Optimization method, system and ultrasound imaging device for ultrasonic plane wave inverse problem imaging

Abstract

The application provides an ultrasonic plane wave inverse problem imaging optimization method and system and an ultrasonic imaging device, belongs to the technical field of ultrasonic imaging, and constructs a spatial coordinate system of an image in ultrasonic plane wave imaging; a sampling matrix is constructed according to the spatial coordinate system of the image and parameters of transmitted ultrasonic plane waves, and is weighted in combination with directivity of a transducer element and geometric information of the image to maintain sparsity; a low-rank sparse optimization model of the ultrasonic plane wave inverse problem is constructed based on the weighted sampling matrix; the low-rank sparse optimization model is updated through beam forming, kernel norm updating and sparse term and Lagrange term updating, and an ultimately optimized ultrasonic image is obtained through envelope detection, logarithmic compression and gray scale mapping. The application fully utilizes the advantage of high frame rate of plane wave imaging, retains spot texture, is fast in imaging speed, improves the resolution and contrast of the ultrasonic image, and improves the imaging quality.

Description

Technical Field

[0001] This invention relates to the field of ultrasound imaging technology, specifically to an ultrasound plane wave inverse problem imaging optimization method, system, and ultrasound imaging device. Background Technology

[0002] Plane wave ultrasound imaging has become a popular ultrafast ultrasound imaging method due to its high imaging frame rate, as a single plane wave emission can cover the entire region of interest. Plane wave imaging frame rates can reach 5000 frames per second. However, this non-focused wave imaging mode leads to a low signal-to-noise ratio in the echo signal, resulting in poor image contrast. To address this issue, plane wave coherent recombination technology has been proposed. While image quality is improved, this method increases computational complexity due to the recombination of plane waves at multiple angles. Furthermore, the imaging frame rate decreases as the recombination angle increases, thus failing to fully utilize the high frame rate advantage of non-focused wave imaging.

[0003] The formulation of the inverse problem of ultrasound beamforming has attracted extensive research interest in recent years. Existing inverse problem-based ultrasound beamforming methods typically use Gaussian (2-norm) or Laplacian (1-norm) models as regularization functions to improve the sparsity of the solution. While the latter works well in reconstructing high-resolution and high-contrast ultrasound images, sparse solutions perform poorly in preserving scattering texture, which is an important feature for applications such as motion. Summary of the Invention

[0004] The purpose of this invention is to provide an ultrasound plane wave inversion problem imaging optimization method, system, and ultrasound imaging device to solve at least one of the technical problems existing in the background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

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

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

[0008] A sampling matrix is ​​constructed based on the spatial coordinate system of the image and the parameters of the emitted ultrasonic plane wave, and weighted by combining the directionality of the transducer element and the geometric information of the image to maintain sparsity;

[0009] Based on the weighted sampling matrix, a low-rank sparse optimization model for the inverse problem of ultrasonic plane waves is constructed.

[0010] The low-rank sparse optimization model is updated through beamforming, nuclear norm, and sparse and Lagrange terms. Finally, the optimized ultrasound image is obtained through envelope detection, logarithmic compression, and grayscale mapping.

[0011] As a further limitation of the first aspect of the present invention, the number of abscissas N in the spatial coordinate system is... x =3×L, where L is the number of array elements required to emit ultrasonic plane waves; N is the number of vertical coordinates in the image spatial coordinate system. y =2×B×f c / f s Where B is the number of echo signals received along the vertical axis from the emitted ultrasonic plane wave, f c f is the center frequency for transmitting ultrasonic plane waves. s The sampling frequency for the ultrasonic plane wave transmission system.

[0012] As a further limitation of the first aspect of the present invention, each row of the sampling matrix represents the contribution of each pixel in the image to the acquired prebeam data sample, wherein the elements of the sampling matrix are weighted according to the difference between the propagation delay and the reception time of the pixels to ensure the sparsity of the matrix.

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

[0014]

[0015] in, Let represent the vectorized representation of the ultrasound plane wave image X to be recovered, and b be the observed echo signal data vector. The ultrasound plane wave image to be recovered. The weights of the lower-rank terms. The weights of the sparse terms, For sparse terms, It is a low-rank term.

