An ultrasonic beam forming method fusing narrowband segmentation and directional projection
By combining narrowband segmentation with directional projection and optimizing the ultrasonic beamforming algorithm, the robustness and resolution deficiencies of traditional methods under broadband strongly correlated signals are solved, and high-quality imaging of ultrasonic images is achieved.
Patent Information
- Application Number
- CN202211435743.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-11-16
- Publication Date
- 2026-02-10
- Estimated Expiration
- 2042-11-16
AI Technical Summary
Traditional oblique projection minimum variance ultrasound imaging algorithms are not suitable for broadband strongly correlated ultrasound echo signals, resulting in insufficient imaging robustness and resolution and contrast performance.
By combining narrowband segmentation with directional projection, the ultrasonic echo signal is segmented into narrowband signals, and directional constraint projection optimization is performed based on the narrowband signals to optimize the weights of the minimum variance beamformer, thereby improving the robustness and adaptability of the algorithm.
It improves the resolution and contrast of ultrasound images, reduces beamside lobe levels, and enhances the overall image quality.
Smart Images
Figure CN115932809B_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the technical field of ultrasonic imaging, and relates to an ultrasonic beam forming method fusing narrowband segmentation and directivity projection. BACKGROUND
[0002] Ultrasonic imaging is a noninvasive, safe, real-time and convenient nondestructive detection method, and is widely applied in various detection fields. The core content of ultrasonic imaging is a beam forming technology, and the most widely used beam forming technology at present is a delay and sum (DAS) algorithm. The DAS algorithm is to delay the received echo signals according to the geometric position relationship of the array elements, and then to superimpose the data after alignment. The traditional DAS algorithm has simple principle and fast imaging speed, but the main lobe width is increased due to the use of fixed window function weighting, the resolution is low, and the imaging quality is poor.
[0003] A minimum variance (MV) adaptive beam forming algorithm is based on the principle of keeping the expected direction gain unchanged and minimizing the array output energy. The weighting vector of the focused delay signal is dynamically calculated, and then the vector is multiplied by the input signal. The MV algorithm improves the resolution of the imaging image. In addition, a slant projection minimum variance is proposed to improve the resolution and contrast of the algorithm. However, the traditional minimum variance algorithm and the slant projection minimum variance algorithm are only suitable for narrowband far-field non-correlated signals, and are not suitable for wideband strong correlation ultrasonic echo signals. This factor seriously affects the imaging robustness of the ultrasonic slant projection minimum variance algorithm, thereby greatly limiting the improvement of the resolution and contrast.
[0004] In summary, there is an urgent need to invent a method capable of further improving the resolution and contrast performance of the slant projection minimum variance ultrasonic imaging algorithm to further improve the comprehensive performance of imaging. SUMMARY
[0005] Therefore, the purpose of the present application is to provide an ultrasonic beam forming method fusing narrowband segmentation and directivity projection. The method fuses and optimizes the narrowband segmentation method and the directivity projection, applies the directivity projection to the ultrasonic array element echo signals after the narrowband segmentation, thereby improving the algorithm robustness and adaptability, solving the problem of insufficient resolution and contrast performance of the traditional projection-based minimum variance algorithm, and improving the overall quality of the ultrasonic image.
[0006] To achieve the above purpose, the present application provides the following technical scheme:
[0007] An ultrasonic beam forming method fusing narrowband segmentation and directivity projection, the method comprising the following steps:
[0008] S1: sequentially segment the ultrasonic echo signals received by each array element into multiple narrowband signals to obtain a narrowband time-frequency signal spectrum corresponding to each array element;
[0009] S2: sequentially extract corresponding time-frequency point data in the narrowband time-frequency signal spectrum of each array element to form a time-frequency point input data group as data for calculating corresponding time-frequency points in the final scan line time-frequency signal spectrum;
[0010] S3: constructing a sample covariance matrix under the condition of narrowband segmentation based on the time-frequency point input data group;
[0011] S4: spatially decomposing the obtained sample covariance matrix to obtain a sample signal subspace;
[0012] S5: using the current time-frequency point sample signal subspace and the expected direction vector to construct a directivity constraint projection multiplier of the current time-frequency point;
[0013] S6: calculating the output of the spatial beamformer of the current time-frequency point, and using the directivity constraint projection multiplier of the current time-frequency point to weight and optimize the output of the spatial beamformer as the output of each point in the scan line time-frequency signal spectrum;
[0014] S7: time-frequency converting the obtained scan line time-frequency signal spectrum to reconstruct time-domain scan data.
