Self-adaptive ultrasonic blood flow signal enhancement method

Abstract

The invention provides a self-adaptive ultrasonic blood flow signal enhancement method which is used for improving the image signal-to-noise ratio of ultrasonic blood flow imaging. According to the method, on the basis of singular value decomposition of ultrasonic signals, a spatial feature matrix is partitioned to obtain tissue subspaces and a plurality of blood flow subspaces, corresponding weight vectors are further calculated on the basis of spatial feature matrixes of the blood flow subspaces, and the spatial feature matrixes of the blood flow subspaces are subjected to point-by-point weighted reconstruction through the weight vectors; and finally, combining all subspaces obtained by partitioning, and reconstructing to obtain an ultrasonic image sequence after blood flow signal enhancement. The method provides an ultrasonic blood flow signal enhancement strategy which does not influence the image frame rate, and can be used for performing high-definition ultrasonic imaging on blood flow in microvessels in a low signal-to-noise ratio scene.

Description

Technical Field

[0001] The invention relates to ultrasonic blood flow signal processing technology, and in particular to an adaptive ultrasonic blood flow signal enhancement method, electronic equipment, device, system and computer storage medium. Background Art

[0002] Many physiological studies have shown that changes in microvascular morphology and hemodynamics are closely related to the occurrence and development of various diseases. In recent years, the introduction of ultrafast plane wave (PW) imaging technology and advanced wall filters represented by singular value decomposition (SVD) have greatly improved the sensitivity of ultrasound imaging to blood flow, making ultrasound-based microvascular imaging possible.

[0003] However, ultrasound blood flow imaging is susceptible to various noise interferences, such as electronic noise, noise generated by ultrasound attenuation, speckle noise, etc., and the ultrasound signal transmission process of PW imaging is not focused, which will further reduce the signal-to-noise ratio of the image. In order to solve this problem, the coherent plane wave composite (CPWC) technology is proposed to transmit multiple directional plane waves in sequence at different angles, and then beamform the echo signals received at various angles, and perform coherent summation to obtain a composite image. However, the improvement of ultrasound blood flow image quality by CPWC is still unsatisfactory.

[0004] In order to further improve the signal-to-noise ratio of ultrasound blood flow imaging, most of the current mainstream methods perform denoising from the time-space domain or frequency domain of the ultrasound signal after wall filtering. Typical methods include non-local mean (NLM) denoising, Hessian-based blood flow enhancement, and various morphological filters. These denoising algorithms can enhance blood flow signals while suppressing noise to a certain extent, but they often cannot handle weak blood flow signals that are submerged in noise well.

[0005] The Chinese patent application "Ultrasound Blood Flow Imaging" with patent application number 201780013123.0 discloses a high signal-to-noise ratio blood flow imaging method based on low-rank matrix decomposition and adaptive cutoff value, which performs singular value decomposition on the collected ultrasound signal data to obtain singular values ​​and corresponding singular vectors, and further estimates low-order cutoff values ​​and high-order cutoff values ​​based on the decomposed data, wherein the low-order cutoff value is used to distinguish the signal attributed to the tissue from the signal attributed to the blood flow, and the high-order cutoff value is used to distinguish the signal attributed to the blood flow from the signal attributed to the noise. The patent suppresses the noise signal by setting the singular value greater than the high-order cutoff value to zero, but since the signal attributed to the blood flow and the signal attributed to the noise cannot be effectively separated by singular value decomposition, this technical solution is also difficult to achieve while suppressing the noise signal and enhancing the weak blood flow signal.

[0006] Therefore, there is an urgent need to introduce a technology that can adaptively enhance blood flow signals in ultrasound imaging, especially a technology that can enhance weak blood flow signals that are submerged in heavy background noise. Summary of the invention

[0007] The technical problem to be solved by the present invention is to address the deficiencies of the above-mentioned prior art and to propose an adaptive ultrasonic blood flow signal enhancement method, electronic equipment, device, system and computer storage medium for achieving high signal-to-noise ratio micro blood flow imaging.

