A clutter suppression method and system for high spatio-temporal resolution ultrasonic micro blood flow imaging

Abstract

The application discloses a clutter suppression method and system for high spatio-temporal resolution ultrasonic micro blood flow imaging, and belongs to the technical field of ultrasonic blood flow imaging. The clutter suppression method combines an angle data singular value filtering method and a multi-angle composite method based on p power compression and generalized inter-frame multiplication and superposition. The combined angle singular value filtering method can effectively suppress tissue clutter in a short set by using the multidimensionality and spatio-temporal coherence of angle data, significantly improves the time resolution on the premise of maintaining the sensitivity of micro blood flow extraction. The multi-angle composite method based on p power compression and generalized inter-frame multiplication and superposition enhances coherent blood flow signals and suppresses incoherent noise by using angle coherence, thereby improving the signal-to-noise ratio and spatial resolution of micro blood flow imaging. In addition, the multi-angle composite method requires fewer angles to improve image quality, and can maintain the frame rate and time resolution. In addition, the proposed clutter suppression imaging method can also be used for enhancing other ultrasonic blood flow imaging.

Description

Technical Field

[0001] This invention belongs to the field of ultrasound blood flow imaging technology, and relates to a clutter suppression method and system for high spatiotemporal resolution ultrasound micro-blood flow imaging. Background Technology

[0002] Ultrafast ultrasound micro-blood flow imaging technology, due to its ultra-high frame rate (~10kHz) non-focused wave transmission and large time-accumulated sample size, significantly improves the sensitivity of micro-blood flow detection, enabling dynamic monitoring of micro-blood flow dynamics and microvascular morphological changes. This is of great significance for the assessment of physiological states and the diagnosis and treatment of clinical diseases. However, existing ultrafast ultrasound micro-blood flow imaging technology cannot simultaneously achieve both temporal and spatial resolution, failing to capture transient changes in blood flow while imaging the fine structure of blood vessels. The reasons are as follows: First, existing clutter suppression methods require a large number of frames (long datasets), resulting in a long acquisition time for generating a single blood flow image, leading to low temporal resolution. Second, ultrafast ultrasound imaging uses non-focused wave transmission, making the echo signal highly susceptible to interference from tissue clutter, reverberation, off-axis scattering, and thermal noise, resulting in very low image signal-to-noise ratio and contrast-to-noise ratio, and poor spatial resolution. These factors limit the further application of ultrafast ultrasound micro-blood flow imaging in research and clinical practice. Therefore, simultaneously improving the spatiotemporal resolution of ultrafast ultrasound micro-blood flow imaging is of great value for microvascular visualization and functional ultrasound imaging.

[0003] Clutter filters can suppress stationary or non-stationary tissue motion and some noise, and are crucial for the extraction of minute blood flows. Eigenvalue decomposition (SVD) filters, utilizing spatiotemporal coherence, can effectively separate blood flow, tissue clutter, and noise, and are currently the most widely used method. However, SVD filtering has some drawbacks: First, the performance of SVD increases with the set length, performing poorly in short sets (tens of frames), but long sets mean increased acquisition time, which is not conducive to improving temporal resolution. Second, performing global SVD on a single image is not conducive to filtering out deep noise, and the image signal-to-noise ratio and spatial resolution need to be improved. Higher-order SVD uses the features of multiple subspaces to classify ultrasound signals, which can effectively improve the sensitivity of detecting slow, minute blood flows in short sets, but the computational complexity and cost of higher-order SVD are much higher than those of SVD. Robust principal component analysis (RPCA) and its improved filtering methods can effectively suppress the interference of tissue clutter and noise, improve the detection sensitivity of minute blood flows and the image signal-to-noise ratio, but this method requires a large dataset and has high computational complexity.

