Online clutter suppression method and system for ultrasonic tiny blood flow imaging

Abstract

The invention discloses an online clutter suppression method and system for ultrasonic tiny blood flow imaging, and the method comprises the steps: carrying out the ultrasonic collection based on ultra-high speed, obtaining a high-frame-frequency ultrasonic image, carrying out the modeling of a tissue signal and a blood flow signal through employing a robust principal component analysis IRLS-rPCA method based on iteration reweighted least squares, constructing an online IRLS-rPCA clutter filter, and carrying out the clutter suppression of the ultrasonic tiny blood flow imaging. The ultrasonic image is processed based on the online IRLS-rPCA clutter filter, clutter suppression is carried out, a blood flow signal in the current frame image is extracted and ultrasonic tiny blood flow imaging is carried out by utilizing the space-time correlation between the current frame image and the filtered image in an online calculation mode, and the ultrasonic tiny blood flow image is obtained. The method can better conform to an ultrasonic high-speed acquisition and frame-by-frame reconstruction mechanism, and is easier to realize ultra-fast micro blood flow imaging.

Description

Technical Field

[0001] The present invention relates to the technical field of medical ultrasonic imaging, and in particular to an online clutter suppression method and system for ultrasonic micro-blood flow imaging. Background Art

[0002] Ultrasonic micro-blood flow imaging has been one of the hot topics in the field of ultrasound imaging technology in the past decade. This imaging technology aims to extract blood vessels, especially blood flow signals in micro-vessels, for imaging through ultra-high-speed ultrasound signal acquisition and high-sensitivity spatiotemporal clutter filtering, suppressing tissue signals and noise. In ultrasonic micro-blood flow imaging, spatiotemporal clutter filtering is one of the most critical technologies, and its clutter filter performance has a direct impact on the extraction effect of micro-blood flow signals.

[0003] At present, the mainstream filtering method used for clutter filtering in ultrasound micro-blood flow imaging is robust principal component analysis (rPCA). This method assumes that the continuous ultrasound images acquired at ultra-high speed are composed of three parts: tissue signal, blood flow signal, and noise. Among them, the tissue is in a static or very slow motion state, and the echo signal has a high signal-to-noise ratio, and its signal presents a low-rank feature; the blood flow signal is in a dynamic, high-speed motion state, and the echo signal has a low signal-to-noise ratio. Since the spatial distribution of blood flow is relatively sparse compared to tissue, its signal presents a sparse feature; the noise is generally assumed to be Gaussian white noise that is unrelated to tissue signals and blood flow signals. The clutter filtering method based on rPCA extracts blood flow signals by constructing an optimization model. Although improvements based on the above basic model have been carried out, all of them use static rPCA, that is, the static-rPCA strategy to suppress the clutter and extract the blood flow signal of the tiny blood flow. The existing technology requires obtaining a certain number (usually hundreds of frames) of ultrasound images, and then performing clutter filtering to extract the blood flow signals in these ultrasound images at one time for blood flow imaging. If long-term continuous processing is required, it is necessary to obtain a new batch of ultrasound images, recalculate, and extract new blood flow signals.

[0004] The main drawback of the existing technology is that the static, batch-by-batch processing process is inconsistent with the frame-by-frame, fast reconstruction process of ultrasound images. First, the static rPCA filter needs to obtain a certain amount of ultrasound image data before it can start clutter suppression and blood flow signal extraction. The process of waiting for data slows down the speed of micro-blood flow imaging; secondly, each time a sufficient amount of ultrasound image data is obtained, the static rPCA filter needs to be reconfigured and run. After multiple rounds of iterations, it can achieve better clutter suppression and blood flow signal extraction effects after convergence. It usually takes several seconds to tens of seconds to reconfigure and run the static rPCA filter, which seriously hinders the real-time and even ultra-fast realization of ultrasound micro-blood flow imaging.

