Ultrasonic imaging method, equipment, medium and product

Abstract

The invention discloses an ultrasonic imaging method and device, a medium and a product, and relates to the field of ultrasonic imaging, and the method comprises the steps: carrying out the coherent combination of plane wave echo signals of a negative angle and a positive angle, and determining an auxiliary signal; performing adaptive truncation on the auxiliary signal according to a truncation threshold determined by the auxiliary signal; determining an output weight of a cut-off sidelobe suppression beam former according to the adaptively cut-off signal and a cut-off threshold value; array element dimension delay superposition summation and coherent recombination are carried out on the plane wave echo signals; according to the third plane wave echo signal, an empirical Bayesian method is adopted to obtain a signal variance and a noise variance, and a signal-to-noise ratio adaptive filter is constructed; iterating the signal-to-noise ratio adaptive filter for multiple times, and determining an output weight; and weighting the third plane wave echo signal by using the determined adaptive beam weight, and carrying out final imaging. The method can improve the comprehensive 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 and low cost. Digital beamforming can significantly improve signal quality from the perspective of signal processing. At present, 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 channel, and then aligns and superimposes the delayed echo signal. In the plane wave imaging mode, it is necessary to transmit plane waves at multiple angles, and then coherently compound the received multi-angle plane waves to obtain high-quality ultrasound images.

[0003] In the process of plane wave coherence recombination, weighting the image after DAS by the coherence factor (CF) can effectively improve the contrast of the imaging image and help distinguish cysts from tissues. However, the above algorithm tends to overestimate the noise, resulting in excessive suppression of effective background information, resulting in poor robustness and poor speckle retention performance, which is not conducive to the practical application of B-mode imaging. Adaptive beamforming based on adaptive filtering can significantly improve signal quality, but the current ultrasound adaptive filtering beamforming method is difficult to achieve an effective balance between high resolution, high contrast performance and speckle retention.

[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, which 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 ultrasonic imaging method, the ultrasonic 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 compounding on the plane wave echo signal with a negative angle and the plane wave echo signal with a positive angle respectively 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; the first plane wave echo signal and the second plane wave echo signal both have no 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 according to 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] According to the third plane wave echo signal, the signal variance and the noise variance are obtained by using the empirical Bayes method; and a signal-to-noise ratio adaptive filter is constructed according to the signal variance and the 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 a composite plane wave signal of adaptive beamforming, 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 respectively performing coherent composite 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 include:

[0020] The plane wave echo signal with a negative angle is coherently compounded to obtain the first plane wave echo signal x a (i,j);

[0021] The plane wave echo signal with a positive angle is coherently compounded 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 For x sub (i,j) composed of the signal x sub The mean value, s xsub For x sub (i,j) composed of the signal x sub , δ is the cutoff threshold, i, j are the row and column positions of the pixel points.

[0025] Optionally, the method 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 truncated 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) determine 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 summing 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 according to the third plane wave echo signal; constructing a signal-to-noise ratio adaptive filter according to 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, and w HCR (i, j) is the signal-to-noise ratio adaptive filter.

[0039] Optionally, the step of 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 complex 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 in 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 ultrasonic 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, which obtains the signal variance and noise variance by using the empirical Bayes method according to the third plane wave echo signal; and constructs a signal-to-noise ratio adaptive filter according to the signal variance and noise variance; iterates the signal-to-noise ratio adaptive filter multiple times, that is, the signal-to-noise ratio performs adaptive filtering on the third plane wave echo signal and performs multiple iterations to improve the contrast; and determines the output weight of the truncated sidelobe suppression beamformer according to the adaptively truncated signal and the truncation threshold, which can suppress the detected sidelobe and improve the image resolution; the present application can improve the speckle retention performance and imaging contrast performance while ensuring its high-resolution performance, significantly suppress the artifacts existing in the sound absorption spots, and overcome the trade-off difficulties between the image high resolution, high contrast and speckle retention performance. Therefore, the present application can effectively improve the image resolution and imaging contrast without sacrificing the background speckle information, thereby improving the comprehensive 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 drawings required for use in the embodiments will be briefly introduced below. 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 paying creative work.

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

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

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

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

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

[0053] In order to make the above-mentioned objects, 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 ultrasonic imaging method is provided, the method comprising the following S101 to S107. Wherein:

[0055] S101, acquiring 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, respectively coherently compounding 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; the first plane wave echo signal and the second plane wave echo signal both have no 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 a 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, For x sub (i,j) composed of the signal x sub The mean of For x sub (i,j) composed of the signal x sub , 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 the truncation threshold to obtain an adaptively truncated signal; and determining an output weight of a truncation sidelobe suppression beamformer according to 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) determine 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 summing 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 superimposed 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, according to the third plane wave echo signal, using the empirical Bayes method to obtain the signal variance and the noise variance; and constructing a signal-to-noise ratio adaptive filter according to the signal variance and the noise variance; iterating the signal-to-noise ratio adaptive filter multiple times to determine the 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, and 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 get 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, usually 2 to 3 times.

