of the blood vessel tree.
  • the geometric parameter further includes one or more of a length of a blood vessel segment, a perfusion area, and a branch angle in the blood vessel tree.
  • the blood flow velocity includes a blood flow velocity in a state where the target blood vessel is at a maximum congestion state and a blood flow velocity in a resting state; or the pre-processing of the geometric model includes using medical history information and / or The physiological parameter information is used to modify the geometric model.
  • the present invention further provides a device for obtaining a blood pressure difference, and the device for obtaining a blood pressure difference includes:
  • a data collector for acquiring and storing geometric parameters of a target blood vessel in an anatomical model of a vascular system
  • a pressure difference processor configured to establish a blood flow model of a target blood vessel and a geometric model of a corresponding target blood vessel based on the geometric parameters
  • the pressure difference processor is further configured to modify the geometric model and / or the blood flow model, and obtain a cross-sectional morphology model and a blood vessel pressure difference calculation model based on the modified geometric model and the blood flow model; At the same time, a pressure difference value ⁇ P of the target blood vessel is obtained according to the vascular pressure difference calculation model and hemodynamics.
  • the geometric model is obtained by measuring the image data of the anatomical model and fitting and calibrating; the cross-sectional morphological model is obtained directly / indirectly through the geometric model; or, all
  • the cross-sectional morphological model includes the presence or absence of plaques on each cross-section, the location of plaques, the size of plaques, the angle of plaque formation, the composition of plaques and changes in plaque composition, the shape of plaques, and plaques. Change of shape.
  • the geometric model obtained by the pressure difference processor 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. ; Or the geometric model includes at least one single vessel segment.
  • the apparatus for obtaining a blood pressure difference further includes a speed collector, the speed collector is used to obtain a blood flow velocity of a target blood vessel, and the blood flow velocity is used to estimate a proximal end point of the target blood vessel.
  • the pressure difference value ⁇ P from the distal end;
  • the speed collector includes a speed calculation module and a speed extraction module; the speed extraction module can directly acquire the blood flow velocity through the data collector, and can also directly extract the blood flow velocity through the blood flow model;
  • the speed calculation module includes a speed conversion module and a speed measurement module.
  • the blood flow speed can be obtained by converting the speed of fluid filling in a blood vessel through the speed conversion module, and can also be obtained through the shape of a blood vessel tree in a geometric model. Calculated by the measurement module.
  • the present invention further provides a device for obtaining a blood flow reserve score, and the device for obtaining a blood flow reserve score includes:
  • a data collector for acquiring and storing geometric parameters of a target vessel in an anatomical model of a vascular device
  • a blood flow information processor which is used to establish a blood flow model of a target blood vessel, and establish a geometric model corresponding to the target blood vessel based on the geometric parameters;
  • the blood flow information processor is further configured to modify the geometric model and the blood flow model to obtain a cross-sectional morphological model, and obtain a blood vessel pressure difference calculation model based on the cross-sectional morphological model and the blood flow model. And the maximum blood flow velocity of the target blood vessel; the blood flow reserve fraction FFR is calculated and calculated according to the vascular pressure difference calculation model and the maximum blood flow velocity in combination with hemodynamics.
  • the geometric model is obtained by measuring the image data of the anatomical model and fitting and calibrating;
  • the cross-sectional morphological model is obtained by direct / transformation of the geometric model;
  • the image data collected by the data collector is not less than two groups, and there is a collection angle between any two groups of the image data Poor, and the acquisition angle difference is not less than 20 degrees.
  • 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 geometric model obtained by the blood flow information processor 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 coronations emitted by the aorta. Arteries; or the geometric model includes at least one single vessel segment;
  • the blood flow model established by the blood flow information 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 maximum blood flow velocity may be obtained by calculating a velocity of fluid filling in a blood vessel; or may be obtained by calculating a shape of a blood vessel tree;
  • the morphology of the vascular tree includes at least one or more of the area, volume, and lumen diameter of a vascular segment in the vascular tree; when the maximum blood flow velocity is obtained by morphological calculation of the vascular tree
  • the geometric parameter further includes one or more of a length of a blood vessel segment, a perfusion area, and a branch angle in the blood vessel tree.
  • the device for obtaining a blood flow reserve fraction further includes a speed collector for obtaining a maximum blood flow velocity of a target blood vessel, and the maximum blood flow velocity is used to estimate a proximal end point of the target blood vessel.
  • the present invention further provides a device for obtaining a blood pressure difference of a patient, the device having a processor, wherein the processor is configured to cause the device to perform the following steps:
  • a vascular pressure difference between any two positions of a blood vessel to be detected is determined based on the luminal morphological model and the blood flow velocity.
  • the dimension is a distance between two adjacent cross sections
  • the morphological difference function is obtained by fitting and establishing the luminal morphological model, and is used to represent a function of a change in cross-sectional morphology at different positions of a target blood vessel as a function of a distance x from the position to a reference point; Is a difference function related to the cross-sectional area, diameter, or edge distance of the blood vessel to be examined.
  • the present invention further provides a method for obtaining a vascular pressure difference, which method includes:
  • the cross-sectional morphological model at different scales is fitted to calculate the morphological difference function f (x) of the lumen of the target blood vessel, and the scale is to calculate the morphological difference function f ( x) the distance between two adjacent cross sections;
  • ⁇ 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 greater than or equal to 1;
  • ⁇ 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 correction parameter k is a value obtained directly / indirectly based on individual information
  • the morphological difference function f (x) is a function that represents the change in the cross-sectional morphology at different positions of the target blood vessel as a function of the distance x from the position to the reference point.
  • the beneficial effect of the present invention is that the method for obtaining a pressure difference of a blood vessel of the present invention obtains a planar geometric image at each cross-sectional position of a target blood vessel by establishing a cross-sectional morphological model, and fits the cross-sectional morphological model at different positions.
  • Establish the morphological difference function and introduce the concept of cross-sectional morphology in the calculation of vascular pressure difference, taking into account the influence of plaque position and shape in the lumen on the calculation of vascular pressure difference; so that blood vessels can be obtained by the present invention
  • the vascular pressure difference value calculated by the pressure difference method is more accurate and can accurately reflect the pressure change at both ends of the target blood vessel. It is guaranteed that the vascular pressure difference calculated using the method of the invention is accurate and reliable when applied to the calculation of other blood flow characteristic values. .
  • 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 a blood vessel pressure difference according to the present invention.
  • FIG. 10 is a structural block diagram of an apparatus for obtaining a blood flow reserve score according to the present invention.
  • the present invention provides a method for obtaining a vascular pressure difference.
  • the method for obtaining a vascular pressure difference includes the following steps:
  • the cross-sectional morphological model at different scales is fitted to calculate the morphological difference function f (x) of the lumen of the target blood vessel, and the scale is the morphological difference function f ( x) the distance between two adjacent cross sections;
  • a pressure difference value ⁇ P at any two positions of the target blood vessel is calculated and obtained.
