A novel method and system for clutter suppression and enhancement in ultrasonic micro-blood flow imaging

Abstract

The present invention discloses a novel method and system for clutter suppression and enhancement in ultrasonic micro-blood flow imaging. The method uses multi-angle composite beamforming to obtain orthogonal demodulation data of the original ultrasonic image sequence. Based on the different spatiotemporal characteristics of tissue, blood flow, and noise, a low-rank sparse three-dimensional total variation robust principal component analysis (3DTV-RPCA) theoretical model is constructed. The sparse and continuous characteristics of blood flow signals are used to constrain the model, thereby improving the ability to extract blood flow and enhancing the effect of clutter suppression. Based on the theoretical model, an augmented Lagrangian function is constructed, and the alternating method multiplier method (ADMM) is used to obtain ultrasonic micro-blood flow signals after clutter suppression and enhancement, thereby obtaining ultrasonic micro-vascular imaging and blood flow velocity maps. This method enhances the sensitivity of ultrasonic micro-blood flow imaging by strongly constraining the sparsity and continuity of weak blood flow signals, effectively suppressing clutter interference such as noise and artifacts, and improving the image quality of ultrasonic micro-blood flow imaging.

Description

Technical Field

[0001] The present invention belongs to the field of ultrasonic imaging technology and relates to a novel method and system for clutter suppression and enhancement in ultrasonic micro-blood flow imaging, and in particular to a novel method and system for clutter suppression and enhancement in ultrasonic micro-blood flow imaging based on low-rank sparse three-dimensional total variation robust principal component analysis (3DTV-RPCA). Background Art

[0002] Ultrasound microvascular imaging is a non-invasive imaging technique used to detect and quantify the structural morphology and dynamic properties of microvessels. By observing microvascular parameters such as flow velocity, volume, and caliber in real time, ultrasound microvascular imaging can help physicians promptly detect and assess vascular conditions in a variety of diseases, including insufficient blood supply, tumor angiogenesis, inflammatory responses, and cardiovascular disease. This is crucial for the early detection and diagnosis of cardiovascular disease, cancer, and other conditions. Ultrasound microvascular imaging offers broad application prospects in clinical medicine due to its high resolution, real-time nature, and repeatability. However, current ultrasound microvascular imaging still faces several limitations. Microvascular signals are extremely weak and easily affected by noise, artifacts, and tissue echo signals, reducing imaging quality and reliability. Furthermore, the blood flow contrast and resolution in ultrasound images are limited, making it difficult to accurately display the structural and dynamic properties of microvessels.

[0003] Traditional frequency domain filtering methods can extract blood flow signals by distinguishing between tissue, blood flow and noise in the spectrum, but when the blood flow velocity is low or the tissue movement is large, the two spectra will overlap, affecting effective distinction. Singular Value Decomposition (SVD) can distinguish tissue, blood flow and noise by distinguishing them in terms of the coherence of their spatiotemporal information, but it requires manual selection of thresholds based on different imaging scenarios, and its adaptability is poor. Robust Principal Component Analysis (RPCA) is an emerging ultrasound micro-blood flow imaging enhancement technology, but it only utilizes the spatiotemporal low-rank characteristics of tissue and the spatiotemporal sparse characteristics of blood flow for constraints, and is still ineffective for scenes with micro-blood flow and large tissue movement. Existing clutter suppression algorithms still have difficulty solving the problem of low sensitivity of micro-blood flow signals and the inability to detect micro-vessels, which affects the imaging quality of ultrasound micro-blood flow. Summary of the Invention

[0004] The purpose of the present invention is to solve the problem that the clutter suppression algorithm in the existing technology is difficult to solve the problem of low sensitivity of small blood flow signals and inability to detect small blood vessels, which seriously affects the imaging quality of ultrasonic small blood flows. A new method and system for clutter suppression enhancement of ultrasonic small blood flow imaging is provided.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The present invention proposes a novel method for clutter suppression and enhancement in ultrasonic micro-blood flow imaging, comprising the following steps:

[0007] Obtain an orthogonal demodulated original three-dimensional ultrasound image sequence after beamforming of multi-frame and multi-angle ultrasound signals; construct a low-rank sparse three-dimensional total variation robust principal component analysis theoretical model based on the spatiotemporal characteristics of tissue, blood flow, and noise in the original three-dimensional ultrasound image sequence;

[0008] An augmented Lagrangian function is constructed based on the low-rank sparse three-dimensional total variation robust principal component analysis theoretical model. When the augmented Lagrangian function meets the convergence condition, a two-dimensional space-time matrix of the ultrasonic micro-blood flow signal after clutter suppression and enhancement is obtained.

