Color flow imaging wall filtering method, system, device, and storage medium

Abstract

The application discloses a color blood flow imaging wall filtering method, system, device and storage medium, the method can ensure the coincidence degree of the polynomial regression filter coefficient matrix and the echo signal by combining the initial filter coefficient matrix based on the echo signal, so as to facilitate subsequent coefficient matrix superposition processing; the reliability of the target coefficient matrix can be effectively improved by weighting calculation of the constructed infinite impulse response filter coefficient matrix based on projection initialization and the polynomial regression filter coefficient matrix, and then the target coefficient matrix is obtained, so that the filtering effect of the echo signal can be improved based on the target coefficient matrix, the accuracy of the speed estimation result is improved, and then the accuracy and reliability of imaging are improved.

Description

Technical Field

[0001] This invention relates to the field of ultrasound technology, and in particular to a color Doppler imaging wall filtering method, system, device, and storage medium. Background Technology

[0002] Color Doppler flow imaging uses the Doppler frequency shift of blood flow echo signals to obtain blood flow velocity, which is then displayed simultaneously with the original B-mode image through color encoding. However, the echo signals received by the transducer contain not only blood flow signals but also clutter signals generated by the vessel walls and tissues. The presence of clutter signals affects the display of color Doppler flow, so a wall filter is needed to filter out these clutter signals and retain only the blood flow signal. Because the velocity of blood flow is much greater than the velocity of vessel walls and tissues, the spectrum of blood flow echo signals is higher, while the spectrum of vessel wall and tissue echoes is lower. A high-pass filter is needed to separate the high-frequency signals.

[0003] However, commonly used wall filters include infinite impulse response (IIR) filters and polynomial regression (Pol-Reg) filters. IIR filters are often used in conjunction with initialization techniques, among which projection initialization is the best performing. Pol-Reg filters are a special type of filter based on the least squares fitting algorithm, and are often used as echo wall filters due to their advantages of not losing data points and having a small impulse response. However, because the cutoff frequency of the polynomial regression filter does not match the number of echo points, it is impossible to obtain a filter coefficient matrix with more different cutoff frequencies when the number of direction points is fixed and the echo signal is slow. Moreover, the Pol-Reg filter has a large attenuation gain at high frequencies, which causes errors in the velocity obtained by autocorrelation, affecting the sonographer's judgment of the true blood flow distribution.

[0004] Therefore, in the existing technology, during the wall filtering process of color blood flow imaging, there is a problem that the reliability of color blood flow imaging is not high due to errors in velocity estimation. Summary of the Invention

[0005] In view of this, it is necessary to provide a color flow imaging wall filtering method, system, device and storage medium to solve the problem of low reliability of color flow imaging due to velocity estimation errors in the existing technology during the color flow imaging wall filtering process.

[0006] To address the above problems, this invention provides a color blood flow imaging wall filtering method, comprising:

[0007] Acquire the echo signal and set the cutoff frequency;

[0008] The initial filter coefficient matrix is ​​determined based on the cutoff frequency, and the initial filter coefficient matrix is ​​combined based on the echo signal to obtain the polynomial regression filter coefficient matrix.

[0009] Construct the infinite impulse response filter coefficient matrix based on projection initialization using the echo signal and cutoff frequency;

[0010] The target coefficient matrix is ​​obtained by weighting the coefficient matrix of the polynomial regression filter and the coefficient matrix of the infinite impulse response filter based on projection initialization, and then color blood flow imaging wall filtering is performed based on the target coefficient matrix.

[0011] Furthermore, the initial filter coefficient matrix is ​​determined based on the cutoff frequency, and the initial filter coefficient matrix is ​​combined based on the echo signal to obtain the polynomial regression filter coefficient matrix, including:

[0012] Based on the cutoff frequency, the number of points and the order of the polynomial regression filter are determined by the amplitude-frequency characteristic function;

[0013] Determine the initial filter coefficient matrix based on the number of points, order, and echo signal;

[0014] Obtain the length of the echo signal in the slow-time direction;

[0015] The initial filter coefficient matrix is ​​combined according to its length and number of points to obtain the polynomial regression filter coefficient matrix.

