Improved nonlinear beam forming method

Abstract

The invention relates to an improved nonlinear beam forming method, which belongs to the technical field of ultrasonic imaging, and comprises the following steps: S1, carrying out focusing delay processing on echo signals obtained by ultrasonic array element sampling to obtain time-aligned original echo data; s2, performing p times of root signal compression nonlinear processing on the original echo data; s3, calculating the p-th power of the mean value of the echo data after nonlinear processing, and obtaining the output of the nonlinear beam forming method based on p-th root signal compression; s4, calculating a mean value and a standard deviation of the echo data after nonlinear processing at each sampling point, and dividing the mean value and the standard deviation to obtain a nonlinear weighting factor; s5, multiplying the sign value of the delay superposition method on the basis of the nonlinear weighting factor to obtain a signed nonlinear weighting factor method; and S6, adopting a nonlinear weighting factor and a symbol nonlinear weighting factor to respectively weight an even number of nonlinear beam forming methods and an odd number of nonlinear beam forming methods based on p-number root compression to obtain final beam forming output.

Description

Technical Field

[0001] The present invention belongs to the technical field of ultrasonic imaging, and relates to an improved non-linear beamforming method. Background Art

[0002] Ultrasonic imaging technology has been widely used in the detection fields such as medical and industrial due to its advantages of high sensitivity, accurate defect location, high efficiency, low cost, harmless to the human body and convenient for on-site operation. Among them, digital beamforming is a key step in the ultrasonic imaging process. Currently, the most commonly used beamforming method - Delay and Sum (DAS) imaging has disadvantages such as wide main lobe, high sidelobe, and a lot of clutter, and its imaging quality is poor. Therefore, scholars have improved DAS and proposed many adaptive beamforming algorithms.

[0003] In 1969, Capon proposed the Minimum Variance (MV) method applied to the narrowband radar field, which was later improved and applied to the ultrasonic imaging field. The core of the MV method is to make the output signal or its variance reach the minimum by adaptively adjusting the weighting vector while ensuring the stable gain in the desired direction. Although the MV algorithm has achieved remarkable results in improving image resolution, its suppression effect on clutter and noise is not good. In addition, the MV algorithm needs to perform covariance matrix inversion operation, with a high computational complexity and a long imaging time.

[0004] Another common adaptive beamforming method is the Coherence Factor (CF). CF is defined as the ratio of coherent energy to incoherent energy in the echo signal. However, the CF method will over-suppress incoherent signals, thus generating black spot artifacts and reducing the background level. It can be seen that the above two adaptive beamforming methods cannot meet the requirements of high resolution, high contrast and low complexity at the same time. Therefore, how to improve the imaging quality while reducing the computational complexity is still an urgent problem to be solved.

[0005] In summary, there is an urgent need for an adaptive beamforming method that can significantly improve the resolution and contrast of ultrasonic imaging with a low complexity. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide an improved non-linear beamforming method.

[0007] To achieve the above purpose, the present invention provides the following technical solutions:

[0008] An improved non-linear beamforming method, comprising the following steps:

[0009] S1: Perform focusing delay processing on the echo signals sampled by the ultrasonic array elements to obtain the original echo data x(k) with time alignment;

[0010] S2: Perform non - linear processing of p - th root compression on the original echo data x(k);

[0011] S3: Calculate the p - th power of the mean of the echo data after non - linear processing to obtain the output y of the non - linear beamforming method based on p - th root compression p-NL (k);

[0012] S4: Calculate the mean and standard deviation of the echo data after non - linear processing at each sampling point, and divide them to obtain the non - linear weighting factor NWF(k);

[0013] S5: Multiply the sign value of the delay and sum method on the basis of the non - linear weighting factor to obtain the signed non - linear weighting factor method SNWF(k);

[0014] S6: Use the non - linear weighting factor and the signed non - linear weighting factor to weight the even - numbered and odd - numbered non - linear beamforming methods based on p - th root compression respectively to obtain the outputs y p-NWF (k) and y p-SNWF (k).

[0015] Further, step S2 specifically includes:

[0016] Take the absolute value of the original echo data x(k), take the p - th root, and multiply it by its sign value:

[0017]

[0018] In the formula, sign[·] represents the sign operation, |·| represents the absolute value operation, represents the p - th root operation, x i (k) represents the i - th echo data after time alignment, s i (k) represents the echo data after non - linear processing.

