Ultrasonic plane wave inverse problem imaging optimization method and system and ultrasonic imaging device

Abstract

The invention provides an ultrasonic plane wave inverse problem imaging optimization method and system and an ultrasonic imaging device, and belongs to the technical field of ultrasonic imaging, and the method comprises the steps: constructing a space coordinate system of an image in ultrasonic plane wave imaging; constructing a sampling matrix according to the space coordinate system of the image and the parameters of the transmitted ultrasonic plane waves, and weighting in combination with the directivity of the transducer element and the geometric information of the image to keep sparsity; based on the weighted sampling matrix, constructing a low-rank sparse optimization model of an ultrasonic plane wave inverse problem; and carrying out beam forming updating, nuclear norm updating and sparse item and Lagrange item updating on the low-rank sparse optimization model, and carrying out envelope detection, logarithm compression and gray mapping to obtain a final optimized ultrasonic image. According to the method, the advantage of high frame rate of plane wave imaging is fully utilized, spot textures are reserved, the imaging speed is high, meanwhile, the resolution ratio and the contrast ratio of the ultrasonic image are improved, and the imaging quality is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ultrasonic imaging, in particular to an ultrasonic plane wave inverse problem imaging optimization method and system and an ultrasonic imaging device. BACKGROUND

[0002] Plane wave ultrasonic imaging has become a popular ultrafast ultrasonic imaging method because of its high imaging frame rate. Plane waves can cover the entire region of interest through a single transmission. The plane wave imaging frame rate can reach 5000 frames per second. However, this non-focused wave imaging mode can result in low signal-to-noise ratio of echo signals, and thus poor contrast of the image. To solve this problem, plane wave coherent compounding technology is proposed. The image quality is improved, but this method can increase the amount of calculation due to the compounding of multiple angle plane waves. With the increase of the compounding angle, the imaging frame rate is reduced, so that the advantage of high frame rate of non-focused wave imaging cannot be fully exerted.

[0003] The formulation of inverse problems based on ultrasonic beamforming has attracted extensive research interest in the past few years. Existing ultrasonic beamforming methods based on inverse problems usually use a Gaussian (2-norm) or Laplacian (1-norm) model as a regularization function to improve the sparsity of the solution. Although the latter works well in reconstructing high-resolution and high-contrast ultrasonic images, the sparse solution performs poorly in preserving scattering textures, which are important features for applications such as motion. SUMMARY

[0004] The present application aims to provide an ultrasonic plane wave inverse problem imaging optimization method and system and an ultrasonic imaging device to solve at least one of the technical problems in the background.

[0005] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0006] In a first aspect, the present application provides an ultrasonic plane wave inverse problem imaging optimization method, comprising:

[0007] Constructing a spatial coordinate system of an image in ultrasonic plane wave imaging;

[0008] Constructing a sampling matrix according to the spatial coordinate system of the image and the parameters of the transmitted ultrasonic plane wave, and combining the directivity of the transducer element and the geometric information of the image for weighting to maintain sparsity;

[0009] Based on the weighted sampling matrix, constructing a low-rank sparse optimization model of the ultrasonic plane wave inverse problem;

[0010] Updating the low-rank sparse optimization model through beamforming, kernel norm, and sparse term and Lagrange term, and obtaining the final optimized ultrasonic image through envelope detection, logarithmic compression, and gray scale mapping.

[0011] As a further limitation of the first aspect of the application, the number N of lateral coordinates of the spatial coordinate system of the image x = 3 x L, where L is the number of elements required to transmit the ultrasonic plane wave; the number N of longitudinal coordinates of the spatial coordinate system of the image y = 2 x B x f c / f s , where B is the number of received echo signals along the longitudinal axis of the transmitted ultrasonic plane wave, f c is the center frequency of the transmitted ultrasonic plane wave, and f s is the sampling frequency of the transmitted ultrasonic plane wave system.

[0012] As a further limitation of the first aspect of the application, each row of the sampling matrix represents the contribution of each pixel of the image to the collected pre-beam data samples, wherein the elements of the sampling matrix are weighted according to the difference between the propagation delay and the reception time of the pixels, ensuring the sparsity of the matrix.

