Ultrasonic imaging quality optimization method based on nonlinear adaptive weighting

Abstract

The invention relates to an ultrasonic imaging quality optimization method based on nonlinear adaptive weighting, and belongs to the technical field of ultrasonic imaging, and the method comprises the following steps: S1, carrying out the focusing delay processing of an echo signal obtained through the sampling of an ultrasonic array element, and obtaining the time-aligned ultrasonic echo data x (k); s2, performing nonlinear processing s (k) of absolute value obtaining, n-time root number opening and sign obtaining on the ultrasonic echo data after time alignment; s3, calculating the mean value of the echo data after nonlinear processing, and calculating the nth power of the mean value to obtain the output ypDASn (k) of the nonlinear beam forming method; s4, calculating the total incoherent intensity yNC (k), and dividing the total incoherent intensity yNC (k) by a nonlinear beam forming method to obtain a nonlinear adaptive coherence factor NCFn (k); and S5, weighting the ypDASn (k) by adopting the NCFn (k) to obtain a final beam forming output yNCFn (k).

Description

Technical Field

[0001] The present invention belongs to the technical field of ultrasonic imaging, and relates to an ultrasonic imaging quality optimization method based on non-linear adaptive weighting. 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 lobes, high side lobes, 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 adaptively adjust the weighting vector to minimize the output signal or its variance 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 poor. In addition, the MV algorithm requires covariance matrix inversion operation, with a high computational complexity and a long imaging time. Another common adaptive beamforming method is the Coherence Factor (CF). CF is defined as the ratio between the coherent energy and the incoherent energy in the echo signal. However, the above two adaptive beamforming methods cannot simultaneously meet the requirements of high resolution, high contrast, and low complexity. Therefore, how to improve the imaging quality while reducing the computational complexity is still an urgent problem to be solved.

[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 of the Invention

[0005] In view of this, the purpose of the present invention is to provide an ultrasonic imaging quality optimization method based on non-linear adaptive weighting to improve the resolution and contrast of ultrasonic imaging with low complexity.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] An ultrasonic imaging quality optimization method based on non-linear adaptive weighting, comprising the following steps:

[0008] S1: Perform focusing delay processing on the echo signals sampled by the ultrasonic array elements to obtain time-aligned ultrasonic echo data x(k);

[0009] S2: Perform non-linear processing s(k) of taking the absolute value, taking the nth root, and taking the sign on the time-aligned ultrasonic echo data;

[0010] S3: Calculate the mean value of the echo data after non-linear processing and calculate its nth power to obtain the output y pDASn (k) of the non-linear beamforming method;

[0011] S4: Calculate the total non-coherent intensity y NC (k), and divide it by the non-linear beamforming method to obtain the non-linear adaptive coherence factor NCFn(k);

[0012] S5: Weight the output y pDASn (k) of the non-linear beamforming method with the non-linear adaptive coherence factor NCFn(k) to obtain the output y NCFn (k) of the final beamforming;

[0013] Furthermore, in step S2, take the absolute value, take the nth root of the time-aligned ultrasonic echo data x(k), and multiply it by its sign value:

[0014]

[0015] In the formula, sign[·] represents the operation of obtaining the sign value, |·| represents the absolute value operation, represents the nth root operation, k represents the delay time, x i (k) represents the echo data of the i-th array element after time alignment, s in (k) represents the i-th array element and the echo data after the nth root processing.

[0016] Furthermore, in step S3, calculate the mean value of the echo data after non-linear processing and calculate its nth power to obtain the output y pDASn (k) of the non-linear beamforming method:

[0017]

[0018] In the formula, Σ represents the summation symbol, [·] n represents the nth power operation, N represents the total number of array elements of the ultrasonic array, s in (k) represents the echo data after non-linear processing of taking the nth root of the i-th array element after time alignment, y pDASn (k) represents the output of the non-linear beamforming method.

