A method, device, storage medium and electronic device for estimating the speed of sound

Abstract

This specification discloses a method, apparatus, storage medium, and electronic device for estimating the speed of sound. The echo signals corresponding to each element in a linear array ultrasonic probe are obtained. At each preset imaging grid point, beam synthesis of the echo signals corresponding to multiple elements is performed for each candidate speed of sound included in a preset speed-of-sound sequence. For each depth of the grid points in each layer in the axial direction, the average speed-of-sound value corresponding to the depth of the grid points in this layer is screened out from the candidate speeds of sound according to the signal coherence. The average speed-of-sound value is converted into an average slowness value to obtain information on the variation of the average slowness with depth. The average slowness is converted into an expression of local slowness, and the local slowness is a weighted combination of several step functions. By solving the combination coefficients of the weighted combination, the potential speed-of-sound mutation nodes in the depth direction are identified, and information on the variation of the speed of sound with depth in a stratified medium is obtained, accurately determining the local speed of sound at each depth.

Description

Technical Field

[0001] This specification relates to the technical field of ultrasonic positioning, and particularly to a method and device for estimating sound speed, a storage medium, and an electronic device. Background Art

[0002] Medical ultrasound imaging is an indispensable imaging method in clinical diagnosis, but its imaging quality has long relied on the operator's experience judgment and qualitative analysis. Since different tissue types may present similar echo characteristics, this property leads to subjective differences in image interpretation and is prone to the risk of missed diagnosis and misdiagnosis. To improve the specificity of ultrasound imaging, the academic community has proposed introducing measurement techniques for tissue quantitative parameters (such as sound speed, elastic modulus, etc.). Among them, sound speed imaging technology has shown significant clinical value because it can directly reflect tissue pathological characteristics (such as breast cancer differentiation, fatty liver staging) and can correct the propagation distortion effect of traditional ultrasound.

[0003] Currently, the commonly used sound speed imaging technology is the pulse echo sound speed estimation method, which is mainly based on two principles: one is to iteratively optimize the sound speed parameters through B-mode ultrasound image quality indicators (such as speckle size, brightness), and the other is to estimate the average sound speed (ASS) using the maximum spatial coherence criterion, including the local spatial coherence maximization method and the short-lag spatial coherence technique, etc. Although the existing pulse echo sound speed estimation methods can obtain sound speed estimation values, they only reflect the average propagation speed of ultrasonic waves from the probe to the imaging area and cannot accurately characterize the local sound speed (LSS) distribution of tissues, resulting in errors in ultrasound imaging.

[0004] Based on this, this specification provides a method for estimating sound speed. Summary of the Invention

[0005] This specification provides a method and device for estimating sound speed, a storage medium, and an electronic device to at least partially solve the above problems existing in the prior art.

[0006] This specification adopts the following technical solutions:

[0007] This specification provides a method for estimating sound speed, including:

[0008] Obtain the echo signals corresponding to each element in a linear array ultrasonic probe, where the echo signals are received by all elements;

[0009] At each preset imaging grid point, perform beamforming on the echo signals corresponding to multiple elements for each candidate sound speed included in the preset sound speed sequence, and calculate the signal coherence at the same imaging grid point for different transmission times according to the beamforming result;

[0010] For the depth of each grid point in the axial direction, the average sound velocity value corresponding to the depth of this grid point is screened out from each candidate sound velocity according to the signal coherence;

[0011] Convert the average sound velocity value corresponding to the depth of each grid point into an average slowness value to obtain the information of the change of average slowness with depth;

[0012] Convert the average slowness into the expression of local slowness, and the local slowness is a weighted combination of several step functions;

[0013] Determine the coefficient sequence of the weighted combination through the average slowness value of each grid point depth in the axial direction, and obtain the axial distribution information of the local sound velocity according to the obtained axial distribution information of the local slowness.

[0014] Optionally, at each preset imaging grid point, perform beamforming on the echo signals corresponding to multiple array elements for each candidate sound velocity included in the preset sound velocity sequence, and calculate the signal coherence at the same imaging grid point at different emission times, specifically including:

[0015] At each preset imaging grid point, for each candidate sound velocity included in the preset sound velocity sequence, calculate the beam domain image corresponding to this array element under this candidate sound velocity according to the echo signal corresponding to each array element;

[0016] According to the ratio of the coherent superposition result of the beam domain images corresponding to each array element and the incoherent superposition result of the beam domain images corresponding to each array element under this candidate sound velocity, determine the coherence factor between the echo signals corresponding to each array element under this candidate sound velocity, and the coherence factor is a transverse-axial-sound velocity three-dimensional coherence matrix.

[0017] Optionally, for the depth of each grid point in the axial direction, the average sound velocity value corresponding to the depth of this grid point is screened out from each candidate sound velocity according to the signal coherence, specifically including:

[0018] Average the transverse-axial-sound velocity three-dimensional coherence matrix in the axial direction to obtain a depth-sound velocity two-dimensional coherence matrix;

[0019] For the depth of each grid point in the axial direction, based on the candidate sound velocity corresponding to the maximum coherence factor in the corresponding depth of the depth-sound velocity two-dimensional coherence matrix, determine the average sound velocity value corresponding to the depth of this grid point.

[0020] Optionally, convert the average sound velocity value corresponding to the depth of each grid point into an average slowness value to obtain the information of the change of average slowness with depth, specifically including:

[0021] Respectively convert the average sound velocity values corresponding to the depths of each grid point into average slowness values to obtain the initial slowness values corresponding to the depths of each grid point;

[0022] For each depth of the grid points in the axial direction in sequence, based on the initial slowness value corresponding to the depth of the grid points in the previous layer, correct the initial slowness value corresponding to the grid points in this layer to obtain the average slowness value corresponding to the grid points in this layer;

[0023] Based on the average slowness values corresponding to the depths of each layer of grid points, obtain the information on the variation of the average slowness with depth.