[0015] Further, in step S1, first, the ultrasonic echo signals received by each array element are sequentially segmented into multiple narrowband signals to obtain a narrowband time-frequency signal spectrum corresponding to each array element:
[0016]
[0017] wherein x n (k), n = 1, 2, …, N represents the ultrasonic array echo signal in the time domain, N represents the number of array elements of the ultrasonic array, k is the sampling time; H(k) represents a Hanning window with a length of 64, f n (r, ω) represents the corresponding value of the x n (k) signal in the time-frequency domain, r represents the segment number, ω represents the frequency domain number under the segment number r, i represents the imaginary unit, and e represents the index.
[0018] Further, in step S2, corresponding time-frequency point data in the narrowband time-frequency signal spectrum of each array element is sequentially extracted to form a time-frequency point input data group as data for calculating corresponding time-frequency points in the final scan line time-frequency signal spectrum:
[0019] F(r, ω) = [f1(r, ω), f2(r, ω), …, f n (r, ω), …, f N (r, ω)]
[0020] F(r, ω) represents the signal input data set corresponding to the time-frequency point (r, ω) in the time-frequency signal spectrum, f n (r, ω) represents the time-frequency signal spectrum value corresponding to the time-frequency point (r, ω) of the nth ultrasonic array element.
[0021] Further, in step S3, a sample covariance matrix SF(r, ω) under the narrowband segmentation condition is constructed based on the time-frequency point input data set:
[0022]
[0023] wherein F l (r, ω) = [f1(r, ω), f2(r, ω),..., f l+L-1 (r, ω)] represents the lth subarray data decomposed from the time-frequency point input data set, l = 1, 2,..., N-L+1, N represents the total length of the ultrasonic array, L represents the subarray length of the ultrasonic array, F l (r, ω) H is the conjugate transpose of F l (r, ω).
[0024] Further, in step S4, the sample covariance matrix is spatially decomposed to obtain a sample signal subspace:
[0025] SF(r, ω) = UF s (r, ω)Ψ s (r, ω)UF s (r, ω) H + UF n (r, ω)Ψ n (r, ω)UF n (r, ω) H
[0026] wherein UF s (r, ω) represents a signal subspace obtained according to the time-frequency point input data set F(r, ω), UF n (r, ω) represents a noise subspace obtained according to the time-frequency point input data set F(r, ω); Ψ s (r, ω) and Ψ n (r, ω) represent the eigenvalue sets of the signal subspace and the noise subspace, respectively, UF s (r, ω) H and UF n (r, ω) H are the conjugate transpose matrices of UF s (r, ω) and UF n (r, ω), respectively.
[0027] Further, in step S5, a directivity constraint projection multiplier of the current time-frequency point is constructed by using the current time-frequency point sample signal subspace and the expected direction vector:
[0028] S51: Calculate the directivity constraint steering matrix SF A (r, ω) is expressed as:
[0029] SF A (r, ω) = UF s (r, ω)Ψ s (r, ω)UF s (r, ω) H
[0030] The pseudo-inverse matrix of the directivity constraint steering matrix SF A (r, ω) can be expressed as SF A (r, ω) + , which is calculated as follows:
[0031] SF A (r, ω) + = UF s (r, ω)Ψ s -1 (r, ω)UF s (r, ω) H
[0032] wherein Ψ s (r, ω) is the inverse matrix of the diagonal matrix Ψ s -1 (r, ω), SF A (r, ω) + is the pseudo-inverse matrix of the directivity constraint steering matrix SF A (r, ω);
[0033] S52: According to the obtained SF A (r, ω) + , the directivity constraint projection operator is further calculated, and the specific calculation process is as follows:
[0034] FOB(r, ω) = UF s (r, ω)(fa H SF A (r, ω) + fa) -1 UF s (r, ω) H SF A (r, ω) +
[0035] wherein fa = [1, 1..., 1] TThe desired direction vector of the ultrasound beamformer fusing narrowband segmentation and directional projection is a unit column vector with length L, fa H is the conjugate transpose of fa, UF s (r, ω) represents a signal subspace obtained according to the time-frequency point input data set F(r, ω), UF s (r, ω) H is the conjugate transpose of UF s (r, ω).
