An online clutter suppression method and system for ultrasound micro blood flow imaging

Abstract

Disclosed is an online clutter suppression method and system for ultrasonic micro blood flow imaging, comprising ultrasonic acquisition based on ultrahigh speed, obtaining high-frame-frequency ultrasonic images, modeling tissue signals and blood flow signals respectively by using a robust principal component analysis (IRLS-rPCA) method based on iterative reweighted least squares, constructing an online IRLS-rPCA clutter filter, processing the ultrasonic images based on the online IRLS-rPCA clutter filter, utilizing the space-time correlation between a current frame image and a filtered image, performing clutter suppression in an online calculation manner, extracting blood flow signals in the current frame image, and performing ultrasonic micro blood flow imaging, which can be more consistent with the mechanism of ultrasonic high-speed acquisition and frame-by-frame reconstruction, and is easier to realize ultrarapid micro blood flow imaging.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical ultrasound imaging, in particular to an online clutter suppression method and system for ultrasound micro blood flow imaging. BACKGROUND

[0002] Ultrasound micro blood flow imaging is one of the hotspots in the field of ultrasound imaging technology in recent ten years. The imaging technology aims to suppress tissue signals and noise through high-speed ultrasound signal acquisition and high-sensitivity space-time clutter filtering, and extract blood flow signals in blood vessels, especially micro blood vessels, for imaging. In ultrasound micro blood flow imaging, space-time clutter filtering is one of the most critical technologies, and the performance of the clutter filter has a direct impact on the extraction effect of micro blood flow signals.

[0003] A mainstream filtering method currently used for ultrasound micro blood flow imaging clutter filtering is robust principal component analysis (rPCA). This method assumes that the continuous ultrasound images acquired at high speed are composed of tissue signals, blood flow signals and noise, wherein the tissue is in a static or very slow motion state, and the echo signal has a high signal-to-noise ratio, and the signal presents a low-rank feature; the blood flow signal is in a dynamic and high-speed motion state, and the echo signal has a low signal-to-noise ratio, and the signal presents a sparse feature because the spatial distribution of blood flow is more sparse than that of tissue; and the noise is generally assumed to be Gaussian white noise unrelated to the tissue signal and the blood flow signal. The rPCA-based clutter filtering method extracts blood flow signals by constructing an optimization model. Although improvements based on the above basic model have been made, all of them use the static rPCA (static-rPCA) strategy for micro blood flow clutter suppression and blood flow signal extraction. The existing technology needs to acquire a certain number (usually hundreds) of ultrasound images, then perform clutter filtering, and extract the blood flow signals in these ultrasound images at one time for blood flow imaging. If long-term continuous processing is required, a new batch of ultrasound images needs to be acquired, and the calculation needs to be performed again to extract new blood flow signals.

[0004] The main deficiency of the prior art is that the static, batch-by-batch process is incompatible 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. Second, after obtaining a sufficient amount of ultrasound image data each time, the static rPCA filter needs to be reconfigured and run. After multiple iterations, it can achieve good clutter suppression and blood flow signal extraction effect. It usually takes several seconds to tens of seconds to reconfigure and run the static rPCA filter, which seriously hinders the real-time, even super-fast implementation of ultrasound micro blood flow imaging.

[0005] In summary, the existing rPCA type clutter filter for ultrasound micro blood flow imaging all adopts a static calculation method, which needs to obtain a certain amount of ultrasound images before it can perform clutter suppression and blood flow signal extraction. Moreover, it needs to be reconfigured and run each time image data is obtained. The above process is very time-consuming and is incompatible with the real-time and super-fast nature of ultrasound micro blood flow imaging. SUMMARY

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

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

[0008] S1. Based on high-speed ultrasound acquisition, a high-frame-rate ultrasound image is obtained.

[0009] S2. An iterative reweighted least squares-based robust principal component analysis (IRLS-rPCA) method is used to model tissue signals and blood flow signals respectively, and an online IRLS-rPCA clutter filter is constructed.

[0010] S3. Based on the online IRLS-rPCA clutter filter, the ultrasound image is processed. By utilizing the spatio-temporal correlation between the current frame image and the filtered image, clutter suppression is performed through online calculation, blood flow signals in the current frame image are extracted, and ultrasound micro blood flow imaging is performed.

[0011] Further, the ultrasound image in step S1 is obtained by high-speed ultrasound acquisition of multi-angle plane wave or spherical wave to obtain a series of high-frame-rate ultrasound images. The ultrasound image is where N x , N z is the number of pixels in two dimensions of the ultrasound image, and N tis the number of the ultrasound images, the format of the ultrasound images is the beamforming obtained quadrature demodulation signal.