[0024] Optionally, for each depth of the grid points in the axial direction in sequence, based on the initial slowness value corresponding to the depth of the grid points in the previous layer, correct the initial slowness value corresponding to the grid points in this layer to obtain the average slowness value corresponding to the grid points in this layer, specifically including:

[0025] For each depth of the grid points in the axial direction in sequence, estimate the upper limit of the average slowness at the depth of the grid points in this layer according to the initial slowness value corresponding to the depth of the grid points in the previous layer, the depth value of the grid points in the previous layer, the thickness value of the grid points in this layer, the preset lower limit of the sound velocity, and the depth value of the grid points in this layer; estimate the lower limit of the average slowness at the depth of the grid points in this layer according to the initial slowness value corresponding to the depth of the grid points in the previous layer, the depth value of the grid points in the previous layer, the thickness value of the grid points in this layer, the preset upper limit of the sound velocity, and the depth value of the grid points in this layer;

[0026] Use the upper limit of the average slowness and the lower limit of the average slowness as constraint conditions to correct the average slowness value corresponding to the depth of the grid points in this layer.

[0027] Optionally, convert the average sound velocity values corresponding to the depths of each layer of grid points into average slowness values to obtain the information on the variation of the average slowness with depth, specifically including:

[0028] Convert the average sound velocity values corresponding to the depths of each layer of grid points into average slowness values;

[0029] Denoise the average sound velocity values corresponding to the depths of each layer of grid points by using a preset regularization matrix and adopting third-order difference regularization;

[0030] According to the denoising result, obtain the information on the variation of the average slowness with depth.

[0031] Optionally, convert the average slowness into the expression of local slowness, specifically including:

[0032] For each depth of the grid points in the axial direction in sequence, represent the average slowness value corresponding to the depth of the grid points in this layer as a weighted combination of the local slowness values corresponding to the depths of the grid points in the previous layer.

[0033] This specification provides a sound velocity estimation device, and the device includes:

[0034] A signal acquisition module that acquires the echo signal corresponding to each element in a linear array ultrasonic probe, and the echo signal is received by all elements;

[0035] A beamforming module that performs beamforming on the echo signals corresponding to multiple array elements for each candidate sound speed included in a preset sound speed sequence at each preset imaging grid point, and calculates the signal coherence at the same imaging grid point at different transmission times according to the beamforming result;

[0036] An average sound speed determination module that screens out the average sound speed value corresponding to the depth of each layer of grid points in the axial direction from the candidate sound speeds according to the signal coherence;

[0037] An average slowness determination module that converts the average sound speed values corresponding to the depths of each layer of grid points into average slowness values to obtain the information on the variation of average slowness with depth;

[0038] A local slowness representation module that converts the average slowness into an expression of local slowness, where the local slowness is a weighted combination of several step functions;

[0039] A solving module that determines the coefficient sequence of the weighted combination through the average slowness values at the depths of each layer of grid points, and obtains the axial distribution information of the local sound speed according to the obtained axial distribution information of the local slowness.

[0040] This specification provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the above-mentioned sound speed estimation method is implemented.

[0041] This specification provides an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the above-mentioned sound speed estimation method is implemented.

[0042] At least one of the above technical solutions adopted in this specification can achieve the following beneficial effects:

[0043] In the sound speed estimation method provided in this specification, by determining the information on the variation of average sound speed with depth, and representing the local slowness as a weighted combination of several step functions, potential sound speed mutation nodes in the depth direction are identified. By solving the combination coefficients of the weighted combination, the information on the variation of local slowness with depth is obtained, and the local slowness is converted into local sound speed, that is, the information on the variation of sound speed with depth in a layered medium is obtained. Applying this method can accurately determine the local sound speed at each depth, and thus obtain more accurate ultrasonic imaging. Description of the Drawings

[0044] The drawings described herein are used to provide a further understanding of this specification, and constitute a part of this specification. The schematic embodiments of this specification and their descriptions are used to explain this specification and do not constitute an improper limitation to this specification. In the drawings:

[0045] Figure 1 It is a schematic flow diagram of a sound velocity estimation method in this specification;

[0046] Figure 2 It is the specific implementation process of a sound velocity estimation method in this specification;

[0047] Figure 3 It is a schematic diagram of the sound field used in the simulation in this specification;

[0048] Figure 4 It is a distribution diagram of the average slowness varying with depth obtained from the simulation in this specification;

[0049] Figure 5 It is a distribution diagram of the local sound velocity varying with depth obtained from the simulation in this specification;

[0050] Figure 6 It is a schematic diagram of a sound velocity estimation device in this specification;

[0051] Figure 7 It is provided in this specification corresponding to Figure 1 the schematic diagram of the electronic device. Specific implementation manners

[0052] To make the objectives, technical solutions and advantages of this specification clearer, the technical solutions of this specification will be clearly and completely described below in conjunction with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on the embodiments in this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0053] Existing sound velocity imaging technologies are mainly divided into two systems: ultrasound computed tomography (USCT or UCT) and pulse echo ultrasound imaging. USCT inversely calculates the sound velocity distribution based on the propagation time of multi-angle ultrasonic waves, and a circumferential probe array is required to collect full-angle waveform data. Although this technology can achieve high-precision reconstruction, its complex hardware architecture and strict acquisition conditions limit the popularization of clinical applications.

[0054] In contrast, the pulse echo ultrasound sound velocity estimation technology is more clinically adaptable, but faces the core challenge of missing position information of echo signals. Although such methods can obtain sound velocity estimation values, they only reflect the average propagation speed of ultrasonic waves from the probe to the imaging area and cannot accurately characterize the local sound velocity (LSS) distribution of tissues.