[0016] As a further limitation of the first aspect of the present invention, 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 kernel norm of the matrix.

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

[0018] The first building unit is used to construct the spatial coordinate system of the image in ultrasonic plane wave imaging;

[0019] The second building unit is used to construct a sampling matrix based on the spatial coordinate system of the image and the parameters of the emitted ultrasonic plane wave, and to weight it by combining the directionality of the transducer element and the geometric information of the image to maintain sparsity.

[0020] The third building unit is used to construct a low-rank sparse optimization model for the inverse problem of ultrasonic plane waves based on the weighted sampling matrix.

[0021] The solution unit is used to update the low-rank sparse optimization model through beamforming, nuclear norm, and sparse and Lagrange terms, and obtain the final optimized ultrasound image through envelope detection, logarithmic compression, and grayscale mapping.

[0022] Thirdly, the present invention provides an ultrasonic plane wave inverse problem imaging device, comprising:

[0023] The transmitting module is used to transmit plane waves toward the target object during plane wave imaging.

[0024] The receiving module is used to receive the echo signal of the transmitted plane wave;

[0025] An optimization module is used to optimize the received echo signal using the method described in the first aspect to obtain an optimized ultrasound image;

[0026] The display module is used to display the optimized ultrasound images.

[0027] Thirdly, the present invention 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 as described in the first aspect.

[0028] Fourthly, the present invention provides a computer device including a memory and a processor, the processor and the memory communicating with each other, the memory storing program instructions executable by the processor, and the processor calling the program instructions to execute the ultrasonic plane wave inverse problem imaging optimization method as described in the first aspect.

[0029] Fifthly, the present invention provides an electronic device, comprising: 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 cause the electronic device to execute instructions for implementing the ultrasonic plane wave inverse problem imaging optimization method as described in the first aspect.

[0030] The beneficial effects of this invention are: it fully utilizes the high frame rate advantage of plane wave imaging, preserves speckle texture, and improves the resolution and contrast of ultrasound images while increasing imaging speed, thus improving imaging quality.

[0031] The advantages of additional aspects of the invention will be set forth more clearly in the following description or will be learned by practice of the invention. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart of the ultrasonic plane wave inverse problem imaging optimization method according to an embodiment of the present invention.

[0034] Figure 2 This is a graph showing the contribution of image pixels to a single sample of prebeamforming data, as described in an embodiment of the present invention.

[0035] Figure 3 This invention relates to the reconstruction of point scatterers and anechoic cysts using different methods on different datasets with a plane wave having a turning angle of 0°, as described in the embodiments of the present invention. Detailed Implementation

[0036] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0037] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0038] It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as here.

[0039] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, 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 this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0041] To facilitate understanding of the present invention, the present invention will be further explained and described below with reference to the accompanying drawings and specific embodiments. However, the specific embodiments do not constitute a limitation on the embodiments of the present invention.

[0042] Those skilled in the art should understand that the accompanying drawings are merely schematic diagrams of embodiments, and the components in the drawings are not necessarily essential for implementing the present invention.

[0043] This invention provides an optimization method, system, and ultrasound imaging device for ultrasound plane wave inverse problem imaging, addressing the problem of poor performance in preserving image speckle texture in existing ultrasound plane wave inverse problem imaging methods. The method includes the following steps: First, constructing the spatial coordinate system of the image in ultrasound plane wave imaging. Second, constructing a sampling matrix Φ based on the spatial coordinate system and emission parameters. Next, constructing an optimization model for the ultrasound plane wave inverse problem based on a low-rank sparse model. This model includes sparse terms and low-rank terms. The sparse terms can be optimized using the 0-norm, 1-norm, or p-norm, while the low-rank terms can be optimized using the matrix kernel norm. Then, solving the optimization model using the Alternating Direction Multiplier (ADMM) algorithm, which includes three main steps: beamforming update, kernel norm update, and sparsity and Lagrange multiplier update, ultimately yielding a high-quality ultrasound plane wave image. This method, through the low-rank sparse model and ADMM algorithm, significantly improves the accuracy and efficiency of ultrasound plane wave imaging and has broad application prospects.