[0036] Further, in step S6, the spatial domain beamformer output of the current time-frequency point is obtained, and the spatial domain beamformer output is weighted and optimized by using the directional constraint projection multiplier of the current time-frequency point as the output of each point of the scan line time-frequency signal spectrum:
[0037]
[0038] wherein, SF(r, ω) represents the final weighted value of the ultrasound beamformer fusing narrowband segmentation and directional projection at the time-frequency point (r, ω) of the time-frequency spectrum, SF(r, ω) -1 is the inverse matrix of the sample covariance matrix SF(r, ω), y FOBPMV (r, ω) represents the final output value of the ultrasound beamformer fusing narrowband segmentation and directional projection at the time-frequency point (r, ω) of the time-frequency spectrum.
[0039] Further, in step S7, the obtained scan line time-frequency signal spectrum is subjected to time-frequency conversion to reconstruct time domain scanning data:
[0040]
[0041] wherein, y FOBPMV (r, ω) represents the time-frequency spectrum output value of the ultrasound beamformer fusing narrowband segmentation and directional projection at the time-frequency point (r, ω); y FOBPMV (k) represents the final time domain output value of the ultrasound beamformer fusing narrowband segmentation and directional projection at the sampling time k.
[0042] The present application has the beneficial effects that, compared with the conventional minimum variance algorithm based on projection, the present application can effectively improve the robustness of the algorithm, solve the problems of ultrasonic signal and algorithm adaptability, improve the resolution and contrast of the algorithm, and improve the background quality of the image. The present application can greatly improve the beam width of the ultrasound beamformer, reduce the beam sidelobe level, and further improve the overall quality of the image.
[0043] Additional advantages, objects, and features of the application will be apparent to those skilled in the art upon examination of the following specification. It is intended that the application not be limited by the disclosed BRIEF DESCRIPTION OF DRAWINGS
[0044] In order to make the objects, technical solutions and advantages of the present application clearer, the preferred embodiments of the present application will be described in detail below with reference to the drawings, in which:
[0045] Figure 1 Flow chart of the method of the present application;
[0046] Figure 2 Imaging results of point targets by four algorithms;
[0047] Figure 3 Graph of lateral resolution of point target imaging at 40mm by four algorithms;
[0048] Figure 4 Imaging results of dark spot targets by four algorithms. DETAILED DESCRIPTION
[0049] The present application is described below by way of specific examples. Other advantages and benefits of the present application will be apparent to those skilled in the art from the disclosure herein. The present application can be implemented or applied in other different specific embodiments, and the details in the specification can be modified or changed based on different views and applications without departing from the spirit of the present application. It should be noted that the figures provided in the following examples only illustrate the basic concepts of the present application in a schematic manner, and the following examples and features in the examples can be combined with each other without conflict.
[0050] The accompanying drawings are only used for illustrative purposes, and the representations are only schematic diagrams, not physical drawings, and should not be understood as limitations of the present application. In order to better illustrate the embodiments of the present application, some components in the drawings can be omitted, enlarged or reduced, and do not represent the actual size of the product. It is understandable to those skilled in the art that some well-known structures and their descriptions in the drawings can be omitted.
[0051] The same or similar reference numerals in the drawings of the embodiments of the present application correspond to the same or similar components; in the description of the present application, it is understood that if the orientations or positional relationships indicated by the terms "upper", "lower", "left", "right", "front", "back" and the like are based on the orientations or positional relationships shown in the drawings, they are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, therefore the terms describing the positional relationship in the drawings are only used for exemplary illustration, and cannot be understood as a limitation on the present application, for those skilled in the art, the specific meanings of the above terms can be understood according to the specific circumstances.
[0052] Please refer to Figures 1-4 , Figure 1 The algorithm flowchart of the present application is shown in Figure 1 , the present application designs an ultrasonic beam forming method combining narrowband segmentation and directional projection, comprising the following steps:
[0053] Step S1: the method first segments the ultrasonic echo signals received by each array element into multiple narrowband signals in sequence, and obtains the narrowband time-frequency signal spectrum corresponding to each array element:
[0054]
[0055] Wherein, x n (k), n = 1, 2,..., N represents the ultrasonic array echo signal in time domain, N represents the number of array elements of the ultrasonic array, k is the sampling time; H(k) represents a Hanning window with a length of 64, f n (r, ω) represents the corresponding value of x n (k) signal in time-frequency domain, r represents the segmentation number, ω represents the frequency domain number under the segmentation number r, i represents the imaginary unit, and e represents the index;
[0056] Step S2: sequentially extracting the corresponding time-frequency point data in the narrowband time-frequency signal spectrum of each array element, forming a time-frequency point input data group, as the data of the corresponding time-frequency point in the final scan line time-frequency signal spectrum:
[0057] F(r, ω) = [f1(r, ω), f2(r, ω),..., f n (r, ω),..., f N (r, ω)]
[0058] F(r, ω) represents the signal input data group corresponding to the time-frequency point (r, ω) in the time-frequency signal spectrum, f n (r, ω) represents the time-frequency signal spectrum value of the nth ultrasonic array element corresponding to the time-frequency point (r, ω).
