An amplitude-weighted ultrasound imaging method, device, medium, and product

Abstract

This invention discloses an amplitude-weighted ultrasound imaging method, device, medium, and product, relating to the field of ultrasound imaging. The method includes: acquiring the delayed signals of each array element; applying a constructed received amplitude weighting function with a function value of 1, a received amplitude weighting function with a function value of 1 in the first half and a function value of -1 in the second half, and M pairs of complementary received amplitude weighting functions with a function value of 1 to the delayed signals of each array element, thereby obtaining a delayed superimposed beamforming output signal, a beamforming output signal, and M beamforming signal groups; determining the inversion coefficient with a completely opposite trend to the beamforming output signal and the cross-correlation coefficient after median filtering of the M beamforming signal groups; fusing the inversion coefficient and the median-filtered cross-correlation coefficient to obtain a new weighting matrix; and weighting the delayed superimposed beamforming output signal with the new weighting matrix to obtain the final beamforming output signal, thereby improving the quality of ultrasound imaging.

Description

Technical Field

[0001] This invention relates to the field of ultrasound imaging technology, and in particular to an amplitude-weighted ultrasound imaging method, device, medium, and product. Background Technology

[0002] Ultrasound imaging is widely used in clinical medical diagnosis due to its advantages such as safety, real-time performance, and low cost. Digital beamforming occupies a central position in the entire ultrasound imaging system. The most widely used and simplest beamforming technique in ultrasound imaging is the Delay And Sum (DAS) algorithm. It calculates the delay of the received echo signal based on the geometric position relationship of the array element channels, and then aligns and superimposes the delayed data. Traditional DAS algorithms have low complexity and fast imaging speed, but their use of a fixed window function for weighting leads to an increased main lobe width and lower resolution.

[0003] Minimum variance (MV) beamforming effectively improves image resolution by filtering out interference and noise signals based on the directional difference between the desired and interference signals. However, MV involves numerous complex matrix operations and data processing, resulting in extremely high computational complexity, making real-time imaging challenging. While most adaptive beamforming algorithms can improve performance, they also significantly increase computational complexity, failing to meet the real-time requirements of ultrasound imaging. Therefore, researching computationally efficient and high-performance beamforming algorithms has become an important and challenging topic in the field of beamforming.

[0004] In summary, there is an urgent need for a beamforming algorithm that can maintain high resolution performance, improve imaging contrast, and have low complexity. Summary of the Invention

[0005] The purpose of this invention is to provide an amplitude-weighted ultrasound imaging method, device, medium, and product. This invention can achieve better imaging contrast and lateral resolution performance close to MV, while avoiding covariance matrix inversion, making it suitable for real-time imaging systems.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] In a first aspect, the present invention provides an amplitude-weighted ultrasound imaging method, comprising:

[0008] The echo signal received by the ultrasonic array element is subjected to a first conversion process to obtain ultrasonic echo data; the first conversion process includes amplification processing, AD conversion processing, and time-delay focusing processing.

[0009] The Hilbert transform is applied to the ultrasonic echo data to obtain the delayed signals of each array element after the Hilbert transform.

[0010] The constructed received amplitude weighting function with a function value of 1 is applied to the delayed signals of each array element after the Hilbert transform to obtain the output signal formed by the delayed superimposed beam.

[0011] The received amplitude weighting function, with the first half having a function value of 1 and the second half having a function value of -1, is applied to the delayed signals of each array element after the Hilbert transform to obtain the beamforming output signal.

[0012] Based on the beamforming output signal, determine the inversion coefficient that has a completely opposite trend to the beamforming output signal.

[0013] The constructed M pairs of complementary received amplitude weighting functions with a function value of 1 are applied to the delayed signals of each array element after the Hilbert transform to obtain M beamforming signal groups.

[0014] Calculate the similarity between each beamforming signal group and generate M normalized cross-correlation coefficients; M is a positive integer greater than 1.

[0015] The M normalized cross-correlation coefficients are subjected to a second transformation process to obtain the cross-correlation coefficients after median filtering; the second transformation process includes thresholding, averaging, and median filtering.

[0016] The inverted coefficients are fused with the cross-correlation coefficients after median filtering to obtain a new weighted matrix.