[0004] Besides clutter filtering, advanced beamforming or multi-angle composite techniques utilize signal coherence theory to effectively suppress background noise, off-axis interference, and reverberation, which is key to improving the spatial resolution of non-focused ultrasound imaging. Existing adaptive beamforming algorithms utilize channel coherence to suppress incoherent noise, effectively improving the quality of ultrafast ultrasound imaging, such as Delay Multiplication Summation (DMAS), Minimum Variance (MV), Coherence Factor (CF), and nonlinear beamforming based on p-power compression. However, the above-mentioned adaptive beamforming methods based on channel coherence have two limitations when applied to ultrafast ultrasound micro-blood flow imaging. First, the above methods, when adding adaptive effects, can also lead to phase distortion of the blood flow signal, making them unsuitable for direct application to blood flow imaging, and changing the calculation steps will bring hidden computational burdens. Second, channel coherence-based methods require a very large amount of data, and the computational complexity and cost increase by a factor of N (the number of channels). Multi-angle composite algorithms utilize angular coherence to significantly improve the signal-to-noise ratio (SNR) and spatial resolution of non-focused ultrasound imaging at a lower computational cost. Coherent composite (CC) is currently the most commonly used angular composite method to improve the imaging quality of unfocused waves, but CC has limited effect on image quality improvement, especially when the background noise level is high. Other multi-angle composite strategies, such as inter-frame multiplication and superposition composite (FMAS) and angle grouping composite imaging, can effectively improve the imaging quality of ultrafast ultrasound. However, the above methods require a large number of angle frames to be composited to improve spatial resolution, which is not conducive to improving spatiotemporal resolution. Summary of the Invention

[0005] The purpose of this invention is to solve the problem that existing technologies are unable to effectively suppress tissue clutter and noise in short sets, resulting in ultrafast ultrasound micro-blood flow imaging being unable to simultaneously achieve temporal and spatial resolution. This invention provides a clutter suppression method and system for high spatiotemporal resolution ultrasound micro-blood flow imaging.

[0006] To achieve the above objectives, the present invention employs the following technical solution:

[0007] The present invention proposes a clutter suppression method for high spatiotemporal resolution ultrasound micro-blood flow imaging, comprising the following steps:

[0008] Four-dimensional ultrasound data is reconstructed into two-dimensional ultrasound data, and the two-dimensional ultrasound data is decomposed to obtain singular vectors in angle space.

[0009] The cutoff point is selected in the feature space using the singular vector of the angle space. Based on the cutoff point, the singular component corresponding to the clutter is set to zero, and the processed blood flow signal is obtained after reconstruction.

[0010] Based on the processed blood flow signal, a generalized inter-frame multiplication and superposition composite factor based on p-th power compression is obtained for each frame of multi-angle data. The generalized inter-frame multiplication and superposition composite factor is used as an adaptive weighting factor and multiplied pixel by pixel with the image after adding the multi-angle data of that frame to achieve enhancement of blood flow signal and noise suppression.

[0011] Preferably, the four-dimensional ultrasound data is presented in Cascorati format. Reconstructed into two-dimensional ultrasound data Where M is the number of spatial samples, M = Z × L; Z represents the number of axial samples, perpendicular to the sensor array; L represents the number of lateral samples, along the sensor array; W represents the number of angles; and T represents the number of time frames.

[0012] Preferably, the two-dimensional ultrasound data is decomposed using SVD to obtain S = UΔV. * ;

[0013] in, It is the angular space singular vector contained in the signal S. It is the time singular vector contained in the signal S. It is a diagonal matrix containing the corresponding singular values ​​on its diagonal, and * is the matrix transpose.

[0014] Preferably, the method for selecting the cutoff point in the feature space is as follows:

[0015] Calculate the angular space similarity matrix. Among them, u a Let represent the a-th column vector of the singular vector U in the angle space, with a size of (MW,1); and σ a They represent u respectively a The mean and standard deviation; u b Let be the b-th column vector of the singular vector U in the angle space, with a size of (MW, 1); and σ b They represent u respectively b The mean and standard deviation; a, b are matrix indices. M is the number of spatial samples; W is the number of angles; k is the spatial sample index of the angle;

[0016] By identifying relevant square regions in the angular space similarity matrix, subspaces for tissue, blood, and noise are determined, thereby establishing first cutoff values ​​λ for suppressing tissue and noise, respectively. s1 Second cutoff value λ s2 .