[0005] In summary, the existing rPCA-type clutter filters used for ultrasonic micro-blood flow imaging all adopt a static calculation method, which requires a certain number of ultrasonic images to perform clutter suppression and blood flow signal extraction, and needs to be reconfigured and run each time the image data is acquired. The above process is very time-consuming, which contradicts the real-time and ultrafast characteristics of ultrasonic micro-blood flow imaging. Summary of the invention

[0006] In order to solve the technical problem of time-consuming static calculation in the prior art, the present application proposes an online clutter suppression method and system for ultrasonic micro-blood flow imaging.

[0007] According to one aspect of the present invention, an online clutter suppression method for ultrasonic micro blood flow imaging is proposed, comprising the following steps:

[0008] S1. Based on ultra-high-speed ultrasound acquisition, high frame rate ultrasound images are acquired;

[0009] S2, using the iterative reweighted least squares based robust principal component analysis IRLS-rPCA method to model tissue signals and blood flow signals respectively, and construct an online IRLS-rPCA clutter filter;

[0010] S3. Processing the ultrasound image based on the online IRLS-rPCA clutter filter, utilizing the spatiotemporal correlation between the current frame image and the filtered image, performing clutter suppression through online calculation, extracting the blood flow signal in the current frame image, and performing ultrasonic micro-blood flow imaging.

[0011] Furthermore, the ultrasound image in step S1 is a series of ultrasound images with high frame rate acquired by ultra-high-speed ultrasound acquisition of multi-angle plane waves or spherical waves, and the ultrasound image is Where N x 、N z is the number of pixels in the two dimensions of the ultrasound image, N tis the number of the ultrasonic images, and the format of the ultrasonic image is an orthogonal demodulation signal obtained by beamforming.

[0012] Furthermore, the specific method of tissue signal modeling in step S2 is:

[0013] The beamformed data S(x,z,t) consists of three parts: the tissue signal C(x,z,t), the blood flow signal B(x,z,t), and the noise N(x,z,t), which is expressed as (S=C+B+N) after Casorati dimension transformation.

[0014] The tissue signal is represented as in is a set of bases describing the changes in tissue signals, N s is the N x and N z The product of c is the dimension of the tissue signal and satisfies r c <d c <<min(N s ,N t ), are the coordinate coefficients of the tissue signal under this set of bases;

[0015] A set of weight coefficients W is introduced for the tissue signal c , the tissue signal weight is defined as follows:

[0016]

[0017] Wherein, diag(·) represents the conversion of a vector into a diagonal matrix of corresponding dimension, ∈ is a very small constant greater than 0, and when performing online clutter filtering on the t-th frame image, the modeling method of the tissue signal is expressed as:

[0018]

[0019] Among them, λ c is the tissue signal penalty coefficient, ||·|| F Represents the matrix Frobenius norm.

[0020] Furthermore, the specific method of blood flow signal modeling in step S2 is: introducing a set of weights W for the blood flow signal b , the blood flow signal weight is defined as follows:

[0021] W b (i,j)=(B(,j) 2 +∈) -1 / 2

[0022] Where i represents the i-th pixel after a two-dimensional ultrasound image is arranged into a column vector, j represents the j-th ultrasound image, and the blood flow component B(:, t) in the t-th frame image is modeled as follows:

[0023]

[0024] Among them, λ b is the blood flow signal penalty coefficient, and ⊙ represents the Hadamard product.

[0025] Furthermore, the specific method of clutter suppression and blood flow signal extraction in step S3 is:

[0026] The online IRLS-rPCA clutter filter performs the clutter suppression and blood flow signal extraction by solving the optimization problem:

[0027]

[0028] in, Indicates that at time t, the tissue signal is at U c,t is the coordinate coefficient in the subspace spanned by a set of bases, M t =diag([μ t-1 ,μ t-2 ,…,1]) is a set of preset weighting coefficients that can apply greater weights to filtered images that are closer to the current image frame.