[0019] Further, step S3 specifically includes:

[0020] Calculate the p - th power of the mean of the echo data s i (k) to obtain the output y of the non - linear beamforming method based on p - th root compression p-NL (k):

[0021]

[0022] In the formula, N represents the total number of array elements of the ultrasonic array, mean(·) represents the mean operation, ∑ represents the summation symbol, y p-NL(k) represents the output signal of the non-linear beamforming method based on the p-th root compression.

[0023] Furthermore, step S4 specifically includes the following steps:

[0024] S41: Calculate the mean and standard deviation of the echo data after non-linear processing at each sampling point:

[0025]

[0026] In the formula, mean(·) and std(·) respectively represent the mean and standard deviation operations, represents the square root operation;

[0027] S42: Divide the mean by the standard deviation to obtain the non-linear weighting factor NWF(k):

[0028]

[0029] In the formula, NWF(k) represents the output of the non-linear weighting factor.

[0030] Furthermore, step S5 specifically includes the following steps:

[0031] S51: Calculate the superimposed output value y DAS (k) of the time-aligned delayed echo data:

[0032]

[0033] S52: Multiply the non-linear weighting factor by the sign value of the delayed superposition method to obtain the sign non-linear weighting factor SNWF(k):

[0034]

[0035] In the formula, SNWF(k) represents the output of the sign non-linear weighting factor.

[0036] Furthermore, step S6 specifically includes the following steps:

[0037] S61: Weight the non-linear beamforming method based on the p-th root compression for even numbers using the non-linear weighting factor to obtain y p-NWF (k):

[0038] y p-NWF (k) = NWF(k) · y p-NL (k)

[0039] In the formula, y p-NWF (k) represents the final imaging data when p is an even number;

[0040] S62: Obtain y by weighting the non-linear beamforming method based on the p-th root compression for odd times with a symbol non-linear weighting factor p-SNWF (k):

[0041] y p-SNWF (k) = SNWF(k) · y p-NL (k)

[0042] In the formula, y p-SNWF (k) represents the final imaging data when p is odd.

[0043] The beneficial effects of the present invention are as follows: The present invention can significantly improve the resolution and contrast of ultrasonic imaging, does not involve matrix inversion, only includes simple numerical operations, has a low computational complexity, and can be applied to an ultrasonic imaging system for real-time imaging.

[0044] Other advantages, objectives, and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the following specification. Brief Description of the Drawings

[0045] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be described in detail preferably with reference to the accompanying drawings, where:

[0046] Figure 1 is the flowchart of the improved non-linear beamforming method described in the present invention;

[0047] Figure 2 is the imaging diagram of steel block defects for 6 algorithms;

[0048] Figure 3 is the imaging diagram of aluminum block defects for 6 algorithms;

[0049] Figure 4 is the imaging diagram of steel plate welds for 6 algorithms;

[0050] Figure 5 is the lateral resolution curve diagram of steel plate welds for 6 algorithms. Detailed Embodiments

[0051] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand the other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. All the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0052] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Therefore, only the components related to the present invention are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and proportion of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0053] In the following description, a large number of details are explored to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0054] Embodiment 1

[0055] As Figure 1 shown, the improved non-linear beamforming method provided by the present invention includes the following steps:

[0056] S1: Perform focusing delay processing on the echo signals sampled by the ultrasonic array elements to obtain time-aligned ultrasonic echo signals x(k);

[0057] S2: Perform non-linear processing of p-th root compression on the original echo data; specifically, step S2 includes the following content:

[0058] Take the absolute value of the original echo data, take the p-th root, and multiply it by its sign value:

[0059]

[0060] In the formula, sign[·] represents the sign operation, |·| represents the absolute value operation, represents the p-th root operation, x i (k) represents the i-th time-aligned echo data, s i (k) represents the echo data after time alignment and non-linear processing.

