Non-contrast super-resolution ultrasonic imaging method and system based on radial symmetric single molecule positioning

Abstract

The invention discloses a non-contrast super-resolution ultrasonic imaging method and system based on radial symmetric single molecule localization, and the method comprises the following steps: transmitting a multi-angle composite plane wave to a target object, collecting an echo signal, and carrying out the beam synthesis to obtain an erythrocyte B-type ultrasonic image sequence; filtering, detecting and positioning the erythrocyte B-type ultrasonic image sequence through a radial symmetric single molecule positioning model to obtain an erythrocyte positioning point; and accumulating all red blood cell positioning points, and reconstructing to obtain a super-resolution ultrasonic image. According to the method, the dependence on an ultrasonic contrast agent is avoided, red blood cell-based ULM imaging is realized through accurate positioning of red blood cells in a vascular system, the application range of ULM imaging is expanded, the imaging speed is increased, and the time resolution of a super-resolution ultrasonic imaging technology is effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of ultrasonic imaging, and in particular relates to a non-contrast super-resolution ultrasonic imaging method and system based on radially symmetric single-molecule positioning. Background Art

[0002] Medical imaging technology plays an important role in clinical diagnosis, and widely used technologies include X-ray, CT and MRI. Compared with these medical imaging technologies, ultrasound imaging has the advantages of being non-invasive, safe, portable and inexpensive, so it is widely used in clinical practice. However, the spatial resolution of ultrasound imaging is constrained by the diffraction limit, and its upper limit is half the length of the ultrasound wave used.

[0003] Drawing on relevant technologies in the field of optical microscopy, ultrasound localization microscopy (ULM) imaging technology was proposed. This technology can achieve a spatial resolution of 10 μm without sacrificing penetration depth by locating a single strong ultrasound scattering source (ultrasound contrast agent, microbubble). Specifically, traditional ULM imaging is to reconstruct a B-type ultrasound image sequence by emitting ultrasound waves through an ultrasound transducer and receiving backscattered signals from microbubbles. After filtering out tissue and noise signals, microbubbles are detected and located frame by frame, and then the microbubble location points are superimposed to reconstruct a super-resolution ultrasound image.

[0004] However, the existing ULM imaging technology requires the injection of ultrasound contrast agents (microbubbles) to achieve ultrasound echo signal enhancement, which makes it face problems such as limited application scenarios, low imaging time resolution and limited emission sound pressure, which greatly limits the practical application of ULM technology. Specifically, first of all, as an exogenous imaging marker that needs to be injected, ultrasound contrast agents may have certain side effects, such as allergic reactions or microvascular embolism. Therefore, ultrasound contrast agents are only approved for use in more than 40 countries worldwide. Secondly, ULM imaging technology is generally based on single-molecule localization technology. On the one hand, this technology requires the injected microbubbles to be sparse to determine the sub-pixel position of a single microbubble, so the concentration of injected microbubbles is limited; on the other hand, microbubbles need to be fully perfused in the vascular system to achieve complete reconstruction of the microvascular system. These limitations reduce the temporal resolution of ULM imaging technology, making it difficult to use it to detect rapid biological activities, such as hemodynamics and brain activity. Finally, in order to maintain the stability of microbubbles and prevent them from rupturing, the emission sound pressure is usually also limited, reducing the signal-to-noise ratio of ultrasound images. Summary of the invention

[0005] To solve the above problems, the purpose of the present invention is to provide a non-contrast super-resolution ultrasound imaging method and system based on radially symmetric single-molecule positioning, to realize ULM imaging based on red blood cells, to expand the application scope of ULM imaging, and at the same time to accelerate the imaging speed, thereby effectively improving the temporal resolution of super-resolution ultrasound imaging technology.