[0029] Solution:

[0030] At time t, the online IRLS-rPCA filter obtains the t-th frame image and updates B(:, t) and W alternately. b (:,t),V c (t,:):

[0031]

[0032] Where, (k): number of iterations (kth iteration), until the convergence condition is met:

[0033]

[0034] To U c and W c To perform a one-time update:

[0035]

[0036] Among them, M c,t Represents a set of time-varying weighting coefficients: μ cis a constant between 0 and 1. The filter obtains the image of the t+1th frame and repeats the above operation.

[0037] Furthermore, the clutter suppression and blood flow signal extraction in step S3 are improved by initializing the blood flow weight based on spatiotemporal constraints and jointly estimating the blood flow signal and the tissue signal coefficients.

[0038] Furthermore, the blood flow weight initialization based on time-space constraints includes:

[0039] Using the N before the current frame b The weight W of the blood flow signal in the current frame image to the frame filtered image b (:,t) for initialization:

[0040] W b (:,t)=W b (:,tN b :t-1)ν b

[0041] Among them, the vector ν b is defined as follows (ν∈(0,1]):

[0042]

[0043] Where T stands for transpose.

[0044] Furthermore, the joint estimation of the blood flow signal and tissue signal coefficients includes:

[0045] The blood flow signal component B(:, t) in the current frame image and the coordinate coefficient V of the tissue signal c (t,:) is jointly estimated:

[0046]

[0047] The basis U describing the tissue signal c And the weights of the tissue signals are updated:

[0048]

[0049] According to a second aspect of the present invention, an online clutter suppression system for ultrasonic micro blood flow imaging is proposed, comprising:

[0050] An image acquisition module configured to acquire ultrasound images with a high frame rate;

[0051] A signal modeling module, configured to model tissue signals and blood flow signals respectively according to an iteratively reweighted least squares based robust principal component analysis IRLS-rPCA method, and to construct an online IRLS-rPCA clutter filter;

[0052] An online clutter filtering module, configured to perform clutter suppression and extraction of the blood flow signal according to the online IRLS-rPCA clutter filter;

[0053] The micro blood flow imaging module is configured to perform ultrasonic micro blood flow imaging according to the extracted blood flow signal.

[0054] According to a third aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is processed and executed, the steps of an online clutter suppression method for ultrasonic micro-blood flow imaging are implemented.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] (1) Real-time extraction of blood flow signals in the current frame of ultrasound image to suppress clutter;

[0057] (2) After initialization, the filtering method does not need to be reconfigured during operation, and can continuously and quickly perform online clutter suppression and blood flow signal extraction. Compared with existing technologies, it is more in line with the mechanism of ultrasonic high-speed acquisition and frame-by-frame reconstruction, and is easier to achieve ultra-fast micro blood flow imaging. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated into and constitute a part of this specification. The accompanying drawings illustrate the embodiments and are used together with the description to explain the principles of the present invention. It will be easy to recognize other embodiments and many expected advantages of the embodiments because they become better understood by reference to the following detailed description. The elements of the drawings are not necessarily to scale with each other. The same reference numerals refer to corresponding similar parts.

[0059] Figure 1 A flow chart of the online IRLS-rPCA clutter filtering method according to the present invention is shown;

[0060] Figure 2 The result of continuously processing multiple frames of human liver micro-blood flow power Doppler imaging according to a specific embodiment of the present invention is shown;

[0061] Figure 3 A line graph showing the computational time consumption for processing each frame of ultrasound image (clutter suppression and blood flow signal extraction) according to a specific embodiment of the present invention is shown;

[0062] Figure 4 The schematic diagram of the structure of the online clutter suppression system for ultrasonic micro blood flow imaging of the present invention is shown. DETAILED DESCRIPTION

[0063] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0064] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0065] like Figure 1 An online clutter suppression method for ultrasonic micro-blood flow imaging is shown, comprising the following steps:

[0066] S1. Based on ultra-high-speed ultrasound acquisition, high frame rate ultrasound images are acquired;

[0067] S2, using the iterative reweighted least squares based robust principal component analysis (IRLS-rPCA) method to model tissue signals and blood flow signals respectively, and construct an online IRLS-rPCA clutter filter;

[0068] S3. The ultrasound image is processed based on the online IRLS-rPCA clutter filter. The spatiotemporal correlation between the current frame image and the filtered image is utilized to suppress clutter through online calculation, extract the blood flow signal in the current frame image, and perform ultrasonic micro-blood flow imaging.

