A sidelobe-nulled beamforming method incorporating symbol coherence

Abstract

The application discloses a sidelobe nulling beamforming method combined with symbol coherence, and relates to the field of ultrasonic imaging; the method comprises the following steps: focusing and delaying sampled echo data to obtain time-aligned echo signals; dividing a complete ultrasonic array into two sub-arrays, performing delay and superposition beamforming algorithm on echo signals of the two sub-arrays respectively to obtain two groups of output signals; adding and subtracting the two groups of output signals respectively to preliminarily obtain output signals of a main beamformer and an auxiliary beamformer; simplifying processing of symbol multiplication and coherence factors, and weighting output of the main beamformer to obtain final output of the main beamformer; weighting output of the auxiliary beamformer by using nulling parameters to obtain final output of the auxiliary beamformer; and comparing output data of the main beamformer and the auxiliary beamformer to obtain final imaging data and perform imaging; and the application significantly improves resolution and contrast of ultrasonic imaging.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of ultrasonic imaging, in particular to a sidelobe nulling beamforming method combined with sign coherence. BACKGROUND

[0002] As a detection technology with high sensitivity, accurate defect positioning, high efficiency, low cost, no harm to human body and convenient for on-site use, ultrasonic imaging technology is widely used in medical, industrial and other detection fields. Among them, digital beamforming is the most important part of ultrasonic imaging. The most basic and widely used ultrasonic imaging method is the delay and sum (DAS) beamforming method, which obtains imaging data by simply superimposing the delayed echo data. However, the DAS beamforming method has a wide main lobe and high sidelobe, which leads to a decrease in the resolution and contrast of the imaging, and a poor quality of the obtained ultrasonic image.

[0003] In 1969, Capon first proposed the minimum variance (MV) beamforming method applied to narrowband radar, and then improved and applied it to ultrasonic imaging. The core of the MV algorithm is to minimize the variance of the output signal by adjusting the weight vector under the premise of keeping the expected direction gain unchanged. Although the MV algorithm has a significant effect on improving the image resolution, it has poor effect on processing clutter and noise, and the MV algorithm needs to perform covariance matrix inversion, which has high complexity and long imaging time. Coherence factor (CF) is another adaptive beamforming method. Coherence factor is defined as the ratio of coherent energy to incoherent energy in the echo signal. However, the CF method will excessively suppress the incoherent signal, resulting in black spot artifacts and reducing the background level. The above two adaptive beamforming methods cannot simultaneously meet the requirements of high resolution, high contrast and low complexity. Therefore, it is still a problem to be solved to reduce the computational complexity while improving the imaging quality.

[0004] In summary, there is an urgent need for an adaptive beamforming method that can significantly improve the resolution and contrast of ultrasonic imaging with low complexity. SUMMARY

[0005] The purpose of the present application is to provide a sidelobe nulling beamforming method combined with sign coherence, which can improve the resolution and contrast of ultrasonic imaging with low complexity.

[0006] To achieve the above purpose, the present application provides the following scheme:

[0007] A sidelobe nulling adaptive beamforming method combined with sign coherence, comprising the following steps:

[0008] S1: focus and delay the echo signal sampled from the ultrasonic array element to obtain time-aligned ultrasonic echo signal x(p);

[0009] S2: divide an ultrasonic array of a complete length N into two sub-arrays of length N / 2, and perform delay-and-sum beamforming on the two sub-arrays respectively to obtain two groups of output signals;

[0010] S3: add and subtract the two groups of output data to obtain the output signal of the main beamformer and the output signal of the auxiliary beamformer respectively;

[0011] S4: simplify the processing of symbol multiplication and coherence factor, and weight the main beamformer to obtain the output signal of the main beamformer with clutter and noise suppressed;

[0012] S5: weight the auxiliary beamformer with the eigenvalue parameter to obtain the output signal of the auxiliary beamformer;

[0013] S6: compare the output signal of the main beamformer with the output signal of the auxiliary beamformer to obtain the final imaging data and perform imaging.

[0014] Further, step S2 includes the following content:

[0015] S21, delay-and-sum beamforming is performed on the data of the sub-array:

[0016]

[0017] wherein ∑ represents a summation symbol, N represents the number of array elements, x i (p) and x j (p) represent the time-aligned echo data of the i-th and j-th array elements of the two sub-arrays respectively, and s1(p) and s2(p) represent the output signals of the two sub-arrays after superposition.

[0018] Further, step S3 includes the following content:

[0019] S31, the signals obtained after delay-and-sum beamforming of the sub-arrays are added and subtracted respectively:

[0020] s M (p) = s1(p) + s2(p)

[0021] s A (p) = s1(p) - s2(p)

[0022] wherein s M (p) and s A (p) represent the output signals of the main beamformer and the auxiliary beamformer respectively.

