A novel fast ultrasonic super-resolution imaging method, system, device and medium

Abstract

The present invention discloses a novel rapid ultrasonic super-resolution imaging method, system, equipment and medium. First, ultrasonic B-images are processed to obtain candidate microbubble signal points. In the subsequent microbubble matching tracking, four constraints are taken into account: the flow velocity changes of the same microbubble in the blood vessel should be consistent and the velocity changes should be small; the deflection angle of the microbubble moving in the same blood vessel should not be too large; and the position should be as close as possible to the predicted position of the Kalman filter. Four parameters are proposed: velocity deviation, angle deviation, separation distance deviation and unscented Kalman weight. This makes the tracking process more consistent with the actual structure of the blood vessel. The influence of historical trajectory on the tracking process is introduced to reduce the probability of erroneous blood vessels.

Description

Technical Field

[0001] The present invention relates to the field of ultrasonic imaging technology, and in particular to a novel fast ultrasonic super-resolution imaging method, system, equipment and medium. Background Art

[0002] Submillimeter angiogenesis is a hallmark of tumor growth, and alterations in submillimeter vascular morphology are also common features of neurological diseases. Therefore, studying submillimeter vascular imaging plays a crucial role in clinical diagnosis. CT, MRI, and ultrasound imaging techniques can all be used for vascular imaging, but CT emits radiation, which is harmful to the human body. Conventional MRI resolution cannot reach submillimeter resolution and is time-consuming and expensive. Ultrasound imaging offers advantages such as speed, real-time resolution, and affordability, but due to the diffraction limit, its resolution cannot reach submillimeter resolution. With the advent and continued development of optical localization microscopy, a similar technique, Ultrasound Localization Microscopy (ULM), has emerged in ultrasound imaging. This technique transcends the diffraction limit of conventional ultrasound imaging, enabling submillimeter vascular imaging and providing information on blood flow velocity and direction. It represents a promising new approach for submillimeter vascular imaging.

[0003] Due to their small size, the contrast-enhancing microbubbles used in ultrasound imaging can flow within submillimeter blood vessels. Therefore, the vascular structure can be reconstructed by tracking the trajectories of the microbubbles. When the number of microbubbles is large enough, it can be assumed that every location in the vessel is flowing through them. Therefore, the vascular structure can be obtained by superimposing all the microbubble trajectories. Based on this principle, ULM uses a multi-frame ultrasound image sequence containing microbubble information and a special positioning method to obtain the microbubble position information in each frame. Then, a tracking method is used to match and connect the microbubbles in different frames to form trajectories. Finally, these trajectories are displayed to form a super-resolution ultrasound image containing submillimeter vascular structure information.

[0004] Traditional ULM positioning methods include Gaussian fitting and maximum peak methods. The physical basis of the Gaussian fitting method is that the point spread function of microbubbles in the ultrasound imaging system is similar to a two-dimensional Gaussian function. Therefore, a standard Gaussian function can be used to perform correlation calculations or deconvolution on the image. The position where the microbubbles appear has a higher amplitude, but when microbubbles overlap, this method is difficult to separate them. The maximum peak method obtains the microbubble coordinates by finding the local maximum point of the echo hyperbola. This method needs to ensure that the microbubble echo curve is not disturbed, and has extremely strict concentration requirements. Microbubbles with low echo intensity are easily missed. Tracking methods include nearest neighbor allocation and Hungarian allocation. Both methods assume that the position change of the microbubble is only related to the position of the previous frame during the tracking process. Therefore, the historical trajectory of the microbubble is ignored and not taken into account, so inaccurate tracking may occur.

[0005] A major challenge hindering the clinical translation of ULM is the long acquisition time. This is because sufficient data must be acquired to ensure that microbubbles pass through every location in the blood vessel, significantly extending data acquisition time. Furthermore, the increased data volume increases data processing time. While the simplest and most direct approach is to increase microbubble concentration, this increases the probability of microbubble overlap, leading to a decrease in the accuracy of traditional localization methods. The increased number of microbubbles and the fact that traditional tracking methods do not consider the historical trajectory of microbubbles increase the probability of incorrect vascular structures being tracked. Summary of the Invention

[0006] The present invention aims to provide a novel rapid ultrasound super-resolution imaging method to overcome the problem in the prior art of an increased probability of erroneous vascular structures occurring during the tracking process.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A novel rapid ultrasound super-resolution imaging method comprises the following steps:

[0009] S1: Acquire ultrasound B-mode image data, perform noise reduction processing on the ultrasound B-mode image data, and extract candidate points of microbubble signals.

