Method and device for estimating three-dimensional blood flow velocity based on ultrasonic speckle decorrelation analysis

Abstract

The application discloses a blood vessel three-dimensional flow velocity estimation method and device based on ultrasonic speckle decorrelation analysis. In the prior art, when a one-dimensional ultrasonic transducer array is used to estimate blood vessel flow velocity, only one-dimensional (axial) or two-dimensional (axial and transverse) flow velocity components of the blood vessel can be estimated, and the flow velocity estimation effect is influenced by a detection angle and calibration of an operator. The method of the application reconstructs ultrasonic echo signals by using different receiving apertures in a beam synthesis process, and successfully distinguishes specific contributions of three-dimensional flow velocity components of the blood vessel to attenuation of an autocorrelation function of blood flow signals in combination with ultrasonic speckle decorrelation analysis theory, so that accurate estimation of the three-dimensional flow velocity components of the blood vessel is realized. The device uses the method to estimate the three-dimensional flow velocity. By using the method and the device, the dependence of ultrasonic flow velocity estimation on the detection angle and the calibration of the operator can be reduced, and the three-dimensional flow velocity of the blood vessel can be accurately estimated by using the one-dimensional ultrasonic transducer array.

Description

Technical Field

[0001] This invention belongs to the field of medical devices and methods, specifically relating to a method and apparatus for estimating three-dimensional blood vessel flow velocity based on ultrasound speckle decorrelation analysis. Background Technology

[0002] Ultrasound imaging is one of the most widely used diagnostic tools in basic scientific research and clinical practice. This technology combines the advantages of being non-invasive, contrast-free, having high spatiotemporal resolution, and high imaging depth, enabling the assessment of various parameters such as vascular structure and hemodynamics. It is widely used for the early screening of blood microcirculation diseases. Among these, accurately estimating vascular flow velocity is crucial for assessing hemodynamic parameters.

[0003] Researchers both domestically and internationally have proposed a series of ultrasound flow velocity assessment methods to obtain vascular flow velocity information. Traditional color Doppler ultrasound can measure the axial velocity of blood flow by quantizing frequency shifts. However, this method can only detect the axial velocity component of the blood vessel, and the measured velocity value depends on the angle between the ultrasound probe and the blood vessel being probed, making the flow velocity assessment highly uncertain. Ultrasound vector Doppler, by measuring blood vessel flow velocity at different detection angles, can accurately estimate the two-dimensional (axial and transverse) velocity components of blood flow, but this method still cannot measure the velocity component in the elevation direction (the direction perpendicular to the ultrasound detection plane). Ultrasound localization microscopy based on microbubble tracking can measure the three-dimensional velocity components of blood vessels, but this technology often requires the combination of two-dimensional ultrasound transducers and multiple ultrasound imaging platforms, resulting in high operating and computational costs. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] This invention addresses the problems of existing technologies that can only estimate one-dimensional axial or two-dimensional (axial and transverse) velocity components of blood vessels, and whose velocity assessment results are affected by the detection angle and operator calibration. It provides a method and apparatus for estimating three-dimensional blood vessel velocity based on ultrasonic speckle decorrelation analysis. By reconstructing the ultrasonic echo signal using different receiving apertures during beamforming, modulating the transverse resolution of the reconstructed data, and combining this with ultrasonic speckle decorrelation analysis theory, the specific contribution of the three-dimensional blood vessel velocity components to the attenuation of autocorrelation data is effectively distinguished. This achieves accurate estimation of the three-dimensional blood vessel velocity components, while meeting the requirements of measuring three-dimensional blood vessel velocity using only a one-dimensional ultrasonic transducer array and ensuring that the assessment results are independent of the detection angle and operator calibration. This provides convenience for clinicians to screen for early circulatory system-related diseases.

[0006] (II) Technical Solution

[0007] To address its technical problems, this invention provides a method and apparatus for estimating three-dimensional blood vessel flow velocity based on ultrasonic speckle decorrelation analysis, the specific technical solution of which is as follows.

