Ultrasonic imaging method, device, medium and product

Abstract

The present application discloses an ultrasonic imaging method, device, medium, and product, relating to the field of ultrasonic imaging. The method includes coherently compounding plane wave echo signals of negative and positive angles, respectively, and determining an auxiliary signal; adaptively truncating the auxiliary signal according to a truncation threshold determined by the auxiliary signal; determining the output weight of a truncation sidelobe suppression beamformer according to the adaptively truncation signal and the truncation threshold; performing element-dimensional delayed superposition summation and coherent compounding on the plane wave echo signal; constructing a signal-to-noise ratio adaptive filter based on the signal variance and noise variance obtained using the empirical Bayesian method based on a third plane wave echo signal; iterating the signal-to-noise ratio adaptive filter multiple times to determine the output weight; and weighting the third plane wave echo signal using the determined adaptive beam weight to perform final imaging. The present application can improve the overall imaging quality.

Description

Technical Field

[0001] The present application relates to the field of ultrasonic imaging, and in particular to an ultrasonic imaging method, device, medium and product. Background Art

[0002] Ultrasound imaging is widely used in medical clinical diagnosis due to its advantages such as safety, real-time performance, and low cost. Digital beamforming can significantly improve signal quality from the perspective of signal processing. Currently, the most widely used and simplest beamforming technology in ultrasound imaging is the Delay And Sum (DAS) algorithm, which calculates the delay of the received echo signal based on the geometric position relationship of the array element channels and then aligns and superimposes the delayed echo signals. In plane wave imaging mode, it is necessary to transmit plane waves at multiple angles and then coherently combine the received multi-angle plane waves to obtain high-quality ultrasound images.

[0003] During the plane wave coherent recombination process, weighting the post-DAS image by the coherence factor (CF) can effectively improve image contrast and facilitate the differentiation of cysts from tissue. However, these algorithms tend to overestimate noise, resulting in excessive suppression of effective background information, poor robustness, and poor speckle preservation, hindering the practical application of B-mode imaging. While adaptive beamforming based on adaptive filtering can significantly improve signal quality, current ultrasound adaptive filtering beamforming methods struggle to achieve an effective trade-off between high resolution, high contrast, and speckle preservation.

[0004] In summary, there is an urgent need to provide an ultrasonic imaging method that can maintain high resolution performance, improve imaging contrast, and have the ability to maintain speckle background. Summary of the Invention

[0005] The purpose of this application is to provide an ultrasonic imaging method, device, medium and product that can effectively improve the resolution and imaging contrast of the image without sacrificing background speckle information, thereby improving the overall imaging quality.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides an ultrasound imaging method, comprising:

[0008] Acquire a plane wave echo signal; the plane wave echo signal carries array element information and angle information;

[0009] performing coherent recombination on the plane wave echo signal with a negative angle and the plane wave echo signal with a positive angle to obtain a first plane wave echo signal and a second plane wave echo signal; determining an auxiliary signal based on the first plane wave echo signal and the second plane wave echo signal; and determining a truncation threshold based on the auxiliary signal; the first plane wave echo signal and the second plane wave echo signal both do not carry angle information;

[0010] Adaptively truncating the auxiliary signal using a truncation threshold to obtain an adaptively truncated signal; and determining an output weight of a truncation sidelobe suppression beamformer based on the adaptively truncated signal and the truncation threshold;

[0011] Performing element-dimensional delayed superposition and coherent compounding on the plane wave echo signal to obtain a third plane wave echo signal;

[0012] Based on the third plane wave echo signal, the empirical Bayes method is used to obtain the signal variance and noise variance; a signal-to-noise ratio adaptive filter is constructed based on the signal variance and noise variance; the signal-to-noise ratio adaptive filter is iterated multiple times to determine the final output weight;

[0013] The smaller value of the output weight of the truncated sidelobe suppression beamformer and the final output weight of the signal-to-noise ratio adaptive filter is used as the adaptive beam weight;

[0014] The third plane wave echo signal is weighted by using the adaptive beam weight to obtain an adaptive beamformed composite plane wave signal, and finally imaged.

