Filtering method, device and computer readable storage medium for ultrasound signals

Abstract

The application relates to the technical field of ultrasound, and discloses a filtering method for an ultrasonic signal. The method first acquires original demodulation data generated after ultrasonic waves act on a subject, converts the original demodulation data based on a preset orthogonal matrix to obtain time-space projection matrix, and filters the original demodulation data according to the preset orthogonal matrix and the time-space projection information to obtain filtered ultrasonic blood flow signals. In the filtering process of the ultrasonic signal, the time information of ultrasonic emission and the position information of the subject are considered simultaneously, the time information and the space information are projected simultaneously, and the original demodulation data is filtered according to the projection information. The time information and the space information can distinguish tissues and blood flow more accurately, and the imaging effect of an ultrasonic blood flow image is improved. The application further discloses a filtering device for an ultrasonic signal and a computer readable storage medium.

Description

Technical Field

[0001] The present application relates to the field of ultrasound technology, for example, to a filtering method, device and computer-readable storage medium for ultrasound signals. Background Art

[0002] Ultrasound color flow imaging, an imaging technique that incorporates anatomical information, is highly valuable for diagnosing human diseases. This technique involves transmitting repeated pulse signals to the same tissue site. Because blood is in motion, the received ultrasonic echo signals experience phase differences, and the rate of phase change can be interpreted as a frequency shift. Based on the Doppler principle, the target's velocity can be calculated from the frequency shift. Therefore, the phase difference between echoes from the same sampling volume can be used to estimate blood flow velocity, thereby obtaining information about the motion of the measured blood flow. Finally, the blood flow velocity changes are superimposed on the anatomical grayscale image through color coding.

[0003] In ultrasound color flow imaging, it is often assumed that tissue and blood flow signals have completely different spectral characteristics. Based on this temporal dynamics assumption, conventional clutter filtering methods use standard linear regression to filter the raw ultrasound signal along the time dimension.

[0004] In the process of implementing the embodiments of the present application, it was found that there are at least the following problems in the related art: the clutter filtering method used in most current ultrasonic color blood flow imaging relies only on time information for imaging, resulting in poor clutter suppression and low image quality.

[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 this application, and therefore may include information that does not constitute prior art known to ordinary technicians in this 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 elements or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0007] Embodiments of the present application provide a filtering method, device, and computer-readable storage medium for ultrasonic signals to improve the effect of clutter suppression.

[0008] In some embodiments, the method includes: obtaining original demodulated data generated after ultrasound acts on a subject; wherein the original demodulated data includes time information and space information; converting the original demodulated data based on a preset orthogonal matrix to obtain a time-space projection matrix; filtering the original demodulated data according to the preset orthogonal matrix and the time-space projection matrix to obtain a filtered ultrasonic blood flow signal.

[0009] Optionally, the original demodulated data is converted based on a preset orthogonal matrix to obtain a time-space projection matrix, including: reconstructing the original demodulated data to obtain a two-dimensional time-space matrix; projecting the two-dimensional time-space matrix to a basis vector space through the preset orthogonal matrix to obtain a time-space projection matrix.

[0010] Optionally, the original demodulated data is converted based on a preset orthogonal matrix to obtain a time-space projection matrix, including: inputting the original demodulated data and the preset orthogonal matrix into a pre-trained model to obtain a time-space projection matrix corresponding to the original demodulated data.

[0011] Optionally, the preset orthogonal matrix is ​​determined by obtaining a preset conversion matrix in the following manner, where the conversion matrix is ​​a power function; orthogonalization is performed on the preset conversion matrix to obtain a preset orthogonal matrix composed of basis vectors.

[0012] Optionally, the preset conversion matrix is ​​a power function; and the orthogonalization is Schmidt orthogonalization.

[0013] Optionally, the basis vectors are Legendre polynomial basis vectors or Fourier basis vectors.

[0014] Optionally, the original demodulated data is filtered according to the preset orthogonal matrix and the time-space projection matrix, including: accumulating the modulus values ​​of the time-space projection matrix in the spatial dimension to obtain the basis vector weights of each basis vector in the time-space projection matrix; and filtering the original demodulated data according to the basis vector weights and the preset orthogonal matrix.