[0055] To obtain local sound speed parameters and construct an LSS inversion model, a tomographic sound speed imaging method is introduced into the sound speed imaging of pulse echo. A typical method is to establish a sound speed tomography model through local echo phase information; or design an average compensation framework to achieve layered sound speed estimation through the mathematical conversion from ASS to LSS. However, its iterative inversion process is vulnerable to noise interference, resulting in insufficient stability. In recent years, although the end-to-end sound speed estimation method based on deep learning has made progress, its dependence on simulation data training makes its clinical generalization ability questionable. In specific clinical application scenarios (such as liver steatosis assessment), these existing technologies expose two key defects: 1) The stability of sound speed estimation is sensitive to probe configuration parameters (such as emission frequency, number of array elements). When hardware conditions are limited, the accuracy drops significantly; 2) Traditional tomographic inversion methods ignore the piecewise continuity characteristics of tissue sound speed, resulting in non-physical oscillations in the sound speed distribution in the depth direction.

[0056] This has prompted the urgent need in this field to develop an adaptive sound speed imaging method that incorporates prior constraints, which can improve the clinical applicability and measurement repeatability of LSS estimation while maintaining the simplicity of the pulse echo system.

[0057] The following will, in conjunction with the accompanying drawings, elaborate on the technical solutions provided by each embodiment of this specification.

[0058] Figure 1 It is a schematic flow diagram of a sound speed estimation method in this specification, specifically including the following steps:

[0059] S100: Obtain the echo signals corresponding to each array element in the linear array ultrasonic probe, and the echo signals are received by all array elements.

[0060] This specification does not limit the acquisition method of the linear array ultrasonic probe signals. For example, a synthetic aperture scanning mode of transmitting by each array element and receiving by all array elements, a mode of synchronously transmitting by all array elements and receiving by all array elements, etc. can be adopted.

[0061] The reception time of the echo signals of the linear array ultrasonic probe is related to the signal propagation speed. Therefore, the echo signals corresponding to each array element are the basic information for subsequent estimation of the sound speed of the ultrasonic signals.

[0062] Taking the synthetic aperture scanning mode as an example, each array element serves as an independent emission source in turn to emit broadband ultrasonic pulses, and at the same time, all array elements synchronously receive the reflected echo signals.

[0063] For each array element included in the ultrasonic probe, taking this array element as the transmitting array element, controlling this transmitting array element to emit an ultrasonic pulse signal. After the ultrasonic pulse signal is emitted, the ultrasonic array switches to the signal reception mode, and all array elements receive the echo pulse signals. This echo pulse signal is a three-dimensional data volume including the emission position, reception position, and reception time delay. At the During the first acquisition, the th array element emits an ultrasonic pulse, and the echo pulse signal received by the th array element is denoted as , where represents the reception time delay of the signal.

[0064] S102: At each preset imaging grid point, for each candidate sound speed included in the preset sound speed sequence, beamforming of the echo signals corresponding to multiple array elements is performed, and the signal coherence at the same imaging grid point at different transmission times is calculated according to the beamforming result.

[0065] In ultrasonic imaging, the accuracy of the sound speed has an important impact on the imaging quality. In this method, a preset sound speed range is set in advance according to the characteristics of the medium to be imaged, such as human tissues and structures. Then, according to the preset sound speed interval, multiple candidate sound speeds included in the preset sound speed range are determined to obtain a sound speed sequence. For example, it can be within the preset sound speed range , with as the sound speed interval, divided between the preset minimum sound speed and the preset maximum sound speed to obtain a sound speed sequence composed of multiple candidate sound speeds.

[0066] Because this method needs to determine the variation information of the sound speed along the depth in the medium to be imaged, each candidate sound speed is used to match the accurate sound speed of each depth layer of the imaging medium.

[0067] At each preset imaging grid point, for each candidate sound speed included in the preset sound speed sequence, beamforming of the echo signals corresponding to multiple array elements is performed to obtain a beam domain image of the ultrasonic signal.

[0068] Specifically, before beamforming, an imaging grid needs to be set in advance, and this imaging grid can be set according to the specifications of the ultrasonic probe. For example, with the center of the ultrasonic probe as the reference, the probe width is used as the lateral width of the imaging grid, and the axial range of the ultrasonic probe is the imaging depth of the imaging grid. The grid size can be set according to requirements. The smaller the grid size, the higher the imaging resolution. For example, the grid size can be set to one-fourth of the wavelength of the ultrasonic beam.

[0069] For each candidate sound speed, according to the echo pulse signal collected from the ultrasonic pulse signal emitted by the mth array element, a beam domain image corresponding to the mth array element under this candidate sound speed is generated using a beamforming algorithm.

[0070] This specification does not limit the specific beamforming algorithm to be used. For example, Delay and Sum Beamforming (DAS), Frequency Domain Beamforming (FDB), Linearly Constrained Minimum Variance (LCMV), etc.

[0071] Taking the DAS algorithm as an example, at the candidate sound speed the beam domain image corresponding to the m-th transmitting element of the ultrasonic probe can be determined by the following formula:

[0072]

[0073]

[0074] where and represent the lateral and axial coordinates of the grid point, and represent the lateral and axial coordinates of the -th element, is the geometric propagation time delay, representing the ultrasonic pulse signal emitted by the m-th transmitting element, propagating to the imaging grid ), and then continuing to propagate and being received by the n-th element.

[0075] Finally, a full aperture data set can be generated. This full aperture data set is a four-dimensional tensor, and M is the total number of elements included in the ultrasonic probe. The original echo pulse signal collected is obtained as this full aperture data set after time gain compensation, providing complete wave propagation information for subsequent processing.

[0076] Then, based on the beam domain images corresponding to each element at this candidate sound speed, the coherence factor between the echo signals corresponding to each element at this candidate sound speed can be calculated. This coherence factor is a three-dimensional coherence matrix of lateral - axial - sound speed, used to reflect the coherence between the echo signals corresponding to each element, that is, between the transmitting channels.

[0077] Specifically, according to the ratio of the coherent superposition result and the incoherent superposition result of the beam domain images corresponding to each element at this candidate sound speed, the coherence factor between the echo signals corresponding to each element at this candidate sound speed is determined.