[0069] Ultra-high-speed multi-angle plane waves are used to collect the echo signals of the target, and channel data in radio frequency (RF) format are obtained. The channel data is orthogonally demodulated and downsampled to obtain channel data in quadrature demodulation (in-phase and quadrature phase, IQ) format. The channel data is processed by dynamic focusing delay-and sum (DAS) beam synthesis technology to obtain IQ format ultrasound images at various angles. Then, the images at various angles are spatially coherently compounded to obtain multi-frame IQ data format ultrasound images. The ultrasound images are Where N x 、N z is the number of pixels in the two dimensions of the ultrasound image, N t is the number of ultrasound images, and the format of the ultrasound image is the orthogonal demodulation signal obtained by beamforming.

[0070] Tissue signal modeling:

[0071] The beamformed data S(x,z,t) consists of three parts: tissue signal C(x,z,t), blood flow signal B(x,z,t), and noise N(x,z,t). After Casorati dimension transformation, it is expressed as (S=C+B+N);

[0072] The tissue signal is represented as in is a set of bases describing the changes in tissue signals, N s is the N x and N z The product of c is the dimension of the organizational signal and satisfies r c <d c <<min(N s ,N t ), is the coordinate coefficient of the tissue signal under this set of basis;

[0073] Introduce a set of weight coefficients W for tissue signals c , the tissue signal weight is defined as:

[0074]

[0075] Where diag(·) represents the conversion of a vector into a diagonal matrix of the corresponding dimension, ∈ is a very small constant greater than 0. When performing online clutter filtering on the t-th frame image, the modeling method of the tissue signal is expressed as:

[0076]

[0077] Among them, λ c is the tissue signal penalty coefficient, ||·|| F Represents the matrix Frobenius norm.

[0078] Blood flow signal modeling: A set of weights W is introduced for blood flow signals b , the blood flow signal weight is defined as:

[0079] W b (i,j)=(B(i,j) 2 +∈) -1 / 2

[0080] Where i represents the i-th pixel after a two-dimensional ultrasound image is arranged into a column vector, j represents the j-th ultrasound image, and the blood flow component B(:, t) in the t-th frame image is modeled as follows:

[0081]

[0082] Among them, λ b is the blood flow signal penalty coefficient, and ⊙ represents the Hadamard product.

[0083] Clutter suppression and blood flow signal extraction:

[0084] The online IRLS-rPCA clutter filter performs clutter suppression and blood flow signal extraction by solving the optimization problem:

[0085]

[0086] in, Indicates that at time t, the tissue signal is at U c,t is the coordinate coefficient in the subspace spanned by a set of bases, M t =diag([μ t-1 ,μ t-2 ,…,1]) is a set of preset weighting coefficients that can apply greater weights to filtered images that are closer to the current image frame.

[0087] Clutter suppression and blood flow signal extraction are improved by initializing blood flow weights based on spatiotemporal constraints and jointly estimating blood flow and tissue signal coefficients.

[0088] Blood flow weight initialization based on spatiotemporal constraints:

[0089] Using the N before the current frame b The weight W of the blood flow signal in the current frame image to the frame filtered image b (:,t) for initialization:

[0090] W b (:,t)=W b (:,tN b :t-1)v b

[0091] Among them, the vector v b is defined as follows (v∈(0,1]):

[0092]

[0093] Where T stands for transpose.