[0009] The two-dimensional space-time matrix of ultrasonic micro-blood flow signals is reorganized into a three-dimensional ultrasonic sequence, and the morphological structure of micro-vessels and the velocity of micro-blood flow are obtained through ultrasonic Doppler imaging to achieve ultrasonic micro-blood flow imaging.

[0010] Preferably, an ultrasonic instrument is used to transmit and collect multi-frame multi-angle ultrasonic radio frequency signals to the imaging area, and the original three-dimensional ultrasonic image sequence is obtained by multi-angle composite beam synthesis.

[0011] in, represents the complex field, n z Indicates the number of axial samples in the spatial dimension, n x Indicates the number of horizontal samples in the spatial dimension, n t Represents the number of samples in the time dimension in the spatial dimension;

[0012] The original 3D ultrasound image sequence O is decomposed into a low-rank tissue component T and a sparse component S according to the differences in temporal and spatial characteristics;

[0013] Among them, the sparse component S is decomposed into the motion tissue sparse component E, the blood flow signal B and the noise signal N.

[0014] Preferably, by simultaneously applying sparsity constraints and continuity constraints to blood flow signals, the ability to extract blood flow signals and suppress clutter is enhanced, and a low-rank sparse three-dimensional total variation robust principal component analysis theoretical model is constructed as follows:

[0015]

[0016] Where T is the low-rank tissue component, S is the sparse component, E is the motion tissue sparse component, B is the blood flow signal, N is the noise signal, and O is the original three-dimensional ultrasound image sequence; ‖·‖ *represents the nuclear norm, ‖·‖1 represents the l1-norm, ‖·‖ 3DTV is the three-dimensional total variation norm operator, represents the Frobenius-norm, λ1 is the balance parameter of the sparse component S, λ2 is the balance parameter of the sparse component E of the moving tissue, λ3 is the balance parameter of the blood flow signal B, and λ4 is the balance parameter of the noise signal N.

[0017] Preferably, standard equilibrium parameters are defined Then the balance parameter λ1 of the sparse component S is 0.04 times the standard balance parameter λ, the balance parameter λ2 of the sparse component E of the moving tissue is 2 times the standard balance parameter λ, the balance parameter λ3 of the blood flow signal B is 0.01 times the standard balance parameter λ, and the balance parameter λ4 of the noise signal N is 0.01 times the standard balance parameter λ.

[0018] Preferably, the augmented Lagrangian function L constructed μ as follows:

[0019]

[0020] Where X, Y, and Z are Lagrange multipliers, <·,·> is the matrix inner product operator, μ is the penalty parameter, and the variable H = D(B) is introduced, where D(·) represents the first-order difference operator in three dimensions on the ultrasound imaging sequence.

[0021] Preferably, an alternating multiplier method is used to iteratively solve the tissue, blood flow and noise components and auxiliary variables in sequence, and when the augmented Lagrangian function meets the convergence condition, a two-dimensional space-time matrix of the ultrasonic micro-blood flow signal is obtained;

[0022] During the iteration process, (·) k represents the k-th iteration result of the variable, (·) k+1 Indicates the k+1th iteration result of the variable;

[0023] Update the low-rank organizational subspace T at k+1 iterations k+1 :

[0024]

[0025] Update the sparse subspace S at k+1 iterations k+1 :

[0026]

[0027] Update the sparse background subspace E at k+1 iterations k+1 :

[0028]

[0029] Update the continuous sparse blood flow subspace B at k+1 iterations k+1 :

[0030]

[0031] Update the auxiliary variable H at the k+1 iteration k+1 :

[0032]

[0033] Update the noise subspace N at k+1 iterations k+1 :

[0034]

[0035] Update the Lagrange multiplier X at the k+1 iteration k+1 , Y k+1 and Z k+1 :

[0036]

[0037] Update the penalty parameter μ at k+1 iterations k+1 :

[0038] μ k+1 =min(ρμ k ,μ max ) (10)

[0039] Among them, μ is the penalty parameter, is the soft threshold compression operator, b represents the three-dimensional spatiotemporal form of the blood flow subspace, and are Fourier transform and inverse transform operators, respectively, D T (·) represents the transpose of the three-dimensional difference operator D(·), D x (·), D z (·) and D t (·) represents the first-order difference operator in the horizontal dimension, vertical dimension and time dimension respectively, fold(·) and unfold(·) are the folding operator and the unfolding operator respectively; ρ is the penalty parameter amplification coefficient, μ max is the upper limit of the penalty parameter;

[0040] Iterative relative error When the error is less than the limit ε, the iterative convergence condition is met and the iteration is terminated to obtain the final two-dimensional space-time matrix of the ultrasonic micro-blood flow signal. Among them, the error limit ε=10 -6 ~10 -4 .