[0061] S3: Calculate the p-th power of the mean of the echo data after non-linear processing to obtain the output y of the non-linear beamforming method based on p-th root compression p-NL (k); Specifically, step S3 includes the following content:

[0062] Calculate the p-th power of the mean of the echo data after non-linear processing to obtain the output y of the non-linear beamforming method based on p-th root compression p-NL (k):

[0063]

[0064] where N represents the total number of array elements of the ultrasonic array, mean(·) represents the mean operation, ∑ represents the summation symbol, and y p-NL (k) represents the output signal of the non-linear beamforming method based on p-th root compression

[0065] S4: Calculate the mean and standard deviation of the echo data after non-linear processing at each sampling point, and divide them to obtain the non-linear weighting factor NWF(k); Specifically, step S4 includes the following content:

[0066] S41. Calculate the mean and standard deviation of the echo data after non-linear processing at each sampling point:

[0067]

[0068] where mean(·) and std(·) represent the mean and standard deviation operations respectively, represents the square root operation

[0069] S42. Divide the mean by the standard deviation to obtain the non-linear weighting factor NWF(k):

[0070]

[0071] where NWF(k) represents the output of the non-linear weighting factor

[0072] S5: Multiply the non-linear weighting factor by the sign value of the delay-and-sum method to obtain the signed non-linear weighting factor method SNWF(k); Specifically, step S5 includes the following content:

[0073] S51. Calculate the superimposed output value y DAS (k) of the delay-aligned echo data, that is, the output value y DAS (k) of the delay-and-sum method:

[0074]

[0075] where y DAS (k) represents the output of the delay-and-sum method

[0076] S52. Multiply the symbol value of the delay superposition method by the non - linear weighting factor to obtain the symbol non - linear weighting factor SNWF(k):

[0077]

[0078] Wherein, SNWF(k) represents the output of the symbol non - linear weighting factor.

[0079] S6: Use the non - linear weighting factor and the signed non - linear weighting factor to weight the even - numbered and odd - numbered non - linear beamforming methods based on the p - th root compression respectively to obtain the output of the final beamforming. Specifically, step S6 includes the following contents:

[0080] S61. Use the non - linear weighting factor to weight the even - numbered non - linear beamforming method based on the p - th root compression to obtain y p-NWF (k):

[0081] y p-NWF (k) = NWF(k)·y p-NL (k)

[0082] Wherein, y p-NWF (k) represents the final imaging data when p is even.

[0083] S62. Use the signed non - linear weighting factor to weight the odd - numbered non - linear beamforming method based on the p - th root compression to obtain y p-SNWF (k):

[0084] y p-SNWF (k) = SNWF(k)·y p-NL (k)

[0085] Wherein, y p-SNWF (k) represents the final imaging data when p is odd.

[0086] In this example, the beneficial effects of the present invention are demonstrated by experimental verification:

[0087] In this experiment, ultrasonic echo data was collected using a 5L128 ultrasonic probe produced by a certain company, and the algorithm was verified in MATLAB R2024a. Figure 2Intuitively shows the imaging results of a steel block using the delay superposition method, the delay multiply and sum (DMAS) method, the non-linear beamforming method based on the p-th root compression, and the improved method proposed in the present invention. The results show that the DAS algorithm has the worst imaging effect, with a large amount of noise and clutter. The main lobe width of its defect points is the widest and the sidelobe level is the highest. Compared with DAS, the images of DMAS and p-NL algorithms significantly reduce noise and clutter. Compared with all the above algorithms, the method proposed in the present invention further improves the resolution while suppressing clutter to almost invisible. And the method proposed in the present invention has a better imaging effect when p = 3 than when p = 2. In short, the method proposed in the present invention has the best imaging effect.

[0088] To further compare the imaging performance of various methods, the present invention calculates the full width at half maximum (FWHM), the array performance index (API), and the contrast ratio (CR) of all defect points. The detailed data are shown in Table 1.

[0089] Table 1

[0090]

[0091] According to Table 1, the improved method proposed in the present invention has the smallest FWHM and API values and the largest CR value. In terms of resolution, 3-SNWF has the smallest FWHM and API values, which are 70.5% and 77.3% lower than DAS, 60.1% and 61.6% lower than DMAS, and 55.2% and 62.6% lower than 3-NL. In terms of contrast, 3-SNWF has the largest CR value, which is 172.9% higher than DAS, 78.5% higher than DMAS, and 56.5% higher than 3-NL. 3-SNWF has improved in terms of FWHM, API, and CR performance compared with 2-NWF. It can be concluded that as the p value increases, the resolution and contrast performance of p-NWF / p-SNWF improve.