[0008] The technical solution proposed by the present invention to solve the above-mentioned problem is to provide an adaptive ultrasonic blood flow signal enhancement method, electronic equipment, device, system and computer storage medium, which are used in the process of ultrasonic blood flow imaging to enhance the blood flow signal and suppress the background tissue and noise based on processing U obtained by singular value decomposition S=UΔV*.

[0009] The method comprises the following specific steps: 1. Using an ultrasonic signal acquisition system to acquire and obtain an N-frame ultrasonic echo IQ complex signal matrix S0 of size (X, Z, N) through beam synthesis.

[0010] 2. Transform the three-dimensional matrix S0 into a two-dimensional Casoraday matrix form S1 of size (X×Z,N), and use the singular value decomposition S=UΔV* to obtain the left matrix U, the right matrix V and the singular value matrix Δ; where * represents the conjugate transpose of the matrix.

[0011] 3. Through the adaptive block strategy, U and V are divided into K subspaces according to the descending order of the singular values ​​in Δ, and the left matrix U after block division is obtained k , right matrix V k And the singular value matrix Δ k In the present invention, the number of segments K is preferably 2-10, and the adaptive blocking strategy is preferably an energy-based subspace blocking method.

[0012] The specific steps of the adaptive block strategy are as follows: (1) Sort by singular value size and use the sum of squares of singular values ​​as energy; select a singular value set λ1 whose energy ratio is less than a preset value P, and further form a singular value matrix Δ1 from λ1, and the left block matrix and the right block matrix corresponding to Δ1 are U1 and V1 respectively; in the present invention, P is preferably 98%-99.5%; (2) Divide the remaining energy equally into K-1 parts, and obtain K-1 singular value sets λ k ; further by λ k Composed singular value matrix Δ k , and Δ k Corresponding to K-1 left block matrices U respectively k And K-1 right block matrices Vk ; where k=2,…,K.

[0013] Thus, adaptive segmentation of U and V is achieved to obtain tissue space U1, V1 and Δ1 and blood flow space U k 、V k and Δ k (k=2,…,K).

[0014] 4. Set U1 to zero to remove background tissue and obtain U1'.

[0015] 5. Based on the left matrix U corresponding to the remaining K-1 blood flow subspaces k (k=2,…,K), and the weight vectors B of the corresponding K-1 blood flow subspaces are obtained through the adaptive blood flow reweighting strategy k (k=2,…,K), the specific steps are as follows: (1) For each complex matrix U in the K-1 blood flow subspaces k , take the amplitude value to get U k amp ; Further calculate U k amp The statistical characteristics of each row vector in get K-1 column vectors J of size (X×Z,1) k ; The statistical feature can be the mean or variance or information entropy; (2) J k Transformed into a two-dimensional matrix M of size (X, Z) k ; (3) For M k Perform Min-Max normalization and then perform histogram equalization to obtain M k '; (4) Based on M k ', using the classic background suppression algorithm, we get M k ''; (5) M k ''Reshape into a column vector of size (X×Z,1) to get U k The corresponding weight vector B k .

[0016] In the above steps, the histogram equalization method is preferably a CLAHE algorithm; and the background suppression algorithm is preferably a top hat filtering algorithm.

[0017] 6. U k Each column vector in (k=2,…,K) is related to B k (k=2,…,K) do Hadamard product to get the left matrix U where the blood flow signal is adaptively enhanced and the noise is suppressed k'(k=2,…,K).

[0018] 7. U1' and the remaining K-1 blood flow subspaces corresponding to U k '( k=2,…,K) merge to get the new left matrix U'.

[0019] Based on U', V and Δ, a new N-frame two-dimensional space-time complex matrix S2 is reconstructed through S2=U'ΔV*, and S2 is further transformed into a new N-frame three-dimensional space-time complex matrix S3. Finally, the amplitude of each complex-valued element in S3 is taken to obtain S4; S4 is the N-frame ultrasound image sequence after blood flow signal enhancement.

