Blood flow velocity acquisition method and device and ultrasonic scanning equipment

Abstract

The invention relates to the technical field of medical images, and discloses a blood flow velocity obtaining method and device and ultrasonic scanning equipment. The blood flow velocity obtaining method comprises the steps that Doppler signals of a blood flow area are obtained; eigenvalue decomposition is carried out on the Doppler signals, and a noise matrix is constructed based on eigenvalues obtained through decomposition; performing noise filtering on the Doppler signal based on the noise matrix to obtain an effective Doppler signal; performing self-correlation processing on the effective Doppler signal to obtain a blood flow signal; and solving the blood flow velocity of the blood flow region based on the blood flow signal. By introducing eigenvalue decomposition and noise matrix construction methods, more accurate noise estimation and filtering are realized, and the problem that clutters are mistakenly eliminated or incompletely removed in a traditional high-pass filter is solved. Meanwhile, the method has self-adaptability, a filtering strategy can be adjusted according to individual differences of different patients, and the accuracy and reliability of blood flow velocity estimation are ensured.

Description

Technical Field

[0001] The present disclosure relates to the technical field of medical imaging, for example, to a method and device for acquiring blood flow velocity, and an ultrasonic scanning device. Background Art

[0002] The ultrasound color Doppler imaging signal processing system uses the Doppler effect to calculate the blood flow velocity by detecting the frequency shift caused by the relative movement between red blood cells and the ultrasound probe. This technology encodes blood flow information in color and superimposes this information on traditional grayscale anatomical images, thereby providing visual information about the direction and velocity of blood flow. It is an important tool in medical imaging for evaluating vascular conditions and diagnosing cardiovascular diseases.

[0003] In practical applications, in addition to the Doppler shift caused by the target blood flow, the ultrasound echo signal is also affected by other factors, such as the chronic movement of human tissue, the fluctuation of the blood vessel wall caused by the heartbeat and pulse, and the inherent noise of the imaging system. These factors will produce additional Doppler shifts, called clutter, which is usually characterized by low frequency but strong energy. In order to accurately extract blood flow velocity information, these clutter signals must be effectively filtered out.

[0004] The related technology mainly relies on high-pass filters to remove low-frequency clutter and retain high-frequency blood flow signals. However, this method has some limitations. Specifically, the related technology has the problem of erroneous elimination or incomplete elimination of text. For example, in low-speed blood flow areas, such as near the blood vessel wall, the high-pass filter may mistakenly treat the blood flow signal as clutter and filter it out, or fail to fully remove the clutter, resulting in inaccurate blood flow velocity estimation and blurred blood vessel wall display. In addition, different patients have different physiological characteristics, and the echo and clutter signals generated are also different. Using uniform filtering parameters may not be able to adapt to all situations, affecting the accuracy of the diagnostic results. Therefore, in the related technology, the high-pass filter is prone to erroneous elimination of blood flow signals or incomplete removal of clutter in low-speed blood flow areas, and the uniform filtering parameters are difficult to adapt to the individual differences of different patients, thereby affecting the accuracy and reliability of the diagnostic results.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0006] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0007] The embodiments of the present disclosure provide a method and device for acquiring blood flow velocity, and an ultrasonic scanning device, which overcome the problem of false elimination or incomplete removal of clutter existing in traditional high-pass filters, and can adjust the filtering strategy according to the individual differences of different patients, thereby ensuring the accuracy and reliability of blood flow velocity estimation.

[0008] According to a first aspect of the present disclosure, a method for acquiring blood flow velocity is provided, comprising:

[0009] Acquiring a Doppler signal of the blood flow area, wherein the Doppler signal is obtained after demodulation processing of an echo signal obtained by performing multiple ultrasonic scans on the blood flow area;

[0010] Perform eigenvalue decomposition on the Doppler signal and construct a noise matrix based on the eigenvalues ​​obtained by the decomposition;

[0011] The Doppler signal is filtered out based on the noise matrix to obtain a valid Doppler signal;

[0012] Perform autocorrelation processing on the effective Doppler signal to obtain the blood flow signal;

[0013] The blood flow velocity in the blood flow area is calculated based on the blood flow signal.