  • the blood vessels include coronary blood vessels, branch blood vessels emitted by coronary blood vessels, a blood vessel tree, and a single branch blood vessel segment; and the individual data includes individual general parameters and individual specific parameters.
  • the pressure difference value ⁇ P is calculated by calculating the morphological difference function f (x) at different scales and the blood flow model of the target blood vessel, and the calculation formula of the pressure difference value ⁇ P at different scales is:
  • ⁇ P (c 1 V + c 2 V 2 +... + c m V m ) * [ ⁇ 1 * ⁇ f 1 (x) dx + ⁇ 2 * ⁇ f 2 (x) dx +... + ⁇ n * ⁇ f n (x) dx]
  • V is the blood flow velocity and is obtained directly / indirectly through the blood flow model
  • 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 is greater than A natural number equal to 1 is used to represent the influence of different parameter coefficients on the blood flow velocity V 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 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 cross-sectional morphology model is obtained directly / indirectly through the geometric model, and in the present invention, the geometric model includes at least geometric parameters such as the shape, diameter, and area of the target blood vessel. Further, the geometric parameters are also Including parameters such as the bending angle of the vessel segment that can reflect the actual morphology of the target vessel. Specifically, the establishment of the cross-sectional morphological model includes the following steps:
  • the cross section at the proximal end of the target blood vessel is defined as the reference plane, and the central diameter of the geometric model is obtained through the method of centerline extraction and establishment;
  • the cross-sectional morphology model includes plaque information at each cross-sectional position, and the plaque information is the lesion information of the target blood vessel, and a large amount of data indicates that when the length of the plaque (that is, the lesion) is greater than 20 mm , Will cause the target vascular pressure difference value ⁇ P to increase, further leading to the calculation of blood flow characteristic values such as blood flow reserve fraction FFR; and when the composition of the plaque at the same cross-section is complex or the size of the target blood vessel is narrowed substantially If the rate is high, the target blood vessel pressure difference value ⁇ P will be further increased.
  • 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 for obtaining a blood vessel pressure difference further includes fitting the cross-sectional morphological models at different scales, and calculating a morphological difference function f (x) of a target blood vessel lumen.
  • 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 calculation of the pressure difference value ⁇ P is also related to the blood flow velocity V of the target blood vessel, and in the present invention, the obtaining of the blood flow velocity V may be directly / Obtained indirectly.
  • the blood flow model in the present invention includes a fixed blood flow model and a personalized blood flow model, and the blood flow model may be either a data calculation model or a three-dimensional fluid flow model.
  • the fixed blood flow model is an empirical blood flow model.
  • the blood flow velocity V can be directly obtained from the fixed blood flow model, and is described in the present invention.
  • the blood flow velocity V can also be a fixed parameter; it should be noted that the acquisition of the fixed blood flow model is directly established through the method of large data collection and simulation based on clinical practical experience.
  • the personalized blood flow model includes a resting-state blood flow model and a load-state blood flow model.
  • the blood flow velocity V can be obtained by calculating the velocity of fluid filling.
  • the resting blood flow model is a contrast agent blood flow model, and at this time, the blood flow velocity V is a target blood vessel obtained by using a gray-time fitting function during angiography. The average flow velocity of the agent; or the average flow velocity of the contrast agent during the angiography process obtained by using the TIMI frame method.
  • the blood flow velocity V may be obtained by calculating a morphology of a blood vessel tree, and the shape of the blood vessel tree includes at least an area, a volume, and a blood vessel tree.
  • the geometric parameters also include the length of the blood vessel segment in the blood vessel tree, and the perfusion area And one or more of the branch angles.
  • the blood flow model is a load state blood flow model, and at this time, the blood flow velocity V is a blood flow velocity V after the adenosine injection vessel is sufficiently expanded, and at this time, the blood flow velocity V The blood flow velocity V is the maximum blood flow velocity Vmax.
  • the blood flow velocity V includes a blood flow velocity Vmax in a state where the target blood vessel is at a maximum congestion state and a blood flow velocity Vqc in a resting state.
  • the blood flow rate Velocity V is the blood flow velocity Vmax in the state of maximum congestion.
  • Further blood flow velocity Vmax can be obtained directly from the blood flow model, or can be obtained by converting the blood flow velocity V calculated from the blood flow model.
  • the blood flow velocity V is a blood flow velocity Vqc in a resting state.
  • the cross-sectional morphological model and the blood flow velocity are obtained through the geometric model and the blood flow model.
  • the blood flow model and / or the geometric model need to be modified through medical history information and / or physiological parameter information, and in the present invention, the medical history information includes circulatory system diseases that affect blood flow velocity or blood viscosity. , Respiratory diseases, neurological diseases, skeletal diseases, digestive diseases, metabolic diseases, family history, etc .; the physiological parameters include age, gender, blood pressure, body mass index, and dominant coronary artery types, etc. .
  • factors affecting the pressure difference value ⁇ P further include myocardial microcirculation resistance (IMR) and whether there is a collateral circulation.
  • IMR myocardial microcirculation resistance
  • the myocardial microcirculation resistance exists in the target blood vessel, it will affect the microcirculation perfusion and further affect the blood flow velocity V of the target blood vessel, so that the blood flow velocity V decreases, resulting in a decrease in the target blood vessel pressure difference value ⁇ P, resulting in There is an error in the calculation of blood flow characteristic values such as the blood flow reserve fraction FFR.
  • the maximum blood flow through the target blood vessel will be reduced, thereby reducing the target blood vessel pressure difference value ⁇ P and increasing the calculated value of the blood flow reserve fraction FFR.
  • the present invention also provides a device for obtaining a blood pressure difference
  • the device for obtaining a blood pressure difference includes:
  • a data collector for acquiring and storing geometric parameters of a target blood vessel in an anatomical model of a vascular system
  • a pressure difference processor configured to establish a blood flow model of a target blood vessel and a geometric model of a corresponding target blood vessel based on the geometric parameters
  • the pressure difference processor is further configured to modify the geometric model and / or the blood flow model, and obtain a cross-sectional morphology model and a blood vessel pressure difference calculation model based on the modified geometric model and the blood flow model; At the same time, according to the vascular pressure difference calculation model and hemodynamics, a pressure difference value ⁇ P between the first blood pressure Pa at the proximal end of the target blood vessel and the proximal end and the distal end of the target blood vessel is obtained.