[0094] Joint estimation of blood flow signal and tissue signal coefficients:

[0095] The coordinate coefficient V of the blood flow signal component B(:,t) and the tissue signal in the current frame image c (t,:) is jointly estimated:

[0096]

[0097] The basis U that describes the tissue signal c And update the weights of the organization signals:

[0098]

[0099] Solution:

[0100] At time t, the online IRLS-rPCA filter obtains the t-th frame image and updates B(:, t) and W alternately. b (:,t),V c (t,:):

[0101]

[0102] Where, (k): number of iterations (kth iteration), until the convergence condition is met:

[0103]

[0104] To U c and W c To perform a one-time update:

[0105]

[0106]

[0107] Among them, M c,t Represents a set of time-varying weighting coefficients: μ c is a constant between 0 and 1. The filter obtains the image of the t+1th frame and repeats the above operation.

[0108] like Figure 2 As shown, from left to right are the human liver micro-blood flow power Doppler imaging with acquisition times of 0 seconds, 12.5 milliseconds, 37.5 milliseconds, 87.5 milliseconds, and 187.5 milliseconds. It can be seen from the figure that the online filtering technology proposed in the present invention does not introduce additional artifacts or noise during the calculation process, and can stably achieve clutter filtering and blood flow imaging.

[0109] The computational time of online IRLS-rPCA clutter filter processing each frame of ultrasound image (clutter suppression and blood flow signal extraction) is as follows: Figure 3 As shown, in the processing Figure 2 For the corresponding data, the processing speed reached an average of 0.03±0.003 seconds per frame, and the corresponding blood flow imaging frame rate was about 33 frames per second, achieving fast and real-time micro blood flow imaging.

[0110] Figure 4The present invention is a schematic diagram of the structure of an online clutter suppression system for ultrasonic micro blood flow imaging, which mainly includes four modules: an image acquisition module, configured to acquire ultrasonic images with a high frame rate; a signal modeling module, configured to model tissue signals and blood flow signals respectively according to an iterative reweighted least squares-based robust principal component analysis (IRLS-rPCA) method, and construct an online IRLS-rPCA clutter filter; an online clutter filtering module, configured to perform clutter suppression and extract the blood flow signal according to the online IRLS-rPCA clutter filter; and a micro blood flow imaging module, configured to perform ultrasonic micro blood flow imaging according to the extracted blood flow signal as a final output result.

[0111] A computer-readable storage medium stores a computer program, which, when executed, implements the steps of an online clutter suppression method for ultrasonic micro-blood flow imaging.

[0112] 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 processes 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, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0113] The above describes the specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0114] In the description of the present application, it should be understood that the terms "upper", "lower", "inside", "outside", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. The wording 'comprising' does not exclude the presence of elements or steps not listed in the claims. The wording 'one' or 'an' preceding an element does not exclude the presence of a plurality of such elements. The simple fact that certain measures are recorded in mutually different dependent claims does not indicate that a combination of these measures cannot be used for improvement. Any reference symbols in the claims should not be interpreted as limiting the scope.

Claims

1. An online clutter suppression method for ultrasonic micro blood flow imaging, characterized in that: The steps include: S1. Based on ultra-high-speed ultrasound acquisition, high frame rate ultrasound images are acquired; S2, using the iterative reweighted least squares based robust principal component analysis IRLS-rPCA method to model tissue signals and blood flow signals respectively, and construct an online IRLS-rPCA clutter filter; S3. Processing the ultrasound image based on the online IRLS-rPCA clutter filter, utilizing the spatiotemporal correlation between the current frame image and the filtered image, performing clutter suppression through online calculation, extracting the blood flow signal in the current frame image, and performing ultrasonic micro-blood flow imaging.

2. The online clutter suppression method for ultrasonic micro blood flow imaging according to claim 1, characterized in that: The ultrasound image in step S1 is a series of ultrasound images with high frame rate acquired by ultra-high-speed ultrasound acquisition of multi-angle plane waves or spherical waves. Where N x 、N z is the number of pixels in the two dimensions of the ultrasound image, N t is the number of the ultrasonic images, and the format of the ultrasonic images is an orthogonal demodulation signal obtained by beamforming.