[0041] Preferably, the iterative result is transformed by inverse Casorati transformation Converted into a three-dimensional space-time sequence Along slow time n t The energy of blood flow signal is accumulated to obtain the ultrasonic power Doppler imaging of blood flow signal; t Frequency deviation calculation is performed to obtain ultrasound color Doppler velocity imaging of blood flow signals.

[0042] The present invention proposes a novel ultrasonic micro-blood flow imaging clutter suppression and enhancement system, comprising:

[0043] A model construction module is used to obtain an orthogonal demodulated original three-dimensional ultrasound image sequence after beamforming of multi-frame and multi-angle ultrasound signals; and to construct a low-rank sparse three-dimensional total variation robust principal component analysis theoretical model based on the spatiotemporal characteristics of tissue, blood flow, and noise in the orthogonal demodulated original three-dimensional ultrasound image sequence;

[0044] A clutter suppression and enhancement module is used to construct an augmented Lagrangian function based on a low-rank sparse three-dimensional total variation robust principal component analysis theoretical model, and when the augmented Lagrangian function meets the convergence condition, a two-dimensional space-time matrix of the ultrasonic micro-blood flow signal after clutter suppression and enhancement is obtained;

[0045] The image acquisition module is used to reorganize the two-dimensional space-time matrix of ultrasonic micro-blood flow signals into a three-dimensional ultrasonic sequence, obtain the morphological structure of micro-vessels and the speed of micro-blood flow through ultrasonic Doppler imaging, and realize ultrasonic micro-blood flow imaging.

[0046] A computer device comprises a memory and a processor, wherein the memory stores a computer program and the processor implements the steps of a novel method for clutter suppression and enhancement of ultrasonic micro-blood flow imaging when executing the computer program.

[0047] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a novel method for clutter suppression and enhancement of ultrasonic micro-blood flow imaging.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention proposes a novel clutter suppression and enhancement method for ultrasonic micro-blood flow imaging. The method first constructs a low-rank sparse three-dimensional total variation robust principal component analysis (3DTV-RPCA) theoretical model based on the differences in the spatiotemporal characteristics of tissue, blood flow, and noise in the IQ data of the original ultrasonic image sequence after multi-frame multi-angle composite beam synthesis. The sparse and continuous characteristics of the blood flow signal are used for constraint to improve the blood flow extraction capability and enhance the clutter suppression effect. The corresponding augmented Lagrangian function is constructed based on the 3DTV-RPCA theoretical model. When the augmented Lagrangian function meets the convergence condition, the sparse matrix obtained by solving is the two-dimensional spatiotemporal matrix of the blood flow signal after clutter suppression and enhancement. The matrix is ​​then converted into a three-dimensional ultrasonic sequence, ultimately achieving enhanced suppression of tissue and noise signals and extracting richer morphological characteristics and flow velocity information of micro-vessels. Specifically, when constructing a 3DTV-RPCA model, the present invention adds a three-dimensional total variation constraint to the low-rank sparse model to constrain the spatiotemporal continuity of blood flow signals. This allows for segmentation of weak blood flow signals and moving tissue, improving the contrast and sensitivity of blood flow signals and significantly enhancing the clutter suppression effect of ultrasonic micro-blood flow imaging. Therefore, the enhancement method proposed in the present invention can address the problem of low sensitivity of micro-blood flow signals and the inability to detect micro-vessels in existing clutter suppression algorithms, which affects the quality of ultrasonic micro-blood flow imaging.

[0050] This paper proposes a novel clutter suppression and enhancement system for ultrasonic micro-blood flow imaging. By dividing the system into a model-building module, a clutter suppression and enhancement module, and an image acquisition module, it enhances the clutter suppression effect for ultrasonic micro-blood flow imaging. The modular design makes each module independent, facilitating unified management of all modules. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0052] Figure 1 This is a flow chart of the novel ultrasonic micro-blood flow imaging clutter suppression and enhancement method of the present invention.

[0053] Figure 2 This is a comparison chart of the results of blood flow enhancement using different methods in specific embodiments provided by the present invention.