  • the geometric model is obtained by measuring image data of the anatomical model and fitting and calibrating; specifically, the geometric model obtained by the pressure difference processor includes at least the shape and diameter of the target blood vessel And geometric parameters such as area and area, the geometric parameters also include parameters such as the bending angle of the vessel segment that can reflect the actual shape of the target vessel; that is, in the present invention, the geometric model can be either a single vessel segment or a vessel tree, and The vessel tree includes an aorta and a plurality of coronary arteries emanating from the aorta.
  • the cross-sectional morphological model is obtained directly / indirectly through 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 of the plaque formation, Plaque composition and changes in plaque composition, plaque shape and plaque shape.
  • the device for obtaining a blood pressure difference further includes a speed collector for obtaining a blood flow velocity of a target blood vessel, and the blood flow velocity is used to estimate a first blood vessel at a proximal end of the target blood vessel.
  • the speed collector includes a speed calculation module and a speed extraction module; the speed extraction module can directly obtain the blood flow velocity through the data collector, and can also directly extract the blood flow velocity through the blood flow model.
  • the speed calculation module includes a speed conversion module and a speed measurement module.
  • the blood flow speed can be obtained by converting the speed of fluid filling in a blood vessel through the speed conversion module, and can also be obtained through the speed measurement module through the shape of a blood vessel tree in a geometric model. Calculated.
  • the pressure difference value ⁇ P is calculated by the following formula:
  • ⁇ P (c 1 V + c 2 V 2 +... + c m V m )
  • V is the blood flow velocity, which is obtained directly / indirectly through the 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 factors affecting blood viscosity, Multiple parameter coefficients such as blood turbulence influencing factors and viscosity coefficients;
  • m is a natural number greater than or equal to 1 to represent the effect of different parameter coefficients on the blood flow velocity V, and to correct the pressure difference value ⁇ P to ensure the pressure Accuracy of the difference ⁇ 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 present invention further provides a device for obtaining a blood flow reserve score
  • the device for obtaining a blood flow reserve score includes:
  • a data collector for acquiring and storing geometric parameters of a target vessel in an anatomical model of a vascular device
  • a blood flow information processor which is used to establish a blood flow model of a target blood vessel, and establish a geometric model corresponding to the target blood vessel based on the geometric parameters;
  • the blood flow information processor is further configured to modify the geometric model and the blood flow model to obtain a cross-sectional morphological model, and obtain a blood vessel pressure difference calculation model based on the cross-sectional morphological model and the blood flow model. And the maximum blood flow velocity of the target blood vessel; the blood flow reserve fraction FFR is calculated and calculated according to the vascular pressure difference calculation model and the maximum blood flow velocity in combination with hemodynamics.
  • the geometric model is obtained by the blood flow information processor by measuring the image data of the anatomical model obtained by the data collector and fitting and calibrating; specifically, when the image data of the anatomical model is obtained by CT When acquiring such devices as OCT, OCT, and IVUS, the data collector can directly collect the image data and pass it to the blood flow information processor for fitting to establish a geometric model; and when the image data of the anatomical model When acquired by the method of imaging, when the data collector collects the image data, the image data is not less than two groups, and there is a difference in acquisition angle between any two groups of the image data, and the The collection angle difference is not less than 20 degrees. With this setting, the geometric model obtained by the blood flow information processor can ensure that the geometric model is accurately established.
  • the cross-sectional morphological model is obtained through direct / transformation of the geometric model, and 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 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.
  • the blood flow model established by the blood flow information processor 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 maximum blood flow velocity may be obtained by calculating a velocity of fluid filling in a blood vessel; or may be obtained by calculating a shape of a blood vessel tree.
  • the geometric model includes at least one blood vessel tree, and the blood vessel tree includes at least one aortic blood vessel segment or at least one aorta and the aorta Issued multiple coronary arteries, or the geometric model includes at least one single vessel segment; at this time, the geometric parameter also includes one or more of the length, perfusion area, and branch angle of the vessel segment in 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 device for obtaining a pressure difference of a blood vessel further includes a speed collector for obtaining a maximum blood flow velocity of the target blood vessel, and the maximum blood flow velocity is used to estimate a proximal end of the target blood vessel.
  • 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 present invention also provides a device for obtaining a blood pressure difference of a patient, the device having a processor, wherein the processor is configured to cause the device to perform the following steps:
  • a vascular pressure difference between any two positions of a blood vessel to be examined is determined based on the luminal morphological model and the blood vessel 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 geometric model and the blood flow model, and alternative terms of the blood vessel model may also be a lumen model, a fluid flow model, etc., which can reflect the shape of the blood vessel to be examined and the blood vessel in the individual.
  • the model of the fluid flow situation 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, the number of branch blood vessels, and the like Data related to the geometric topography.
  • 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 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 blood vessel model is established in the same manner as the blood flow model and the geometric model, and the only difference is that the blood vessel model can include both the shape and Blood flow information, so in this embodiment, the specific establishment method of the blood vessel 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 physiological parameters include one or more of physiological information that can be directly obtained, such as age, sex, blood pressure and body mass index .
  • 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 the blood flow velocity, which is obtained directly / indirectly through the 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 factors affecting blood viscosity, Multiple parameter coefficients such as blood turbulence influencing factors and viscosity coefficients;
  • m is a natural number greater than or equal to 1 to represent the effect of different parameter coefficients on blood flow velocity V, and to correct the pressure difference value ⁇ P to ensure blood vessels Accuracy of pressure difference ⁇ 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.
  • 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 present invention also provides another method for obtaining a blood pressure difference, which method includes:
  • the cross-sectional morphological model at different scales is fitted to calculate the morphological difference function f (x) of the lumen of the target blood vessel, and the scale is to calculate the morphological difference function f ( x) the distance between two adjacent cross sections;
  • ⁇ 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 greater than or equal to 1;
  • ⁇ 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 correction parameter k is a value obtained directly / indirectly based on individual information, that is, in the present invention, the correction parameter k is data obtained directly / indirectly through estimation or testing equipment, and the correction parameter k may be related to the individual Specific information or general information.
  • the morphological difference function f (x) is a function used to represent a change in cross-sectional morphology at different positions of a target blood vessel.
  • the morphological difference function f (x) includes:
  • 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). That is, the morphological difference function f (x) is a function related to a change in a cross-sectional area of two cross sections of a target blood vessel, a change in a diameter at each position, or a change in an edge distance at each position.
  • the cross-sectional morphological model includes plaque information at each cross-sectional position, where the plaque information is the lesion information of the target blood vessel, and during the establishment of the cross-sectional morphological model, the plaques
  • the 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 was formed, the composition of the plaque and the changes in the plaque composition, the shape of the plaque, and the change in the shape of the plaque.
  • 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 target blood vessel is extracted by a center line extraction method, and a center diameter line of the geometric model is established and obtained;
  • the planar geometric image of the lumen cross section at each position needs to refer to the coordinate system established in step S2.
  • the position of the plaque on each cross section can be clarified to facilitate the subsequent formulation of the cross-section morphological model.