[0017] The new weighting matrix is ​​used to weight the output signal of the delayed superimposed beamforming to obtain the final beamforming output signal.

[0018] Optionally, the expression for the beamforming output signal is:

[0019]

[0020] Where, x n (k) represents the delayed echo signal corresponding to the nth array element, where k is the sampling time, and y SUB (k) is the beamforming signal at the k-th sampling time of a scanline signal, Apod_SUB n The value of the received amplitude weighting function corresponding to the nth array element is the sum of the first half of the function value being 1 and the second half being -1. It is a positive integer greater than 1.

[0021] Optionally, the expression for the inverted coefficient is:

[0022]

[0023] Among them, w SUB (k,h) are the inverse coefficients, ρ' SUB (k,h) represents the beamforming output signal after thresholding. ρ SUB (k,h) is the beamforming output signal after the absolute value of the beamforming signal is normalized at the k-th sampling time of the h-th column scan line signal in the imaging region.

[0024] Optionally, the expression for the beamforming signal group is:

[0025]

[0026] Among them, BF1 r (k) represents the first adaptive beam signal at the k-th sampling time of a scan line signal, BF2 r (k) represents the second adaptive beam signal at the k-th sampling time of a scanline signal, x n (k) represents the delayed echo signal corresponding to the nth array element, where k is the sampling time. For the first sub-amplitude weighting function, For the second sub-amplitude weighting function, r = 1, 2...M, where N is a positive integer greater than 1.

[0027] Optionally, the expression for the normalized cross-correlation coefficient is:

[0028]

[0029] Where, ρ r (k,h) represents the normalized cross-correlation coefficient, k is the sampling time, A is the sample length taken upwards or downwards at the k-th sampling time, and BF1 r (p,h) represents the first adaptive beam signal at the p-th sampling time of the h-th column scan line signal in the imaging region, BF2 r (p,h) represents the second adaptive beam signal at the p-th sampling time of the h-th scan line signal in the imaging region.

[0030] Optionally, the expression for the new weighting matrix is:

[0031]

[0032] Among them, w LCH (k,h) is the new weighting matrix, w SUB (k,h) are the inverse coefficients. This represents the cross-correlation coefficient after median filtering.

[0033] Optionally, the expression for the final beamforming output signal is:

[0034] y LCH (k,h)=w LCH (k,h)·y DAS (k,h);

[0035] Among them, y LCH (k,h) represents the final beamforming output signal, y DAS (k,h) represents the beamforming signal at the k-th sampling time of the h-th scan line signal in the imaging region, y DAS (k,h) represents y DAS (k) in two-dimensional representation, y DAS (k) is the beamforming signal at the k-th sampling time of a scan line signal. Apod_DAS n The value of the received amplitude weighting function corresponding to the nth array element, Apod_DAS n = 1, 1≤n≤N, where N is a positive integer greater than 1, x n (k) represents the delayed echo signal corresponding to the nth array element, k is the sampling time, and w LCH (k,h) is the new weighting matrix.

[0036] In a second aspect, the present invention provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the amplitude-weighted ultrasound imaging method described in the first aspect.

[0037] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when executed by a processor, the computer program implements the steps of the amplitude-weighted ultrasound imaging method described in the first aspect.

[0038] Fourthly, the present invention provides a computer program product, including a computer program, characterized in that, when executed by a processor, the computer program implements the steps of the amplitude-weighted ultrasound imaging method described in the first aspect.

[0039] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0040] This invention provides an amplitude-weighted ultrasound imaging method, device, medium, and product. Compared to existing beamforming algorithms, this invention achieves near-MV algorithm-level high resolution with extremely low complexity, while simultaneously improving imaging contrast performance. This invention can significantly suppress artifacts in sound-absorbing spots, overcoming the difficulty of balancing high image resolution, high contrast, maintaining background quality, and low complexity. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a schematic flowchart of an amplitude-weighted ultrasound imaging method provided in Embodiment 1 of the present invention;

[0043] Figure 2 This is a structural framework diagram of an amplitude-weighted ultrasound imaging method provided in Embodiment 1 of the present invention;

