An adaptive ultrasound blood flow signal enhancement method

Abstract

The application provides an adaptive ultrasonic blood flow signal enhancement method for improving the image signal-to-noise ratio of ultrasonic blood flow imaging. On the basis of singular value decomposition of an ultrasonic signal, a spatial feature matrix is divided into a tissue subspace and a plurality of blood flow subspaces, a corresponding weight vector is calculated based on the spatial feature matrix of the blood flow subspace, the spatial feature matrix of the blood flow subspace is point-by-point weighted reconstruction through the weight vector, and finally, the ultrasonic image sequence after blood flow signal enhancement is reconstructed by merging all the subspaces obtained by division. The method provides an ultrasonic blood flow signal enhancement strategy without affecting the image frame rate, and can be used for high-definition ultrasonic imaging of blood flow in microvessels in a low signal-to-noise ratio scene.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ultrasonic blood flow signal processing, and in particular to an adaptive ultrasonic blood flow signal enhancement method, an electronic device, an apparatus, a system and a computer storage medium. BACKGROUND

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

[0003] However, the ultrasonic blood flow imaging process is easily disturbed by various noises, such as electronic noise, noise generated by ultrasonic attenuation, speckle noise, etc., and the ultrasonic signal transmission process of PW imaging is not focused, which further reduces the signal-to-noise ratio of the image. In order to solve this problem, the coherent plane wave compounding (CPWC) technology is proposed, which sequentially transmits multiple directional plane waves at different angles, then performs beamforming on the received echo signals of each angle, and performs coherent summation to obtain a composite image. However, the CPWC still cannot satisfactorily improve the quality of ultrasonic blood flow images.

[0004] In order to further improve the signal-to-noise ratio of ultrasonic blood flow imaging, the current mainstream methods mostly perform denoising processing on the time-space domain or frequency domain of the wall-filtered ultrasonic signals. Typical methods include non-local mean (NLM) denoising, blood flow enhancement based on hessian, and various morphological filters. These denoising algorithms can enhance blood flow signals to some extent while suppressing noise, but often cannot well process weak blood flow signals submerged in noise.

[0005] The Chinese patent application No. 201780013123.0 “Ultrasonic blood flow imaging” discloses a high signal-to-noise ratio blood flow imaging method based on low-rank matrix decomposition and adaptive threshold, which performs singular value decomposition on the collected ultrasonic signal data to obtain singular values and corresponding singular vectors, and further estimates low-order and high-order threshold values based on the decomposed data, wherein the low-order threshold value is used to distinguish signals attributed to tissue from signals attributed to blood flow, and the high-order threshold value is used to distinguish signals attributed to blood flow from signals attributed to noise. This patent suppresses noise signals by setting singular values greater than the high-order threshold value to zero. However, since signals attributed to blood flow and signals attributed to noise cannot be effectively separated by singular value decomposition, this technical solution also cannot enhance weak blood flow signals while suppressing noise signals.

[0006] Therefore, it is urgent to introduce a technology capable of adaptively enhancing blood flow signals in ultrasonic imaging, especially capable of enhancing weak blood flow signals submerged in severe background noise. SUMMARY

[0007] The technical problem solved by the present application is to address the deficiencies of the prior art, and to provide an adaptive ultrasonic blood flow signal enhancement method, electronic device, apparatus, system and computer storage medium for realizing small blood flow imaging with high signal-to-noise ratio.

[0008] The technical solution provided by the present application to solve the above problems is to provide an adaptive ultrasonic blood flow signal enhancement method, electronic device, apparatus, system and computer storage medium, which is used to enhance blood flow signals and suppress background tissues and noise in the process of ultrasonic blood flow imaging based on processing U obtained by singular value decomposition S=UΔV*.

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

[0010] 2. Deform the three-dimensional matrix S0 into a two-dimensional Cauchy matrix S1 with size (X×Z, N), and obtain left matrix U, right matrix V and singular value matrix Δ by singular value decomposition S=UΔV*; wherein * represents the conjugate transpose of the matrix.

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

[0012] The specific steps of the adaptive block strategy are as follows:

[0013] (1) Sort the singular values according to their size, and take the sum of the squares of the singular values as the energy; select a singular value set λ1 whose energy ratio is less than a preset value P, 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 application, P is preferably 98%-99.5%;

[0014] (2) Divide the remaining energy into K-1 parts to obtain K-1 singular value sets λ k ; further form a singular value matrix Δ k from λ k , and Δ k corresponds to K-1 left block matrices Uk and K-1 right block matrices V k Where k=2,…,K.