[0008] This invention discloses a method for estimating three-dimensional blood vessel flow velocity based on ultrasound speckle decorrelation analysis, characterized by the following steps:

[0009] Step A: Use a one-dimensional ultrasonic transducer array to emit ultrasonic pulses and receive the echo signal reflected by the sample;

[0010] Step B: The echo signal is reconstructed using beamforming. Two different receiving apertures are used in the beamforming process, namely the first receiving aperture and the second receiving aperture, to obtain two sets of reconstructed data with different lateral resolutions, namely IQ1 and IQ2. Both the first receiving aperture and the second receiving aperture are smaller than the effective receiving aperture of the one-dimensional ultrasonic transducer array.

[0011] Step C: Extract the dynamic blood flow signals sIQ1 and sIQ2 contained in the reconstructed data IQ1 and IQ2 respectively;

[0012] Step D: Perform autocorrelation calculations on the dynamic blood flow signals sIQ1 and sIQ2 respectively to obtain two sets of autocorrelation data g with different attenuation characteristics. 1，exp (τ) and g 2，exp (τ), the g 1，exp (τ) and g 2，exp The amplitude of (τ) and the three-dimensional velocity component V of the blood vessel x V y With V z All are correlated, with the imaginary part only related to the axial velocity component V of the blood vessel. Z Relatedly, the three dimensions refer to the horizontal, elevation, and axial directions in three spatial orientations.

[0013] Step E: For the autocorrelation data g 1，exp Phase analysis is performed on the imaginary part of (τ) to calculate the axial velocity component V. z ;

[0014] Step F: Utilize the autocorrelation data g 1，exp (τ) divided by the autocorrelation data g 2，exp (τ) Obtain the quotient Δg 1，exp (τ), the Δg 1，exp (τ) is only related to the unknown transverse velocity component of the blood vessel V. x Based on the principle of least squares fitting, the transverse velocity component V of the blood vessel was calculated. x ;

[0015] Step G: Calculate the transverse velocity component V from step F. x The axial velocity component V calculated in step E z Substitute into the autocorrelation data g 1，exp (τ), the velocity component in the direction of the blood vessel's elevation angle affects g. 1，exp (τ) is the only unknown quantity of amplitude. Based on the principle of least squares fitting, the velocity component V in the elevation direction of the blood vessel is calculated. y The elevation angle direction is perpendicular to the ultrasonic detection surface.

[0016] Preferably, in step B, different receiving apertures are used to change only the lateral resolution of the reconstructed data, the reconstructed data IQ １ The three-dimensional resolution of IQ2 satisfies: σ x1 ≠σ x2 , σ y1 =σ y2 , σ z1 =σ z2 The σ x1 , σ y1 With σ z1 To reconstruct the lateral, elevation, and axial resolutions corresponding to data IQ1, the σ x2 , σ y2 With σ z2 To reconstruct the lateral, elevation, and axial resolutions corresponding to the data IQ2.

[0017] Preferably, in step D, autocorrelation calculations are performed on the dynamic blood flow signals sIQ1 and sIQ2 respectively to obtain two sets of autocorrelation data g with different attenuation characteristics. 1，exp (τ) and g 2，exp The formula for calculating (τ) is:

[0018]

[0019]

[0020] Where * represents complex conjugate, <…> t τ represents the average time of the entire population, where t is the total time and τ is the time delay.

[0021] Preferably, in step E, the autocorrelation data g 1，exp Phase analysis is performed on the imaginary part of (τ) to calculate the axial velocity component V. z The calculation formula is:

[0022]

[0023] Where λ0 is the wavelength of the ultrasonic pulse emitted in step A, and τ cFor the autocorrelation data g in step E 1,exp The time corresponding to the first extremum of the imaginary part of (τ).