[0014] In some embodiments, the Doppler signal includes a blood flow velocity matrix obtained during each ultrasonic scan, and the blood flow velocity matrix includes transverse sampling points and depth direction sampling points.

[0015] In some embodiments, performing eigenvalue decomposition on the Doppler signal and constructing a noise matrix based on the eigenvalues ​​obtained by the decomposition includes:

[0016] For each blood flow velocity matrix, singular value decomposition is performed on the blood flow velocity matrix to obtain corresponding singular values;

[0017] Identify the smallest singular value of each blood velocity matrix;

[0018] Based on the minimum singular value of each blood flow velocity matrix, a blood flow velocity noise matrix of each blood flow velocity matrix is ​​constructed.

[0019] In some embodiments, performing singular value decomposition on the blood flow velocity matrix to obtain corresponding singular values ​​includes:

[0020] Decomposing the blood flow velocity matrix into a product representation of a spatial singular vector matrix, a temporal singular vector matrix and a singular value matrix;

[0021] The elements on the main diagonal of the singular value matrix are determined as the singular values ​​of the blood flow velocity matrix.

[0022] In some embodiments, based on the minimum singular value of each blood flow velocity matrix, a blood flow velocity noise matrix of each blood flow velocity matrix is constructed, including:

[0023] Determine the temporal singular vector corresponding to the minimum singular value of the blood flow velocity matrix in the temporal singular vector matrix;

[0024] Copy and reconstruct the temporal singular vector corresponding to the minimum singular value of the blood flow velocity matrix to obtain the blood flow velocity noise matrix of the blood flow velocity matrix.

[0025] In some embodiments, copying and reconstructing the temporal singular vector corresponding to the minimum singular value of the blood flow velocity matrix to obtain the blood flow velocity noise matrix of the blood flow velocity matrix includes:

[0026] Perform moving average filtering on the temporal singular vector corresponding to the minimum singular value of the blood flow velocity matrix to obtain a smoothed temporal singular vector;

[0027] Copy and reconstruct the smoothed temporal singular vector corresponding to the minimum singular value of the blood flow velocity matrix to obtain the blood flow velocity noise matrix of the blood flow velocity matrix.

[0028] In some embodiments, the noise matrix includes the blood flow velocity noise matrix of each blood flow velocity matrix; based on the noise matrix, noise filtering is performed on the Doppler signal to obtain an effective Doppler signal, including: performing noise filtering on each blood flow velocity matrix based on the blood flow velocity noise matrix of each blood flow velocity matrix to obtain an effective blood flow velocity matrix of each blood flow velocity matrix.

[0029] In some embodiments, autocorrelation processing is performed on the effective Doppler signal to obtain a blood flow signal, including: performing autocorrelation processing on the effective blood flow velocity matrices of all blood flow velocity matrices to obtain a blood flow signal.

[0030] According to a second aspect of the present disclosure, there is provided a blood flow velocity acquisition device, including a processor and a memory storing program instructions, the processor being configured to execute the blood flow velocity acquisition method provided in the first aspect of the present disclosure when running the program instructions.

[0031] According to a third aspect of the present disclosure, there is provided an ultrasonic scanning device, including the blood flow velocity acquisition device provided in the second aspect of the present disclosure.

[0032] The blood flow velocity acquisition method provided by the embodiment of the present disclosure is to decompose the Doppler signal of the blood flow area into eigenvalues ​​of different sizes by eigenvalues, and accurately construct a noise matrix according to the eigenvalues ​​of corresponding sizes. Based on the noise matrix, the noise in the Doppler signal can be estimated and removed more accurately without affecting the blood flow signal, thereby avoiding the problem of misjudgment or incomplete removal of clutter in the low-frequency region by the traditional high-pass filter. Moreover, the noise component can be dynamically identified and estimated through the noise matrix. This adaptive method can adjust the noise filtering strategy according to the specific situation of each patient and generate a reasonable noise matrix instead of relying on fixed filtering parameters. Therefore, it can better adapt to the individual differences of different patients and improve the accuracy and reliability of the diagnosis results. The effective Doppler signal has effectively removed the interference, and then the time difference of the blood flow signal can be more accurately captured through autocorrelation processing, thereby improving the accuracy of blood flow velocity calculation and obtaining a more accurate blood flow velocity. In summary, the embodiment of the present disclosure achieves more accurate noise estimation and filtering by introducing the method of eigenvalue decomposition and noise matrix construction, and overcomes the problem of mis-elimination or incomplete removal of clutter in the traditional high-pass filter. At the same time, this method is adaptive and can adjust the filtering strategy according to the individual differences of different patients, ensuring the accuracy and reliability of blood flow velocity estimation.