[0017] Preferably, the processed blood flow signal is acquired as follows:

[0018] in, λs1 λ is the first cutoff value; s2 This is the second cutoff value; It is the diagonal matrix containing the singular values ​​of the blood flow signal after filtering, U is the angular space singular vector contained in signal S, and V is the time singular vector contained in signal S.

[0019] Preferably, the generalized inter-frame multiplication and superposition composite factor based on p-th power compression for each frame of multi-angle data is as follows:

[0020]

[0021] in, It is the ultrasonic signal after angular singularity filtering. W represents the number of angles; Represents the t-th frame; p is the compression factor. f p-GFMAS The method for obtaining (t) is as follows:

[0022]

[0023] Preferably, f is calculated for each frame. p-GFMAS (t) is used as an adaptive weighting factor and multiplied pixel by pixel in the composite image to enhance the blood flow signal and suppress unrelated noise and side lobes.

[0024] This invention proposes a clutter suppression system for high spatiotemporal resolution ultrasound micro-blood flow imaging, comprising:

[0025] An ultrasound data decomposition module is used to reassemble four-dimensional ultrasound data into two-dimensional ultrasound data, and to decompose the two-dimensional ultrasound data to obtain angular space singular vectors.

[0026] The blood flow signal acquisition module is used to select a cutoff point in the feature space using the singular vector of the angle space, set the singular component corresponding to the clutter to zero based on the cutoff point, and then reconstruct and acquire the processed blood flow signal.

[0027] A multi-angle composite module is used to obtain a p-th power compression-based generalized inter-frame multiplication and superposition composite factor for each frame of multi-angle data based on the processed blood flow signal; the generalized inter-frame multiplication and superposition composite factor is used as an adaptive weighting factor and multiplied pixel by pixel with the image after multi-angle addition of that frame to achieve blood flow signal enhancement and noise suppression.

[0028] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement steps of a clutter suppression method for high spatiotemporal resolution ultrasound micro-blood flow imaging.

[0029] A computer-readable storage medium storing a computer program that, when executed by a processor, implements steps of a clutter suppression method for high spatiotemporal resolution ultrasound micro-blood flow imaging.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] This invention proposes a clutter suppression method for high spatiotemporal resolution ultrasound micro-blood flow imaging. Applied to time-accumulated multi-angle combined data, i.e., angle datasets, the method leverages the multidimensionality, angular coherence, and spatiotemporal coherence of angle data to effectively suppress tissue clutter and incoherent noise while enhancing blood flow signals within short datasets. This simultaneously improves the temporal and spatial resolution of ultrasound micro-blood flow imaging. Furthermore, the relatively small data volume of angle data allows for lower algorithm complexity and computational cost. Specifically, singular value filtering of the angle data, utilizing its multidimensionality and spatiotemporal coherence, effectively suppresses tissue clutter and improves the sensitivity of blood flow extraction within short datasets, significantly enhancing temporal resolution. A p-power compression-based generalized inter-frame multiplication and superposition composite factor, used as an adaptive weighting factor, is multiplied pixel-by-pixel with the composite image obtained from multiplying the multi-angle data. This effectively enhances blood flow signals within limited angle transmission data and further suppresses incoherent noise and sidelobes, improving not only spatial resolution but also frame rate and temporal resolution. In addition, the p-th power compression-based generalized inter-frame multiplication and superposition composite factor can be used not only for ultrafast ultrasound micro-blood flow imaging, but also to improve the spatial resolution of other ultrafast ultrasound imaging, such as ultrafast B-mode imaging.