[0010] S2: Counting the change characteristics of the amplitude and gradient in the 8-directional fields of the microbubble signal candidate points, and then determining the possibility of the existence of microbubbles at the microbubble signal candidate points through a mixed criterion, screening the microbubble signal candidate points that are microbubbles, and obtaining the precise positioning of the screened microbubble signal candidate points, and taking all the precisely positioned microbubble signal candidate points as the microbubble signal point set;

[0011] S3: Recording the historical motion speed and deflection angle in the same microbubble trajectory, using an unscented Kalman filter to predict the nonlinear motion trajectory of all microbubble signal candidate points in the microbubble signal point set, obtaining the unscented Kalman weight, and calculating the speed deviation, angle deviation, and separation distance deviation of all microbubble signal candidate points in the microbubble signal point set; determining the motion trajectory of all microbubble signal candidate points in the microbubble signal point set based on the speed deviation, angle deviation, separation distance deviation, and unscented Kalman weight of all microbubble signal candidate points;

[0012] S4: performing an interpolation operation on the motion trajectories of all candidate microbubble signal points in the microbubble signal point set and calculating their velocity information to obtain a super-resolution image, a velocity map, and a flow direction map.

[0013] Preferably, the ultrasound B-mode image is obtained by beamforming using the video signal output from a commercial machine or using the RF data output from a Verasonics programmable ultrasound machine.

[0014] Preferably, the mixed criterion formula is:

[0015]

[0016] in The coordinates in the image are There is a possibility of microbubbles at the point of and are the score weights of amplitude and gradient changes, and are amplitude score and gradient change score respectively.

[0017] Preferably, the specific method of reducing noise in S1 is:

[0018] Pixels with brightness lower than or equal to the threshold in the ultrasound B-mode image are defaulted to background and set to zero. The connected domain area of ​​the brightness region higher than the threshold is calculated, and the connected domain area with an area greater than an integer multiple of the theoretical point spread function is removed.

[0019] Preferably, the precise positioning method for obtaining the candidate microbubble signal points for screening microbubbles in S2 is to interpolate the ultrasound B image and obtain the points using a weighted average algorithm.

[0020] A novel fast ultrasound super-resolution imaging system, based on a novel fast ultrasound super-resolution imaging method, includes:

[0021] Extraction module: used to obtain ultrasound B-mode image data, perform noise reduction processing on the ultrasound B-mode image data, and extract candidate points of microbubble signals;

[0022] Determination module: used to determine the possibility of the existence of microbubbles in all microbubble signal candidate points, screen the microbubble signal candidate points that are microbubbles, obtain the precise positioning of the screened microbubble signal candidate points that are microbubbles, and use all the precisely positioned microbubble signal candidate points as a microbubble signal point set;

[0023] Trajectory acquisition module: used to record the historical motion speed and deflection angle of all microbubble signal candidate points in the microbubble signal point set, use the unscented Kalman filter to predict the nonlinear motion trajectory of all microbubble signal candidate points in the microbubble signal point set, obtain the unscented Kalman weight, and calculate the speed deviation, angle deviation, and distance deviation of all microbubble signal candidate points in the microbubble signal point set. The motion trajectory of all microbubble signal candidate points in the microbubble signal point set is determined based on the speed deviation, angle deviation, distance deviation, and unscented Kalman weight of all microbubble signal candidate points;

[0024] Image acquisition module: used to interpolate the motion trajectories of all candidate microbubble signal points in the microbubble signal point set and calculate their velocity information to obtain super-resolution images, velocity maps and flow direction maps.

[0025] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the novel fast ultrasonic super-resolution imaging method are implemented.

[0026] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a novel fast ultrasonic super-resolution imaging method.