[0092] To further study the performance of the algorithm, this example images the defects of an aluminum block, and the imaging results are as Figure 3As shown. The imaging effect of the DAS algorithm is the worst, with a large amount of noise and clutter. The main lobe width of its defect points is the widest and the sidelobe level is the highest. Compared with DAS, the images of the DMAS and p-NL algorithms significantly reduce noise and clutter. Compared with all the above algorithms, the method proposed in the present invention further improves the resolution while almost completely suppressing clutter and noise. On this basis, we calculated the FWHM, API, and CR values of steel plate weld imaging with different algorithms, as shown in Table 2.

[0093] Table 2

[0094]

[0095] As can be seen from Table 2, compared with DAS, the FWHM of 3-SNWF is reduced by 79.8%, the API is reduced by 90.6%, and the CR is increased by 132.4%. Compared with DMAS, the FWHM of 3-SNWF is reduced by 73.5%, the API is reduced by 84.4%, and the CR is increased by 58.3%. Compared with 3-NL, the FWHM of 3-SNWF is reduced by 70.1%, the API is reduced by 81.7%, and the CR is increased by 40.7%. The improved method proposed in the present invention has the smallest FWHM and API values and the largest CR value.

[0096] In addition, this example images the defects of the steel plate weld, and the imaging results are as Figure 4 shown. The results show that the imaging effect of the DAS algorithm is the worst, with a large amount of noise and clutter. The improved method proposed in the present invention maximally improves the resolution and greatly suppresses noise and clutter. This example plots the lateral resolution curve at the depth of the steel plate weld, as Figure 5 shown. It can be seen that the main lobe width of DAS is the widest. Compared with DAS, the main lobe widths of DMAS and 2-NL are reduced. The main lobe width of 3-SNWF is the lowest, having the best resolution performance.

[0097] Table 3

[0098]

[0099] On this basis, we calculated the FWHM, API, and CR values of steel plate weld imaging with different algorithms, as shown in Table 3. The results show that compared with DAS, the FWHM of 3-SNWF is reduced by 79.8%, the API is reduced by 75.1%, and the CR is increased by 289.1%. The improved method proposed in the present invention has the smallest FWHM and API values and the largest CR value.

[0100] In summary, the improved method proposed in the present invention has the highest resolution and contrast performance and the best imaging effect.

[0101] Example 2

[0102] A computer device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the improved non-linear beamforming method in Embodiment 1.

[0103] Embodiment 3

[0104] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the improved non-linear beamforming method in Embodiment 1 is implemented.

[0105] Embodiment 4

[0106] A computer program product, comprising a computer program, and when the computer program is executed by a processor, the improved non-linear beamforming method in Embodiment 1 is implemented.

[0107] Embodiment 5

[0108] A computer device, which may be a database. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, 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. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes 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 the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store transactions to be processed. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, the improved non-linear beamforming method in Embodiment 1 is implemented.

[0109] In the above embodiments, the reference to "this embodiment" in the specification means that the specific features, structures, or characteristics described in combination with the embodiment are included in at least some embodiments, but not necessarily all embodiments. Multiple occurrences of "this embodiment" do not necessarily all refer to the same embodiment.

[0110] In the above embodiments, although the present invention has been described in conjunction with specific embodiments of the present invention, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other storage structures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed. Embodiments of the present invention are intended to cover all such alternatives, modifications, and variations that fall within the broad scope of the appended claims.

[0111] For the computer-readable storage medium in this embodiment, those of ordinary skill in the art can understand that all or part of the steps for implementing the above method embodiments can be completed by hardware related to a computer program. The foregoing computer program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the foregoing storage medium includes: various media such as ROM, RAM, magnetic disk, or optical disc that can store program code.

[0112] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver, and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication therebetween. The memory is used to store a computer program, the communication interface is used for communication, and the processor and the transceiver are used to run the computer program so that the electronic terminal executes each step of the above method.

[0113] In this embodiment, the memory may include a random access memory (Random Access Memory, abbreviated as RAM), and may also include a non-volatile memory, such as at least one disk memory.