[0016] Furthermore, the amplitude-frequency response function is:

[0017]

[0018]

[0019]

[0020] Where H(w) is the frequency response function, N is the number of points, K is the order, and B... i (w) is an intermediate variable, j is an imaginary number, and b i (m) is a vector b i The m-th number in the vector b i For the preset matrix R N×K The column vectors of the orthonormal basis, where w is the angular frequency.

[0021] Furthermore, the initial filter coefficient matrix is ​​combined according to its length and number of points to obtain the polynomial regression filter coefficient matrix, including:

[0022] When the length is greater than the number of points, supplementary combinations are made based on the data in the first column of the initial filter coefficient matrix until the polynomial regression filter coefficient matrix is ​​obtained.

[0023] When the length equals the number of points, the initial filter coefficient matrix is ​​determined to be the polynomial regression filter coefficient matrix.

[0024] Furthermore, based on the echo signal and the cutoff frequency, a projection-initialized infinite impulse response filter coefficient matrix is ​​constructed, including:

[0025] The filter coefficients are determined by calculating based on the echo signal and cutoff frequency using a preset recursive filter.

[0026] Construct an infinite impulse response filter coefficient matrix based on projection initialization using the filter coefficients.

[0027] Furthermore, the preset recursive filter includes at least one of the following: Butterworth filter, Chebyshev Type I filter, Chebyshev Type II filter, and elliptic filter.

[0028] Furthermore, the target coefficient matrix is ​​obtained by weighting the coefficient matrix of the polynomial regression filter and the coefficient matrix of the infinite impulse response filter based on projection initialization, including:

[0029] The target coefficient matrix is ​​obtained by weighting the coefficient matrix of the polynomial regression filter and the coefficient matrix of the infinite impulse response filter based on projection initialization according to the weighted calculation formula of the target coefficient matrix.

[0030] The weighted calculation formula for the target coefficient matrix is ​​as follows:

[0031] A Reg_IIR =k1A Reg +k2A IIR

[0032] k1+k2≤1

[0033] Among them, A Reg_IIR Let A be the target coefficient matrix. Reg Let A be the coefficient matrix of the polynomial regression filter. IIR Let k1 and k2 be the coefficient matrix of the infinite impulse response filter initialized based on projection, where k1 and k2 are constants.

[0034] To address the above problems, the present invention also provides a color blood flow imaging wall filtering system, comprising:

[0035] The parameter setting module is used to acquire the echo signal and set the cutoff frequency;

[0036] The polynomial regression filter coefficient matrix acquisition module is used to determine the initial filter coefficient matrix based on the cutoff frequency, and to combine the initial filter coefficient matrix based on the echo signal to obtain the polynomial regression filter coefficient matrix.

[0037] The module for obtaining the coefficient matrix of an infinite impulse response filter based on projection initialization is used to construct the coefficient matrix of the infinite impulse response filter based on projection initialization according to the echo signal and the cutoff frequency.

[0038] The color Doppler imaging wall filtering module is used to perform weighted calculations on the coefficient matrix of the polynomial regression filter and the coefficient matrix of the infinite impulse response filter based on projection initialization to obtain the target coefficient matrix, and then perform color Doppler imaging wall filtering based on the target coefficient matrix.

[0039] To address the aforementioned problems, the present invention also provides a color Doppler imaging wall filtering device, comprising a memory and a processor, wherein,

[0040] Memory, used to store programs;

[0041] A processor, coupled to memory, is used to execute programs stored in memory to implement the steps in the color flow imaging wall filtering methods, systems, devices, and storage media described above.

[0042] To address the aforementioned problems, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instruction, which, when executed by a processor, enables the implementation of the steps in the color blood flow imaging wall filtering method, system, device, and storage medium described above.