[0023] Further, step S4 comprises the following contents:

[0024] S41, when x(p) is positive, let b(p) be +1; when x(p) is zero, let b(p) be 0; when x(p) is negative, let b(p) be -1:

[0025]

[0026] In the formula, b(p) represents the sign value of the echo data corresponding to the array element, and x(p) represents the echo data of each array;

[0027] S42, calculate SMCF and simplify it to obtain the improved SMCF, i.e. ISMCF:

[0028]

[0029] In the formula, ∑ represents the summation symbol, N represents the number of array elements, b i (p) and b j (p) represent the sign values of the i-th and j-th array data, respectively;

[0030] S43, use ISMCF to weight the output signal of the main beamformer to obtain the final output of the main beamformer:

[0031] s FM (p) = ISMCF(p)·s M (p)

[0032] In the formula, s FM (p) represents the final output signal of the main beamformer.

[0033] Further, step S5 comprises the following contents:

[0034] S51, use the weighting of the anechoic parameter on the output of the auxiliary beamformer:

[0035] s FA (p) = ω·s A (p)

[0036] In the formula, ω represents the anechoic parameter, s FA (p) represents the output signal of the auxiliary beamformer.

[0037] Further, step S6 comprises the following contents:

[0038] S61, comparing the output signals of the main beamformer and the auxiliary beamformer, when the output signal of the main beamformer is greater than the output signal of the auxiliary beamformer, the final output is equal to the output of the main beamformer; when the output signal of the main beamformer is less than the output signal of the auxiliary beamformer, the final output is equal to the output of the main beamformer divided by the annulling parameter:

[0039]

[0040] In the formula, s CSC-SC (p) represents the final imaging data, ω represents the annulling parameter, s FA (p) represents the output signal of the auxiliary beamformer, s FM (p) represents the final output signal of the main beamformer.

[0041] According to the specific embodiments provided by the application, the following technical effects are disclosed: the application can significantly improve the resolution and contrast of ultrasonic imaging, does not involve matrix inversion, only contains simple numerical operation, has low calculation complexity, and can be applied to ultrasonic imaging systems for real-time imaging. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0043] Figure 1 The method flowchart of the present application;

[0044] Figure 2 The steel block defect imaging graph of the four algorithms;

[0045] Figure 3 The steel block imaging lateral resolution curve graph of the four algorithms;

[0046] Figure 4 The steel block imaging parameter comparison graph of the four algorithms;

[0047] Figure 5 The steel plate weld imaging graph of the four algorithms;

[0048] Figure 6 The steel plate weld imaging lateral resolution curve graph of the four algorithms;

[0049] Figure 7 The steel plate weld imaging parameter comparison graph of the four algorithms. DETAILED DESCRIPTION

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] The purpose of this invention is to provide a sidelobe camouflage beamforming method that combines symbolic coherence, which solves the problems of insufficient resolution and contrast and high computational complexity in existing ultrasound imaging, and is suitable for real-time ultrasound imaging.

[0052] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0053] Example 1

[0054] like Figure 1 As shown, the sidelobe cloaking beamforming method combining symbolic coherence provided by the present invention includes the following steps:

[0055] S1: The echo signal sampled by the ultrasonic array element is focused and delayed to obtain the time-aligned ultrasonic echo signal x(p);

[0056] S2: Divide a complete ultrasonic array of length N into two subarrays of length N / 2, and perform delayed superposition beamforming on the two subarrays respectively to obtain two sets of output signals;

[0057] Specifically, step S2 includes the following:

[0058] S21. Delayed superposition beamforming of data from the subarray:

[0059]

[0060] In the formula, ∑ represents the summation symbol, N represents the number of array elements, and x i (p) and x j (p) represent the time-aligned echo data of the i-th and j-th elements of the two subarrays, respectively, and s1(p) and s2(p) represent the output signals after the two subarrays are superimposed.

[0061] S3: Add and subtract the two sets of output data to obtain the output signal of the main beamformer and the output signal of the auxiliary beamformer, respectively;

[0062] Specifically, step S3 includes the following:

[0063] S31, add and subtract the signals obtained after the subarray delay-and- sum beamforming respectively;

[0064] s M (p) = s1(p) + s2(p)

[0065] s A (p) = s1(p) - s2(p)

[0066] In the formula, s M (p) and s A (p) represent the output signals of the main beamformer and the auxiliary beamformer respectively.

