• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    An ultrasonic system for detecting a flow of a fluid in a medium The invention relates to an ultrasonic system for detecting a flow of a fluid in a medium, comprising a probe configured for ultrasonic insonification of the medium and receiving a signal from the medium. echoes, a control device configured to construct a sequence of images based on the signal received from the echoes, filter the images by a temporal high pass filter, and determine a local displacement of the flow between two successive images by maximizing the similarity between blocks extracted from the two images. The invention also relates to an ultrasonic method for detecting a flow of a fluid in a medium. Figure for the abstract: Fig. 2.
    公开号:FR3099586A1
    申请号:FR1908627
    申请日:2019-07-29
    公开日:2021-02-05
    发明作者:David SAVÉRY；Elsa GIRAUDAT
    申请人:SuperSonic Imagine SA；
    IPC主号:
    专利说明:

    [0001] The invention generally relates to ultrasound. More specifically it relates to ultrasound systems, for example for medical ultrasound investigation. The invention relates in particular to ultrasonic systems for detecting a flow of a fluid in a medium, for example to make it possible to automatically estimate the direction of the blood flow.
    [0002] In a manner known in the state of the art, an ultrasound system can be used to determine the Doppler angle during a measurement of blood velocity (or of another liquid). The Doppler angle is the angle between the axis of the ultrasound beam and the velocity vector of the flowing blood. The Doppler method consists in measuring an average frequency fDoppler from a plurality of shots fired at a constant rate PRF (Pulse Repetition Frequency), and in using the Doppler formula to deduce the velocity Vz projected on the axis of the beam Vz = c0 * fDoppler / f0 / 2 where c0 = speed of sound and f0 = center frequency of the transmitted acoustic wave. In addition, the absolute speed Va = Vz / cos (Doppler angle) can be calculated. However, in the classical Doppler method, the scale of the Doppler spectrum is determined in a single region (i.e. the sampling volume) and therefore in a single beam.
    [0003] In this case, the user must manually identify and select this sampling volume in the ultrasound image, in order to apply the Doppler method. Therefore, the angle must be positioned manually by the user in pulsed Doppler (PD) mode, which does not allow automatic estimation of the blood flow direction. For example, the user visually determines the direction of the vessel by inspection of the grayscale ultrasound image, then indicates via the user interface the estimated flow direction.
    [0004] For example, US 6,618,493 B1 proposes to generate (as allowed by plane wave insonification) K speckle images from a packet size of N with 1 < K < N ( with K = 1 corresponding to conventional color Doppler). Application of a matrix-like temporal high-pass filter, which cancels out stationary tissue, allows Color Power Imaging images to be displayed to the user at a frame rate typically increased by a factor K over the conventional color Doppler imaging. This cadence makes it possible to show a "flow" of speckle which also moves laterally.
    [0005] American patent application US 2004/0249284 A1 describes a method for obtaining the velocity vector by the use of two ultrasonic beams making it possible to obtain two Doppler projections of the velocity, and thus the determination of the Doppler angle in the imaging plane.
    [0006] Furthermore, WO 2013/059659 proposes to use unfocused (plane) insonifications to image the Doppler vector, to use different angles to evaluate the projections and to combine these projections to calculate the Doppler vector. Also a single angle can be used, then a high pass filter to calculate a spatio-temporal map F(p,t) where p denotes the pixels of the image. A method using the spatial gradient of F(p,t) is used to calculate the velocity vector at each point p at time t.
    [0007] Furthermore, US 9247927 B2 describes another ultrasound system comprising a processor for visualizing the flow of a liquid using a Doppler method.
    [0008] In addition, it was proposed to compare two speckle tracking methods on unfiltered B-mode data: the so-called standard method which maximizes the spatial intercorrelation index and the fast method which minimizes the sum of the absolute values of the differences (SAD), cf. for example :
    [0009] Bohs, L.N., & Trahey, G.E. (1991). A novel method for angle independent ultrasonic imaging of blood flow and tissue motion. IEEE Transactions on Biomedical Engineering, 38(3), 280-286.