[0019] Furthermore, step S4 includes the following steps:

[0020] S41: Calculate the total incoherent intensity y NC (k):

[0021]

[0022] Wherein, represents the square root operation of order 2, [·] 2 represents the power operation of order 2, y NC (k) represents the total incoherent intensity;

[0023] S42: Divide the nonlinear beamforming method by the total incoherence to obtain the nonlinear adaptive coherence factor NCFn(k):

[0024]

[0025] Wherein, NCFn(k) represents the nonlinear adaptive coherence factor.

[0026] Furthermore, in step S5, the output y DASn (k) of the nonlinear beamforming method obtained in step S3 is weighted by the nonlinear adaptive coherence factor NCFn(k) obtained in step S4 to obtain the final beamforming output y NCFn (k):

[0027] y NCFn (k) = NCFn(k) · y pDASn (k)

[0028] Wherein, y NCFn (k) represents the final beamforming output.

[0029] 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.

[0030] 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 realized and obtained through the following specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] 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, wherein:

[0032] Figure 1Flowchart of the method for optimizing ultrasonic imaging quality based on non - linear adaptive weighting according to the present invention;

[0033] Figure 2 Imaging diagrams of steel block defects for 6 algorithms;

[0034] Figure 3 Imaging diagrams of steel block defects for 6 algorithms;

[0035] Figure 4 Imaging diagrams of aluminum block defects for 6 algorithms. Specific embodiments

[0037] The following uses specific examples to illustrate the embodiments of the present invention. Those skilled in the art can easily understand 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. 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 schematically. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0038] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention schematically. 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.

[0039] 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.

[0040] Embodiment 1

[0041] As Figure 1 shown, a method for optimizing ultrasonic imaging quality based on non - linear adaptive weighting provided by the present invention includes the following steps:

[0042] S1: Perform focusing delay processing on the echo signal sampled by the ultrasonic array element to obtain the time - aligned ultrasonic echo signal x(k);

[0043] S2: Perform non - linear processing s(k) of taking the absolute value, taking the nth root, and taking the sign on the time - aligned ultrasonic echo data; Step S2 includes the following content:

[0044] Take the absolute value of the original echo data, take the nth root, and multiply it by its sign value:

[0045]

[0046] In the formula, sign[·] represents the operation of finding the sign value, |·| represents the absolute value operation, represents the nth root operation, k represents the delay time, x i (k) represents the echo data after time alignment of the i-th array element, s in (k) represents the i-th array element, and the echo data after the nth root processing.

[0047] S3: Calculate the mean value of the echo data after non-linear processing, and calculate its nth power to obtain the output y pDASn (k); Step S3 includes the following contents:

[0048] Calculate the mean value of the echo data after non-linear processing, and calculate its nth power to obtain the output y pDASn (k):

[0049]

[0050] In the formula, ∑ represents the summation symbol, [·] n represents the nth power operation, N represents the total number of array elements of the ultrasonic array, s in (k) represents the echo data after non-linear processing of the i-th array element with the nth root taken after time alignment, y pDASn (k) represents the output of the non-linear beamforming method.

[0051] S4: Calculate the total non-coherent intensity y NC (k), and divide it by the non-linear beamforming method to obtain the non-linear adaptive coherence factor NCFn(k); Step S4 includes the following contents:

[0052] S41. Calculate the total non-coherent intensity y NC (k):

[0053]

[0054] In the formula, represents the square root operation, [·] 2 represents the square operation, y NC (k) represents the total non-coherent intensity.

[0055] S42. Divide the non-linear beamforming method by the total non-coherence to obtain the non-linear adaptive coherence factor NCFn(k):

[0056]

[0057] Wherein, NCFn(k) represents the non - linear adaptive coherence factor.

[0058] S5: Weight the output y pDASn (k) of the non - linear beamforming method obtained in S3 with the non - linear adaptive coherence factor NCFn(k) obtained in S4 to obtain the final beamforming output y NCFn (k); Step S5 includes the following content:

[0059] S51: Weight the output y DASn (k) of the non - linear beamforming method obtained in S3 with the non - linear adaptive coherence factor NCFn(k) obtained in S4 to obtain the final beamforming output y NCFn (k):

[0060] y NCFn (k)=NCFn(k)·y pDASn (k)

[0061] Wherein, y NCFn (k) represents the final beamforming output.