  • the effect of different plaque morphologies on vascular pressure difference is further clarified.
  • the method for obtaining vascular pressure difference obtains a planar geometric image at each cross-sectional position of a target blood vessel by establishing a cross-sectional morphological model, and establishes a morphology by fitting cross-sectional morphological models at different positions.
  • the difference function introduces the concept of cross-sectional morphology in the calculation of vascular pressure difference, and comprehensively considers the influence of plaque position and shape in the lumen on the calculation of vascular pressure difference; so that the vascular pressure difference can be obtained by the present invention
  • the vascular pressure difference value calculated by the method is more accurate and can accurately reflect the pressure change at both ends of the target blood vessel. It is guaranteed that the vascular pressure difference calculated by the method of the present invention is accurate and reliable when applied to the calculation of other blood flow characteristic values.

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Abstract

一种获取血管压力差的方法和装置，该获取血管压力差的方法包括：接收一部分血管段的解剖数据，根据解剖数据获取目标血管的几何模型；根据解剖数据并结合个体数据，获取目标血管的血流模型和目标血管的血流速度V；对几何模型进行预处理，建立横截面形态模型，计算目标血管管腔的形态差异函数f(x)，基于目标血管管腔的形态差异函数f(x)和血流速度V，计算获得目标血管任意两位置处的压力差数值ΔP。该获取血管压力差的方法，通过引入形态学的概念，明确血管形态等对血管压力差计算的影响，提高血管压力差计算的准确性。

Description

获取血管压力差的方法及装置 技术领域
本发明涉及一种获取血管压力差的方法及装置，属于医疗技术领域。
背景技术
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，我国现有冠心病患者约1100万人，心血管介入手术治疗患者数量每年增长大于10％。
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。
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，成为冠心病患者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。
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。
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。
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；同时，不同的病史信息和生理特征也会对血流压力的差值ΔP产生影响；因此，现有技术中，通过血流压力的差值ΔP计算获得的FFR多会偏离实际值，致使通过FFR评价冠脉狭窄功能的结果存在误差。
有鉴于此，确有必要提供一种新的获取血管压力差的方法，以解决上述问题。
发明内容
本发明的目的在于提供一种获取血管压力差的方法，以至少解决现有技术中存在的技术问题之一。本发明提供的获取血管压力差的方法，通过引入形态学的概念，明确斑块信息等对血管压力差计算的影响，提高血管压力差计算的准确性。
为实现上述发明目的，本发明提供了一种获取血管压力差的方法，所述获取血管压力差的方法包括：
接收血管的解剖数据，根据所述解剖数据获取目标血管的几何模型；
根据所述解剖数据并结合个体数据，获取目标血管的血流模型，并根据所述血流模型获取目标血管的血流速度V；
对所述几何模型进行预处理，建立目标血管在近端终点和远端终点之间各个位置处的横截面形态模型；
以目标血管的近端终点为参考点，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)，所述尺度为计算形态差异函数f(x)时相邻两横截面之间的距离；
基于所述目标血管管腔的形态差异函数f(x)和血流速度V，计算获得所述目标血管任意两位置处的压力差数值ΔP。
作为本发明的进一步改进，所述血管包括冠脉血管、由冠脉血管发出的分支血管、血管树和单支血管段；所述个体数据包括个体普遍参数和个体特异性参数；所述血流模型至少包括所述目标血管的血流速度V。
作为本发明的进一步改进，所述压力差数值ΔP通过目标血管管腔在不同尺度下的形态差异函数f(x)和目标血管的血流速度V计算获得，所述ΔP在不同尺度下的计算公式为：
ΔP＝(c 1V+c 2V 2+…+c mV m)
*[α 1*∫f 1(x)dx+α 2*∫f 2(x)dx+…+α n*∫f n(x)dx]
其中，V为血流速度，为通过所述血流模型直接/间接获取；
c 1、c 2、…、c m分别代表血流速度V的参数系数；
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；
m为大于等于1的自然数；
n为尺度为大于等于1的自然数；
所述不同尺度包括第一尺度、第二尺度、……、第n尺度；
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；
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；
……
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。
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：
S1、定义目标血管近端终点处的横截面为参考面，通过中心线提取与建立方法获得所述几何模型的中心径线；
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。
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。
作为本发明的进一步改进，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；
所述形态差异函数f(x)的获取包括：
基于横截面形态模型，建立各横截面的形态函数；所述形态函数包括面积函数、直径函数和边缘位置函数；
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；
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，获取形态差异函数f(x)。
作为本发明的进一步改进，所述血流速度V的获取还包括通过病史信息和/或生理参数信息对所述血流模型进行修正，并通过修正后的血流模型获得；所述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，所述血流速度可通过血管内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；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。
作为本发明的进一步改进，所述血流速度包括目标血管处于最大充血状态下的血流速度和静息状态下的血流速度；或者，所述几何模型的预处理包括通过病史信息和/或生理参数信息对几何模型进行修正。
为实现上述发明目的，本发明还提供了一种获取血管压力差的装置，所述获取血管压力差的装置包括：
数据采集器，所述数据采集器用于获取及存储血管系统的解剖模型中目标血管的几何参数；
压力差处理器，所述压力差处理器用于建立目标血管的血流模型，和基于所述几何参数建立的对应目标血管的几何模型；
所述压力差处理器还用于对所述几何模型和/或血流模型进行修正，并基于修正后的所述几何模型和所述血流模型获取横截面形态模型和血管压力差计算模型；同时，根据所述血管压力差计算模型和血流动力学，获取目标血管的压力差数值ΔP。
作为本发明的进一步改进，所述几何模型为通过对所述解剖模型的图像数据进行测算，并拟合校准获得；所述横截面形态模型为通过所述几何模型 直接/间接获得；或者，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。
作为本发明的进一步改进，所述压力差处理器获取的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；或者所述几何模型包括至少一段单支血管段。
作为本发明的进一步改进，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目标血管的血流速度，所述血流速度用以推算所述目标血管近端终点与远端终点之间的压力差数值ΔP；
所述速度采集器包括速度计算模块及速度提取模块；所述速度提取模块可通过所述数据采集器直接采集获得血流速度，也可通过所述血流模型直接提取血流速度；
所述速度计算模块包括速度转换模块及速度测算模块，所述血流速度可通过血管中流体充盈的速度经所述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所述速度测算模块计算获得。
为实现上述发明目的，本发明还提供了一种获取血流储备分数的装置，所述获取血流储备分数的装置包括：
数据采集器，所述数据采集器用于获取及存储血管装置解剖模型中目标血管的几何参数；
血流信息处理器，所述血流信息处理器用于建立目标血管的血流模型，和基于所述几何参数建立对应目标血管的几何模型；