[0044] Example 1

[0045] In this embodiment 1, an ultrasound plane wave inverse problem imaging optimization system is first provided, including: a first construction unit for constructing the spatial coordinate system of the image in ultrasound plane wave imaging; a second construction unit for constructing a sampling matrix based on the spatial coordinate system of the image and the parameters of the emitted ultrasound plane wave, and weighting it by combining the directionality of the transducer element and the geometric information of the image to maintain sparsity; a third construction unit for constructing a low-rank sparse optimization model of the ultrasound plane wave inverse problem based on the weighted sampling matrix; and a solution unit for updating the low-rank sparse optimization model through beamforming, kernel norm, and sparse and Lagrange terms, and obtaining the final optimized ultrasound image through envelope detection, logarithmic compression, and grayscale mapping.

[0046] In this embodiment 1, the above-described system can be used to realize an ultrasound plane wave inverse problem imaging optimization method. The method includes: using a first building unit to construct the spatial coordinate system of the image in ultrasound plane wave imaging; then using a second building unit to construct a sampling matrix based on the spatial coordinate system of the image and the parameters of the emitted ultrasound plane wave, and weighting it in combination with the directionality of the transducer element and the geometric information of the image to maintain sparsity; using a third building unit to construct a low-rank sparse optimization model of the ultrasound plane wave inverse problem based on the weighted sampling matrix; finally, using a solution unit to update the low-rank sparse optimization model through beamforming, kernel norm, and sparse and Lagrange terms, and obtaining the final optimized ultrasound image through envelope detection, logarithmic compression, and grayscale mapping.

[0047] Wherein, the number of abscissas in the spatial coordinate system is N x =3×L, where L is the number of array elements required to emit ultrasonic plane waves; N is the number of vertical coordinates in the image spatial coordinate system. y =2×B×f c / f s Where B is the number of echo signals received along the vertical axis from the emitted ultrasonic plane wave, f c f is the center frequency for transmitting ultrasonic plane waves. s The sampling frequency for the ultrasonic plane wave transmission system.

[0048] The constructed sampling matrix Φ is established by calculating the contribution of each pixel to the echo data and weighting it based on the directivity of the transducer elements and the geometric settings of the image. Specifically, each row of matrix Φ represents the contribution of each pixel in the image to the acquired pre-beam data sample, where the matrix elements are weighted according to the difference between the pixel's propagation delay and reception time, ensuring the sparsity of the matrix. In this method, the calculation of matrix Φ can be pre-calculated and adjusted according to the imaging settings to improve imaging efficiency and reduce computational overhead.

[0049] The final low-rank sparse optimization model for the inverse problem of ultrasonic plane waves is as follows:

[0050]

[0051] in, Let represent the vectorized representation of the ultrasound plane wave image X to be recovered, and b be the observed echo signal data vector. The ultrasound plane wave image to be recovered. The weights of the lower-rank terms. The weights of the sparse terms, For sparse terms, It is a low-rank term.

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

[0053] Example 2

[0054] like Figure 1 As shown in Embodiment 1, this invention provides an inverse problem image optimization method based on a low-rank sparse model. In this embodiment, a spatial coordinate system and sampling matrix for ultrasonic plane wave imaging are constructed, and the inverse problem of ultrasonic plane waves is optimized based on a low-rank sparse model, ultimately yielding the optimized image.

[0055] Specifically, the following steps are included:

[0056] (1) Constructing the spatial coordinate system of the image in ultrasonic plane wave imaging: First, define L piezoelectric elements of the ultrasonic probe to emit sound waves into the medium, and N receiving elements to receive the backscattered waves. This process is repeated multiple times depending on the imaging technique and probe type. Assuming the transducer spacing is p, and a specific sampling frequency (f... s The backscattered signal is recorded. The beamforming grid is decomposed into a certain number of pixels on the axis, and the pixel sizes in the wave propagation direction and the transverse direction (z, x) are dz = c / 2 × fs and dz = c / 2 × fs, respectively. Assuming the speed of sound in the medium is constant, denoted as c, and the m-th actual time of signal recording is... ,in ={1, 2, ... The total flight time τ from each spatial beamforming position (x, z) to each transducer element n can be expressed as: Where 0 is the Rx offset after the event is sent. Transmission delay. The calculation is as follows: Receive delay The calculation is as follows: , It is the turning angle of the plane wave. Propagation delay. The same applies to different receiving elements, but different grid positions (x, z) and turning angles apply to different regions of interest. They are different; reception delay The difference is for element n and position (x, z), but across angles They are the same.