[0024] Preferably, in step F, the least squares fitting process involves the quotient theory model Δg. 1,theory (τ), the expression is:

[0025]

[0026] Among them, g 1,theory (τ) and g 2,theory (τ) refers to the autocorrelation data theory model corresponding to the reconstructed data IQ1 and IQ2, respectively.

[0027] The autocorrelation data theory model is determined by the three-dimensional resolution of the reconstructed data and the three-dimensional flow velocity components of the blood vessels. These three dimensions represent the horizontal, elevation, and axial directions, respectively. 1,theory (τ) and g 2,theory The expressions for (τ) are as follows:

[0028]

[0029]

[0030] Where e is the natural exponent, τ is the time delay, and σ x1 , σ y1 With σ z1 These represent the three-dimensional resolution of the reconstructed data IQ1, σ and σ, respectively. x2 , σ y2 With σ z2 V represents the 3D resolution of the reconstructed data IQ2, respectively. x V y With V z Let i and k0 represent the three-dimensional velocity components of the blood vessel, respectively, where i is the imaginary unit and k0 is the wavenumber. The formula for their calculation is: λ0 is the wavelength of the ultrasonic pulse emitted in step A.

[0031] According to step B, the three-dimensional resolutions of the reconstructed data IQ1 and IQ2 respectively satisfy σ x1 ≠σ x2 , σ y1 =σ y2 , σ z1 =σ z2 Theoretical model Δg 1,theory The expression can be replaced with:

[0032]

[0033] Where e is the natural index, V xσ represents the transverse velocity component of the blood vessel. x1 With σ x2 τ represents the horizontal resolution of the reconstructed data IQ1 and IQ2, respectively, and τ is the time delay.

[0034] Preferably, in step F, the lateral flow velocity component V of the blood vessel x Determined by the fitting factor R, a series of velocity values ​​V x，i Substituting into R, where V maximizes R. x，i This refers to the transverse velocity component of the blood vessel, where R is calculated using the following formula:

[0035]

[0036] Where e is the natural index, σ x1 With σ x2 τ represents the horizontal resolution of the reconstructed data IQ1 and IQ2, respectively, τ is the time delay, <...> represents the overall time average, and |...| represents taking the absolute value.

[0037] Preferably, in step G, the velocity component V in the elevation direction of the blood vessel y Determined by the fitting factor RR, V calculated in step E is... z V calculated in step F x And a series of speed values ​​V y，i Substituting this into RR, where V maximizes RR. y，i This refers to the velocity component along the elevation angle of the blood vessel, where RR is calculated using the following formula:

[0038]

[0039] Among them, g 1,exp (τ) represents the autocorrelation data corresponding to the dynamic blood flow signal sIQ1, g 1,theory (τ) represents the corresponding autocorrelation data theoretical model, e is the natural exponent, τ is the time delay, <...> represents the overall time average, and |...| represents taking the absolute value.

[0040] Preferably, the method further includes the step of extracting the total flow velocity of the blood vessel, wherein the magnitude of the total velocity vector synthesized from the three blood flow velocity components is expressed by the following formula:

[0041]

[0042] Preferably, this method is applicable not only to the three-dimensional flow velocity estimation of blood vessels, but also to the three-dimensional flow velocity assessment of other flowing fluids with ultrasonic reflectivity.

[0043] The present invention also discloses a three-dimensional vascular flow velocity estimation device based on ultrasonic speckle decorrelation analysis, which uses the aforementioned three-dimensional vascular flow velocity estimation method based on ultrasonic speckle decorrelation analysis to estimate the three-dimensional flow velocity.

[0044] (III) Beneficial Effects

[0045] Compared with the prior art, the present invention has significant positive technical effects, and its beneficial effects are reflected in at least the following aspects.