[0015] Optionally, acquiring a plane wave echo signal specifically includes:

[0016] Acquiring plane wave echo data received by the ultrasonic array element;

[0017] Perform dynamic gain amplification and AD conversion on the plane wave echo data to obtain ultrasonic echo data;

[0018] The ultrasonic echo data is bandpass filtered and Hilbert transformed to obtain a plane wave echo signal with array element information and angle information.

[0019] Optionally, the steps of performing coherent combination on the plane wave echo signal with a negative angle and the plane wave echo signal with a positive angle to obtain a first plane wave echo signal and a second plane wave echo signal; determining an auxiliary signal based on the first plane wave echo signal and the second plane wave echo signal; and determining a truncation threshold based on the auxiliary signal specifically include:

[0020] Perform coherent recombination on the negative angle plane wave echo signal to obtain the first plane wave echo signal x a (i,j);

[0021] The plane wave echo signal with positive angle is coherently combined to obtain the second plane wave echo signal x b (i,j);

[0022] Using the formula x sub (i,j)=x a (i,j)-x b (i, j) determine the auxiliary signal;

[0023] Using the formula d = m xsub +2s xsub Determine the cutoff threshold;

[0024] Among them, x sub (i, j) is the auxiliary signal, μ xsub is x sub (i,j) composed of the signal x sub The mean value, s xsub is x sub (i,j) composed of the signal x sub The variance of , δ is the cutoff threshold, i, j are the row and column positions of the pixel points.

[0025] Optionally, the step of adaptively truncating the auxiliary signal by using a truncation threshold to obtain an adaptively truncated signal; and determining an output weight of a truncation sidelobe suppression beamformer according to the adaptively truncation signal and the truncation threshold, specifically includes:

[0026] Using the formula x TSS (i,j)=max(x sub (i, j),δ) determines the adaptively truncated signal;

[0027] Using the formula w TSS (i,j)=d / x TSS (i, j) determines the output weights of the truncated sidelobe suppression beamformer;

[0028] Among them, x TSS (i, j) is the adaptively truncated signal, w TSS (i, j) is the output weight of the truncated sidelobe suppression beamformer, and max is the maximum value.

[0029] Optionally, performing element-dimensional delayed superposition and coherent compounding on the plane wave echo signal to obtain a third plane wave echo signal specifically includes:

[0030] The plane wave echo signal s(t,n,θ) is delayed and summed in the element dimension to obtain the signal x(i,j,θ) with all angle information;

[0031] For the signal x(i,j,θ) with all angle information, the formula is used Perform coherent recombination to obtain the third plane wave echo signal y(i,j);

[0032] Where n is the number of array elements, θ is the number of angles, t is the sampling time, θ = 1, 2...M, M is the number of emission angles, and i, j are the row and column positions of the pixels.

[0033] Optionally, the method of obtaining a signal variance and a noise variance by using an empirical Bayesian method based on the third plane wave echo signal; constructing a signal-to-noise ratio adaptive filter based on the signal variance and the noise variance; and iterating the signal-to-noise ratio adaptive filter multiple times to determine a final output weight specifically includes:

[0034] Using the formula determining a signal variance of the third plane wave echo signal;

[0035] Using the formula determining a noise variance of the third plane wave echo signal;

[0036] Using the formula Construct a signal-to-noise ratio adaptive filter;

[0037] Iterate the signal-to-noise ratio adaptive filter multiple times to determine the optimal weight vector, and perform median filtering on the weight matrix composed of the optimal weight vector to obtain the final output weight of the signal-to-noise ratio adaptive filter;

[0038] in, is the signal variance, is the noise variance, ‖·‖ is the 2-norm, w HCR (i, j) is the signal-to-noise ratio adaptive filter.