[0006] The technical solution provided by the present invention is: a non-contrast super-resolution ultrasound imaging method based on radially symmetric single-molecule localization, comprising the following steps:

[0007] Transmit multi-angle composite plane waves to the target object, collect echo signals and perform beam synthesis to obtain a red blood cell B-mode ultrasound image sequence;

[0008] The red blood cell B-type ultrasound image sequence is filtered, detected and located by using a radially symmetric single-molecule localization model to obtain a red blood cell localization point;

[0009] All red blood cell positioning points are accumulated to reconstruct a super-resolution ultrasound image.

[0010] Preferably, filtering the red blood cell B-type ultrasound image sequence by the radially symmetric single molecule localization model further includes using a spatiotemporal filtering algorithm based on singular value decomposition to separate the red blood cell signal from the tissue signal and the noise signal to obtain an ultrasound image sequence with a high signal-to-noise ratio:

[0011] The red blood cell ultrasound image sequence of shape s(x,z,t) corresponds to n x ×n z ×n t A sampling set, where n x , n z and n t Represents the number of spatial sampling in the x direction, the number of spatial sampling in the z direction and the number of time sampling respectively. The shape of s(x,z,t) converted to the Casorati matrix form is (n x ×n z ,n t ) is a two-dimensional space-time sequence matrix S, and the following singular value decomposition is performed on it: S = UΔV * , where V is a non-square diagonal matrix (n x ×n z ,n t ), U(n x ×n z ,n x ×n z ) and V(n t ,n t ) is a standard orthogonal matrix; the columns of U and V correspond to the spatial and temporal singular vectors of S respectively;

[0012] The singular value decomposition of the matrix S is a series of matrices A k The ordered weighted sum of k is divisible, Among them, u k , v k are the k-th columns of U and V respectively;

[0013] By calculating the shape (n t ,n t ) k |,k∈[[1,n t ]], the cross-correlation matrix C between them can determine different subspaces: In the formula, and σ n Respectively represent u n The mean and standard deviation of the index, C is the spatial similarity matrix;

[0014] In the visualized spatial similarity matrix C, the boundaries of the cross-correlation square regions of the tissue and red blood cell subspaces represent the optimal SVD cutoff thresholds. Can be rewritten as: S f =SVI f V * =UΔ f V * , where S f represents the filtered ultrasound data set, I f Represents the matrix filter, which gives a truncated diagonal matrix Δ f , whose singular values ​​correspond to the optimal SVD cutoff threshold for removing tissue signals and noise signals.

[0015] Preferably, detecting the red blood cell B-mode ultrasound image sequence by using a radially symmetric single molecule localization model further includes determining the region where the point spread function of a single red blood cell is located:

[0016] Perform 2× interpolation upsampling on each frame of the filtered ultrasound image;

[0017] The area that coincides with the PSF of the red blood cell signal is defined according to the full width at half maximum, which is called the localization nucleus and has a size of 5 × 5 pixels;

[0018] For each frame of ultrasound image that has been interpolated and upsampled, find all local maximum points, sort them in descending order, and only keep the first 300. If the number of local maximum points is less than 300, all of them are kept.

[0019] The image is cropped with the retained local maximum point as the center according to the defined positioning kernel size. If there are only one or two local maximum points in the cropped positioning kernel, the red blood cell separation condition is met and further red blood cell positioning is performed; if there are three or more local maximum points, positioning is not performed in the positioning kernel.

[0020] Preferably, positioning the red blood cell B-mode ultrasound image sequence by using the radially symmetric single-molecule localization model further includes, in each localization nucleus obtained by the red blood cell detection algorithm, solving the central position of the red blood cell by using the RS localization algorithm:

[0021] In the positioning nucleus, on the symmetric intensity plane centered on the maximum intensity point of the red blood cell, the intensity gradient of each point points to the maximum point, and the distance from the maximum point to the equipotential line on the symmetric intensity plane is minimized to determine the coordinates of the maximum point, thereby determining the center of the red blood cell and realizing accurate positioning of the red blood cell.