[0046] (1) This invention employs the ultrasonic speckle decorrelation analysis theory, while existing technologies mostly use the Doppler principle. Doppler-based ultrasonic velocimetry can only estimate the one-dimensional (axial) or two-dimensional (axial and transverse) velocity components of blood vessels. This invention can accurately analyze the three-dimensional velocity components of blood flow and further recover the total blood flow velocity of the blood vessels, making it more accurate and efficient than existing technologies.

[0047] (2) This invention utilizes ultrasonic speckle decorrelation analysis theory to achieve three-dimensional vascular flow velocity assessment, and the accuracy of the assessment is unaffected by the angle between the ultrasonic transducer and the blood flow to be detected. In contrast, existing technologies often require operators to adjust the ultrasonic transducer to a suitable angle to assess blood flow velocity, a process highly dependent on the operator's experience. This invention can accurately assess three-dimensional vascular flow velocity at any detection angle, effectively reducing the experience requirements for ultrasonic flow velocity assessment.

[0048] (3) This invention can analyze the three-dimensional flow velocity of blood vessels using a single one-dimensional ultrasound transducer array and ultrasound imaging platform; while existing technologies often require two-dimensional ultrasound transducers and multiple ultrasound imaging platforms to achieve three-dimensional flow velocity assessment. This invention can reduce the complexity and imaging cost of three-dimensional flow velocity assessment systems. Attached Figure Description

[0049] Figure 1 This is a flowchart of a method for estimating three-dimensional blood vessel flow velocity based on ultrasound speckle decorrelation analysis.

[0050] Figure 2 This is a schematic diagram for encoding different receiving apertures.

[0051] Figure 3 This is a graph showing the autocorrelation function attenuation of the same pixel under different receiving apertures.

[0052] Figure 4 This is a schematic diagram showing the first extremum of the imaginary part of the autocorrelation function.

[0053] Figure 5 To calculate the transverse blood flow velocity V x The curve fitting graph.

[0054] Figure 6 To calculate the blood flow velocity V in the direction perpendicular to the ultrasound imaging plane y The curve fitting graph. Detailed Implementation

[0055] To address its technical problems, this invention provides a method and apparatus for estimating three-dimensional blood vessel flow velocity based on ultrasonic speckle decorrelation analysis. The technical solution of this invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0056] This embodiment proposes a flow velocity assessment method based on ultrasonic speckle decorrelation analysis, which can measure the three-dimensional flow velocity components of blood vessels at any measurement angle, and can avoid the dependence on measurement angle and operator calibration in the flow velocity assessment process. Figure 1 This is a flowchart of the three-dimensional blood vessel flow velocity estimation method provided in an embodiment of this application. The specific implementation steps are as follows:

[0057] Step 1: The Prodigy 256-channel ultrasound research platform drives a one-dimensional ultrasound transducer array to transmit pulsed ultrasound signals with a center frequency of 7.2MHz and a repetition frequency of 25kHz. The target sample is continuously probed to obtain 1000 sets of ultrasound echo signals. The center frequency and repetition frequency used in this embodiment are only examples to illustrate the main idea of ​​the invention. Other pulsed ultrasound signals with different center frequencies and repetition frequencies can be selected according to the actual application scenario.

[0058] Step 2: Perform phase orthogonal demodulation on each set of ultrasonic echo signals using Hilbert transform, and use the delay-superposition algorithm to reconstruct the image of the demodulated signal. In this process, two different receiving apertures are selected to obtain two sets of reconstructed data with different lateral resolutions, namely IQ1 and IQ2.

[0059] It should be noted that, as Figure 2 As shown, this embodiment selects receiving aperture values ​​of 1.0 and 0.3 for image reconstruction of the demodulated signal during the reconstruction process. Using different receiving apertures only changes the lateral resolution of the reconstructed data, resulting in resolutions of [σ]. x1 =0.5, σ y1 =1.0, σ z1 =0.4]mm and [σ x2 =1.0, σ y2 =1.0, σ z2 Two sets of reconstructed data with a resolution of 0.4 mm.