[0059] Step S3: Constructing sample covariance matrix SF(r,ω) in narrowband division based on time-frequency point input data set:
[0060]
[0061] Wherein, F l (r,ω)=[f1(r,ω),f2(r,ω),...,f l+L-1 (r,ω) represents the decomposed first subarray data of time-frequency point input data set, l=1,2,...,N-L+1, N represents the total length of ultrasonic array, L represents the subarray length of ultrasonic array, F l (r,ω) H is the conjugate transpose of F l (r,ω);
[0062] Step S4: Spatially decomposing the obtained sample covariance matrix to obtain sample signal subspace:
[0063] SF(r,ω)=UF s (r,ω)Ψ s (r,ω)UF s (r,ω) H +UF n (r,ω)Ψ n (r,ω)UF n (r,ω) H
[0064] Wherein, UF s (r,ω) represents the signal subspace obtained according to time-frequency point input data set F(r,ω), UF n (r,ω) represents the noise subspace obtained according to time-frequency point input data set F(r,ω).Ψ s (r,ω) andΨ n (r,ω) represent the eigenvalue set of signal subspace and noise subspace respectively, UF s (r,ω) H and UF n (r,ω) H respectively represent the conjugate transpose matrix of UF s (r,ω) and UF n (r,ω).
[0065] Step S5: Using current time-frequency point sample signal subspace and expected direction vector to construct directionality constraint projection multiplier of current time-frequency point:
[0066] S51: First, calculate time-frequency spectrum direction constraint guide matrix SF A (r,ω), which is expressed as:
[0067] SF A (r,ω)=UF s (r,ω)Ψ s (r,ω)UF s (r,ω) H
[0068] Directional constraint guidance matrix SF A The pseudo-inverse matrix of (r,ω) can be represented as SF A (r,ω) + The calculation is as follows:
[0069] SF A (r,ω) + =UF s (r,ω)Ψ s -1 (r,ω)UF s (r,ω) H
[0070] Among them, Ψ s (r,ω) is a diagonal matrix Ψ s -1 The inverse matrix of (r,ω), SF A (r,ω) + Directional constraint guide matrix SF A The pseudo-inverse matrix of (r,ω).
[0071] S52: Based on the obtained SF A (r,ω) + The direction constraint projection operator is further calculated, and the specific calculation process is as follows:
[0072] FOB(r,ω)=UF s (r,ω)(fa H SF A (r,ω) + fa) -1 UF s (r,ω) H SF A (r,ω) +
[0073] Where fa = [1, 1, ..., 1] T The desired direction vector of the ultrasonic beamformer, which integrates narrowband segmentation and directional projection, is a unit column vector of length L, fa H It is the conjugate transpose of fa, UF s (r,ω) represents the signal subspace obtained based on the input data set F(r,ω) at the time and frequency points, UF s (r,ω) H It is UF sThe conjugate transpose of (r,ω).
[0074] Step S6: Obtain the spatial beamformer output at the current time-frequency point, and use the directional constraint projection multiplier at the current time-frequency point to perform weighted optimization on the spatial beamformer output, which is then used as the output of each point in the time-frequency signal spectrum of the scan line:
[0075]
[0076] in, SF(r,ω) represents the final weighted value of the ultrasonic beam at the time-frequency point (r,ω) in the time spectrum, which combines narrowband segmentation and directional projection. -1 It is the inverse of the sample covariance matrix SF(r,ω), y FOBPMV (r,ω) represents the final output value of the ultrasonic beam generator that integrates narrowband segmentation and directional projection at the time frequency point (r,ω) in the time spectrum.