所述血流信息处理器还用于，对所述几何模型及血流模型进行修正以获取横截面形态模型，并基于所述横截面形态模型和所述血流模型，获取血管压力差计算模型和目标血管的最大血流速度；根据所述血管压力差计算模型和所述最大血流速度并结合血流动力学，计算获取血流储备分数FFR。
作为本发明的进一步改进，所述几何模型通过对所述解剖模型的图像数 据进行测算，并拟合校准获得；所述横截面形态模型为通过所述几何模型直接/转换获得；
所述数据采集器接收到的所述图像数据为目标血管的造影图像数据时，所述数据采集器采集的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。
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。
作为本发明的进一步改进，所述血流信息处理器获取的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；或者所述几何模型包括至少一段单支血管段；
所述血流信息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；
所述血流模型为静息态血流模型时，所述最大血流速度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；所述最大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、灌注面积及分支角度中的一种或几种。
作为本发明的进一步改进，所述获取血流储备分数的装置还包括速度采集器，其用于获取目标血管的最大血流速度，所述最大血流速度用以推算所述目标血管近端终点处的第一血流压力Pa及目标血管近端终点与远端终点之间的压力差数值ΔP。
为实现上述发明目的，本发明还提供了一种用于获取患者血管压力差的设备，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：
收集患者待检血管的解剖数据；
根据所述解剖数据建立患者待检血管的血管模型并获取血流速度；
基于所述血管模型进一步建立不同尺度下管腔形态模型；
根据预设的形态差异函数，基于所述管腔形态模型以及所述血流速度确定待检血管任意两位置间的血管压力差。
作为本发明的进一步改进，所述尺度为相邻两横截面之间的距离；
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为与所述待检血管的横截面积、直径或边缘距离有关的差异函数。
为实现上述发明目的，本发明还提供了一种获取血管压力差的方法，所述方法包括：
接收血管的解剖数据，根据所述解剖数据获取目标血管的几何模型；
对所述几何模型进行预处理，建立目标血管在近端终点和远端终点之间各个位置处的横截面形态模型；
以目标血管的近端终点为参考点，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)，所述尺度为计算形态差异函数f(x)时相邻两横截面之间的距离；
所述目标血管任意两位置处的压力差数值ΔP在不同尺度下的计算公式为：
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为大于等于1的常数；
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；
所述不同尺度包括第一尺度、第二尺度、……、第n尺度；
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；
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；
……
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。
作为本发明的进一步改进，所述修正参数k为基于个体信息直接/间接获取的数值；
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。
本发明的有益效果是：本发明的获取血管压力差的方法通过建立横截面形态模型，获取目标血管各个横截面位置处的平面几何图像，并通过对不同位置处的横截面形态模型进行拟合建立形态差异函数，在血管压力差计算的过程中引入了横截面形态的概念，综合考虑了管腔中斑块的位置、形状等因素对血管压力差计算的影响；使得通过本发明的获取血管压力差的方法计算得到的血管压力差数值更加准确，可准确反映目标血管两端的压力变化；保证使用本发明的方法算得的血管压力差在应用至其它血流特征值的计算时，结果准确可靠。
附图说明
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。
图2是图1中D 1位置处横截面形态模型的结构示意图。
图3是图1中D 2位置处横截面形态模型的结构示意图。
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。
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。
图6是图5中D 1位置处横截面形态模型的结构示意图。
图7是图5中D 2位置处横截面形态模型的结构示意图。
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。
图9是本发明获取血管压力差的装置的结构框图。
图10是本发明获取血流储备分数的装置的结构框图。
具体实施方式
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。
本发明提供了一种获取血管压力差的方法，所述获取血管压力差的方法包括以下步骤：
接收血管的解剖数据，根据所述解剖数据获取目标血管的几何模型；
根据所述解剖数据并结合个体数据，获取目标血管的血流模型；
对所述几何模型进行预处理，建立目标血管在近端终点和远端终点之间各个位置处的横截面形态模型；
以目标血管的近端终点为参考点，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)，所述尺度为计算形态差异函数f(x)时相邻两横截面之间的距离；
基于所述目标血管管腔的形态差异函数f(x)和血流速度V，计算获得所述目标血管任意两位置处的压力差数值ΔP。
其中，所述血管包括冠脉血管、由冠脉血管发出的分支血管、血管树和单支血管段；所述个体数据包括个体普遍参数和个体特异性参数。
进一步的，所述压力差数值ΔP为通过不同尺度下的形态差异函数f(x)和目标血管的血流模型计算获得，且所述压力差数值ΔP在不同尺度下的计算公式为：
ΔP＝(c 1V+c 2V 2+…+c mV m)*[α 1*∫f 1(x)dx+α 2*∫f 2(x)dx+…+α n*∫f n(x)dx]
其中，V为血流速度，为通过所述血流模型直接/间接获取；
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。
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。
具体来讲，所述不同尺度包括第一尺度、第二尺度、……、第n尺度；
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；
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；
……
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。
所述横截面形态模型为通过所述几何模型直接/间接获得，且在本发明中所述几何模型至少包括所述目标血管的形状、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目标血管实际形态的参数。具体来讲，所述横截面形态模型的建立包括以下步骤：
S1、定义目标血管近端终点处的横截面为参考面，通过中心线提取与建立方法，获得所述几何模型的中心径线；
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。
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，所述斑块信息即为目标血管的病变信息，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；同时当所述斑块处于不同的位置处时，目标血管所供应的心肌体积区域不同，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，从而导致目标血管压力差数值ΔP发生偏差。
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。
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。
所述获取血管压力差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：
基于横截面形态模型，建立各横截面的形态函数；
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；
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)。
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)。
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。
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。
进一步的，在本发明中，所述压力差数值ΔP的计算还与目标血管的血流速度V有关，且在本发明中，所述血流速度V的获取可通过所述血流模型直接/间接获取。
具体来讲，在本发明中所述血流模型包括固定血流模型及个性化血流模型，且所述血流模型既可为数据计算模型也可为三维流体流动模型。其中所述固定血流模型即为经验值血流模型，当所述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，且在本发明中所述血流速度V还可为固定的参数；需要说明的是，所述固定血流模型的获取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；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目标血管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目标血管在造影过程中造影剂的平均流动速度。
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；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。
在本发明的另一实施例中，所述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。