[0020] Through the above method steps, it is convenient to adaptively enhance the blood flow signal without affecting the ultrasonic imaging frame rate, thereby achieving ultrasonic blood flow imaging with a high signal-to-noise ratio.

[0021] The electronic device for adaptive ultrasonic blood flow signal enhancement includes a processor, a memory storing executable instructions, and a storage medium; wherein the processor is used to execute a computer program corresponding to the adaptive ultrasonic blood flow signal enhancement method; and the storage medium is responsible for storing the computer program corresponding to the adaptive ultrasonic blood flow signal enhancement method as well as ultrasonic signals and image data.

[0022] The measuring device for adaptively enhancing ultrasonic blood flow signals comprises: a singular value decomposition module M1, used for obtaining U, V and Δ based on the singular value decomposition of the ultrasonic echo IQ complex signal S0; an adaptive block module M2, used for dividing the singular value decomposition feature space into blocks to obtain a tissue subspace and a blood flow subspace; a background tissue suppression module M3, used for setting elements in the tissue subspace to zero to suppress background tissue signals; and a blood flow subspace weight vector calculation module M4, used for obtaining a blood flow subspace weight vector B k ; Adaptive blood flow enhancement module M5, used to enhance the blood flow of the spatial vector in the blood flow subspace; matrix merging module M6, used to merge the blocked tissue subspace and blood flow subspace; ultrasonic blood flow image reconstruction module M7, used to reconstruct an ultrasonic image sequence with enhanced blood flow signals.

[0023] The measurement system for adaptive ultrasonic blood flow signal enhancement comprises: an ultrasonic signal acquisition device and the electronic device or imaging device.

[0024] The computer storage medium for adaptive ultrasonic blood flow signal enhancement includes: a computer program for storing the adaptive ultrasonic blood flow signal enhancement method, as well as ultrasonic signals and image data, which is used for the electronic device to execute the adaptive ultrasonic blood flow signal enhancement method. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] FIG1 is a schematic block diagram of a method for adaptively enhancing ultrasonic blood flow signals according to an embodiment of the present invention.

[0026] FIG. 2 is a schematic block diagram of an adaptive blocking strategy calculation process according to an embodiment of the present invention.

[0027] FIG3 is a schematic block diagram of a calculation process of an adaptive blood flow re-weighting weight vector according to an embodiment of the present invention.

[0028] FIG. 4 is a schematic diagram of a module of an adaptive ultrasonic blood flow signal enhancement processing device according to an embodiment of the present invention.

[0029] FIG. 5 is an electronic device according to an embodiment of the present invention. FIG6 is a schematic diagram of a two-dimensional visualization of an adaptive blood flow re-weighting weight vector according to an embodiment of the present invention. FIG. 7 is a schematic diagram of the denoising effect before and after processing by the adaptive ultrasonic blood flow signal enhancement method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The present invention is further described in detail below in conjunction with the accompanying drawings: Figure 1 It is an overall flow chart of the adaptive ultrasonic blood flow signal enhancement method of the present invention.

[0031] like Figure 1 As shown: N frames of continuous ultrasound signals of the target part are collected by the ultrasound collection system and the ultrasound probe, as indicated by step 101.

[0032] In a specific embodiment of the present invention, an ultrasound probe with a center frequency of 15 MHz is used to collect 500 frames of ultrasound signals at a mouse tumor site, wherein the blood vessels at the tumor site grow in a disorderly manner and the ultrasound imaging signal-to-noise ratio is relatively low, which is suitable as a specific implementation object of the present invention.

[0033] The collected IQ complex signal matrix S0 is transformed into a two-dimensional Casoraday matrix S1, and singular value decomposition is performed on S1 to obtain a singular value matrix Δ and two singular vector matrices U and V. The singular values ​​are sorted in descending order, where low-order singular values ​​are greater than high-order singular values.