[0062] In this example, the beneficial effects of the present invention are demonstrated by experimental verification:

[0063] In this experiment, ultrasonic echo data were collected using a 5L128 ultrasonic probe produced by a certain company and the algorithm was verified in MATLAB R2024a. Figure 2 Intuitively shows the imaging results of two groups of steel blocks using the Delay and Sum (DAS) method and the method proposed in the present invention (Nonlinear Coherence Factor, NCF) when n = 2, 3, 4, 5, and 6. 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 NCFn algorithm image significantly reduces noise and clutter. As n increases, the imaging effect of NCFn gradually improves. When n is greater than 4, the noise and clutter in the image are almost completely suppressed, and it has a high - quality imaging effect.

[0064] To further compare the imaging performance of various methods, the present invention calculates the Array Performance Index (API), Contrast Ratio (CR), and Signal - to - noise Contrast (SNR) of all defect points. The detailed data are shown in Table 1.

[0065] Table 1

[0066]

[0067] As can be seen from Table 1, the method proposed in the present invention has the smallest API value and the largest CR and SNR values. In terms of resolution, NCF6 has the smallest API value, which is 68.9% lower than that of DAS. In terms of contrast, NCF6 has the largest CR and SNR values, which are 193.3% and 151.3% higher than those of DAS. NCF has improved in terms of API, CR, and SNR performance compared to DAS. As the value of n increases, the resolution and contrast performance of NCF improve. When n is greater than 4, the noise and clutter in the image are almost completely suppressed, and the imaging effect is excellent. In summary, the method proposed in the present invention can significantly improve the resolution and contrast of the DAS algorithm and generate high-quality ultrasonic images.

[0068] The imaging results of the second group of steel blocks are as Figure 3 shown. According to the imaging results, 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 image of the NCF algorithm significantly reduces noise and clutter. As n increases, the imaging effect of NCFn gradually improves. When n is greater than 4, the method proposed in the present invention further improves the resolution while almost completely suppressing clutter and noise. On this basis, the present invention calculates the API, CR, and SNR values of the ultrasonic image, as shown in Table 2.

[0069] Table 2

[0070]

[0071] As can be seen from Table 2, compared with DAS, the API of NCF6 is reduced by 49.6%, the CR is increased by 154.8%, and the SNR is increased by 129.5%. The improved method proposed in the present invention has the smallest API value and the largest CR and SNR values.

[0072] In addition, this example images the defects of the aluminum block, 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. It can be seen that the main lobe width of DAS is the widest. Compared with DAS, the main lobe width of NCF2 is reduced. The main lobe width of NCF6 is the lowest, and it has the best resolution performance.

[0073] Table 3

[0074]

[0075] On this basis, the API, CR, and SNR values of steel plate weld imaging by different algorithms were calculated, as shown in Table 3. It can be seen from the results that compared with DAS, the API of NCF6 decreased by 60.7%, the CR increased by 147.1%, and the SNR increased by 119.7%. The method proposed in the present invention has the minimum API value, the maximum CR and SNR values.

[0076] In summary, the improved method proposed in the present invention has the highest resolution and contrast performance, and the best imaging effect.

[0077] Example 2

[0078] A computer device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement an ultrasonic imaging quality optimization method based on non-linear adaptive weighting in Example 1.

[0079] Example 3

[0080] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements an ultrasonic imaging quality optimization method based on non-linear adaptive weighting in Example 1.

[0081] Example 4

[0082] A computer program product includes a computer program, which, when executed by a processor, implements an ultrasonic imaging quality optimization method based on non-linear adaptive weighting in Example 1.

[0083] Example 5

[0084] 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. The computer program, when executed by the processor, implements an ultrasonic imaging quality optimization method based on non-linear adaptive weighting in Example 1.

[0085] In the above embodiments, the reference in the specification to "this embodiment" means that the specific features, structures, or characteristics described in connection 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.