特别地，在本发明中所述血流速度V包括目标血管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，当目标血管位于冠脉区域时，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可直接通过血流模型获取，或通过血流模型计算的血流速度V转换获得；当目标血管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。
需要说明的是，为保证本发明的获取血管压力差的方法获得的压力差数值ΔP结果准确，在通过所述几何模型和所述血流模型获取所述横截面形态模型和所述血流速度V时，需通过病史信息和/或生理参数信息对所述血流模型和/或所述几何模型进行修正，且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、代谢性疾病及家族史等；所述生理参数包括年龄、性别、血压、身体质量指数及冠状动脉优势类型等可直接获取的生理信息。
进一步的，影响所述压力差数值ΔP的因素还包括心肌微循环阻力(IMR)及是否存在侧支循环。具体来讲，当目标血管存在心肌微循环阻力时，将影响微循环灌注，进一步影响目标血管的血流速度V，使得血流速度V减小，致使目标血管压力差数值ΔP的降低，从而导致血流特征值如血流储备分数FFR的计算出现误差。当目标血管存在侧支循环时，将导致流过目标血管的最大血流量减少，从而目标血管压力差数值ΔP的降低，血流储备分数FFR的计算值升高。
请参参阅图9所示，本发明还提供了一种获取血管压力差的装置，所述获取血管压力差的装置包括：
数据采集器，所述数据采集器用于获取及存储血管系统的解剖模型中目标血管的几何参数；
压力差处理器，所述压力差处理器用于建立目标血管的血流模型，和基于所述几何参数建立的对应目标血管的几何模型；
所述压力差处理器还用于对所述几何模型和/或血流模型进行修正，并基于修正后的所述几何模型和所述血流模型获取横截面形态模型和血管压力差计算模型；同时，根据所述血管压力差计算模型和血流动力学，获取目标血管近端终点处的第一血流压力Pa与目标血管近端终点和远端终点之间的压力差数值ΔP。
进一步的，所述几何模型为通过对所述解剖模型的图像数据进行测算，并拟合校准获得；具体来讲，所述压力差处理器获取的几何模型至少包括所述目标血管的形状、直径和面积等几何参数，所述几何参数还包括血管段的弯曲角度等可以反映目标血管实际形态的参数；即在本发明中，所述几何模型既可为单一血管段也可为血管树，且所述血管树包括主动脉和由所述主动脉发出的多个冠状动脉。
所述横截面形态模型为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。
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目标血管的血流速度，所述血流速度用以推算所述目标血管近端终点处的第一血流压力Pa及目标血管近端终点与远端终点之间的压力差数值ΔP。
所述速度采集器包括速度计算模块及速度提取模块；所述速度提取模块可通过所述数据采集器直接采集获得血流速度，也可通过所述血流模型直接提取血流速度。
所述速度计算模块包括速度转换模块及速度测算模块，所述血流速度可通过血管中流体充盈的速度经所述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计算获得。
优选的，所述压力差数值ΔP通过如下公式计算获得：
ΔP＝(c 1V+c 2V 2+…+c mV m)
*[α 1*∫f 1(x)dx+α 2*∫f 2(x)dx+…+α n*∫f n(x)dx]
其中，V为血流速度，为通过所述血流模型直接/间接获取；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。
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。
请参参阅图10所示，本发明还提供了一种获取血流储备分数的装置，所述获取血流储备分数的装置包括：
数据采集器，所述数据采集器用于获取及存储血管装置解剖模型中目标血管的几何参数；
血流信息处理器，所述血流信息处理器用于建立目标血管的血流模型，和基于所述几何参数建立对应目标血管的几何模型；
所述血流信息处理器还用于，对所述几何模型及血流模型进行修正以获取横截面形态模型，并基于所述横截面形态模型和所述血流模型，获取血管压力差计算模型和目标血管最大血流速度；根据所述血管压力差计算模型和所述最大血流速度并结合血流动力学，计算获取血流储备分数FFR。
所述几何模型为所述血流信息处理器通过对所述数据采集器获取的解剖模型的图像数据进行测算，并拟合校准获得；具体来讲，当所述解剖模型的图像数据为通过CT、OCT和IVUS等设备获取时，所述数据采集器可直接对所述图像数据进行收集，并传递至所述血流信息处理器进行拟合建立几何模型；而当所述解剖模型的图像数据为通过造影的方法获取时，所述数据采集器在对所述图像数据进行采集时，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，所述血流信息处理器获取的几何模型时，可保证几何模型的建立准确。
进一步的，所述横截面形态模型为通过所述几何模型直接/转换获得，且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。
所述血流信息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。
当所述血流模型为静息态血流模型时，所述最大血流速度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。当所述最大血流速度通过所述血管树的形态计算获得时，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个主动脉血管段或者至少一个主动脉和由所述主动脉发出的多个冠状动脉，或者所述几何模型包括至少一个单支血管段；此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，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。
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目标血管的最大血流速度，所述最大血流速度用以推算所述目标血管近端终点处的第一血流压力Pa及目标血管近端终点与远端终点之间的压力差数值ΔP。
优选的，所述压力差数值ΔP的计算公式为：
ΔP＝(c 1V+c 2V 2+…+c mV m)
*[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。
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。
本发明还提供了一种用于获取患者血管压力差的设备，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：
收集患者待检血管的解剖数据；
根据所述解剖数据建立患者待检血管的血管模型；
基于所述血管模型进一步建立不同尺度下管腔形态模型；
根据预设的形态差异函数，基于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。
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几何模型和所述血流模型中的一种，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。
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：
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；
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。
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。
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。
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。
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。
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。
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；
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；
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；
……
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。
进一步的，在本发明中所述血管模型的建立方式与所述血流模型和所述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。
当然，在本装置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；所述生理参数包括年龄、性别、血压及身体质量指数等可直接获取的生理信息中的一个或多个。
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：
ΔP＝(c 1V+c 2V 2+…+c mV m)
*[α 1*∫f 1(x)dx+α 2*∫f 2(x)dx+…+α n*∫f n(x)dx]
其中，V为血流速度，为通过所述血流模型直接/间接获取；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。
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。
本发明还提供了另一种获取血管压力差的方法，所述方法包括：
接收血管的解剖数据，根据所述解剖数据获取目标血管的几何模型；
对所述几何模型进行预处理，建立目标血管在近端终点和远端终点之间各个位置处的横截面形态模型；
以目标血管的近端终点为参考点，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)，所述尺度为计算形态差异函数f(x)时相邻两横截面之间的距离；
此时，所述目标血管任意两位置处的压力差数值ΔP，所述ΔP在不同尺度下的计算公式为：
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为大于等于1的常数；
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；
优选的，所述不同尺度包括第一尺度、第二尺度、……、第n尺度；
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；