[0015] Thus, adaptive block division of U and V is achieved, resulting in tissue space U1, V1, and Δ1, as well as blood flow space U. k V k and Δ k (k=2,…,K).

[0016] 4. Set the value in U1 to zero to remove the background tissue, resulting in U1'.

[0017] 5. Based on the left matrix U corresponding to the remaining K-1 blood flow subspaces k (k=2,…,K), through an adaptive blood flow reweighting strategy, the weight vector B of the corresponding K-1 blood flow subspaces is obtained. k (k=2,…,K), the specific steps are as follows:

[0018] (1) For each complex matrix U in the K-1 blood flow subspaces k The amplitude value is U k amp Further calculate U separately k amp By analyzing the statistical characteristics of each row vector, we obtain K-1 column vectors J, each with a size of (X×Z,1). k The statistical characteristics can be the mean, variance, or information entropy.

[0019] (2) J k Transformed into a two-dimensional matrix M of size (X,Z) k ;

[0020] (3) For M k Min-Max normalization is performed, followed by histogram equalization to obtain M. k ';

[0021] (4) Based on M k Using the classic background suppression algorithm, M is obtained. k '';

[0022] (5) M k Reshape it into a column vector of size (X×Z,1) to obtain U k The corresponding weight vector B k .

[0023] In the above steps, the histogram equalization method is preferably the CLAHE algorithm; the background suppression algorithm is preferably the top-hat filtering algorithm.

[0024] 6. Regarding Uk each column vector in B k (k=2,…,K) to obtain left matrix U k (k=2,…,K).

[0025] 7. Merge U1' and U k (k=2,…,K) corresponding to the remaining K-1 blood flow subspaces to obtain new left matrix U'.

[0026] Based on U', V and Δ, reconstruct new N frames of two-dimensional space-time complex matrix S2 by S2=U'ΔV*, further deform S2 into new N frames of three-dimensional space-time complex matrix S3, and finally take the amplitude of each complex value element in S3 to obtain S4; S4 is the N frames of ultrasound image sequence after blood flow signal enhancement.

[0027] Through the above method steps, the blood flow signal can be adaptively enhanced without affecting the frame rate of ultrasound imaging, so as to realize high signal-to-noise ratio ultrasound blood flow imaging.

[0028] The electronic device for adaptive ultrasound blood flow signal enhancement comprises a processor, a memory storing executable instructions, and a storage medium; wherein the processor is used to execute the computer program corresponding to the adaptive ultrasound blood flow signal enhancement method; and the storage medium is responsible for storing the computer program corresponding to the adaptive ultrasound blood flow signal enhancement method and ultrasound signal and image data.

[0029] The measurement device for adaptive ultrasound blood flow signal enhancement comprises: a singular value decomposition module M1, which is used to obtain U, V and Δ based on singular value decomposition of ultrasound echo IQ complex signal S0; an adaptive blocking module M2, which is used to block the characteristic space of singular value decomposition to obtain tissue subspaces and blood flow subspaces; a background tissue suppression module M3, which is used to set the elements in the tissue subspaces to zero to suppress the background tissue signal; a blood flow subspace weight vector calculation module M4, which is used to obtain blood flow subspace weight vector B k ; an adaptive blood flow enhancement module M5, which is used to perform blood flow enhancement on the spatial vectors in the blood flow subspaces; a matrix merging module M6, which is used to merge the blocked tissue subspaces and blood flow subspaces; and an ultrasound blood flow image reconstruction module M7, which is used to reconstruct the ultrasound image sequence after blood flow signal enhancement.

[0030] The measurement system for adaptive ultrasound blood flow signal enhancement comprises an ultrasound signal acquisition device and the electronic device or imaging device.

[0031] The computer storage medium for adaptive ultrasound blood flow signal enhancement comprises: responsible for storing the computer program corresponding to the adaptive ultrasound blood flow signal enhancement method and the ultrasound signal and image data, for the electronic device to execute the adaptive ultrasound blood flow signal enhancement method. BRIEF DESCRIPTION OF DRAWINGS

[0032] Fig. 1 is a flowchart of an adaptive ultrasound blood flow signal enhancement method according to an embodiment of the present application.

[0033] Fig. 2 is a flowchart of an adaptive block strategy calculation according to an embodiment of the present application.

[0034] Fig. 3 is a flowchart of an adaptive blood flow reweighting weight vector calculation according to an embodiment of the present application.