[0043] The beneficial effects of the above embodiments are as follows: This invention provides a color blood flow imaging wall filtering method, system, device, and storage medium. This method, by combining the initial filter coefficient matrix based on the echo signal, can ensure the conformity between the polynomial regression filter coefficient matrix and the echo signal, so as to facilitate subsequent coefficient matrix superposition processing. By weighting the constructed infinite impulse response filter coefficient matrix based on projection initialization and the polynomial regression filter coefficient matrix to obtain the target coefficient matrix, the reliability of the target coefficient matrix can be effectively improved. Therefore, color blood flow imaging wall filtering based on the target coefficient matrix can improve the filtering effect of the echo signal, improve the accuracy of velocity estimation results, and thus improve the accuracy and reliability of imaging. Attached Figure Description

[0044] Figure 1 A schematic flowchart of an embodiment of the color blood flow imaging wall filtering method provided by the present invention;

[0045] Figure 2 This is a schematic flowchart illustrating an embodiment of the present invention for obtaining the coefficient matrix of a multinomial regression filter.

[0046] Figure 3A , Figure 3B and Figure 3CA schematic diagram of the results of the first set of comparative experiments provided by this invention;

[0047] Figure 4A , Figure 4B and Figure 4C This is a schematic diagram of the results of the second set of comparative experiments provided by the present invention;

[0048] Figure 5 This is a structural block diagram of an embodiment of the color blood flow imaging wall filtering system provided by the present invention;

[0049] Figure 6 This is a structural block diagram of an embodiment of the color blood flow imaging wall filtering device provided by the present invention. Detailed Implementation

[0050] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0051] Color Doppler flow imaging uses the Doppler frequency shift of blood flow echo signals to obtain blood flow velocity, which is then displayed simultaneously with the original B-mode image through color encoding. However, the echo signals received by the transducer contain not only blood flow signals but also clutter signals generated by the vessel walls and tissues. The presence of clutter signals affects the display of color Doppler flow, so a wall filter is needed to filter out these clutter signals and retain only the blood flow signal. Because the velocity of blood flow is much greater than the velocity of vessel walls and tissues, the spectrum of blood flow echo signals is higher, while the spectrum of vessel wall and tissue echoes is lower. A high-pass filter is needed to separate the high-frequency signals.

[0052] However, commonly used wall filters include infinite impulse response (IIR) filters and polynomial regression (Pol-Reg) filters. IIR filters are often used in conjunction with initialization techniques, among which projection initialization is the best performing. Pol-Reg filters are a special type of filter based on the least squares fitting algorithm, and are often used as echo wall filters due to their advantages of not losing data points and having a small impulse response. However, because the cutoff frequency of the polynomial regression filter does not match the number of echo points, it is impossible to obtain a filter coefficient matrix with more different cutoff frequencies when the number of direction points is fixed and the echo signal is slow. Moreover, the Pol-Reg filter has a large attenuation gain at high frequencies, which causes errors in the velocity obtained by autocorrelation, affecting the sonographer's judgment of the true blood flow distribution.

[0053] Therefore, in the existing technology, during the wall filtering process of color blood flow imaging, there is a problem that the reliability of color blood flow imaging is not high due to errors in velocity estimation.

[0054] To address the aforementioned problems, this invention provides a color blood flow imaging wall filtering method, system, device, and storage medium, which will be described in detail below.

[0055] Figure 1 This is a flowchart illustrating an embodiment of the color Doppler imaging wall filtering method provided by the present invention, as shown below. Figure 1 As shown, the wall filtering method for color blood flow imaging includes:

[0056] Step S101: Acquire the echo signal and set the cutoff frequency;

[0057] Step S102: Determine the initial filter coefficient matrix based on the cutoff frequency, and combine the initial filter coefficient matrix based on the echo signal to obtain the polynomial regression filter coefficient matrix.

[0058] Step S103: Construct the coefficient matrix of the infinite impulse response filter based on projection initialization according to the echo signal and the cutoff frequency;

[0059] Step S104: Perform weighted calculations on the coefficient matrix of the polynomial regression filter and the coefficient matrix of the infinite impulse response filter based on projection initialization to obtain the target coefficient matrix, and perform color blood flow imaging wall filtering based on the target coefficient matrix.