[0033] The foregoing general description and the following description are exemplary and explanatory only and are not intended to limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] One or more embodiments are exemplarily described by corresponding drawings, which do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements, and the drawings do not constitute a scale limitation, and wherein:

[0035] Figure 1 is a schematic diagram of an ultrasonic scanning device provided by an embodiment of the present disclosure;

[0036] Figure 2 is a schematic diagram of another ultrasonic scanning device provided by an embodiment of the present disclosure;

[0037] Figure 3 is a flow chart of a method for obtaining blood flow velocity provided by an embodiment of the present disclosure;

[0038] Figure 4 is a flow chart of another method for obtaining blood flow velocity provided by an embodiment of the present disclosure;

[0039] Figure 5 is a flow chart of another method for obtaining blood flow velocity provided by an embodiment of the present disclosure;

[0040] Figure 6It is a structural schematic diagram of a blood flow velocity acquisition device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0041] In order to be able to understand the features and technical contents of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0042] The terms "first", "second", etc. in the specification and claims of the embodiments of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of the present disclosure described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0043] Unless otherwise stated, the term "plurality" means two or more.

[0044] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B indicates: A or B.

[0045] The term "and / or" is a description of the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0046] The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0047] The present disclosure provides an ultrasonic scanning device, combined with Figure 1 As shown, the ultrasonic scanning device includes a blood flow velocity acquisition device and an ultrasonic scanning device connected to the blood flow velocity acquisition device. The ultrasonic scanning device can send ultrasonic waves to the blood flow area of ​​the patient, receive echoes from the blood flow area, and generate a Doppler signal of the blood flow area according to the echoes of the blood flow area. The ultrasonic scanning device can send the Doppler signal to the blood flow velocity acquisition device, and the blood flow velocity acquisition device acquires the blood flow velocity of the blood flow area based on the Doppler signal.

[0048] Combination Figure 2As shown, the ultrasonic scanning device includes a pulse transmitter, an ultrasonic probe, a receiving chip, a beam synthesizer and a demodulation module. The pulse transmitter is used to generate high voltage pulses to drive the ultrasonic probe to transmit ultrasonic waves. The ultrasonic probe is connected to the pulse transmitter and the receiving chip. The ultrasonic probe can convert electrical signals into mechanical vibrations (i.e., ultrasonic waves) and send ultrasonic waves to the blood flow area. At the same time, it can also receive echoes from the blood flow area and convert them back into electrical signals.

[0049] The receiving chip amplifies the micro-echo signal received by the probe to ensure that the signal strength is sufficient for subsequent processing. The receiving chip is connected to the probe and outputs the amplified echo signal to the beamformer. The beamformer controls the delay and weighting of each channel, synthesizes the echo signals from different directions, and forms a focused RF (Radio Frequency) signal.

[0050] The demodulation module is connected to the beam synthesizer, and the beam synthesizer can send the synthesized RF signal to the demodulation module. The demodulation module converts the RF signal into a demodulation signal of a lower frequency (usually an intermediate frequency or baseband signal). For ease of understanding and expression, the demodulation signal is defined as the Doppler signal of the blood flow area. The demodulation module sends the Doppler signal of the blood flow area to the blood flow velocity acquisition device, and the blood flow velocity acquisition device acquires the blood flow velocity of the blood flow area based on the Doppler signal.

[0051] In combination with the ultrasonic scanning device provided in the embodiment of the present disclosure, the embodiment of the present disclosure provides a method for obtaining blood flow velocity, the execution subject of the method is a blood flow velocity obtaining device (hereinafter referred to as the device), Figure 3 As shown, the blood flow velocity acquisition method includes:

[0052] S301, the device acquires Doppler signals in the blood flow area.