[0057] (2) During reception, a single output sample is generated only when the sum of the propagation times of the arriving propagation wave ἡ and the returning propagation wave Ἳ is the same. To account for digitization errors, pixels that meet the following conditions will contribute to the output of this element: Each sample of the RF channel data can be linearly modeled as a combination of image pixel values. The forward model is: ,in These are the vectorized versions of the prebeamform data and the desired image, respectively. Represents a weighted matrix. It is electronic noise that affects the original data, and it has been shown that it can be approximated by additive white high noise.

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

[0059]

[0060] in It corresponds to the actual time of the sample output by the component. ) and pixel propagation delay time ( The maximum absolute difference between the pixels. Matrix Φ is highly sparse because only a small fraction of the pixels satisfy the condition. . also, It is data-independent and can be pre-calculated based on known imaging settings. Finally, the matrix... It is multiplied with the receiver apodization matrix commonly used in DAS beamforming, which takes into account the directivity of the transducer elements and the f-number is fixed for the entire image.

[0061] (3) Based on the forward model, construct an optimization model for the inverse problem of ultrasonic plane waves based on a low-rank sparse model: ,in, Vectorization representation of the ultrasound plane wave image to be recovered. The observed RF channel data vector, The ultrasound plane wave image to be recovered. The weights of the lower-rank terms. Let be the weights of the sparse terms, s be the sparse term, and L be the low-rank term. The ADMM method is used to find the solution to the equation, and its convergence has been proven for this optimization problem.

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

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

[0064]

[0065] in, and Representing the image and its sparse representation, respectively. For the sampling matrix, and This is the regularization parameter.

[0066] The equation can be reformulated using its augmented Lagrange function as follows:

[0067] Step 1 Beamforming Update: Update Update by minimizing the 2-norm problem The BFGS method can be used to solve this problem.

[0068] Step 2: Nuclear Norm Update: Update , , This is a matrix completion problem, and the solution is through singular value thresholding: ,in It is an operation that performs soft thresholding on singular values.

[0069] Step 3: Sparsity and Lagrange Multiplier Update

[0070] renew , The 1-norm optimization problem updates the value using a soft thresholding method. , .

[0071] Update Lagrange multipliers and :

[0072] 0-norm solution: Unlike the 1-norm solution, the 0-norm solution uses the non-convex and non-smooth 0-norm to measure the sparsity of the image. This is achieved by introducing auxiliary variables. The optimization problem can be expressed as:

[0073] in, Is with Auxiliary variables of the same size, ⊙ represents the Hadamard product.

[0074] Step 1 Beamforming Update: Update and This step is the same as the update method in the 1-norm solution, updating by solving the 2-norm problem. and .

[0075] Step 2: Nuclear Norm Update: Update , .

[0076] Step 3 Sparsity and Lagrange Multipliers Update:: Update , Update Lagrange multipliers , , , .

[0077] (5) The final result obtained from the inverse problem is used to obtain the final ultrasound image through envelope detection, logarithmic compression, and grayscale mapping. Figure 2 Reconstruction results are presented for several different datasets, where SR (Simulated Resolution): Images containing horizontally and vertically distributed point targets against an anechoic background are used to evaluate spatial resolution. SC (Simulated Contrast): Images containing horizontally and vertically distributed anechoic cysts against a fully developed blob background are used to evaluate contrast. ER (Experimental Resolution): Images acquired from a CIRS Phantom (model 040GSE) containing wire-target regions against a blob background are used to evaluate spatial resolution. EC (Experimental Contrast): This image, also acquired from the same Phantom, contains anechoic cyst regions against a blob background and is used to evaluate contrast. In this embodiment, the Kolmogorov-Smirnov (KS) test, designed by the PICMUS organizers, is used to verify whether the reconstructed images preserve blob texture. A significance level of α = 0.05 is considered for a region of the image to determine whether scattering statistics are preserved.