[0078] For each candidate sound speed , calculate the beam domain images corresponding to each element at this candidate sound speed ​The coherence factor between them, where represents the horizontal and vertical coordinates of the grid points of the preset imaging grid, and the coherence factor is the three-dimensional coherence matrix of transverse-axial-sound speed.

[0079] Specifically, when the sound speed is , calculate the corresponding coherence factor (coherence factor, CF) according to the following formula:

[0080]

[0081] In the above formula, the numerator is the coherent superposition result of the beam domain images of each array element, that is, the power of the coherent superposition of the signals between the transmitting channels, and the denominator is the incoherent superposition result of the beam domain images of each array element, that is, the sum of the independent powers of each transmitting channel.

[0082] S104: For each layer of grid point depth in the axial direction, screen out the average sound speed value corresponding to the depth of this layer of grid points from the candidate sound speeds according to the signal coherence.

[0083] Because the obtained coherence factor is the three-dimensional coherence matrix of transverse-axial-sound speed, in order to eliminate the influence of the transverse structure on the sound speed and determine the variation information of the sound speed with depth under the layered medium, perform a transverse average on the three-dimensional coherence matrix of transverse-axial-sound speed of the coherence factor according to the total number of grid points in the transverse direction of the preset imaging grid to obtain the two-dimensional coherence matrix of depth-sound speed.

[0084] In the two-dimensional coherence matrix of depth-sound speed, the closer the value of CF is to 1, the higher the phase consistency of the signals of each transmitting channel, and the corresponding sound speed is closer to the true sound speed.

[0085] Therefore, for each layer of grid point depth in the axial direction, based on the candidate sound speed corresponding to the maximum coherence factor in the corresponding depth of the two-dimensional coherence matrix of depth-sound speed, determine the average sound speed value corresponding to the depth of this layer of grid points. Based on the average sound speed values corresponding to the depths of each layer of grid points, obtain the variation information of the average sound speed with depth.

[0086] Specifically, along the axial grid depth direction, search layer by layer for the candidate sound speed value corresponding to the maximum coherence factor to determine the estimated sound speed corresponding to each layer of grid depth. That is, for each grid depth , traverse all candidate sound speeds , and select the candidate sound speed that makes the largest .

[0087] This process can be expressed by the following formula:

[0088]

[0089] Among them, is the estimated sound speed corresponding to the j-th grid depth, and the estimated sound speed is the average sound speed from the top of the grid to the j-th grid depth.

[0090] Further, in one embodiment, after obtaining the depth-sound speed two-dimensional coherence matrix, in order to improve the accuracy of the estimated sound speed corresponding to each layer of grid depth determined, bilinear interpolation can be performed on the sound speed based on the depth-sound speed two-dimensional coherence matrix to improve the sound speed resolution. For example, if the sound speed interval in S100 is initially set to 2 m / s, after interpolation it can be increased to 0.1 m / s.

[0091] S106: Convert the average sound speed values corresponding to the depths of each layer of grid points into average slowness values to obtain the information of the change of average slowness with depth.

[0092] For the convenience of subsequent cumulative processing of the sound speed in the time dimension, starting from this step, the sound speed is converted into slowness for subsequent operations. Then, the information of the change of average sound speed with depth obtained above is also correspondingly converted into the information of the change of average slowness with depth. Slowness is the reciprocal of speed, with the unit of s / m. Slowness is linearly related to time, which is convenient for subsequent construction of a linear model for solution.

[0093] This step can be expressed by the following formula:

[0094]

[0095] where is the average slowness corresponding to the j-th layer of grid depth.

[0096] S108: Convert the average slowness into an expression of local slowness, and the local slowness is a weighted combination of several step functions.

[0097] Based on the stratified propagation model, establish a sound speed inversion framework, and realize the accurate solution of ASS through sparse constraint optimization. First, discretize the axial detection area into a multi-layer structure, and establish a linear mapping model according to the transfer relationship between LSS and ASS.

[0098] For each layer of grid point depth in the axial direction in turn, represent the average slowness value corresponding to the depth of this layer of grid points as a weighted combination of the local slowness values corresponding to the depths of the previous layers of grid points.

[0099] Specifically, according to the following formula, represent the average slowness of the n-th layer of grid point depth as the weighted sum of the local slownesses corresponding to the depths of the previous n layers of grid points:

[0100]

[0101] where represents the average slowness vector, represents the local slowness vector, is the transformation matrix, , represents the depth of the n-th layer grid.

[0102] It can be seen that the transformation matrix is a lower triangular matrix. For the element in the n-th row and k-th column of , its value can be expressed as follows:

[0103]

[0104] Through this transformation matrix T, the average slowness of the n-th layer is expressed as a weighted sum of the local slownesses of the first n layers, is the layer thickness of the n-th layer grid depth , so the weight corresponding to the local slowness of each layer is the proportion of the layer thickness .

[0105] S108: Determine the coefficient sequence of the weighted combination through the average slowness value of each layer grid point depth, and obtain the axial distribution information of the local sound speed according to the obtained axial distribution information of the local slowness.

[0106] Based on the piecewise constant characteristic of the sound speed distribution of the medical tissue, construct a sparse representation space composed of step function bases, and represent the local sound speed as a weighted combination of a finite number of step changes.

[0107] The local slowness corresponding to the depth of the n-th layer grid can be expressed by the following formula:

[0108]

[0109]

[0110] where is the piecewise constant basis function, is the threshold. This threshold can be set as needed, for example, it can be set to two wavelengths.

[0111] Based on the conversion relationship between the local slowness of each grid depth and the piecewise constant basis function, a sparse representation model can be obtained, and its matrix form is .

[0112] where, the element in the matrix , its meaning represented is whether the depth of the j-th layer grid exceeds the threshold of the i-th basis function. The coefficient matrix represents the sparse coefficient of the local sound speed distribution, elements Most of them are close to 0, which can represent the amplitude and position information of the segmented change of the sound speed with depth.