[0013] As a further limitation of the first aspect of the application, the constructed low-rank sparse optimization model of the ultrasonic plane wave inverse problem is:

[0014]

[0015] where x represents the vectorized representation of the ultrasonic plane wave image X to be recovered, b is the observed echo signal data vector, X is the ultrasonic plane wave image to be recovered, λ is the weight of the low-rank term, μ is the weight of the sparse term, ||·||1 S is the sparse term, and ||·||* L is the low-rank term.

[0016] As a further limitation of the first aspect of the application, the sparse term of the low-rank sparse optimization model is optimized using the 0-norm, 1-norm or p-norm; the low-rank term of the low-rank sparse optimization model is optimized using the kernel norm of the matrix.

[0017] In a second aspect, the application provides an ultrasonic plane wave inverse problem imaging optimization system, comprising:

[0018] a first construction unit for constructing a spatial coordinate system of an image in ultrasonic plane wave imaging;

[0019] a second construction unit for constructing a sampling matrix according to the spatial coordinate system of the image and the parameters of the transmitted ultrasonic plane wave, and combining the directivity of the transducer elements and the geometric information of the image to perform weighting and maintain sparsity;

[0020] a third construction unit for constructing a low-rank sparse optimization model of the ultrasonic plane wave inverse problem based on the weighted sampling matrix;

[0021] The solving unit is used for updating the low-rank sparse optimization model through beam forming, kernel norm updating, and sparse term and Lagrange term updating, and obtaining the final optimized ultrasonic image through envelope detection, logarithmic compression and gray mapping.

[0022] In a third aspect, the present application provides an ultrasonic plane wave inverse problem imaging device, comprising:

[0023] The transmitting module is configured to transmit a plane wave to a target object during a plane wave imaging process.

[0024] The receiving module is configured to receive an echo signal of the transmitted plane wave.

[0025] The optimization module is configured to optimize the received echo signal by using the method of the first aspect to obtain an optimized ultrasonic image.

[0026] The display module is configured to display the optimized ultrasonic image.

[0027] In a third aspect, the present application provides a non-transitory computer readable storage medium for storing computer instructions, which, when executed by a processor, implement the ultrasonic plane wave inverse problem imaging optimization method of the first aspect.

[0028] In a fourth aspect, the present application provides a computer device comprising a memory and a processor, the processor and the memory being in communication with each other, the memory storing program instructions executable by the processor, and the processor invoking the program instructions to execute the ultrasonic plane wave inverse problem imaging optimization method of the first aspect.

[0029] In a fifth aspect, the present application provides an electronic device comprising a processor, a memory and a computer program, wherein the processor is connected with the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to make the electronic device execute instructions for implementing the ultrasonic plane wave inverse problem imaging optimization method of the first aspect.

[0030] The present application has the advantages of making full use of the high frame rate advantage of plane wave imaging, retaining spot texture, improving the resolution and contrast of ultrasonic images, and improving the imaging quality while improving the imaging speed.

[0031] The advantages of the additional aspects of the present application will be more apparent from the following description section or be understood through the practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without any creative effort.

[0033] Figure 1 The flow chart of the ultrasonic plane wave inverse problem imaging optimization method described in the embodiments of the present application.

[0034] Figure 2 The contribution degree diagram of the image pixel to the single sample of the pre-beamforming data described in the embodiments of the present application.

[0035] Figure 3 The image reconstructed by using different methods on different data sets of point scatterers and echo-free cysts using one plane wave with a steering angle of 0° described in the embodiments of the present application. DETAILED DESCRIPTION

[0036] The embodiments of the present application will be described in detail below, and the examples of the embodiments are shown in the drawings, wherein the same or similar notations represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with the drawings are exemplary and are only used to explain the present application, and cannot be interpreted as a limitation to the present application.

[0037] Those skilled in the art can understand that, unless otherwise defined, all the terms (including technical terms and scientific terms) used herein have the same meaning as that generally understood by those skilled in the art in the field of the present application.

[0038] It should also be understood that the terms such as those defined in the general dictionary should be understood as having the same meaning as in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless defined as such.

[0039] Those skilled in the art can understand that, unless otherwise stated, the singular forms "a", "an" and "the" used herein also include the plural forms. It should be further understood that the phrase "comprising" used in the specification of the present application means that the features, integers, steps, operations, elements and / or groups exist, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements and / or groups thereof.