[0022] Preferably, in each positioning kernel obtained by the red blood cell detection algorithm, solving the center position of the red blood cell using the RS positioning algorithm further comprises:

[0023] The intensity at each pixel in the positioning kernel is {I ij (x ij ,y ij )}, where I is the pixel intensity, i and j represent the rows and columns of the 2D grid respectively. For the pixels with 4 indices {(i,j),(i+1,j),(i,j+1),(i+1,j+1)}, the point (x k ,y k ) is calculated as follows:

[0024]

[0025] For a slope of m k And through the point (x k ,y k ) is: y = y k +m k (xx k ), for any point (x c ,y c ), the distance between it and the straight line is d k Calculated by the following formula:

[0026]

[0027] For all midpoints (x k ,y k ), we need to determine the best point (x c ,yc ), so that the total distance Minimize, where w k It can be calculated by the following formula:

[0028]

[0029] By setting χ 2 x c With y c The derivative of is 0, so we can find x c With y c as follows:

[0030]

[0031] Among them, Δ can be calculated by the following formula:

[0032]

[0033] The red blood cell center within each localized nucleus is calculated, achieving sub-pixel localization of red blood cells.

[0034] Preferably, emitting multi-angle composite plane waves to the target object, collecting echo signals and performing beam synthesis to obtain a red blood cell B-mode ultrasonic image sequence further comprises:

[0035] The delay of each array element emission is calculated according to the selected angle, and the array elements of the transducer are stimulated in turn according to the calculated delay, so that the ultrasonic wave forms a multi-angle plane wave and is emitted at a predetermined angle;

[0036] Each array element switches to receiving state after transmission, collecting and storing the ultrasonic echo signal reflected by the red blood cells;

[0037] The collected echo signals are processed using a delay superposition algorithm to compensate for the delay difference between transmission and reception, and the echo data obtained at different angles are coherently superimposed;

[0038] The synthesized data is subjected to envelope detection to obtain a red blood cell B-type ultrasonic image sequence.

[0039] Preferably, accumulating all red blood cell positioning points to reconstruct a super-resolution ultrasound image further comprises:

[0040] Based on precise time control and spatial alignment, the acquired images containing the positioning information of a single or a small number of red blood cells are superimposed to obtain a super-resolution ultrasound image.

[0041] Based on the same concept, the present invention also provides a non-contrast super-resolution ultrasound imaging system based on radially symmetric single-molecule localization, comprising:

[0042] An acquisition module is used to transmit multi-angle composite plane waves to the target object, collect echo signals and perform beam synthesis to obtain a red blood cell B-type ultrasonic image sequence;

[0043] A radially symmetric single-molecule localization model is used to filter, detect and locate the red blood cell B-type ultrasound image sequence to obtain the red blood cell localization point;

[0044] The reconstruction module is used to accumulate all red blood cell positioning points to reconstruct a super-resolution ultrasound image.

[0045] Based on the same concept, the present invention also provides an electronic device, comprising:

[0046] A memory, the memory being used to store a processing program;

[0047] A processor, wherein when executing the processing program, the processor implements the non-contrast super-resolution ultrasound imaging method based on radially symmetric single-molecule positioning.

[0048] Based on the same concept, the present invention also provides a readable storage medium, on which a processing program is stored. When the processing program is executed by a processor, the non-contrast super-resolution ultrasound imaging method based on radially symmetric single-molecule localization is implemented.

[0049] Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the prior art:

[0050] Compared with the existing ultrasound localization microscopic imaging methods, the present invention avoids the dependence on ultrasound contrast agents, and realizes ULM imaging with ultra-high resolution by accurately positioning red blood cells in the vascular system, thereby improving the application flexibility of ULM imaging technology and expanding its application scope; in addition, the radially symmetric positioning algorithm adopted has high computational efficiency and good positioning accuracy, and can realize fast and accurate reconstruction; finally, since red blood cells are densely distributed in blood vessels, the number of frames required for reconstruction of accumulated positioning points is effectively reduced, thereby improving the temporal resolution of the method. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The specific embodiments of the present invention are further described in detail below with reference to the accompanying drawings, wherein:

[0052] Figure 1 This is a flow chart of the non-contrast super-resolution ultrasound imaging method based on radially symmetric single-molecule localization of the present invention;

[0053] Figure 2 An embodiment is a radially symmetric single-molecule localization model for red blood cells;

[0054] Figure 3This is an imaging effect diagram obtained based on the non-contrast super-resolution ultrasonic imaging method based on radially symmetric single-molecule positioning of the present invention. DETAILED DESCRIPTION

[0055] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The advantages and features of the present invention will become more apparent from the following description and claims. It should be noted that the drawings are all in a very simplified form and use non-precise ratios, which are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention.

[0056] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0057] The purpose of the present invention is to propose a non-contrast super-resolution ultrasound imaging method based on radially symmetric single-molecule positioning to address the shortcomings of existing super-resolution ultrasound imaging technology, to achieve ULM imaging based on red blood cells, to expand the application scope of ULM imaging, and at the same time to accelerate the imaging speed, thereby effectively improving the temporal resolution of super-resolution ultrasound imaging technology.

[0058] To achieve the above object, the idea of ​​the present invention is:

[0059] A radially symmetric single-molecule localization model for red blood cells is constructed, including a spatiotemporal filtering algorithm for the red blood cell ultrasound image sequence, a detection algorithm for red blood cells, and a localization algorithm for red blood cells; in the non-contrast mode, the acquisition of high-density, weakly scattered red blood cell ultrasound echo signals is achieved by emitting multi-angle ultrafast ultrasonic plane waves, and the red blood cell ultrasound image sequence is obtained by composite multi-angle data and beam synthesis; finally, the experimentally collected data is processed by the constructed single-molecule localization model to obtain the red blood cell location point sequence, and all the location points are superimposed to reconstruct a super-resolution image, thereby realizing non-contrast super-resolution ultrasonic imaging.

[0060] Example

[0061] like Figure 1 As shown, a schematic diagram of a non-contrast super-resolution ultrasound imaging method based on radially symmetric single-molecule localization is shown. A non-contrast super-resolution ultrasound imaging method based on radially symmetric single-molecule localization comprises the following steps:

[0062] Transmit multi-angle composite plane waves to the target object, collect echo signals and perform beam synthesis to obtain a red blood cell B-mode ultrasound image sequence;

[0063] The red blood cell B-type ultrasound image sequence is filtered, detected and located by using a radially symmetric single-molecule localization model to obtain a red blood cell localization point;

[0064] All red blood cell positioning points are accumulated to reconstruct a super-resolution ultrasound image.

[0065] When constructing the radially symmetric single-molecule localization model, the following work was done:

[0066] Specifically, we first construct a spatiotemporal filtering algorithm for the red blood cell ultrasound image sequence. For the red blood cell ultrasound image sequence, we use a spatiotemporal filtering algorithm based on singular value decomposition (SVD) to separate the red blood cell signal from the tissue signal and the noise signal, and obtain an ultrasound image sequence with a high signal-to-noise ratio, which is convenient for the subsequent detection of red blood cell signals and the positioning of red blood cells. Specifically, for a red blood cell ultrasound image sequence of the form s(x,z,t), it corresponds to n x ×n z ×n t A sampling set of x , n z and n t Represents the number of spatial samples in the x direction, the number of spatial samples in the z direction, and the number of time samples. Convert s(x,z,t) to a Casorati matrix in the form of (n x ×n z ,n t ) is obtained by calculating the two-dimensional space-time sequence matrix S, and then performing the following singular value decomposition on it:

[0067] S=UΔV *

[0068] Where V is a non-square diagonal matrix (n x ×n z ,n t ), U(n x ×n z ,n x ×n z ) and V(n t ,n t ) is a standard orthogonal matrix; the columns of U and V correspond to the spatial and temporal singular vectors of S, respectively.