[0015] Optionally, the original demodulated data is filtered according to the basis vector weights and the preset orthogonal matrix, including: performing logarithmic mapping on the basis vector weights to obtain a mapping curve; determining a suppression threshold based on the curvature of the mapping curve; updating the basis vector weights based on the suppression threshold to obtain updated basis vector weights; and filtering the original demodulated data according to the updated basis vector weights and the preset orthogonal matrix.

[0016] Optionally, the basis vector weight is updated based on the suppression threshold, including: when the basis vector weight is less than the suppression threshold, updating the current basis vector weight to a preset weight value; when the basis vector weight is greater than or equal to the suppression threshold, keeping the current basis vector weight unchanged.

[0017] In some embodiments, the device includes: an acquisition module, configured to acquire original demodulated data generated after ultrasound waves act on a subject; wherein the original demodulated data includes time information and space information; a conversion module, configured to convert the original demodulated data based on a preset orthogonal matrix to obtain a time-space projection matrix; a filtering module, configured to filter the original demodulated data according to the preset orthogonal matrix and the time-space projection matrix to obtain a filtered ultrasonic blood flow signal.

[0018] The filtering method, device, and computer-readable storage medium for ultrasonic signals provided in the embodiments of the present application can achieve the following technical effects:

[0019] The embodiment of the present application first obtains the original demodulated data generated after the ultrasound acts on the subject, converts the original demodulated data based on a preset orthogonal matrix to obtain a time-space projection matrix, and filters the original demodulated data according to the preset orthogonal matrix and the time-space projection information to obtain a filtered ultrasonic blood flow signal. In the filtering process of the above-mentioned ultrasonic signal, the time information of the ultrasound emission and the position information of the ultrasound acting on the subject are simultaneously considered, the time information and the space information are projected simultaneously, and the original demodulated data is filtered according to the projection information. The time information and the space information can more accurately distinguish between tissue and blood flow, thereby improving the imaging effect of the ultrasonic blood flow image.

[0020] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] One or more embodiments are exemplarily described by corresponding drawings. These exemplary descriptions and drawings do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements. The drawings do not constitute a scale limitation. In addition,

[0022] Figure 1 A schematic diagram of an ultrasound imaging process;

[0023] Figure 2 A flowchart of a filtering method for ultrasonic signals provided in an embodiment of the present application;

[0024] Figure 3A flowchart of another method for filtering ultrasonic signals provided in an embodiment of the present application;

[0025] Figure 4 A flowchart of another method for filtering ultrasonic signals provided in an embodiment of the present application;

[0026] Figure 5 A flowchart of a filtering method for ultrasonic signals in a practical application scenario provided in an embodiment of the present application;

[0027] Figures 6a to 6b A schematic diagram showing blood flow before and after the filtering method for ultrasound signals provided by an embodiment of the present application;

[0028] Figure 7 A schematic structural diagram of a filtering device for ultrasonic signals provided in an embodiment of the present application;

[0029] Figure 8 A schematic structural diagram of a filtering device for ultrasonic signals provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to be able to understand the features and technical contents of the embodiments of the present application in more detail, the implementation of the embodiments of the present application is described in detail below in conjunction with the accompanying drawings. The accompanying drawings are for reference only and are not used to limit the embodiments of the present application. In the following technical description, for the sake of 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.

[0031] In the description and claims of the embodiments of the present application and the accompanying drawings, the terms "first," "second," and the like are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate for the purposes of describing the embodiments of the present application. In addition, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions.

[0032] Unless otherwise stated, the term "plurality" means two or more.

[0033] In the embodiments of the present application, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0034] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0035] Ultrasound color flow imaging, an imaging technology that incorporates anatomical information, is highly instructive for diagnosing human diseases. This technology involves transmitting repeated pulse signals to the same tissue site. Because blood is in motion, the received ultrasonic echo signals experience phase differences, and the rate of phase change can be interpreted as a frequency shift. Based on the Doppler principle, the target's velocity can be calculated from the frequency shift. Therefore, the phase difference between echoes from the same sampling volume can be used to estimate blood flow velocity, thereby obtaining information about the motion of the measured blood flow. Finally, the blood flow velocity changes are superimposed on the anatomical grayscale image through color coding. Color flow imaging is now available on nearly all commercial ultrasound systems and holds immense value in market applications.