[0054] Figure 3 This is a diagram of the novel ultrasonic micro-blood flow imaging clutter suppression and enhancement system of the present invention. DETAILED DESCRIPTION

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0056] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0057] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0058] In the description of the embodiments of the present invention, it should be noted that if the terms "upper," "lower," "horizontal," "inner," etc. appear, the orientation or positional relationship indicated is based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the inventive product is typically placed when in use. These terms are merely for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on the present invention. In addition, the terms "first," "second," etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0059] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0060] In the description of the embodiments of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0061] The present invention is described in further detail below with reference to the accompanying drawings:

[0062] The present invention proposes a new method for clutter suppression and enhancement of ultrasonic micro blood flow imaging, such as Figure 1 As shown, the following steps are included:

[0063] S1. Obtain an orthogonal demodulated original three-dimensional ultrasound image sequence after beamforming of multi-frame and multi-angle ultrasound signals; construct a low-rank sparse three-dimensional total variation robust principal component analysis theoretical model based on the spatiotemporal characteristics of tissue, blood flow, and noise in the original three-dimensional ultrasound image sequence;

[0064] Use ultrasound equipment to transmit and collect multi-frame and multi-angle ultrasound radio frequency signals in the imaging area, and obtain the original three-dimensional ultrasound image sequence through multi-angle composite beam synthesis

[0065] in, represents the complex field, n z Indicates the number of axial samples in the spatial dimension, n x Indicates the number of horizontal samples in the spatial dimension, n t Represents the number of samples in the time dimension over the spatial dimension.

[0066] The original 3D ultrasound image sequence O is decomposed into a low-rank tissue component T and a sparse component S according to the differences in temporal and spatial characteristics;

[0067] Among them, the sparse component S is decomposed into the motion tissue sparse component E, the blood flow signal B and the noise signal N.

[0068] By simultaneously applying sparsity constraints and continuity constraints to blood flow signals, the ability to extract blood flow signals and suppress clutter is enhanced. The theoretical model of low-rank sparse three-dimensional total variation robust principal component analysis is constructed as follows:

[0069]

[0070] Where T is the low-rank tissue component, S is the sparse component, E is the motion tissue sparse component, B is the blood flow signal, N is the noise signal, and O is the original three-dimensional ultrasound image sequence; ‖·‖ * represents the nuclear norm, ‖·‖1 represents the l1-norm, ‖·‖ 3DTV is the three-dimensional total variation norm operator, represents the Frobenius-norm, λ1 is the balance parameter of the sparse component S, λ2 is the balance parameter of the sparse component E of the moving tissue, λ3 is the balance parameter of the blood flow signal B, and λ4 is the balance parameter of the noise signal N.

[0071] Defining standard equilibrium parameters Then the balance parameter λ1 of the sparse component S is 0.04 times the standard balance parameter λ, the balance parameter λ2 of the sparse component E of the moving tissue is 2 times the standard balance parameter λ, the balance parameter λ3 of the blood flow signal B is 0.01 times the standard balance parameter λ, and the balance parameter λ4 of the noise signal N is 0.01 times the standard balance parameter λ.

[0072] S2. An augmented Lagrangian function is constructed based on a low-rank sparse three-dimensional total variation robust principal component analysis theoretical model. When the augmented Lagrangian function meets the convergence condition, a two-dimensional space-time matrix of the ultrasonic micro-blood flow signal after clutter suppression and enhancement is obtained;

[0073] The constructed augmented Lagrangian function L μ as follows:

[0074]

[0075] Where X, Y, and Z are Lagrange multipliers, <·,·> is the matrix inner product operator, μ is the penalty parameter, and the variable H = D(B) is introduced, where D(·) represents the first-order difference operator in three dimensions on the ultrasound imaging sequence.

[0076] The alternating multiplier method is used to iteratively solve the tissue, blood flow, noise components and auxiliary variables in turn. When the augmented Lagrangian function meets the convergence condition, the two-dimensional space-time matrix of the ultrasonic micro-blood flow signal is obtained.