[0113] Up to this step, for the local sound speed at each grid depth, it is the quantity to be solved. By solving the coefficient matrix , the coefficient sequence of the weighted combination of step functions can be obtained, and the axial distribution information of the piecewise constant local sound speed that conforms to the characteristics of biological tissues can be inversely calculated.

[0114] In this specification, the coefficient matrix can be solved by the least squares method. The specific solution method is as follows:

[0115] First, construct the least squares optimization function: .

[0116] Second, solve the above optimization function, and the coefficient matrix can be obtained. Any existing least squares solution method can be used for solving. In this specification, the fast iterative shrinkage-thresholding algorithm (FISTA) is used as an example to illustrate the solution steps.

[0117] The solution steps are as follows. Initialize .

[0118] Then, iteratively update ( ):

[0119]

[0120]

[0121]

[0122] Among them, is the soft threshold operation with a threshold of , which is used to sparsify the coefficients. is the iteration step size. is the acceleration factor, which is used to accelerate convergence.

[0123] Iterate K times to obtain the solution result of the coefficient sequence.

[0124] After obtaining the coefficient sequence , according to , the local slowness matrix can be obtained. By converting the local slowness matrix into local sound speed, the axial distribution information of the local sound speed can be obtained.

[0125] According toFigure 1 For the described method, first, a multi - sound - velocity beam synthesis system is constructed. Coherence factor feature maps at different candidate sound velocities are generated through a beam synthesis algorithm. The average slowness is extracted based on the maximum coherence criterion. The local slowness is expressed as a linear combination of a finite number of step functions. A sparse - constraint inversion framework is constructed through the compressive sensing theory, and the sound - velocity boundary location is transformed into an optimization problem with a regularization term. The FISTA algorithm is used to efficiently solve the layered interface.

[0126] The standardized full - process design and deterministic algorithm architecture ensure a high degree of consistency in measurement results. The full - aperture data acquisition scheme suppresses random errors through the spatial synthesis effect of multi - channel signals, eliminates the differences in manual parameter adjustment with fixed parameter settings, reduces the fluctuation range of repeated test results, and meets the stringent requirements of clinical diagnosis for repeatability.

[0127] In the above - mentioned step S102, based on the prior sound - velocity constraint mechanism, the effective sound - velocity range of the current layer can also be dynamically adjusted according to the upper - layer sound - velocity estimation result, and a hard - threshold truncation strategy is adopted to eliminate abnormal estimated values in the estimated average sound velocity.

[0128] First, the average sound - velocity values corresponding to the depths of grid points in each layer are respectively converted into average slowness values to obtain the initial slowness values corresponding to the depths of grid points in each layer.

[0129] That is, according to the method in the above - mentioned step S102, the candidate sound velocity corresponding to each layer of grid depth determined based on the maximum coherence factor is used as the initial sound - velocity value to be corrected corresponding to each layer of grid depth. Then, according to the reciprocal of the initial sound - velocity value, the initial sound - velocity value is converted into an initial slowness value.

[0130] Then, for each layer of grid - point depth in the axial direction in turn, according to the initial slowness value corresponding to the grid - point depth of the upper layer, the initial slowness value corresponding to the grid points of this layer is corrected to obtain the average slowness value corresponding to the grid points of this layer.

[0131] Specifically, for each layer of grid - point depth in the axial direction in turn, according to the initial slowness value corresponding to the grid - point depth of the upper layer, the grid - point depth value of the upper layer, the thickness value of the grid points of this layer, the preset lower - limit value of the sound velocity, and the grid - point depth value of this layer, the upper - limit value of the average slowness of the grid - point depth of this layer is estimated; according to the initial slowness value corresponding to the grid - point depth of the upper layer, the grid - point depth value of the upper layer, the thickness value of the grid points of this layer, the preset upper - limit value of the sound velocity, and the grid - point depth value of this layer, the lower - limit value of the average slowness of the grid - point depth of this layer is estimated. The upper - limit value of the average slowness and the lower - limit value of the average slowness are used as constraint conditions to correct the average slowness value corresponding to the grid - point depth of this layer.

[0132] For the nth layer grid along the depth direction of the preset imaging grid, the initial slowness corresponding to the depth of the nth layer grid is corrected according to the initial slowness corresponding to the depth of the (n - 1)th layer grid, and the average slowness corresponding to the depth of the nth layer grid is obtained.

[0133] The following is the recurrence relation of the initial slowness with depth:

[0134] Among them, represents the initial slowness corresponding to the depth of the nth layer grid, represents the initial slowness corresponding to the depth of the (n - 1)th layer grid, represents the depth of the nth layer grid, represents the depth of the (n - 1)th layer grid, represents the layer thickness of the nth layer grid, .

[0135] The numerator in the recurrence relation is represents the total slowness integral of the depths of the first (n - 1) layer grids, and the sum with represents the slowness integral of the current nth layer. The denominator is the total depth of the nth layer grid , and this recurrence formula can represent the overall average slowness from the surface to the current grid depth layer .

[0136] The local slowness at the depth of the nth grid is the quantity to be estimated. In this embodiment, according to prior knowledge, a sound speed constraint is introduced to restrict the local slowness. Ensure that the local sound speed falls within the range , that is, the local slowness satisfies .

[0137] It can be deduced that for the depth of the nth grid, when , the sound speed is the slowest, and the upper limit value of the average slowness is obtained, that is, the maximum allowable average slowness . When , the sound speed is the fastest, and the lower limit value of the average slowness is obtained, the minimum allowable average slowness .

[0138] Based on this, the upper limit value and the lower limit value of the average slowness can be used as constraint conditions to construct the hard upper and lower bounds of the average slowness of this grid depth layer. According to the following formula:

[0139]

[0140] For the initial slowness of the nth layer grid depth Perform correction to obtain the average slowness corresponding to the grid depth of the nth layer .

[0141] It can be seen from the hard constraints of the upper and lower bounds that this embodiment is a layer-by-layer dynamic correction process. The initial slowness is corrected for each layer of grid depth, depending on the average slowness obtained after correcting the initial slowness of the previous layer, so that the average slowness of the current layer is within a reasonable range and outliers are suppressed.