[0036] One of the key challenges in ultrasound color flow imaging is suppressing clutter signals from stationary and slowly moving tissue. This is particularly true in two clinical application scenarios: one involving low blood flow velocities, such as microvascular identification in placental and tumor diagnosis; and the other involving identifying blood flow in rapidly moving tissue, such as in cardiac or abdominal examinations. Human tissue echoes typically have an amplitude 40 to 60 dB higher than that of blood. The module that suppresses tissue clutter signals is called a clutter suppression filter. It removes tissue clutter signals from the echoes, leaving only the blood flow signal. Improper filter design can result in tissue motion noise in the blood flow image or the elimination of low-velocity blood flow signals.

[0037] In ultrasound color flow imaging, it is generally assumed that tissue signals and blood flow signals have completely different spectral characteristics. Based on this temporal dynamics assumption, conventional clutter filtering methods use standard linear regression to filter the raw ultrasound signal along the time dimension. Most current clutter filtering methods used in ultrasound color flow imaging have limited generalization capabilities and only utilize temporal information. However, the spatial characteristics of tissue and blood flow signals are also different. Tissue motion can be viewed as the spatial movement of speckle patterns, while blood flow motion is more like the reorganization of scatterers, resulting in different speckle patterns. Most existing methods use the same filtering parameters for different patients, resulting in different results for different individuals. In clinical use in hospitals, faced with a large number of patients with varying physical conditions, adaptive clutter filtering methods that can stably output color flow images are particularly important.

[0038] Based on this, an embodiment of the present invention provides a filtering method for ultrasound signals. This method weights clutter filtering basis vectors based on the spatiotemporal characteristics of blood flow signals from different patients. During clutter suppression, both temporal and spatial characteristics are considered. Simultaneously, the weights of the basis vectors are adaptively updated, and the ultrasound echo signal in the color flow mode is decomposed into the space spanned by the basis vectors. An adaptive method is then used to estimate the clutter suppression threshold, effectively suppressing clutter on the signal. This improves the sensitivity and stability of the color flow image display, thereby enhancing the clutter suppression effect and image quality.

[0039] Figure 1 The following is a flowchart of the overall process of ultrasonic color flow imaging using the clutter filtering method. First, an ultrasonic probe continuously transmits and receives at a specific pulse repetition frequency at each transmission line. The receiving chip performs signal amplification and A / D conversion to generate channel data. This is then beamformed to generate RF (Radio Frequency) data. The RF data is then filtered and demodulated to generate IQ (Demodulated) data. This IQ data is then clutter filtered to generate filtered blood flow data. Autocorrelation, smoothing, and velocity estimation are performed on the blood flow data to produce the output ultrasonic blood flow image.

[0040] The filtering method provided in the embodiment of the present application is Figure 1 The clutter filtering process shown has been improved. In the process of clutter filtering on IQ data, both time characteristics and spatial characteristics are considered, and the weights of basis vectors are adaptively updated.

[0041] Combine Figure 2 As shown in FIG, a filtering method for ultrasonic signals provided in an embodiment of the present application is provided, such as Figure 2 As shown, the method specifically includes the following steps:

[0042] S201: Acquire original demodulated data generated after ultrasound waves act on a subject; wherein the original demodulated data includes time information and spatial information;

[0043] The original demodulated data is the data received by using the ultrasound probe to act on the same position of the subject for multiple times. Figure 1 The demodulated data after the demodulation process. Each time it is used, a data is generated, which includes time information and spatial information. The spatial information is the spatial demodulation data of the test site. In the embodiment of the present application, the variable s(x, z, t) is used to represent the original demodulated data, and the data size is n x ×n z ×n t , where n x ,n z ,n tThey represent the number of sampling points in the horizontal scanning space dimension, the number of sampling points in the vertical depth space dimension, and the number of sampling points in the time dimension of repeated transmission and reception at the same position.