[0044] Figure 3 This is a schematic diagram of the imaging results of four algorithms for point targets and sound-absorbing spot targets provided in Embodiment 1 of the present invention;

[0045] Figure 4 for Figure 3 Lateral resolution curves of the four algorithms at the depth of the midpoint target;

[0046] Figure 5 This is a schematic diagram of the imaging results of four algorithms for the geabr_0 experimental data provided in Embodiment 1 of the present invention;

[0047] Figure 6a for Figure 5 Horizontal resolution curves of the four algorithms at the depth of the fourth target point in the middle;

[0048] Figure 6b for Figure 5 Horizontal resolution curves of four algorithms at the depth of the 6th target point in the middle;

[0049] Figure 7 This is an internal structural diagram of a computer device provided in Embodiment 4 of the present invention. Detailed Implementation

[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 an amplitude-weighted ultrasound imaging method, device, medium, and product. This invention can achieve better imaging contrast and lateral resolution performance close to MV, while avoiding covariance matrix inversion, making it suitable for real-time imaging systems.

[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 and Figure 2 As shown, this embodiment provides an amplitude-weighted ultrasound imaging method, including:

[0055] S1: Perform a first conversion process on the echo signal received by the ultrasonic array element to obtain ultrasonic echo data; the first conversion process includes amplification processing, AD conversion processing and time-delay focusing processing.

[0056] S2: Apply the Hilbert transform to the ultrasonic echo data to obtain the delayed signals of each array element after the Hilbert transform.

[0057] S3: Apply the constructed received amplitude weighting function with a function value of 1 to the delayed signals of each array element after the Hilbert transform to obtain the output signal formed by the delayed superimposed beam.

[0058] S4: The received amplitude weighting function with the first half function value of 1 and the second half function value of -1 is applied to the delayed signal of each array element after the Hilbert transform to obtain the beamforming output signal.

[0059] S5: Based on the beamforming output signal, determine the inversion coefficient that has a completely opposite trend to the beamforming output signal.

[0060] S6: The constructed M pairs of complementary received amplitude weighting functions with a function value of 1 are applied to the delayed signals of each array element after the Hilbert transform to obtain M beamforming signal groups.

[0061] S7: Calculate the similarity between each beamforming signal group and generate M normalized cross-correlation coefficients; M is a positive integer greater than 1.

[0062] S8: Perform a second transformation process on the M normalized cross-correlation coefficients to obtain the cross-correlation coefficients after median filtering; the second transformation process includes thresholding, averaging, and median filtering.

[0063] S9: The inverted coefficients are fused with the cross-correlation coefficients after median filtering to obtain a new weighted matrix.

[0064] S10: The new weighting matrix is ​​used to weight the output signal of the delayed superimposed beamforming to obtain the final beamforming output signal.

[0065] As an optional implementation method provided in this embodiment, step S3 specifically includes:

[0066] S31: For a sensor array with N equally spaced elements, construct a received amplitude weighting function with a function value of 1:

[0067] Apod_DAS n =1, 1≤n≤N;

[0068] Among them, Apod_DAS n Let n be the value of the received amplitude weighting function corresponding to the nth array element, where n = 1, 2...N, and N is a positive integer greater than 1.

[0069] S32: The received amplitude weighting function with a function value of 1 is used to process the delayed signals of each array element after the Hilbert transform, to obtain the output signal formed by the delayed superimposed beam:

[0070]

[0071] Where, x n (k) represents the delayed echo signal corresponding to the nth array element, where k is the sampling time, and y DAS (k) is the beamforming signal at the kth sampling time of a scan line signal.

[0072] As an optional implementation method provided in this embodiment, step S4 specifically includes:

[0073] S41: For a sensor array with N equally spaced elements, construct a received amplitude weighting function with a function value of 1 in the first half and a function value of -1 in the second half:

[0074]

[0075] Among them, Apod_SUB nLet n be the first half of the function value corresponding to the nth array element, where n = 1, 2, ..., N, and N is a positive integer greater than 1.

[0076] S42: The received amplitude weighting function, with a function value of 1 in the first half and a function value of -1 in the second half, is used to process the delayed signals of each array element after the Hilbert transform to obtain the beamforming output signal:

[0077]

[0078] Where, x n (k) represents the delayed echo signal corresponding to the nth array element, where k is the sampling time, and y SUB (k) is the beamforming signal at the kth sampling time of a scan line signal.