[0027] Compared with the prior art, the present invention has the following beneficial effects: the present invention provides a novel fast ultrasound super-resolution imaging method, firstly, the ultrasound B image is processed to obtain candidate microbubble signal points, and in the subsequent microbubble matching tracking, four constraints are taken into account: the flow velocity changes of the same microbubble in the blood vessel should be consistent and the velocity changes should be small; the deflection angle of the microbubble moving in the same blood vessel should not be too large; and it should be as close as possible to the predicted position of the Kalman filter. Four parameters are proposed, namely, velocity deviation, angle deviation, separation distance deviation, and unscented Kalman weight, so that the tracking process is more consistent with the actual structure of the blood vessel, and the influence of historical trajectory on the tracking process is introduced, so that the probability of erroneous blood vessels is reduced.

[0028] Furthermore, the present invention utilizes the physical fact that the brightness of microbubbles in ultrasound images is high and is similar to the gradient of a two-dimensional Gaussian function, thereby changing the traditional method of using only peak location or Gaussian fitting location.

[0029] Furthermore, the present invention proposes a hybrid criterion based on amplitude and gradient features, which combines the advantages of the peak location method and the Gaussian fitting method, and can improve the accuracy and precision of positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flowchart of a fast super-resolution imaging method based on feature localization and physical constraint tracking.

[0031] Figure 2 This is a schematic diagram of the trajectory allocation method of the dynamic constraint tracking algorithm.

[0032] Figure 3 This is a comparison of positioning result parameters between the positioning method of the present invention and the traditional Gaussian fitting method in two different simulation data sets of high frequency and low frequency. a is the positioning result of the traditional Gaussian fitting method, and b is the positioning result of the positioning method of the present invention.

[0033] Figure 4 This is a comparison between the super-resolution image obtained in the present invention and the super-resolution image obtained by the traditional Gaussian fitting method. a is the super-resolution image reconstructed by the tracking algorithm of the present invention, and b is the super-resolution image obtained by using Gaussian fitting positioning and traditional nearest neighbor tracking. DETAILED DESCRIPTION

[0034] The present invention will be further described in detail below with reference to specific embodiments, which are intended to explain the present invention rather than to limit it.

[0035] like Figure 1 The present invention provides a novel fast ultrasound super-resolution imaging method. The method proposed in the present invention can use the video signal output by a commercial machine to obtain a B-mode image, and can also use the RF data output by a programmable ultrasound machine such as Verasonics to perform beam synthesis to obtain a B-mode image.

[0036] After obtaining a B-mode image, an appropriate threshold must be set and a theoretical point spread function (PSF) calculated based on the probe center frequency used during data acquisition. Specifically, pixels in the B-mode image with brightness below the threshold are considered background pixels and thus reset to zero. This effectively suppresses the impact of low-brightness speckle noise on localization. The connected domain area of ​​high-brightness regions is then calculated. Any connected domain area greater than a certain multiple of the PSF is considered tissue noise. After preprocessing the B-mode image, the remaining local brightness maxima are used as candidate microbubble locations.

[0037] In ultrasound images, the microbubble point spread function exhibits a characteristic of decreasing amplitude from the center outward and increasing gradient inward. Therefore, we can calculate the amplitude and gradient variations within the eight-directional neighborhood of each candidate point to determine its likelihood of microbubble presence, removing those with low likelihood. Finally, we interpolate the original image and use a weighted averaging algorithm to accurately localize microbubbles to the decimal level.

[0038] After obtaining the precise positioning of the candidate points, a nonlinear motion model needs to be designed. An unscented Kalman filter is then used to predict the microbubble trajectory. The noise perturbation of the unscented Kalman filter system is related to the size of the microbubble point spread function, allowing for controllable positioning errors to reduce the impact on trajectory consistency. The historical trajectory of successfully tracked microbubbles is recorded. The impact of four factors—speed deviation, angular deviation, separation distance, and unscented Kalman prediction weights—is simultaneously considered during the allocation of new frames. This historical trajectory is used to dynamically constrain the tracking process, resulting in a microbubble trajectory that is more consistent with flow theory and improves the resolution between adjacent blood vessels.

[0039] After interpolating the tracked trajectory and calculating its velocity information, it can be mapped to the upsampled coordinate matrix to form a super-resolution density map and velocity map.

[0040] Specific calculation process:

[0041] Step 1: Threshold segmentation is performed on each frame in the ultrasound B-mode image sequence, and pixels below the threshold are set to 0. Calculate the area of ​​the theoretical point spread function , the connected area in the image is greater than times The area of ​​​​is set to 0, and the resulting image is recorded as .