[0034] For ultrasound signals, the singular vector matrices U and V correspond to the spatial and temporal characteristics of ultrasound signals, respectively. Tissue signals are usually located in the singular vectors corresponding to low-order singular values, while blood flow signals and noise are located in the singular vectors corresponding to higher-order singular values.

[0035] In order to enhance the blood flow signal in the singular vector and suppress the tissue signal and noise, the singular vector matrices U and V are partitioned using an adaptive partitioning strategy, as indicated in step 104 .

[0036] In a specific embodiment of the present invention, the number of blocks is set to 6 and the blocks are divided according to energy.

[0037] The calculation process of the adaptive block strategy is as follows: Figure 2 As shown: Since the eigenvectors obtained by SVD are normalized, their corresponding energy is completely contained in the corresponding singular values. The relationship between energy and singular values ​​is described by the Frobenius norm, so the sequential sum of squares of the singular values ​​can be calculated as the energy.

[0038] Considering that tissue signals usually have much higher energy than blood flow signals, we first select singular values ​​whose energy proportion is less than the preset value P to obtain the singular vectors corresponding to the tissue subspace, which are denoted as U1 and V1. In a specific embodiment of the present invention, the preset value P is 99.5%.

[0039] Next, the energy contained in the remaining singular values ​​is divided into 5 parts, and 5 groups of singular values ​​are obtained. The singular vectors corresponding to each group of singular values ​​are regarded as an independent blood flow subspace, denoted as U k and V k , where k=2,…,6.

[0040] Next, the U corresponding to the divided tissue subspace and blood flow subspace is k Process them separately.

[0041] The tissue signal corresponding to U1 is considered as a "clutter signal" in the context of blood flow imaging, so all elements therein can be set to zero to suppress the tissue signal.

[0042] For the remaining blood flow subspaces, each blood flow subspace contains different blood flow characteristics, and the U corresponding to each blood flow subspace can be k Adaptive blood flow reweighting is performed to enhance the blood flow signal.

[0043] In a specific embodiment of the present invention, based on U k Calculate the corresponding weight matrix B k , B k The calculation process is as follows Figure 2 As shown: complex matrix U k Each element in is taken as the magnitude to obtain the real matrix U k amp , based on the fact that the noise signal is evenly distributed along the singular value dimension and has lower energy, it can be calculated by k amp The statistical characteristics of the row vector distinguish it from the blood flow signal.

[0044] In a specific embodiment of the present invention, U is calculated k ampThe mean of each row vector in gets the corresponding column vector J k .

[0045] In order to further suppress background noise and enhance blood flow signals, J k The image is converted into a two-dimensional image and processed using an image processing algorithm. In a specific embodiment of the present invention, Min-Max normalization, adaptive histogram equalization (CLAHE) and top hat filtering algorithms are used to process J k The two-dimensional image form M k Processing to obtain M k '', M k ''Transform back to column vector to get weight vector B k .

[0046] Weight vector B k The meaning of B is that k The location with a larger weight value is more likely to have a blood flow signal, and the location with a smaller weight value is more likely to be background noise.

[0047] Figure 6 The weight vector B corresponding to each of the five blood flow subspaces of the tumor ultrasound signal is shown. k Two-dimensional visualization of M k '', M k In '', the blood flow signal is highlighted, the background noise is suppressed, and the M corresponding to each blood flow subspace k '' reflects different blood flow signal characteristics.

[0048] Using the weight matrix B k U of the corresponding blood flow subspace k Weighted, that is, U k The singular vectors in are reconstructed point by point by weighted reconstruction, as indicated in step 107.

[0049] In a specific embodiment of the present invention, k and the corresponding B k Make the Hadamard product so that B k with U k Each column vector in is multiplied element by element, with the aim of enhancing the blood flow signal carried by each spatial singular vector in each blood flow subspace and suppressing the noise signal.