[0079] As an optional implementation method provided in this embodiment, step S5 specifically includes:

[0080] S51: The beamforming signal at the k-th sampling time of the h-th column scan line signal in the imaging region, i.e., the beamforming output y SUB Normalize the absolute value of (k,h), y DAS (k,h) represents y DAS The two-dimensional representation of (k) is used to obtain normalized variables ranging from 0 to 1, and the normalized variables are then thresholded.

[0081]

[0082] Where, ρ' SUB (k,h) represents the beamforming output signal after thresholding, ρ SUB (k,h) is the beamforming output signal after the absolute value of the beamforming signal is normalized at the k-th sampling time of the h-th column scan line signal in the imaging region.

[0083] S52: In order to obtain inversion coefficients that exhibit a completely opposite trend to the thresholded beamforming output signal, the inversion coefficients are defined based on the thresholded beamforming output signal as follows:

[0084]

[0085] Among them, w SUB (k,h) are the inverse coefficients.

[0086] As an optional implementation method provided in this embodiment, step S6 specifically includes:

[0087] S61: For a sensor array with N equally spaced array elements, M array elements are extracted alternately to construct two received amplitude weighting functions Apod1. n and Apod2 n Where n = 1, 2... N; further divide these two received amplitude weighting functions into M sub-amplitude weighting function groups:

[0088]

[0089] Where r = 1, 2, ..., M, N is a positive integer greater than 1, M is a positive integer greater than 1, and k is the sampling time. For the first sub-amplitude weighting function, This is the second sub-amplitude weighting function.

[0090] S62: The delayed signals of each array element after Hilbert transform are processed using the first sub-amplitude weighting function and the second sub-amplitude weighting function to obtain M beamforming signal groups:

[0091]

[0092] Among them, BF1 r (k) represents the first adaptive beam signal at the k-th sampling time of a scan line signal, BF2 r (k) represents the second adaptive beam signal at the k-th sampling time of a scanline signal, x n (k) represents the delayed echo signal corresponding to the nth array element, where k is the sampling time.

[0093] As an optional implementation provided in this embodiment, in step S7, the expression for the normalized cross-correlation coefficient is:

[0094]

[0095] Where, ρ r (k,h) represents the normalized cross-correlation coefficient, k is the sampling time, A is the sample length taken upwards or downwards at the k-th sampling time, and BF1 r (p,h) represents the first adaptive beam signal at the p-th sampling time of the h-th column scan line signal in the imaging region, BF2 r (p,h) represents the second adaptive beam signal at the p-th sampling time of the h-th scan line signal in the imaging region.

[0096] As an optional implementation method provided in this embodiment, step S8 specifically includes:

[0097] S81: Threshold the M normalized cross-correlation coefficients to obtain the thresholded cross-correlation coefficients:

[0098]

[0099] Where, ρ' r (k,h) is the cross-correlation coefficient after thresholding, and δ is the threshold, set to 0.001.

[0100] S82: Average the cross-correlation coefficients after thresholding to obtain the averaged cross-correlation coefficients:

[0101]

[0102] in, This is the cross-correlation coefficient after taking the average value.

[0103] S83: Perform median filtering on the averaged cross-correlation coefficients to obtain the median-filtered cross-correlation coefficients; specifically:

[0104] For all pixels in the imaging area Median filtering is performed on the weighted matrix to obtain For any point (k, h) in the matrix, a median filter window ω will be designed centered on that point. kh Then, the values ​​contained in the window are arranged in ascending order, and the median value of the arrangement is taken as the filtering result of the (k,h) point. The window size is 3λ×10, where λ is the number of sampling points for beamforming, which is obtained by dividing the sampling frequency in the ultrasound system by the center frequency.

[0105] As an optional implementation provided in this embodiment, in step S9, the expression of the new weighting matrix is:

[0106]

[0107] Among them, w LCH (k,h) is the new weighting matrix, w SUB (k,h) are the inverted coefficients, and ρ'(k,h) is the cross-correlation coefficient after median filtering.