[0032] Furthermore, singular value filtering of angle data, by calculating a similarity matrix in angle space to select two suitable cutoff values ​​to filter out tissue clutter and noise respectively, can effectively utilize the performance of the filter. The generalized inter-frame multiplication and superposition multi-angle composite algorithm based on p-power compression exhibits different adaptive effects depending on the p-value. A larger p-value results in a stronger adaptive effect but may lead to the loss of weak blood flow signals; a smaller p-value results in a relatively weaker adaptive effect and a weaker ability to suppress incoherent noise. When p-value is 1, p-GFMAS does not perform amplitude compression and recovery.

[0033] This invention proposes a clutter suppression system for high spatiotemporal resolution ultrasound micro-blood flow imaging. By dividing the system into an ultrasound data decomposition module, a blood flow signal acquisition module, and a multi-angle composite module, it achieves enhancement of blood flow signals and suppression of tissue clutter and noise. The modular approach ensures that each module is independent, facilitating unified management of all modules. Attached Figure Description

[0034] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a flowchart of the clutter suppression method for high spatiotemporal resolution ultrasound micro-blood flow imaging according to the present invention.

[0036] Figure 2 This is a comparison chart of the results of different clutter suppression methods of the present invention.

[0037] Figure 3 This is a diagram of the clutter suppression system for high spatiotemporal resolution ultrasound micro-blood flow imaging according to the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0039] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0040] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0041] The present invention will now be described in further detail with reference to the accompanying drawings:

[0042] See the figure for a clutter suppression method used in high spatiotemporal resolution ultrasound micro-blood flow imaging, such as... Figure 1 As shown, it includes the following steps:

[0043] S1. Reconstruct the four-dimensional ultrasound data into two-dimensional ultrasound data, decompose the two-dimensional ultrasound data, and obtain the singular vector of the angle space.

[0044] S2. Use the singular vector in the angle space to select the cutoff point in the feature space, set the singular component corresponding to the clutter to zero based on the cutoff point, and obtain the processed blood flow signal after reconstruction.

[0045] S3. Obtain the p-th power compression-based generalized inter-frame multiplication and superposition composite factor for each frame of multi-angle data based on the processed blood flow signal; multiply the generalized inter-frame multiplication and superposition composite factor as an adaptive weighting factor with the image after multi-angle addition of the frame pixel by pixel to achieve blood flow signal enhancement and noise suppression.

[0046] The method is described in detail below:

[0047] First, load the four-dimensional IQ data synthesized from multiple frames and multiple angle beamforming based on time accumulation. Where Z represents the number of axial samples (perpendicular to the sensor array), L represents the number of lateral samples (along the sensor array), W represents the number of angles, and T represents the number of time frames.

[0048] The beamforming four-dimensional angle IQ data has the following dimensions: scan width (along the sensor array), scan depth (perpendicular to the sensor array), angle, and time. The time sample size is ≥15 frames, preferably ≤100 frames, and the number of angles is ≥2, preferably ≤15.

[0049] Second, the angular space samples of the dataset are reconstructed into two-dimensional ultrasound data in Cascorati format. M = Z × L. The recombined data becomes two dimensions: angular space and time.

[0050] Third, the reconstructed signal is decomposed using SVD to obtain the reconstructed two-dimensional ultrasound data. It is decomposed into: S = UΔV * ,in, and The signal S contains angular spatial singular vectors and temporal singular vectors. It is a diagonal matrix containing the corresponding singular values ​​on its diagonal, and * represents the matrix transpose.

[0051] Fourth, based on the singular vector U in the angle space, a suitable cutoff point is selected in the feature space to suppress tissue clutter signals and reduce noise levels. The angle space similarity matrix is ​​calculated. Where u a Let U represent the a-th column vector of the singular vector U in angle space, with a size of (MW, 1). and σ a They represent u respectively a The mean and standard deviation. b Let be the b-th column vector of the singular vector U in the angle space, with a size of (MW, 1); and σ b They represent u respectively b The mean and standard deviation; a, b are matrix indices. M represents the number of spatial samples; W represents the number of angles; k represents the index of the angular spatial samples; by identifying the relevant square regions clearly identifiable in the angular spatial similarity matrix, the subspaces of tissue, blood, and noise can be determined, thereby determining two cutoff values ​​for suppressing tissue and noise respectively: the first cutoff value λ s1 Second cutoff value λ s2 .