    [0010] The following publication uses a speckle tracking method by minimizing the SAD on a window at t and its shifted window at t + 1/PRF of the candidate displacement where the intensity is calculated post wall filter ("after the application of a filter high pass"). The shots are focused here and the line density is reduced compared to mode B to ensure the necessary rate to avoid too much decorrelation between two successive acquisitions:
    [0011] Swillens, A., Segers, P., Torp, H., & Lovstakken, L. (2010). Two-dimensional blood velocity estimation with ultrasound: speckle tracking versus crossed-beam vector Doppler based on flow simulations in a carotid bifurcation model. IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 57(2), 327-339.
    [0012] The aim of the invention is to overcome all or part of the aforementioned drawbacks, in particular to allow automatic estimation of the direction of blood flow in one or more vessels in an ultrasound image.
    [0013] To this end, the invention proposes an ultrasonic system for detecting a flow of a fluid in a medium, comprising:
    [0014] - a probe configured for ultrasonic insonification of the environment and reception of the echo signal,
    [0015] - a control device configured for:
    [0016] - construct (in other words "reconstruct" or "build") a sequence of images based on the signal received from the echoes,
    [0017] - filter the images by a temporal high-pass filter, and
    [0018] - determining a local displacement of the flow between two successive images by maximizing the similarity between blocks extracted from the two images.
    [0019] Therefore, with such a control device, the direction of blood flow in one or more vessels in an ultrasound image can be estimated automatically. In particular, the method implemented by the system makes it possible to display speed spectra of which, for example, the Doppler angle is determined automatically.
    [0020] Furthermore, the system according to the invention makes it possible to estimate the Doppler spectra at any point in the medium examined by the system. This allows the estimation of blood flow direction in multiple vessels simultaneously. In comparison, in classical Doppler as described above, the scale of the Doppler spectrum is determined in a single region (the sampling volume) and the user must identify and manually select this volume in the ultrasound image.
    [0021] In particular, the system allows the evaluation of angles on a plurality of elements in the image (for example blood vessels).
    [0022] Thus, it is possible to evaluate the uncertainty of the estimated angles, which is useful for judging the precision and/or the variance of the measurement.
    [0023] Advantageously, the disclosed method makes it possible to obtain a spatial resolution equivalent to the coherent image formed by a large aperture and a set of firing angles which is therefore better than that obtained by using a sub-aperture in reception.
    [0024] In addition, compared to the known methods known as “speckle tracking” methods on B-mode data, the system according to the invention is advantageously more sensitive to blood flows. The Doppler data can for example be filtered by a selective wall filter of blood flow and suppressor of stationary tissues,
    [0025] Moreover, the method made available by the system according to the invention is better in the measurement of the absolute speed than the other known Doppler methods, with multiple beams, in particular when the Doppler angle is large (and therefore the projection error is large), since the absolute speed measurement is all the greater as the Doppler angle is large, the speed amplitude being proportional to the inverse of the cosine of the Doppler angle. For example, when the inspected vessel is horizontal on an ultrasound image, and when the Doppler beams are quasi-vertical (for reasons of directivity of the transducer elements, and therefore of signal-to-noise ratio), then the Doppler angle(s) are large, and the absolute velocity error due to the angle uncertainty is high.
    [0026] The system makes it possible to calculate new clinical diagnostic indices on, for example, the liver (for example, to evaluate the isotropy/anisotropy of hepatic perfusion by characterizing a plurality of angles on several vessels).
    [0027] The images can be filtered by a temporal high-pass filter for example, making it possible to eliminate the signal coming from the stationary medium.
    [0028] The similarity between two blocks can be expressed for example by the spatial inter-correlation between the two blocks.
    [0029] The medium can be stationary or quasi-stationary. It can be a tissue, for example an organ (for example the liver) or a muscle.
    [0030] The blocks can include at least a first reference block in the first of the two images and at least a second comparison block having the same size in the second of the two images. For example, the block-matching algorithm can be applied in order to optimally determine which can be the two blocks. Indeed, this algorithm makes it possible to locate similar blocks between two images.
    [0031] The similarity can be maximized by optimizing (changing) the position of the second block, for example to find the first block in the second image.
    [0032] The controller may be configured to calculate an average angle and/or average fluid velocity based on the determined local displacement.
    [0033] The angle can be defined for example with respect to a reference axis of the probe.
    [0034] The high-pass filter may comprise a high-pass infinite impulse response filter, or an orthogonal projection matrix filter on a predefined subspace.
    [0035] The determination of the local displacement of the flow can comprise calculating in each pixel the angle of the local displacement.