[0060] In this embodiment, firstly, the echo signal is acquired and the cutoff frequency is set; secondly, the initial filter coefficient matrix is ​​determined based on the cutoff frequency, and the initial filter coefficient matrix is ​​combined based on the echo signal to obtain the polynomial regression filter coefficient matrix; then, the infinite impulse response filter coefficient matrix based on projection initialization is constructed based on the echo signal and the cutoff frequency; finally, the polynomial regression filter coefficient matrix and the infinite impulse response filter coefficient matrix based on projection initialization are weighted and calculated to obtain the target coefficient matrix, and color Doppler imaging wall filtering is performed based on the target coefficient matrix.

[0061] In this embodiment, by combining the initial filter coefficient matrix based on the echo signal, the consistency between the polynomial regression filter coefficient matrix and the echo signal can be guaranteed, facilitating subsequent coefficient matrix superposition. By weighting the constructed infinite impulse response filter coefficient matrix based on projection initialization with the polynomial regression filter coefficient matrix to obtain the target coefficient matrix, the reliability of the target coefficient matrix can be effectively improved. Therefore, color Doppler imaging wall filtering based on the target coefficient matrix can improve the filtering effect of the echo signal, improve the accuracy of velocity estimation results, and thus improve the accuracy and reliability of imaging.

[0062] In a preferred embodiment, in step S102, in order to determine the initial filter coefficient matrix based on the cutoff frequency, and to combine the initial filter coefficient matrix based on the echo signal to obtain the polynomial regression filter coefficient matrix, as follows: Figure 2 As shown, Figure 2 A flowchart illustrating an embodiment of the present invention for obtaining the coefficient matrix of a multinomial regression filter includes:

[0063] Step S121: Determine the number of points and order of the polynomial regression filter based on the cutoff frequency and the amplitude-frequency characteristic function;

[0064] Step S122: Determine the initial filter coefficient matrix based on the number of points, order, and echo signal;

[0065] Step S123: Obtain the length of the echo signal in the slow time direction;

[0066] Step S124: Combine the initial filter coefficient matrix according to the length and number of points to obtain the polynomial regression filter coefficient matrix.

[0067] In this embodiment, the initial filter coefficient matrix is ​​obtained by inverse solving the amplitude-frequency characteristic function, and then the initial filter coefficient matrix is ​​combined according to the length of the echo signal to obtain the polynomial regression filter coefficient matrix, so as to make effective use of the polynomial regression filter coefficient matrix in the future.

[0068] It should be noted that the echo signal includes both fast and slow time dimensions.

[0069] The fast time dimension refers to the signals associated with the same ultrasound image acquired per unit time, while the slow time dimension refers to multiple signals acquired at the same scatter point in an image at consecutive time points.

[0070] In one specific embodiment, the low-frequency components of the echo signal are fitted into a state subspace using the least squares method. Then, the blood flow signal is obtained by subtracting the projection of this subspace onto the original echo signal. This can be expressed by the formula:

[0071] y = A reg x

[0072] Where x is the input echo signal, y is the filtered output blood flow, and A reg It is the coefficient matrix of the polynomial regression filter, with a size of N×N.

[0073] Furthermore, it can be expressed as:

[0074]

[0075] Where A is the clutter projection matrix, a1 is a row vector of length N for the clutter projection matrix, P is a projection operator of size K×N, N and K are the number of points and the order used to construct the filter coefficient matrix, respectively, with the order not exceeding 4, I is an identity matrix of length N×N, and R... i,: Let R represent all the data in the i-th row of matrix R. N,K Let represent the data in the Nth row and Kth column of matrix R, T denotes finding the transpose of the matrix, and -1 denotes finding the inverse of the matrix.

[0076] R is a predefined matrix of size N×K, represented as:

[0077]

[0078] Performing Schmitt orthogonalization on matrix R yields a set of orthonormal bases {b0, b1, ..., b}. K-1}, b K It is a vector of length N.

[0079] In one specific embodiment, the amplitude-frequency response function is:

[0080]

[0081]

[0082]

[0083] Where H(w) is the frequency response function, N is the number of points, K is the order, and B... i (w) is an intermediate variable, j is an imaginary number, and b i (m) is a vector b i The m-th number in the vector b i For the preset matrix R N×K The column vectors of the orthonormal basis, where w is the angular frequency.