3. The online clutter suppression method for ultrasonic micro blood flow imaging according to claim 2, characterized in that: The specific method of tissue signal modeling in step S2 is: The beamformed data S(x, z, t) consists of three parts: the tissue signal C(x, z, t), the blood flow signal B(x, z, t), and the noise N(x, z, t), which is expressed as (S=C+B+N) after Casorati dimension transformation; The tissue signal is represented as in is a set of bases describing the changes in tissue signals, N s is the N x and N z The product of c is the dimension of the tissue signal and satisfies r c <d c < <min(N s , N t ), are the coordinate coefficients of the tissue signal under this set of bases; A set of weight coefficients W is introduced for the tissue signal c , the tissue signal weight is defined as follows: Wherein, diag(·) represents the conversion of a vector into a diagonal matrix of corresponding dimension, ∈ is a very small constant greater than 0, and when performing online clutter filtering on the t-th frame image, the modeling method of the tissue signal is expressed as: Among them, λ c is the tissue signal penalty coefficient, ||·|| F Represents the matrix Frobenius norm.

4. The online clutter suppression method for ultrasonic micro blood flow imaging according to claim 2, characterized in that: The specific method of blood flow signal modeling in step S2 is: introducing a set of weights W for the blood flow signal b , the blood flow signal weight is defined as follows: W b (i,j)=(B(i,j) 2 +∈) -1 / 2 Where i represents the i-th pixel after a two-dimensional ultrasound image is arranged into a column vector, j represents the j-th ultrasound image, and the modeling method of the blood flow component B(:, t) in the t-th frame image is: Among them, λ b is the blood flow signal penalty coefficient, and ⊙ represents the Hadamard product.

5. The online clutter suppression method for ultrasonic micro-blood flow imaging according to claim 1, characterized in that: The specific method of clutter suppression and blood flow signal extraction in step S3 is: The online IRLS-rPCA clutter filter performs the clutter suppression and blood flow signal extraction by solving the optimization problem: in, Indicates that at time t, the tissue signal is at U c，t is the coordinate coefficient in the subspace spanned by a set of bases, M t =diag([μ t-1 , μ t-2 , ..., 1]) is a set of preset weighting coefficients that can apply greater weights to filtered images that are closer to the current image frame.

6. The online clutter suppression method for ultrasonic micro-blood flow imaging according to claim 4, characterized in that: The clutter suppression and blood flow signal extraction in step S3 are improved by initializing the blood flow weight based on spatiotemporal constraints and jointly estimating the blood flow signal and the tissue signal coefficients.

7. The online clutter suppression method for ultrasonic micro-blood flow imaging according to claim 5, characterized in that: The blood flow weight initialization based on time-space constraints includes: Using the N before the current frame b The weight W of the blood flow signal in the current frame image to the frame filtered image b (:, t) to initialize: W b (:,t)=W b (:,t-N b :t-1)v b Among them, the vector v b is defined as follows (v∈(0,1]): Where T stands for transpose.

8. The online clutter suppression method for ultrasonic micro blood flow imaging according to claim 5, characterized in that: The joint estimation of the blood flow signal and tissue signal coefficients includes: The blood flow signal component B(:, t) in the current frame image and the coordinate coefficient V of the tissue signal c (t,:) perform joint estimation: The basis U describing the tissue signal c And the weights of the tissue signals are updated:

9. An online clutter suppression system for ultrasonic micro-blood flow imaging, characterized in that: include: An image acquisition module configured to acquire ultrasound images with a high frame rate; A signal modeling module, configured to model tissue signals and blood flow signals respectively according to an iteratively reweighted least squares based robust principal component analysis IRLS-rPCA method, and to construct an online IRLS-rPCA clutter filter; An online clutter filtering module, configured to perform clutter suppression and extraction of the blood flow signal according to the online IRLS-rPCA clutter filter; The micro blood flow imaging module is configured to perform ultrasonic micro blood flow imaging according to the extracted blood flow signal.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is processed and executed, the steps of the online clutter suppression method for ultrasonic micro blood flow imaging as described in any one of claims 1 to 8 are implemented.

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