[0077] During the iteration process, (·) k represents the k-th iteration result of the variable, (·) k+1 Indicates the k+1th iteration result of the variable;

[0078] Update the low-rank organizational subspace T at k+1 iterations k+1 :

[0079]

[0080] Update the sparse subspace S at k+1 iterations k+1 :

[0081]

[0082] Update the sparse background subspace E at k+1 iterations k+1 :

[0083]

[0084] Update the continuous sparse blood flow subspace B at k+1 iterations k+1 :

[0085]

[0086] Update the auxiliary variable H at the k+1 iteration k+1 :

[0087]

[0088] Update the noise subspace N at k+1 iterations k+1 :

[0089]

[0090] Update the Lagrange multiplier X at the k+1 iteration k+1 , Y k+1 and Z k+1 :

[0091]

[0092] Update the penalty parameter μ at k+1 iterations k+1 :

[0093] μ k+1 = min(ρμ k ,μ max ) (10)

[0094] Among them, μ is the penalty parameter, is the soft threshold compression operator, b represents the three-dimensional spatiotemporal form of the blood flow subspace, and are Fourier transform and inverse transform operators, respectively, D T (·) represents the transpose of the three-dimensional difference operator D(·), D x (·), D z (·) and D t (·) represents the first-order difference operator in the horizontal dimension, vertical dimension and time dimension respectively, fold(·) and unfold(·) are the folding operator and the unfolding operator respectively; ρ is the penalty parameter amplification coefficient, μ max is the upper limit of the penalty parameter;

[0095] Iterative relative error When the error is less than the limit ε, the iterative convergence condition is met and the iteration is terminated to obtain the final two-dimensional space-time matrix of the ultrasonic micro-blood flow signal. Among them, the error limit ε=10 -6 ~10 -4 .

[0096] S3. Reorganize the two-dimensional space-time matrix of ultrasonic micro-blood flow signals into a three-dimensional ultrasonic sequence, obtain the morphological structure of micro-vessels and the velocity of micro-blood flow through ultrasonic Doppler imaging, and realize ultrasonic micro-blood flow imaging.

[0097] The iterative results are transformed by inverse Casorati Converted into a three-dimensional space-time sequence Along slow time n t The energy of blood flow signal is accumulated to obtain the ultrasonic power Doppler imaging of blood flow signal; t Frequency deviation calculation is performed to obtain ultrasound color Doppler velocity imaging of blood flow signals.

[0098] The specific steps include:

[0099] Step 1: Use an ultrasonic instrument to collect ultrasonic signals from the imaging area, and obtain an orthogonal demodulated ultrasonic original image sequence through multi-angle composite beam synthesis.

[0100] The obtained ultrasound original image sequence data is Among them, n z , n x and n t Represents the number of samples in the axial, transverse and time dimensions in the spatial dimension respectively.

[0101] According to the spatiotemporal coherence, the original ultrasound image sequence O consists of a low-rank tissue component T and a sparse component S. The sparse component S can be further decomposed into a motion tissue sparse component E, a blood flow signal B, and a noise signal N.

[0102] Step 2: Based on the spatiotemporal coherence and continuity of tissue, blood flow and noise in ultrasound image sequences, a low-rank sparse three-dimensional total variation RPCA theoretical model is established.

[0103] The low-rank sparse three-dimensional total variation RPCA theoretical model is constructed as follows:

[0104]

[0105] in,‖·‖ * represents the nuclear norm, ‖·‖1 represents the l1-norm, ‖·‖ 3DTV is the three-dimensional total variation norm operator, represents the Frobenius norm. λ1 is the first balance parameter of the corresponding term, λ2 is the second balance parameter of the corresponding term, λ3 is the third balance parameter of the corresponding term, and λ4 is the fourth balance parameter of the corresponding term.

[0106] make The equilibrium parameters λ1, λ2, λ3 and λ4 in the above theoretical model are recommended to be selected as 0.04, 2, 0.01 and 0.01 times of λ respectively according to empirical values.

[0107] Step 3: Construct the augmented Lagrangian function of the low-rank sparse three-dimensional total variation RPCA theoretical model, and iteratively solve the minimum optimization problem of the function according to the alternating direction multiplier method to obtain the iterative result of the blood flow signal subspace.

[0108] In order to reduce the computational complexity, the alternating multiplier method is used to solve the multivariable convex optimization problem, and the augmented Lagrangian function is constructed as follows:

[0109]

[0110] Where X, Y, and Z are Lagrange multipliers, 〈·,·〉 is the matrix inner product operator, and μ is the penalty parameter. The variable H = D(B) is introduced, where D(·) represents the first-order difference operator in three dimensions on the ultrasound imaging sequence.

[0111] Step 4: Perform inverse Casorati transform on the blood flow signal subspace to convert the result into a three-dimensional ultrasound sequence. The morphological structure of microvessels is obtained by ultrasound power Doppler accumulation, and the velocity estimation of microblood flow is obtained by color Doppler frequency shift calculation.