[0052] Fifth, after selecting a suitable cutoff point, set the singular components corresponding to the tissue and noise to zero to achieve the filtering effect. Combine the angular singular values ​​and filter the singular values. for:

[0053]

[0054] in, It is a diagonal matrix containing the singular values ​​corresponding to the blood flow signal after filtering.

[0055] The filtered blood flow signal was reconstructed as And restore the original signal dimension. Here, U is the angular singular vector contained in signal S, and V is the temporal singular vector contained in signal S.

[0056] Sixth, the filtered data is synthesized from multiple frames and angles based on time accumulation. Spatial samples are recombined in Cascorati form

[0057] Seventh, calculate the p-GFMAS composite factor (p-GFMAS) f of the p-th power compression-based generalized inter-frame multiplication and superposition of multi-angle data for each frame. p-GFMAS :

[0058]

[0059]

[0060] in, Represents the t-th frame. f calculated for each frame p-GFMAS (t) is used as an adaptive weighting factor and multiplied pixel by pixel in the composite image to enhance the blood flow signal and suppress unrelated noise and side lobes.

[0061] Eighth, then calculate f for each frame. p-GFMASAs an adaptive weighting factor, it is applied to each pixel of the frame image. That is, the generalized inter-frame multiplication is superimposed and multiplied with each pixel of the frame image to obtain the composite blood flow signal, so as to enhance the blood flow signal and suppress incoherent noise.

[0062] This invention proposes a clutter suppression method for high spatiotemporal resolution ultrasound micro-blood flow imaging. The effectiveness is influenced by the following factors: the number and range of angles, the number of frames, the cutoff value, and the p-value. Preferably, the number of angles is less than 15, and the angle range is less than 10°. Within the preferred number and range of angles, a higher number of angles results in better filtering but also increases computational complexity; conversely, a lower number of angles results in worse filtering but reduces computational complexity. Preferably, the number of frames is greater than 15. Within the preferred frame number range, a higher number of frames results in better filtering but also increases computational complexity; conversely, a lower number of frames results in worse filtering but reduces computational complexity. Regarding the cutoff value, if the cutoff value for filtering tissue motion is too small, tissue interference cannot be filtered out; if the cutoff value for filtering tissue motion is too large, blood signals will be lost. If the cutoff value for filtering noise is too small, blood signals will be lost; if the cutoff value for filtering noise is too large, noise interference cannot be filtered out.

[0063] Figure 2 Comparison of power Doppler results for rat kidneys using different clutter suppression methods. Figure 2 (a) is a traditional ultrafast power Doppler image, which uses CC multi-angle composite followed by SVD for clutter suppression. It can be seen that the power Doppler image is significantly affected by tissue clutter and noise. (b) is the ultrafast power Doppler image after filtering using this method, and its image quality is significantly better than (a). To quantitatively compare the two methods, the signal-to-noise ratio (SNR) and contrast ratio of the ROI region were calculated. The SNR and contrast ratio calculated by this method after filtering are 28.92 dB and 14.69 dB higher, respectively, than those of the traditional ultrafast Doppler method.

[0064] This invention proposes a clutter suppression system for high spatiotemporal resolution ultrasound micro-blood flow imaging, such as... Figure 3 As shown, it includes an ultrasound data decomposition module, a blood flow signal acquisition module, and a multi-angle composite module;

[0065] The ultrasound data decomposition module is used to reconstruct four-dimensional ultrasound data into two-dimensional ultrasound data, and decompose the two-dimensional ultrasound data to obtain angular space singular vectors.