    [0036] The determination of the local displacement of the flow can comprise a segmentation into homogeneous zones of the images. Each of the zones can correspond to a channel, for example a vessel, comprising the fluid.
    [0037] Consequently, the control device can be configured to detect one or more channels in the medium comprising the fluid by segmenting homogeneous zones in the images.
    [0038] The controller can be configured to calculate an average angle and/or an average fluid velocity for each of the channels.
    [0039] The segmentation can be based on a predefined decision rule relating to the amplitude and/or the average frequency of the high-pass filtered signals.
    [0040] The determination of the local displacement of the stream can include the use of a block-matching algorithm, for example by locating similar blocks between two successive images. The block-matching algorithm can use for example a predefined block size and a predefined pixel step size.
    [0041] The block-matching algorithm can be configured to maximize the spatial inter-correlation between two windows of two successive filtered images
    [0042] The block-matching algorithm can use an increasing function of the envelope of the filtered Doppler signals between two successive images to determine a local displacement in a set of pixels (for example a set of pixels in a channel).
    [0043] The block-matching algorithm can be configured to use the envelope of the signals filtered between two successive images to determine a local vector displacement in a set of pixels (for example a set of pixels in a channel).
    [0044] The block-matching algorithm may include at least one of the following algorithms:
    [0045] - temporal and/or spatial averaging to estimate the 2D spatial inter-correlation function then maximize it, and
    [0046] - minimization of the function sum of the absolute values of the differences.
    [0047] The controller can be configured to calculate the circular variance of the flux angle based on the local displacement determined over a set of pixels (e.g. a set of pixels in a channel).
    [0048] The set of pixels can be a set of pixels in a channel or pixels that form the channel on the images.
    [0049] The probe can be configured for ultra-fast insonification, for example with a rate of at least 500 shots per second, more preferably at least 3000 shots per second.
    [0050] The probe can be configured for insonification at different angles.
    [0051] The probe can be configured for insonification with a succession of shots of ultrasonic plane waves from variable angles or ultrasonic cylindrical waves from variable source points.
    [0052] The monitoring device can be configured to build a sequence of baseband demodulated images for a typical shot repetition rate of for example at least 500 Hz, more preferably for example at least 3000 Hz.
    [0053] The probe may comprise an array of ultrasonic transducers and/or an array of ultrasonic transducers.
    [0054] The invention also proposes an ultrasonic method for detecting a flow of a fluid in a medium, comprising the following steps:
    [0055] - ultrasonic insonification of the environment and reception of an echo signal,
    [0056] - build a sequence of images based on the signal received from the echoes,
    [0057] - filter the images by a temporal high-pass filter, and
    [0058] - determining a local displacement of the flow between two successive images by maximizing the similarity between blocks extracted from the two images.
    [0059] The characteristics and advantages of the invention will appear on reading the following description, given solely by way of non-limiting example, and made with reference to the appended drawings.
    [0060] FIG. 1 is a schematic view of an architecture of an ultrasound system according to the invention.
    [0061] FIG. 2 is a schematic view of a method for processing ultrasound constructed data to determine a flow of a fluid in a medium according to the invention.
    [0062] Figure 3 schematically illustrates an example of a determined block in a vessel at a time t,
    [0063] Figure 4 schematically illustrates the calculation of the local displacement of the block of Figure 3 at a time t+1/PRF,
    [0064] Figure 5 schematically illustrates the calculation of the local velocity resulting from the local displacement of the block of Figure 4, i.e., Figure 5 schematically illustrates the cross-correlation function between the two windows illustrated in Figures 3 and 4,
    [0065] Figure 6 schematically illustrates an example of a calculation of local velocities and angles.
    [0066] Description of embodiments
    [0067] Figure 1 is a schematic view of an architecture of an ultrasound system 1 according to the invention. The system 1 may in particular be an electronic display system and it may for example be configured for the determination of the Doppler angle during a medical ultrasound investigation. For example, it can automatically estimate the direction of blood flow.
    [0068] System 1 comprises a probe 3 configured for ultrasonic insonification I of the medium and reception of a signal from echoes E. The probe may comprise an array of ultrasonic transducers and/or an array of ultrasonic transducers.