[0142] Through this embodiment, a prior sound speed constraint mechanism and a dynamic compensation strategy are introduced, effectively overcoming the technical bottleneck that the traditional method is sensitive to hardware parameters. The hard threshold truncation design limits the sound speed estimation values at each depth within a physically reasonable range, blocking the longitudinal transmission path of errors; the third-order derivative regularization process ensures the continuity of the sound speed curve by suppressing non-smooth fluctuations. Under extreme conditions such as low-frequency probes or missing data in some channels, this method can still maintain a stable sound speed output, showing stronger environmental adaptability compared with the traditional coherent factor method.

[0143] By iteratively correcting the systematic offset of the average sound speed curve, the estimation accuracy of the shallow layer is significantly improved. The sparse regularization model based on tissue characteristics accurately captures the sound speed mutation interface and shows better interlayer resolution ability in multi-layer medium scenarios.

[0144] In another embodiment, in step S104, the third-order derivative regularization method can also be used to smooth the sound speed curve and suppress noise perturbations.

[0145] Specifically, through a preset regularization matrix , third-order difference regularization is used to denoise the average slowness corresponding to the grid depth of the nth layer. According to the denoising result, the information of the average slowness varying with depth is obtained.

[0146] For the average slowness obtained at the grid depth of the nth layer, third-order difference regularization is used to suppress noise perturbations. The regularization process is specifically expressed as follows:

[0147]

[0148] Among them, is the smoothness parameter, is the regularization matrix, The specific form of is as follows:

[0149]

[0150] What is obtained in this embodiment is the final estimated average slowness-depth curve after prior constraint compensation.

[0151] Next, in combination with Figure 2The process of the illustrated embodiment uses specific ultrasonic echo experimental data to illustrate the specific implementation of the present invention. The embodiment of the present invention uses a simulation method to implement the estimation process of the layered sound velocity. Specifically, this method is implemented by reconstructing the sound velocity from the measurement data of the two-dimensional acoustic simulation of the numerical phantom. In the simulation, a linear array ultrasonic transducer is used, which includes 80 uniformly arranged ultrasonic transmitting and receiving array elements. The center frequency of each element is 2.7 MHz, the width is 0.206 mm, the element spacing is 0.254 mm, and the fractional bandwidth of the element frequency is 74%. These transmitting and receiving elements sequentially emit acoustic pulses and scan the numerical phantom in turn. The entire data acquisition process includes 80 emission steps, with each element emitting individually, and all the other transducers being responsible for recording the acoustic wave field signals.

[0152] The sound velocity in the simulation domain is shown in the appendix Figure 3 , Figure 3 and is the schematic diagram of the sound domain used in the simulation in this specification. Figure 3 The left and right in it respectively represent two simulation sound domains. z represents the axial direction, x represents the transverse direction. On the left are 2 layers of media (1500 m / s and 1540 m / s from top to bottom), and on the right are 3 layers of media (1500 m / s, 1580 m / s, and 1540 m / s from top to bottom). Different media are marked with different shaped backgrounds, and the medium density is 1000 kg / m 3 . In order to simulate random speckles, Gaussian white noise with a standard deviation of 3 m / s is added to the density. The spatial and time grid settings in the simulation are shown in Table 1.

[0153] Table 1 Simulation parameters

[0154]

[0155] Above, the simulation settings are completed.

[0156] Execute Step 1: In the simulation environment, in the sequential emission mode, use the ultrasonic probe to sequentially emit ultrasonic pulses and collect the acoustic pressure signals to obtain the RF signals of the echoes. Repeat the collection 10 times to evaluate the repeatability of the method.

[0157] Execute Step 2: Preset the imaging grid before beamforming. The transverse width is 25 mm, and the axial depth is 5 mm - 95 mm. Generate a candidate sound velocity sequence based on a preset sound velocity range of 1460 m / s - 1620 m / s (step size 2 m / s). Perform DAS beamforming on each candidate value, calculate the signal coherence between the emission channels, and construct a three-dimensional coherence matrix of transverse - axial - sound velocity. Generate a depth - sound velocity two-dimensional coherence feature map by axial stratification and transverse averaging, combine the upper layer sound velocity estimation to dynamically constrain the effective sound velocity range of the current layer, and use a hard threshold to truncate the outliers. Further, use third-order derivative regularization to iteratively smooth the sound velocity curve, and the regularization parameter is 1e7. The average slowness distribution is obtained as shown in the appendix Figure 4 , Figure 4 is the distribution diagram of the average slowness varying with depth obtained by simulation in this specification. The solid line represents the true value of the sound velocity, and each dashed line represents the experimental result of one time. Figures (a) and (b) respectively correspond to Figure 3 the two media in

[0158] Step three is executed: constructing a sparse representation space composed of step function bases, with an interval of two wavelengths, and the regularization parameter is set to 0.01. The final output axial LSS distribution curve is as shown in the appendix Figure 5 , Figure 5 is the distribution diagram of the local sound velocity varying with depth obtained by simulation in this specification. The solid line represents the true value of the sound velocity, and the dashed line represents the estimated result of the local sound velocity varying with depth. Figures (a) and (b) respectively correspond to Figure 3 the two media in Figure 5 It can be seen that the present invention can accurately estimate the variation curve of LSS along the depth, and this information can provide the lesion conditions in the body for medical diagnosis through quantitative evaluation.

[0159] The above is the sound velocity estimation method provided by this specification. Based on the same idea, this specification also provides a corresponding sound velocity estimation device, as shown in Figure 6 .

