WO2022237161A1 - 脑动脉波强度和波功率的测量方法、终端设备及存储介质 - Google Patents

脑动脉波强度和波功率的测量方法、终端设备及存储介质 Download PDF

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WO2022237161A1
WO2022237161A1 PCT/CN2021/138058 CN2021138058W WO2022237161A1 WO 2022237161 A1 WO2022237161 A1 WO 2022237161A1 CN 2021138058 W CN2021138058 W CN 2021138058W WO 2022237161 A1 WO2022237161 A1 WO 2022237161A1
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artery
cerebral
target
internal carotid
model
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PCT/CN2021/138058
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French (fr)
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张现成
刘嘉
张志珺
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中国科学院深圳先进技术研究院
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30016—Brain
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular
    • G06T2207/30104—Vascular flow; Blood flow; Perfusion
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/30—Assessment of water resources

Definitions

  • the present application belongs to the technical field of image processing, and in particular relates to a method for measuring cerebral artery wave intensity and wave power, a terminal device, and a computer-readable storage medium.
  • Cerebral arterial wave intensity (Wave intensity, WI) and wave power (Wave power, WP) are important markers to predict the risk of Alzheimer's disease. Therefore, measurement of cerebral artery WI and WP is required.
  • a combined echo-tracking and Doppler system is used to simultaneously acquire common carotid artery diameter and flow velocity waveforms, which are estimated by calibrating the diameter waveforms to the systolic and diastolic pressures of the arm cuff The pressure waveform of the common carotid artery is obtained to calculate the WI of the common carotid artery.
  • This measurement method assumes that the vessel diameter waveform and the pressure waveform are the same, and uses arm pressure to calibrate, ignoring the difference between arm pressure and carotid artery pressure, resulting in inaccurate pressure assessment and affecting the measurement accuracy of wave intensity WI.
  • Embodiments of the present application provide a method, device, terminal device, and computer-readable storage medium for measuring wave intensity and wave power of cerebral arteries, which can accurately measure wave intensity and wave power of each cerebral artery.
  • the embodiment of the present application provides a method for measuring cerebral artery wave intensity and wave power, which may include:
  • the nuclear magnetic resonance image including brain information and neck information can be obtained, and the cerebral hemodynamic model can be constructed according to the nuclear magnetic resonance image; the left internal carotid artery, right internal carotid artery, left vertebral artery and The target ultrasonic flow velocity spectrum waveform of the right vertebral artery is used to determine the wave intensity and wave power of each cerebral artery according to the cerebral hemodynamic model and each target ultrasonic flow velocity spectrum waveform. That is, the embodiment of the present application realizes the measurement of WI and WP of the whole cerebral artery through the combination of non-invasive measurement and numerical simulation, which greatly improves the measurement accuracy of WI and WP of the cerebral artery, and has strong usability and practicability.
  • the cerebral hemodynamic model performs blood flow simulation on any cerebral artery according to the following formula:
  • A is the target cross-sectional area of the cerebral artery at time t at position x
  • U is the average blood flow velocity at the cerebral artery at time t at position x
  • P is the average blood pressure at the cerebral artery at time t at position x
  • ⁇ Blood viscosity
  • ⁇ is the blood density
  • ⁇ is the viscous friction constant
  • ⁇ 0.0035Pa ⁇ s
  • P ext is the pressure applied on the outer wall of the cerebral artery
  • P 0 is the reference Pressure
  • K is the stiffness parameter of the cerebral artery
  • A0 is the initial cross-sectional area of the cerebral artery.
  • the determining the wave intensity and wave power of each cerebral artery according to the cerebral hemodynamic model and each of the target ultrasonic velocity spectrum waveforms may include:
  • the wave intensity and wave power of each cerebral artery are determined according to the average blood flow velocity waveform, the average flow waveform and the average blood pressure waveform of each cerebral artery.
  • the determining the outlet boundary condition of the cerebral hemodynamic model may include:
  • Outlet boundary conditions for the cerebral hemodynamic model are determined based on the target resistance and target compliance for each of the terminal arteries.
  • the cerebral artery is calculated according to the blood pressure of the brachial artery, the target ultrasonic velocity spectrum waveform and the initial cross-sectional area of the left internal carotid artery, the right internal carotid artery, the left vertebral artery, and the right vertebral artery
  • the total resistance and total compliance of the system which can include:
  • a total resistance and a total compliance of the cerebral arterial system are determined based on the second mean flow and the brachial blood pressure.
  • the determining the total resistance and total compliance of the cerebral arterial system according to the second average flow rate and the brachial artery blood pressure may include:
  • the total resistance and total compliance of the cerebral arterial system were determined according to the following formula:
  • RT is the total resistance
  • CT is the total compliance
  • P m is the mean arterial pressure of the brachial artery
  • P cap is the intracranial capillary pressure
  • P icp is the intracranial pressure
  • P cap 25mmHg
  • P icp 11mmHg
  • PP SBP-DBP
  • SBP is the systolic pressure of the brachial artery
  • DBP is the diastolic pressure of the brachial artery
  • ⁇ V T is the total pulsation volume of the cerebral arterial system.
  • the determining the target resistance and target compliance of each terminal artery according to the total resistance and total compliance of the cerebral arterial system may include:
  • the total resistance and total compliance of the cerebral arterial system are distributed to each of the terminal arteries by using a two-step distribution method of the arterial flow ratio and the arterial cross-sectional area ratio to obtain the target resistance and target compliance of each of the terminal arteries.
  • the constructing the cerebral hemodynamic model according to the nuclear magnetic resonance image may include:
  • An 0D model is respectively connected to each terminal artery of the 1D pulse wave transmission model to obtain the cerebral hemodynamic model, wherein the 0D model is used to simulate the target resistance and target compliance of the peripheral vascular bed of the terminal artery sex.
  • the embodiment of the present application provides a device for measuring cerebral artery wave intensity and wave power, which may include:
  • the nuclear magnetic resonance image acquisition module is used to acquire the nuclear magnetic resonance image including brain information and neck information;
  • a dynamic model construction module used to construct a cerebral hemodynamic model according to the nuclear magnetic resonance image
  • the spectrum waveform acquisition module is used to obtain the target ultrasonic velocity spectrum waveform of the left internal carotid artery, the right internal carotid artery, the left vertebral artery and the right vertebral artery respectively;
  • the wave intensity and wave power determination module is used to determine the wave intensity and wave power of each cerebral artery according to the cerebral hemodynamic model and each of the target ultrasonic velocity spectrum waveforms.
  • an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and operable on the processor.
  • the processor executes the computer program, The method for measuring cerebral artery wave intensity and wave power described in any one of the above-mentioned first aspects is realized.
  • an embodiment of the present application provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements any one of the above-mentioned first aspects.
  • the embodiment of the present application provides a computer program product.
  • the terminal device executes the calculation of cerebral artery wave intensity and wave power described in any one of the above first aspects. Measurement methods.
  • Fig. 1 is a schematic flow chart of a method for measuring cerebral artery wave intensity and wave power provided by an embodiment of the present application
  • FIGS. 1 and Figure 3 are schematic diagrams of the construction of a cerebral hemodynamic model provided by an embodiment of the present application
  • Fig. 4 is a schematic diagram of a scene for determining a target ultrasonic velocity spectrum waveform provided by an embodiment of the present application
  • Fig. 5 is an example diagram of a measurement position provided by an embodiment of the present application.
  • Fig. 6 is a calculation example diagram of the pulsation volume of the right internal carotid artery
  • Figure 7 is an illustration of the left middle cerebral artery and its peripheral blood vessels
  • Fig. 8 is a blood flow simulation diagram of the cerebral hemodynamic model provided by the embodiment of the present application.
  • Fig. 9 is a schematic structural diagram of a measuring device for cerebral artery wave intensity and wave power provided by an embodiment of the present application.
  • FIG. 10 is a schematic structural diagram of a terminal device provided by an embodiment of the present application.
  • the term “if” may be construed, depending on the context, as “when” or “once” or “in response to determining” or “in response to detecting “.
  • the phrase “if determined” or “if [the described condition or event] is detected” may be construed, depending on the context, to mean “once determined” or “in response to the determination” or “once detected [the described condition or event] ]” or “in response to detection of [described condition or event]”.
  • references to "one embodiment” or “some embodiments” or the like in the specification of the present application means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application.
  • appearances of the phrases “in one embodiment,” “in some embodiments,” “in other embodiments,” “in other embodiments,” etc. in various places in this specification are not necessarily All refer to the same embodiment, but mean “one or more but not all embodiments” unless specifically stated otherwise.
  • the terms “including”, “comprising”, “having” and variations thereof mean “including but not limited to”, unless specifically stated otherwise.
  • cerebral artery wave intensity WI and wave power WP are important markers for predicting the risk of Alzheimer's disease.
  • the analysis of cerebral artery WI and WP can predict the risk of cognitive decline 10 years in advance, and the prediction results are not affected by other risk factors. Therefore, accurate measurement of cerebral artery WI and WP has important guiding significance for the early diagnosis, prevention and precise treatment of Alzheimer's disease.
  • common carotid artery WI Due to the particularity of cerebrovascular anatomy and the limitations of measurement techniques, it is extremely difficult to measure cerebral artery WI and WP.
  • common carotid artery diameter and flow velocity waveforms are simultaneously acquired using a combined echo-tracing and Doppler system, and the common carotid artery pressure waveform is estimated by calibrating the diameter waveform to the systolic and diastolic pressures of the arm cuff to calculate the total carotid artery pressure waveform.
  • Artery WI is a combined echo-tracing and Doppler system
  • this measurement method assumes that the vessel diameter waveform and the pressure waveform are the same, and uses brachial pressure to calibrate, ignoring the difference between brachial pressure and carotid artery pressure, resulting in inaccurate pressure assessment and affecting the measurement accuracy of wave intensity WI. Moreover, this measurement method can only measure the WI of the common carotid artery, but cannot measure the WI of other cerebral vessels, nor can it measure the WP of the cerebral artery.
  • the embodiment of the present application provides a method for measuring cerebral artery wave intensity and wave power, which can acquire MRI images containing brain information and neck information; construct cerebral hemodynamic parameters based on MRI images According to the cerebral hemodynamic model and each target ultrasonic flow velocity spectrum waveform, the wave intensity of each cerebral artery is determined WI and wave power WP, to realize the measurement of WI and WP of the whole cerebral artery through the combination of non-invasive measurement and numerical simulation, which greatly improves the measurement accuracy of WI and WP of cerebral artery, and has strong ease of use and practicability .
  • FIG. 1 shows a schematic flow chart of a method for measuring cerebral artery wave intensity WI and wave power WP provided by an embodiment of the present application.
  • the measurement method can be applied to terminal devices such as mobile phones, tablet computers, notebook computers, and desktop computers, and the embodiment of the present application does not impose any limitation on specific types of terminal devices.
  • the measurement method may include:
  • the nuclear magnetic resonance image refers to the magnetic resonance imaging (magnetic resonance imaging, MRI) scan of the head and neck of the user, and the obtained nuclear magnetic resonance image (that is, the MRI image), that is, the MRI image may include cerebral arteries. information and carotid information.
  • MRI magnetic resonance imaging
  • the terminal device can use vascular reconstruction software to segment and reconstruct cerebral arteries on the MRI image to obtain a three-dimensional geometric model of the cerebral arterial system. Then, the terminal device can determine the centerline of each cerebral artery according to the three-dimensional geometric model, and obtain the length and initial cross-sectional area of each centerline, so as to establish the whole cerebral artery topology according to the length and initial cross-sectional area of each centerline (ie 1D pulse wave propagation model of cerebral arterial system).