[0044] S202: Converting the original demodulated data based on a preset orthogonal matrix to obtain a time-space projection matrix;

[0045] The preset orthogonal matrix can be determined before performing the filtering, or it can be temporarily generated during each filtering process, which is not limited in the embodiment of the present application. The original demodulated data is projected onto the time-space projection matrix, and the original demodulated data is subjected to noise removal operations based on the time dimension and the space dimension.

[0046] S203: Filtering the original demodulated data according to the preset orthogonal matrix and the time-space projection matrix to obtain a filtered ultrasonic blood flow signal.

[0047] The above method provided in the embodiment of the present application first obtains the original demodulated data generated after the ultrasound acts on the subject, converts the original demodulated data based on a preset orthogonal matrix to obtain a time-space projection matrix, and filters the original demodulated data according to the preset orthogonal matrix and the time-space projection information to obtain a filtered ultrasonic blood flow signal. In the filtering process of the above ultrasonic signal, the time information of the ultrasound emission and the position information of the ultrasound acting on the subject are simultaneously considered, the time information and the space information are projected simultaneously, and the original demodulated data is filtered according to the projection information. The time information and the space information can more accurately distinguish between tissue and blood flow, thereby improving the imaging effect of the ultrasonic blood flow image.

[0048] Optionally, in the above embodiment, the original demodulated data is converted based on a preset orthogonal matrix to obtain a time-space projection matrix, including: reconstructing the original demodulated data to obtain a two-dimensional space-time matrix; projecting the two-dimensional space-time matrix to a basis vector space through the preset orthogonal matrix to obtain a time-space projection matrix.

[0049] In some embodiments, in order to observe the velocity component of the blood flow toward the probe, it is necessary to continuously transmit and receive T times at the same position. The IQ data is obtained through the process described in the previous section, and the IQ data is represented by the variable s(x,z,t). The data size is n x ×n z ×n t , where n x ,n z ,n tRepresents the number of sampling points in the horizontal scanning space dimension, the number of sampling points in the vertical depth space dimension, and the number of sampling points in the time dimension of repeated transmission and reception at the same position. Among them, x represents the horizontal position and z represents the vertical position. The original demodulated data s(x,z,t) is reconstructed into the Casorati matrix form, that is, the time series is converted into a two-dimensional space-time matrix S, the matrix size is (n x ×n z ,n t ).

[0050] Conventional clutter filtering methods mostly only process the time dimension, and all position points are independent of each other. The raw demodulated data in the embodiments of this application contains both time and space information. By projecting the two-dimensional space-time matrix into the basis vector space, filtering can be performed separately based on the basis vectors of each dimension. Compared with existing technologies, the information contained can more comprehensively reflect the differences between blood flow and tissue, thereby improving the clutter suppression effect of ultrasonic blood flow images and enhancing the imaging quality of ultrasonic blood flow images.

[0051] Optionally, in the above embodiment, the original demodulated data is converted based on a preset orthogonal matrix to obtain a time-space projection matrix, including: inputting the original demodulated data and the preset orthogonal matrix into a pre-trained model to obtain a time-space projection matrix corresponding to the original demodulated data.

[0052] The pre-trained model is obtained by training with training data, which consists of multiple sets of labels containing demodulated data, orthogonal matrices, and corresponding time-space projection matrices. This allows the pre-trained model to be directly fed with the preset orthogonal matrix and the original demodulated data to obtain the time-space projection matrix, improving the efficiency of clutter suppression.

[0053] Optionally, the preset orthogonal matrix is ​​determined by: obtaining a preset conversion matrix, which is a power function; and performing orthogonal processing on the preset conversion matrix to obtain a preset orthogonal matrix composed of basis vectors.

[0054] Optionally, in the above embodiment, the preset conversion matrix is ​​a power function; and the orthogonalization is Schmidt orthogonalization.

[0055] Optionally, the basis vectors are Legendre polynomial basis vectors or Fourier basis vectors.