[0084] It should be noted that N is an integer not less than 8, and the value of K ranges from [1, 4], where K is an integer.

[0085] w = 2π × PRF, where PRF is the pulse repetition frequency.

[0086] In a preferred embodiment, in step S124, in order to combine the initial filter coefficient matrix, specifically, when the length is greater than the number of points, supplementary combination is performed based on the data in the first column of the initial filter coefficient matrix until the polynomial regression filter coefficient matrix is ​​obtained.

[0087] When the length equals the number of points, the initial filter coefficient matrix is ​​determined to be the polynomial regression filter coefficient matrix.

[0088] In one specific embodiment, the amplitude-frequency response of the polynomial regression filter is affected by the number of points N and the order K. However, given the number of points M in the direction of slow echo, only a finite number of coefficients with different cutoff frequency characteristics can be generated, and the desired cutoff frequency cannot be obtained. Therefore, it is necessary to find the corresponding filter coefficient matrix A based on the selected cutoff frequency. N×N Combine the coefficients into a coefficient matrix A of length M×M. Reg The combined polynomial regression filter coefficient matrix is ​​then expressed as:

[0089]

[0090] Furthermore, to clearly describe the above combination method, specifically, matrix A... reg The first column is written into matrix A. Reg In the first column, rows 1 to N, then matrix A reg The first column is written into matrix A. Reg In the second column, rows 2 to (N+1). Further, matrix A... reg The first column is written into matrix A. Reg In the i-th column, from row i to (N+i-1), until i = MN, matrix A is... reg Write to matrix A Reg In the (MN) to (M) columns and rows (MN) to (M).

[0091] In a preferred embodiment, in step S103, in order to construct an infinite impulse response filter coefficient matrix based on projection initialization according to the echo signal and the cutoff frequency, firstly, the filter coefficients are determined by calculating according to the echo signal and the cutoff frequency using a preset recursive filter; then, an infinite impulse response filter coefficient matrix based on projection initialization is constructed according to the filter coefficients.

[0092] It should be noted that the preset recursive filter includes at least one of the following: Butterworth filter, Chebyshev Type I filter, Chebyshev Type II filter, and elliptic filter.

[0093] In one specific embodiment, the low-frequency components of the echo signal can be represented by a state subspace. Then, by subtracting the projection of this subspace onto the original echo signal, the blood flow signal can be obtained, expressed by the formula:

[0094] y = A IIR x

[0095] Where x is the input echo signal, y is the filtered output blood flow, and A IIR It is the coefficient matrix of the infinite impulse response filter based on projection initialization, with a size of M×M.

[0096] Typically, a K-order recursive filter can be represented by the following difference equation:

[0097]

[0098] Among them, a k b k Here, y(m) is the filter coefficient, y(mk) is the output at point m, y(mk) is the output before point m, x(mk) is the input before point m, and K is the order of the filter.

[0099] The recursive filter is described using a state-space equation, with the state vector chosen as follows:

[0100] v(m)=[v1(m) v2(m)...v k (m)] T

[0101] Among them, v i (m) is the content of the i-th storage register in the above filter.

[0102] The state space is then described as follows:

[0103] v(m+1)=Fv(m)+qx(m)

[0104] y(m)=g T v(m)+dx(m)

[0105] The expressions for the element matrices F, q, and g are as follows:

[0106]

[0107] Both the input and output sequences are vectors of size M×1:

[0108] x = [x(0)x(1)...x(M-1)] T ,y=[y(0)y(1)...y(M-1)] T Therefore, the relationship between the input and output of the filter can be expressed by the following formula:

[0109] y = Bv(0) + Cx

[0110]

[0111] Since the length of the sampling dataset for wall filtering is limited, typically only 8-16 data points are available. This results in a transient response at the filter output. According to linear system theory, the transient response of the output signal is equivalent to the response when the input signal is zero. Let x = 0 in y = Bv(0) + Cx. It can be seen that the transient response is a subspace composed of the column vectors of matrix B, and the projection matrix P...B =B(B T B) -1 B T This is the projection of the transient space. Therefore, the corresponding filter coefficients are:

[0112] A IIR =(IB(B T B) -1 B T C

[0113] T represents finding the transpose of a matrix, and -1 represents finding the inverse of a matrix.