[0160] Figure 6 is a schematic diagram of a sound velocity estimation device provided by this specification, specifically including:

[0161] A signal acquisition module 200, configured to acquire echo signals corresponding to each element in a linear array ultrasonic probe, and the echo signals are received by all elements;

[0162] A beam synthesis module 202, configured to perform beam synthesis on the echo signals corresponding to multiple elements for each candidate sound velocity included in a preset sound velocity sequence at each preset imaging grid point, and calculate the signal coherence at the same imaging grid point at different transmission times according to the beam synthesis result;

[0163] An average sound velocity determination module 204, configured to screen out the average sound velocity value corresponding to the depth of each layer of grid points in the axial direction from each candidate sound velocity according to the signal coherence;

[0164] An average slowness determination module 206, configured to convert the average sound velocity values corresponding to the depths of each layer of grid points into average slowness values to obtain the information of the average slowness varying with depth;

[0165] A local slowness representation module 208, configured to convert the average slowness into an expression of local slowness, where the local slowness is a weighted combination of a plurality of step functions;

[0166] A solution module 210, configured to determine a coefficient sequence of the weighted combination based on the average slowness value at each grid point depth of each layer, and obtain the axial distribution information of the local sound speed according to the obtained axial distribution information of the local slowness.

[0167] Optionally, the average sound speed determination module 204 is specifically configured to, at each preset imaging grid point, for each candidate sound speed included in the preset sound speed sequence, calculate the beam domain image corresponding to each array element at the candidate sound speed according to the echo signal corresponding to each array element, and determine the coherence factor between the echo signals corresponding to each array element at the candidate sound speed according to the ratio of the coherent superposition result and the incoherent superposition result of the beam domain images corresponding to each array element at the candidate sound speed, where the coherence factor is a transverse-axial-sound speed three-dimensional coherence matrix.

[0168] Optionally, the average sound speed determination module 204 is specifically configured to perform averaging on the transverse-axial-sound speed three-dimensional coherence matrix in the axial direction to obtain a depth-sound speed two-dimensional coherence matrix, and determine the average sound speed value corresponding to the depth of each grid point layer based on the candidate sound speed corresponding to the maximum coherence factor in the corresponding depth of the depth-sound speed two-dimensional coherence matrix for each grid point depth in the axial direction.

[0169] Optionally, the average slowness determination module 206 is specifically configured to convert the average sound speed values corresponding to the depths of each grid point layer into average slowness values respectively to obtain the initial slowness values corresponding to the depths of each grid point layer, and sequentially for each grid point depth in the axial direction, correct the initial slowness value corresponding to the grid point layer according to the initial slowness value corresponding to the upper grid point depth to obtain the average slowness value corresponding to the grid point layer, and obtain the variation information of the average slowness with depth based on the average slowness values corresponding to the depths of each grid point layer.

[0170] Optionally, the average slowness determination module 206 is specifically configured to sequentially for each grid point depth in the axial direction, estimate the upper limit value of the average slowness of the grid point depth according to the initial slowness value corresponding to the upper grid point depth, the upper grid point depth value, the thickness value of the grid point layer, the preset lower limit value of the sound speed, and the grid point depth value, estimate the lower limit value of the average slowness of the grid point depth according to the initial slowness value corresponding to the upper grid point depth, the upper grid point depth value, the thickness value of the grid point layer, the preset upper limit value of the sound speed, and the grid point depth value, and use the upper limit value of the average slowness and the lower limit value of the average slowness as constraint conditions to correct the average slowness value corresponding to the grid point depth.

[0171] Optionally, the average slowness determination module 206 is specifically configured to convert the average sound velocity values corresponding to the depths of the grid points of each layer into average slowness values, perform denoising on the average sound velocity values corresponding to the depths of the grid points of each layer by using third-order difference regularization through a preset regularization matrix, and obtain the information on the variation of the average slowness with depth according to the denoising result.

[0172] Optionally, the local slowness representation module 208 is specifically configured to, for each layer of grid point depth in the axial direction in sequence, represent the average slowness value corresponding to the depth of this layer of grid points as a weighted combination of the local slowness values corresponding to the depths of the previous layers of grid points.

[0173] This specification also provides a computer-readable storage medium storing a computer program, which can be used to execute the above Figure 1 provided sound velocity estimation method.

[0174] This specification also provides Figure 7 a schematic structural diagram of the electronic device shown. At the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include other hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the above Figure 1 described sound velocity estimation method. Of course, in addition to the software implementation manner, this specification does not exclude other implementation manners, such as the manner of logic devices or the combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, and can also be hardware or logic devices.

[0175] Improvements to a technology can be clearly distinguished as either hardware improvements (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software improvements (improvements to method flows). However, with the development of technology, many method flow improvements today can be regarded as direct improvements to hardware circuit structures. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement to a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is an integrated circuit whose logical function is determined by the user programming the device. Designers can program themselves to "integrate" a digital system onto a single PLD without having to ask a chip manufacturer to design and fabricate a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly implemented using "logic compiler" software, which is similar to the software compiler used in program development and writing. The original code before compilation also has to be written in a specific programming language, which is called a hardware description language (HDL), and there is not just one type of HDL, but many types, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones currently are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also be aware that by simply performing a little logical programming on the method flow using the above-mentioned several hardware description languages and programming it into the integrated circuit, it is easy to obtain the hardware circuit that implements the logical method flow.

[0176] The controller can be implemented in any suitable manner. For example, the controller can take the form of, for example, a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of the controller include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in the form of pure computer-readable program code, it is entirely possible to make the controller implement the same function in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as either software modules for implementing the method or the structures within the hardware component.

[0177] The systems, devices, modules, or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0178] For the convenience of description, when describing the above devices, the functions are divided into various units for separate description. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0179] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0180] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and combinations of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce means for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0181] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0182] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0183] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0184] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash RAM. The memory is an example of computer-readable media.

[0185] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0186] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0187] It should be understood by those skilled in the art that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0188] This specification may 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. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0189] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key points of each embodiment are the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the description of the method embodiment.

[0190] The above description is only for the embodiments of this specification and is not intended to limit this specification. For those skilled in the art, various modifications and changes can be made to this specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this specification shall be included within the scope of the claims of this application.