[0056] Specifically, in order to obtain an orthogonal matrix, we first define the sequence x = [-N / 2:N / 2] and create a transformation matrix M of size N×N. Each column of the M matrix is ​​defined as a power function of the sequence x, that is, M = {x 0 ,x 1 ,x 2 ,...,x N}, perform Schmidt orthogonalization on the matrix M to obtain the orthogonal matrix L composed of Legendre polynomial basis vectors.

[0057] Combine Figure 3 As shown, another filtering method for ultrasonic signals provided by an embodiment of the present application is provided. This method further calculates the weight of each basis vector based on the consideration of the temporal and spatial characteristics of the ultrasonic signal, and filters the ultrasonic signal based on the weight information. Specifically, the method includes the following steps:

[0058] S301: Acquire original demodulated data generated after ultrasound waves act on a subject; wherein the original demodulated data includes time information and spatial information;

[0059] S302: Converting the original demodulated data based on a preset orthogonal matrix to obtain a time-space projection matrix;

[0060] S303: Accumulate the module values ​​of the time-space projection matrix in the spatial dimension to obtain the basis vector weight of each basis vector in the time-space projection matrix;

[0061] Specifically, the original demodulated data S is projected onto the time-space matrix spanned by Legendre polynomial basis vectors through a preset orthogonal matrix L, and a time-space projection matrix S'=SL is obtained.

[0062] The time-space projection matrix S' is modulo-added in the spatial dimension to obtain the weights w(t) of all dimensions of the Legendre polynomial basis vectors.

[0063] The specific formula can be expressed as:

[0064]

[0065] S304: Filter the original demodulated data according to the basis vector weights and the preset orthogonal matrix.

[0066] S305: Obtaining a filtered ultrasonic blood flow signal.

[0067] In the above embodiment, basis vector weights are obtained based on the modulus of the spatial dimension, and filtering processing is performed based on the weights, which fully considers the spatial information of the ultrasound signal, can better distinguish tissue and blood flow, and thus improve the imaging effect.

[0068] In order to make the ultrasound imaging process more compatible with the actual application scenario, after obtaining the basis vector weights, the weights are further adaptively updated. Figure 4 As shown, another filtering method for ultrasonic signals provided in an embodiment of the present application includes:

[0069] S401: Acquire original demodulated data generated after ultrasound waves act on a subject; wherein the original demodulated data includes time information and spatial information;

[0070] S402: Converting the original demodulated data based on a preset orthogonal matrix to obtain a time-space projection matrix;

[0071] S403: Accumulate the module values ​​of the time-space projection matrix in the spatial dimension to obtain the basis vector weight of each basis vector in the time-space projection matrix;

[0072] Steps S401-S403 are similar to the corresponding steps in the above embodiments of the present application and will not be repeated here.

[0073] S404: Perform logarithmic mapping on the basis vector weights to obtain a mapping curve;

[0074] S405: determining a suppression threshold according to the curvature of the mapping curve;

[0075] S406: updating the basis vector weight based on the suppression threshold to obtain an updated basis vector weight;

[0076] After determining the basis vector weights of each basis vector, the basis vector weights can be further updated. Specifically, a logarithmic mapping curve can be drawn based on the basis vector weights, and the suppression threshold can be determined from the mapping curve. For example, the minimum value in the curve can be taken as the suppression threshold. In this way, the threshold is determined based on all basis vector weights, and the basis vector weights are determined based on information about the spatial dimensions. The resulting suppression threshold is effectively adjusted based on the subject's own information, realizing an adaptive adjustment process based on different subjects.

[0077] S407: Filter the original demodulated data according to the updated basis vector weights and the preset orthogonal matrix.

[0078] S408: Obtain the filtered ultrasonic blood flow signal.

[0079] Specifically, the original demodulated information S is subjected to a clutter filtering operation to obtain the blood flow signal S after clutter removal. f , the formula is S f =SLI f L * , where I f =diag(w'(t)). Get the blood flow signal S f After that, subsequent operations such as autocorrelation and velocity estimation are performed to output the ultrasonic blood flow image.