[0114] The frequency response function H(w) of the infinite impulse response filter based on projection initialization is expressed as:

[0115]

[0116]

[0117] Where M is the number of points used to construct the filter coefficient matrix, and B i (w) is an intermediate variable, A mi It is vector A IIR The number corresponding to the m-th row and i-th column is j, which is the imaginary number representation, and w is the angular frequency.

[0118] In a preferred embodiment, in step S104, in order to perform weighted calculation on the coefficient matrix of the polynomial regression filter and the coefficient matrix of the infinite impulse response filter based on projection initialization to obtain the target coefficient matrix, specifically, the coefficient matrix of the polynomial regression filter and the coefficient matrix of the infinite impulse response filter based on projection initialization are weighted according to the target coefficient matrix weighted calculation formula to obtain the target coefficient matrix.

[0119] The weighted calculation formula for the target coefficient matrix is ​​as follows:

[0120] A Reg_IIR =k1A Reg +k2A IIR

[0121] Among them, A Reg_IIR Let A be the target coefficient matrix. Reg Let A be the coefficient matrix of the polynomial regression filter. IIR Let k1 and k2 be the coefficient matrix of the infinite impulse response filter initialized based on projection, where k1 and k2 are constants.

[0122] In one specific embodiment, k1 and k2 are preferably 0.5, and the sum of k1 and k2 is preferably 1.

[0123] In other embodiments, the values ​​of k1 and k2 can be adjusted according to actual needs, and no restrictions are imposed here.

[0124] Finally, based on the determined target coefficient matrix, in order to perform wall filtering for color Doppler imaging, a combined filter coefficient matrix A is used. Reg_IIR The output signal is obtained by filtering the echo signal in the slower direction. The output signal is y = A. Reg_IIR x=(k1A Reg +k2A IIR )x.

[0125] It should be noted that in practical use, the coefficients can be retrieved simply by looking up a table. Filtering with these coefficients is equivalent to performing two filters: a polynomial regression filter and a projection initialization IIR filter. This ensures both computational speed and reduces the speed estimation error caused by the polynomial regression filter alone.

[0126] Furthermore, to verify the filtering characteristics of the target coefficient matrix, two sets of comparative experiments were specifically constructed. The experiments revealed that:

[0127] Please refer to Figure 3A , Figure 3B and Figure 3C , Figure 3A In the example, with 5 points and 3 orders, the cutoff frequency is calculated to be 0.3PRF using the frequency response function formula, where PRF is the pulse repetition frequency. The amplitude-frequency response curve is shown in the figure. It is observed that the attenuation gain gradually increases above 0.4, corresponding to the high-velocity blood flow component. Figure 3B In the same setting, with a cutoff frequency of 0.3PRF, the amplitude-frequency characteristic curve of the infinite impulse response filter based on projection initialization is shown in the figure. Above 0.4, the attenuation gain gradually decreases.

[0128] from Figure 3C It can be concluded that when the amplitude-frequency response curve corresponding to the coefficients of the new combined filter is above 0.4, the attenuation gain remains at a relatively low level, which is a significant improvement compared to the individual infinite impulse response filter and polynomial regression filter based on projection initialization.

[0129] Combination Figure 4A , Figure 4B and Figure 4C As shown, Figure 4A In the example, with 5 points and 2 orders, the cutoff frequency is calculated to be 0.2PRF using the frequency response function formula. The amplitude-frequency response curve is shown in the figure. It is observed that there is a significant attenuation gain between 0.35 and 0.4 normalized frequencies, corresponding to the high-velocity blood flow component. Figure 4BIn the same setting, with a cutoff frequency of 0.2PRF, the amplitude-frequency response curve of the generated projection initialization IIR filter is shown in the figure. After 0.35, the attenuation gain is very low.

[0130] from Figure 4C It can be concluded that when the amplitude-frequency characteristic curve corresponding to the coefficients of the new filter is above 0.35, the attenuation gain remains at a relatively low level, effectively reducing the attenuation gain of the polynomial regression filter at high frequencies.