[0086] In the above embodiments, although the present invention has been described in connection 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 in light of 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.

[0087] 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 of 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 disk that can store program code.

[0088] 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 to cause the electronic terminal to execute each step of the above method.

[0089] 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.

[0090] The above-mentioned processor may be a general-purpose processor, including a Central Processing Unit (CPU for short), a Network Processor (NP for short), etc.; it may also be a Digital Signal Processor (DSP for short), an Application Specific Integrated Circuit (ASIC for short), a Field-Programmable Gate Array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0091] 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, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on.

[0092] 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.

[0093] 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 ultrasonic imaging quality optimization method based on non - linear adaptive weighting, characterized in that: Including the following steps: S1: Perform focusing delay processing on the echo signals sampled by the ultrasonic array elements to obtain time-aligned ultrasonic echo data x(k); S2: Perform non-linear processing s(k) of taking the absolute value, taking the nth root, and taking the sign on the time-aligned ultrasonic echo data; S3: Calculate the mean of the echo data after non-linear processing and calculate its n-th power to obtain the output y of the non-linear beamforming method pDASn (k); S4: Calculate the total incoherent intensity y NC (k), and divide it by the nonlinear beamforming method to obtain the nonlinear adaptive coherence factor NCFn(k); S5: The output y of the non - linear beamforming method weighted by the non - linear adaptive coherence factor NCFn(k) is taken pDASn (k), and the final output y NCFn (k) of the beamforming is obtained.

2. The ultrasonic imaging quality optimization method based on non - linear adaptive weighting according to claim 1, characterized in that: In step S2, take the absolute value, take the nth root of the time-aligned ultrasonic echo data x(k), and multiply it by its sign value: where sign[·] represents the operation of obtaining the sign value, and |·| represents the absolute value operation. represents the nth root operation, k represents the delay time, and x i (k) represents the echo data after time alignment of the ith array element, and s in (k) represents the echo data of the ith array element after the nth root processing.

3. The ultrasonic imaging quality optimization method based on non - linear adaptive weighting according to claim 1, characterized in that: In step S3, calculate the mean of the echo data after non-linear processing and calculate its n-th power to obtain the output y of the non-linear beamforming method pDASn (k): In the formula, ∑ represents the summation symbol, [·] n represents the nth power operation, N represents the total number of array elements of the ultrasonic array, s in (k) represents the echo data after non-linear processing of the ith array element taking the nth root after time alignment, y pDASn (k) represents the output of the non-linear beamforming method.

4. The method for optimizing ultrasonic imaging quality based on non - linear adaptive weighting according to claim 1, characterized in that: Step S4 includes the following steps: S41: Calculate the total incoherent intensity y NC (k): In the formula, represents the square root operation of degree 2, [·] 2 represents the power operation of degree 2, y NC (k) represents the total incoherent intensity; S42: Obtain the non-linear adaptive coherence factor NCFn(k) by dividing the non-linear beamforming method by the total non-coherence; In the formula, NCFn(k) represents the non-linear adaptive coherence factor.

5. The method for optimizing ultrasonic imaging quality based on non-linear adaptive weighting according to claim 1, characterized in that: In step S5, the output y DASn (k) of the non-linear beamforming method obtained in step S3 is weighted by the non-linear adaptive coherence factor NCFn(k) obtained in step S4 to obtain the final beamforming output y NCFn (k): DASn (k), NCFn (k): y NCFn (k) = NCFn(k)·y pDASn (k) where y NCFn (k) represents the final beamforming output.

6. A computer device, characterized in that: Including: 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 ultrasonic imaging quality optimization method based on non-linear adaptive weighting according to any one of claims 1-5.

7. A computer-readable storage medium, characterized in that: Stored thereon is a computer program, which when executed by the processor implements the ultrasonic imaging quality optimization method based on non-linear adaptive weighting according to any one of claims 1-5.

8. A computer program product, characterized in that: Including a computer program, which when executed by the processor implements the ultrasonic imaging quality optimization method based on non-linear adaptive weighting according to any one of claims 1-5.

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