[0077] Step S7: Perform time-frequency conversion on the obtained scan line time-frequency signal spectrum to reconstruct the time-domain scan data:
[0078]
[0079] Among them, y FOBPMV (r,ω) represents the time-frequency output value of the ultrasonic beamformer that integrates narrowband segmentation and directional projection at the time-frequency domain point (r,ω); y FOBPMV (k) represents the final time-domain output value of the ultrasonic beamformer that integrates narrowband segmentation and directional projection at sampling time k.
[0080] Verification experiment:
[0081] FieldII is an ultrasonic experimental simulation platform developed by the Technical University of Denmark based on acoustic principles, and it has gained widespread recognition and use in theoretical research. To verify the effectiveness of the proposed algorithm, FieldII was used to perform imaging comparison analysis on point scattering targets and dark spot targets commonly used in ultrasonic imaging.
[0082] In the point target simulation experiment, eight point targets were set up in two columns with a vertical spacing of 5 mm and a depth distribution between 55 mm and 70 mm. A fixed-point focusing method for transmission and dynamic focusing for reception were used, with the transmission focus fixed at 60 mm and the image dynamic range set to 60 dB. A dark spot imaging experiment was also conducted: a circular dark spot with a center of 40 mm and a radius of 3 mm was used, with 100,000 scattering points randomly distributed in the outer background area. Four different algorithms were used for imaging, with the imaging dynamic range set to 60 dB. For the two sets of experimental targets, a Delayed Stacking (DAS) algorithm, a Minimum Variance (MV) algorithm, a Feature Space Minimum Variance (ESBMV) algorithm, and the present invention's Ultrasonic Beamforming Method integrating narrowband segmentation and directional projection (FOBPMV) were used for comparative imaging experiments.
[0083] Figure 2 The point target imaging results of four algorithms are presented, from Figure 2 As can be seen, the DAS algorithm has the lowest image resolution, the most lateral artifacts, and the worst image quality compared to the other three algorithms. The MV algorithm eliminates some interference noise, significantly reducing lateral artifacts at the focal point of the scattering point compared to the DAS algorithm, lowering the overall sidelobes, reducing lateral artifacts, and improving resolution. The ESBMV algorithm further improves resolution and contrast based on MV, with almost no lateral artifacts for imaging scattering points at 50mm. This is because the ESBMV algorithm removes the interference noise subspace component from the weighted values through feature space projection. The FOBPMV algorithm of this invention decomposes the ultrasonic echo signal into a narrowband signal and optimizes the minimum variance beamformer weights based on the narrowband signal using directional constraint projection, thus significantly improving resolution and contrast.
[0084] Figure 3 A comparison of the lateral resolution at 60mm for the four algorithms is shown in the graph. It can be seen that the FOBPMV algorithm of this invention has the highest resolution, exceeding that of ESBMV. The lateral resolution curve of FOBPMV shows significant improvements in both main lobe width and side lobe grade, significantly higher than the DAS and MV algorithms. To more intuitively compare the imaging resolution of the four algorithms, Table 1 presents a comparison of the half-peak width (FWHM) data at -6dB for the four algorithms at different depths. Table 1 shows that DAS has the lowest resolution, while the FOBPMV algorithm has the highest resolution. Compared to the DAS algorithm, the FOBPMV algorithm of this invention reduces the -6dB main lobe width at 65mm by 75.641%.
[0085] Table 1 Comparison of FWHM (Further Works Metric) of Four Algorithms at Different Depths (-6dB)
[0086] FWHM 55 mm 60 mm 65 mm DAS 1.06 1.23 1.56 MV 0.43 0.53 0.73 ESBMV 0.36 0.42 0.53 FOBPMV 0.25 0.28 0.38
[0087] Figure 4The imaging results of dark spot targets using four algorithms are presented. Figure 4 As can be seen, all four algorithms can clearly display the outline of the dark spot. Compared with the other three algorithms, the DAS algorithm exhibits significant interference noise and numerous artifacts within the dark spot target, resulting in the worst imaging effect. The MV and ESBMV algorithms show improved noise suppression compared to DAS, and the artifacts within the dark spot are well suppressed. The FOBPMV algorithm of this invention significantly outperforms the other three algorithms in dark spot imaging, demonstrating the best performance in suppressing noise within the spot.