[0112] The alternating multiplier method is used to iterate each subspace in turn. The iteration method is as follows:

[0113] Update the low-rank organizational subspace T at k+1 iterations k+1 :

[0114]

[0115] Among them, μ is the penalty parameter.

[0116] Update the sparse subspace S at k+1 iterations k+1 :

[0117]

[0118] in, is a soft threshold compression operator.

[0119] Update the sparse background subspace E at k+1 iterations k+1 :

[0120]

[0121] Update the continuous sparse blood flow subspace B at k+1 iterations k+1 :

[0122]

[0123] Among them, b represents the three-dimensional space-time form of the blood flow subspace, and are Fourier transform and inverse transform operators, respectively, D T (·) represents the transpose of the three-dimensional difference operator D(·), D x (·), D z (·) and D t (·) represents the first-order difference operator in the horizontal dimension of space, the vertical dimension of space and the time dimension respectively, fold(·) and unfold(·) represent the folding operator and the unfolding operator respectively.

[0124] Update the auxiliary variable H at the k+1 iteration k+1 :

[0125]

[0126] Update the noise subspace N at k+1 iterations k+1 :

[0127]

[0128] Update the Lagrange multiplier X at the k+1 iteration k+1 , Y k+1 and Z k+1 :

[0129]

[0130] Update the penalty parameter μ at k+1 iterations k+1 :

[0131] μ k+1 = min(ρμ k ,μ max ) (10)

[0132] Among them, ρ is the penalty parameter amplification coefficient, μ max is the upper limit of the penalty parameter. F <ε‖O‖ F Stop the iteration when , and get the final blood flow subspace result Where ε is the iteration error limit, which is usually set to 10 -6 .

[0133] The two-dimensional space-time matrix is ​​transformed by inverse Casorati transformation Converted into a three-dimensional space-time sequence Along slow time n t The energy of blood flow signal is accumulated to obtain the power Doppler result of blood flow signal; t Perform frequency deviation analysis to obtain velocity estimation of blood flow signals.

[0134] Example

[0135] A novel clutter suppression and enhancement method for ultrasound micro-blood flow imaging was used to image micro-blood flows in the brain of a living craniotomized mouse. The specific implementation steps are as follows:

[0136] To investigate the effect of imaging on slow blood flow in microvessels, brain microvessel imaging was performed on craniotomized mice. The mice were anesthetized during the experiment by intraperitoneal injection of urethane (100-120 mg / 100 g). The mouse heads were fixed with a positioning device and craniotomy was performed. A programmable ultrasound device, Vantage 128, was used to transmit 5 (±6) degrees of scintigraphy at a center frequency of 12 MHz. ° ,±3 ° ,0 ° ), a 1-cycle multi-angle ultrasonic plane wave signal with a pulse repetition frequency of 2.5kHz. Verasonics then performs multi-angle composite plane wave beamforming to output IQ data at a frame rate of 500Hz, collecting data for a total of 1s, or 500 frames.

[0137] According to formula (1), a low-rank sparse three-dimensional total variation RPCA theoretical model is constructed, and the equilibrium parameter is set as According to empirical values, the balance parameters λ1, λ2, λ3 and λ4 are selected as 0.04, 2, 0.01 and 0.01 times of λ respectively.

[0138] According to formula (2), the augmented Lagrangian function of the low-rank sparse three-dimensional total variation RPCA theoretical model is constructed, and the alternating multiplier method is used to iteratively solve each sub-item. In the k+1 iteration, each component is solved according to formulas (3)-(10). When ‖OTBEN‖ F <ε‖O‖ F Stop the iteration when , and get the final blood flow subspace result where ε = 10 -6 .

[0139] Blood flow signal subspace Perform inverse Casorati transform to convert the result into a three-dimensional ultrasound sequence The morphological structure of microvessels is obtained by ultrasound power Doppler accumulation, and the velocity estimation of microblood flow is obtained by color Doppler frequency shift calculation.

[0140] Figure 2 This figure compares the brain power Doppler results of mice undergoing craniotomy using different blood flow enhancement methods in a specific embodiment of the present invention. Compared to traditional clutter suppression filters, this method offers a better signal-to-noise ratio and increased sensitivity, enabling the detection of previously undetectable microvessels.