[0053] In the disclosed embodiment, the Doppler signal is obtained after demodulation processing of the echo signal obtained by performing multiple ultrasonic scans on the blood flow area. As mentioned above, the ultrasonic scanning device can send ultrasonic waves to the patient's blood flow area, and receive the echo of the blood flow area, and generate the Doppler signal of the blood flow area according to the echo of the blood flow area. The ultrasonic scanning device can send the Doppler signal to the blood flow velocity acquisition device, and the blood flow velocity acquisition device thereby obtains the Doppler signal of the blood flow area.

[0054] S302, the device performs eigenvalue decomposition on the Doppler signal, and constructs a noise matrix based on the eigenvalues ​​obtained by the decomposition.

[0055] S303, the device performs noise filtering on the Doppler signal based on the noise matrix to obtain a valid Doppler signal.

[0056] In the actual example of the present disclosure, the noise matrix is ​​a matrix used to represent and estimate noise components. The Doppler signal is subjected to noise filtering based on the noise matrix, and the Doppler signal after noise filtering is defined as a valid Doppler signal.

[0057] S304, the device performs autocorrelation processing on the effective Doppler signal to obtain a blood flow signal.

[0058] In the disclosed embodiment, autocorrelation processing is a signal processing technique for analyzing the similarity between a signal and its own time-delayed copy. It is achieved by calculating the average value of the product of a signal and itself at different time offsets. By performing autocorrelation processing on the effective Doppler signal, the blood flow signal of the blood flow area is obtained.

[0059] S305, the device calculates the blood flow velocity in the blood flow area based on the blood flow signal.

[0060] The blood flow velocity acquisition method provided by the embodiment of the present disclosure is to decompose the Doppler signal of the blood flow area into eigenvalues ​​of different sizes by eigenvalues, and accurately construct a noise matrix according to the eigenvalues ​​of corresponding sizes. Based on the noise matrix, the noise in the Doppler signal can be estimated and removed more accurately without affecting the blood flow signal, thereby avoiding the problem of misjudgment or incomplete removal of clutter in the low-frequency region by the traditional high-pass filter. Moreover, the noise component can be dynamically identified and estimated through the noise matrix. This adaptive method can adjust the noise filtering strategy according to the specific situation of each patient and generate a reasonable noise matrix instead of relying on fixed filtering parameters. Therefore, it can better adapt to the individual differences of different patients and improve the accuracy and reliability of the diagnosis results. The effective Doppler signal has effectively removed the interference, and then the time difference of the blood flow signal can be more accurately captured through autocorrelation processing, thereby improving the accuracy of blood flow velocity calculation and obtaining a more accurate blood flow velocity. In summary, the embodiment of the present disclosure achieves more accurate noise estimation and filtering by introducing the method of eigenvalue decomposition and noise matrix construction, and overcomes the problem of mis-elimination or incomplete removal of clutter in the traditional high-pass filter. At the same time, this method is adaptive and can adjust the filtering strategy according to the individual differences of different patients, ensuring the accuracy and reliability of blood flow velocity estimation.

[0061] In the disclosed embodiment, the Doppler signal includes a blood flow velocity matrix obtained by each ultrasonic scan, and the blood flow velocity matrix includes transverse sampling points and depth sampling points. The Doppler signal can be represented as a matrix S, and the size of the matrix S is M*N*T, where T is the number of ultrasonic scans, M is the number of transverse sampling points in the blood flow velocity matrix, and N is the number of depth sampling points in the blood flow velocity matrix. It can be understood that the matrix S includes T blood flow velocity matrices, and the blood flow velocity matrix obtained by the i-th ultrasonic scan can be represented as Si , the matrix S i The size is M*N.

[0062] In some embodiments, the eigenvalue is a singular value. Performing eigenvalue decomposition on the Doppler signal and constructing a noise matrix based on the eigenvalues ​​obtained by the decomposition includes: for each blood flow velocity matrix, performing singular value decomposition on the blood flow velocity matrix to obtain corresponding singular values; identifying the minimum singular value of each blood flow velocity matrix; and constructing a blood flow velocity noise matrix of each blood flow velocity matrix based on the minimum singular value of each blood flow velocity matrix.