[0108] As an optional implementation provided in this embodiment, in step S10, the expression for the final beamforming output signal is:

[0109] y LCH (k,h)=w LCH (k,h)·y DAS (k,h);

[0110] Among them, y LCH (k,h) represents the final beamforming output signal, y DAS(k,h) represents the beamforming signal at the k-th sampling time of the h-th scan line signal in the imaging region, y DAS (k,h) represents y DAS (k) in two-dimensional form, w LCH (k,h) is the new weighting matrix.

[0111] The quality of ultrasound imaging is improved by using the final beamforming output signal.

[0112] Experimental verification:

[0113] Field II, developed by the Technical University of Denmark based on acoustic principles, is an ultrasonic experimental simulation platform that has gained widespread recognition and use in theoretical research. To verify the effectiveness of the algorithm in this invention, Field II was used to image point scattering targets and sound-absorbing spot targets commonly used in ultrasonic imaging, and imaging comparison experiments were conducted using actual experimental data geabr_0. In the simulation experiments of point targets and sound-absorbing spot targets, the longitudinal imaging range was between 30 and 50 mm, and the lateral imaging range was between -10 and 10 mm. It included two strong point targets and a circular sound-absorbing spot with a radius of 4 mm. The positions of the strong point targets were (-4.5 mm, 46.5 mm) and (4.5 mm, 46.5 mm), respectively, and the center of the sound-absorbing spot was located at (0 mm, 40 mm). Within the imaging area, there were 200,000 randomly distributed scattering points. The amplitude of the scattering points in the background area was 400 times that of the internal area of ​​the sound-absorbing spot, and the amplitude of the strong point targets was 150 times that of the background area. The imaging dynamic range of the image was set to 65 dB. The experiment used an array with a center frequency of 2.6 MHz, 64 elements, an element width of 0.3 mm, an element height of 5 mm, an element spacing of 0.05 mm, a sampling frequency of 25 MHz, and a sound velocity of 1540 m / s. The experiment also used an array with a center frequency of 3.33 MHz, 64 elements, a spacing of 0.2413 mm, a sampling frequency of 17.76 MHz, and a sound velocity of 1500 m / s. The imaging dynamic range was set to 65 dB.

[0114] Comparative imaging experiments were conducted on the four experimental targets using the Delayed Superposition (DAS) algorithm, the Multiple Amplitude Weighted Cross-Correlation (MAX) algorithm, the Minimum Variance Distortionless (MV) algorithm, and the Low Complexity High Quality Ultrasound Imaging Method (LCH) based on amplitude weighting.

[0115] Figure 3 Imaging results for point targets and sound-absorbing spot targets using four algorithms are presented, from... Figure 3It can be seen that the edges of the acoustic cysts are not clear enough in DAS and MV images, and there are many artifacts and clutter inside the cysts. Compared with DAS and MV, MAX and LCH make the cyst targets easier to detect, with less internal clutter and clearer edges, and show significant improvements in artifact suppression and contrast enhancement within dark spots. Regarding point targets, MAX and DAS have similar resolution, while MV and the proposed LCH have the best resolution. However, compared with MV, LCH has very low complexity and is easier to image in real time.

[0116] Figure 4 For 4 algorithms in Figure 3 The comparison of lateral resolution at the point targets clearly shows that LCH achieves a resolution close to that of the MV algorithm without affecting background quality. To more intuitively compare the imaging resolution of the four algorithms, Table 1 presents a comparison of the -6dB full peak width (FWHM) data for the four algorithms. Table 1 shows that DAS has the lowest resolution, while MAX's resolution is similar to DAS. The MV algorithm has the highest resolution, and LCH's FWHM is close to that of MV. Compared to DAS and MAX, LCH's FWHM is reduced by 64.23% and 63.64% respectively at the point targets on the left, and by 63.32% and 62.88% respectively at the point targets on the right. However, due to... Figure 3 It can be seen that the sidelobe suppression capability of LCH is significantly improved compared to MV. In summary, LCH has better overall performance.