[0039] Optionally, weighting the third plane wave echo signal by using an adaptive beam weight to obtain an adaptive beamformed composite plane wave signal and performing final imaging specifically includes:

[0040] Using the formula y'(i,j)=w iHCR-TSS (i, j)·y(i, j) determines the composite plane wave signal for adaptive beamforming;

[0041] Where y'(i,j) is the composite plane wave signal of adaptive beamforming at the pixel point in the i-th row and j-th column of the imaging area, y(i,j) is the third plane wave echo signal, and w iHCR-TSS (i,j) is the adaptive beam weight, w TSS (i, j) is the output weight of the truncated sidelobe suppression beamformer, is the final output weight of the signal-to-noise ratio adaptive filter, and k is the number of iterations.

[0042] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the ultrasonic imaging method.

[0043] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the ultrasonic imaging method when executed by a processor.

[0044] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which implements the ultrasound imaging method when executed by a processor.

[0045] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0046] The present application provides an ultrasonic imaging method, device, medium, and product. The method utilizes an empirical Bayesian approach to obtain signal variance and noise variance based on a third plane wave echo signal. A signal-to-noise ratio adaptive filter is constructed based on the signal variance and noise variance. The adaptive filter is iterated multiple times, adaptively filtering the third plane wave echo signal with the signal-to-noise ratio and performing multiple iterations to improve contrast. Furthermore, the output weight of a truncated sidelobe suppression beamformer is determined based on the adaptively truncated signal and a truncation threshold, thereby suppressing detected sidelobes and improving image resolution. The method improves speckle preservation and imaging contrast while maintaining high resolution, significantly suppressing artifacts present in acoustic speckles, and overcoming the trade-off between high image resolution, high contrast, and speckle preservation. This method effectively improves image resolution and contrast without sacrificing background speckle information, thereby enhancing overall imaging quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0048] Figure 1 A schematic flow chart of an ultrasonic imaging method provided in one embodiment of the present application;

[0049] Figure 2 Schematic diagram of the imaging results of point targets in the four algorithm experiments;

[0050] Figure 3The lateral resolution curves of the four algorithms at 40mm for point target imaging are shown;

[0051] Figure 4 Schematic diagram of the imaging results of acoustic spot targets in experiments using four algorithms. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0053] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0054] In an exemplary embodiment, Figure 1 As shown, an ultrasound imaging method is provided, which includes the following S101 to S107.

[0055] S101, obtaining a plane wave echo signal; the plane wave echo signal carries array element information and angle information;

[0056] S101 specifically includes:

[0057] Acquiring plane wave echo data received by the ultrasonic array element;

[0058] Perform dynamic gain amplification and AD conversion on the plane wave echo data to obtain ultrasonic echo data;

[0059] The ultrasonic echo data is bandpass filtered and Hilbert transformed to obtain a plane wave echo signal with array element information and angle information.

[0060] S102, coherently combining the plane wave echo signal with a negative angle and the plane wave echo signal with a positive angle to obtain a first plane wave echo signal and a second plane wave echo signal; determining an auxiliary signal based on the first plane wave echo signal and the second plane wave echo signal; and determining a truncation threshold based on the auxiliary signal; the first plane wave echo signal and the second plane wave echo signal do not carry angle information;

[0061] S102 specifically includes:

[0062] The negative angle plane wave echo signal s(t,n,θ) is coherently compounded to obtain the first plane wave echo signal x a (i,j);

[0063] The plane wave echo signal s(t,n,θ) with positive angle is coherently compounded to obtain the second plane wave echo signal x b (i,j);

[0064] Using the formula x sub (i,j)=x a (i,j)-x b (i, j) determine the auxiliary signal;

[0065] Using the formula Determine the cutoff threshold;

[0066] Among them, x sub (i,j) is the auxiliary signal, is x sub (i,j) composed of the signal x sub The mean of is x sub (i,j) composed of the signal x sub The variance of , δ is the cutoff threshold, i, j are the row and column positions of the pixel points.