[0040] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. The person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples, without contradiction.

[0041] In order to facilitate the understanding of the present application, the present application will be further explained and described in specific embodiments in combination with the accompanying drawings, and the specific embodiments do not constitute a limitation on the embodiments of the present application.

[0042] The person skilled in the art should understand that the drawings are only schematic diagrams of the embodiments, and the components in the drawings are not necessarily necessary for the implementation of the present application.

[0043] The present application provides an ultrasonic plane wave inverse problem imaging optimization method, system and ultrasonic imaging device, which is used to solve the problem that the existing ultrasonic plane wave inverse problem imaging method performs poorly in preserving image speckle texture. The method comprises the following steps: first, constructing a spatial coordinate system of the image in ultrasonic plane wave imaging. Second, constructing a sampling matrix Φ according to the spatial coordinate system and the transmission parameters. Third, constructing an ultrasonic plane wave inverse problem optimization model based on a low-rank sparse model, which includes a sparse term and a low-rank term. The sparse term can be optimized by 0-norm, 1-norm or p-norm, and the low-rank term can be optimized by matrix kernel norm. Then, the multiplication factor alternating direction method (ADMM) is used to solve the optimization model. The ADMM method includes three main steps of beamforming update, kernel norm update and sparsity and Lagrange multiplier update, and finally a high-quality ultrasonic plane wave image is obtained. The method significantly improves the accuracy and efficiency of ultrasonic plane wave imaging through the low-rank sparse model and the ADMM algorithm, and has a wide application prospect.

[0044] Embodiment 1

[0045] In this embodiment 1, first provide an ultrasonic plane wave inverse problem imaging optimization system, comprising: a first construction unit for constructing the spatial coordinate system of the image in ultrasonic plane wave imaging; a second construction unit for constructing a sampling matrix according to the spatial coordinate system of the image and the parameters of the transmitted ultrasonic plane wave, and combining the directivity of the transducer element and the geometric information of the image for weighting, maintaining sparsity; a third construction unit for constructing a low-rank sparse optimization model of the ultrasonic plane wave inverse problem based on the weighted sampling matrix; a solving unit for updating the low-rank sparse optimization model through beamforming, kernel norm, and sparse term and Lagrange term, and obtaining the final optimized ultrasonic image through envelope detection, logarithmic compression, and gray scale mapping.

[0046] In this embodiment 1, the above system can be used to realize an ultrasonic plane wave inverse problem imaging optimization method, which comprises: constructing the spatial coordinate system of the image in ultrasonic plane wave imaging by the first construction unit; then constructing a sampling matrix according to the spatial coordinate system of the image and the parameters of the transmitted ultrasonic plane wave by the second construction unit, and combining the directivity of the transducer element and the geometric information of the image for weighting, maintaining sparsity; constructing a low-rank sparse optimization model of the ultrasonic plane wave inverse problem based on the weighted sampling matrix by the third construction unit; finally, updating the low-rank sparse optimization model through beamforming, kernel norm, and sparse term and Lagrange term by the solving unit, and obtaining the final optimized ultrasonic image through envelope detection, logarithmic compression, and gray scale mapping.

[0047] The number of horizontal coordinates of the spatial coordinate system N x = 3 x L, where L is the number of array elements required for transmitting ultrasonic plane waves; the number of vertical coordinates of the image spatial coordinate system N y = 2 x B x f c / f s , where B is the number of received echo signals along the vertical axis of the transmitted ultrasonic plane wave, f c is the center frequency of the transmitted ultrasonic plane wave, and f s is the sampling frequency of the transmitted ultrasonic plane wave system.

[0048] The constructed sampling matrix Φ is established by calculating the contribution of each pixel to the echo data, and is weighted in combination with the directivity of the transducer element and the geometric setting of the image. Specifically, each row of the matrix Φ represents the contribution of each pixel in the image to the collected pre-beam data samples, wherein the matrix elements are weighted according to the difference between the propagation delay and the reception time of the pixel, ensuring the sparsity of the matrix. In this method, the calculation of the matrix Φ can be pre-calculated in advance and adjusted according to the imaging setting to improve the imaging efficiency and reduce the computational overhead.

