Motion correction and registration method and device in ultrasonic microangiography, medium and product

Abstract

The invention discloses a motion correction and registration method and device in ultrasonic microvascular imaging, a medium and a product, and relates to the field of ultrasonic microvascular imaging, and the method comprises the steps: obtaining an ultrasonic sequence image changing along with time; dividing the ultrasonic sequence image by using the threshold value of the data frame correlation coefficient; determining a reconstructed image of the static frame and a reconstructed image of the relative static frame according to a clustering result obtained by clustering the correlation coefficient between the static frames; respectively carrying out image registration on the reconstructed image of the static frame and the reconstructed image of the relative static frame to obtain a reconstructed image after registration of the static frame and a reconstructed image after registration of the relative static frame; and superposing the reconstructed image after the static frame registration, the reconstructed image after the relative static frame registration and the reconstructed image without breathing movement to obtain a registered image. According to the invention, the ultrasonic super-resolution microvascular imaging performance and quality can be improved.

Description

Technical Field

[0001] The present application relates to the field of ultrasonic microvascular imaging, and in particular to a motion correction and registration method, device, medium and product in ultrasonic microvascular imaging. Background Art

[0002] Super-resolution imaging reconstructs the microvascular network by locating and tracking sparsely distributed microbubble contrast agents. The size of the visualized microvessels reaches 2μm-20μm, and under ideal conditions, the capillary network can be drawn. However, sparse targets lead to a long imaging acquisition time, generally between 1 minute and 10 minutes. Ideally, during this acquisition time, the imaging target of super-resolution localization microscopy (ULM) imaging is completely stationary. However, in actual in vivo experimental applications, the position and morphology of microvessels in ultrasound images will change over time due to factors such as animal breathing, heart beats, or external pressure, resulting in certain tissue movement. Since super-resolution imaging is reconstructed by superimposing many microbubble localizations accumulated over time, the movement between frames will significantly affect the visualization of microvascular images. In addition, the impact of the motion scale that appears in clinical scans is often greater than the super-resolution itself, which will directly affect the clarity and accuracy of the image. Therefore, accurate motion correction of the acquired data is required during the long acquisition process.

[0003] In 2014, Jeffries et al. used in vivo ULM technology and post-processing algorithms to achieve a relatively high-definition presentation of microvessels. However, the human body needed to remain still and the mouse head needed to remain fixed. It can be seen that the above method caused a certain degree of inconvenience to subsequent practical applications.

[0004] In 2017, Foiret et al. eliminated frames with excessive respiratory motion and reduced the impact of motion, achieving maximum axial and lateral resolutions of 2.1 μm and 6.1 μm for rat renal microvessels, respectively. However, this was based on the assumption that only rigid motion existed in the experiment. In reality, there are some non-rigid motions in human tissues that cannot be ignored, so it is not easy to directly transfer it to practical applications.

[0005] In 2019, Piepenbrock et al. proposed applying the optical flow method to non-separated B-Mode images. In the process of non-rigid motion estimation, the microbubble area was excluded, and the motion displacement error of the simulation experiment was as low as 6μm. Although the new technology improved the robustness of the results, its steps are still to select all combinations of two frames from tens of thousands of frames for synthesis, so there is still not much improvement in the key point of speed.

[0006] In 2019, Hao et al. proposed a speckle tracking technology based on 2D normalized cross correlation (NCC) to estimate liver displacement, and used it for non-rigid motion correction of the center position of microbubbles. Although it effectively improved the accuracy and resolution of mouse liver ULM, the underlying technology it used was still to perform registration operations for every two frames of images, so the time cost was not reduced.

[0007] In 2021, Taghavi et al. used B-Mode images for local motion correction and applied the estimated motion field to detect the location of microbubbles in contrast images of rat kidneys. However, the dynamic effect was not very good, and three sets of data had low accuracy due to the movement of the mouse.

[0008] It can be seen that the existing technology for ultrasonic microvascular imaging has the following problems:

[0009] (1) Long processing time: Ultrasound localization microscopy (ULM) usually requires the collection of tens of thousands of frames of ultrasound data to reconstruct the final image, and most of the existing registration technologies use the synthesis of any two frames for registration. The resulting permutations and combinations require more calculations, which in turn consumes a lot of time.

[0010] (2) Direct ultrasound data registration is difficult: Ultrasound images are based on tissue scattered echo signals. In some cases, the tissue structure information is not obvious. Direct motion registration of ultrasound data may result in large errors.

[0011] (3) Low data utilization: Based on the above characteristics of large data volume, some technologies will discard some data, such as some microbubble individuals, or ignore some tissue movement modes (only considering rigid registration but not non-rigid registration, etc.) in order to reduce the running time, which will greatly reduce the data utilization.

[0012] (4) The scope of applicable scenarios is slightly narrow: Most previous technologies are only suitable for objects that are still or as still as possible, such as asking the subject to hold their breath or fix a certain part of their body, which greatly limits the versatility and flexibility of the technology.