[0060] Step 3: High-pass filtering is performed on the reconstructed data IQ1 and IQ2 using a singular value decomposition-based filtering method to extract the dynamic blood flow signal contained therein, resulting in sIQ1 and sIQ2.

[0061] It should be noted that, because red blood cells flow with the blood, the echo signal in the vascular region exhibits high-frequency fluctuations; however, the echo signal intensity of red blood cells is much lower than that in the tissue region, causing the blood flow signal to be submerged by the low-frequency static tissue signal; therefore, before performing autocorrelation calculation, it is necessary to suppress the low-frequency tissue signal and extract the high-frequency blood flow signal using a high-pass filtering method.

[0062] Step 4: Perform time autocorrelation calculations on the two extracted sets of dynamic blood flow signals sIQ1 and sIQ2 respectively to obtain two sets of autocorrelation data g with different attenuation characteristics. 1，exp (τ) and g 2，exp (τ).

[0063] Preferably, the autocorrelation is calculated using the following formula:

[0064]

[0065]

[0066] Where * represents complex conjugate; <…> t τ represents the overall time average; τ is the time delay.

[0067] It should be noted that the decay characteristics of autocorrelation data are jointly determined by the three-dimensional resolution of the reconstructed data and the three-dimensional velocity components of blood flow. Because the lateral resolutions of the reconstructed IQ1 and IQ2 data are different, g 1，exp (τ) and g 2，exp (τ) has different attenuation characteristics, such as Figure 3 As shown.

[0068] Step 5: Apply autocorrelation data g 1，exp Phase analysis of the imaginary part of (τ) can be used to solve for the axial velocity component V of the blood vessel. z .

[0069] It should be noted that the autocorrelation data theory model g 1，theory The expression for (τ) is:

[0070]

[0071] Where e is the natural exponent, τ is the time delay, and σ x1 , σ y1 With σ z1 V represents the 3D resolution of the reconstructed data IQ1, respectively. x V y With V z The three-dimensional velocity components representing blood vessels, where i is the imaginary unit and k0 is the wavenumber, are calculated using the following formula: λ0 is the wavelength of the ultrasonic pulse emitted in step 1.

[0072] It can be observed that the imaginary part of the autocorrelation data is only related to the axial velocity component in the blood flow velocity. Therefore, by analyzing g... 1，exp Phase analysis of the imaginary part of (τ) can be used to solve for the axial velocity component V of the blood vessel. z .

[0073] Preferably, such as Figure 4 As shown, by solving g 1，exp The first extreme time τ of the imaginary part of (τ) c The axial velocity component V was calculated. z The specific calculation formula is as follows:

[0074]

[0075] Step 6: As Figure 5 As shown, using autocorrelation data g 1，exp (τ) divided by the autocorrelation data g 2，exp (τ), to obtain the quotient Δg 1，exp (τ).

[0076] It should be noted that the quotient theory model Δg 1，theory The expression for (τ) is:

[0077]

[0078] It can be observed that Δg 1，theory (τ) is only related to the transverse velocity component of blood flow. Based on the least squares fitting principle, the transverse velocity component V of the blood vessel is calculated. x .

[0079] Preferably, the lateral velocity component V of the blood vessel x Determined by the fitting factor R, a series of velocity values ​​V x，i Substituting into R, where V maximizes R. x，i This refers to the transverse velocity component of the blood vessel, where R is calculated using the following formula:

[0080]

[0081]

[0082] Wherein, Δg 1,exp (τ) is the quotient value, Δg 1,theory (τ) is the quotient theory model, e is the natural index, and σ is the natural index. x1 With σ x2 τ represents the horizontal resolution of the reconstructed data IQ1 and IQ2 respectively, τ is the time delay, <...) represents the overall time average, and |...| represents taking the absolute value.

[0083] Step 7: Calculate the transverse velocity component V x With axial velocity component V z Substitute into the autocorrelation data model g 1,theory (τ), then g 1,exp (τ) and g 1,theory (τ) Perform nonlinear fitting (e.g.) Figure 6 As shown), the velocity component V in the elevation direction of the blood vessel is obtained. y .