[0042] Step 2, calculate The amplitude and gradient change characteristics of all local extreme points in the 8-direction neighborhood are calculated, and the possibility of the existence of microbubbles at this point is determined by a mixed criterion. , When the value is greater than the set threshold, it is considered to be a microbubble. Then the area is interpolated and the weighted average algorithm is used to obtain the precise location of the microbubble. The point set after fine positioning is recorded as .

[0043] The mixed criterion formula is:

[0044]

[0045] in The coordinates in the image are There is a possibility of microbubbles at the point of and are the score weights of amplitude and gradient changes, and Score for amplitude and gradient change.

[0046] Step 3, get the microbubble point set in each frame Then start the microbubble matching, The microbubble point set corresponding to the frame image is recorded as . Assume that now in If a match is made, then calculate Zhongyu The distance is within a certain wavelength range, due to the There may be multiple points with The distance meets the requirements, so the point set that meets the requirements is recorded as .

[0047] Step 4, assume in The points belong to the trajectory , There are multiple historical points in , which are expressed as shown in the formula.

[0048]

[0049] Trajectory prediction using unscented Kalman filter exist The position in the frame is recorded as , and calculate Add each point to the trajectory Velocity deviations of all historical points in the subsequent trajectory , angle deviation , distance deviation , and calculate Each point in Position distance deviation .

[0050] Step 5: Assume in step 4 Each point is added to the trajectory The corresponding four parameters are weighted summed, where is the weight of the corresponding parameter, Add tracks to corresponding points The possibility of middle The smallest point joins the trajectory .

[0051]

[0052] Step 6: Repeat steps 3 to 5 for each positioning point in each frame to obtain the microbubble trajectory in the entire ultrasound B-mode image sequence, and then interpolate the image to obtain a super-resolution structure density map.

[0053] See also Figure 2 , a schematic diagram of the trajectory allocation method of the dynamic constraint tracking algorithm. In the figure, t1 and T1 represent two different trajectories, ✚ represents the points that have been successfully assigned for tracking, ◆ is the noise point that has not been assigned, and ● is the point that needs to be assigned in the current frame. When the two trajectories t1 and T1 are assigned to points t4 and T4, the four ● in the figure can all be used as alternative points for the two trajectories, but the dynamic constraint algorithm in the present invention will jointly affect the allocation of alternative points based on four factors: the trajectory's motion speed deviation, angle deviation, separation distance, and unscented Kalman prediction weight. In the actual tracking process, the relative weights of the above four influencing factors can be adjusted. For example, when the weight of the angle deviation is larger, the possibility of point T7 being assigned to the T1 trajectory and T6 being assigned to the t1 trajectory is greater. When the weight of the separation distance is larger, the possibility of point T5 being assigned to the t1 trajectory and T8 being assigned to the T1 trajectory is greater. Therefore, it is necessary to reasonably adjust the relative weights of the four factors to obtain a reasonable super-resolution image.

[0054] See also Figure 3 Comparison of positioning results using the proposed positioning method and a traditional Gaussian fitting method for two different high-frequency and low-frequency simulation datasets. The high-frequency and low-frequency simulation datasets were generated using L11-4v and GE M5Sc-D probes, respectively, with probe transmission center frequencies of 7.24 MHz and 2.84 MHz. In the figure, ✚ represents the results obtained using the proposed positioning method, ○ represents the results obtained using the traditional Gaussian fitting method, and ☆ represents the actual microbubble locations.

[0055] As shown in Table 1, since the simulation data can obtain the true coordinates of microbubbles, two quantitative evaluation metrics, Precision and Recall, can be calculated for the positioning method proposed in this invention and the traditional Gaussian positioning method. A positioning point is considered correct when the distance between the positioning point and the true coordinates of a microbubble is within half a wavelength. Therefore, Precision can be expressed as the ratio of the number of correct positioning points to the total number of positioning points, and Recall can be expressed as the ratio of the number of correct positioning points to the actual number of microbubbles. These two parameters can reflect the quality of the positioning results from different perspectives.