[0091] For 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 complex 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 in 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, the experimental data obtained by collecting the CIRS 040GSE phantom using the Vantage 256 system was used to conduct an imaging comparison experiment. The imaging area includes point scattering targets and sound absorption spot targets commonly used in ultrasonic imaging. In the experiment, the sound speed was set to 1540m / s, and plane wave echo signals at 75 angles were used during the transmission and acquisition process, with inclination 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 speed is 1540m / s, and the imaging dynamic range is set to 60dB.

[0103] 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 the above two imaging areas were used for comparative imaging experiments. Among them, 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 (full width at half maximum, FWHM), contrast (contrast ratio, CR), contrast-to-noise ratio (contrast-to-noise ratio, CNR), speckle signal-to-noise ratio (sSNR) and generalized contrast-to-noise ratio (generalizedCNR, 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 from part (d) of the figure, iHCR-TSS has the highest image resolution and outperforms other algorithms in the near field, midfield, and far field. It can be seen that under the premise of ensuring the speckle background, iHCR-TSS has the highest imaging resolution among the four algorithms.

[0104] Figure 3 The lateral resolution comparison of the four algorithms at 60 mm 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 the resolution improvement is the highest. In order to more intuitively compare the imaging resolution of the four algorithms, Table 1 gives the -6dB half-peak 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, 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 spot target using four algorithms are shown in Figure 2. Figure 4 It can be seen that the four algorithms can image the dark spot contour more accurately and clearly. Due to the insufficient clutter suppression capability inside the sound absorption spot, there are a large number of sidelobe artifacts inside the DAS spot. The CF algorithm can suppress the clutter inside the sound absorption spot well, but due to the poor noise estimation of the CF algorithm and the excessively high estimated noise power, the background speckle information is not sufficiently maintained, and the background intensity of the image is significantly reduced. In medical imaging, it is not conducive to distinguishing between cysts and tissue backgrounds. 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 inside the sound absorption spot and improve contrast, and the speckle information is more perfect. 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-absorbing 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, and at the same time, the sSNR has also been improved. More importantly, the gCNR of iHCR is higher than that of DAS and CF, which shows that its ability to distinguish between sound-absorbing spots and speckles has been improved. It is worth noting that although the iHCR algorithm can slightly surpass iHCR-TSS in some parameters, according to Figure 2 As shown in Table 1, iHCR-TSS can achieve higher resolution while maintaining contrast and speckle preservation performance, which is crucial for the comprehensive performance of ultrasound imaging. In summary, the iHCR-TSS algorithm achieves a good trade-off in improving image resolution and contrast as well as speckle preservation performance.

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

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

[0114] In an exemplary embodiment, a computer program product is provided, including a computer program, and 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 of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed 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 the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can 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 can 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 database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0118] The technical features of the above embodiments may 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 article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will 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 compounding on the plane wave echo signal with a negative angle and the plane wave echo signal with a positive angle respectively 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; the first plane wave echo signal and the second plane wave echo signal both have no 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 according to the adaptively truncated signal and the truncation threshold; Performing array element dimension delay superposition summation and coherent compounding on the plane wave echo signal to obtain a third plane wave echo signal; According to the third plane wave echo signal, the signal variance and the noise variance are obtained by using the empirical Bayes method; and a signal-to-noise ratio adaptive filter is constructed according to the signal variance and the 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 a composite plane wave signal of adaptive beamforming, and finally imaged.

2. The ultrasonic imaging method according to claim 1, characterized in that: The obtaining 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, characterized in that: The method comprises: performing coherent composite 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: The plane wave echo signal with a negative angle is coherently compounded to obtain the first plane wave echo signal x a (i,j); The plane wave echo signal with a positive angle is coherently compounded 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 For x sub (i,j) composed of the signal x sub The mean value, s xsub For x sub (i,j) composed of the signal x sub , 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, characterized in that: Adaptively truncating the auxiliary signal by using the truncation threshold to obtain an adaptively truncated signal; and determining the output weight of the truncation sidelobe suppression beamformer according to 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) determine the output weights of the truncated sidelobe suppression beamformer; Among them, x TSS (i,j) is the signal after adaptive truncation, 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, characterized in that: The performing element-dimensional delayed superposition summing 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 uses an empirical Bayesian method to obtain a signal variance and a 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, and w HCR (i, j) is the signal-to-noise ratio adaptive filter.

7. The ultrasonic imaging method according to claim 1, characterized in that: The method of 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 specifically includes: Using the formula y'(i,j)=w iHCR-TSS (i,j)·y(i,j) determines the complex 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 in 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.

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