[0063] In some embodiments, the noise matrix includes a blood flow velocity noise matrix of each blood flow velocity matrix. Based on the noise matrix, the Doppler signal is filtered to obtain a valid Doppler signal, including: based on the blood flow velocity noise matrix of each blood flow velocity matrix, each blood flow velocity matrix is ​​filtered to obtain a valid blood flow velocity matrix of each blood flow velocity matrix.

[0064] In some embodiments, performing autocorrelation processing on the effective Doppler signal to obtain the blood flow signal includes: performing autocorrelation processing on the effective blood flow velocity matrices of all blood flow velocity matrices to obtain the blood flow signal.

[0065] Combination Figure 4 As shown, the embodiment of the present disclosure provides another method for obtaining blood flow velocity, and the method for obtaining blood flow velocity includes:

[0066] S401, the device acquires Doppler signals in the blood flow area.

[0067] S402: The device performs singular value decomposition on each blood flow velocity matrix to obtain corresponding singular values.

[0068] S403, the device identifies the minimum singular value of each blood flow velocity matrix.

[0069] In the disclosed embodiment, the singular value represents the signal energy size, the large singular value represents the tissue signal, the middle singular value represents the blood signal, and the small singular value represents the noise signal. The singular values ​​are sorted to find the minimum singular value, and the minimum singular value is used to construct the noise matrix for noise estimation.

[0070] S404: The device constructs a blood flow velocity noise matrix for each blood flow velocity matrix based on the minimum singular value of each blood flow velocity matrix.

[0071] S405: The device performs noise filtering on each blood flow velocity matrix based on the blood flow velocity noise matrix of each blood flow velocity matrix to obtain an effective blood flow velocity matrix for each blood flow velocity matrix.

[0072] S406, the device performs autocorrelation processing on the effective blood flow velocity matrices of all blood flow velocity matrices to obtain blood flow signals.

[0073] S407, the device calculates the blood flow velocity in the blood flow area based on the blood flow signal.

[0074] In some embodiments, singular value decomposition is performed on the blood flow velocity matrix to obtain corresponding singular values, including: decomposing the blood flow velocity matrix into a product representation of a spatial singular vector matrix, a temporal singular vector matrix and a singular value matrix; and determining the elements on the main diagonal of the singular value matrix as the singular values ​​of the blood flow velocity matrix.

[0075] In some embodiments, based on the minimum singular value of each blood flow velocity matrix, a blood flow velocity noise matrix of each blood flow velocity matrix is ​​constructed, including: determining the time singular vector corresponding to the minimum singular value of the blood flow velocity matrix in the time singular vector matrix; copying and reconstructing the time singular vector corresponding to the minimum singular value of the blood flow velocity matrix to obtain the blood flow velocity noise matrix of the blood flow velocity matrix.

[0076] Combination Figure 5 As shown, the embodiment of the present disclosure provides another method for obtaining blood flow velocity, and the method for obtaining blood flow velocity includes:

[0077] S501, the device acquires Doppler signals in the blood flow area.

[0078] S502, the device decomposes the blood flow velocity matrix into a product representation of a spatial singular vector matrix, a temporal singular vector matrix and a singular value matrix for each blood flow velocity matrix.

[0079] The blood flow velocity matrix S obtained by the i-th ultrasound scan i For example, the blood flow velocity matrix S i The size is M*N. For the blood flow velocity matrix S i Perform singular value decomposition and transform S i Decomposed into the product of spatial singular vector matrix, temporal singular vector matrix and singular value matrix. Specifically, S i =UΔV * , where U is the spatial singular vector matrix, V * is the time singular vector matrix, Δ is the singular value matrix, the size of the spatial singular vector matrix is ​​M*M, the size of the time singular vector matrix is ​​N*N, and the size of the singular value matrix is ​​M*N.

[0080] S503: The device determines the elements on the main diagonal of each singular value matrix as the singular values ​​of the corresponding blood flow velocity matrix.

[0081] In the embodiment of the present disclosure, all elements outside the main diagonal of the singular value matrix are zero. Here, the elements on the main diagonal are determined as the singular values ​​of the corresponding blood flow velocity matrix.

[0082] S504: The device identifies the minimum singular value of each blood flow velocity matrix.

[0083] In the disclosed embodiment, the singular value represents the signal energy size, the large singular value represents the tissue signal, the middle singular value represents the blood signal, and the small singular value represents the noise signal. The singular values ​​are sorted to find the minimum singular value, and the minimum singular value is used to construct the noise matrix for noise estimation.