[0078] Figure 3It can be seen that the inverse problem imaging method using only the 1-norm sparse model has relatively large image noise, while the inverse problem method using the kernel norm and the 1-norm or 0-norm low-rank sparse model has high image quality and better preserves the speckle texture of the image. Table 1 shows the quantitative comparison results of different beamforming methods on simulated datasets (SR, ER) and experimental datasets (SC, EC).

[0079] Table 1

[0080]

[0081] The method combining the nuclear norm and the 1-norm achieved the best axial (FWHM_A = 0.24) and lateral resolution (FWHM_L = 0.42) on the ER dataset, demonstrating superior spatial resolution in simulated environments. In contrast, the method combining the nuclear norm and the 0-norm exhibited the highest contrast-to-noise ratio (CNR = 13.51, 10.05) and generalized CNR (gCNR = 0.91, 0.84) on the experimental datasets SC and EC, showing superior contrast performance in real-world scenarios. Notably, both nuclear norm-based methods passed the KS test on all datasets, while the method using the 1-norm alone failed. These results indicate that introducing nuclear norm regularization can significantly improve image resolution and contrast, especially the model combining the 0-norm, which shows greater advantage on experimental data.

[0082] Example 3

[0083] In this embodiment 3, an ultrasonic plane wave inverse imaging device is provided. The device includes: a transmitting module for transmitting plane waves towards a target object during plane wave imaging; a receiving module for receiving the echo signal of the transmitted plane waves; an optimization module for optimizing the received echo signal using the image optimization method described in Embodiment 1 or Embodiment 2 to obtain an optimized ultrasonic image; and a display module for displaying the optimized ultrasonic image.

[0084] Example 4

[0085] This embodiment 4 provides a non-transitory computer-readable storage medium for storing computer instructions. When executed by a processor, the computer instructions implement an ultrasonic plane wave inverse problem imaging optimization method, which includes:

[0086] Constructing the spatial coordinate system of an image in ultrasonic plane wave imaging;

[0087] A sampling matrix is ​​constructed based on the spatial coordinate system of the image and the parameters of the emitted ultrasonic plane wave, and weighted by combining the directionality of the transducer element and the geometric information of the image to maintain sparsity;

[0088] Based on the weighted sampling matrix, a low-rank sparse optimization model for the inverse problem of ultrasonic plane waves is constructed.

[0089] The low-rank sparse optimization model is updated through beamforming, nuclear norm, and sparse and Lagrange terms. Finally, the optimized ultrasound image is obtained through envelope detection, logarithmic compression, and grayscale mapping.

[0090] Example 5

[0091] This embodiment 5 provides a computer device, including a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute an ultrasonic plane wave inverse problem imaging optimization method, the method including:

[0092] Constructing the spatial coordinate system of an image in ultrasonic plane wave imaging;

[0093] A sampling matrix is ​​constructed based on the spatial coordinate system of the image and the parameters of the emitted ultrasonic plane wave, and weighted by combining the directionality of the transducer element and the geometric information of the image to maintain sparsity;

[0094] Based on the weighted sampling matrix, a low-rank sparse optimization model for the inverse problem of ultrasonic plane waves is constructed.

[0095] The low-rank sparse optimization model is updated through beamforming, nuclear norm, and sparse and Lagrange terms. Finally, the optimized ultrasound image is obtained through envelope detection, logarithmic compression, and grayscale mapping.

[0096] Example 6

[0097] This embodiment 6 provides an electronic device, including: a processor, a memory, and a computer program; wherein, the processor is connected to 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 to cause the electronic device to execute instructions for implementing the ultrasonic plane wave inverse problem imaging optimization method as described above, the method including:

[0098] Constructing the spatial coordinate system of an image in ultrasonic plane wave imaging;

[0099] A sampling matrix is ​​constructed based on the spatial coordinate system of the image and the parameters of the emitted ultrasonic plane wave, and weighted by combining the directionality of the transducer element and the geometric information of the image to maintain sparsity;

[0100] Based on the weighted sampling matrix, a low-rank sparse optimization model for the inverse problem of ultrasonic plane waves is constructed.

[0101] The low-rank sparse optimization model is updated through beamforming, nuclear norm, and sparse and Lagrange terms. Finally, the optimized ultrasound image is obtained through envelope detection, logarithmic compression, and grayscale mapping.