  • vascular reconstruction software to segment and reconstruct cerebral arteries on the MRI image to obtain a three-dimensional geometric model of the cerebral arterial system. Then, the terminal device can determine the centerline of each cerebral artery according to the three-dimensional geometric model, and obtain the length and initial cross-sectional area of each centerline, so as to establish the whole cerebral artery topology according to the length and initial cross-sectional area of each centerline (ie 1D pulse wave propagation model of cerebral arterial system).
  • the terminal device can obtain multiple cross-sectional areas uniformly distributed along the centerline, and use the average value of the multiple cross-sectional areas as the initial cross-sectional area of the centerline, for example Obtain five cross-sectional areas evenly distributed along the center line, and use the average value of these five cross-sectional areas as the initial cross-sectional area of the center line, that is, as the initial cross-sectional area of the cerebral artery corresponding to the center line .
  • FIG. 2 and FIG. 3 are schematic diagrams showing the construction of the cerebral hemodynamic model provided by the embodiment of the present application.
  • the terminal device can use the blood vessel reconstruction software to perform cerebral artery segmentation and reconstruction on the MRI image, for example, an interactive medical image control system can be used materialize mimics the cerebral artery segmentation and reconstruction of the MRI image, obtain the three-dimensional geometric model of the cerebral arterial system as shown in (b) in Figure 2, and determine the centerline of each cerebral artery in the cerebral arterial system, for example, you can use mimics The Fit Centerline tool under the ANALYZE menu in China determines the centerline of each cerebral artery, and an example map of the centerline as shown in (c) in Figure 2 is obtained.
  • the terminal device can obtain the length and initial cross-sectional area of each centerline. For example, you can use the Distance tool under the MEASURE menu in mimics to obtain the length of each centerline, and you can use the Sectional Area tool under the MEASURE menu to obtain the correspondence between each centerline.
  • multiple cross-sectional areas of each cerebral artery, and the initial cross-sectional area A 0 of each cerebral artery can be determined according to the multiple cross-sectional areas corresponding to each cerebral artery.
  • the terminal device can establish a 1D pulse wave transmission model as shown in (d) in Figure 2 according to the length of the center line and the initial cross-sectional area corresponding to each cerebral artery.
  • the 1D pulse wave transmission model can include 45 segments of cerebral arteries .
  • the terminal device can connect an 0D model to each terminal artery of the 1D pulse wave transfer model, for example, the 0D model can be a ternary Windkessel model, with By simulating the resistance and compliance of peripheral vascular beds (such as arterioles, arterioles, capillaries, etc.) of the cerebral arterial network, a 0-1D multi-scale cerebral hemodynamic model as shown in Figure 3 is obtained.
  • the 0D model can include two resistors and a capacitor, the resistor Characterization of characteristic impedance, resistance Characterizes end resistance, capacitance C characterizes compliance. It should be noted that FIG. 3 is only an example of the 0D model of the terminal arterial connection in the 20th paragraph.
  • the cerebral hemodynamics model can use the following 1D Navier-Stokes equation to perform blood flow simulation on each cerebral artery:
  • A is the target cross-sectional area of the cerebral artery at time t at position x
  • U is the average blood flow velocity at the cerebral artery at time t at position x
  • P is the average blood pressure at the cerebral artery at time t at position x
  • ⁇ Blood viscosity
  • ⁇ blood density
  • ⁇ viscous friction constant
  • the average blood flow velocity and average blood pressure in the embodiments of the present application refer to spatial averages. That is, the position x refers to a certain cross-section of the cerebral artery, and the average blood flow velocity and average blood pressure refer to the average value of the entire cross-section.
  • N may be specifically determined according to actual conditions, which is not limited in this embodiment of the present application.
  • P ext is the pressure applied on the outer wall of the cerebral artery.
  • P ext is equal to the intracranial pressure.
  • P ext can be set to 0, and P 0 is the reference pressure. In the embodiment of the present application, it can be The diastolic pressure DBP of the brachial artery was set as P 0 , A 0 was the initial cross-sectional area of each cerebral artery, and K was the stiffness parameter of each cerebral artery.
  • brachial artery blood pressure (including systolic blood pressure SBP and diastolic blood pressure DBP) can be measured in the following manner: use a digital sphygmomanometer to first measure the brachial artery blood pressure in one arm, and perform three measurements at intervals of one minute, requiring The error of each measurement is less than 5mmHg. Then, measure the blood pressure in the brachial artery of the other arm in the same way. Subsequently, the average value of the systolic blood pressure SBP and the average value of the diastolic blood pressure DBP obtained by the six measurements are calculated, and the average value of the diastolic blood pressure DBP obtained by the six measurements can be determined as P 0 in formula three.
  • the terminal device can estimate the initial K m of each cerebral artery according to the following empirical formulas 4 and 5:
  • c 0 is the initial pulse wave velocity of the cerebral artery
  • r 0 is the radius of the cerebral artery
  • k 1 , k 2 and k 3 are empirical constants
  • k 1 300000kg ⁇ s -2 ⁇ m -1
  • k 2 -900m -1
  • k 3 33700kg ⁇ s -2 ⁇ m -1 .
  • the selection criterion of ⁇ is: the percentage error between the average arterial pressure of the left and right internal carotid arteries and the average arterial pressure P m of the brachial artery obtained by the cerebral hemodynamic model simulation is within 5%.
  • the average pulsation pressure P m of the brachial artery DBP+0.422*(SBP-DBP), where the DBP is the average value of the diastolic blood pressure DBP obtained from the aforementioned six measurements, and the SBP here is obtained from the aforementioned six measurements Mean value of systolic blood pressure SBP.
  • the average arterial pressure of the left and right internal carotid arteries (the average blood pressure of the left internal carotid artery + the average blood pressure of the right internal carotid artery)/2, the average blood pressure of the left internal carotid artery
  • the average blood pressure of the right internal carotid artery refers to the average value of the pressure waveform of the C1 segment of the right internal carotid artery within one cardiac cycle obtained by the simulation of the cerebral hemodynamic model.
  • the terminal device can further adjust the correction coefficient ⁇ of the intracranial arteries.
  • the adjustment standard of ⁇ is: the percentage error between the peak value of the blood flow velocity of the intracranial artery obtained by the simulation of the cerebral hemodynamic model and the peak value of the blood flow velocity of the intracranial artery obtained by the TCD measurement is within 5%.
  • cerebral arteries that can measure blood flow velocity by TCD include: left internal carotid artery C7 segment (22), left anterior cerebral artery A1 segment (16), left middle cerebral artery M1 segment (17), left cerebral artery P1 segment of posterior artery (31), V4 segment of left vertebral artery (41), C7 segment of right internal carotid artery (6), A1 segment of right anterior cerebral artery (7), M1 segment of right middle cerebral artery (9), right posterior cerebral artery
  • the specific measurement position of each intracranial artery may be the middle position of each intracranial artery, such as the black dot shown in FIG. 3 .
  • the ultrasonic velocity spectrum waveform (hereinafter referred to as the first ultrasonic velocity spectrum waveform) of multiple (for example, three) cardiac cycles of each intracranial artery can be measured by TCD, and then according to each intracranial artery corresponding
  • the average value of the multiple first ultrasonic flow velocity spectrum waveforms determines the final target ultrasonic flow velocity spectrum waveform of each intracranial artery.
  • the user when the flow rate is measured by the TCD, the user can be placed in a supine posture.
  • the following will take the C7 segment of the left internal carotid artery as an example to illustrate the determination of the target ultrasonic velocity spectrum waveform of each intracranial artery.
  • FIG. 4 shows a schematic diagram of a scene for determining a target ultrasonic velocity spectrum waveform provided by an embodiment of the present application.
  • the terminal device may measure the first ultrasonic velocity spectrum waveform of the C7 segment of the left internal carotid artery for at least three cardiac cycles through the TCD.
  • the profiles of the three first ultrasonic velocity spectrum waveforms can be respectively extracted to generate ultrasonic velocity spectrum waveforms (hereinafter referred to as the second ultrasonic velocity spectrum waveform) of three cardiac cycles, for example
  • the second ultrasonic velocity spectrum waveform can be imported into OriginPro 2016 Digitizer respectively, and the contour point coordinates of the three first ultrasonic flow velocity spectrum waveforms can be respectively exported to generate the second ultrasonic flow velocity of three cardiac cycles according to the contour point coordinates Spectral waveforms (ie U 1 , U 2 and U 3 ).
  • the terminal device may calculate the average value of the three generated second ultrasonic flow velocity spectrum waveforms, and fit the ultrasonic flow velocity spectrum waveform of one cardiac cycle (hereinafter referred to as the third ultrasonic flow velocity spectrum waveform) through the average value.
  • TCD measurement the brachial artery blood pressure measurement and the subsequent color Doppler ultrasound equipment measurement can be performed simultaneously.
  • color Doppler ultrasound equipment can be used to measure the ultrasonic velocity spectrum waveforms of the C1 segment of the left internal carotid artery, the C1 segment of the right internal carotid artery, the V2 segment of the left vertebral artery, and the V2 segment of the right vertebral artery for at least three cardiac cycles (hereinafter called the fourth ultrasonic velocity spectrum waveform).
  • the specific measurement positions of the C1 segment of the left internal carotid artery, the C1 segment of the right internal carotid artery, the V2 segment of the left vertebral artery, and the V2 segment of the right vertebral artery can be the middle positions of the arteries, for example, the black dots shown in Figure 5 place.
  • the terminal device may calculate the average value of the three fourth ultrasonic velocity spectrum waveforms corresponding to the C1 segment of the left internal carotid artery, and determine the target ultrasonic velocity spectrum waveform of the left internal carotid artery according to the average value corresponding to the C1 segment of the left internal carotid artery.
  • the terminal device can calculate the average value of the three fourth ultrasonic flow velocity spectrum waveforms corresponding to the C1 segment of the right internal carotid artery, and determine the target ultrasonic flow velocity spectrum waveform of the right internal carotid artery according to the average value corresponding to the C1 segment of the right internal carotid artery ; Calculate the average value of the three fourth ultrasonic velocity spectrum waveforms corresponding to the left vertebral artery V2 segment, and determine the target ultrasonic velocity spectrum waveform of the left vertebral artery according to the corresponding average value of the left vertebral artery V2 segment, and calculate the right vertebral artery V2 segment The corresponding average value of the three fourth ultrasonic velocity spectrum waveforms, and determine the target ultrasonic velocity spectrum waveform of the right vertebral artery according to the average value corresponding to the V2 segment of the right vertebral artery.
  • the method for determining the target ultrasonic velocity spectrum waveform of the left internal carotid artery, the right internal carotid artery, the left vertebral artery, and the right vertebral artery is the same as the determination of the target ultrasonic velocity spectrum waveform of the C7 segment of the left internal carotid artery described above.
  • the manner is the same, and for specific content, reference may be made to the foregoing description, and for the sake of brevity, details are not repeated here.
  • the terminal device may use the target ultrasonic flow velocity spectrum waveform of the left internal carotid artery (indicated by left internal carotid artery velocity in FIG. 3 ), the target ultrasonic flow velocity spectrum waveform of the right internal carotid artery (Fig.