[0035] Fig. 4 is a schematic diagram of a module of an adaptive ultrasound blood flow signal enhancement method processing device according to an embodiment of the present application.

[0036] Fig. 5 is an electronic device according to an embodiment of the present application.

[0037] Fig. 6 is a two-dimensional visualization of an adaptive blood flow reweighting weight vector according to an embodiment of the present application.

[0038] Fig. 7 is a schematic diagram of the denoising effect before and after the adaptive ultrasound blood flow signal enhancement method according to an embodiment of the present application. DETAILED DESCRIPTION

[0039] The present application will be further described in detail below with reference to the accompanying drawings: Figure 1 Fig. 1 is a flowchart of an adaptive ultrasound blood flow signal enhancement method according to an embodiment of the present application.

[0040] As shown in Fig. 1, the method comprises the following steps: Figure 1 Step 101: Collecting continuous N frames of ultrasound signals of a target part through an ultrasound acquisition system and an ultrasound probe.

[0041] In one embodiment of the present application, a 15MHz center frequency ultrasound probe is used to collect 500 frames of ultrasound signals of a mouse tumor part, where the tumor part has chaotic blood vessel growth and low signal-to-noise ratio of ultrasound imaging, which is suitable as a specific implementation object of the present application.

[0042] The collected IQ complex signal matrix S0 is deformed into a two-dimensional Cauchy matrix S1, singular value decomposition is performed on S1 to obtain a singular value matrix Δ and two singular vector matrices U and V, and the singular values are sorted in descending order, where the low-order singular values are greater than the high-order singular values.

[0043] For the ultrasound signals, the singular vector matrices U and V correspond to the spatial and temporal characteristics of the ultrasound signals respectively, and the tissue signals are usually located in the singular vectors corresponding to the low-order singular values, while the blood flow signals and the noise are located in the singular vectors corresponding to the higher-order singular values.

[0044] In order to enhance the blood flow signals in the singular vectors and suppress the tissue signals and the noise, the singular vector matrices U and V are blocked using an adaptive blocking strategy, as indicated by step 104.

[0045] In one specific embodiment of the present application, the number of blocks is set to 6 and the blocks are divided according to the energy.

[0046] The calculation process of the adaptive blocking strategy is shown in Figure 2 Since the eigenvectors obtained by SVD are normalized, the corresponding energy is completely contained in the corresponding singular value, and the relationship between the energy and the singular value is described by the Frobenius norm, so the sequential sum of squares of the singular values can be calculated as the energy.

[0047] Considering that the tissue signals usually have much higher energy than the blood flow signals, the singular vectors corresponding to the tissue subspace are first obtained by selecting the singular values with an energy proportion less than a preset value P, denoted as U1 and V1, and in one specific embodiment of the present application, the preset value P is 99.5%.

[0048] Next, the energy contained in the remaining singular values is divided into 5 parts, obtaining 5 groups of singular values, and 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.

[0049] Next, the U k corresponding to the blocked tissue subspace and blood flow subspace are processed respectively.

[0050] The tissue signals corresponding to U1 are considered as "clutter signals" in the context of blood flow imaging, so all the elements in U1 can be set to zero to suppress the tissue signals.

[0051] For the remaining blood flow subspaces, the blood flow characteristics contained in each blood flow subspace are different, and the U k corresponding to each blood flow subspace can be adaptively blood flow reweighted to enhance the blood flow signals therein.

[0052] In one specific embodiment of the present application, the corresponding weight matrix B k is calculated based on U k , and the calculation process of B k is shown in Figure 2 : The complex matrix U kEach element in the matrix is ​​taken to obtain the real matrix U. k amp Based on the fact that the noise signal is uniformly distributed along the singular value dimension and has lower energy, it can be calculated that U k amp The statistical characteristics of row vectors distinguish them from blood flow signals.

[0053] In a specific embodiment of the present invention, U is calculated. k amp The mean of each row vector in the vector is used to obtain the corresponding column vector J. k .

[0054] To further suppress background noise and enhance blood flow signals, J k The image is converted to a two-dimensional image and processed using image processing algorithms. In one specific embodiment of the invention, Min-Max normalization, adaptive histogram equalization (CLAHE), and top-hat filtering algorithms are used to process J. k Two-dimensional image form M k Processing yields M k '', will M k The weight vector B can be obtained by transforming it back into a column vector. k .

[0055] Weight vector B k The significance lies in B k Locations with larger weight values ​​are more likely to have blood flow signals, while locations with smaller weight values ​​are more likely to have background noise.