    [0069] Furthermore, the system comprises a control device 2 coupled with the probe 3 and able to capture a series of images of a medium using the probe 3. The device 2 in an example of use, constructed ( or reconstructs) the sequence of images based on the signal received from the echoes, filters the images by a temporal high-pass filter, and determines a local displacement of the flow between two successive images by maximizing the similarity between blocks extracted from the two images. Advantageously, the device can therefore automatically estimate the direction of flow of the liquid (for example blood) in one or more vessels in one of the ultrasound images obtained using the probe. In particular, the device according to the disclosure makes it possible to display speed spectra of which, for example, the Doppler angle is determined automatically.
    [0070] Furthermore, the system can comprise a first screen 4 and optionally also a second screen 5 which can be touch-sensitive. Screen 5 can be a single-touch or multi-touch screen. At least one of the screens can be used to display the velocity spectra.
    [0071] The disclosed system is able to perform the following steps:
    [0072] 1. Insonification (preferably ultrafast) of the medium by plane ultrasound waves at different angles (typically for example 3 angles, insonification clocked for example at 9000 Hz) or by cylindrical ultrasound waves at different source points and reception by probe 3, then,
    [0073] The following steps are performed by controller 2:
    [0074] 2. Construction of a sequence of IQ images for a typical PRF rate of for example 3000 Hz;
    [0075] 3. Filtering by wall filter to eliminate the signal coming from stationary tissues;
    [0076] 4. Block matching using the envelope of Doppler signals filtered between two successive instants to determine a local displacement in a set of pixels (which are the centers of the blocks). The block matching method can consist in maximizing the spatial inter-correlation between two successive filtered Doppler images.
    [0077] 5. (optional) Calculation in each pixel of the local displacement angle, and
    [0078] 6. (optional) Calculation on each ship of an average angle (spatial average restricted to a ship area), an average speed, and/or an average speed vector.
    [0079] Steps 2. to 6. will be described in more detail in the context of Figure 2.
    [0080] Figure 2 is a schematic view of a method of processing ultrasound constructed data to determine a flow of a fluid in a medium according to the invention. The method may include one or more of the following steps:
    [0081] In a step S1 a sequence of images constructed on the basis of the echo signal is obtained. This step may include the sub-steps of:
    [0082] - Periodic insonification at a typical PRF rate of for example 0.5 to 10 kHz of the medium by a succession of EL shots of ultrasonic plane waves of variable angles or of ultrasonic cylindrical waves of apex (center of curvature of the wave fronts ) variables emitted by an array or matrix of ultrasonic transducers.
    [0083] - Reception and sampling of waves backscattered by the medium (amplification and associated filtering).
    [0084] - Construction of a plurality of demodulated images, coherent summation on the angles or on the apexes, to obtain a series of complex IQ(z,x,t) images, where the slow time is sampled at the frequency PRF and where the number of demodulated images obtained is denoted by EL, Ensemble Length or “packet size”.
    [0085] In a step S2 the images are filtered by a temporal high-pass filter (for example a "wall filter"). This step may include the following sub-steps:
    [0086] - High-pass (“wall”) filtering of the sequence of images for the suppression of fixed or quasi-fixed echoes to obtain a new sequence WFIQ(z,x,t). This filtering can take the form of a linear operation characterized by a shape-dependent pixel-dependent EL * EL transformation matrix:
    [0087] [WFIQ(z,x,1) … WFIQ(z,x,EL)] T = M(z,x) [IQ(z,x,1) … IQ(z,x,EL)] T (eq. 1)
    [0088] M(z,x) can be obtained from the impulse response of a linear invariant filter with finite or infinite impulse response, or can be defined as a projection matrix on a subspace of C EL (polynomials of degrees above above a fixed order, trigonometric polynomials, etc.), this subspace characterizing the ultrasonic echoes of flowing blood. M(z,x) can vary with the pixel (z,x) or be constant in the image.
    [0089] In a step S3, an average velocity map and an average amplitude map are calculated. This step may include the following sub-steps:
    [0090] - Extraction of the average frequency CFI(z,x) (color flow image) and of the average amplitude CPI(z,x) (color power image) in each pixel of coordinates (z,x) from the vector [WFIQ (z,x,1) … WFIQ(z,x,EL)] . This average frequency and this average amplitude can be calculated for example from the following relations (* designates the complex conjugate, arg the argument on [-pi,pi] of a complex number):
    [0091]
    [0092] The two maps CFI and CPI thus obtained are then used to make it possible to segment and label the vessels. Plus |CFI(z,x)| and CPI(z,x) are large, the greater the probability that (z,x) is in a vessel.