  • the wave intensity WI is the product of the first derivative of P and U at the same position in the blood vessel with respect to time t, namely
  • the wave power WP is the product of the first derivative of P and Q at the same position in the blood vessel with respect to time t, that is
  • the terminal device determines the second average flow rate of total cerebral perfusion within a cardiac cycle
  • the first average flow rate of the left internal carotid artery the initial blood flow velocity of the left internal carotid artery*the initial cross-sectional area of the left internal carotid artery
  • the initial blood flow velocity of the left internal carotid artery is The average blood flow velocity corresponding to the target ultrasonic flow velocity spectrum waveform of the artery.
  • the initial blood flow velocity of the right internal carotid artery is the obtained right carotid artery in the aforementioned S103
  • the first average flow rate of the left vertebral artery the initial blood flow velocity of the left vertebral artery*the initial cross-sectional area of the left vertebral artery
  • the initial blood flow velocity of the left vertebral artery is the average blood flow velocity corresponding to the target ultrasonic flow velocity spectrum waveform of the left vertebral artery obtained in the aforementioned S103
  • the first average flow rate of the right vertebral artery the initial blood flow velocity of the right vertebral artery*the initial cross-sectional area of the right vertebral artery, right The initial blood flow velocity of the
  • the terminal device can calculate R T and C T of the cerebral arterial system according to the following formulas 6 and 7:
  • P m is the mean arterial pressure of the brachial artery
  • P cap is the intracranial capillary pressure
  • P icp is the intracranial pressure
  • P cap 25mmHg
  • P icp 11mmHg
  • PP is the pulse pressure
  • PP SBP-DBP
  • ⁇ V T is the total pulsating volume of the entire cerebral artery (ie, cerebral arterial system).
  • the total pulsation volume of the entire cerebral artery is the sum of the pulsation volumes of the left and right internal carotid arteries and the left and right vertebral arteries, namely ⁇ V i is the pulse volume.
  • the pulsatile volume represents the change in circulatory volume of the cerebral arteries required to attenuate pulsatile arterial flow to non-pulsatile capillary flow.
  • the pulsatile volume is the difference between the maximum value and the minimum value of the cumulative integral in one cardiac cycle after the instantaneous flow waveform minus the average flow, and it is also equivalent to the instantaneous flow at the intersection of the systolic instantaneous flow waveform and the average flow.
  • the cumulative integral of the difference between the waveform and the average flow rate, that is, the pulsation volume can be determined by the following formula 8:
  • Q(t) is the instantaneous flow rate
  • t 1 and t 2 are the instantaneous flow Q(t) and the average flow in systole respectively The time at the intersection.
  • FIG. 6 shows an example diagram of calculating the pulsation volume of the right internal carotid artery.
  • the terminal device can obtain the instantaneous flow rate of the right internal carotid artery as shown in (a) in Figure 6 according to the target ultrasonic velocity spectrum waveform of the right internal carotid artery in one cardiac cycle and the initial cross-sectional area of the right internal carotid artery. Flow waveform and average flow.
  • the instantaneous flow waveform of the right internal carotid artery is calculated by multiplying the target ultrasonic flow velocity spectrum waveform by the initial cross-sectional area
  • the average flow rate of the right internal carotid artery is calculated by multiplying the average blood flow velocity corresponding to the target ultrasonic flow velocity spectrum waveform by the initial cross-sectional area. Calculated cross-sectional area.
  • the cumulative integral of the instantaneous flow waveform of the right internal carotid artery minus the average flow in one cardiac cycle represents the volume change of the right internal carotid artery in one cardiac cycle.
  • the pulsation volume ⁇ V i of the internal carotid artery is also equal to the area shaded in gray in (a) in FIG. 6 .
  • the terminal device determines the target resistance and target compliance of the 0D model of the terminal artery.
  • the terminal device can divide the total resistance R T and the total compliance C T Reasonable allocation to the entire cerebral arterial network to individualize the target resistance and target compliance of the 0D model of the terminal artery. It can be understood that the assignment methods of the target resistance and the target compliance of the 0D model of each terminal artery are basically the same, and the left middle cerebral artery will be taken as an example for illustration below.
  • FIG. 7 shows an example diagram of the left middle cerebral artery and its peripheral blood vessels.
  • ICA is the left internal carotid artery
  • ACA is the left anterior cerebral artery
  • MCA is the left middle cerebral artery
  • the terminal device may first obtain the average flow Q MCA of the left middle cerebral artery in one cardiac cycle, wherein Q MCA may be obtained by multiplying the average blood flow velocity corresponding to the target ultrasonic flow velocity spectrum waveform of the left middle cerebral artery obtained by the aforementioned TCD measurement by The initial cross-sectional area of the left middle cerebral artery was calculated. Subsequently , the end-device can Calculate the total resistance R MCA and total compliance C MCA of the vessels downstream of the left middle cerebral artery. Then, the terminal device can calculate the target resistance and target compliance of the peripheral 0D vascular bed according to the cross-sectional area ratio of the left middle cerebral artery terminal artery.
  • the terminal device can calculate the R MCA and C MCA of the vessels downstream of the left middle cerebral artery according to the following formulas 9 and 10, and then can calculate the target resistance and target of the peripheral OD vascular bed according to the following formulas 11 and 12 Compliance:
  • ⁇ is the blood density
  • c j is the pulse wave velocity of artery j
  • a j is the initial cross-sectional area of arterial j
  • a k is the initial cross-sectional area of arterial k
  • L j is the vessel length of arterial j.
  • cj can be determined based on the aforementioned hardness parameter K of each artery, namely K j is the stiffness parameter of artery j.
  • the outlet boundary conditions of the cerebral hemodynamic model may be the average flow rate Q 1D , the average blood pressure P 1D and the average blood flow velocity U 1D at the terminal nodes of each terminal artery.
  • the average flow rate Q 1D , the average blood pressure P 1D , and the average blood flow velocity U 1D at the terminal node of the terminal artery j will be illustrated below with the example diagram shown in FIG. 7 .
  • the terminal device may determine the flow Q 1D , blood pressure P 1D and flow velocity U 1D at the end node of the terminal artery j according to the following formulas 13, 14, 15, 16 and 17:
  • a 1D is the target cross-sectional area at the terminal node of the terminal artery j
  • P a is the peripheral vascular bed pressure
  • W f is the characteristic variable transmitted forward
  • a 0 is the terminal artery j
  • A is the target cross-sectional area at each moment of the terminal artery j
  • Q p is the flow
  • Q d is the flow through
  • Pcap is the capillary pressure
  • c is the actual pulse wave velocity of the terminal artery j
  • P is the average blood pressure of the terminal artery j at each moment.
  • Pa can be derived from Pa at the previous time
  • Q d can be derived from Q d at the previous time
  • W f can also be derived from W f at the previous time.
  • t n is the current time
  • t n-1 is the last time
  • x m is the end node
  • t n-1 is the propagating velocity of the characteristic variable passed forward at t n-1 , which is equal to the pulse wave velocity at t n-1 plus the blood flow velocity.
  • the wave intensity WI and wave power WP of each cerebral artery can be separated into forward The propagating and backward propagating components, where the forward component (i.e. the forward propagating wave intensity WI + and the forward propagating wave power WP + ) represents the influence of the upstream of the blood vessel, and the backward component (i.e. the backward reflected wave intensity WI - and the back-reflected wave power WP - ) represent the effect downstream of the vessel.
  • the forward wave intensity WI + , the backward reflected wave intensity WI - , the forward wave power WP + and the backward reflected wave power WP - are respectively:
  • FIG. 8 shows a blood flow simulation diagram of the cerebral hemodynamic model provided by the embodiment of the present application.
  • the cerebral blood flow of the target user can be simulated through the cerebral hemodynamic model, and the right anterior cerebral artery as shown in (a) in FIG. 8 is respectively obtained, as shown in FIG. 8
  • the left anterior cerebral artery shown in (b), the right middle cerebral artery shown in (c) in Figure 8, the left middle cerebral artery shown in (d) in Figure 8, the left middle cerebral artery shown in Figure 8 Flow velocity waveforms of the right posterior cerebral artery shown in (e) and the left posterior cerebral artery shown in (f) in FIG. 8 .
  • each flow velocity waveform in Fig. 8 is time (time is s), and the vertical axis is blood flow velocity (unit is cm/s). It can be seen from Figure 8 that the flow velocity waveforms of the left anterior cerebral artery, right anterior cerebral artery, left middle cerebral artery, right middle cerebral artery, left posterior cerebral artery, and right posterior cerebral artery simulated by the cerebral hemodynamic model are consistent with the TCD The actual measurement data are very consistent, indicating that the cerebral hemodynamic model provided by the embodiment of the present application has strong validity and reliability.
  • nuclear magnetic resonance images of the brain and neck can be obtained; a cerebral hemodynamic model can be constructed according to the nuclear magnetic resonance images; left internal carotid artery, right internal carotid artery, left vertebral artery and right vertebral artery can be obtained Spectral waveform of the target ultrasonic flow velocity; determine the wave intensity and wave power of each cerebral artery according to the cerebral hemodynamic model and each target ultrasonic velocity spectrum waveform, so as to realize the WI and WP of the whole cerebral artery through the combination of non-invasive measurement and numerical simulation Measurement, improve the accuracy of cerebral artery WI and WP measurement, with strong ease of use and practicability.
  • Fig. 9 shows a structural block diagram of the measuring device for cerebral artery wave intensity and wave power provided by the embodiment of the present application. For the convenience of description, only Parts related to the embodiments of the present application are shown.
  • the measuring device may include:
  • a nuclear magnetic resonance image acquisition module 901 configured to acquire a nuclear magnetic resonance image including brain information and neck information;
  • a dynamic model construction module 902 configured to construct a cerebral hemodynamic model according to the nuclear magnetic resonance image
  • the spectrum waveform acquisition module 903 is used to respectively acquire the target ultrasonic velocity spectrum waveforms of the left internal carotid artery, the right internal carotid artery, the left vertebral artery and the right vertebral artery;
  • the wave intensity and wave power determination module 904 is configured to determine the wave intensity and wave power of each cerebral artery according to the cerebral hemodynamic model and each of the target ultrasonic velocity spectrum waveforms.
  • the cerebral hemodynamic model performs blood flow simulation on any cerebral artery according to the following formula:
  • A is the target cross-sectional area of the cerebral artery at time t at position x
  • U is the average blood flow velocity at the cerebral artery at time t at position x
  • P is the average blood pressure at the cerebral artery at time t at position x
  • ⁇ Blood viscosity
  • ⁇ is the blood density
  • ⁇ is the viscous friction constant
  • ⁇ 0.0035Pa ⁇ s
  • P ext is the pressure applied on the outer wall of the cerebral artery
  • P 0 is the reference Pressure
  • K is the stiffness parameter of the cerebral artery
  • A0 is the initial cross-sectional area of the cerebral artery.
  • the wave intensity and wave power determining module 904 may include:
  • an inlet boundary determination unit configured to determine each of the target ultrasonic velocity spectrum waveforms as an inlet boundary condition of the cerebral hemodynamic model
  • an outlet boundary determination unit configured to determine an outlet boundary condition of the cerebral hemodynamic model
  • a cerebral arterial blood pressure determination unit configured to determine the average blood flow velocity waveform, the average flow waveform, and the Average blood pressure waveform
  • the wave intensity and wave power determining unit is configured to determine the wave intensity and wave power of each of the cerebral arteries according to the average blood flow velocity waveform, the average flow waveform and the average blood pressure waveform of each of the cerebral arteries.