[0069] In physical terms, the singular value decomposition of the matrix S can be regarded as decomposing it into a series of matrices A k The ordered weighted sum of k is divisible, that is Among them, u k , v kare the kth columns of U and V respectively. In other words, the matrix S can be decomposed as follows:

[0070]

[0071] Therefore, singular value decomposition can be used to perform temporal and spatial filtering on ultrasound sequence data, and each v k Represents a length of n t The time signal, each u k Represents a length of n x ×n z The spatial signal of the ultrasound image sequence is (n x ,n z )'s two-dimensional space image I k That is, the singular value decomposition of the matrix S transforms it into a series of time signals v k Independently modulated image I k Considering that tissues are distributed in the entire field of view, they have a higher temporal and spatial correlation and a larger singular value, while red blood cells only flow in blood vessels, they have a lower temporal and spatial correlation and a smaller singular value. Therefore, tissue signals and red blood cell signals have different spatial distributions, that is, |u k |The vectors should be cross-correlated in the tissue subspace and the red blood cell subspace respectively, but not between the two subspaces. That is, by calculating the shape (n t ,n t ) k |,k∈[[1,n t ]], the cross-correlation matrix C between them can determine different subspaces:

[0072]

[0073] In the formula, and σ n Respectively represent u n The mean and standard deviation of the index, C, is also called the spatial similarity matrix.

[0074] In the visualized spatial similarity matrix C, the cross-correlation square regions of the tissue and red blood cell subspaces can be clearly distinguished, and their boundaries represent the optimal SVD cutoff threshold. Can be rewritten as:

[0075] S f =SVI f V * =UΔ f V *

[0076] In the formula, S f represents the filtered ultrasound data set, I frepresents the matrix filter, i.e., the diagonal identity matrix with the first diagonal element being zero, thus obtaining a truncated diagonal matrix Δ f , whose singular values ​​correspond to the above optimal cutoff thresholds, which can remove tissue signals and noise signals.

[0077] Next, we construct a detection algorithm for red blood cells. In order to locate a single red blood cell, we first need to detect the single red blood cell, that is, determine the area where the point spread function (PSF) of the single red blood cell is located. Considering that the echo signal of red blood cells is weak, that is, the area corresponding to its PSF is small, each frame of the ultrasound image after filtering is firstly upsampled by 2× interpolation; then, the area overlapping with its PSF is defined according to the full width at half maximum (FWHM) of the red blood cell signal, which is called the positioning kernel, with a size of 5×5 pixels; for each frame of ultrasound image after interpolation upsample, all local maximum points are found, and only the first 300 are retained after arranging them in descending order (that is, the maximum number of red blood cell detections in each frame, if the number of local maximum points is less than 300, all are retained); finally, for the retained local maximum points, the image is cropped according to the defined positioning kernel size with them as the center. If there are only one or two local maximum points in the cropped positioning kernel, it is considered that the red blood cell separation condition is met and the red blood cell positioning can be further performed; if there are three or more local maximum points, different red blood cell echo signals may interfere with each other, so positioning is not performed in the positioning kernel.

[0078] Finally, a positioning algorithm for red blood cells is constructed. In each positioning kernel obtained by the red blood cell detection algorithm, the RS positioning algorithm is used to solve the center position of the red blood cell. Specifically, the positioning algorithm utilizes the following fact: for a radially symmetric particle intensity distribution, a line passing through any point and parallel to the gradient of that point will intersect with the center of the particle. For a noisy pixelated image, these lines may not intersect at one point, but the center of the particle can still be estimated as the point that minimizes the sum of its distances from all lines. That is, within the positioning kernel, on the symmetric intensity plane centered on the maximum point of red blood cell intensity, the intensity gradient of each point points to the maximum point. Therefore, if the distance from the maximum point to the equipotential lines on the above-mentioned symmetric intensity plane is minimized, the coordinates of the maximum point, that is, the center of the red blood cell, can be determined, thereby achieving accurate positioning of the red blood cell. The radially symmetric single-molecule positioning model for red blood cells is shown in the attached figure. Figure 2 shown.