[0056] Figure 6 The weight vector B is shown for each of the five blood flow subspaces of the tumor ultrasound signal. k Two-dimensional visualization, namely M k '', M k The blood flow signal is highlighted, background noise is suppressed, and the M corresponding to each blood flow subspace is... k '' reflects different blood flow signal characteristics.

[0057] Using weight matrix B k U of the corresponding blood flow subspace k Weighting, that is, weighting U k The singular vectors in the data are reconstructed point by point using weighted methods, as indicated in step 107.

[0058] In one specific embodiment of the present invention, for U k and the corresponding B k Perform the Adama product so that B k with U keach column vector in U1 is multiplied by U1 to enhance the blood flow signal carried by each singular vector in each blood flow subspace while suppressing the noise signal.

[0059] U1 is set to zero to obtain U1' and U k U is obtained by adaptively weighting U k ' (k = 2, …, 6) and merging U k (k = 1, …, 6) along the column vector dimension, so that the singular vectors and singular values of the merged U' can be one-to-one corresponding.

[0060] In one specific embodiment of the present application, U k ' k k (k = 1, …, 6) along the column vector dimension, so that the singular vectors and singular values of the merged U' can be one-to-one corresponding.

[0061] Using the processed U', combining V and Δ, and reconstructing a new two-dimensional ultrasound space-time complex matrix S2 through U' Δ V*, the blood flow signal is enhanced, S2 is deformed into a three-dimensional matrix S3, and the amplitude of the elements is obtained. N frames of ultrasound image sequence after blood flow signal enhancement.

[0062] In one specific embodiment of the present application, the processing result of the above specific implementation method on the tumor blood flow signal is shown in Figure 7 .

[0063] Compared with before processing, the signal-to-noise ratio of the blood flow image is significantly improved, as shown in the white dashed line box marked area in the figure, after processing by the present application, some weak blood flow signals submerged by noise are significantly enhanced.

[0064] Corresponding to the above specific implementation method, Figure 4 a module schematic diagram of an adaptive ultrasound blood flow signal enhancement processing device provided by one specific embodiment of the present application is shown, which comprises:

[0065] (1) An ultrasound signal acquisition module M0 responsible for obtaining an ultrasound echo IQ complex signal S0 of an imaging part;

[0066] (2) A singular value decomposition module M1 responsible for obtaining U, V and Δ based on the ultrasound echo IQ complex signal S0 through singular value decomposition;

[0067] (3) An adaptive blocking module M2 responsible for obtaining K subspaces and corresponding U k , V k and Δ k based on U, V and Δ by using an energy adaptive blocking strategy;

[0068] (4) A background tissue suppression module M3 responsible for setting all elements in U1 to zero to obtain U1';

[0069] (5) a blood flow sub-space weight vector calculation module M4, responsible for calculating blood flow sub-space weight vectors B k (k=2,...,K) based on U k (k=2,...,K) through an adaptive blood flow re-weighting strategy.

[0070] (6) an adaptive blood flow enhancement module M5, responsible for calculating U k (k=2,...,K) and B k (k=2,...,K) based on U k (k=2,...,K) and B k (k=2,...,K) through calculating the Hadamard product of U k (k=2,...,K) and B

[0071] (7) a matrix merging module M6, responsible for merging U' k (k=1,...,K) in descending order of singular values to obtain U';

[0072] (8) an ultrasound blood flow image reconstruction module M7, responsible for obtaining S2 through S2=U'ΔV* based on U', V and Δ, and further obtaining S3 through matrix deformation, and finally obtaining the ultrasound image sequence S4 after blood flow signal enhancement by taking the amplitude part of the elements of S3.

[0073] In addition, the electronic device of the embodiment of the present application is shown in Figure 5 .

[0074] As shown in the figure, the electronic device 20 includes one or more processors 201 and a memory 202.

[0075] The processor 201 can be a central processing unit (CPU) or other forms of processing units with data processing and / or instruction execution capabilities, and can control other components in the electronic device 20 to perform desired functions.

[0076] The memory 202 can include one or more computer program products, which can include various forms of computer readable storage media, such as volatile memory and / or non-volatile memory.

[0077] The volatile memory may, for example, include random access memory (RAM), cache memory (cache), etc.

[0078] The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions can be stored on the computer readable storage medium, and the processor 201 can run the program instructions to implement the adaptive ultrasound blood flow signal enhancement method of the various embodiments described above and / or other desired functions.