  • the outlet boundary determination unit may include:
  • the total resistance determination sub-unit is used to calculate according to the brachial artery blood pressure and the target ultrasonic velocity spectrum waveform and initial cross-sectional area of the left internal carotid artery, the right internal carotid artery, the left vertebral artery and the right vertebral artery total resistance and total compliance of the cerebral arterial system;
  • a target resistance determination subunit configured to determine the target resistance and target compliance of each terminal artery according to the total resistance and total compliance of the cerebral arterial system
  • the outlet boundary determination subunit is used to determine the outlet boundary condition of the cerebral hemodynamic model according to the target resistance and target compliance of each terminal artery.
  • the total resistance determination sub-unit may include:
  • the first average flow rate determining subunit is used to determine one The first average flow rate of left internal carotid artery, right internal carotid artery, left vertebral artery and right vertebral artery in cardiac cycle;
  • the second average flow determination subunit is configured to determine a second average flow of total brain perfusion within one cardiac cycle according to each of the first average flows;
  • a total resistance determining subunit configured to determine the total resistance and total compliance of the cerebral arterial system according to the second average flow rate and the brachial artery blood pressure.
  • the total resistance determination subunit is specifically configured to determine the total resistance and total compliance of the cerebral arterial system according to the following formula:
  • RT is the total resistance
  • CT is the total compliance
  • P m is the mean arterial pressure of the brachial artery
  • P cap is the intracranial capillary pressure
  • P icp is the intracranial pressure
  • P cap 25mmHg
  • P icp 11mmHg
  • PP SBP-DBP
  • SBP is the systolic pressure of the brachial artery
  • DBP is the diastolic pressure of the brachial artery
  • ⁇ V T is the total pulsation volume of the cerebral arterial system.
  • the target resistance determination subunit is specifically configured to allocate the total resistance and total compliance of the cerebral arterial system to each of the terminal arteries by using a two-step allocation method of the arterial flow ratio and the arterial cross-sectional area ratio , to obtain the target resistance and target compliance of each terminal artery.
  • the dynamic model building module 902 may include:
  • a cerebral artery segmentation unit configured to perform cerebral artery segmentation and reconstruction on the nuclear magnetic resonance image, to obtain a three-dimensional geometric model of the cerebral artery system
  • a centerline determining unit configured to determine the centerline of each cerebral artery according to the three-dimensional geometric model, and obtain the length and initial cross-sectional area of each centerline;
  • a pulse wave transfer model establishing unit configured to establish a 1D pulse wave transfer model of the cerebral arterial system according to the length and initial cross-sectional area of each of the centerlines;
  • a dynamic model construction unit configured to connect an 0D model to each terminal artery of the 1D pulse wave transmission model to obtain the cerebral hemodynamic model, wherein the 0D model is used to simulate the periphery of the terminal artery Target resistance and target compliance of the vascular bed.
  • FIG. 10 is a schematic structural diagram of a terminal device provided by an embodiment of the present application.
  • the terminal device 10 of this embodiment includes: at least one processor 1000 (only one is shown in FIG. 10 ), a memory 1001 and stored in the memory 1001 and can be used in the at least one processor 1000
  • a computer program 1002 running on the computer when the processor 1000 executes the computer program 1002, the steps in any of the above-mentioned embodiments of the method for measuring cerebral artery wave intensity and wave power are realized.
  • the terminal device 10 may be a computing device such as a desktop computer, a notebook, a palmtop computer, or a cloud server.
  • the terminal device may include, but not limited to, a processor 1000 and a memory 1001 .
  • FIG. 10 is only an example of the terminal device 10, and does not constitute a limitation to the terminal device 10. It may include more or less components than those shown in the figure, or combine certain components, or different components. , for example, may also include input and output devices, network access devices, and so on.
  • the processor 1000 can be a central processing unit (central processing unit, CPU), and the processor 1000 can also be other general processors, digital signal processors (digital signal processor, DSP), application specific integrated circuits (application specific integrated circuits) , ASIC), field-programmable gate array (field-programmable gate array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • a general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.
  • the storage 1001 may be an internal storage unit of the terminal device 10 in some embodiments, for example, a hard disk or a memory of the terminal device 10 .
  • the memory 1001 may also be an external storage device of the terminal device 10 in other embodiments, such as a plug-in hard disk equipped on the terminal device 10, a smart media card (smart media card, SMC), a secure digital (secure digital, SD) card, flash memory card (flash card), etc. Further, the memory 1001 may also include both an internal storage unit of the terminal device 10 and an external storage device.
  • the memory 1001 is used to store operating systems, application programs, boot loaders (BootLoader), data and other programs, such as program codes of the computer programs.