[0067] S4: simplify the processing of symbol multiplication and coherent factor, and weight the main beamformer to obtain the output signal of the main beamformer with clutter and noise suppressed;

[0068] Specifically, step S4 includes the following contents:

[0069] S41, when x(p) is positive, let b(p) be +1; when x(p) is zero, let b(p) be 0; when x(p) is negative, let b(p) be -1:

[0070]

[0071] In the formula, b(p) represents the sign value of the echo data corresponding to the array element, and x(p) represents the echo data of each array;

[0072] S42, calculate the SMCF and simplify the processing to obtain the improved SMCF, i.e. ISMCF:

[0073]

[0074] In the formula, ∑ represents the summation symbol, N represents the number of array elements, b i (p) and b j (p) represent the sign values of the i-th and j-th array data respectively;

[0075] S43, weight the output signal of the main beamformer using the ISMCF to obtain the final output of the main beamformer:

[0076] s FM (p) = ISMCF(p) · s M (p)

[0077] In the formula, s FM (p) represents the final output signal of the main beamformer.

[0078] S5: weight the auxiliary beamformer using the anechoic parameter to obtain the output signal of the auxiliary beamformer;

[0079] Specifically, step S5 includes the following contents:

[0080] S51, the output of the auxiliary beamformer is weighted with the anechoic parameter:

[0081] s FA (p)=ω·s A (p)

[0082] In the formula, ω represents the anechoic parameter, s FA (p) represents the output signal of the auxiliary beamformer.

[0083] S6: comparing the output signal of the main beamformer with the output signal of the auxiliary beamformer to obtain the final imaging data and performing imaging;

[0084] Specifically, step S6 includes the following contents:

[0085] S61, comparing the output signals of the main beamformer and the auxiliary beamformer, when the output signal of the main beamformer is greater than the output signal of the auxiliary beamformer, the final output is equal to the output of the main beamformer; when the output signal of the main beamformer is less than the output signal of the auxiliary beamformer, the final output is equal to the output of the main beamformer divided by the anechoic parameter:

[0086]

[0087] In the formula, s CSC-SC (p) represents the final imaging data, ω represents the anechoic parameter, s FA (p) represents the output signal of the auxiliary beamformer, s FM (p) represents the final output signal of the main beamformer.

[0088] In this example, the beneficial effects of the present application are demonstrated by experimental verification:

[0089] In this experiment, the 5L128 ultrasonic probe produced by Shantou Ultrasonic Electronics Co., Ltd. in Guangdong is used to collect ultrasonic echo data, and the algorithm verification is performed in MATLAB R2024a. The imaging target region of the steel block experiment is arranged with 12 equally spaced (with a spacing of 2 mm) and 8 mm deep defect points. Figure 2The imaging results of the steel block by using the delay and sum (DAS) algorithm, the coherence factor (CF) algorithm, the sign coherence factor (SCF) algorithm and the method (Sidelobe Concealment Combined with Sign Correlation, CSC-SC) proposed in the application are intuitively shown. The results show that the DAS algorithm has the worst imaging effect, has a large amount of noise and clutter, and has the widest main lobe width and the highest side lobe level of the defect point. Compared with the DAS, the CF and SCF algorithms significantly reduce the noise and clutter. Compared with all the above algorithms, the CSC-SC further improves the resolution while suppressing the clutter to almost invisible. The CSC-SC has the highest resolution and contrast. In summary, the algorithm proposed in the present application has the best imaging effect.

[0090] Figure 3 For the lateral resolution curves of the defect points of the steel block of different algorithms, it can be seen that the sidelobe suppression effect of the application is the best and the main lobe width is the smallest. On this basis, we calculate the full width at half maximum (FWHM) of all defect points, the array performance index (API) and the contrast ratio (CR), and the detailed data are shown in Table 1.

[0091] Table 1 FWMH, API and CR values of steel block imaging of different algorithms

[0092]

[0093] According to Table 1, the CSC-SC method proposed in the application has the smallest FWHM and API values and the largest CR value. In order to intuitively compare the performance of each algorithm, we visualize the parameters in the form of a column chart, as shown in Figure 4 It can be seen that the CSC-SC method proposed in the application has the best imaging resolution and contrast.

[0094] In order to further the performance of the algorithm, the steel plate weld defects are imaged, and the imaging results are shown in Figure 5 The results show that the DAS algorithm has the worst imaging effect and has a large amount of noise and clutter. The CSC-SC method proposed in the application maximizes the resolution and greatly suppresses the noise and clutter.

[0095] Figure 6The lateral resolution curves of the defect points in the steel plate welds of different algorithms can be seen from the graphs, and the sidelobe suppression effect of the application is the best, and the main lobe width is the smallest. On this basis, we calculate the FWHM, API and CR values of the steel plate weld imaging of different algorithms, as shown in Table 2.

[0096] Table 2 FWMH, API and CR values of steel plate weld imaging of different algorithms

[0097]

[0098] According to Table 2, the CSC-SC method proposed in the application has the smallest FWHM and API values and the largest CR value. The comparison of CR and API of different methods is shown in Figure 7 The CSC-SC method proposed in the application has the lowest API and FWHM values and the highest CR value among all algorithms.

[0099] In summary, the CSC-SC method proposed in the application has the highest resolution, contrast and best imaging effect among the four methods.