Claims

1. A method for estimating the speed of sound, characterized in that, Including: Obtaining echo signals corresponding to each array element in a linear array ultrasonic probe, where the echo signals are received by all array elements; At each preset imaging grid point, for each candidate sound speed included in a preset sound speed sequence, performing beam synthesis on the echo signals corresponding to multiple array elements, and calculating the signal coherence at the same imaging grid point at different transmission times according to the beam synthesis result; For each layer of grid point depth in the axial direction, screening out the average sound speed value corresponding to the depth of this layer of grid points from each candidate sound speed according to the signal coherence; Converting the average sound speed values corresponding to each layer of grid point depth into average slowness values to obtain the information of the variation of average slowness with depth; Converting the average slowness into an expression of local slowness, where the local slowness is a weighted combination of several step functions; Determining the coefficient sequence of the weighted combination through the average slowness value of each layer of grid point depth, and obtaining the axial distribution information of local sound speed according to the obtained axial distribution information of local slowness.

2. The method according to claim 1, wherein, At each preset imaging grid point, for each candidate sound speed included in a preset sound speed sequence, performing beam synthesis on the echo signals corresponding to multiple array elements, and calculating the signal coherence at the same imaging grid point at different transmission times, specifically including: At each preset imaging grid point, for each candidate sound speed included in a preset sound speed sequence, calculating the beam domain image corresponding to this array element at this candidate sound speed according to the echo signal corresponding to each array element; Determining the coherence factor between the echo signals corresponding to each array element at this candidate sound speed according to the ratio of the coherent superposition result and the incoherent superposition result of the beam domain images corresponding to each array element at this candidate sound speed, where the coherence factor is a transverse-axial-sound speed three-dimensional coherence matrix; 3. The method according to claim 2, wherein For each layer of grid point depth in the axial direction, screening out the average sound speed value corresponding to the depth of this layer of grid points from each candidate sound speed according to the signal coherence, specifically including: Averaging the transverse-axial-sound speed three-dimensional coherence matrix in the axial direction to obtain a depth-sound speed two-dimensional coherence matrix; For each layer of grid point depth in the axial direction, based on the candidate sound speed corresponding to the maximum coherence factor in the corresponding depth of the depth-sound speed two-dimensional coherence matrix, determining the average sound speed value corresponding to the depth of this layer of grid points.

4. The method according to claim 1, wherein Converting the average sound speed values corresponding to each layer of grid point depth into average slowness values to obtain the information of the variation of average slowness with depth, specifically including: Respectively converting the average sound speed values corresponding to each layer of grid point depth into average slowness values to obtain the initial slowness values corresponding to each layer of grid point depth; Successively for each layer of grid point depth in the axial direction, correcting the initial slowness value corresponding to this layer of grid points according to the initial slowness value corresponding to the previous layer of grid point depth to obtain the average slowness value corresponding to this layer of grid points; Based on the average slowness values corresponding to each layer of grid point depth, obtaining the information of the variation of average slowness with depth.

5. The method according to claim 4, wherein Successively for each layer of grid point depth in the axial direction, correcting the initial slowness value corresponding to this layer of grid points according to the initial slowness value corresponding to the previous layer of grid point depth to obtain the average slowness value corresponding to this layer of grid points, specifically including: For each depth of the grid points in the axial direction in sequence, estimate the upper limit of the average slowness of the depth of the grid points of this layer according to the initial slowness value corresponding to the depth of the grid points in the previous layer, the depth value of the grid points in the previous layer, the thickness value of the grid points of this layer, the preset lower limit of the sound speed, and the depth value of the grid points of this layer; estimate the lower limit of the average slowness of the depth of the grid points of this layer according to the initial slowness value corresponding to the depth of the grid points in the previous layer, the depth value of the grid points in the previous layer, the thickness value of the grid points of this layer, the preset upper limit of the sound speed, and the depth value of the grid points of this layer. Take the upper limit of the average slowness and the lower limit of the average slowness as constraint conditions to correct the average slowness value corresponding to the depth of the grid points of this layer.

6. The method according to claim 1, wherein Convert the average sound speed value corresponding to the depth of each layer of grid points into an average slowness value to obtain the information of the change of the average slowness with depth, specifically including: Convert the average sound speed value corresponding to the depth of each layer of grid points into an average slowness value. Denoise the average sound speed value corresponding to the depth of each layer of grid points by using third-order difference regularization through a preset regularization matrix. Obtain the information of the change of the average slowness with depth according to the denoising result.

7. The method according to claim 1, wherein Convert the average slowness into the expression of the local slowness, specifically including: For each depth of the grid points in the axial direction in sequence, express the average slowness value corresponding to the depth of the grid points of this layer as a weighted combination of the local slowness values corresponding to the depths of the grid points in the previous layer.

8. An apparatus for estimating the speed of sound, characterized in that, Include: A signal acquisition module that acquires the echo signal corresponding to each element in the linear array ultrasonic probe, and the echo signal is received by all elements. A beamforming module that performs beamforming on the echo signals corresponding to multiple elements for each candidate sound speed included in the preset sound speed sequence at each preset imaging grid point, and calculates the signal coherence at the same imaging grid point at different transmission times according to the beamforming result. An average sound speed determination module that screens out the average sound speed value corresponding to the depth of the grid points of this layer from the candidate sound speeds according to the signal coherence for each depth of the grid points in the axial direction. An average slowness determination module that converts the average sound speed value corresponding to the depth of each layer of grid points into an average slowness value to obtain the information of the change of the average slowness with depth. A local slowness representation module that converts the average slowness into the expression of the local slowness, and the local slowness is a weighted combination of several step functions. A solution module that determines the coefficient sequence of the weighted combination according to the average slowness value of each depth of the grid points, and obtains the axial distribution information of the local sound speed according to the obtained axial distribution information of the local slowness.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of claims 1 to 7 above is implemented.

10. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, the method described in any one of claims 1 to 7 above is implemented.

Images