[0050] U1' and U k (k=2,…,6) Adaptively weighted U k '(k=2,…,6) are combined to obtain U', as indicated in step 108.

[0051] In a specific embodiment of the present invention, U k ' (k=1,…,6) are merged along the column vector dimension so that the singular vectors and singular values ​​of U' after merging can correspond one to one.

[0052] The processed U' is combined with V and Δ to reconstruct a new two-dimensional ultrasonic spatiotemporal complex matrix S2 through U'ΔV*. S2 is transformed into a three-dimensional matrix S3 and the amplitude of its elements is taken to obtain an N-frame ultrasonic image sequence after blood flow signal enhancement.

[0053] In a specific embodiment of the present invention, the processing result of the tumor blood flow signal by the above specific implementation method is Figure 7 Shown in.

[0054] Compared with before processing, the signal-to-noise ratio of the blood flow image is significantly improved, as shown in the area marked by the white dotted box in the figure. After processing by the present invention, some weak blood flow signals that were submerged by noise are significantly enhanced.

[0055] Corresponding to the above specific implementation method, Figure 4 A schematic diagram of a module of an adaptive ultrasonic blood flow signal enhancement processing device provided by a specific embodiment of the present invention is shown, comprising: (1) Ultrasonic signal acquisition module M0, responsible for obtaining the ultrasonic echo IQ complex signal S0 of the imaging part; (2) Singular value decomposition module M1, responsible for obtaining U, V and Δ based on the ultrasonic echo IQ complex signal S0 through singular value decomposition; (3) Adaptive partitioning module M2 is responsible for obtaining K subspaces and corresponding U based on U, V and Δ using an energy adaptive partitioning strategy. k 、V k and Δ k ; (4) Background tissue suppression module M3, which is responsible for setting all elements in U1 to zero to obtain U1'; (5) Blood flow subspace weight vector calculation module M4 is responsible for k (k=2,…,K), the corresponding weight vector B is obtained through the adaptive blood flow reweighting strategy k (k=2,…,K); (6) Adaptive blood flow enhancement module M5, responsible for k (k=2,…,K) and B k (k=2,…,K), by finding U k (k=2,…,K) and B k The Hadamard product of (k=2,…,K) gives U k '(k=2,…,K); (7) Matrix merging module M6, responsible for k'(k=1,…,K), merge in descending order of singular values ​​to get U'; (8) Ultrasonic blood flow image reconstruction module M7 is responsible for obtaining S2 based on U', V and Δ through S2 = U'ΔV* and further obtaining S3 through matrix deformation. Finally, the amplitude part of the S3 element is taken to obtain the ultrasound image sequence S4 after the blood flow signal is enhanced.

[0056] In addition, the electronic device according to the embodiment of the present invention is Figure 5 Shown in.

[0057] As shown in the figure, the electronic device 20 includes one or more processors 201 and a memory 202 .

[0058] The processor 201 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 20 to perform desired functions.

[0059] Memory 202 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory.

[0060] The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache).

[0061] The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 201 may execute the program instructions to implement the adaptive ultrasonic blood flow signal enhancement method of each embodiment described above and / or other desired functions.

[0062] The computer storable medium may also store information such as the ultrasonic echo IQ complex signal S0 of the imaging part, the singular value matrix Δ obtained by SVD, the singular matrices U and V, and the singular matrix U after block division. k , weight vector B k And other contents.

[0063] In a specific embodiment of the present invention, the electronic device 20 may further include: an input device 203 and an output device 204, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown). The input device 203 may include, for example, a keyboard, a mouse, and the like.

[0064] The output device 204 can output various information to the outside, including the ultrasonic echo IQ complex signal S0 of the imaging part, the singular value matrix Δ obtained by SVD, the singular matrices U and V, and the singular matrix U after block division. k , weight vector B k The output device 204 may include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0065] Of course, to simplify, Figure 5 Only some of the components related to the present invention in the electronic device 20 are shown, and components such as buses, input / output interfaces, etc. are omitted.