[0084] Preferably, the velocity component V in the elevation direction of the blood vessel y Determined by the fitting factor RR, the calculated V z With V x And a series of speed values ​​V y，i Substituting this into RR, where V maximizes RR. y，i This refers to the velocity component along the elevation angle of the blood vessel, where RR is calculated using the following formula:

[0085]

[0086] Among them, g 1,exp (τ) represents the autocorrelation data corresponding to the dynamic blood flow signal sIQ1, g 1，theory (τ) is the autocorrelation data theory model, e is the natural exponent, τ is the time delay, <...> represents the overall time average, and |...| represents taking the absolute value.

[0087] Step 8: Calculate the obtained three-dimensional blood vessel velocity component V x V y and V z Calculate the total blood flow velocity V at the corresponding location. total .

[0088] The vascular flow velocity assessment method in this embodiment is based on ultrasound speckle decorrelation analysis. Compared to ultrasound Doppler methods that assess one-dimensional axial or two-dimensional (axial and transverse) vascular flow velocity components based on spectral analysis, this method can calculate the three-dimensional velocity components and total blood flow velocity of the blood vessel, offering superior accuracy and efficiency compared to existing technologies. Furthermore, this embodiment eliminates the need for experienced operators to position the ultrasound transducer array at specific angles for accurate measurement of the three-dimensional vascular flow velocity, effectively reducing reliance on operator calibration during the velocity assessment process. Moreover, the main hardware required for calculating the three-dimensional vascular flow velocity in this embodiment includes only a single one-dimensional ultrasound transducer array and an ultrasound research platform. Compared to methods and devices based on two-dimensional area ultrasound transducers and multiple ultrasound research platforms, this significantly reduces system complexity and imaging costs, facilitating the widespread adoption of this method.

[0089] The specific embodiments described in this application are merely illustrative examples of the main ideas of the present invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A method for estimating three-dimensional blood vessel flow velocity based on ultrasound speckle decorrelation analysis, characterized in that, The method includes the following steps: Step A: Use a one-dimensional ultrasonic transducer array to emit ultrasonic pulses and receive the echo signals reflected by the sample; Step B: The echo signal is reconstructed using beamforming. Two different receiving apertures, a first receiving aperture and a second receiving aperture, are used during beamforming to obtain two different sets of reconstructed data, IQ1 and IQ2. Both the first and second receiving apertures are smaller than the effective receiving aperture of the one-dimensional ultrasonic transducer array. The three-dimensional resolution of the reconstructed data IQ1 and IQ2 satisfies the following: , , The , and To reconstruct the lateral, elevation, and axial resolutions corresponding to data IQ1, the following... , and To reconstruct the lateral, elevation, and axial resolutions corresponding to the IQ2 data; Step C: Extract the dynamic blood flow signals sIQ1 and sIQ2 contained in the reconstructed data IQ1 and IQ2 respectively; Step D: Perform autocorrelation calculations on the dynamic blood flow signals sIQ1 and sIQ2 respectively to obtain two sets of autocorrelation data with different attenuation characteristics. and The and The amplitude and the three-dimensional flow velocity components of the blood vessel , and All are correlated; the imaginary part is only correlated with the axial velocity component of the blood vessel. Relatedly, the three dimensions refer to the horizontal, elevation, and axial directions in three spatial orientations. Step E: For the autocorrelation data Phase analysis was performed on the imaginary part to calculate the axial velocity component. ; Step F: Utilize the autocorrelation data Divided by the aforementioned autocorrelation data Gain quotient The Only with unknown transverse vascular velocity components Based on the principle of least squares fitting, the transverse velocity component of the blood vessel was calculated. ; Step G: Calculate the transverse velocity component from step F. The axial velocity component calculated in step E Substitute into the autocorrelation data theory model The velocity component in the elevation direction of the blood vessel To influence The only unknown quantity of the amplitude is the velocity component in the elevation direction of the blood vessel, calculated based on the principle of least squares fitting. The elevation angle direction is perpendicular to the ultrasonic detection surface.