[0084] S505, the device determines the time singular vector corresponding to the minimum singular value of the blood flow velocity matrix in the time singular vector matrix.

[0085] In the embodiment of the present disclosure, the number of the minimum singular values ​​may be one or more. If the number of the minimum singular values ​​is multiple, the time singular vectors corresponding to each minimum singular value in the time singular vector matrix should be determined.

[0086] S506, the device copies and reconstructs the time singular vector corresponding to the minimum singular value of the blood flow velocity matrix to obtain a blood flow velocity noise matrix of the blood flow velocity matrix.

[0087] In some embodiments, a time singular vector corresponding to the minimum singular value of the blood flow velocity matrix may be subjected to sliding average filtering to obtain a smoothed time singular vector. After obtaining the smoothed time singular vector, the smoothed time singular vector corresponding to the minimum singular value of the blood flow velocity matrix is ​​copied and reconstructed to obtain a blood flow velocity noise matrix of the blood flow velocity matrix.

[0088] Optionally, a local weighted average method may be used to perform sliding average filtering on the time singular vector corresponding to the minimum singular value of the blood flow velocity matrix, thereby obtaining a smoothed time singular vector.

[0089] S507: The device performs noise filtering on each blood flow velocity matrix based on the blood flow velocity noise matrix of each blood flow velocity matrix to obtain an effective blood flow velocity matrix for each blood flow velocity matrix.

[0090] In the embodiment of the present disclosure, each blood flow velocity matrix can be subtracted from its blood flow velocity noise matrix to achieve noise filtering, thereby obtaining an effective blood flow velocity matrix. When the blood flow velocity matrix has multiple blood flow velocity noise matrices, the blood flow velocity matrix should be subtracted from all of its blood flow velocity noise matrices to achieve noise filtering.

[0091] In the embodiment of the present disclosure, the blood flow velocity noise matrix of the blood flow velocity matrix can be expressed as S N, the effective blood flow velocity matrix of the blood flow velocity matrix is ​​expressed as S f The blood flow velocity matrix S obtained by the i-th ultrasound scan is i For example, the blood flow velocity matrix S i The blood flow velocity noise matrix is ​​S Ni , blood flow velocity matrix S i The effective blood flow velocity matrix is The noise filtering process can be expressed as:

[0092] S508, the device performs autocorrelation processing on the effective blood flow velocity matrices of all blood flow velocity matrices to obtain blood flow signals.

[0093] In the embodiment of the present disclosure, the blood flow signal can be expressed as S A , blood flow signal S A It can be obtained by the following formula: In this formula, S f(i-1) Represents the blood flow velocity matrix S (i-1) The effective blood velocity matrix, The effective blood flow velocity matrix is ​​expressed as The complex conjugate of .

[0094] S509, the device calculates the blood flow velocity in the blood flow area based on the blood flow signal.

[0095] In the disclosed embodiment, the time difference of the blood flow signal is converted into a frequency difference, thereby obtaining the blood flow velocity in the blood flow area.

[0096] Combination Figure 6 As shown, the embodiment of the present disclosure provides a blood flow velocity acquisition device 600, which includes a processor (processor) 601 and a memory (memory) 602. Optionally, the blood flow velocity acquisition device 600 may also include a communication interface (Communication Interface) 603 and a bus 604. Among them, the processor 601, the communication interface 603, and the memory 602 can communicate with each other through the bus 604. The communication interface 603 can be used for information transmission. The processor 601 can call the logic instructions in the memory 602 to execute the blood flow velocity acquisition method of the above embodiment.

[0097] In addition, the logic instructions in the memory 602 described above may be implemented in the form of software functional units and when sold or used as independent products, may be stored in a computer-readable storage medium.

[0098] The memory 602 is a computer-readable storage medium that can be used to store software programs and computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 601 executes the function application and data processing by running the program instructions / modules stored in the memory 602, that is, the blood flow velocity acquisition method in the above embodiment is implemented.

[0099] The memory 602 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 602 may include a high-speed random access memory and may also include a non-volatile memory.

[0100] An embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to execute the above-mentioned blood flow velocity acquisition method.