[0066] In addition, the electronic device 20 may further include any other appropriate components according to specific application scenarios.

[0067] In addition to the above-mentioned methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the adaptive ultrasonic blood flow signal enhancement method according to various embodiments of the present invention described above in this specification.

[0068] The computer program product can be written in any combination of one or more programming languages ​​to write program codes for executing the operations of the embodiments of the present invention, and the programming languages ​​include object-oriented programming languages, such as Java, C++, etc., and also include conventional procedural programming languages, such as "C" language or similar programming languages.

[0069] The program code may execute entirely on the user's computing device, partly on the user's computing device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server.

[0070] In addition, an embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enables the processor to execute the steps of the adaptive ultrasonic blood flow signal enhancement method according to various embodiments of the present invention described above in this specification.

[0071] The computer readable storage medium may take the form of any combination of one or more computer readable media.

[0072] The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof.

[0073] More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more conductors, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM, an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0074] The above is a detailed description of the implementation mode of the present invention in conjunction with the accompanying drawings, but the present invention is not limited to the above implementation mode. Various changes can be made within the knowledge scope of ordinary technicians in the field without departing from the purpose of the present invention. The above is only a preferred embodiment of the present invention, and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modify the technical content disclosed above to an equivalent embodiment of equivalent changes without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent replacement and improvement of the above embodiments made according to the technical essence of the present invention, within the spirit and principle of the present invention, still belongs to the protection scope of the technical solution of the present invention.

Claims

1. An adaptive ultrasonic blood flow signal enhancement method, characterized in that: Based on the singular value decomposition S = UΔV* of the complex signal S of the N-frame ultrasound echo IQ, the weight vectors B corresponding to K-1 blood flow patterns are obtained by using the statistical characteristics of U along the singular value dimension k (k=2,…,K); further use the weight vector B k Update U to U', and finally reconstruct the adaptive blood flow signal enhanced video S' through S'=U'ΔV*; the specific steps include: (1) Acquire N frames of ultrasonic echo IQ complex signals S0; S0 is a three-dimensional complex value matrix of size (X, Z, N); X is the number of pixels in the array element direction, Z is the number of pixels in the depth direction, and the size of each frame of IQ complex signal is X×Z; (2) Transform S0 into a Casoladay matrix form S1, and use the singular value decomposition S = UΔV* to obtain the left matrix U, the right matrix V, and the singular value matrix Δ; where S1 is a two-dimensional complex-valued matrix of size (X×Z,N), and * represents the conjugate transpose of the matrix; (3) Through the adaptive block strategy, U and V are divided into K subspaces in descending order of the singular values ​​in Δ, and the left matrix U after block division is obtained k , right matrix V k And the singular value matrix Δ k ; where k=1 corresponds to tissue space, k=2,…,K corresponds to K-1 blood flow subspaces; where K=2-10; (4) Set all elements in U1 to zero to remove background tissue and obtain U1'; (5) Based on the left matrix U corresponding to the remaining K-1 blood flow subspaces k (k=2,…,K), and the weight vectors B of the corresponding K-1 blood flow subspaces are obtained through the adaptive blood flow reweighting strategy k (k=2,…,K); where B k is a column vector of size (X×Z,1); (6) U k Each column vector in (k=2,…,K) is related to B k (k=2,…,K) do Hadamard product to get the new left matrix U k '(k=2,…,K);U k 'That is, the blood flow signal is adaptively enhanced and the noise is suppressed; (7) U1' and the remaining K-1 blood flow subspaces corresponding to U k '( k=2,…,K) merge to get the new left matrix U'; (8) Based on U', V and Δ, a new N-frame two-dimensional space-time complex matrix S2 is reconstructed through S2 = U'ΔV*, and S2 is further transformed into a new N-frame three-dimensional space-time complex matrix S3. Finally, the amplitude of each complex-valued element in S3 is taken to obtain S4; S4 is the N-frame ultrasound image sequence after blood flow signal enhancement.