[0114] The above-mentioned processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, abbreviated as CPU), a network processor (Network Processor, abbreviated as NP), etc.; it may also be a digital signal processor (Digital Signal Processing, abbreviated as DSP), an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC), a field-programmable gate array (Field-Programmable Gate Array, abbreviated as FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0115] The present invention can be used in numerous general-purpose or special-purpose computing system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on.

[0116] The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the present technical solution, and they should all be covered by the scope of the claims of the present invention.

Claims

1. An improved nonlinear beamforming method, characterized in that: The following steps are involved: S1: Perform focusing delay processing on the echo signal obtained by sampling the ultrasonic array element to obtain the time-aligned original echo data x(k); S2: Perform nonlinear processing of p-th root compression on the original echo data x(k); S3: Calculate the p-th power of the mean value of the echo data after nonlinear processing to obtain the output y of the nonlinear beamforming method based on p-th root compression p-NL (k); S4: Calculate the mean and standard deviation of the echo data after nonlinear processing at each sampling point, and divide them to obtain the nonlinear weighting factor NWF(k); S5: multiplying the sign value of the delay and superposition method by the nonlinear weighting factor to obtain a signed nonlinear weighting factor method SNWF(k); S6: Use nonlinear weighting factors and sign nonlinear weighting factors to weight the even-numbered and odd-numbered p-th root compression-based nonlinear beamforming methods respectively to obtain the final beamforming output y p-NWF (k) and y p-SNWF (k).

2. The improved nonlinear beamforming method according to claim 1, characterized in that: Step S2 specifically includes: Take the absolute value of the original echo data x(k), take the pth root, and multiply it by its sign value: In the formula, sign[·] represents symbol operation, |·| represents absolute value operation, Indicates the p-th square root operation, x i (k) represents the echo data after time alignment of the ith time, s i (k) shows the echo data after nonlinear processing.

3. The improved nonlinear beamforming method according to claim 1, characterized in that: Step S3 specifically includes: Calculate the echo data s after nonlinear processing i (k) The p-th power of the mean value, and the output y of the nonlinear beamforming method based on p-th root compression is obtained p-NL (k): Where N represents the total number of array elements in the ultrasonic array, mean(·) represents the mean operation, ∑ represents the summation symbol, and y p-NL (k) represents the output signal of the nonlinear beamforming method based on p-th root compression.

4. The improved nonlinear beamforming method according to claim 1, characterized in that: Step S4 specifically includes the following steps: S41: Calculate the mean and standard deviation of the echo data after nonlinear processing at each sampling point: In the formula, mean(·) and std(·) represent mean and standard deviation operations respectively. Indicates the square root operation; S42: Divide the mean by the standard deviation to obtain a nonlinear weighting factor NWF(k): Where NWF(k) represents the output of the nonlinear weighting factor.

5. The improved nonlinear beamforming method according to claim 1, characterized in that: Step S5 specifically includes the following steps: S51: Calculate the superposition output value y after the delayed echo data is time aligned DAS (k): S52: The nonlinear weighting factor is multiplied by the symbol value of the delay superposition method to obtain a symbol nonlinear weighting factor SNWF(k): Where SNWF(k) represents the output of the symbolic nonlinear weighting factor.

6. The improved nonlinear beamforming method according to claim 1, characterized in that: Step S6 specifically includes the following steps: S61: Use nonlinear weighting factors to weight the even-order p-th root compression-based nonlinear beamforming method to obtain y p-NWF (k): and p-NWF (k)=NWF(k)·y p-NL (k) In the formula, y p-NWF (k) represents the final imaging data when p is an even number; S62: Use the symbol nonlinear weighting factor to weight the odd-numbered p-th root compression-based nonlinear beamforming method to obtain y p-SNWF (k): y p-SNWF (k)=SNWF(k)·y p-NL (k) In the formula, y p-SNWF (k) represents the final imaging data when p is an odd number.

7. A computer device, characterized in that: include: 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 improved nonlinear beamforming method according to any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the improved nonlinear beamforming method according to any one of claims 1 to 6 is implemented.

9. A computer program product, characterized in that: The invention comprises a computer program, which, when executed by a processor, implements the improved nonlinear beamforming method according to any one of claims 1 to 6.

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