[0080] Optionally, in the above embodiment, the basis vector weight is updated based on the suppression threshold, including: when the basis vector weight is less than the suppression threshold, updating the current basis vector weight to a preset weight value; when the basis vector weight is greater than or equal to the suppression threshold, keeping the current basis vector weight unchanged.

[0081] Specifically, the curvature of the mapped curve is calculated, and the first local minimum of the curvature is used as the clutter suppression threshold N. The weights of the basis vectors that are less than the suppression threshold are set to 0, and the weights that are greater than the suppression threshold are retained, that is, w(t<N)=0, to obtain the updated weight w'(t);

[0082] The updated weights obtained by the above method are obtained by accumulating all the position points of the entire image, that is, the spatial information of the entire image is utilized. In ultrasonic imaging, tissue signals have higher spatial coherence than blood signals. The deformation of tissue is far less than the change in the arrangement of red blood cells in plasma. A small movement of the tissue can be regarded as a spatial displacement of the speckle pattern, while the movement of red blood cells means the reorganization of scatterers, resulting in different speckle patterns. Therefore, spatial information is very important for distinguishing between tissue signals and blood flow signals. The embodiment of the present application utilizes the spatiotemporal information of blood flow signals, takes more into account spatial information, better distinguishes between tissue and blood flow, and thus improves the quality of ultrasonic blood flow images after imaging.

[0083] S501: Reconstruct the demodulated data into a time-space matrix form S.

[0084] S502: Obtain Legendre polynomial basis vectors by Schmidt orthogonalization of the power function;

[0085] First, define the sequence x = [-N / 2:N / 2] and create a transformation matrix M of size N×N. Each column of the M matrix is ​​defined as a power function of the sequence x, that is, M = {x 0 ,x 1 ,x 2 ,...,x N}, perform Schmidt orthogonalization on the matrix M to obtain the orthogonal matrix L composed of Legendre polynomial basis vectors.

[0086] S503: Projecting the demodulated data in the time-space matrix form into the Legendre polynomial basis vector space;

[0087] Project S to the space spanned by Legendre polynomial basis vectors through the orthogonal matrix L to obtain the projection matrix S'=SL.

[0088] S504: Calculate the weight of each basis vector in the basis vector space;

[0089] The projection matrix S' is modulo-added in the spatial dimension to obtain the weights w(t) of all dimensions of the Legendre polynomial basis vectors.

[0090] S505: Calculating a clutter suppression threshold based on all basis vector weights;

[0091] Perform logarithmic mapping on the weight w(t) and calculate the curvature of the mapped curve. The first local minimum of the curvature is defined as the clutter suppression threshold N. The portion of the weight vector less than the threshold is set to 0, and the portion greater than the threshold is retained, i.e., w(t<N)=0, to obtain the new weight w'(t);

[0092] S506: Update basis vector weights using the clutter suppression threshold;

[0093] S507: Based on the updated basis vector weights, clutter suppression is performed on the demodulated data, and suppressed blood flow data is output.

[0094] In order to verify the effect of the filtering method provided in the embodiment of the present application, IQ data of ultrasonic blood flow of a real human kidney was collected by ultrasound equipment, and the filtering method process provided in the above embodiment was implemented on the IQ data through offline simulation. Figure 6(a) is a schematic diagram of blood flow after using the relevant filtering technology, and Figure 6(b) is a schematic diagram of blood flow after using the filtering method provided in the embodiment of the present application. It can be seen that the filtering method provided in the embodiment of the present application has a better effect on clutter suppression and a clearer blood flow display effect.

[0095] Combine Figure 7 FIG. 7 is a schematic diagram of a structure of a filtering device 700 for ultrasonic signals provided in an embodiment of the present application, as shown in FIG. Figure 7 As shown, the device includes:

[0096] The acquisition module 701 is configured to acquire original demodulated data generated after ultrasound waves act on a subject; wherein the original demodulated data includes time information and spatial information;

[0097] The conversion module 702 is configured to convert the original demodulated data based on a preset orthogonal matrix to obtain a time-space projection matrix;

[0098] The filtering module 703 is configured to filter the original demodulated data according to the preset orthogonal matrix and the time-space projection matrix to obtain a filtered ultrasonic blood flow signal.