[0067] S103, adaptively truncating the auxiliary signal using a truncation threshold to obtain an adaptively truncated signal; and determining an output weight of a truncation sidelobe suppression beamformer based on the adaptively truncated signal and the truncation threshold;

[0068] S103 specifically includes:

[0069] Using the formula x TSS (i,j)=max(x sub (i, j),δ) determines the adaptively truncated signal;

[0070] Using the formula w TSS (i,j)=d / x TSS (i, j) determines the output weights of the truncated sidelobe suppression beamformer;

[0071] Among them, x TSS (i, j) is the adaptively truncated signal, w TSS (i, j) is the output weight of the truncated sidelobe suppression beamformer, and max is the maximum value.

[0072] S104, performing element-dimensional delayed superposition and coherent compounding on the plane wave echo signal to obtain a third plane wave echo signal;

[0073] S104 specifically includes:

[0074] The plane wave echo signal s(t,n,θ) is delayed and summed in the element dimension to obtain the signal x(i,j,θ) with all angle information. At this time, θ=1,2...M;

[0075] For the signal x(i,j,θ) with all angle information, the formula is used Perform coherent recombination to obtain the third plane wave echo signal y(i,j);

[0076] Where n is the number of array elements, θ is the number of angles, t is the sampling time, θ = 1, 2...M, M is the number of emission angles, and i, j are the row and column positions of the pixels.

[0077] S105, using the empirical Bayesian method to obtain the signal variance and noise variance based on the third plane wave echo signal; constructing a signal-to-noise ratio adaptive filter based on the signal variance and noise variance; iterating the signal-to-noise ratio adaptive filter multiple times to determine a final output weight;

[0078] S105 specifically includes:

[0079] Using the formula determining a signal variance of the third plane wave echo signal;

[0080] Using the formula determining a noise variance of the third plane wave echo signal;

[0081] Using the formula Construct a signal-to-noise ratio adaptive filter;

[0082] Iterate the signal-to-noise ratio adaptive filter multiple times to determine the optimal weight vector, and perform median filtering on the weight matrix composed of the optimal weight vector to obtain the final output weight of the signal-to-noise ratio adaptive filter;

[0083] in, is the signal variance, is the noise variance, ‖·‖ is the 2-norm, w HCR (i, j) is the signal-to-noise ratio adaptive filter.

[0084] Among them, the process of constructing the signal-to-noise ratio adaptive filter is:

[0085] Using the formula Determine the high contrast adaptive filter;

[0086] According to the signal-to-noise ratio SNR, the adaptive parameter α(i,j) = M / SNR is obtained, and then the signal variance and noise variance are used to obtain Further obtain the signal-to-noise ratio adaptive filter:

[0087]

[0088] The multiple iterations of the signal-to-noise ratio adaptive filter are as follows:

[0089]

[0090] Here, k is the number of iterations, which is usually 2 to 3.

[0091] To The weight matrix Perform median filtering with a window size of 3*3, as follows:

[0092]

[0093] in, is the final output weight of the signal-to-noise ratio adaptive filter.

[0094] S106, using the smaller value of the output weight of the truncated sidelobe suppression beamformer and the final output weight of the signal-to-noise ratio adaptive filter as the adaptive beam weight;

[0095] S106 specifically includes:

[0096] Using the formula determining adaptive beam weights;

[0097] Among them, w iHCR-TSS (i, j) is the adaptive beam weight, and min is the minimum value.

[0098] S107 , weighting the third plane wave echo signal by using the adaptive beam weight to obtain a composite plane wave signal formed by adaptive beamforming, and performing final imaging.

[0099] S107 specifically includes:

[0100] Using the formula y'(i,j)=w iHCR-TSS (i, j)·y(i, j) determines the composite plane wave signal for adaptive beamforming;

[0101] Where y'(i,j) is the composite plane wave signal of adaptive beamforming at the pixel point in the i-th row and j-th column of the imaging area, y(i,j) is the third plane wave echo signal, and w iHCR-TSS (i, j) is the adaptive beam weight.