[0102] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0103] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0104] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0105] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment, whereby a series of operational steps are performed to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0106] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that, based on the technical solutions disclosed in the present invention, various modifications or variations that can be made by those skilled in the art without creative effort should be included within the scope of protection of the present invention.

Claims

1. An optimization method for ultrasonic plane wave inverse problem imaging, characterized in that, include: Constructing the spatial coordinate system of an image in ultrasonic plane wave imaging; A sampling matrix is ​​constructed based on the spatial coordinate system of the image and the parameters of the emitted ultrasonic plane wave, and weighted by combining the directionality of the transducer element 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 ultrasonic plane wave problem is constructed. Each row of the sampling matrix represents the contribution of each pixel in the image to the acquired prebeam data samples. The elements of the sampling matrix are weighted according to the difference between the pixel's propagation delay and reception time to ensure the sparsity of the matrix. The constructed low-rank sparse optimization model for the inverse ultrasonic plane wave problem is as follows: ;in, Let represent the vectorized representation of the ultrasound plane wave image X to be recovered, and b be the observed echo signal data vector. The ultrasound plane wave image to be recovered. The weights of the lower-rank terms. The weights of the sparse terms, For sparse terms, For low-rank terms, a single output sample is generated when the sum of the propagation times of the arriving propagation wave ẋ and the returning propagation wave ẋ is the same; pixels that meet the following conditions contribute to the output: f s The sampling frequency of the ultrasonic plane wave transmission system; each sample of the RF channel data is linearly modeled as a combination of image pixel values, and the forward model is: ,in These are the vectorized versions of the prebeamform data and the desired image, respectively. Represents a weighted matrix. The electronic noise affecting the original data is used to update the low-rank sparse optimization model through beamforming, nuclear norm, and sparse and Lagrange terms. Finally, the 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 abscissas N in the spatial coordinate system x =3×L, where L is the number of array elements required to emit ultrasonic plane waves; N is the number of vertical coordinates in the image spatial coordinate system. y =2×B×f c / f s Where B is the number of echo signals received along the vertical axis from the emitted ultrasonic plane wave, f c f is the center frequency for transmitting ultrasonic plane waves. s The sampling frequency for the ultrasonic plane wave transmission system.

3. 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 kernel norm of the matrix.

4. An ultrasonic plane wave inverse problem imaging optimization system based on the ultrasonic plane wave inverse problem imaging optimization method as described in any one of claims 1-3, characterized in that, include: The first building unit is used to construct the spatial coordinate system of the image in ultrasonic plane wave imaging; The second building unit is used to construct a sampling matrix based on the spatial coordinate system of the image and the parameters of the emitted ultrasonic plane wave, and to weight it by combining the directionality of the transducer element and the geometric information of the image to maintain sparsity. The third building unit is used to construct a low-rank sparse optimization model for the inverse problem of ultrasonic plane waves based on the weighted sampling matrix. The solution unit is used to update the low-rank sparse optimization model through beamforming, nuclear norm, and sparse and Lagrange terms, and obtain the final optimized ultrasound image through envelope detection, logarithmic compression, and grayscale mapping.

5. An ultrasonic plane wave inverse problem imaging device, characterized in that, include: The transmitting module is used to transmit plane waves toward the target object during plane wave imaging. The receiving module is used to receive the echo signal of the transmitted plane wave; An optimization module is used to optimize the received echo signal using the method described in any one of claims 1-3 to obtain an optimized ultrasound image; The display module is used to display the obtained ultrasound images.

6. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, which, when executed by a processor, implement the ultrasonic plane wave inverse problem imaging optimization method as described in any one of claims 1-3.

7. A computer device, characterized in that, The device includes a memory and a processor, the processor and the memory communicating with each other, the memory storing program instructions that can be executed by the processor, and the processor calling the program instructions to execute the ultrasonic plane wave inverse problem imaging optimization method as described in any one of claims 1-3.

8. An electronic device, characterized in that, include: The device includes 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 cause the electronic device to execute instructions for implementing the ultrasonic plane wave inverse problem imaging optimization method as described in any one of claims 1-3.

Images