[0049] The low-rank sparse optimization model of the inverse problem of the finally constructed ultrasonic plane wave is as follows:

[0050]

[0051] wherein x represents the vectorized representation of the ultrasonic plane wave image X to be recovered, b is the observed echo signal data vector, X is the ultrasonic plane wave image to be recovered, λ is the weight of the low-rank term, μ is the weight of the sparse term, ||·||0 S is the sparse term, and ||·||* L is the low-rank term.

[0052] The ultrasonic plane wave image obtained by solving the above optimization model is solved by using a multiplier alternating direction algorithm (ADMM). The ADMM method includes the following three main steps: beamforming update, kernel norm update, and sparsity and Lagrange multiplier update. The sparse term can be optimized by using 0 norm, 1 norm or p norm; and the low-rank term can be optimized by using the kernel norm of a matrix.

[0053] Embodiment 2

[0054] As shown in Figure 1 , the embodiment 1 provides an inverse problem image optimization method based on a low-rank sparse model. In this embodiment, a spatial coordinate system of ultrasonic plane wave imaging is constructed and a sampling matrix is constructed, the inverse problem of ultrasonic plane wave is optimized based on a low-rank sparse model, and finally an optimized image is obtained by solving.

[0055] Specifically, the following steps are included:

[0056] (1) Constructing the spatial coordinate system of the image in the ultrasonic plane wave imaging, first defining that L piezoelectric elements of the ultrasonic probe emit sound waves into the medium, and N receiving elements receive the backscattered waves. According to the imaging technology and the probe type, the process is repeated multiple times. Assuming that the transducer spacing is p, the backscattered signals are recorded at a certain sampling frequency (f s ). The beamforming grid is decomposed into a certain number of pixels on the axis, and the pixel size in the wave propagation direction and the transverse direction (z, x) is dz=c / 2*fs and dx=p respectively. Assuming that the sound speed in the medium is constant, denoted as c, the mth actual time of signal recording is t=(m-1) / fs, where m={1, 2…M}. The total flight time τ from each spatial beamforming position (x, z) to each transducer element n can be expressed as: τ=τ0+τt+τr, wherein v0 is the Rx offset after the transmission event. The transmission delay τt is calculated as: The reception delay τr is calculated as: θ kis the steering angle of the plane wave. The transmission delay τt is the same for different receiving elements, but is different for different grid locations of the region of interest (x, z) and steering angles θ k The receiving delay τr is different for element n and location (x, z), but is the same across angles θ k .

[0057] (2) In the receiving process, only when the sum of the propagation time of the transmission wave τt and the returned propagation wave τr reaching the pixel is the same, a single output sample will be generated. In order to consider the digitization error, the pixels meeting the following condition will contribute to the output of the element: Each sample of the RF channel data can be linearly modeled as a combination of image pixel values, the forward model is: y = Φx + v, where y, x ∈ R MN are the vectorized versions of the pre-beamformed data and the desired image respectively, Φ ∈ R MN *MN represents the weighting matrix, v is the electronic noise affecting the original data, which has been proved to be approximated by additive white high noise.

[0058] Each row of the matrix Φ represents the contribution of an image pixel to a single sample of the pre-formed data. Specifically, the pixels are weighted using the following equation:

[0059]

[0060] where t max is the actual time (t i ) corresponding to the sample of the element output and the propagation delay (τ j ) of the pixel contributing to the following. The matrix Φ is highly sparse, because only a small part of the pixels meet In addition, Φ is independent of the data and can be pre-computed based on the known imaging setup. Finally, the matrix Φ is multiplied with the receive apodization matrix commonly used in DAS beamforming, where the directivity of the transducer elements is considered and the f-number is fixed for the entire image.

[0061] (3) According to the forward model, an optimization model of the ultrasonic plane wave inverse problem based on the low-rank sparse model is constructed: where x is the vectorized representation of the ultrasonic plane wave image to be recovered, b is the observed RF channel data vector, X is the ultrasonic plane wave image to be recovered, λ is the weight of the low-rank term, μ is the weight of the sparse term, s is the sparse term, and L is the low-rank term. The ADMM method is used to find the solution to the equation, and its convergence has been proved for this optimization problem.

[0062] (4) The ultrasonic plane wave image is obtained by solving the above optimization model by different methods.