[0102] In order to verify the effectiveness of this application, an imaging comparison experiment was conducted using experimental data obtained from the CIRS 040GSE phantom collected by the Vantage 256 system. The imaging area included point scattering targets and sound absorption spot targets commonly used in ultrasound imaging. In the experiment, the sound velocity was set to 1540m / s, and plane wave echo signals at 75 angles were used during transmission and acquisition, with tilt angles ranging from -16o to +16o. The imaging probe (L11-5v) has 128 chips with a spacing size of 0.3mm and a chip width of 0.27mm. The transmitted pulse has a duration of two cycles, a center frequency of 6.25MHz, and the RF signal is sampled at 25MHz. The sound velocity is 1540m / s, and the imaging dynamic range is set to 60dB.

[0103] The two imaging areas mentioned above were subjected to comparative imaging experiments using the delayed superposition algorithm (DAS), coherence factor (CF), iterative high contrast adaptive filtering (iHCR), and the iterative high contrast adaptive filtering weighted truncated sidelobe suppression (iHCR-TSS) based on this application. The number of iterations of iHCR-TSS is 2. Image evaluation indicators include subjective image evaluation and objective indicator evaluation; objective indicator evaluation includes full width at half maximum (FWHM), contrast (CR), contrast-to-noise ratio (CNR), speckle signal-to-noise ratio (sSNR) and generalized contrast-to-noise ratio (gCNR). Figure 2 The point target imaging results of four algorithms are given, such as Figure 2 As shown in Figure 2, the images obtained by DAS, CF, and iHCR algorithms have low resolution and severe sidelobe artifacts. Figure 2 From part (b), we can see that the CF background information is strongly suppressed and the speckle information is severely eliminated at the edge of the image. Figure 2 From part (c), we can see that the speckle background information of iHCR is effectively preserved compared with CF. Figure 2 As can be seen in part (d), iHCR-TSS achieves the highest image resolution, outperforming the other algorithms in the near field, midfield, and far field. This shows that while ensuring the speckle background, iHCR-TSS achieves the highest imaging resolution among the four algorithms.

[0104] Figure 3 The comparison of the lateral resolution of the four algorithms at 60mm for point targets is shown in Figure 2. Figure 3It can be seen intuitively that the main lobe of iHCR-TSS is the narrowest and has the greatest improvement in resolution. In order to more intuitively compare the imaging resolution of the four algorithms, Table 1 shows the -6dB half-maximum width (FWHM) data comparison of the four algorithms at different depths. As can be seen from Table 1, DAS has the lowest resolution, and the resolution of iHCR is similar to that of DAS, and its FWHM is almost the same as that of DAS at different depths. The iHCR-TSS algorithm has the highest resolution, which is Figure 3 It can be seen that the main lobe reduction capability of iHCR-TSS is significantly improved compared with the other three algorithms. In summary, in terms of objective indicators, iHCR-TSS has better high-resolution imaging performance.

[0105] Table 1 Comparison of -6dB FWHM of four algorithms at different depths

[0106] FWHM 10mm 30mm 50mm Mean DAS 0.34 0.36 0.48 0.39 CF 0.29 0.30 0.35 0.31 iHCR 0.34 0.36 0.48 0.39 iHCR-TSS 0.09 0.09 0.23 0.14

[0107] Figure 4 The imaging results of the acoustic speckle target using four algorithms are shown in Figure 2. Figure 4 It can be seen that all four algorithms can accurately and clearly image the dark spot outline. Due to the insufficient ability to suppress clutter within the sound-absorbing spot, a large number of sidelobe artifacts exist within the DAS spot. The CF algorithm can effectively suppress clutter within the sound-absorbing spot. However, due to the poor noise estimation of the CF algorithm and the excessively high estimated noise power, the background speckle information is insufficiently preserved and the background intensity of the image is significantly reduced. In medical imaging, this is not conducive to distinguishing between cysts and tissue background. The iHCR algorithm can adaptively adjust the noise estimation ratio according to the signal-to-noise ratio, and its edge contour is clearer. It has significant improvements in the ability to suppress artifacts within the sound-absorbing spot and improve contrast, while maintaining more complete speckle information. In summary, under the premise of ensuring the speckle background, iHCR-TSS has the highest imaging contrast among the four algorithms.