[0079] Within the positioning kernel, the intensity at each pixel is {I ij (x ij ,y ij)}, where I is the pixel intensity, i and j represent the row and column of the 2D grid respectively. For the 4 pixels with index {(i,j),(i+1,j),(i,j+1),(i+1,j+1)}, the point (x k ,y k ) is calculated as follows:

[0080]

[0081] Therefore, the slope is m k And through the point (x k ,y k ) is: y = y k +m k (xx k ). For any point (x c ,y c ), the distance between it and the straight line is d k It can be calculated by the following formula:

[0082]

[0083] For all midpoints (x k ,y k ), we need to determine the best point (x c ,y c ), so that the total distance Minimize. Among them, w k It can be calculated by the following formula:

[0084]

[0085] By setting χ 2 x c With y c The derivative of is 0, so we can find x c With y c as follows:

[0086]

[0087] Among them, Δ can be calculated by the following formula:

[0088]

[0089] Based on this, the red blood cell center in each positioning nucleus can be calculated, achieving sub-pixel positioning of red blood cells. And because this algorithm is a non-iterative analytical method, it is more efficient than numerical iterative methods such as maximum likelihood estimation (Gaussian fitting method).

[0090] In order to obtain a red blood cell B-type ultrasound image sequence and realize the acquisition of high-density, weak-echo red blood cell ultrasound echo signals, the present application adopts plane wave technology to realize ultrafast ultrasound imaging, and improves the signal-to-noise ratio of the ultrasound image by compounding multi-angle ultrasound signals, and then performs beam synthesis on the collected radio frequency data.

[0091] Based on the constructed single-molecule localization model for red blood cells, the red blood cell ultrasound image sequence obtained in the above experiment is filtered, detected and located to obtain the location points of red blood cells. Then, the location points of all red blood cells are accumulated to reconstruct a super-resolution ultrasound image in a non-contrast mode, as shown in the attached figure. Figure 3 shown.

[0092] Compared with the existing ultrasound localization microscopic imaging methods, the present invention avoids the dependence on ultrasound contrast agents, and realizes ULM imaging with ultra-high resolution by accurately positioning red blood cells in the vascular system, thereby improving the application flexibility of ULM imaging technology and expanding its application scope; in addition, the radially symmetric positioning algorithm adopted has high computational efficiency and good positioning accuracy, and can realize fast and accurate reconstruction; finally, since red blood cells are densely distributed in blood vessels, the number of frames required for reconstruction of accumulated positioning points is effectively reduced, thereby improving the temporal resolution of the method.

[0093] Based on the same concept, the present invention also provides a non-contrast super-resolution ultrasound imaging system based on radially symmetric single-molecule localization, comprising:

[0094] An acquisition module is used to transmit multi-angle composite plane waves to the target object, collect echo signals and perform beam synthesis to obtain a red blood cell B-type ultrasonic image sequence;

[0095] A radially symmetric single-molecule localization model is used to filter, detect and locate the red blood cell B-type ultrasound image sequence to obtain the red blood cell localization point;

[0096] The reconstruction module is used to accumulate all red blood cell positioning points to reconstruct a super-resolution ultrasound image.

[0097] Based on the same concept, the present invention also provides an electronic device, comprising: a memory, the memory being used to store a processing program; a processor, the processor implementing any one of the above-mentioned non-contrast super-resolution ultrasound imaging methods based on radially symmetric single-molecule positioning when executing the processing program.

[0098] Based on the same concept, the present invention also provides a readable storage medium having a processing program stored thereon, and when the processing program is executed by a processor, the non-contrast super-resolution ultrasound imaging method based on radially symmetric single-molecule localization as described above is implemented.

[0099] If the non-contrast super-resolution ultrasound imaging method based on radially symmetric single-molecule positioning is implemented in the form of program instructions and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this embodiment is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of software, and the computer software is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0100] Those skilled in the art can clearly understand that, for the sake of convenience and brevity in description, the identification content specifically executed by the above-described system and device can refer to the corresponding process in the aforementioned method embodiment.