[0131] Therefore, it is obvious that the color blood flow imaging wall filter provided by the present invention can achieve the effect of multiple cutoff frequencies of a polynomial regression filter with a fixed number of echo points through a coefficient combination method. Moreover, by combining the coefficients with the projection initialization IIR filter, the attenuation gain at high echo frequencies can be reduced, the error of subsequent velocity estimation can be reduced, and thus a more accurate color blood flow image can be output.

[0132] By combining the initial filter coefficient matrix based on the echo signal in the above manner, the consistency between the polynomial regression filter coefficient matrix and the echo signal can be guaranteed, which facilitates subsequent coefficient matrix superposition. By weighting the constructed infinite impulse response filter coefficient matrix based on projection initialization with the polynomial regression filter coefficient matrix to obtain the target coefficient matrix, the reliability of the target coefficient matrix can be effectively improved. Therefore, color Doppler imaging wall filtering based on the target coefficient matrix can improve the filtering effect of the echo signal, improve the accuracy of velocity estimation results, and thus improve the accuracy and reliability of imaging.

[0133] To address the aforementioned problems, the present invention also provides a color blood flow imaging wall filtering system, such as... Figure 5 As shown, Figure 5 This is a structural block diagram of an embodiment of the color Doppler imaging wall filtering system provided by the present invention. The color Doppler imaging wall filtering system 500 includes:

[0134] The parameter setting module 501 is used to acquire the echo signal and set the cutoff frequency;

[0135] The polynomial regression filter coefficient matrix acquisition module 502 is used to determine the initial filter coefficient matrix based on the cutoff frequency, and to combine the initial filter coefficient matrix based on the echo signal to obtain the polynomial regression filter coefficient matrix.

[0136] The infinite impulse response filter coefficient matrix acquisition module 503 based on projection initialization is used to construct the infinite impulse response filter coefficient matrix based on projection initialization according to the echo signal and the cutoff frequency.

[0137] The color blood flow imaging wall filtering module 504 is used to perform weighted calculations on the coefficient matrix of the polynomial regression filter and the coefficient matrix of the infinite impulse response filter based on projection initialization to obtain the target coefficient matrix, and to perform color blood flow imaging wall filtering based on the target coefficient matrix.

[0138] The present invention also provides a color blood flow imaging wall filtering device, such as... Figure 6 As shown, Figure 6 This is a structural block diagram of an embodiment of the color Doppler imaging wall filtering device provided by the present invention. The color Doppler imaging wall filtering device 600 can be a computing device such as a mobile terminal, desktop computer, laptop, handheld computer, and server. The color Doppler imaging wall filtering device 600 includes a processor 601 and a memory 602, wherein the memory 602 stores a color Doppler imaging wall filtering program 603.

[0139] In some embodiments, memory 602 may be an internal storage unit of a computer device, such as a hard disk or memory. In other embodiments, memory 602 may be an external storage device of a computer device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc. Further, memory 602 may include both internal and external storage units of the computer device. Memory 602 is used to store application software and various types of data installed on the computer device, such as program code for installing the computer device. Memory 602 can also be used to temporarily store data that has been output or will be output. In one embodiment, the color Doppler imaging wall filtering program 603 can be executed by processor 601 to implement the color Doppler imaging wall filtering method, system, device, and storage medium of the various embodiments of the present invention.

[0140] In some embodiments, processor 601 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in memory 602 or process data, such as executing a color blood flow imaging wall filtering program.

[0141] This embodiment also provides a computer-readable storage medium storing a color flow imaging wall filtering program thereon. When the computer processor executes the program, it implements the color flow imaging wall filtering method, system, device, and storage medium as described above.