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；
……
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。
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，即在本发明中所述修正参数k为通过估算或测试设备直接/间接获取的数据，所述修正参数k可与个体的特异性信息或常规信息相关。
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，所述形态差异函数f(x)的获取包括：
基于横截面形态模型，建立各横截面的形态函数；
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；
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)。
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)。即，所述形态差异函数f(x)为与目标血管的两横截面的横截面面积变化、各个位置处的直径变化或各个位置处的边缘距离变化有关的函数。
进一步的，所述横截面形态模型中包括各横截面位置处的斑块信息，其中所述斑块信息即为目标血管的病变信息，所述横截面形态模型在建立过程中，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，且在本实施例中，所述横截面形态模型的建立包括以下步骤：
S1、定义目标血管近端终点处的横截面为参考面，通过中心线提取方法提取目标血管的中心线，并建立获取所述几何模型的中心径线；
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。
其中，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，如此设置，可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，进一步明确不同的斑块形态的差异对血管压力差的影响。
需要指出的是，本说明书中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。
综上所述，本发明的获取血管压力差的方法通过建立横截面形态模型，获取目标血管各个横截面位置处的平面几何图像，并通过对不同位置处的横截面形态模型进行拟合建立形态差异函数，在血管压力差计算的过程中引入了横截面形态的概念，综合考虑了管腔中斑块的位置、形状等因素对血管压力差计算的影响；使得通过本发明的获取血管压力差的方法计算得到的血管压力差数值更加准确，可准确反映目标血管两端的压力变化；保证使用本发明的方法算得的血管压力差在应用至其它血流特征值的计算时，结果准确可靠。
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。

Claims (21)

  1. 一种获取血管压力差的方法，其特征在于，所述方法包括：
    接收血管的解剖数据，根据所述解剖数据获取目标血管的几何模型；
    根据所述解剖数据并结合个体数据，获取目标血管的血流模型；
    对所述几何模型进行预处理，建立目标血管在近端终点和远端终点之间各个位置处的横截面形态模型；
    以目标血管的近端终点为参考点，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)，所述尺度为计算形态差异函数f(x)时相邻两横截面之间的距离；
    基于所述目标血管管腔的形态差异函数f(x)和血流模型，计算获得所述目标血管任意两位置处的压力差数值ΔP。
  2. 根据权利要求1所述的获取血管压力差的方法，其特征在于：所述血管包括冠脉血管、由冠脉血管发出的分支血管、血管树和单支血管段；所述个体数据包括个体普遍参数和个体特异性参数；所述血流模型至少包括所述目标血管的血流速度V。
  3. 根据权利要求1所述的获取血管压力差的方法，其特征在于：所述压力差数值ΔP通过目标血管管腔在不同尺度下的形态差异函数f(x)和目标血管的血流模型计算获得，所述ΔP在不同尺度下的计算公式为：
    ΔP＝(c 1V+c 2V 2+…+c mV m)
    *[α 1*∫f 1(x)dx+α 2*∫f 2(x)dx+…+α n*∫f n(x)dx]
    其中，V为血流速度，为通过所述血流模型直接/间接获取；
    c 1、c 2、…、c m分别代表血流速度V的参数系数；
    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；
    m为大于等于1的自然数；
    n为尺度为大于等于1的自然数；
    所述不同尺度包括第一尺度、第二尺度、……、第n尺度；
    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；
    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；
    ……
    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。
  4. 根据权利要求1所述的获取血管压力差的方法，其特征在于：所述横截面形态模型的建立包括：
    S1、定义目标血管近端终点处的横截面为参考面，通过中心线提取与建立方法获得所述几何模型的中心径线；
    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。
  5. 根据权利要求4所述的获取血管压力差的方法，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。
  6. 根据权利要求1所述的获取血管压力差的方法，其特征在于：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；
    所述形态差异函数f(x)的获取包括：
    基于横截面形态模型，建立各横截面的形态函数；所述形态函数包括面积函数、直径函数和边缘位置函数；
    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；
    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，获取形态差异函数f(x)。
  7. 根据权利要求2所述的获取血管压力差的方法，其特征在于：所述血流模型的获取还包括通过病史信息和/或生理参数信息对所述血流模型进行修正，并通过修正后的血流模型获得；所述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可通过血管内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；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。
  8. 根据权利要求2所述的获取血管压力差的方法，其特征在于：所述血流速度V包括目标血管处于最大充血状态下的血流速度和静息状态下的血流速度；或者，所述几何模型的预处理包括通过病史信息和/或生理参数信息对几何模型进行修正。
  9. 一种获取血管压力差的装置，其特征在于，包括：
    数据采集器，所述数据采集器用于获取及存储血管系统的解剖模型中目标血管的几何参数；
    压力差处理器，所述压力差处理器用于建立目标血管的血流模型，和基于所述几何参数建立的对应目标血管的几何模型；
    所述压力差处理器还用于对所述几何模型和/或血流模型进行修正，并基于修正后的所述几何模型和所述血流模型获取横截面形态模型和血管压力差计算模型；同时，根据所述血管压力差计算模型和血流动力学，获取目标血管的压力差数值ΔP。
  10. 根据权利要求9所述的获取血管压力差的装置，其特征在于：所述几何 模型为通过对所述解剖模型的图像数据进行测算，并拟合校准获得；所述横截面形态模型为通过所述几何模型直接/间接获得；或者，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。
  11. 根据权利要求9所述的获取血管压力差的装置，其特征在于：所述压力差处理器获取的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；或者所述几何模型包括至少一段单支血管段。
  12. 根据权利要求9所述的获取血管压力差的装置，其特征在于：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目标血管的血流速度，所述血流速度用以推算所述目标血管近端终点与远端终点之间的压力差数值ΔP；
    所述速度采集器包括速度计算模块及速度提取模块；所述速度提取模块可通过所述数据采集器直接采集获得血流速度，也可通过所述血流模型直接提取血流速度；
    所述速度计算模块包括速度转换模块及速度测算模块，所述血流速度可通过血管中流体充盈的速度经所述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所述速度测算模块计算获得。
  13. 一种获取血流储备分数的装置，其特征在于，包括：
    数据采集器，所述数据采集器用于获取及存储血管装置解剖模型中目标血管的几何参数；
    血流信息处理器，所述血流信息处理器用于建立目标血管的血流模型，和基于所述几何参数建立对应目标血管的几何模型；
    所述血流信息处理器还用于，对所述几何模型及血流模型进行修正以获取横截面形态模型，并基于所述横截面形态模型和所述血流模型，获取血管压力差计算模型和目标血管的最大血流速度；根据所述血管压力差计算模型和所述最大血流速度并结合血流动力学，计算获取血流储备分数FFR。
  14. 根据权利要求13所述的获取血流储备分数的装置，其特征在于：所述几何模型通过对所述解剖模型的图像数据进行测算，并拟合校准获得；所述横截面形态模型为通过所述几何模型直接/转换获得；
    所述数据采集器接收到的所述图像数据为目标血管的造影图像数据时，所述数据采集器采集的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。
  15. 根据权利要求13所述的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。
  16. 根据权利要求13所述的获取血流储备分数的装置，其特征在于：所述血流信息处理器获取的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；或者所述几何模型包括至少一段单支血管段；
    所述血流信息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；