[0101] The embodiments of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments. Even if various changes are made to the present invention, if these changes fall within the scope of the claims of the present invention and their equivalents, they still fall within the protection scope of the present invention.

Claims

1. A non-contrast super-resolution ultrasound imaging method based on radially symmetric single-molecule localization, characterized in that: The following steps are involved: Transmit multi-angle composite plane waves to the target object, collect echo signals and perform beam synthesis to obtain a red blood cell B-mode ultrasound image sequence; The red blood cell B-type ultrasound image sequence is filtered, detected and located by using a radially symmetric single-molecule localization model to obtain a red blood cell localization point; All red blood cell positioning points are accumulated to reconstruct a super-resolution ultrasound image.

2. The non-contrast super-resolution ultrasound imaging method based on radially symmetric single-molecule localization according to claim 1, characterized in that: Filtering the red blood cell B-mode ultrasound image sequence by the radially symmetric single molecule localization model further includes using a spatiotemporal filtering algorithm based on singular value decomposition to separate the red blood cell signal from the tissue signal and the noise signal to obtain an ultrasound image sequence with a high signal-to-noise ratio: The red blood cell ultrasound image sequence of shape s(x,z,t) corresponds to n x ×n z ×n t A sampling set, where n x , n z and n t Represents the number of spatial sampling in the x direction, the number of spatial sampling in the z direction and the number of time sampling respectively. The shape of s(x,z,t) converted to the Casorati matrix form is (n x ×n z ,n t ) is a two-dimensional space-time sequence matrix S, and the following singular value decomposition is performed on it: S = UΔV * , where V is a non-square diagonal matrix (n x ×n z ,n t ), U(n x ×n z ,n x ×n z ) and V(n t ,n t ) is a standard orthogonal matrix; the columns of U and V correspond to the spatial and temporal singular vectors of S respectively; The singular value decomposition of the matrix S is a series of matrices A k The ordered weighted sum of k is divisible, Among them, u k , v k are the k-th columns of U and V respectively; By calculating the shape (n t ,n t ) k |,k∈[[1,n t ]], the cross-correlation matrix C between them can determine different subspaces: In the formula, and σ n Respectively represent u n The mean and standard deviation of the index, C is the spatial similarity matrix; In the visualized spatial similarity matrix C, the boundaries of the cross-correlation square regions of the tissue and red blood cell subspaces represent the optimal SVD cutoff thresholds. Can be rewritten as: S f =SVI f V * =UΔ f V * , where S f represents the filtered ultrasound data set, I f Represents the matrix filter, which gives a truncated diagonal matrix Δ f , whose singular values ​​correspond to the optimal SVD cutoff threshold for removing tissue signals and noise signals.

3. The non-contrast super-resolution ultrasound imaging method based on radially symmetric single-molecule localization according to claim 1, characterized in that: Detecting the red blood cell B-mode ultrasound image sequence by using the radially symmetric single molecule localization model further includes determining the region where the point spread function of a single red blood cell is located: Perform 2× interpolation upsampling on each frame of the filtered ultrasound image; The area that coincides with the PSF of the red blood cell signal is defined according to the full width at half maximum, which is called the localization nucleus and has a size of 5 × 5 pixels; For each frame of ultrasound image that has been interpolated and upsampled, find all local maximum points, sort them in descending order, and only keep the first 300. If the number of local maximum points is less than 300, all of them are kept. The image is cropped with the retained local maximum point as the center according to the defined positioning kernel size. If there are only one or two local maximum points in the cropped positioning kernel, the red blood cell separation condition is met and further red blood cell positioning is performed; if there are three or more local maximum points, positioning is not performed in the positioning kernel.