[0063] 1 -norm solution: This method uses the ADMM framework to solve the ultrasound low-rank sparse beamforming problem. The goal is to minimize the following optimization problem:

[0064]

[0065] s.t. X = R(x), x = u

[0066] where X and x represent the image and its sparse representation, respectively, Φ is the sampling matrix, and μ and λ are regularization parameters.

[0067] By using its augmented Lagrangian function, the equation is reformulated as follows:

[0068]

[0069] Step 1: Beamforming update: Update x by minimizing the 2-norm problem, which can be solved using the BFGS method:

[0070] Step 2: Core norm update: Update X, X is a matrix completion problem, which can be solved by singular value thresholding: where S is the soft thresholding operation on singular values.

[0071] Step 3: Sparsity and Lagrange multiplier update:

[0072] Update u, The 1 -norm optimization problem updates u by the soft thresholding method:

[0073] Update Lagrange multipliers Λ1 and Λ2: Λ1 = Λ1 + β1(X - R(x)), Λ2 = Λ2 + β2(x - u)

[0074] 0 -norm solution: Unlike the 1 -norm, the 0 -norm solution uses the non-convex and non-smooth 0 -norm to measure the sparsity of the image. By introducing an auxiliary variable v, the optimization problem can be represented as:

[0075] s.t. X = R(x), x = u, v ⊙ |u| = 0

[0076] where v is an auxiliary variable of the same size as u, and ⊙ represents the Hadamard product.

[0077] Step 1: Beamforming update: Update x and X, which is the same as the update method in the 1 -norm solution, by solving the 2-norm problem to update x and X.

[0078] Step 2: Core norm update: Update v,

[0079] Step 3 Sparsity and Lagrange multiplier update: update u, Update Lagrange multipliers Λ1, Λ2, Λ3, Λ1 = Λ1 + β1(X-R(x)), Λ2 = Λ2 + β2(x-u), Λ3 = Λ3 + β3(v⊙|u|).

[0080] (5) The final result of the inverse problem is obtained by envelope detection, logarithmic compression, and gray scale mapping to obtain the final ultrasound image. Figure 2 The reconstruction results for several different data sets, where SR (simulated resolution): the image contains horizontally and vertically distributed point targets, and the background is designed to be anechoic, aiming to evaluate spatial resolution. SC (simulated contrast): the image contains horizontally and vertically distributed anechoic cysts, and the background is fully developed speckle, aiming to evaluate contrast. ER (experimental resolution): the image is collected from a CIRS Phantom (model 040GSE), containing a wire target region in a speckle background, for evaluating spatial resolution. EC (experimental contrast): this image is also collected from the same Phantom, containing an anechoic cyst region in a speckle background, for evaluating contrast. In this embodiment, the Kolmogorov-Smirnov (KS) test designed by the PICMUS organizers is used to verify whether the reconstructed image retains the speckle texture. In a region of the image, a significance level of α = 0:05 is considered to determine whether to retain the scattering statistics.

[0081] Figure 3 It can be seen that the image noise of the inverse problem imaging method using only the sparse model of the 1-norm is large, and the image quality of the inverse problem method using the low-rank sparse model of the kernel norm and the 1-norm or the 0-norm is high and better preserves the speckle texture of the image. Table 1 shows the quantitative comparison results of different beamforming methods on simulated data sets (SR, ER) and experimental data sets (SC, EC).

[0082] Table 1

[0083]

[0084] The method combining the kernel norm with the 1-norm achieved the best axial (FWHM_A = 0.24) and lateral resolution (FWHM_L = 0.42) in the ER dataset, indicating its superior spatial resolution in the simulation environment. In contrast, the method combining the kernel norm with the 0-norm showed the highest contrast noise ratio (CNR = 13.51, 10.05) and generalized contrast noise ratio (gCNR = 0.91, 0.84) in the experimental datasets SC and EC, demonstrating its superior contrast performance in real-world scenarios. Notably, both kernel norm-based methods passed the KS test on all datasets, while the method using the 1-norm alone failed to pass. This result indicates that the introduction of the kernel norm regularization can significantly improve the resolution and contrast of the image, and the model combining the 0-norm is more advantageous in experimental data.