2. The method for estimating three-dimensional blood vessel flow velocity based on ultrasound speckle decorrelation analysis according to claim 1, characterized in that, In step D, autocorrelation calculations are performed on the dynamic blood flow signals sIQ1 and sIQ2 respectively to obtain two sets of autocorrelation data with different attenuation characteristics. and The calculation formula is: Where * represents complex conjugate, Represents the overall time average, For the total time, This is a time delay.

3. The method for estimating three-dimensional blood vessel flow velocity based on ultrasound speckle decorrelation analysis according to claim 1, characterized in that, In step E, the autocorrelation data Phase analysis was performed on the imaginary part to calculate the axial velocity component. The calculation formula is: in, The wavelength of the ultrasonic pulse emitted in step A is... For the autocorrelation data in step D The time corresponding to the first extreme value of the imaginary part.

4. The method for estimating three-dimensional blood vessel flow velocity based on ultrasound speckle decorrelation analysis according to claim 1, characterized in that, In step F, the least squares fitting process involves a quotient theory model. The expression is: in, It is a natural index. The lateral velocity component representing blood vessels. and These represent the lateral resolutions of the reconstructed data IQ1 and IQ2, respectively. This is a time delay.

5. The method for estimating three-dimensional blood vessel flow velocity based on ultrasound speckle decorrelation analysis according to claim 1, characterized in that, In step F, the lateral velocity component of the blood vessel Determined by the fitting factor R, a series of velocity values Substituting into R, we find the value that maximizes R. This refers to the transverse velocity component of the blood vessel, where R is calculated using the following formula: in, The quotient value in step F, For the quotient theory model, It is a natural index. and These represent the lateral resolutions of the reconstructed data IQ1 and IQ2, respectively. For time delay, Represents the overall time average, This represents taking the absolute value.

6. The method for estimating three-dimensional blood vessel flow velocity based on ultrasound speckle decorrelation analysis according to claim 1, characterized in that, In step G, the least squares fitting process involves an autocorrelation data theoretical model. The expression is: in, It is a natural index. For time delay, , and These represent the lateral, elevation, and axial resolutions of the reconstructed IQ1 data, respectively. , , These represent the transverse, elevation, and axial velocity components of the blood vessel, respectively. It is the imaginary unit. It is the wave number, and its calculation formula is: , The wavelength of the ultrasonic pulse emitted in step A.

7. The method for estimating three-dimensional blood vessel flow velocity based on ultrasound speckle decorrelation analysis according to claim 1, characterized in that, In step G, the velocity component in the elevation direction of the blood vessel Determined by the fitting factor RR, the result calculated in step E... The result calculated in step F and a series of speed values Substituting into RR, we find the value that maximizes RR. This refers to the velocity component along the elevation angle of the blood vessel, where RR is calculated using the following formula: in, The autocorrelation data corresponding to the dynamic blood flow signal sIQ1, For the corresponding autocorrelation data theoretical model, It is a natural index. For time delay, Represents the overall time average, This represents taking the absolute value.

8. The method for estimating three-dimensional blood vessel flow velocity based on ultrasound speckle decorrelation analysis according to claim 1, characterized in that, The method also includes the step of extracting the total blood flow velocity, which is the magnitude of the total velocity vector synthesized from the three blood flow velocity components, expressed by the following formula: 。 9. A device for estimating three-dimensional blood vessel flow velocity based on ultrasonic speckle decorrelation analysis, characterized in that, It uses the three-dimensional flow velocity estimation method based on ultrasound speckle decorrelation analysis as described in any one of claims 1-8 to perform three-dimensional flow velocity estimation.

Images