[0066] The blood flow signal acquisition module is used to select a cutoff point in the feature space using the singular vector of the angle space, set the singular component corresponding to the clutter to zero based on the cutoff point, and then reconstruct and acquire the processed blood flow signal.

[0067] The multi-angle composite module is used to obtain a p-th power compression-based generalized inter-frame multiplication and superposition composite factor for each frame of multi-angle data based on the processed blood flow signal; the generalized inter-frame multiplication and superposition composite factor is used as an adaptive weighting factor and multiplied pixel by pixel with the image after multi-angle addition of that frame to achieve blood flow signal enhancement and noise suppression.

[0068] The terminal device provided in this embodiment of the invention includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the various method embodiments described above. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the various device embodiments described above.

[0069] The computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention.

[0070] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0071] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0072] The memory can be used to store the computer program and / or module. The processor implements various functions of the terminal device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0073] If the modules / units integrated into the terminal device are implemented as software functional 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 methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0074] Therefore, the clutter suppression method proposed in this invention is applied to the angle data set before multi-angle composite after beamforming. It effectively suppresses tissue clutter and incoherent noise and enhances blood flow signals in short sets by utilizing the multidimensionality, angular coherence, and spatiotemporal coherence of angle data. This simultaneously improves the temporal and spatial resolution of ultrafast ultrasound micro-blood flow imaging, with relatively low computational cost. Specifically, singular value filtering of angle data effectively suppresses tissue clutter in short sets by utilizing the multidimensionality and spatiotemporal coherence of angle data, significantly improving temporal resolution while maintaining the sensitivity of micro-blood flow extraction. The p-power compression-based generalized inter-frame multiplication and superposition multi-angle composite method enhances coherent blood flow signals and suppresses incoherent noise using angular coherence, thereby improving the signal-to-noise ratio and spatial resolution of micro-blood flow imaging. Furthermore, the p-power compression-based generalized inter-frame multiplication and superposition multi-angle composite method requires fewer angles to improve image quality, further increasing frame rate and temporal resolution. Additionally, the four-dimensional data volume based on angles is small, resulting in relatively low computational cost.

[0075] The angular singular value filtering provided by this invention calculates a similarity matrix in angular space to select two suitable cutoff values ​​for filtering out tissue clutter and noise, effectively maximizing the filter's performance. For the generalized inter-frame multiplication and superposition multi-angle composite algorithm based on p-power compression provided by this invention, different p values ​​lead to different adaptive effects. A larger p value results in a stronger adaptive effect but may lead to the loss of weak blood flow signals; a smaller p value results in a relatively weaker adaptive effect and weaker incoherent noise suppression. When p is 1, p-GFMAS does not perform amplitude compression and recovery. The preferred p value is 1.5. Furthermore, this invention provides a generalized inter-frame multiplication and superposition multi-angle composite algorithm based on p-power compression, which can be used not only for ultrafast ultrasound micro-blood flow imaging but also for enhancing other ultrafast ultrasound imaging methods, such as ultrafast ultrasound B-mode imaging. The clutter suppression method and system proposed in this invention, in addition to applications in ultrafast power Doppler, can also be used to enhance other ultrafast ultrasound micro-blood flow imaging methods.