[0085] Embodiment 3

[0086] In this embodiment 3, an ultrasonic plane wave inverse problem imaging device is provided, which comprises: a transmitting module for transmitting a plane wave to a target object during plane wave imaging. A receiving module for receiving the echo signal of the transmitted plane wave. An optimization module for optimizing the received echo signal using the image optimization method as described in embodiment 1 or embodiment 2 to obtain an optimized ultrasonic image; a display module for displaying the optimized ultrasonic image.

[0087] Embodiment 4

[0088] This embodiment 4 provides a non-transitory computer readable storage medium for storing computer instructions, which when executed by a processor, implements an ultrasonic plane wave inverse problem imaging optimization method, the method comprising:

[0089] Constructing a spatial coordinate system of the image in ultrasonic plane wave imaging;

[0090] Constructing a sampling matrix according to the spatial coordinate system of the image and the parameters of the transmitted ultrasonic plane wave, and combining the directivity of the transducer element and the geometric information of the image for weighting to maintain sparsity;

[0091] Based on the weighted sampling matrix, a low-rank sparse optimization model of the ultrasonic plane wave inverse problem is constructed;

[0092] The low-rank sparse optimization model is updated by beamforming, kernel norm, and sparse term and Lagrange term, and through envelope detection, logarithmic compression, and gray scale mapping, an ultimately optimized ultrasonic image is obtained.

[0093] Embodiment 5

[0094] The embodiment 5 provides a computer device, comprising a memory and a processor, the processor and the memory are in communication with each other, the memory stores program instructions which can be executed by the processor, and the processor calls the program instructions to execute an ultrasonic plane wave inverse problem imaging optimization method, the method comprises the following steps:

[0095] constructing a spatial coordinate system of an image in ultrasonic plane wave imaging;

[0096] constructing a sampling matrix according to the spatial coordinate system of the image and parameters of the transmitted ultrasonic plane wave, and performing weighting in combination with directivity of a transducer element and geometric information of the image to maintain sparsity;

[0097] constructing a low-rank sparse optimization model of the ultrasonic plane wave inverse problem based on the weighted sampling matrix;

[0098] updating the low-rank sparse optimization model through beam forming, kernel norm updating and sparse term and Lagrange term updating, and obtaining a final optimized ultrasonic image through envelope detection, logarithmic compression and gray scale mapping.

[0099] Embodiment 6

[0100] The embodiment 6 provides an electronic device, comprising a processor, a memory and a computer program; wherein the processor is connected with the memory, and the computer program is stored in the memory; when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes instructions for realizing an ultrasonic plane wave inverse problem imaging optimization method, the method comprising the following steps:

[0101] constructing a spatial coordinate system of an image in ultrasonic plane wave imaging;

[0102] constructing a sampling matrix according to the spatial coordinate system of the image and parameters of the transmitted ultrasonic plane wave, and performing weighting in combination with directivity of a transducer element and geometric information of the image to maintain sparsity;

[0103] constructing a low-rank sparse optimization model of the ultrasonic plane wave inverse problem based on the weighted sampling matrix;

[0104] updating the low-rank sparse optimization model through beam forming, kernel norm updating and sparse term and Lagrange term updating, and obtaining a final optimized ultrasonic image through envelope detection, logarithmic compression and gray scale mapping.

[0105] Those skilled in the art will appreciate that embodiments of the present application can be readily used as software, hardware, or a combination of software and hardware. In a software embodiment, the methods can be tangibly embodied in a machine-readable storage medium having stored thereon instructions that can be used to program a computer to perform any of the methods. The software implementation can be initialized by loading and executing a set of instructions arranged to perform one of the methods into the computer's memory. Alternatively, hard-wired circuitry can be used in place of, or in combination with, software instructions. Thus, the

[0106] The present application is described in reference to the flowchart and / or block diagrams of the method, apparatus (system) and computer program product according to embodiments of the present application. It will be understood that each block of the flowchart and / or block diagrams, and combinations of blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in one or more of the flowchart and / or block diagram block or blocks. Figure 1 means for performing the function specified by the flowchart and / or block diagram block or blocks.

[0107] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in one or more of the flowchart and / or block diagram block or blocks. Figure 1 means for performing the function specified by the flowchart and / or block diagram block or blocks.

[0108] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the function specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in one or more of the flowchart and / or block diagram block or blocks. Figure 1 means for performing the function specified by the flowchart and / or block diagram block or blocks.