4. The non-contrast super-resolution ultrasound imaging method based on radially symmetric single-molecule localization according to claim 3, characterized in that: Positioning the red blood cell B-mode ultrasound image sequence by using the radially symmetric single-molecule localization model further includes solving the central position of the red blood cell by using the RS localization algorithm in each localization nucleus obtained by the red blood cell detection algorithm: In the positioning nucleus, on the symmetric intensity plane centered on the maximum intensity point of the red blood cell, the intensity gradient of each point points to the maximum point, and the distance from the maximum point to the equipotential line on the symmetric intensity plane is minimized to determine the coordinates of the maximum point, thereby determining the center of the red blood cell and realizing accurate positioning of the red blood cell.

5. The non-contrast super-resolution ultrasound imaging method based on radially symmetric single-molecule localization according to claim 4, characterized in that: In each positioning kernel obtained by the red blood cell detection algorithm, solving the center position of the red blood cell using the RS positioning algorithm further includes: The intensity at each pixel in the positioning kernel is {I ij (x ij ,y ij )}, where I is the pixel intensity, i and j represent the rows and columns of the 2D grid respectively. For the pixels with 4 indices {(i,j),(i+1,j),(i,j+1),(i+1,j+1)}, the point (x k ,y k ) is calculated as follows: For a slope of m k And through the point (x k ,y k ) is: y = y k +m k (xx k ), for any point (x c ,y c ), the distance between it and the straight line is d k Calculated by the following formula: For all midpoints (x k ,y k ), we need to determine the best point (x c ,y c ), so that the total distance Minimize, where w k It can be calculated by the following formula: By setting χ 2 x c With y c The derivative of is 0, so we can find x c With y c as follows: Among them, Δ can be calculated by the following formula: The red blood cell center within each localized nucleus is calculated, achieving sub-pixel localization of red blood cells.

6. The non-contrast super-resolution ultrasound imaging method based on radially symmetric single-molecule localization according to claim 1, characterized in that: Transmitting multi-angle composite plane waves to the target object, collecting echo signals and performing beam synthesis to obtain a red blood cell B-mode ultrasound image sequence further includes: The delay of each array element emission is calculated according to the selected angle, and the array elements of the transducer are stimulated in turn according to the calculated delay, so that the ultrasonic wave forms a multi-angle plane wave and is emitted at a predetermined angle; Each array element switches to receiving state after transmission, collecting and storing the ultrasonic echo signal reflected by the red blood cells; The collected echo signals are processed using a delay superposition algorithm to compensate for the delay difference between transmission and reception, and the echo data obtained at different angles are coherently superimposed; The synthesized data is subjected to envelope detection to obtain a red blood cell B-type ultrasonic image sequence.

7. The non-contrast super-resolution ultrasound imaging method based on radially symmetric single-molecule localization according to claim 1, characterized in that: Accumulating all red blood cell positioning points to reconstruct a super-resolution ultrasound image further includes: Based on precise time control and spatial alignment, the acquired images containing the positioning information of a single or a small number of red blood cells are superimposed to obtain a super-resolution ultrasound image.

8. A non-contrast super-resolution ultrasound imaging system based on radially symmetric single-molecule localization, characterized in that: include: An acquisition module is used to transmit multi-angle composite plane waves to the target object, collect echo signals and perform beam synthesis to obtain a red blood cell B-type ultrasonic image sequence; A radially symmetric single-molecule localization model is used to filter, detect and locate the red blood cell B-type ultrasound image sequence to obtain the red blood cell localization point; The reconstruction module is used to accumulate all red blood cell positioning points to reconstruct a super-resolution ultrasound image.

9. An electronic device, characterized in that: include: A memory, the memory being used to store a processing program; A processor, wherein when executing the processing program, the processor implements the non-contrast super-resolution ultrasound imaging method based on radially symmetric single-molecule localization as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that: The readable storage medium stores a processing program, and when the processing program is executed by the processor, the non-contrast super-resolution ultrasound imaging method based on radially symmetric single-molecule localization as described in any one of claims 1 to 7 is implemented.

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