[0101] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes.

[0102] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible changes. Unless explicitly required, separate components and functions are optional, and the order of operation may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of listings containing one or more associated ones. In addition, when used in the present application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the presence of other identical elements in the process, method or device comprising the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0103] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described functions, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians may clearly understand that, for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0104] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units can be only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0105] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A method for obtaining blood flow velocity, characterized in that: include: Acquiring a Doppler signal of the blood flow area, wherein the Doppler signal is obtained after demodulation processing of an echo signal obtained by performing multiple ultrasonic scans on the blood flow area; Perform eigenvalue decomposition on the Doppler signal and construct a noise matrix based on the eigenvalues ​​obtained by the decomposition; The Doppler signal is filtered out based on the noise matrix to obtain a valid Doppler signal; Perform autocorrelation processing on the effective Doppler signal to obtain the blood flow signal; The blood flow velocity in the blood flow area is calculated based on the blood flow signal.

2. The method for obtaining blood flow velocity according to claim 1, characterized in that: The Doppler signal includes a blood flow velocity matrix obtained in each ultrasonic scan, and the blood flow velocity matrix includes transverse sampling points and depth direction sampling points.

3. The method for obtaining blood flow velocity according to claim 2, characterized in that: Perform eigenvalue decomposition on the Doppler signal and construct a noise matrix based on the eigenvalues ​​obtained by decomposition, including: For each blood flow velocity matrix, singular value decomposition is performed on the blood flow velocity matrix to obtain corresponding singular values; Identify the smallest singular value of each blood velocity matrix; Based on the minimum singular value of each blood flow velocity matrix, a blood flow velocity noise matrix of each blood flow velocity matrix is ​​constructed.

4. The method for obtaining blood flow velocity according to claim 3, characterized in that: Perform singular value decomposition on the blood flow velocity matrix to obtain the corresponding singular values, including: Decomposing the blood flow velocity matrix into a product representation of a spatial singular vector matrix, a temporal singular vector matrix and a singular value matrix; The elements on the main diagonal of the singular value matrix are determined as the singular values ​​of the blood flow velocity matrix.

5. The method for obtaining blood flow velocity according to claim 4, characterized in that: Based on the minimum singular value of each blood flow velocity matrix, a blood flow velocity noise matrix of each blood flow velocity matrix is ​​constructed, including: Determine the time singular vector corresponding to the minimum singular value of the blood flow velocity matrix in the time singular vector matrix; The time singular vector corresponding to the minimum singular value of the blood flow velocity matrix is ​​copied and reconstructed to obtain the blood flow velocity noise matrix of the blood flow velocity matrix.

6. The method for obtaining blood flow velocity according to claim 5, characterized in that: The time singular vector corresponding to the minimum singular value of the blood flow velocity matrix is ​​copied and reconstructed to obtain the blood flow velocity noise matrix of the blood flow velocity matrix, including: Perform sliding average filtering on the time singular vector corresponding to the minimum singular value of the blood flow velocity matrix to obtain a smoothed time singular vector; The smoothed time singular vector corresponding to the minimum singular value of the blood flow velocity matrix is ​​copied and reconstructed to obtain the blood flow velocity noise matrix of the blood flow velocity matrix.

7. The method for obtaining blood flow velocity according to claim 2, characterized in that: The noise matrix includes a blood flow velocity noise matrix for each blood flow velocity matrix; The Doppler signal is filtered out based on the noise matrix to obtain an effective Doppler signal, including: filtering out the noise of each blood flow velocity matrix based on the blood flow velocity noise matrix of each blood flow velocity matrix to obtain an effective blood flow velocity matrix of each blood flow velocity matrix.

8. The method for obtaining blood flow velocity according to claim 7, characterized in that: Performing autocorrelation processing on the effective Doppler signal to obtain the blood flow signal includes: performing autocorrelation processing on the effective blood flow velocity matrices of all blood flow velocity matrices to obtain the blood flow signal.

9. A blood flow velocity acquisition device, comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to execute the blood flow velocity acquisition method according to any one of claims 1 to 8 when running the program instructions.

10. An ultrasonic scanning device, characterized in that: It comprises the blood flow velocity acquisition device as described in claim 9.

Images