[0117] Table 14 Comparison of FWHM (6dB) for Different Algorithms

[0118]

[0119] Table 2 provides the imaging performance metrics for different algorithms. It can be seen that the DAS algorithm has an excessively high internal average power, resulting in weak intra-speculiar artifact suppression and thus poor imaging quality. The MV algorithm has the advantage of improved internal average power, but its poor external average power prevents effective improvement in CR. LCH significantly improves contrast compared to MV, and its sidelobe and clutter suppression capabilities are significantly better than MV, while maintaining good background speckle quality. LCH improves CR by 134.1% and 132.6% compared to DAS and MV, respectively. In summary, LCH exhibits excellent dark spot imaging performance.

[0120] Table 2 Comparison of Imaging Performance Indicators of Different Algorithms

[0121]

[0122] Figure 5 Four algorithms were used to generate imaging results on the GEABR_0 data. From... Figure 5 , Figure 6a and Figure 6b As can be seen, DAS exhibits significant dark spot sidelobe artifacts, and the image is severely affected by noise. While MV improves image resolution, its contrast is poor. MAX effectively suppresses sidelobe artifacts within the spot, resulting in a clean and clear image with significantly improved imaging quality, while maintaining the resolution of DAS. LCH, while approaching the narrow point target width of MV, greatly improves contrast and maintains background quality. LCH avoids matrix inversion operations, significantly reducing complexity compared to MV, and holds promise for application in real-time imaging systems.

[0123] Table 3 presents a comparison of the -6dB half-peak width (FWHM) data for the four algorithms. At the second target point, LCH's FWHM is reduced by 30.53% and 30.53% compared to DAS and MAX, respectively. At the third target point, LCH's FWHM is reduced by 44.76% and 44.76% compared to DAS and MAX, respectively. At the fourth target point, LCH's FWHM is reduced by 41.18% and 41.18% compared to DAS and MAX, respectively. At the fifth target point, LCH's FWHM is reduced by 35.11% and 34.62% compared to DAS and MAX, respectively. At the sixth target point, LCH's FWHM is reduced by 29.25% and 28.77% compared to DAS and MAX, respectively. Furthermore, LCH achieves a lateral resolution closest to MV with extremely low complexity.

[0124] Table 34 Comparison of FWHM for 6dB Algorithms

[0125]

[0126]

[0127] Table 4 compares the imaging performance metrics of different algorithms. LCH and MAX improve contrast to the same extent. Compared with DAS and MV, at the first sound-absorbing cyst depth, LCH improves the CR by 38.70% and 69.71%, respectively. At the second sound-absorbing cyst depth, LCH improves the CR by 55.91% and 90.67%, respectively. At the third sound-absorbing cyst depth, LCH improves the CR by 78.47% and 100.0%, respectively. The experimental results and simulation results are basically consistent, verifying that the proposed LCH algorithm significantly outperforms other methods in terms of overall performance in improving image resolution and contrast while maintaining background speckle quality.

[0128] Table 4 Comparison of Imaging Performance Indicators of Different Algorithms

[0129]

[0130] Example 2:

[0131] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the amplitude-weighted ultrasound imaging method described in Example 1.

[0132] Example 3:

[0133] A computer program product includes a computer program that, when executed by a processor, implements the steps of the amplitude-weighted ultrasound imaging method described in Example 1.

[0134] Example 4:

[0135] A computer device, which may be a database, may have an internal structure diagram as shown below. Figure 7 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The database stores pending transactions. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements the amplitude-weighted ultrasound imaging method described in Example 1.