  • the memory 1001 can also be used to temporarily store data that has been output or will be output.
  • the embodiment of the present application also provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the foregoing method embodiments can be implemented.
  • An embodiment of the present application provides a computer program product.
  • the computer program product runs on a terminal device, the terminal device can implement the steps in the foregoing method embodiments when executed.
  • the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • all or part of the processes in the methods of the above embodiments in the present application can be completed by instructing related hardware through computer programs, and the computer programs can be stored in a computer-readable storage medium.
  • the computer program When executed by a processor, the steps in the above-mentioned various method embodiments can be realized.
  • the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form.
  • the computer-readable storage medium may at least include: any entity or device capable of carrying computer program codes to the device/terminal device, recording medium, computer memory, read-only memory (read-only memory, ROM, ), random access Memory (random access memory, RAM), electrical carrier signals, telecommunication signals, and software distribution media.
  • computer readable storage media may not be electrical carrier signals and telecommunication signals based on legislation and patent practice.
  • the disclosed apparatus/terminal device and method may be implemented in other ways.
  • the device/terminal device embodiments described above are only illustrative.
  • the division of the modules or units is only a logical function division.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

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Abstract

本申请适用于图像处理技术领域，尤其涉及一种脑动脉波强度和波功率的测量方法、终端设备及计算机可读存储介质。所述测量方法可以获取包含脑部信息和颈部信息的核磁共振图像，并根据核磁共振图像构建脑血流动力学模型；分别获取左颈内动脉、右颈内动脉、左椎动脉和右椎动脉的目标超声流速频谱波形，以根据脑血流动力学模型和各目标超声流速频谱波形确定各脑动脉的波强度和波功率。即本申请实施例通过无创测量和数值模拟相结合的方式实现全脑动脉WI和WP的测量，极大地提高了脑动脉WI和WP测量的精度，具有较强的易用性和实用性。

Description

脑动脉波强度和波功率的测量方法、终端设备及存储介质 技术领域
本申请属于图像处理技术领域，尤其涉及一种脑动脉波强度和波功率的测量方法、终端设备及计算机可读存储介质。
背景技术
脑动脉波强度(wave intensity，WI)和波功率(wave power，WP)是预测老年痴呆风险的重要标志物。因此，需要测量脑动脉WI和WP。目前，仅能实现颈总动脉WI的测量，即采用组合的回声跟踪和多普勒系统同时获取颈总动脉直径和流速波形，通过将直径波形校准为手臂袖带的收缩压和舒张压而估算出颈总动脉压力波形，以计算颈总动脉WI。这种测量方式是假定血管直径波形和压力波形相同，并采用臂压来校准，忽略了臂压和颈动脉压的差异性，导致压力评估不准确，影响波强度WI的测量精度。
发明内容
本申请实施例提供了一种脑动脉波强度和波功率的测量方法、装置、终端设备及计算机可读存储介质，可以准确地测量各脑动脉的波强度和波功率。
第一方面，本申请实施例提供了一种脑动脉波强度和波功率的测量方法，可以包括：
获取包含脑部信息和颈部信息的核磁共振图像；
根据所述核磁共振图像构建脑血流动力学模型；
分别获取左颈内动脉、右颈内动脉、左椎动脉和右椎动脉的目标超声流速频谱波形；
根据所述脑血流动力学模型和各所述目标超声流速频谱波形确定各脑动脉 的波强度和波功率。
通过上述的测量方法，可以获取包含脑部信息和颈部信息的核磁共振图像，并根据核磁共振图像构建脑血流动力学模型；分别获取左颈内动脉、右颈内动脉、左椎动脉和右椎动脉的目标超声流速频谱波形，以根据脑血流动力学模型和各目标超声流速频谱波形确定各脑动脉的波强度和波功率。即本申请实施例通过无创测量和数值模拟相结合的方式实现全脑动脉WI和WP的测量，极大地提高了脑动脉WI和WP测量的精度，具有较强的易用性和实用性。
示例性的，所述脑血流动力学模型根据下述公式对任一脑动脉进行血流模拟：
Figure PCTCN2021138058-appb-000001
Figure PCTCN2021138058-appb-000002
Figure PCTCN2021138058-appb-000003
其中，A为该脑动脉t时刻x位置处的目标横截面积，U为该脑动脉t时刻x位置处的平均血流速度，P为该脑动脉t时刻x位置处的平均血压，μ为血液黏度，ρ为血液密度，ξ为粘性摩擦常数，且μ＝0.0035Pa·s，ρ＝1050kg·m -3，ξ＝22，P ext为施加在该脑动脉外壁的压力，P 0为参考压力，K为该脑动脉的硬度参数，A 0为该脑动脉的初始横截面积。
在第一方面的一种可能的实现方式中，所述根据所述脑血流动力学模型和各所述目标超声流速频谱波形确定各脑动脉的波强度和波功率，可以包括：
将各所述目标超声流速频谱波形确定为所述脑血流动力学模型的入口边界条件；
确定所述脑血流动力学模型的出口边界条件；
根据所述入口边界条件、所述出口边界条件和所述脑血流动力学模型确定各所述脑动脉任一位置处的平均血流速度波形、平均流量波形以及平均血压波 形；
根据各所述脑动脉的平均血流速度波形、平均流量波形以及平均血压波形确定各所述脑动脉的波强度和波功率。
可选地，所述确定所述脑血流动力学模型的出口边界条件，可以包括：
根据肱动脉血压以及所述左颈内动脉、所述右颈内动脉、所述左椎动脉和所述右椎动脉的目标超声流速频谱波形和初始横截面积计算脑动脉系统的总阻力和总顺应性；
根据所述脑动脉系统的总阻力和总顺应性确定各末端动脉的目标阻力和目标顺应性；
根据各所述末端动脉的目标阻力和目标顺应性确定所述脑血流动力学模型的出口边界条件。
示例性的，所述根据肱动脉血压以及所述左颈内动脉、所述右颈内动脉、所述左椎动脉和所述右椎动脉的目标超声流速频谱波形和初始横截面积计算脑动脉系统的总阻力和总顺应性，可以包括：
根据所述左颈内动脉、所述右颈内动脉、所述左椎动脉和所述右椎动脉的目标超声流速频谱波形和初始横截面积分别确定一个心动周期内的左颈内动脉、右颈内动脉、左椎动脉以及右椎动脉的第一平均流量；
根据各所述第一平均流量确定一个心动周期内脑总灌注的第二平均流量；
根据所述第二平均流量和所述肱动脉血压确定所述脑动脉系统的总阻力和总顺应性。
具体地，所述根据所述第二平均流量和所述肱动脉血压确定所述脑动脉系统的总阻力和总顺应性，可以包括：
根据下述公式确定所述脑动脉系统的总阻力和总顺应性：
Figure PCTCN2021138058-appb-000004
Figure PCTCN2021138058-appb-000005
其中，R T为所述总阻力，C T为所述总顺应性，P m为肱动脉的平均动脉压，P cap为颅内毛细血管压，P icp为颅内压，且P cap＝25mmHg，P icp＝11mmHg，
Figure PCTCN2021138058-appb-000006
为所述第二平均流量，PP＝SBP-DBP，SBP为肱动脉的收缩压，DBP为肱动脉的舒张压，ΔV T为所述脑动脉系统的总脉动体积。
可选地，所述根据所述脑动脉系统的总阻力和总顺应性确定各末端动脉的目标阻力和目标顺应性，可以包括：
利用动脉流量比和动脉横截面积比的两步分配法将所述脑动脉系统的总阻力和总顺应性分配给各所述末端动脉，得到各所述末端动脉的目标阻力和目标顺应性。
在第一方面的一种可能的实现方式中，所述根据所述核磁共振图像构建脑血流动力学模型，可以包括：
对所述核磁共振图像进行脑动脉分割与重建，得到脑动脉系统的三维几何模型；
根据所述三维几何模型确定各脑动脉的中心线，并获取各中心线的长度和初始横截面积；
根据各所述中心线的长度和初始横截面积建立所述脑动脉系统的1D脉搏波传递模型；
在所述1D脉搏波传递模型的各末端动脉中分别连接一0D模型，得到所述脑血流动力学模型，其中，所述0D模型用于模拟末端动脉的外周血管床的目标阻力和目标顺应性。
第二方面，本申请实施例提供了一种脑动脉波强度和波功率的测量装置，可以包括：
核磁共振图像获取模块，用于获取包含脑部信息和颈部信息的核磁共振图像；
动力学模型构建模块，用于根据所述核磁共振图像构建脑血流动力学模型；
频谱波形获取模块，用于分别获取左颈内动脉、右颈内动脉、左椎动脉和 右椎动脉的目标超声流速频谱波形；
波强度和波功率确定模块，用于根据所述脑血流动力学模型和各所述目标超声流速频谱波形确定各脑动脉的波强度和波功率。
第三方面，本申请实施例提供了一种终端设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现上述第一方面中任一项所述的脑动脉波强度和波功率的测量方法。
第四方面，本申请实施例提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现上述第一方面中任一项所述的脑动脉波强度和波功率的测量方法。
第五方面，本申请实施例提供了一种计算机程序产品，当计算机程序产品在终端设备上运行时，使得终端设备执行上述第一方面中任一项所述的脑动脉波强度和波功率的测量方法。
可以理解的是，上述第二方面至第五方面的有益效果可以参见上述第一方面中的相关描述，在此不再赘述。
附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本申请一实施例提供的脑动脉波强度和波功率的测量方法的流程示意图；
图2和图3是本申请一实施例提供的脑血流动力学模型的构建示意图；
图4是本申请一实施例提供的确定目标超声流速频谱波形的场景示意图；
图5是本申请一实施例提供的测量位置的示例图；
图6是右颈内动脉的脉动体积的计算示例图；
图7是左大脑中动脉及其外周血管的示例图；
图8是本申请实施例提供的脑血流动力学模型的血流模拟图；
图9是本申请实施例提供的脑动脉波强度和波功率的测量装置的结构示意图；
图10是本申请实施例提供的终端设备的结构示意图。
具体实施方式
以下描述中，为了说明而不是为了限定，提出了诸如特定系统结构、技术之类的具体细节，以便透彻理解本申请实施例。然而，本领域的技术人员应当清楚，在没有这些具体细节的其它实施例中也可以实现本申请。在其它情况中，省略对众所周知的系统、装置、电路以及方法的详细说明，以免不必要的细节妨碍本申请的描述。
应当理解，当在本申请说明书和所附权利要求书中使用时，术语“包括”指示所描述特征、整体、步骤、操作、元素和/或组件的存在，但并不排除一个或多个其它特征、整体、步骤、操作、元素、组件和/或其集合的存在或添加。
还应当理解，在本申请说明书和所附权利要求书中使用的术语“和/或”是指相关联列出的项中的一个或多个的任何组合以及所有可能组合，并且包括这些组合。
如在本申请说明书和所附权利要求书中所使用的那样，术语“如果”可以依据上下文被解释为“当...时”或“一旦”或“响应于确定”或“响应于检测到”。类似地，短语“如果确定”或“如果检测到[所描述条件或事件]”可以依据上下文被解释为意指“一旦确定”或“响应于确定”或“一旦检测到[所描述条件或事件]”或“响应于检测到[所描述条件或事件]”。