[0079] The computer-storable medium may also store, for example, the complex IQ signal S0 of the ultrasound echo from the imaging site, the singular value matrix Δ obtained by SVD, the singular matrices U and V, and the block-divided singular matrix U. k Weight vector B k Various other content.

[0080] In one specific embodiment of the invention, the electronic device 20 may further include an input device 203 and an output device 204, these components being interconnected via a bus system and / or other forms of connection mechanism (not shown). The input device 203 may include, for example, a keyboard, a mouse, etc.

[0081] The output device 204 can output various information to the outside, including, for example, the complex IQ signal S0 of the ultrasound echo of the imaging site, the singular value matrix Δ obtained by SVD, the singular matrices U and V, and the block-divided singular matrix U. k Weight vector B k Various content, etc. The output device 204 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0082] Of course, for the sake of simplicity, Figure 5 Only some of the components of the electronic device 20 that are relevant to the present invention are shown, and components such as buses, input / output interfaces, etc. are omitted.

[0083] In addition, depending on the specific application, the electronic device 20 may include any other suitable components.

[0084] In addition to the methods and devices described above, embodiments of the present invention may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the adaptive ultrasound blood flow signal enhancement methods according to various embodiments of the present invention described above.

[0085] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages.

[0086] The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0087] Furthermore, embodiments of the present application can also be a computer readable storage medium having stored thereon computer program instructions which, when run by a processor, cause the processor to perform steps of the adaptive ultrasound blood flow signal enhancement method according to various embodiments of the present application described above in the specification.

[0088] The computer readable storage medium can take any combination of one or more of the following.

[0089] The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium may, for example, include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the above.

[0090] More specific examples (a non-exhaustive list) of the readable storage medium include an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0091] The embodiments of the present application described above are merely preferred embodiments, and the present application is not limited to the above embodiments. Within the knowledge of those skilled in the art, various changes can be made to the embodiments without departing from the spirit of the present application. The above description is merely a preferred embodiment of the present application, and is not intended to limit the present application in any form. Although the present application has been disclosed with reference to the preferred embodiments above, the present application is not limited to the above embodiments. Within the knowledge of those skilled in the art, various changes can be made to the embodiments without departing from the spirit of the present application. The above description is merely a preferred embodiment of the present application, and is not intended to limit the present application in any form. Although the present application has been disclosed with reference to the preferred embodiments above, the present application is not limited to the above embodiments. Within the knowledge of those skilled in the art, various changes can be made to the embodiments without departing from the spirit of the present application. The above description is merely a preferred embodiment of the present application, and is not intended to limit the present application in any form. Although the present application has been disclosed with reference to the preferred embodiments above, the present application is not limited to the above embodiments. Within the knowledge of those skilled in the art, various changes can be made to the embodiments without departing from the spirit of the present application. The above description is merely a preferred embodiment of the present application, and is not intended to limit the present application in any form. Although the present application has been disclosed with reference to the preferred embodiments above, the present application is not limited to the above embodiments. Within the knowledge of those skilled in the art, various changes can be made to the embodiments without departing from the spirit of the present application. The above description is merely a preferred embodiment of the present application, and is not intended to limit the present application in any form. Although the present application has been disclosed with reference to the preferred embodiments above, the present application is not limited to the above embodiments. Within the knowledge of those skilled in the art, various changes can be made to the embodiments without departing from the spirit of the present application. The above description is merely a preferred embodiment of the present application, and is not intended to limit the present application in any form.