    所述血流模型为静息态血流模型时，所述最大血流速度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；所述最大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、灌注面积及分支角度中的一种或几种。
  17. 根据权利要求13所述的获取血流储备分数的装置，其特征在于：所述获取血流储备分数的装置还包括速度采集器，其用于获取目标血管的最大血流速度，所述最大血流速度用以推算所述目标血管近端终点处的第一血流压力Pa及目标血管近端终点与远端终点之间的压力差数值ΔP。
  18. 一种用于获取患者血管压力差的设备，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：
    收集患者待检血管的解剖数据；
    根据所述解剖数据建立患者待检血管的血管模型；
    基于所述血管模型进一步建立不同尺度下管腔形态模型；
    根据预设的形态差异函数，基于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。
  19. 根据权利要求18所述的用于获取患者血管压力差的设备，其特征在于：所述尺度为相邻两横截面之间的距离；
    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包括与所述目标血管的横截面积或直径或边缘距离有关的差异函数。
  20. 一种获取血管压力差的方法，其特征在于，所述方法包括：
    接收血管的解剖数据，根据所述解剖数据获取目标血管的几何模型；
    对所述几何模型进行预处理，建立目标血管在近端终点和远端终点之间各个位置处的横截面形态模型；
    以目标血管的近端终点为参考点，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)，所述尺度为计算形态差异函数f(x)时相邻两横截面之间的距离；
    所述目标血管任意两位置处的压力差数值ΔP在不同尺度下的计算公式为：
    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为大于等于1的常数；
    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；
    所述不同尺度包括第一尺度、第二尺度、……、第n尺度；
    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；
    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；
    ……
    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。
  21. 根据权利要求20所述的获取血管压力差的方法，其特征在于：所述修正参数k为基于个体信息直接/间接获取的数值；
    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。
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Families Citing this family (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109770930B (zh) * 2019-01-29 2021-03-09 浙江大学 一种冠状动脉微循环阻力的确定方法和装置
JP7532402B2 (ja) 2019-04-01 2024-08-13 キャスワークス リミテッド 血管造影画像選択のための方法および装置
WO2020210948A1 (zh) * 2019-04-15 2020-10-22 博动医学影像科技（上海）有限公司 获取冠脉血流量及血流速度的方法和装置
US12039685B2 (en) 2019-09-23 2024-07-16 Cathworks Ltd. Methods, apparatus, and system for synchronization between a three-dimensional vascular model and an imaging device
CN110786840B (zh) * 2019-11-04 2021-06-08 苏州润迈德医疗科技有限公司 基于生理参数获取血管评定参数的方法、装置及存储介质
CN113487588B (zh) * 2020-03-02 2024-03-22 上海博动医疗科技股份有限公司 冠状动脉的血流速度的计算方法、装置及电子设备
CN113180631A (zh) * 2021-04-29 2021-07-30 博动医学影像科技（上海）有限公司 基于血管内成像的血流速度、血流储备分数的分析方法
CN113876304B (zh) * 2021-09-08 2024-08-06 深圳市中科微光医疗器械技术有限公司 一种基于oct图像和造影图像确定ffr的方法及装置
CN115758945B (zh) * 2023-02-13 2023-06-09 首都医科大学附属北京友谊医院 数值模型构建方法、装置、电子设备及存储介质
CN116746893B (zh) * 2023-06-16 2024-05-31 上海博动医疗科技股份有限公司 基于单体位造影数据的血管压力差计算方法及装置

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104854592A (zh) * 2012-09-12 2015-08-19 哈特弗罗公司 用于根据血管几何形状和生理学估计血流特性的系统和方法
US20150268039A1 (en) * 2014-03-18 2015-09-24 Medis Associated B.V. Method and device for determining deviation in pressure in a blood vessel
CN105326486A (zh) * 2015-12-08 2016-02-17 上海交通大学 血管压力差与血流储备分数的计算方法及系统
CN107491636A (zh) * 2017-07-26 2017-12-19 武汉大学 一种基于计算流体力学的脑血管储备力仿真系统和方法
CN107730540A (zh) * 2017-10-09 2018-02-23 全景恒升（北京）科学技术有限公司 基于高精度匹配模型的冠脉参数的计算方法
CN108022650A (zh) * 2017-12-07 2018-05-11 博动医学影像科技（上海）有限公司 基于主支血管和边支血管参数的管腔建模及计算血管压力差的方法与系统

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8315812B2 (en) * 2010-08-12 2012-11-20 Heartflow, Inc. Method and system for patient-specific modeling of blood flow
US10433740B2 (en) * 2012-09-12 2019-10-08 Heartflow, Inc. Systems and methods for estimating ischemia and blood flow characteristics from vessel geometry and physiology
US9785746B2 (en) 2014-03-31 2017-10-10 Heartflow, Inc. Systems and methods for determining blood flow characteristics using flow ratio
US9195801B1 (en) 2014-08-05 2015-11-24 Heartflow, Inc. Systems and methods for treatment planning based on plaque progression and regression curves
CA2991784A1 (en) 2015-07-17 2017-01-26 Heartflow, Inc. Systems and methods for assessing the severity of plaque and/or stenotic lesions using contrast distribution predictions and measurements
EP4393386A3 (en) * 2017-01-23 2024-09-11 Shanghai United Imaging Healthcare Co., Ltd. Method and system for analyzing blood flow condition
CN107115108B (zh) * 2017-04-27 2020-09-15 博动医学影像科技（上海）有限公司 快速计算血管压力差的方法及系统
EP3456249A1 (en) * 2017-09-15 2019-03-20 Koninklijke Philips N.V. Using archived patient data to correct intravascular measurements for patient co-morbidities
CA3103478A1 (en) * 2018-06-19 2019-12-26 Abiomed, Inc. Systems and methods for system identification

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104854592A (zh) * 2012-09-12 2015-08-19 哈特弗罗公司 用于根据血管几何形状和生理学估计血流特性的系统和方法
US20150268039A1 (en) * 2014-03-18 2015-09-24 Medis Associated B.V. Method and device for determining deviation in pressure in a blood vessel
CN105326486A (zh) * 2015-12-08 2016-02-17 上海交通大学 血管压力差与血流储备分数的计算方法及系统
CN107491636A (zh) * 2017-07-26 2017-12-19 武汉大学 一种基于计算流体力学的脑血管储备力仿真系统和方法
CN107730540A (zh) * 2017-10-09 2018-02-23 全景恒升（北京）科学技术有限公司 基于高精度匹配模型的冠脉参数的计算方法
CN108022650A (zh) * 2017-12-07 2018-05-11 博动医学影像科技（上海）有限公司 基于主支血管和边支血管参数的管腔建模及计算血管压力差的方法与系统

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