    [0093] In a step S4, a vessel is located in the average velocity map. This step may include:
    [0094] - Segmentation into homogeneous zones Lk, k = 1 … Nk (which can be reduced to a pixel) according to the values of CPI and CFI, each of the zones corresponding to a vessel. One segmentation method that can be used is to split the CPI image into connected components whose value CPI(z,x) and |CFI(z,x)| are above predefined thresholds.
    [0095] In a step S5, a local displacement is determined for each pixel in the vessel, for example using the block-matching algorithm. This step may include the following sub-steps:
    [0096] - For each pixel (z,x) of each homogeneous zone Lk, and for a pair of times t1 < t2, the calculation of the local velocity vector v = (dz,dx)*1/(t1-t2) is performed by maximizing the correlation index C(dz,dx) defined by:
    [0097]
    [0098] where "window" designates a window centered in (z,x) whose size is predefined in (z,x).
    [0099] As shown in the example in Figure 5, the correlation index can be estimated more robustly by averaging its values both spatially and over time t1 and t2, considering pairs t1 and t2 such that t2- t1 is a constant.
    [0100] As shown in the example in Figure 6, if the maximized correlation index does not exceed a predefined threshold, then the local displacement estimate (i.e. each arrow shown in Figure 6) can be poor quality and the local speed [Vz(z,x) Vx(z,x)] could not be taken into account in the following for the calculation of certain statistics for example. Therefore, the predefined threshold advantageously improves the estimation of the average speed and angle by eliminating aberrant measurements.
    [0101] In an optional step S6, for each homogeneous zone L k , a mean angle calculation α k can then be extracted, as well as a circular variance var k . These values can for example be calculated using the following formulas:
    [0102] e(z,x) = [Vx(z,x) + i Vz(z,x)] / |Vx(z,x) + i Vz(z,x)| (eq. 4)
    [0103] α k = arg(<e(z,x)> k ) (eq. 5)
    [0104] var k = 1 - |<e(z,x)> k |, (eq. 6)
    [0105] where the taking of mean <> k being on the homogeneous zone L k .
    [0106] In this context, an example is illustrated in figures 3 to 6.
    [0107] Figures 3 and 4 schematically illustrate respectively an example of a determined block in a vessel at a time t, and the calculation of the local displacement of the block at a time t+1/PRF. In particular, Figure 3 illustrates an image of the amplitude of a Doppler image of a tilted vessel at time t1. The white rectangle represents a window F1 (that is to say a block, for example determined by the block-matching algorithm) of chosen size. Figure 4 illustrates the image of the amplitude of a Doppler image of a tilted vessel at, for example, a time t2 = t1 + 5.7 ms (or another predefined value). The white rectangle represents the window F2(dz,dx) which maximizes the spatial intercorrelation between F1 and a shifted window. The displacement evaluated at the center of F1 corresponds to the white vector.
    [0108] FIG. 5 schematically illustrates the calculation of the local velocity resulting from the local displacement of the block of FIG. 4, that is to say, the intercorrelation function between the two windows illustrated in FIGS. 3 and 4. In particular, FIG. 5 illustrates the image of the spatial intercorrelation (and its level lines) between F1 and F2(dz,dx). It is maximized in for example dx = 3.4 mm and dz = 0.3 mm for an index of for example 0.97. It is the estimator of the displacement vector between t1 and t2 at the center of F1.
    [0109] Figure 6 schematically illustrates an example of a calculation of local velocities and angles. In particular, Figure 6 illustrates an image of velocity vectors calculated on a pixel grid of an inclined vessel.
    权利要求:
    Claims (16)
    [0001]
    Ultrasonic system for detecting a flow of a fluid in a medium, comprising:a probe configured for ultrasonic insonification of the environment and reception of a signal from the echoes, a control device configured to: build a sequence of images based on the signal received from the echoes, filter the images by a temporal high-pass filter, and determining a local displacement of the flow between two successive images by maximizing the similarity between blocks extracted from the two images.
    [0002]
    Detection system according to claim 1, in which the blocks comprise at least a first reference block in the first of the two images and at least a second comparison block having the same size in the second of the two images, and in which the similarity is maximized by optimizing the position of the second block.