[0076] The above are merely preferred embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A clutter suppression method for high spatiotemporal resolution ultrasound micro-blood flow imaging, characterized in that, Includes the following steps: Four-dimensional ultrasound data is reconstructed into two-dimensional ultrasound data, and the two-dimensional ultrasound data is decomposed to obtain singular vectors in angle space. The cutoff point is selected in the feature space using the singular vector of the angle space. Based on the cutoff point, the singular component corresponding to the clutter is set to zero, and the processed blood flow signal is obtained after reconstruction. Based on the processed blood flow signal, a generalized inter-frame multiplication and superposition composite factor based on p-th power compression is obtained for each frame of multi-angle data. The generalized inter-frame multiplication and superposition composite factor is used as an adaptive weighting factor and multiplied pixel by pixel with the image after adding the multi-angle data of that frame to achieve blood flow signal enhancement and noise suppression. The method for selecting the cutoff point in the feature space is as follows: Calculate the angular space similarity matrix. ; By identifying relevant square regions in the angular space similarity matrix, subspaces for tissue, blood, and noise are determined, thereby establishing first cutoff values ​​for suppressing tissue and noise, respectively. Second cutoff value ; The generalized inter-frame multiplication and superposition composite factor based on p-th power compression for each frame of multi-angle data is as follows: The method to obtain it is as follows: in, Represents singular vectors in angle space The Column vector, size is ; and They represent The mean and standard deviation; Singular vectors in angle space The Column vector, size is ; and They represent The mean and standard deviation; , For matrix indexing, ; This represents the number of spatial samples. The number of angles; An index for angular space samples; It is the ultrasonic signal after angular singularity filtering. ; Indicates the number of angles; , representing the frame; As the compression factor, .

2. The clutter suppression method for high spatiotemporal resolution ultrasound micro-blood flow imaging according to claim 1, characterized in that, Four-dimensional ultrasound data in Cascorati format Reconstructed into two-dimensional ultrasound data ;in, For spatial sample number, ; Indicates the number of samples along the axial direction, perpendicular to the sensor array; This indicates the number of samples in the horizontal direction, along the sensor array; Indicates the number of angles; Indicates the number of time frames.

3. The clutter suppression method for high spatiotemporal resolution ultrasound micro-blood flow imaging according to claim 1, characterized in that, SVD decomposition was performed on the two-dimensional ultrasound data to obtain ; in, It is a signal Includes singular vectors in the angle space. It is a signal Includes time singular vectors, It is a diagonal matrix containing the corresponding singular values ​​on its diagonal. This is the matrix transpose.

4. The clutter suppression method for high spatiotemporal resolution ultrasound micro-blood flow imaging according to claim 1, characterized in that, The processed blood flow signal is obtained as follows ; in, , This is the first cutoff value; This is the second cutoff value; It is the filtered diagonal matrix containing the singular values ​​corresponding to the blood flow signal. It is a signal Includes singular vectors in the angle space. It is a signal It contains time singular vectors.

5. The clutter suppression method for high spatiotemporal resolution ultrasound micro-blood flow imaging according to claim 1, characterized in that, After each frame is calculated As an adaptive weighting factor, it is multiplied pixel-by-pixel with the composite image to enhance the blood flow signal and suppress irrelevant noise and side lobes.

6. A clutter suppression system for high spatiotemporal resolution ultrasound micro-blood flow imaging, characterized in that, The clutter suppression method for high spatiotemporal resolution ultrasound micro-blood flow imaging according to any one of claims 1 to 5 includes: An ultrasound data decomposition module is used to reassemble four-dimensional ultrasound data into two-dimensional ultrasound data, and to decompose the two-dimensional ultrasound data to obtain angular space singular vectors. The blood flow signal acquisition module is used to select a cutoff point in the feature space using the singular vector of the angle space, set the singular component corresponding to the clutter to zero based on the cutoff point, and then reconstruct and acquire the processed blood flow signal. A multi-angle composite module is used to obtain a p-th power compression-based generalized inter-frame multiplication and superposition composite factor for each frame of multi-angle data based on the processed blood flow signal; the generalized inter-frame multiplication and superposition composite factor is used as an adaptive weighting factor and multiplied pixel by pixel with the image after multi-angle addition of that frame to achieve blood flow signal enhancement and noise suppression.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes a computer program, it implements the steps of the clutter suppression method for high spatiotemporal resolution ultrasound micro-blood flow imaging as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the clutter suppression method for high spatiotemporal resolution ultrasound micro-blood flow imaging as described in any one of claims 1 to 5.

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