[0088] Table 2 provides the image quality indicators for dark spot backgrounds using different algorithms. As shown in Table 2, the DAS algorithm has the lowest contrast ratio (20.35 dB) and the highest internal average power, indicating weak artifact suppression within the dark spot and thus poor imaging quality. However, it has a relatively high speckle signal-to-noise ratio (sSNR), demonstrating the best algorithm robustness. The MV algorithm reduces the internal average power of the dark spot but also lowers the external average power, resulting in a contrast ratio (CR) increase of approximately 9 dB compared to the DAS algorithm. However, its lower speckle signal-to-noise ratio (sSNR) indicates poor background uniformity. The ESBMV algorithm shows a significant improvement in contrast ratio (CR) compared to DAS and MV, but it has the lowest contrast-to-noise ratio (CNR). The FOBPMV algorithm of this invention shows the largest increase in the absolute value of internal average power, indicating the best internal noise suppression effect. Its CR value is 16.05 dB and 7.17 dB higher than DAS and MV, respectively, and also higher than ESBMV. Its CNR value is significantly higher than the ESBMV algorithm, while simultaneously reducing the speckle signal-to-noise ratio (sSNR) of MV, thus improving background imaging quality.
[0089] Table 2 Comparison of background quality indicators for dark spots using different algorithms
[0090]
[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A method for forming an ultrasonic beam that integrates narrowband segmentation and directional projection, characterized in that: The method includes the following steps: S1: The ultrasonic echo signals received by each array element are sequentially divided into multiple narrowband signals to obtain the narrowband time-frequency signal spectrum corresponding to each array element: in, Represents the time-domain echo signal of the ultrasonic array. N This indicates the number of elements in the ultrasonic array. k Sampling time; This refers to a Hanning window with a length of 64. express The corresponding value of the signal in the time-frequency domain Indicates the segment number. Indicates the segment number The frequency domain index below, Represents the imaginary unit. Indicates an exponent; S2: Sequentially extract the corresponding time-frequency point data from the narrowband time-frequency signal spectrum of each array element to form a time-frequency point input data group, which is used as the data to obtain the corresponding time-frequency point in the final scan line time-frequency signal spectrum: Represents the time-frequency point in the time-frequency signal spectrum The corresponding signal input data group Indicates the first n Each ultrasonic element at the time-frequency point The corresponding time-frequency signal spectrum value; S3: Constructing the sample covariance matrix for narrowband segmentation based on the input data set at time and frequency points. : in, Indicates the first decomposition of the input data set at the time and frequency points. Subarray data, , This represents the total length of the ultrasound array. This indicates the subarray length of the ultrasound array. for The conjugate transpose of; S4: Perform spatial decomposition on the obtained sample covariance matrix to obtain the sample signal subspace: in, Indicates input data group based on time and frequency points The obtained signal subspace, Indicates input data group based on time and frequency points The resulting noise subspace; and Let them represent the sets of eigenvalues in the signal subspace and the noise subspace, respectively. and They represent and The conjugate transpose of ; S5: Construct the directional constraint projection multiplier for the current time-frequency point using the sample signal subspace of the current time-frequency point and the desired direction vector: S51: Spectral Direction Constraint Guiding Matrix for Calculation , is represented as: Directional constraint guide matrix The pseudo-inverse matrix is represented as The calculation is as follows: in, diagonal matrix The reverse formation, Directional constraint guide matrix The pseudo-inverse matrix; S52: Based on the obtained The direction constraint projection operator is calculated, and the specific calculation process is as follows: in, The desired direction vector of an ultrasonic beamformer that integrates narrowband segmentation and directional projection is of length . L unit column vector, yes The conjugate transpose of . Indicates input data group based on time and frequency points The obtained signal subspace, yes The conjugate transpose of; S6: Obtain the spatial beamformer output at the current time-frequency point, and use the directional constraint projection multiplier at the current time-frequency point to perform weighted optimization on the spatial beamformer output, which is then used as the output of each point in the time-frequency signal spectrum of the scan line: in, This represents the time-frequency point of an ultrasonic beam that integrates narrowband segmentation and directional projection in the time spectrum. The final weighted value, It is the sample covariance matrix The reverse formation, This represents the time-frequency point of an ultrasonic beam that integrates narrowband segmentation and directional projection in the time spectrum. The final output value; S7: Perform time-frequency conversion on the obtained scan line time-frequency signal spectrum to reconstruct the time-domain scan data: in, This represents the time-frequency domain point of an ultrasonic beamformer that integrates narrowband segmentation and directional projection. The time-spectrum output value; This indicates that the ultrasonic beamformer, which integrates narrowband segmentation and directional projection, is at the sampling time. k The final time-domain output value.