另外，在本申请说明书和所附权利要求书的描述中，术语“第一”、“第二”、“第三”等仅用于区分描述，而不能理解为指示或暗示相对重要性。
在本申请说明书中描述的参考“一个实施例”或“一些实施例”等意味着在本申请的一个或多个实施例中包括结合该实施例描述的特定特征、结构或特点。由此，在本说明书中的不同之处出现的语句“在一个实施例中”、“在一些实施例中”、“在其他一些实施例中”、“在另外一些实施例中”等不是必然都参考相同的实施例，而是意味着“一个或多个但不是所有的实施例”，除非是以其他方式另外特别强调。术语“包括”、“包含”、“具有”及它们的变形都意味着“包括但不限于”，除非是以其他方式另外特别强调。
据世界卫生组织(World Health Organization，WHO)报道，目前世界范围内约有5000万痴呆患者，近60％生活在中低收入国家。2019年欧洲痴呆年鉴指出欧洲目前约有1000万痴呆患者。在美国，65岁以上老年人口中约有500万痴呆患者。2020年1月发布在《柳叶刀-神经病学》杂志的研究显示，我国65岁以上的老年人口中约有1000万痴呆症患者，每年造成的经济损失高达17000亿元。我国人口老龄化程度正在加速加深，预计到2050年老年痴呆患者人数将会超过4000万，痴呆将成为危害我国人民健康和晚年生活质量最为严重的公共卫生问题。最新研究发现，脑动脉波强度WI和波功率WP是预测老年痴呆风险的重要标志物，脑动脉WI和WP的分析可提前10年预测认知下降风险，预测结果不受其他风险因子的影响。因此，准确测量脑动脉WI和WP对老年痴呆症的早期诊断、预防和精准治疗具有重要的指导意义。
由于脑血管解剖结构的特殊性和测量技术的局限性，脑动脉WI和WP的测量极其困难，目前仅能实现颈总动脉WI的无创测量。例如，采用组合的回声跟踪和多普勒系统同时获取颈总动脉直径和流速波形，通过将直径波形校准为手臂袖带的收缩压和舒张压而估算出颈总动脉压力波形，以计算颈总动脉WI。但这种测量方法是假定血管直径波形和压力波形相同，并且采用臂压来校准，忽略了臂压和颈动脉压的差异性，导致压力评估不准确，影响波强度WI的测量精度。且这种测量方法仅能测量颈总动脉WI，无法测量其他脑血管的WI，也无法测量脑动脉WP。
为解决上述问题，本申请实施例提供了一种脑动脉波强度和波功率的测量方法，该方法可以获取包含脑部信息和颈部信息的核磁共振图像；根据核磁共振图像构建脑血流动力学模型；分别获取左颈内动脉、右颈内动脉、左椎动脉和右椎动脉的目标超声流速频谱波形；根据脑血流动力学模型和各目标超声流速频谱波形确定各脑动脉的波强度WI和波功率WP，以通过无创测量和数值模拟相结合的方式实现全脑动脉WI和WP的测量，极大地提高了脑动脉WI和WP测量的精度，具有较强的易用性和实用性。
请参阅图1，图1示出了本申请实施例提供的脑动脉波强度WI和波功率WP的测量方法的示意性流程图。其中，所述测量方法可以应用于手机、平板电脑、笔记本电脑、桌上型计算机等终端设备上，本申请实施例对终端设备的具体类型不作任何限制。如图1所示，所述测量方法，可以包括：
S101、获取包含脑部信息和颈部信息的核磁共振图像。
其中，核磁共振图像是指对用户的头部和颈部进行磁共振成像(magnetic resonance imaging，MRI)扫描，所得到的核磁共振图像(即MRI图像)，即该MRI图像中可以包含脑动脉的信息和颈动脉的信息。
S102、根据所述核磁共振图像构建脑血流动力学模型。
具体地，终端设备可以利用血管重建软件对MRI图像进行脑动脉分割与重建，得到脑动脉系统的三维几何模型。然后，终端设备可以根据三维几何模型确定各脑动脉的中心线，并获取各中心线的长度和初始横截面积，以根据各中心线的长度和初始横截面积建立全脑动脉拓扑结构(即脑动脉系统)的1D脉搏波传递模型。需要说明的是，对于任一中心线，终端设备可以获取沿该中心线均匀分布的多个横截面积，并将这多个横截面积的平均值作为该中心线的初始横截面积，例如获取沿该中心线均匀分布的五个横截面积，并将这五个横截面积的平均值作为该中心线的初始横截面积，即作为该中心线所对应的脑动脉的初始横截面积。
请参阅图2和图3，图2和图3示出了本申请实施例提供的脑血流动力学模 型的构建示意图。示例性的，在通过MRI扫描得到图2中的(a)所示的MRI图像后，终端设备可以利用血管重建软件对该MRI图像进行脑动脉分割与重建，例如可以利用交互式医学影像控制系统materialise mimics对该MRI图像进行脑动脉分割与重建，得到如图2中的(b)所示的脑动脉系统的三维几何模型，并确定脑动脉系统中各脑动脉的中心线，例如可以使用mimics中ANALYZE菜单下的Fit Centerline工具确定各脑动脉的中心线，得到如图2中的(c)所示的中心线的示例图。随后，终端设备可以获取各中心线的长度和初始横截面积，例如可以使用mimics中MEASURE菜单下的Distance工具获取各中心线的长度，以及可以使用MEASURE菜单下的Sectional Area工具获取各中心线对应的多个横截面积，并可以分别根据各脑动脉对应的多个横截面积确定各脑动脉的初始横截面积A 0。最后，终端设备可以根据各脑动脉对应的中心线的长度和初始横截面积建立如图2中的(d)所示的1D脉搏波传递模型，该1D脉搏波传递模型可以包括45段脑动脉。
在构建图2中的(d)所示的1D脉搏波传递模型后，终端设备可以在1D脉搏波传递模型的各末端动脉分别连接一个0D模型，例如，0D模型可以三元的Windkessel模型，以模拟脑动脉网络的外周血管床(例如小动脉、微动脉和毛细血管等)的阻力和顺应性，得到如图3所示的0-1D多尺度的脑血流动力学模型。如图3所示，0D模型可以包括两个电阻和一个电容，电阻
Figure PCTCN2021138058-appb-000007
表征特征阻抗，电阻
Figure PCTCN2021138058-appb-000008
表征末端阻力，电容C表征顺应性。需要说明的是，图3仅在第20段的末端动脉连接0D模型为例进行示例性说明。
示例性的，脑血流动力学模型可以采用下述的1D纳维-斯托克斯方程对各脑动脉进行血流模拟：
Figure PCTCN2021138058-appb-000009
Figure PCTCN2021138058-appb-000010
其中，A为该脑动脉t时刻x位置处的目标横截面积，U为该脑动脉t时刻 x位置处的平均血流速度，P为该脑动脉t时刻x位置处的平均血压，μ为血液黏度，ρ为血液密度，ξ为粘性摩擦常数，且μ＝0.0035Pa·s，ρ＝1050kg·m -3，ξ＝22。
应理解，本申请实施例中的平均血流速度和平均血压等是指空间上的平均。即位置x指的是脑动脉中的某一个横截面，平均血流速度和平均血压等指该整个横截面上的平均值。
本申请实施例，可以将各脑动脉沿中心轴向划分N个节点，假定两节点之间的距离为Δx，则节点i的位置x＝i*Δx，i＝1，2，…，N。在此，N可以根据实际情况具体确定，本申请实施例对此不作任何限制。
可以理解的是，A、U和P均是随心脏跳动连续变化的，即A、U和P均是随时间变化的，也就是说，本申请实施例所获取的各脑动脉的A、U和P均为随时间变化的波形曲线。
上述的1D纳维-斯托克斯方程组中有2个方程和3个未知数(即A、U以及P)，为了关闭控制方程，本申请实施例中可以引入管定律：
Figure PCTCN2021138058-appb-000011
其中，P ext为施加在脑动脉外壁的压力，对于颅内动脉，P ext等于颅内压，对于其他动脉，P ext可以设置为0，P 0为参考压力，本申请实施例中，可以将肱动脉的舒张压DBP设置为P 0，A 0为各脑动脉的初始横截面积，K为各脑动脉的硬度参数。
示例性的，肱动脉血压(包含收缩压SBP和舒张压DBP)可以通过下述方式测量得到：使用数字血压计先测量一只手臂的肱动脉血压，以一分钟的间隔分别进行三次测量，要求每次测量的误差小于5mmHg。然后，以同样的方式测量另一只手臂的肱动脉血压。随后，计算六次测量得到的收缩压SBP的平均值和舒张压DBP的平均值，并可以将六次测量得到的舒张压DBP的平均值确定为公式三中的P 0。
下面对各脑动脉的硬度参数K的确定过程进行详细说明。
(一)终端设备可以根据下述经验公式四和公式五估算各脑动脉的初始K m：
Figure PCTCN2021138058-appb-000012
Figure PCTCN2021138058-appb-000013
其中，c 0为该脑动脉初始的脉搏波速度，r 0为该脑动脉的半径，k 1、k 2和k 3为经验常数，且
Figure PCTCN2021138058-appb-000014
k 1＝300000kg·s -2·m -1，k 2＝-900m -1，k 3＝33700kg·s -2·m -1。
(二)终端设备可以确定各脑动脉的修正系数δ，并将各脑动脉的初始K m分别乘以修正系数δ，得到各脑动脉最终的硬度参数K，即K＝δ*K m。
在此，δ的选择标准为：使得脑血流动力学模型仿真得到左右颈内动脉的平均动脉压与肱动脉的平均动脉压P m之间的百分比误差在5％以内。其中，肱动脉的平均脉动圧P m＝DBP+0.422*(SBP-DBP)，此处的DBP为前述六次测量得到的舒张压DBP的平均值，此处的SBP为前述六次测量得到的收缩压SBP的平均值。左右颈内动脉的平均动脉压＝(左颈内动脉的平均血压+右颈内动脉的平均血压)/2，左颈内动脉的平均血压是指脑血流动力学模型仿真得到的左颈内动脉C1段的一个心动周期内的压力波形的平均值。同样地，右颈内动脉的平均血压是指脑血流动力学模型仿真得到的右颈内动脉C1段的一个心动周期内的压力波形的平均值。
(三)对于可以通过经颅多普勒超声(Transcranial Doppler，TCD)测量血流速度的颅内动脉，终端设备还可以进一步对该颅内动脉的修正系数δ进行调整。其中，δ的调整标准为：使得脑血流动力模型仿真得到的该颅内动脉的血流速度的峰值与TCD测量得到的该颅内动脉的血流速度的峰值的百分比误差在5％以内。
如图3所示，可以通过TCD测量血流速度的脑动脉包括：左颈内动脉C7段(22)、左大脑前动脉A1段(16)、左大脑中动脉M1段(17)、左大脑后动脉P1段(31)、左椎动脉V4段(41)、右颈内动脉C7段(6)、右大脑前 动脉A1段(7)、右大脑中动脉M1段(9)、右大脑后动脉P1段(30)、右椎动脉V4段(40)和基底动脉上部(36)。其中，各颅内动脉的具体测量位置可以为各颅内动脉的中间位置，例如可以为图3所示的黑色圆点处。
为消除测量误差，本申请实施例可以通过TCD测量各颅内动脉多个(例如三个)心动周期的超声流速频谱波形(以下称为第一超声流速频谱波形)，然后根据各颅内动脉对应的多个第一超声流速频谱波形的平均值确定各颅内动脉最终的目标超声流速频谱波形。其中，在通过TCD进行流速测量时，可以让用户处于仰卧姿态。以下将以左颈内动脉C7段为例示例性说明各颅内动脉的目标超声流速频谱波形的确定。
请参阅图4，图4示出了本申请实施例提供的确定目标超声流速频谱波形的场景示意图。如图4中的(a)所示，终端设备可以通过TCD测量左颈内动脉C7段至少三个心动周期的第一超声流速频谱波形。如图4中的(b)所示，然后可以分别提取这三个第一超声流速频谱波形的轮廓，生成三个心动周期的超声流速频谱波形(以下称为第二超声流速频谱波形)，例如可以将这三个第一超声流速频谱波形分别导入OriginPro 2016 Digitizer，分别导出这三个第一超声流速频谱波形的轮廓点坐标，以根据各轮廓点坐标分别生成三个心动周期的第二超声流速频谱波形(即U 1、U 2和U 3)。随后，终端设备可以计算所生成的三个第二超声流速频谱波形的平均值，并通过平均值拟合成一个心动周期的超声流速频谱波形(以下称为第三超声流速频谱波形)。由于TCD测量的第一超声流速频谱波形为血管中心的最大流速，而本申请实施例中所需要的是整个血管横截面的平均流速，因此，如图4中的(c)所示，终端设备可以将拟合成的第三超声流速频谱波形除以2，以得到左颈内动脉C7段最终的目标超声流速频谱波形，即左颈内动脉C7段最终的目标超声流速频谱波形U m＝(U 1+U 2+U 3)/6。
需要说明的是，TCD测量、肱动脉血压的测量以及后续彩色多普勒超声设备的测量可以同时进行。
S103、分别获取左颈内动脉、右颈内动脉、左椎动脉和右椎动脉的目标超 声流速频谱波形。
具体地，可以先使用彩色多普勒超声设备测量左颈内动脉C1段、右颈内动脉C1段、左椎动脉V2段和右椎动脉V2段至少三个心动周期的超声流速频谱波形(以下称为第四超声流速频谱波形)。其中，左颈内动脉C1段、右颈内动脉C1段、左椎动脉V2段和右椎动脉V2段的具体测量位置可以为各动脉的中间位置，例如可以为图5所示的黑色圆点处。然后，终端设备可以计算左颈内动脉C1段对应的三个第四超声流速频谱波形的平均值，并根据左颈内动脉C1段对应的平均值确定左颈内动脉的目标超声流速频谱波形。同样的，终端设备可以计算右颈内动脉C1段对应的三个第四超声流速频谱波形的平均值，并根据右颈内动脉C1段对应的平均值确定右颈内动脉的目标超声流速频谱波形；计算左椎动脉V2段对应的三个第四超声流速频谱波形的平均值，并根据左椎动脉V2段对应的平均值确定左椎动脉的目标超声流速频谱波形，以及计算右椎动脉V2段对应的三个的第四超声流速频谱波形的平均值，并根据右椎动脉V2段对应的平均值确定右椎动脉的目标超声流速频谱波形。
可以理解的是，左颈内动脉、右颈内动脉、左椎动脉以及右椎动脉的目标超声流速频谱波形的确定方式与前述所述的左颈内动脉C7段的目标超声流速频谱波形的确定方式相同，具体内容可以参照前述描述，为简明起见，在此不再赘述。
S104、根据所述脑血流动力学模型和各所述目标超声流速频谱波形确定各脑动脉的波强度和波功率。