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

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

[0014] The tissue signal is expressed as wherein is a set of bases describing the change of the tissue signal, N s is the N x product of and N z , d c is the dimension of the tissue signal and satisfies r c < d c < min(N s , N t ), is the coordinate coefficient of the tissue signal under the set of bases;

[0015] A set of weight coefficients W c is introduced for the tissue signal, and the definition of the tissue signal weight is:

[0016]

[0017] wherein diag(·) represents converting a vector into a diagonal matrix of the corresponding dimension, ∈ is a very small constant greater than 0, and the modeling method of the tissue signal is expressed as:

[0018]

[0019] wherein λ c is the tissue signal penalty coefficient, and ||·||F F represents the matrix Frobenius norm.

[0020] Further, the specific method of modeling the blood flow signal in step S2 is: introducing a set of weights W b for the blood flow signal, and the definition of the blood flow signal weight is:

[0021] W b (i, j) = (B(i, j) 2 + ∈) -1 / 2

[0022] where i denotes the i-th pixel after arranging a two-dimensional ultrasound image into a column vector, j denotes the j-th ultrasound image, and B(:, t) denotes the blood flow component in the t-th frame of images.

[0023]

[0024] where λ b is a blood flow signal penalty coefficient, and denotes the Hadamard product.

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

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

[0027]

[0028] where denotes the coordinate coefficient of the tissue signal in the subspace spanned by a set of bases U c,t at time t, and M t = diag([μ t-1 , μ t-2 , …, 1]) is a set of preset weighting coefficients, which can exert greater weight on the filtered image frames that are closer to the current image frame.

[0029] Solving method:

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

[0031]

[0032] where (k): iteration number (k-th iteration), until the convergence condition is met:

[0033]

[0034] The U c and W c are updated once:

[0035]

[0036] where M c,t denotes a set of time-varying weighting coefficients: μ cis a constant between 0 and 1, the filter obtains the image of the t+1 frame, and the above operation is repeated.

[0037] Further, the clutter suppression and blood flow signal extraction in step S3 is improved by blood flow weight initialization based on spatial-temporal constraints, and joint estimation of the blood flow signal and the tissue signal coefficients.

[0038] Further, the blood flow weight initialization based on spatial-temporal constraints comprises:

[0039] N b frames before the current frame are used to initialize the blood flow signal weight W b (:,t) in the current frame image:

[0040] W b (:,t)=W b (:,t-N b :t-1)ν b

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

[0042]

[0043] wherein T represents transposition.

[0044] Further, the joint estimation of the blood flow signal and the tissue signal coefficients comprises:

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

[0046]

[0047] updating the basis U c describing the tissue signal and the weight of the tissue signal:

[0048]

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

[0050] an image acquisition module configured to acquire high frame rate ultrasound images;

[0051] a signal modeling module, configured to model tissue signals and blood flow signals respectively according to an iterative reweighted least squares-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 blood flow signal extraction according to the online IRLS-rPCA clutter filter;

[0053] a micro blood flow imaging module, configured to perform ultrasound micro blood flow imaging according to the extracted blood flow signal.

[0054] According to a third aspect of the present application, a computer readable storage medium is provided, which stores a computer program, the computer program being executed by a processor to implement the steps of the online clutter suppression method for ultrasound micro blood flow imaging.

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

[0056] (1) Blood flow signals in a current frame of ultrasound image are extracted in real time, and clutter is suppressed;

[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 the prior art, the present application is more suitable for ultrasound high-speed acquisition and frame-by-frame reconstruction, and is more conducive to realizing ultrafast micro blood flow imaging. BRIEF DESCRIPTION OF DRAWINGS

[0058] The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated in and constitute a part of this specification. The drawings illustrate the embodiments and, together with the description, serve to explain the principles of the present application. Other embodiments and many of the intended advantages of the present application will be readily appreciated as the same becomes better understood by reference to the following detailed description when considered in connection with the accompanying drawings. The elements of the drawings are not necessarily to scale relative to each other. Like reference numerals designate corresponding similar parts.

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

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

[0061] Figure 3 a calculation time consumption broken line graph of processing each frame of ultrasound image (clutter suppression and blood flow signal extraction) according to one specific embodiment of the present application is shown;

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

[0063] The application will be further described in detail below with reference to the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related application, and not to limit the application. In addition, it should be noted that only the parts related to the application are shown in the drawings for ease of description.

[0064] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and embodiments.

[0065] As Figure 1 shown, an online clutter suppression method for ultrasonic micro blood flow imaging includes the following steps:

[0066] S1, based on ultrahigh-speed ultrasonic acquisition, high-frame-rate ultrasonic images are obtained;

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

[0068] S3, based on the online IRLS-rPCA clutter filter, the ultrasonic images are processed, the spatiotemporal correlation between the current frame image and the filtered image is utilized, the clutter suppression is performed through online calculation, the blood flow signals in the current frame image are extracted, and ultrasonic micro blood flow imaging is performed.