[0109] The above description is implemented with reference to the specific embodiments of the present application, but is not intended to limit the scope of protection of the present application. Those skilled in the art should understand that various modifications or variations can be made to the technical solutions disclosed in the present application without creative labor, and all should be covered within the scope of protection of the present application.

Claims

1. An ultrasonic plane wave inverse problem imaging optimization method, characterized in that: include: Constructing the spatial coordinate system of images in ultrasonic plane wave imaging; The sampling matrix is ​​constructed based on the spatial coordinate system of the image and the parameters of the transmitted ultrasonic plane wave, and is weighted by combining the directionality of the transducer elements and the geometric information of the image to maintain sparsity; Based on the weighted sampling matrix, a low-rank sparse optimization model for the inverse problem of ultrasonic plane waves is constructed; The low-rank sparse optimization model is updated through beamforming, nuclear norm, sparse terms and Lagrangian terms, and the final optimized ultrasound image is obtained through envelope detection, logarithmic compression and grayscale mapping.

2. The ultrasonic plane wave inverse problem imaging optimization method according to claim 1, characterized in that: The number of horizontal coordinates N of the spatial coordinate system x =3×L, where L is the number of array elements required to transmit ultrasonic plane waves; the number of ordinates in the image space coordinate system N y =2×B×f c / f s , where B is the number of echo signals received along the vertical axis by transmitting ultrasonic plane waves, f c is the center frequency of the transmitted ultrasonic plane wave, f s is the sampling frequency of the transmitting ultrasonic plane wave system.

3. The ultrasonic plane wave inverse problem imaging optimization method according to claim 1, characterized in that: Each row of the sampling matrix represents the contribution of each pixel in the image to the collected pre-beam data sample, where the elements of the sampling matrix are weighted according to the difference between the propagation delay and the reception time of the pixel to ensure the sparsity of the matrix.

4. The ultrasonic plane wave inverse problem imaging optimization method according to claim 1, characterized in that: The constructed low-rank sparse optimization model for the ultrasonic plane wave inverse problem is: Where x represents the vectorized representation of the ultrasonic plane wave image X to be restored, b is the observed echo signal data vector, X is the ultrasonic plane wave image to be restored, λ is the weight of the low-rank term, μ is the weight of the sparse term, and ||·|| S is a sparse term, ||·|| L is a low-rank term.

5. The ultrasonic plane wave inverse problem imaging optimization method according to claim 1, characterized in that: The sparse terms of the low-rank sparse optimization model are optimized using the 0-norm, 1-norm or p-norm; the low-rank terms of the low-rank sparse optimization model are optimized using the nuclear norm of the matrix.

6. An ultrasonic plane wave inverse problem imaging optimization system, characterized in that: include: A first construction unit is used to construct a spatial coordinate system of an image in ultrasonic plane wave imaging; a second construction unit, configured to construct a sampling matrix according to the spatial coordinate system of the image and the parameters of the transmitted ultrasonic plane wave, and weighting the matrix based on the directionality of the transducer elements and the geometric information of the image to maintain sparsity; The third construction unit is used to construct a low-rank sparse optimization model for the ultrasonic plane wave inverse problem based on the weighted sampling matrix; The solving unit is used to update the low-rank sparse optimization model through beamforming, nuclear norm, sparse terms and Lagrangian terms, and obtain the final optimized ultrasound image through envelope detection, logarithmic compression and grayscale mapping.

7. An ultrasonic plane wave inverse problem imaging device, characterized in that: include: A transmitting module, used for transmitting a plane wave toward a target object during plane wave imaging; A receiving module, used for receiving an echo signal of the transmitted plane wave; an optimization module, configured to optimize the received echo signal using the method according to any one of claims 1 to 5 to obtain an optimized ultrasound image; The display module is used to display the obtained ultrasound image.

8. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the ultrasonic plane wave inverse problem imaging optimization method according to any one of claims 1 to 5 is implemented.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the ultrasonic plane wave inverse problem imaging optimization method according to any one of claims 1 to 5.

10. An electronic device, characterized in that: include: A processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to execute instructions for implementing the ultrasonic plane wave inverse problem imaging optimization method according to any one of claims 1 to 5.

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