示例性的，如图3所示，终端设备可以将左颈内动脉的目标超声流速频谱波形(图3中用左颈内动脉流速来表示)、右颈内动脉的目标超声流速频谱波形(图3中用右颈内动脉流速来表示)、左椎动脉的目标超声流速频谱波形(图3中用左椎动脉流速来表示)以及右椎动脉的目标超声流速频谱波形(图3中用右椎动脉流速来表示)均作为脑血流动力学模型的入口边界条件，以使得脑血流动力学模型根据该入口边界条件计算各脑动脉任一位置处的A、U和P波形。 然后，终端设备可以根据Q＝A*U分别计算各脑动脉任一位置处的流量Q波形，从而可以根据各脑动脉的P、U以及Q波形确定各脑动脉的波强度WI和波功率WP。
其中，波强度WI为血管内同一位置处的P和U对时间t的一阶导数的乘积，即
Figure PCTCN2021138058-appb-000015
波功率WP为血管内同一位置处的P和Q对时间t的一阶导数的乘积，即
Figure PCTCN2021138058-appb-000016
在一种可能的实现方式中，终端设备还可以根据末端动脉的0D模型确定脑血流动力学模型的出口边界条件，以使得脑血流动力学模型可以根据入口边界条件和出口边界条件来计算各脑动脉任一位置处的A、U和P波形。随后，终端设备可以根据各脑动脉的Q＝A*U计算各脑动脉任一位置处的Q波形，从而可以根据各脑动脉的P、U以及Q波形来确定各脑动脉的波强度WI和波功率WP。
下面将对脑血流动力学模型的出口边界条件的确定过程进行详细说明。
(一)终端设备确定一个心动周期内脑总灌注的第二平均流量
Figure PCTCN2021138058-appb-000017
具体地，
Figure PCTCN2021138058-appb-000018
为一个心动周期内左右颈内动脉和左右椎动脉的第一平均流量之和。其中，左颈内动脉的第一平均流量＝左颈内动脉的初始血流速度*左颈内动脉的初始横截面积，左颈内动脉的初始血流速度为前述S103中的获取左颈内动脉的目标超声流速频谱波形对应的平均血流速度。同样地，右颈内动脉的第一平均流量＝右颈内动脉的初始血流速度*右颈内动脉的初始横截面积，右颈内动脉的初始血流速度为前述S103中的获取右颈内动脉的目标超声流速频谱波形对应的平均血流速度；左椎动脉的第一平均流量＝左椎动脉的初始血流速度*左椎动脉的初始横截面积，左椎动脉的初始血流速度为前述S103中的获取左椎动脉的目标超声流速频谱波形对应的平均血流速度；右椎动脉的第一平均流量＝右椎动脉的初始血流速度*右椎动脉的初始横截面积，右椎动脉的初始血流速度为前述S103中的获取右椎动脉的目标超声流速频谱波形对应的平均血流速度。
(二)终端设备根据
Figure PCTCN2021138058-appb-000019
计算脑动脉系统的总阻力R T和总顺应性C T。
具体地，终端设备可以根据下述公式六和公式七计算脑动脉系统的R T和C T：
Figure PCTCN2021138058-appb-000020
Figure PCTCN2021138058-appb-000021
其中，P m为肱动脉的平均动脉压，P cap为颅内毛细血管压，P icp为颅内压，且P cap＝25mmHg，P icp＝11mmHg，
Figure PCTCN2021138058-appb-000022
为一个心动周期内脑总灌注的第一平均流量，PP为脉压，且PP＝SBP-DBP，ΔV T为整个脑动脉(即脑动脉系统)的总脉动体积。
可以理解的是，整个脑动脉的总脉动体积也就是左右颈内动脉和左右椎动脉的脉动体积之和，即
Figure PCTCN2021138058-appb-000023
ΔV i为脉动体积。假设脑血流在毛细血管中是非脉动性的，则脉动体积表示将脉动性动脉血流衰减为非脉动性毛细血管血流所需要的脑动脉的循环体积变化。数学上，脉动体积是瞬时流量波形减去平均流量之后在一个心动周期累积积分的最大值和最小值之差，同时也等同于收缩期瞬时流量波形与平均流量的交点处时间段内的瞬时流量波形与平均流量之差的累积积分，即脉动体积可以通过下述公式八确定：
Figure PCTCN2021138058-appb-000024
其中，Q(t)为瞬时流量，
Figure PCTCN2021138058-appb-000025
为一个心动周期内的平均流量，t 1和t 2分别为收缩期内瞬时流量Q(t)和平均流量
Figure PCTCN2021138058-appb-000026
交点处的时间。
以下以右颈内动脉为例示例性说明脉动体积的计算。请参阅图6，图6示出了右颈内动脉的脉动体积的计算示例图。具体地，终端设备可以根据一个心动周期内右颈内动脉的目标超声流速频谱波形和右颈内动脉的初始横截面积，得到如图6中的(a)所示的右颈内动脉的瞬时流量波形和平均流量。可以理解的是，右颈内动脉的瞬时流量波形由目标超声流速频谱波形乘以初始横截面积计算得到，右颈内动脉的平均流量由目标超声流速频谱波形对应的平均血流速度 乘以初始横截面积计算得到。如图6中的(b)所示，右颈内动脉的瞬时流量波形减去平均流量后在一个心动周期的累积积分，代表右颈内动脉在一个心动周期内的体积变化。其中，右颈内动脉的脉动体积ΔV i则为图6中的(b)所示的体积变化的最大值V max与最小值V min之差，即ΔV i＝V max-V min，同时右颈内动脉的脉动体积ΔV i也等于图6中的(a)中灰色阴影的面积。
(三)终端设备确定末端动脉的0D模型的目标阻力和目标顺应性。
在此，终端设备可以基于血管流量比-血管横截面积比(也称为动脉流量比-动脉横截面积比)的两步分配法将脑动脉系统的总阻力R T和总顺应性C T合理分配到整个脑动脉网络，以对末端动脉的0D模型的目标阻力和目标顺应性进行个体化赋值。可以理解的是，各末端动脉的0D模型的目标阻力和目标顺应性的赋值方式基本相同，以下将以左大脑中动脉为例进行示例性说明。
请参阅图7，图7示出了左大脑中动脉及其外周血管的示例图。如图7所示，ICA为左颈内动脉，ACA为左大脑前动脉，MCA为左大脑中动脉，
Figure PCTCN2021138058-appb-000027
为动脉j的特征阻抗，
Figure PCTCN2021138058-appb-000028
为动脉j的末端阻力。
具体地，终端设备可以先获取左大脑中动脉一个心动周期的平均流量Q MCA，其中，Q MCA可以由前述TCD测量得到的左大脑中动脉的目标超声流速频谱波形对应的平均血流速度乘以左大脑中动脉的初始横截面积计算得到。随后，终端设备可以根据Q MCA与脑总灌注的
Figure PCTCN2021138058-appb-000029
的比值，计算左大脑中动脉下游血管的总阻力R MCA和总顺应性C MCA。然后，终端设备可以根据左大脑中动脉末端动脉的横截面积比，计算外周0D血管床的目标阻力和目标顺应性。
具体地，终端设备可以根据下述公式九和公式十计算左大脑中动脉下游血管的R MCA和C MCA，然后可以根据下述公式十一和公式十二计算外周0D血管床的目标阻力和目标顺应性：
Figure PCTCN2021138058-appb-000030
Figure PCTCN2021138058-appb-000031
Figure PCTCN2021138058-appb-000032
Figure PCTCN2021138058-appb-000033
其中，
Figure PCTCN2021138058-appb-000034
ρ为血液密度，c j为动脉j的脉搏波速度，A j为动脉j的初始横截面积，A k为动脉k的初始横截面积，L j为动脉j的血管长度。本申请实施例中，为了建模方便，可以假定各动脉的脉搏波速度是不变的，是一个常数，在此，c j可以基于前述各动脉的硬度参数K确定，即
Figure PCTCN2021138058-appb-000035
K j为动脉j的硬度参数。
(四)根据0D模型的目标阻力和目标顺应性确定脑血流动力学模型的出口边界条件。
其中，脑血流动力学模型的出口边界条件可以为各末端动脉中末端节点处的平均流量Q 1D、平均血压P 1D和平均血流速度U 1D。下面将以图7所示的示例图来对末端动脉j末端节点处的平均流量Q 1D、平均血压P 1D和平均血流速度U 1D进行示例性说明。
具体地，终端设备可以根据下述公式十三、公式十四、公式十五、公式十六和公式十七确定末端动脉j末端节点处的流量Q 1D、血压P 1D和流速U 1D：
Figure PCTCN2021138058-appb-000036
Figure PCTCN2021138058-appb-000037
Figure PCTCN2021138058-appb-000038
Figure PCTCN2021138058-appb-000039
Figure PCTCN2021138058-appb-000040
其中，A 1D为末端动脉j末端节点处的目标横截面积，P a为外周血管床压力，W f为向前传递的特征变量，τ∈[A 0,A]，A 0为末端动脉j的初始横截面积，A为末端动脉j各时刻的目标横截面积，Q p为流过
Figure PCTCN2021138058-appb-000041
的流量，Q d为流过
Figure PCTCN2021138058-appb-000042
的流量，P cap为毛细血管压，c为末端动脉j实际的脉搏波速度，P为末端动脉j各时刻的平均血压。
在此，P a可以根据上一时间的P a推导得到，Q d可以根据上一时间的Q d推导得到，W f也可以根据上一时间的W f推导得到。其中，
Figure PCTCN2021138058-appb-000043
t n为当前时间，t n-1为上一时间，x m为末端节点，
Figure PCTCN2021138058-appb-000044
为t n-1时向前传递的特征变量的传播速度，其等于t n-1时的脉搏波速度加上血流速度。
本申请实施例中，在通过上述的测量方法测量得到各脑动脉的波强度WI和波功率WP后，可以通过波分离技术，将各脑动脉的波强度WI和波功率WP分别分离为向前传播和向后传播的分量，其中，向前的分量(即向前传递波强度WI +和向前传递波功率WP +)代表血管上游的影响，向后的分量(即向后反射波强度WI -和向后反射波功率WP -)代表血管下游的影响。具体地，向前传递波强度WI +、向后反射波强度WI -、向前传递波功率WP +以及向后反射波功率WP -分别为：
Figure PCTCN2021138058-appb-000045
Figure PCTCN2021138058-appb-000046
请参阅图8，图8示出了本申请实施例提供的脑血流动力学模型的血流模拟图。本申请实施例中，可以通过该脑血流动力学模型模拟目标用户(如老年痴呆患者)的脑血流，分别得到如图8中的(a)所示的右大脑前动脉、如图8中的(b)所示的左大脑前动脉、如图8中的(c)所示的右大脑中动脉、如图8 中的(d)所示的左大脑中动脉、如图8中的(e)所示的右大脑后动脉以及如图8中的(f)所示的左大脑后动脉的流速波形。其中，图8中各流速波形的横轴为时间(时间为s)，纵轴为血流速度(单位为cm/s)。由图8可知，该脑血流动力学模型模拟出的左大脑前动脉、右大脑前动脉、左大脑中动脉、右大脑中动脉、左大脑后动脉以及右大脑后动脉的流速波形与TCD的实际测量数据非常吻合，表明了本申请实施例提供的脑血流动力学模型具有较强的有效性和可靠性。
另外，利用本申请实施例提供的脑血流动力学模型计算老年痴呆患者和健康对照者的脑动脉波强度WI和波功率WP后，通过波分析和对比，发现与健康人相比，老年痴呆患者的脑近端动脉(即左右颈内动脉和基底动脉)的平均向前传递波强度(FCWI)较高，而总的向前传递波功率(FCWP)较低。上述发现与临床观察相一致，表明脑动脉WI和WP的分析可应用于认知损伤和老年痴呆风险的评估的可能性，从而对老年痴呆症的早期诊断、预防和精准治疗具有重要的指导意义。
本申请实施例中，可以获取脑部和颈部的核磁共振图像；根据核磁共振图像构建脑血流动力学模型；分别获取左颈内动脉、右颈内动脉、左椎动脉和右椎动脉的目标超声流速频谱波形；根据脑血流动力学模型和各目标超声流速频谱波形确定各脑动脉的波强度和波功率，以通过无创测量和数值模拟相结合的方式实现全脑动脉WI和WP的测量，提高脑动脉WI和WP测量的精度，具有较强的易用性和实用性。
应理解，上述实施例中各步骤的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本申请实施例的实施过程构成任何限定。
对应于上文实施例所述的脑动脉波强度和波功率的测量方法，图9示出了本申请实施例提供的脑动脉波强度和波功率的测量装置的结构框图，为了便于说明，仅示出了与本申请实施例相关的部分。
参照图9，所述测量装置可以包括：
核磁共振图像获取模块901，用于获取包含脑部信息和颈部信息的核磁共振图像；
动力学模型构建模块902，用于根据所述核磁共振图像构建脑血流动力学模型；
频谱波形获取模块903，用于分别获取左颈内动脉、右颈内动脉、左椎动脉和右椎动脉的目标超声流速频谱波形；
波强度和波功率确定模块904，用于根据所述脑血流动力学模型和各所述目标超声流速频谱波形确定各脑动脉的波强度和波功率。
示例性的，所述脑血流动力学模型根据下述公式对任一脑动脉进行血流模拟：
Figure PCTCN2021138058-appb-000047
Figure PCTCN2021138058-appb-000048
Figure PCTCN2021138058-appb-000049
其中，A为该脑动脉t时刻x位置处的目标横截面积，U为该脑动脉t时刻x位置处的平均血流速度，P为该脑动脉t时刻x位置处的平均血压，μ为血液黏度，ρ为血液密度，ξ为粘性摩擦常数，且μ＝0.0035Pa·s，ρ＝1050kg·m -3，ξ＝22，P ext为施加在该脑动脉外壁的压力，P 0为参考压力，K为该脑动脉的硬度参数，A 0为该脑动脉的初始横截面积。
在一种可能的实现方式中，所述波强度和波功率确定模块904，可以包括：
入口边界确定单元，用于将各所述目标超声流速频谱波形确定为所述脑血流动力学模型的入口边界条件；
出口边界确定单元，用于确定所述脑血流动力学模型的出口边界条件；
脑动脉血压确定单元，用于根据所述入口边界条件、所述出口边界条件和 所述脑血流动力学模型确定各所述脑动脉任一位置处的平均血流速度波形、平均流量波形以及平均血压波形；