[0108] Table 2 Comparison of imaging performance indicators of different algorithms (near field / far field)

[0109]

[0110]

[0111] Table 2 provides the imaging performance indicators of different algorithms. It can be seen that DAS has the lowest CR, that is, the ability to suppress intra-spot artifacts is weak, so the imaging effect is not good. Although CF improves CR, its gCNR is lower than DAS, indicating that in this area, CF is weaker than DAS in the ability to distinguish between sound absorption spots and background, and the distinction ability has not been substantially improved. At the same time, its CNR and sSNR are significantly lower than DAS. The advantage of the iHCR algorithm is that the CR has been greatly improved. At the same time, the sSNR has also been improved. More importantly, the gCNR of iHCR is higher than DAS and CF, which shows that its ability to distinguish between sound absorption spots and speckles has been improved. It is worth noting that although the iHCR algorithm can surpass iHCR-TSS with a slight advantage in some parameters, according to Figure 2 As can be seen from Table 1, iHCR-TSS can achieve higher resolution while maintaining contrast and speckle preservation, which is crucial for the overall performance of ultrasound imaging. In summary, the iHCR-TSS algorithm achieves a good trade-off between improving image resolution and contrast while maintaining speckle preservation.

[0112] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0113] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0114] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0115] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0116] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0117] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0118] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0119] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. An ultrasonic imaging method, characterized in that: The ultrasonic imaging method comprises: Acquire a plane wave echo signal; the plane wave echo signal carries array element information and angle information; performing coherent recombination on the plane wave echo signal with a negative angle and the plane wave echo signal with a positive angle to obtain a first plane wave echo signal and a second plane wave echo signal; determining an auxiliary signal based on the first plane wave echo signal and the second plane wave echo signal; and determining a truncation threshold based on the auxiliary signal; the first plane wave echo signal and the second plane wave echo signal both do not carry angle information; Adaptively truncating the auxiliary signal using a truncation threshold to obtain an adaptively truncated signal; and determining an output weight of a truncation sidelobe suppression beamformer based on the adaptively truncated signal and the truncation threshold; Performing element-dimensional delayed superposition and coherent compounding on the plane wave echo signal to obtain a third plane wave echo signal; Based on the third plane wave echo signal, the empirical Bayes method is used to obtain the signal variance and noise variance; a signal-to-noise ratio adaptive filter is constructed based on the signal variance and noise variance; the signal-to-noise ratio adaptive filter is iterated multiple times to determine the final output weight; The smaller value of the output weight of the truncated sidelobe suppression beamformer and the final output weight of the signal-to-noise ratio adaptive filter is used as the adaptive beam weight; The third plane wave echo signal is weighted by using the adaptive beam weight to obtain an adaptive beamformed composite plane wave signal, and finally imaged.

2. The ultrasonic imaging method according to claim 1, wherein: The acquiring of the plane wave echo signal specifically includes: Acquiring plane wave echo data received by the ultrasonic array element; Perform dynamic gain amplification and AD conversion on the plane wave echo data to obtain ultrasonic echo data; The ultrasonic echo data is bandpass filtered and Hilbert transformed to obtain a plane wave echo signal with array element information and angle information.