[0136] 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 used for analysis, data stored, data displayed, etc.) involved in this invention are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0137] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided by this invention 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 can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided by this invention may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided by this invention may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0138] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0139] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. An amplitude-weighted ultrasound imaging method, characterized in that, include: The echo signal received by the ultrasonic array element is subjected to a first conversion process to obtain ultrasonic echo data; the first conversion process includes amplification processing, AD conversion processing, and time-delay focusing processing. The Hilbert transform is applied to the ultrasonic echo data to obtain the delay signals of each array element after the Hilbert transform; The received amplitude weighting function with a function value of 1 is applied to the delayed signal of each array element after the Hilbert transform to obtain the output signal formed by the delayed superimposed beam. The received amplitude weighting function, with the first half function value being 1 and the second half function value being -1, is applied to the delayed signal of each array element after the Hilbert transform to obtain the beamforming output signal. Based on the beamforming output signal, determine the inversion coefficient that has a completely opposite trend to the beamforming output signal; The constructed M pairs of complementary received amplitude weighting functions with a function value of 1 are applied to the delayed signals of each array element after the Hilbert transform to obtain M beamforming signal groups. Calculate the similarity between each beamforming signal group and generate M normalized cross-correlation coefficients; M is a positive integer greater than 1; The M normalized cross-correlation coefficients are subjected to a second transformation process to obtain the cross-correlation coefficients after median filtering; the second transformation process includes thresholding, averaging, and median filtering. The inverted coefficients are fused with the cross-correlation coefficients after median filtering to obtain a new weighting matrix; The new weighting matrix is ​​used to weight the output signal of the delayed superimposed beamforming to obtain the final beamforming output signal.

2. The amplitude-weighted ultrasound imaging method according to claim 1, characterized in that, The expression for the beamforming output signal is: Where, x n (k) represents the delayed echo signal corresponding to the nth array element, where k is the sampling time, and y SUB (k) represents the beamforming output signal at the k-th sampling time of a scanline signal, Apod_SUB n The value of the received amplitude weighting function corresponding to the nth array element is the sum of the first half of the function value being 1 and the second half being -1. N is a positive integer greater than 1.

3. The amplitude-weighted ultrasound imaging method according to claim 1, characterized in that, The expression for the inversion coefficient is: Among them, w SUB (k,h) are the inverse coefficients, ρ' SUB (k,h) represents the beamforming output signal after thresholding. ρ SUB (k,h) is the beamforming output signal after the absolute value of the beamforming signal is normalized at the k-th sampling time of the h-th column scan line signal in the imaging region.

4. The amplitude-weighted ultrasound imaging method according to claim 1, characterized in that, The expression for the beamforming signal group is: Among them, BF1 r (k) represents the first adaptive beam signal at the k-th sampling time of a scan line signal, BF2 r (k) represents the second adaptive beam signal at the k-th sampling time of a scanline signal, x n (k) represents the delayed echo signal corresponding to the nth array element, where k is the sampling time. For the first sub-amplitude weighting function, For the second sub-amplitude weighting function, r = 1, 2...M, where N is a positive integer greater than 1.

5. The amplitude-weighted ultrasound imaging method according to claim 1, characterized in that, The expression for the normalized cross-correlation coefficient is: Where, ρ r (k,h) represents the normalized cross-correlation coefficient, k is the sampling time, A is the sample length taken upwards or downwards at the k-th sampling time, and BF1 r (p,h) represents the first adaptive beam signal at the p-th sampling time of the h-th column scan line signal in the imaging region, BF2 r (p,h) represents the second adaptive beam signal at the p-th sampling time of the h-th scan line signal in the imaging region.

6. The amplitude-weighted ultrasound imaging method according to claim 1, characterized in that, The expression for the new weighting matrix is: Among them, w LCH (k,h) is the new weighting matrix, w SUB (k,h) are the inverse coefficients. This represents the cross-correlation coefficient after median filtering.

7. The amplitude-weighted ultrasound imaging method according to claim 1, characterized in that, The expression for the final beamforming output signal is: y LCH (k,h)=w LCH (k,h)·y DAS (k,h); Among them, y LCH (k,h) represents the final beamforming output signal, y DAS (k,h) represents the beamforming signal at the k-th sampling time of the h-th scan line signal in the imaging region, y DAS (k,h) represents y DAS (k) in two-dimensional representation, y DAS (k) is the beamforming signal at the k-th sampling time of a scan line signal. Apod_DAS n The value of the received amplitude weighting function corresponding to the nth array element, Apod_DAS n = 1, 1≤n≤N, where N is a positive integer greater than 1, x n (k) represents the delayed echo signal corresponding to the nth array element, k is the sampling time, and w LCH (k,h) is the new weighting matrix.

8. A computer device, comprising: The memory and processor contain a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the amplitude-weighted ultrasound imaging method according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the amplitude-weighted ultrasound imaging method according to any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the amplitude-weighted ultrasound imaging method according to any one of claims 1-7.

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