[0100] Embodiment 2

[0101] A computer device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the computer program to implement the sidelobe nulling beamforming method combined with symbol coherence in embodiment 1.

[0102] Embodiment 3

[0103] A computer readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the sidelobe nulling beamforming method combined with symbol coherence in embodiment 1.

[0104] Embodiment 4

[0105] A computer program product comprising a computer program, the computer program being executed by a processor to implement the sidelobe nulling beamforming method combined with symbol coherence in embodiment 1.

[0106] Embodiment 5

[0107] A computer device can be a database. The computer device comprises a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store to-be-processed transactions. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through network connection. The computer program is executed by the processor to implement the sidelobe blanking beam forming method combined with symbol coherence in embodiment 1.

[0108] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0109] Those skilled 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. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided by 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 storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided by the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided by the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0110] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0111] The principles and implementation modes of the present application are described by using specific examples in this paper, and the above-mentioned embodiments are only used to help understand the method and core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In conclusion, the content of the present application should not be understood as a limitation.

Claims

1. A method of sidelobe canceller beamforming with symbol coherence combining, the method comprising: The method comprises the following steps: S1: focusing and delaying the echo signal sampled from the ultrasonic array element to obtain time-aligned ultrasonic echo signal x(p); S2: dividing an ultrasonic array with a complete length of N into two sub-arrays with a length of N / 2, and performing delay and superposition beam forming on the two sub-arrays respectively to obtain two groups of output signals; S3: adding and subtracting the two groups of output data to obtain the output signal of the main beam former and the output signal of the auxiliary beam former respectively; S4: simplifying the processing of the sign multiplication coherent factor, and weighting the main beam former to obtain the output signal of the main beam former with clutter and noise suppressed; the specific steps are as follows: S41: when x(p) is positive, let b(p) be +1; when x(p) is 0, let b(p) be 0; when x(p) is negative, let b(p) be -1: In the formula, b(p) represents the sign value of the echo data corresponding to the array element, and x(p) represents the echo data of each array; S42: calculating the SMCF and simplifying the processing thereof to obtain an improved SMCF, namely ISMCF: where ∑ denotes a summation symbol, N denotes the number of array elements, b i (p) and b j (p) denote the sign values of the i-th and j-th array data, respectively; S43: weighting the output signal of the main beam former by using the ISMCF to obtain the final output of the main beam former: s FM (p) = ISMCF(p) s M (p) where s FM (p) denotes the output signal of the final main beamformer; S5: weighting the auxiliary beam former by using the mute parameter to obtain the output signal of the auxiliary beam former; S6: comparing the output signal of the main beam former with the output signal of the auxiliary beam former to obtain the final imaging data and performing imaging.

2. The method of claim 1, wherein: Step S2: dividing an ultrasonic array with a complete length of N into two sub-arrays with a length of N / 2, and performing delay and superposition beam forming on the two sub-arrays respectively to obtain two groups of output signals, the specific steps are as follows: S21: performing delay and superposition beam forming on the data of the sub-array: where ∑ denotes summation, N denotes the number of elements, x i (p) and x j (p) respectively denote the time-aligned echo data of the i-th and j-th elements of the two sub-arrays, and s1(p) and s2(p) respectively denote the output signals of the two sub-arrays after superposition.

3. The method of claim 1, wherein: Step S3: Add and subtract the two sets of output data to obtain the output signals s M (p) and s A (p) of the main and auxiliary beamformers, respectively, as follows: S31: adding and subtracting the signals obtained after delay and superposition beam forming on the sub-array respectively: s M (p) = s1(p) + s2(p) s A (p) = s1(p) - s2(p) where s M (p) and s A (p) represent the output signals of the primary and secondary beamformers, respectively.

4. The method of claim 1, wherein: Step S5: weighting the auxiliary beam former by using the mute parameter to obtain the output signal of the auxiliary beam former, the specific steps are as follows: S51: weighting the output of the auxiliary beam former by using the mute parameter: s FA (p) = ω · s A (p) where ω denotes the stego parameter, s FA (p) denotes the output signal of the auxiliary beamformer.

5. The method of claim 1, wherein: Step S6: comparing the output signal of the main beam former with the output signal of the auxiliary beam former to obtain the final imaging data and performing imaging, the specific steps are as follows: S61: comparing the output signals of the main beam former and the auxiliary beam former, when the output signal of the main beam former is greater than the output signal of the auxiliary beam former, the final output is equal to the output of the main beam former; when the output signal of the main beam former is less than the output signal of the auxiliary beam former, the final output is equal to the output of the main beam former divided by the mute parameter: where s CSC-SC (p) denotes the final imaged data, ω denotes the steepest descent parameter, s FA (p) denotes the output signal of the auxiliary beamformer, s FM (p) denotes the output signal of the final main beamformer.

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