[0141] The present invention proposes a new type of ultrasonic micro blood flow imaging clutter suppression and enhancement system, such as Figure 3As shown, it includes a model building module, a clutter suppression enhancement module and an image acquisition module;

[0142] The model building module is used to obtain an orthogonal demodulated original three-dimensional ultrasound image sequence after multi-frame multi-angle ultrasound signal beam synthesis; based on the spatiotemporal characteristics of tissue, blood flow and noise in the orthogonal demodulated original three-dimensional ultrasound image sequence, a low-rank sparse three-dimensional total variation robust principal component analysis theoretical model is constructed;

[0143] The clutter suppression and enhancement module is used to construct an augmented Lagrangian function based on a low-rank sparse three-dimensional total variation robust principal component analysis theoretical model. When the augmented Lagrangian function meets the convergence condition, a two-dimensional space-time matrix of the ultrasonic micro-blood flow signal after clutter suppression and enhancement is obtained;

[0144] The image acquisition module is used to reorganize the two-dimensional space-time matrix of ultrasonic micro-blood flow signals into a three-dimensional ultrasonic sequence, obtain the morphological structure of micro-vessels and the speed of micro-blood flow through ultrasonic Doppler imaging, and realize ultrasonic micro-blood flow imaging.

[0145] An embodiment of the present invention provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of each of the aforementioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of each module / unit in each of the aforementioned apparatus embodiments are implemented.

[0146] The computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to accomplish the present invention.

[0147] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0148] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0149] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0150] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0151] This paper proposes a novel method and system for clutter suppression and enhancement in ultrasonic micro-blood flow imaging. This method is applied to the original sequence of ultrasonic images after multi-angle beamforming. By leveraging the low-rank characteristics of tissue signals in three-dimensional space and time, the sparse characteristics of dynamic backgrounds, and the continuity of blood flow signals, a low-rank sparse three-dimensional total variation (RPCA) model is constructed to extract blood flow signals. This effectively suppresses noise, artifacts, and signal interference, improving the contrast and clarity of blood flow signals. By constraining the sum of the first-order differences of blood flow signals in the horizontal and vertical spatial dimensions, as well as the temporal dimension, the 3D total variation (RPCA) model preserves image edge information and details, enhances sensitivity and detail display, and significantly improves the image quality of ultrasonic micro-blood flow imaging.

[0152] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A novel method for clutter suppression and enhancement in ultrasonic micro-blood flow imaging, characterized in that: The steps include: Obtain an orthogonal demodulated original three-dimensional ultrasound image sequence after beamforming of multi-frame and multi-angle ultrasound signals; construct a low-rank sparse three-dimensional total variation robust principal component analysis theoretical model based on the spatiotemporal characteristics of tissue, blood flow, and noise in the original three-dimensional ultrasound image sequence; An augmented Lagrangian function is constructed based on the low-rank sparse three-dimensional total variation robust principal component analysis theoretical model. When the augmented Lagrangian function meets the convergence condition, a two-dimensional space-time matrix of the ultrasonic micro-blood flow signal after clutter suppression and enhancement is obtained. The two-dimensional space-time matrix of ultrasonic micro-blood flow signals is reorganized into a three-dimensional ultrasonic sequence, and the morphological structure of micro-vessels and the velocity of micro-blood flow are obtained through ultrasonic Doppler imaging to achieve ultrasonic micro-blood flow imaging.

2. The novel method for clutter suppression and enhancement of ultrasonic micro-blood flow imaging according to claim 1 is characterized in that: Use ultrasound equipment to transmit and collect multi-frame and multi-angle ultrasound radio frequency signals in the imaging area, and obtain the original three-dimensional ultrasound image sequence through multi-angle composite beam synthesis ; in, represents the complex field, Indicates the number of samples in the axial direction of the spatial dimension, Indicates the number of horizontal samples in the spatial dimension, Represents the number of samples in the time dimension in the spatial dimension; Original 3D ultrasound image sequence Decompose into low-rank organizational components based on the differences in spatiotemporal characteristics and sparse components ; Among them, the sparse component Decomposition into sparse components of motion tissue , blood flow signal and noise signal .

3. The novel method for clutter suppression and enhancement of ultrasonic micro-blood flow imaging according to claim 1 is characterized in that: By simultaneously applying sparsity constraints and continuity constraints to blood flow signals, the ability to extract blood flow signals and suppress clutter is enhanced. The theoretical model of low-rank sparse three-dimensional total variation robust principal component analysis is constructed as follows: in, T is the low-rank organizational component, is the sparse component, is the sparse component of motion organization, is the blood flow signal, is the noise signal, is the original three-dimensional ultrasound image sequence; represents the nuclear norm, express -norm, is the three-dimensional total variation norm operator, represents the Frobenius-norm, is the sparse component The equilibrium parameters, Organizing sparse components for motion The equilibrium parameters, Blood flow signal The equilibrium parameters, is the noise signal The balance parameters.