2. The method for adaptively enhancing ultrasonic blood flow signals according to claim 1, characterized in that: Based on U, V and Δ, the feature space is divided into K subspaces in descending order of singular values ​​through an adaptive block strategy, and the left matrix U after block division is obtained. k , right matrix V k And the singular value matrix Δ k ; The specific steps include: (1) Sort by singular value size and use the sum of squares of singular values ​​as energy; select a singular value set λ1 whose energy ratio is less than a preset value P, and further form a singular value matrix Δ1 from λ1, and the left block matrix and right block matrix corresponding to Δ1 are U1 and V1 respectively; where P is 98%-99.5%; (2) Divide the remaining energy into K-1 parts and obtain K-1 singular value sets λ k ; further by λ k Composed singular value matrix Δ k , and Δ k Corresponding to K-1 left block matrices U respectively k And K-1 right block matrices V k ; where k=2,…,K.

3. The adaptive ultrasonic blood flow signal enhancement method according to claim 1, characterized in that: Based on the left matrix U corresponding to the remaining K-1 blood flow subspaces k (k=2,…,K), and the weight vector B of the corresponding blood flow subspace is obtained through the adaptive blood flow reweighting strategy k (k=2,…,K); the specific steps include: (1) For each complex matrix U in the K-1 blood flow subspaces k , take the amplitude value to get U k amp ; Further calculate U k amp The statistical characteristics of each row vector in get K-1 column vectors J of size (X×Z,1) k ; The statistical feature is mean or variance or information entropy; k=2,…,K; (2) J k Transformed into a two-dimensional matrix M of size (X, Z) k ; (3) For M k Perform Min-Max normalization and then perform histogram equalization to obtain M k '; (4) Based on M k ', using the classic background suppression algorithm, we get M k ''; (5) M k ''Reshape into a column vector of size (X×Z,1) to get U k The corresponding weight vector B k .

4. An electronic device, comprising a processor and a computer storage medium, wherein the computer storage medium stores instructions executable by the processor, and the processor is configured to execute the instructions to implement the method according to any one of claims 1 to 3.

5. A measuring device, characterized in that: The measuring device is used to implement the measuring method of claims 1 to 3, comprising: (a) Singular value decomposition module M1 is responsible for obtaining U, V and Δ through singular value decomposition based on the ultrasonic echo IQ complex signal S0; (b) Adaptive partitioning module M2 is responsible for obtaining K subspaces and the corresponding U based on U, V and Δ using an energy adaptive partitioning strategy. k 、V k and Δ k ; (c) Background tissue suppression module M3, which is responsible for setting all elements in U1 to zero to obtain U1'; (d) Blood flow subspace weight vector calculation module M4 is responsible for k (k=2,…,K), the corresponding weight vector B is obtained through the adaptive blood flow reweighting strategy k (k=2,…,K); (e) Adaptive blood flow enhancement module M5, responsible for k (k=2,…,K) and B k (k=2,…,K), by finding U k (k=2,…,K) and B k The Hadamard product of (k=2,…,K) gives U k '(k=2,…,K); (f) Matrix merging module M6, responsible for k '(k=1,…,K), merge in descending order of singular values ​​to get U'; (g) Ultrasonic blood flow image reconstruction module M7 is responsible for obtaining S2 based on U', V and Δ through S2=U'ΔV* and further obtaining S3 through matrix deformation. Finally, the amplitude part of the S3 element is taken to obtain the ultrasound image sequence S4 after the blood flow signal is enhanced.

6. A measurement system, characterized in that: include: (1) The acquisition device M0 is responsible for acquiring the ultrasonic echo IQ complex signal S0; (2) The electronic device according to claim 4 or the measuring device according to claim 5.

7. A computer storage medium, characterized in that: The computer storage medium is used to store processor-executable instructions so that the processor executes the measurement method according to any one of claims 1 to 3.

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