3. The ultrasonic imaging method according to claim 1, wherein: The method comprises: performing coherent combination on the plane wave echo signal with a negative angle and the plane wave echo signal with a positive angle to obtain a first plane wave echo signal and a second plane wave echo signal; determining an auxiliary signal according to the first plane wave echo signal and the second plane wave echo signal; and determining a truncation threshold according to the auxiliary signal, specifically comprising: Perform coherent recombination on the negative angle plane wave echo signal to obtain the first plane wave echo signal x a (i,j); The plane wave echo signal with positive angle is coherently combined to obtain the second plane wave echo signal x b (i,j); Using the formula x sub (i,j)=x a (i,j)-x b (i, j) determine the auxiliary signal; Using the formula d = m xsub +2s xsub Determine the cutoff threshold; Among them, x sub (i, j) is the auxiliary signal, μ xsub is x sub (i,j) composed of the signal x sub The mean value, s xsub is x sub (i,j) composed of the signal x sub The variance of , δ is the cutoff threshold, i, j are the row and column positions of the pixel points.

4. The ultrasonic imaging method according to claim 3, wherein: Adaptively truncating the auxiliary signal using the truncation threshold to obtain an adaptively truncated signal; and determining an output weight of a truncation sidelobe suppression beamformer based on the adaptively truncated signal and the truncation threshold, specifically includes: Using the formula x TSS (i,j)=max(x sub (i, j),δ) determines the adaptively truncated signal; Using the formula w TSS (i,j)=d / x TSS (i, j) determines the output weights of the truncated sidelobe suppression beamformer; Among them, x TSS (i, j) is the adaptively truncated signal, w TSS (i, j) is the output weight of the truncated sidelobe suppression beamformer, and max is the maximum value.

5. The ultrasonic imaging method according to claim 1, wherein: The performing element-dimensional delayed superposition and coherent compounding on the plane wave echo signal to obtain a third plane wave echo signal specifically includes: The plane wave echo signal s(t,n,θ) is delayed and summed in the element dimension to obtain the signal x(i,j,θ) with all angle information; For the signal x(i,j,θ) with all angle information, the formula is used Perform coherent recombination to obtain the third plane wave echo signal y(i,j); Where n is the number of array elements, θ is the number of angles, t is the sampling time, θ = 1, 2...M, M is the number of emission angles, and i, j are the row and column positions of the pixels.

6. The ultrasonic imaging method according to claim 5, characterized in that: The method adopts the empirical Bayes method to obtain the signal variance and the noise variance according to the third plane wave echo signal; and constructs a signal-to-noise ratio adaptive filter according to the signal variance and the noise variance; Iterate the signal-to-noise ratio adaptive filter multiple times to determine the final output weights, including: Using the formula determining a signal variance of the third plane wave echo signal; Using the formula determining a noise variance of the third plane wave echo signal; Using the formula Construct a signal-to-noise ratio adaptive filter; Iterate the signal-to-noise ratio adaptive filter multiple times to determine the optimal weight vector, and perform median filtering on the weight matrix composed of the optimal weight vector to obtain the final output weight of the signal-to-noise ratio adaptive filter; in, is the signal variance, is the noise variance, ‖·‖ is the 2-norm, w HCR (i, j) is the signal-to-noise ratio adaptive filter.

7. The ultrasonic imaging method according to claim 1, wherein: The step of weighting the third plane wave echo signal by using the adaptive beam weight to obtain an adaptive beamformed composite plane wave signal and performing final imaging specifically includes: Using the formula y'(i,j)=w iHCR-TSS (i, j)·y(i, j) determines the composite plane wave signal for adaptive beamforming; Where y'(i,j) is the composite plane wave signal of adaptive beamforming at the pixel point in the i-th row and j-th column of the imaging area, y(i,j) is the third plane wave echo signal, and w iHCR-TSS (i,j) is the adaptive beam weight, w TSS (i, j) is the output weight of the truncated sidelobe suppression beamformer, is the final output weight of the signal-to-noise ratio adaptive filter, and k is the number of iterations.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the ultrasonic imaging method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the ultrasonic imaging method according to any one of claims 1 to 7 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the ultrasonic imaging method according to any one of claims 1 to 7 is implemented.

Images