4. The novel method for clutter suppression and enhancement in ultrasonic micro-blood flow imaging according to claim 3 is characterized in that: Defining standard equilibrium parameters , then the sparse component The balance parameter Standard balance parameters 0.04 times, sparse component of motion tissue The balance parameter Standard balance parameters 2 times, blood flow signal The balance parameter Standard balance parameters 0.01 times the noise signal The balance parameter Standard balance parameters 0.01 times, Represents the number of axial samples of the original three-dimensional ultrasound image sequence in the spatial dimension, Represents the number of horizontal samples of the original three-dimensional ultrasound image sequence in the spatial dimension, Represents the number of samples in the time dimension of the original 3D ultrasound image sequence in the spatial dimension.

5. The novel method for clutter suppression and enhancement in ultrasonic micro-blood flow imaging according to claim 3 is characterized in that: Constructed augmented Lagrangian function as follows: in, , and is the Lagrange multiplier, is the matrix inner product operator, Is the penalty parameter, introducing the variable , represents the first-order difference operator in three dimensions on the ultrasound imaging sequence.

6. The novel method for clutter suppression and enhancement in ultrasonic micro-blood flow imaging according to claim 5 is characterized in that: The alternating multiplier method is used to iteratively solve the tissue, blood flow, noise components and auxiliary variables in turn. When the augmented Lagrangian function meets the convergence condition, the two-dimensional space-time matrix of the ultrasonic micro-blood flow signal is obtained. During the iteration process, Indicates the variable The result of the iteration, Indicates the variable Iteration results; renew The low-rank organizational component at iteration : renew The sparse component at iteration : renew The sparse component of motion organization at iteration : renew The blood flow signal at the iteration : renew Auxiliary variables at iteration : renew Noise signal at iteration : renew Lagrange multiplier at the iteration , and : renew The penalty parameter at the iteration : in, is the penalty parameter, is the soft threshold compression operator, Represents the three-dimensional space-time form of the blood flow subspace, and are the Fourier transform and inverse transform operators, respectively. Represents a three-dimensional difference operator The transpose of , and Represent the first-order difference operators in the horizontal dimension of space, the vertical dimension of space, and the time dimension, respectively. and Folding operator and unfolding operator respectively; is the penalty parameter amplification coefficient, is the upper limit of the penalty parameter; Iterative relative error Less than the error limit When the iterative convergence condition is met, the iteration is terminated to obtain the final two-dimensional space-time matrix of the ultrasonic micro-blood flow signal ; Among them, the error limit The value range is .

7. The novel method for clutter suppression and enhancement in ultrasonic micro-blood flow imaging according to claim 6, characterized in that: The iterative results are transformed by inverse Casorati Converted into a three-dimensional space-time sequence , along slow time The energy of blood flow signal is accumulated to obtain the ultrasonic power Doppler imaging of blood flow signal; Frequency deviation calculation is performed to obtain ultrasound color Doppler velocity imaging of blood flow signals.

8. A new type of ultrasonic micro-blood flow imaging clutter suppression and enhancement system, characterized by: include: A model building module, wherein the model building module is used to obtain an orthogonal demodulated original three-dimensional ultrasound image sequence after beamforming of multi-frame and multi-angle ultrasound signals; According to the spatiotemporal characteristics of tissue, blood flow and noise in the orthogonal demodulated original 3D ultrasound image sequence, a low-rank sparse 3D total variation robust principal component analysis theoretical model is constructed. A clutter suppression and enhancement module is used to construct an augmented Lagrangian function based on a low-rank sparse three-dimensional total variation robust principal component analysis theoretical model, and when the augmented Lagrangian function meets the convergence condition, a two-dimensional space-time matrix of the ultrasonic micro-blood flow signal after clutter suppression and enhancement is obtained; The image acquisition module is used to reorganize the two-dimensional space-time matrix of ultrasonic micro-blood flow signals into a three-dimensional ultrasonic sequence, obtain the morphological structure of micro-vessels and the speed of micro-blood flow through ultrasonic Doppler imaging, and realize ultrasonic micro-blood flow imaging.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the novel method for clutter suppression and enhancement of ultrasonic micro-blood flow imaging as claimed in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the novel method for clutter suppression and enhancement of ultrasonic micro-blood flow imaging as claimed in any one of claims 1 to 7 are implemented.

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