[0099] The above-mentioned device provided in the embodiment of the present application first obtains the original demodulated data generated after the ultrasound acts on the subject, converts the original demodulated data based on a preset orthogonal matrix to obtain a time-space projection matrix, and filters the original demodulated data according to the preset orthogonal matrix and the time-space projection information to obtain a filtered ultrasonic blood flow signal. In the filtering process of the above-mentioned ultrasonic signal, the time information of the ultrasound emission and the position information of the ultrasound acting on the subject are simultaneously considered, the time information and the space information are projected simultaneously, and the original demodulated data is filtered according to the projection information. The time information and the space information can more accurately distinguish between tissue and blood flow, thereby improving the imaging effect of the ultrasonic blood flow image.

[0100] Optionally, the conversion module 702 is specifically configured to reconstruct the original demodulated data to obtain a two-dimensional space-time matrix; and project the two-dimensional space-time matrix to a basis vector space through the preset orthogonal matrix to obtain a space-time projection matrix.

[0101] Optionally, the conversion module 702 is specifically configured to input the original demodulated data and a preset orthogonal matrix into a pre-trained model to obtain a time-space projection matrix corresponding to the original demodulated data.

[0102] Optionally, the preset orthogonal matrix is ​​determined by: obtaining a preset conversion matrix, which is a power function; and performing orthogonal processing on the preset conversion matrix to obtain a preset orthogonal matrix composed of basis vectors.

[0103] Optionally, the preset conversion matrix is ​​a power function; and the orthogonalization is Schmidt orthogonalization.

[0104] Optionally, the basis vectors are Legendre polynomial basis vectors or Fourier basis vectors.

[0105] Optionally, the filtering module 703 is specifically configured to accumulate the modulus values ​​of the time-space projection matrix in the spatial dimension to obtain the basis vector weights of each basis vector in the time-space projection matrix; and filter the original demodulated data according to the basis vector weights and the preset orthogonal matrix.

[0106] Optionally, the original demodulated data is filtered according to the basis vector weights and the preset orthogonal matrix, including: performing logarithmic mapping on the basis vector weights to obtain a mapping curve; determining a suppression threshold based on the curvature of the mapping curve; updating the basis vector weights based on the suppression threshold to obtain updated basis vector weights; and filtering the original demodulated data according to the updated basis vector weights and the preset orthogonal matrix.

[0107] Optionally, the basis vector weight is updated based on the suppression threshold, including: when the basis vector weight is less than the suppression threshold, updating the current basis vector weight to a preset weight value; when the basis vector weight is greater than or equal to the suppression threshold, keeping the current basis vector weight unchanged.

[0108] Combine Figure 8 As shown, an embodiment of the present application provides a filtering device 800 for ultrasonic signals, including a processor 100 and a memory 101. Optionally, the device may also include a communication interface 102 and a bus 103. The processor 100, the communication interface 102, and the memory 101 may communicate with each other via the bus 103. The communication interface 102 may be used for information transmission. The processor 100 may call the logic instructions in the memory 101 to execute the filtering method for ultrasonic signals of the above embodiment.

[0109] In addition, the logic instructions in the memory 101 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0110] The memory 101 is a computer-readable storage medium that can be used to store software programs and computer-executable programs, such as the program instructions / modules corresponding to the methods in the embodiments of the present application. The processor 100 executes the program instructions / modules stored in the memory 101 to perform functional applications and data processing, thereby implementing the filtering method for ultrasonic signals in the above-mentioned embodiment.

[0111] The memory 101 may include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data generated based on the use of the terminal device. Furthermore, the memory 101 may include high-speed random access memory and non-volatile memory.

[0112] An embodiment of the present application provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to execute the filtering method for ultrasonic signals of the above embodiment.

[0113] An embodiment of the present application provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the filtering method for ultrasonic signals of the above embodiment.

[0114] The aforementioned computer-readable storage medium may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0115] The technical solution of the embodiment of the present application 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 can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiment of the present application. The aforementioned storage medium can be a non-transient storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program code, or a transient storage medium.