[0069] The echo signals of the target are acquired by ultrahigh-speed multi-angle plane wave acquisition, channel data in radio frequency (RF) format are obtained, the channel data are subjected to quadrature demodulation and downsampling to obtain channel data in in-phase and quadrature phase (IQ) format, the channel data are processed by dynamic focusing delay-and-sum (DAS) beam synthesis technology to obtain IQ format ultrasonic images of each angle, then the images of each angle are subjected to spatial coherence compounding to obtain multiple frames of ultrasonic images in IQ data format, and the ultrasonic images are wherein N x , N z are the pixel numbers of two dimensions of the ultrasonic images, N t is the number of ultrasonic images, and the format of the ultrasonic images is the quadrature demodulation signal obtained by beam synthesis.

[0070] Tissue signal modeling:

[0071] The data S(x,z,t) of beamforming is composed of tissue signal C(x,z,t), blood flow signal B(x,z,t) and noise N(x,z,t), which is expressed as (S=C+B+N) after Casorati dimension transformation;

[0072] The tissue signal is expressed as wherein is a set of bases describing the change of tissue signal, N s is the product of the N x and N z , d c is the dimension of tissue signal and satisfies r c <d c <<min(N s ,N t ), is the coordinate coefficient of tissue signal under the set of bases;

[0073] A set of weight coefficients W c is introduced for tissue signal, and the definition of tissue signal weight is as follows:

[0074]

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

[0076]

[0077] wherein, λ c is a tissue signal penalty coefficient, and ||·||F F represents the matrix Frobenius norm.

[0078] Blood flow signal modeling: a set of weights W b is introduced for blood flow signal, and the definition of blood flow signal weight is as follows:

[0079] W b (i,j)=(B(i,j) 2 +∈) -1 / 2

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

[0081]

[0082] where λ b is the blood flow signal penalty coefficient, and ⊙ denotes 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 an optimization problem:

[0085]

[0086] where denotes the coordinate coefficient of the tissue signal in the subspace spanned by the set of bases U c,t at time t, and M t = diag([μ t-1 ,μ t-2 ,…,1]) is a set of pre-defined weighting coefficients, which can impose greater weight on the filtered images that are closer to the current image frame.

[0087] Clutter suppression and blood flow signal extraction are improved by blood flow weight initialization based on spatiotemporal constraints and joint estimation of blood flow signal and tissue signal coefficients.

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

[0089] The blood flow signal weight W b (:,t) in the current frame is initialized using the filtered images of the previous N b frames:

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

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

[0092]

[0093] where T denotes the transpose.

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

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

[0096]

[0097] The basis U c and the weight of the tissue signal are updated:

[0098]

[0099] Solving method:

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

[0101]

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

[0103]

[0104] U c and W c are updated once:

[0105]

[0106]

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

[0108] As shown in Figure 2 , from left to right are the human liver micro blood flow power Doppler imaging collected at 0 seconds, 12.5 milliseconds, 37.5 milliseconds, 87.5 milliseconds and 187.5 milliseconds, and it can be seen from the figure that the online filtering technology proposed in the application does not introduce additional artifacts or noise in the calculation process, and can stably realize clutter filtering and blood flow imaging.

[0109] The calculation time of the online IRLS-rPCA clutter filter processing each frame of ultrasonic image (clutter suppression and blood flow signal extraction) is as shown in Figure 3 , when processing the data corresponding to Figure 2 , the processing speed reaches 0.03±0.003 seconds per frame on average, and the corresponding blood flow imaging frame frequency is about 33 frames per second, reaching fast real-time micro blood flow imaging.

[0110] Figure 4A structural diagram of an online clutter suppression system for ultrasonic micro blood flow imaging is provided in the application, which mainly comprises four modules: an image acquisition module configured to acquire high frame rate ultrasonic images; a signal modeling module configured to model tissue signals and blood flow signals respectively according to an iterative reweighted least squares 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 blood flow signal extraction 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 the final output result.

[0111] A computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the online clutter suppression method for ultrasonic micro blood flow imaging.

[0112] If the modules / units of the terminal device are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can implement the steps of the above-mentioned various method embodiments when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0113] The specific embodiments of the application are described above, but the protection scope of the application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the application, which should be covered in the protection scope of the application. Therefore, the protection scope of the application should be subject to the protection scope of the claims.