[0142] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0143] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A color Doppler flow imaging wall filtering method, characterized in that, include: Acquire the echo signal and set the cutoff frequency; Determining an initial filter coefficient matrix based on the cutoff frequency, and combining the initial filter coefficient matrix based on the echo signal to obtain a polynomial regression filter coefficient matrix, includes: determining the number of points and order of the polynomial regression filter using an amplitude-frequency characteristic function based on the cutoff frequency; determining the initial filter coefficient matrix based on the number of points, the order, and the echo signal; obtaining the length of the echo signal in the slow-time direction; and combining the initial filter coefficient matrix based on the length and the number of points to obtain the polynomial regression filter coefficient matrix. Construct an infinite impulse response filter coefficient matrix based on projection initialization using the echo signal and the cutoff frequency; The coefficient matrix of the polynomial regression filter and the coefficient matrix of the infinite impulse response filter based on projection initialization are weighted to obtain the target coefficient matrix, and color blood flow imaging wall filtering is performed based on the target coefficient matrix.

2. The color Doppler imaging wall filtering method according to claim 1, characterized in that, The amplitude-frequency characteristic function is: in, It is the frequency response function. N For the number of points, K For the order, As an intermediate variable, It is the imaginary number representation. It is a vector The Middle Count, vector For the preset matrix The column vectors of an orthonormal basis. It is angular frequency.

3. The color Doppler imaging wall filtering method according to claim 1, characterized in that, The step of combining the initial filter coefficient matrix according to the length and the number of points to obtain the polynomial regression filter coefficient matrix includes: When the length is greater than the number of points, the data in the first column of the initial filter coefficient matrix is ​​supplemented and combined until the polynomial regression filter coefficient matrix is ​​obtained. When the length is equal to the number of points, the initial filter coefficient matrix is ​​determined to be the polynomial regression filter coefficient matrix.

4. The color Doppler imaging wall filtering method according to claim 1, characterized in that, The step of constructing the infinite impulse response filter coefficient matrix based on projection initialization according to the echo signal and the cutoff frequency includes: The filter coefficients are determined by calculating the echo signal and the cutoff frequency using a preset recursive filter. Construct an infinite impulse response filter coefficient matrix based on projection initialization using the filter coefficients.

5. The color Doppler imaging wall filtering method according to claim 4, characterized in that, The preset recursive filter includes at least one of the following: Butterworth filter, Chebyshev Type I filter, Chebyshev Type II filter, and elliptic filter.

6. The color Doppler imaging wall filtering method according to claim 1, characterized in that, The weighted calculation of the coefficient matrix of the polynomial regression filter and the coefficient matrix of the infinite impulse response filter based on projection initialization to obtain the target coefficient matrix includes: The target coefficient matrix is ​​obtained by weighting the coefficient matrix of the polynomial regression filter and the coefficient matrix of the infinite impulse response filter based on projection initialization according to the weighted calculation formula of the target coefficient matrix. The weighted calculation formula for the target coefficient matrix is ​​as follows: k 1 +k 2≤1 in, The target coefficient matrix, Let be the coefficient matrix of the polynomial regression filter. The coefficient matrix of the infinite impulse response filter based on projection initialization is given. and It is a constant.

7. A color blood flow imaging wall filtering system, characterized in that, include: The parameter setting module is used to acquire the echo signal and set the cutoff frequency; A polynomial regression filter coefficient matrix acquisition module is used to determine an initial filter coefficient matrix based on the cutoff frequency, and to combine the initial filter coefficient matrix based on the echo signal to obtain a polynomial regression filter coefficient matrix. The module includes: determining the number of points and order of the polynomial regression filter using an amplitude-frequency characteristic function based on the cutoff frequency; determining the initial filter coefficient matrix based on the number of points, the order, and the echo signal; obtaining the length of the echo signal in the slow-time direction; and combining the initial filter coefficient matrix based on the length and the number of points to obtain the polynomial regression filter coefficient matrix. The infinite impulse response filter coefficient matrix acquisition module based on projection initialization is used to construct the infinite impulse response filter coefficient matrix based on projection initialization according to the echo signal and the cutoff frequency. The color Doppler imaging wall filtering module is used to perform weighted calculations on the coefficient matrix of the polynomial regression filter and the coefficient matrix of the infinite impulse response filter based on projection initialization to obtain a target coefficient matrix, and to perform color Doppler imaging wall filtering based on the target coefficient matrix.

8. A color blood flow imaging wall filter device, characterized in that, Including memory and processor, among which, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the color blood flow imaging wall filtering method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the color blood flow imaging wall filtering method according to any one of claims 1 to 6.

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