    [0003]
    A detection system according to claim 1 or 2, wherein the monitoring device is configured to:calculate an average angle and/or an average velocity of the fluid based on the determined local displacement, the angle being defined relative to a reference axis of the probe.
    [0004]
    A system according to any preceding claim, wherein the high pass filter comprises a high pass infinite impulse response filter, or an orthogonal projection matrix filter on a predefined subspace.
    [0005]
    System according to any one of the preceding claims, in which the determination of the local displacement of the flux comprises the step of calculating in each pixel the angle of the local displacement.
    [0006]
    System according to any one of the preceding claims, in which the control device is configured to detect one or more channels in the medium comprising the fluid by segmenting homogeneous areas in the images, and in which the control device is configured to calculate a average angle and/or an average fluid velocity for each of the channels.
    [0007]
    System according to any one of the preceding claims, in which the segmentation is based on a predefined decision rule relating to the amplitude and/or the average frequency of the high-pass filtered signals.
    [0008]
    A system according to any preceding claim, wherein the determination of the local displacement of the stream comprises the use of a block-matching algorithm by locating similar blocks between two successive images.
    [0009]
    System according to the preceding claim, in which the block-matching algorithm is configured to maximize the spatial inter-correlation between two windows of two successive filtered images.
    [0010]
    System according to one of the preceding claims 8 or 9, in which the block-matching algorithm uses an increasing function of the envelope of the filtered Doppler signals between two successive images to determine a local displacement in a set of pixels.
    [0011]
    System according to any one of claims 8 to 10, in which the block-matching algorithm is configured to use the envelope of the signals filtered between two successive images to determine a local vector displacement in a set of pixels.
    [0012]
    System according to any one of the preceding claims 8 to 11, in which the block-matching algorithm comprises at least one of the following algorithms:temporal and spatial averaging to estimate the 2D spatial inter-correlation function then maximize it, and minimization of the function sum of the absolute values of the differences.
    [0013]
    A system according to any preceding claim, wherein the control device is configured to calculate the circular variance of the flux angle based on the local displacement determined over a set of pixels.
    [0014]
    A system according to any of claims 10 to 13, wherein the set of pixels is a set of pixels in a channel.
    [0015]
    A detection system according to any preceding claim, wherein the probe is configured to:ultra-rapid insonification with a rate of at least 500 shots per second, and/or insonification at different angles, and/or insonification with a succession of shots of ultrasonic plane waves from variable angles or ultrasonic cylindrical waves from variable sources, and/or the control device is configured to construct a series of baseband demodulated images for a shot repetition rate of at least 500Hz.
    [0016]
    Ultrasonic method for detecting a flow of a fluid in a medium, comprising the steps:ultrasonic insonification of the environment and reception of an echo signal, construction of a series of images based on the signal received from the echoes, filtering of the images by a temporal high-pass filter, and determination of a local displacement of the flow between two successive images by maximizing the similarity between blocks extracted from the two images.
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    同族专利:
    公开号 | 公开日
    FR3099586B1|2021-08-06|
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    法律状态:
    2020-07-27| PLFP| Fee payment|Year of fee payment: 2 |
    2021-02-05| PLSC| Publication of the preliminary search report|Effective date: 20210205 |
    2021-07-26| PLFP| Fee payment|Year of fee payment: 3 |
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    申请号 | 申请日 | 专利标题
    FR1908627|2019-07-29|
    FR1908627A|FR3099586B1|2019-07-29|2019-07-29|Ultrasonic system for detecting a flow of a fluid in a medium|FR1908627A| FR3099586B1|2019-07-29|2019-07-29|Ultrasonic system for detecting a flow of a fluid in a medium|
    KR1020200093182A| KR20210014081A|2019-07-29|2020-07-27|Ultrasonic system for detecting fluid flow in an environment|
    EP20188352.7A| EP3771927A1|2019-07-29|2020-07-29|Ultrasound system for detecting a flow of a fluid in a medium|
    CN202010741595.1A| CN112294359A|2019-07-29|2020-07-29|Ultrasound system for detecting fluid flow in an environment|
    US16/941,865| US20210033440A1|2019-07-29|2020-07-29|Ultrasonic system for detecting fluid flow in an environment|
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