[0114] In the description of the application, it needs to be understood that the terms "upper", "lower", "inner", "outer" and the like indicate the orientation or positional relationship shown in the drawings based on the orientation or positional relationship shown in the drawings, and are only for the purpose of facilitating the description of the application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the application. The word 'comprising' does not exclude the existence of elements or steps not listed in the claims. The word 'a' or 'an' in front of an element does not exclude the existence of multiple such elements. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that the combination of these measures cannot be used to improve. Any reference signs 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 method comprises the following steps: S1, based on the ultrasonic acquisition of super speed, obtaining high frame frequency ultrasonic images; S2, using the robust principal component analysis IRLS-rPCA method based on iterative reweighting least squares to model the tissue signal and the blood flow signal respectively, and constructing an online IRLS-rPCA clutter filter; S3, based on the online IRLS-rPCA clutter filter, processing the ultrasonic images, using the space-time correlation between the current frame image and the filtered image, through online calculation, clutter suppression, extracting the blood flow signal in the current frame image, and performing ultrasonic micro blood flow imaging.

2. The online clutter suppression method for ultrasound micro blood flow imaging according to claim 1, characterized in that, The ultrasound images in step S1 are based on high-speed ultrasound acquisition of multi-angle plane waves or spherical waves to obtain a series of high-frame-rate ultrasound images, and the ultrasound images are where N x , N z are the pixel numbers of two dimensions of the ultrasound images, N t is the number of the ultrasound images, and a format of the ultrasound images is a quadrature demodulation signal obtained by beamforming.

3. The method for online clutter suppression for ultrasound micro blood flow imaging according to claim 2, characterized in that, The specific method for modeling the tissue signal in step S2 is: The data S(x, z, t) of the beam synthesis is composed of the tissue signal C(x, z, t), the blood flow signal B(x, z, t), and noise N(x, z, t), and is expressed as S=C+B+N after Casorati dimension transformation; representing the tissue signal as wherein is a set of bases describing changes in the tissue signal, N s is the N x th basis vector of the set of bases, and N z is the number of bases in the set of bases, d c is the dimension of the tissue signal and satisfies r c < d c < min(N s , N t ), is the coordinate of the tissue signal in the coordinate system under the set of bases; introducing a set of weight coefficients W for the tissue signal c the tissue signal weights are defined in such a way that Wherein, diag(·) represents converting a vector into a corresponding dimension diagonal matrix, ∈ is a very small constant greater than 0, and the modeling method of the tissue signal when performing online clutter filtering on the tth frame image is: where λ c is the organization signal penalty coefficient, ||·||F F denotes the matrix Frobenius norm.

4. The online clutter suppression method for ultrasound micro blood flow imaging according to claim 2, wherein, The specific method for modeling the blood flow signal in step S2 is: introducing a set of weights W for the blood flow signal b The definition of the blood flow signal weights is: W b (i, j) = (B(i, j) 2 + ∈) -1 / 2 Wherein, i represents the i th pixel after arranging a two-dimensional ultrasonic image into a column vector, j represents the j th ultrasonic image, and the modeling method of the blood flow component B( :, t) in the tth frame image is: where λ b is a blood flow signal penalty coefficient, and denotes the Hadamard product.

5. The method for online clutter suppression for ultrasound micro blood flow imaging according to claim 1, wherein, The specific method for 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 an optimization problem: wherein, represents the tissue signal at time t in the subspace spanned by the set of basis U c，t is the coordinate coefficient of the tissue signal in the subspace spanned by the set of basis U t = diag([μ t-1 , μ t-2 ,..., 1]) is a set of pre-defined weighting coefficients, which can impose more weight on the filtered image that is closer to the current image frame.

6. The method for online clutter suppression for ultrasound micro blood flow imaging according to claim 4, wherein, The clutter suppression and blood flow signal extraction in step S3 are improved through blood flow weight initialization based on space-time constraints and joint estimation of the blood flow signal and the tissue signal coefficients.

7. The method for online clutter suppression for ultrasound micro blood flow imaging according to claim 5, wherein, The blood flow weight initialization based on space-time constraints comprises: N frames before the current frame are used to initialize the filter b The blood flow signal weight W is initialized using the filtered image of the N frames before the current frame b ( :, t) W b ( :, t ) = W b ( :, t - N b : t - 1 ) v b where the vector v b is defined as follows (v e (0, 1]): Wherein, T represents transposition.

8. The method for online clutter suppression for ultrasound micro blood flow imaging according to claim 5, wherein, The joint estimation of the blood flow signal and the tissue signal coefficients comprises: a blood flow signal component B(i, t) in the current frame image and a coordinate system coefficient V of the tissue signal c jointly estimate: updating the basis U describing the tissue signal c and the weight of the tissue signal 9. An online clutter suppression system for ultrasonic micro blood flow imaging, characterized by Comprise: An image acquisition module configured to acquire high frame frequency ultrasonic images; A signal modeling module configured to model the tissue signal and the blood flow signal according to the robust principal component analysis IRLS-rPCA method based on iterative reweighting least squares, and 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; A micro blood flow imaging module 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, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1-9. The computer program is processed and executed to realize the steps of the online clutter suppression method for ultrasonic micro blood flow imaging in any one of claims 1-8.

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