波强度和波功率确定单元，用于根据各所述脑动脉的平均血流速度波形、平均流量波形以及平均血压波形确定各所述脑动脉的波强度和波功率。
可选地，所述出口边界确定单元，可以包括：
总阻力确定分单元，用于根据肱动脉血压以及所述左颈内动脉、所述右颈内动脉、所述左椎动脉和所述右椎动脉的目标超声流速频谱波形和初始横截面积计算脑动脉系统的总阻力和总顺应性；
目标阻力确定分单元，用于根据所述脑动脉系统的总阻力和总顺应性确定各末端动脉的目标阻力和目标顺应性；
出口边界确定分单元，用于根据各所述末端动脉的目标阻力和目标顺应性确定所述脑血流动力学模型的出口边界条件。
示例性的，所述总阻力确定分单元，可以包括：
第一平均流量确定子单元，用于根据所述左颈内动脉、所述右颈内动脉、所述左椎动脉和所述右椎动脉的目标超声流速频谱波形和初始横截面积分别确定一个心动周期内的左颈内动脉、右颈内动脉、左椎动脉以及右椎动脉的第一平均流量；
第二平均流量确定子单元，用于根据各所述第一平均流量确定一个心动周期内脑总灌注的第二平均流量；
总阻力确定子单元，用于根据所述第二平均流量和所述肱动脉血压确定所述脑动脉系统的总阻力和总顺应性。
具体地，所述总阻力确定子单元，具体用于根据下述公式确定所述脑动脉系统的总阻力和总顺应性：
Figure PCTCN2021138058-appb-000050
Figure PCTCN2021138058-appb-000051
其中，R T为所述总阻力，C T为所述总顺应性，P m为肱动脉的平均动脉压，P cap为颅内毛细血管压，P icp为颅内压，且P cap＝25mmHg，P icp＝11mmHg，
Figure PCTCN2021138058-appb-000052
为所述第二平均流量，PP＝SBP-DBP，SBP为肱动脉的收缩压，DBP为肱动脉的舒张压，ΔV T为所述脑动脉系统的总脉动体积。
可选地，所述目标阻力确定分单元，具体用于利用动脉流量比和动脉横截面积比的两步分配法将所述脑动脉系统的总阻力和总顺应性分配给各所述末端动脉，得到各所述末端动脉的目标阻力和目标顺应性。
在一种可能的实现方式中，所述动力学模型构建模块902，可以包括：
脑动脉分割单元，用于对所述核磁共振图像进行脑动脉分割与重建，得到脑动脉系统的三维几何模型；
中心线确定单元，用于根据所述三维几何模型确定各脑动脉的中心线，并获取各中心线的长度和初始横截面积；
脉搏波传递模型建立单元，用于根据各所述中心线的长度和初始横截面积建立所述脑动脉系统的1D脉搏波传递模型；
动力学模型构建单元，用于在所述1D脉搏波传递模型的各末端动脉中分别连接一0D模型，得到所述脑血流动力学模型，其中，所述0D模型用于模拟末端动脉的外周血管床的目标阻力和目标顺应性。
需要说明的是，上述装置/单元之间的信息交互、执行过程等内容，由于与本申请方法实施例基于同一构思，其具体功能及带来的技术效果，具体可参见方法实施例部分，此处不再赘述。
所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，仅以上述各功能单元、模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能单元、模块完成，即将所述装置的内部结构划分成不同的功能单元或模块，以完成以上描述的全部或者部分功能。实施例中的各功能单元、模块可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中，上述集成的单元既可以采用硬 件的形式实现，也可以采用软件功能单元的形式实现。另外，各功能单元、模块的具体名称也只是为了便于相互区分，并不用于限制本申请的保护范围。上述系统中单元、模块的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
图10为本申请一实施例提供的终端设备的结构示意图。如图10所示，该实施例的终端设备10包括：至少一个处理器1000(图10中仅示出一个)、存储器1001以及存储在所述存储器1001中并可在所述至少一个处理器1000上运行的计算机程序1002，所述处理器1000执行所述计算机程序1002时实现上述任意各个脑动脉波强度和波功率的测量方法实施例中的步骤。
所述终端设备10可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算设备。该终端设备可包括，但不仅限于，处理器1000、存储器1001。本领域技术人员可以理解，图10仅仅是终端设备10的举例，并不构成对终端设备10的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件，例如还可以包括输入输出设备、网络接入设备等。
所述处理器1000可以是中央处理单元(central processing unit，CPU)，该处理器1000还可以是其他通用处理器、数字信号处理器(digital signal processor，DSP)、专用集成电路(application specific integrated circuit，ASIC)、现场可编程门阵列(field-programmable gate array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
所述存储器1001在一些实施例中可以是所述终端设备10的内部存储单元，例如终端设备10的硬盘或内存。所述存储器1001在另一些实施例中也可以是所述终端设备10的外部存储设备，例如所述终端设备10上配备的插接式硬盘，智能存储卡(smart media card，SMC)，安全数字(secure digital，SD)卡，闪存卡(flash card)等。进一步地，所述存储器1001还可以既包括所述终端设备10的内部存储单元也包括外部存储设备。所述存储器1001用于存储操作系统、 应用程序、引导装载程序(BootLoader)、数据以及其他程序等，例如所述计算机程序的程序代码等。所述存储器1001还可以用于暂时地存储已经输出或者将要输出的数据。
本申请实施例还提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时可实现上述各个方法实施例中的步骤。
本申请实施例提供了一种计算机程序产品，当计算机程序产品在终端设备上运行时，使得终端设备执行时可实现上述各个方法实施例中的步骤。
所述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读存储介质中。基于这样的理解，本申请实现上述实施例方法中的全部或部分流程，可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一计算机可读存储介质中，该计算机程序在被处理器执行时，可实现上述各个方法实施例的步骤。其中，所述计算机程序包括计算机程序代码，所述计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述计算机可读存储介质至少可以包括：能够将计算机程序代码携带到装置/终端设备的任何实体或装置、记录介质、计算机存储器、只读存储器(read-only memory，ROM，)、随机存取存储器(random access memory，RAM，)、电载波信号、电信信号以及软件分发介质。例如U盘、移动硬盘、磁碟或者光盘等。在某些司法管辖区，根据立法和专利实践，计算机可读存储介质不可以是电载波信号和电信信号。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述或记载的部分，可以参见其它实施例的相关描述。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现 所描述的功能，但是这种实现不应认为超出本申请的范围。
在本申请所提供的实施例中，应该理解到，所揭露的装置/终端设备和方法，可以通过其它的方式实现。例如，以上所描述的装置/终端设备实施例仅仅是示意性的，例如，所述模块或单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通讯连接可以是通过一些接口，装置或单元的间接耦合或通讯连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
以上所述实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围，均应包含在本申请的保护范围之内。

Claims (10)

  1. 一种脑动脉波强度和波功率的测量方法，其特征在于，包括：
    获取包含脑部信息和颈部信息的核磁共振图像；
    根据所述核磁共振图像构建脑血流动力学模型；
    分别获取左颈内动脉、右颈内动脉、左椎动脉和右椎动脉的目标超声流速频谱波形；
    根据所述脑血流动力学模型和各所述目标超声流速频谱波形确定各脑动脉的波强度和波功率。
  2. 如权利要求1所述的方法，其特征在于，所述脑血流动力学模型根据下述公式对任一脑动脉进行血流模拟：
    Figure PCTCN2021138058-appb-100001
    Figure PCTCN2021138058-appb-100002
    Figure PCTCN2021138058-appb-100003
    其中，A为该脑动脉t时刻x位置处的目标横截面积，U为该脑动脉t时刻x位置处的平均血流速度，P为该脑动脉t时刻x位置处的平均血压，μ为血液黏度，ρ为血液密度，ξ为粘性摩擦常数，且μ＝0.0035Pa·s，ρ＝1050kg·m -3，ξ＝22，P ext为施加在该脑动脉外壁的压力，P 0为参考压力，K为该脑动脉的硬度参数，A 0为该脑动脉的初始横截面积。
  3. 如权利要求1所述的方法，其特征在于，所述根据所述脑血流动力学模型和各所述目标超声流速频谱波形确定各脑动脉的波强度和波功率，包括：
    将各所述目标超声流速频谱波形确定为所述脑血流动力学模型的入口边界条件；
    确定所述脑血流动力学模型的出口边界条件；
    根据所述入口边界条件、所述出口边界条件和所述脑血流动力学模型确定 各所述脑动脉任一位置处的平均血流速度波形、平均流量波形以及平均血压波形；
    根据各所述脑动脉的平均血流速度波形、平均流量波形以及平均血压波形确定各所述脑动脉的波强度和波功率。
  4. 如权利要求3所述的方法，其特征在于，所述确定所述脑血流动力学模型的出口边界条件，包括：
    根据肱动脉血压以及所述左颈内动脉、所述右颈内动脉、所述左椎动脉和所述右椎动脉的目标超声流速频谱波形和初始横截面积计算脑动脉系统的总阻力和总顺应性；
    根据所述脑动脉系统的总阻力和总顺应性确定各末端动脉的目标阻力和目标顺应性；
    根据各所述末端动脉的目标阻力和目标顺应性确定所述脑血流动力学模型的出口边界条件。
  5. 如权利要求4所述的方法，其特征在于，所述根据肱动脉血压以及所述左颈内动脉、所述右颈内动脉、所述左椎动脉和所述右椎动脉的目标超声流速频谱波形和初始横截面积计算脑动脉系统的总阻力和总顺应性，包括：
    根据所述左颈内动脉、所述右颈内动脉、所述左椎动脉和所述右椎动脉的目标超声流速频谱波形和初始横截面积分别确定一个心动周期内的左颈内动脉、右颈内动脉、左椎动脉以及右椎动脉的第一平均流量；
    根据各所述第一平均流量确定一个心动周期内脑总灌注的第二平均流量；
    根据所述第二平均流量和所述肱动脉血压确定所述脑动脉系统的总阻力和总顺应性。
  6. 如权利要求5所述的方法，其特征在于，所述根据所述第二平均流量和所述肱动脉血压确定所述脑动脉系统的总阻力和总顺应性，包括：
    根据下述公式确定所述脑动脉系统的总阻力和总顺应性：
    Figure PCTCN2021138058-appb-100004
    Figure PCTCN2021138058-appb-100005
    其中，R T为所述总阻力，C T为所述总顺应性，P m为肱动脉的平均动脉压，P cap为颅内毛细血管压，P icp为颅内压，且P cap＝25mmHg，P icp＝11mmHg，
    Figure PCTCN2021138058-appb-100006
    为所述第二平均流量，PP＝SBP-DBP，SBP为肱动脉的收缩压，DBP为肱动脉的舒张压，ΔV T为所述脑动脉系统的总脉动体积。
  7. 如权利要求4所述的方法，其特征在于，所述根据所述脑动脉系统的总阻力和总顺应性确定各末端动脉的目标阻力和目标顺应性，包括：
    利用动脉流量比和动脉横截面积比的两步分配法将所述脑动脉系统的总阻力和总顺应性分配给各所述末端动脉，得到各所述末端动脉的目标阻力和目标顺应性。
  8. 如权利要求1至7中任一项所述的方法，其特征在于，所述根据所述核磁共振图像构建脑血流动力学模型，包括：
    对所述核磁共振图像进行脑动脉分割与重建，得到脑动脉系统的三维几何模型；
    根据所述三维几何模型确定各脑动脉的中心线，并获取各中心线的长度和初始横截面积；
    根据各所述中心线的长度和初始横截面积建立所述脑动脉系统的1D脉搏波传递模型；
    在所述1D脉搏波传递模型的各末端动脉中分别连接一0D模型，得到所述脑血流动力学模型，其中，所述0D模型用于模拟末端动脉的外周血管床的目标阻力和目标顺应性。
  9. 一种终端设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现如权利要求1至8任一项所述的脑动脉波强度和波功率的测量方法。
  10. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程 序，其特征在于，所述计算机程序被处理器执行时实现如权利要求1至8任一项所述的脑动脉波强度和波功率的测量方法。
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