[0056] Table 1

[0057]

[0058] The data in Table 1 are the average values ​​of the positioning results of 20 frames of B-mode. It can be seen from the figure that in high-frequency data, the positioning method proposed by the present invention has a significant improvement over the results of traditional Gaussian fitting, with Precision and Recall increased by 11.1% and 14.7%, respectively. This shows that the method proposed by the present invention can not only improve the accuracy of positioning, but also locate more real microbubbles in the picture. In low-frequency data, due to the small number of microbubble points in each frame of B-mode image, there is no significant difference in Recall between the two methods, but Precision is still higher for the method proposed by the present invention.

[0059] See also Figure 4 , a is the super-resolution image reconstructed by the tracking algorithm invented in this paper, and b is the super-resolution image obtained by using Gaussian fitting positioning and traditional nearest neighbor tracking. The data used in the generation process is a high-frequency simulation data set. The super-resolution image obtained by the method proposed in this invention has the following advantages over the results of the original method. First, the overall shape of the trajectory is smoother and more in line with the actual geometric shape of human blood vessels; second, the tortuosity of the trajectory is smaller. In the results of the traditional method, some trajectories even show a trend of tortuous oscillation, which is impossible in the actual blood flow process; third, the connectivity of the trajectory is better. It can be clearly seen that the long trajectory on the left side of the left image is more connected than the trajectory at the same position in the right image. The rest of the trajectories also have the same improvement; fourth, the number of trajectories has increased, showing more details. The reason is that the method proposed in the present invention takes into account the influence of historical tracks, and incorporates four factors, namely motion speed deviation, angle deviation, separation distance and unscented Kalman prediction weight, into the optimal allocation of the trajectory, so that the shape, connectivity and tortuosity of the trajectory are improved. Secondly, the positioning method proposed in the present invention also affects the generation of the trajectory. It is obvious that the positioning method proposed in the present invention improves the two important parameters of Precision and Recall by 11.1% and 14.7% respectively compared with the results of the traditional method, which proves that the positioning method can provide more correct microbubble positioning points for microbubble tracking, reduce more noise points, and locate more real microbubble existence points. These improvements can greatly improve the level of super-resolution results.

[0060] A novel fast ultrasound super-resolution imaging system, comprising:

[0061] Extraction module: used to obtain ultrasound B-mode image data, perform noise reduction processing on the ultrasound B-mode image data, and extract candidate points of microbubble signals;

[0062] Determination module: used to count the change characteristics of the amplitude and gradient in the 8-directional field of the microbubble signal candidate points, and then determine the possibility of the presence of microbubbles at the microbubble signal candidate points through a mixed criterion, screen the microbubble signal candidate points that are microbubbles, and obtain the precise positioning of the microbubble signal candidate points that are screened as microbubbles, and all the microbubble signal candidate points that have obtained precise positioning are regarded as the microbubble signal point set;

[0063] Trajectory acquisition module: used to record the historical motion speed and deflection angle of all microbubble signal candidate points in the microbubble signal point set, use the unscented Kalman filter to predict the nonlinear motion trajectory of all microbubble signal candidate points in the microbubble signal point set, obtain the unscented Kalman weight, and calculate the speed deviation, angle deviation, and distance deviation of all microbubble signal candidate points in the microbubble signal point set, and determine the motion trajectory of all microbubble signal candidate points in the microbubble signal point set according to the speed deviation, angle deviation, distance deviation and unscented Kalman weight of all microbubble signal candidate points; record the historical motion speed and deflection angle in the same microbubble trajectory, use the unscented Kalman filter to predict the nonlinear motion trajectory of all microbubble signal candidate points in the microbubble signal point set, obtain the unscented Kalman weight, and calculate the speed deviation, angle deviation, and distance deviation of all microbubble signal candidate points in the microbubble signal point set, and determine the motion trajectory of all microbubble signal candidate points in the microbubble signal point set according to the speed deviation, angle deviation, distance deviation and unscented Kalman weight of all microbubble signal candidate points;

[0064] Image acquisition module: used to interpolate the motion trajectories of all candidate microbubble signal points in the microbubble signal point set and calculate their velocity information to obtain super-resolution images, velocity maps, and flow direction maps.

[0065] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in a computer device, used to store programs and data. It is understood that the computer-readable storage medium herein may include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides storage space, which stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for being loaded and executed by a processor. These instructions may be one or more computer programs (including program code). It should be noted that the computer-readable storage medium herein may be a high-speed RAM memory or a non-volatile memory, such as at least one disk drive. The processor may load and execute the one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the glass-channel-welded thermocouple temperature compensation method described in the above-mentioned embodiment.