[0116] The above description and the accompanying drawings fully illustrate the embodiments of the present application 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 variations. Unless explicitly required, separate components and functions are optional, and the order of operations may vary. Parts and features of some embodiments may be included in or replace 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 claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to also include plural forms. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of one or more associated listings. In addition, when used in this application, the term "comprise" and its variations "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 of these. In the absence of further restrictions, an element defined by the sentence "comprising a..." does not exclude the presence of other identical elements in the process, method or device that includes the element. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments can be referenced 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 be found in the description of the method part.

[0117] 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 with 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 to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present application. The technicians will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0118] 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 merely illustrative. For example, the division of the units can be merely a logical functional division. In actual implementation, there may be other division methods, 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 shown 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, the functional units in the embodiments of the present application may be integrated into a processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.

[0119] The flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to the embodiments of the present application. 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 part of the module, program segment or 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 an order different from that 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 flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that 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 function or action, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A filtering method for ultrasonic signals, characterized in that: include: Acquiring original demodulated data generated after ultrasound waves act on a subject; wherein the original demodulated data includes time information and spatial information; Converting the original demodulated data based on a preset orthogonal matrix to obtain a time-space projection matrix; Accumulating the module values ​​of the time-space projection matrix in the spatial dimension to obtain a basis vector weight of each basis vector in the time-space projection matrix; Performing logarithmic mapping on the basis vector weights to obtain a mapping curve; determining a suppression threshold according to the curvature of the mapping curve; Updating the basis vector weights based on the suppression threshold to obtain updated basis vector weights; The original demodulated data is filtered according to the updated basis vector weights and the preset orthogonal matrix to obtain a filtered ultrasonic blood flow signal.

2. The method according to claim 1, characterized in that The original demodulated data is converted based on a preset orthogonal matrix to obtain a time-space projection matrix, including: Reconstructing the original demodulated data to obtain a two-dimensional space-time matrix; The two-dimensional space-time matrix is ​​projected onto a basis vector space through the preset orthogonal matrix to obtain a space-time projection matrix.

3. The method according to claim 1, characterized in that The original demodulated data is converted based on a preset orthogonal matrix to obtain a time-space projection matrix, including: The original demodulated data and the preset orthogonal matrix are input into the pre-trained model to obtain the time-space projection matrix corresponding to the original demodulated data.

4. The method according to claim 1, wherein The preset orthogonal matrix is ​​determined by: Get the preset transformation matrix, which is a power function; Orthogonalization is performed on the preset transformation matrix to obtain a preset orthogonal matrix composed of basis vectors.

5. The method according to claim 4, characterized in that The preset conversion matrix is ​​a power function; and the orthogonalization is Schmidt orthogonalization.

6. The method according to claim 4, characterized in that The basis vectors are Legendre polynomial basis vectors or Fourier basis vectors.

7. The method according to claim 1, characterized in that Updating the basis vector weights based on the suppression threshold comprises: When the basis vector weight is less than the suppression threshold, updating the current basis vector weight to a preset weight value; When the basis vector weight is greater than or equal to the suppression threshold, the current basis vector weight is kept unchanged.

8. A filtering device for ultrasonic signals, characterized in that: include: An acquisition module is configured to acquire original demodulated data generated after ultrasound waves act on a subject; wherein the original demodulated data includes time information and spatial information; a conversion module, configured to convert the original demodulated data based on a preset orthogonal matrix to obtain a time-space projection matrix; The filtering module is configured to accumulate the modulus values ​​of the time-space projection matrix in the spatial dimension to obtain the basis vector weights of each basis vector in the time-space projection matrix; perform logarithmic mapping on the basis vector weights to obtain a mapping curve; determine a suppression threshold based on the curvature of the mapping curve; update the basis vector weights based on the suppression threshold to obtain updated basis vector weights; and filter the original demodulated data based on the updated basis vector weights and the preset orthogonal matrix to obtain a filtered ultrasonic blood flow signal.

9. A filtering device for ultrasonic signals, comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to perform the filtering method for ultrasonic signals according to any one of claims 1 to 7 when executing the program instructions.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are configured to execute the filtering method for ultrasonic signals according to any one of claims 1 to 7.

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