Claims

1. An adaptive ultrasound blood flow signal enhancement method, characterized by, Based on singular value decomposition S=UΔV* of N-frame ultrasound echo IQ complex signal S, using the statistical characteristics of U along the singular value dimension to obtain the weight vector B corresponding to K-1 blood flow modes k (k=2,...,K); further using the weight vector B k Update U to U', and finally reconstruct the adaptive blood flow signal enhancement video S' through S'=U'ΔV*; the specific steps include: (1) collect N frames of ultrasonic echo IQ complex signals S0; wherein S0 is a three-dimensional complex matrix with a size of (X, Z, N); wherein 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 signals is X×Z; (2) deform S0 into a Cauchy matrix S1, and obtain left matrix U, right matrix V and singular value matrix Δ by singular value decomposition S=UΔV*; wherein S1 is a two-dimensional complex matrix with a size of (X×Z, N), and * represents the conjugate transpose of the matrix; (3) By adaptive block strategy, U and V are divided into K subspaces in the descending order of singular values in Δ, and the left matrix U k , the right matrix V k and the singular value matrix Δ k are obtained after blocking; wherein k=1 corresponds to the tissue space, and k=2,..., K corresponds to K-1 blood flow subspaces; wherein K=2-10; (4) set all elements in U1 to zero to remove background tissue and obtain U1'; (5) Left matrix U corresponding to the remaining K-1 blood flow subspaces k (k=2,...,K), through the adaptive blood flow reweighting strategy, the weight vector B of the corresponding K-1 blood flow subspaces is obtained k (k=2,...,K); wherein B k is a column vector with a size of (X×Z,1); (6) U k (k = 2,..., K) with B k (k = 2,..., K) to obtain a new left matrix U k (k = 2,..., K); U k ' is the result of the blood flow signal being adaptively enhanced and the noise being suppressed. (7) combine U1' and the U corresponding to the remaining K-1 blood flow subspaces to obtain a new left matrix U k '(k=2,...,K) to obtain a new left matrix U (8) based on U', V and Δ, reconstruct a new N frames of two-dimensional space-time complex matrix S2 by S2=U'ΔV*, further deform S2 into a new N frames of three-dimensional space-time complex matrix S3, and finally take the amplitude of each complex value element in S3 to obtain S4; S4 is the N frames of ultrasonic image sequence after blood flow signal enhancement.

2. The adaptive ultrasound blood flow signal enhancement method of claim 1, wherein, Based on U, V and Δ, The feature space is divided into K subspaces in descending order of singular value by an adaptive blocking strategy, to obtain a left matrix U k , a right matrix V k , and a singular value matrix Δ k after blocking; the specific steps include: (1) sort the singular values according to their sizes, and take the singular value set λ1 whose energy ratio is less than a preset value P as the singular value set; further, compose 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; wherein P is 98%-99.5%; (2) Divide the remaining energy into K-1 equal parts to obtain K-1 sets of singular values ​​λ. k Further by λ k Forming the singular value matrix Δ k , and Δ k Each corresponds to one of the K-1 left block matrices U k and K-1 right block matrices V k Where k=2,...,K.

3. The adaptive ultrasound blood flow signal enhancement method of claim 1, wherein, Based on the left matrix U corresponding to the remaining K-1 blood flow subspaces k (k=2,...,K), through the adaptive blood flow reweighting strategy, the weight vector B of the corresponding blood flow subspace is obtained k (k=2,...,K); the specific steps include: (1) For each complex matrix U in K-1 blood flow subspaces k , take the magnitude to get U k amp ; further calculate U k amp statistical features of each row vector, resulting in K-1 column vectors J of size (X x Z, 1) k ; where the statistical features are mean values or variance or information entropy; k=2,...,K; (2) J k is transformed into a two-dimensional matrix M of size (X,Z) k ; (3) M k Min-Max normalization, and then histogram equalization processing, to obtain M k '; (4) Based on M k , using the classical background suppression algorithm, M k "; (5) M k is reshaped into a column vector of size (X x Z, 1) to obtain U k The corresponding weight vector B k .

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

5. A measuring device, characterized in that The measurement device is used to implement the method of any one of claims 1 to 3, comprising: (a) a singular value decomposition module M1 responsible for obtaining U, V and Δ by singular value decomposition based on the ultrasonic echo IQ complex signal S0; (b) 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 ; (c) a background tissue suppression module M3 responsible for setting all elements in U1 to zero to obtain U1'; (d) a blood flow subspace weight vector computation module M4, responsible for computing blood flow subspace weight vectors based on U k (k = 2,..., K) by adaptive blood flow reweighting The policy gets a corresponding weight vector B k (k = 2,..., K); (e) an adaptive blood flow enhancement module M5, responsible for enhancing the blood flow based on U k (k = 2,..., K) and B k (k = 2,..., K) by solving Uk (k = 2,..., K) and B k The Hadamard product of U (k = 2,..., K) and B k (k = 2,..., K); (f) a matrix merging module M6, responsible for merging U k (k = 1,..., K) in descending order of singular values to obtain U'; (g) an ultrasonic blood flow image reconstruction module M7 responsible for obtaining S2 by S2=U'ΔV* based on U', V and Δ, and further obtaining S3 by matrix deformation, and finally taking the amplitude part of S3 elements to obtain the ultrasonic image sequence S4 after blood flow signal enhancement.

6. A measurement system characterized by, comprising: (1) a collection device M0 responsible for collecting ultrasonic echo IQ complex signals S0; (2) the electronic device of claim 4 or the measurement device of 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 method of any one of the above claims 1 to 3.

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