[0066] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0067] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0068] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0069] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A novel fast ultrasonic super-resolution imaging method, characterized in that: The following steps are involved: S1: Acquire ultrasound B-mode image data, perform noise reduction processing on the ultrasound B-mode image data, and extract candidate microbubble signal points; S2: Counting the change characteristics of the amplitude and gradient in the 8-directional fields of the microbubble signal candidate points, and then determining the possibility of the existence of microbubbles at the microbubble signal candidate points through a mixed criterion, screening the microbubble signal candidate points that are microbubbles, and obtaining the precise positioning of the screened microbubble signal candidate points, and taking all the precisely positioned microbubble signal candidate points as the microbubble signal point set; S3: Recording the historical motion speed and deflection angle in the same microbubble trajectory, using an unscented Kalman filter to predict the nonlinear motion trajectory of all microbubble signal candidate points in the microbubble signal point set, obtaining the unscented Kalman weight, and calculating the speed deviation, angle deviation, and separation distance deviation of all microbubble signal candidate points in the microbubble signal point set; determining the motion trajectory of all microbubble signal candidate points in the microbubble signal point set based on the speed deviation, angle deviation, separation distance deviation, and unscented Kalman weight of all microbubble signal candidate points; S4: performing interpolation operations on the motion trajectories of all candidate microbubble signal points in the microbubble signal point set and calculating their velocity information to obtain a super-resolution image, a velocity map, and a flow direction map; The mixed criterion formula is: in The coordinates in the image are There is a possibility of microbubbles at the point of and are the score weights of amplitude and gradient changes, and are amplitude score and gradient change score respectively.

2. A novel fast ultrasonic super-resolution imaging method according to claim 1, characterized in that: Ultrasound B-mode images were obtained using beamforming of video signals from commercial ultrasound machines or RF data from Verasonics programmable ultrasound machines.

3. A novel fast ultrasonic super-resolution imaging method according to claim 1, characterized in that: The specific method of noise reduction in S1 is: Pixels with brightness lower than or equal to the threshold in the ultrasound B-mode image are defaulted to background and set to zero. The connected domain area of ​​the brightness region higher than the threshold is calculated, and the connected domain area with an area greater than an integer multiple of the theoretical point spread function is removed.

4. A novel fast ultrasonic super-resolution imaging method according to claim 1, characterized in that: The method for obtaining the precise positioning of the candidate points of the microbubble signal for screening the microbubbles in S2 is to interpolate the ultrasound B image and obtain it using a weighted average algorithm.

5. A novel fast ultrasound super-resolution imaging system, based on the novel fast ultrasound super-resolution imaging method according to any one of claims 1 to 4, comprising: Extraction module: used to obtain ultrasound B-mode image data, perform noise reduction processing on the ultrasound B-mode image data, and extract candidate points of microbubble signals; Determination module: used to count the change characteristics of the amplitude and gradient in the 8-directional field of the microbubble signal candidate points, and then determine the possibility of the presence of microbubbles at the microbubble signal candidate points through a mixed criterion, screen the microbubble signal candidate points that are microbubbles, and obtain the precise positioning of the microbubble signal candidate points that are screened as microbubbles, and all the microbubble signal candidate points that have obtained precise positioning are regarded as the microbubble signal point set; Trajectory acquisition module: used to record the historical motion speed and deflection angle in the same microbubble trajectory, use the unscented Kalman filter to predict the nonlinear motion trajectory of all microbubble signal candidate points in the microbubble signal point set, obtain the unscented Kalman weight, and calculate the speed deviation, angle deviation, and distance deviation of all microbubble signal candidate points in the microbubble signal point set. The motion trajectory of all microbubble signal candidate points in the microbubble signal point set is determined based on the speed deviation, angle deviation, distance deviation and unscented Kalman weight of all microbubble signal candidate points; Image acquisition module: used to interpolate the motion trajectories of all candidate microbubble signal points in the microbubble signal point set and calculate their velocity information to obtain super-resolution images, velocity maps and flow direction maps.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the novel fast ultrasonic super-resolution imaging method as claimed in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the novel fast ultrasonic super-resolution imaging method as claimed in any one of claims 1 to 4 are implemented.

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