[0013] Therefore, taking the above problems into consideration, it is urgent to provide a new microvascular image registration method that can make full use of the huge and effective data volume while shortening the data processing time while ensuring the accuracy and stability of the algorithm. Summary of the invention

[0014] The purpose of this application is to provide a motion correction and registration method, device, medium and product in ultrasonic microvascular imaging, which can improve the performance and quality of ultrasonic super-resolution microvascular imaging.

[0015] To achieve the above objectives, this application provides the following solutions:

[0016] In a first aspect, the present application provides a motion correction and registration method in ultrasonic microvascular imaging, the motion correction and registration method in ultrasonic microvascular imaging comprising:

[0017] Acquire ultrasound sequence images that change with time; the ultrasound sequence images include: sequence images of microbubble scatterer flow and sequence images of tissue scatterer movement caused by respiratory movement;

[0018] The ultrasound sequence images are divided by using a threshold value of the data frame correlation coefficient to obtain a reconstructed image with respiratory motion and a reconstructed image without respiratory motion; the threshold value of the data frame correlation coefficient is used to determine whether the ultrasound sequence images have respiratory motion;

[0019] The clustering result of clustering processing based on the correlation coefficient between the still frames determines the reconstructed image of the still frame and the reconstructed image of the relative still frame; the still frame is the resting state between two breaths; the relative still frame is the state between after inhalation and before exhalation;

[0020] Performing image registration on the reconstructed image of the still frame and the reconstructed image of the relative still frame respectively, to obtain a reconstructed image after still frame registration and a reconstructed image after relative still frame registration;

[0021] The reconstructed image after the still frame registration, the reconstructed image after the relative still frame registration, and the reconstructed image without respiratory motion are superimposed to obtain a registered image.

[0022] Optionally, the step of dividing the ultrasound sequence images by using a threshold value of the data frame correlation coefficient to obtain a reconstructed image with respiratory motion and a reconstructed image without respiratory motion specifically includes:

[0023] The ultrasound sequence images are divided using the threshold of the data frame correlation coefficient;

[0024] The divided ultrasound sequence images are subjected to SVD filtering, positioning, tracking and reconstruction respectively to obtain reconstructed images with and without respiratory motion.

[0025] Optionally, the image registration method is a registration method that adopts a rigid and affine two-stage transformation followed by a symmetric normalization deformation.

[0026] Optionally, the motion correction and registration method in ultrasonic microvascular imaging further includes:

[0027] The ultrasound microvascular network model was jointly simulated using MATLAB Ultrasound Toolbox and COMSOL Multiphysics software; and the ultrasound microvascular network model was used for evaluation.

[0028] Optionally, the ultrasound microvascular network model is jointly simulated using MATLAB Ultrasound Toolbox and COMSOL Multiphysics software, specifically including:

[0029] Obtaining a geometric model of microvessels;

[0030] The geometric model was imported into COMSOL Multiphysics for finite element simulation to obtain the blood flow velocity field in the microvessels and the displacement field caused by the movement of the tissue due to respiratory motion.

[0031] According to the blood flow velocity field in the microvessels and the displacement field caused by respiratory movement, the MUST toolkit was used to simulate the ULM imaging process and obtain a joint simulation ultrasound microvascular network model; the joint simulation ultrasound microvascular network model was used to obtain sequence images of the flow of microbubble scatterers that change with time and sequence images of the movement of tissue scatterers caused by respiratory movement.

[0032] Optionally, the ultrasound microvascular network model is jointly simulated using the MATLAB Ultrasound Toolbox and COMSOL Multiphysics software, and then includes:

[0033] Optimize the ultrasound microvascular network model.

[0034] In a second aspect, the present application further provides a motion correction and registration device in ultrasonic microvascular imaging, the motion correction and registration device in ultrasonic microvascular imaging comprising:

[0035] An ultrasound sequence image acquisition module, used to acquire ultrasound sequence images that change over time; the ultrasound sequence images include: sequence images of microbubble scatterer flow and sequence images of tissue scatterer movement caused by respiratory movement;

[0036] An ultrasound sequence image segmentation module is used to segment the ultrasound sequence images using a threshold value of a data frame correlation coefficient to obtain a reconstructed image with respiratory motion and a reconstructed image without respiratory motion; the threshold value of the data frame correlation coefficient is used to determine whether the ultrasound sequence images have respiratory motion;

[0037] The module for determining the reconstructed image of a still frame and the reconstructed image of a relative still frame is used to determine the reconstructed image of a still frame and the reconstructed image of a relative still frame according to the clustering result of clustering processing of the correlation coefficients between the still frames; the still frame is the resting state between two breaths; the relative still frame is the state between after inhalation and before exhalation;

[0038] An image registration module is used to perform image registration on the reconstructed image of the still frame and the reconstructed image of the relative still frame, respectively, to obtain the reconstructed image after still frame registration and the reconstructed image after relative still frame registration;

[0039] The registered image determination module is used to superimpose the reconstructed image after static frame registration, the reconstructed image after relative static frame registration and the reconstructed image without respiratory motion to obtain a registered image.

[0040] In a third aspect, the present application also provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the motion correction and registration method in ultrasound microvascular imaging.

[0041] In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the motion correction and registration method in ultrasonic microvascular imaging.

[0042] In a fifth aspect, the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the motion correction and registration method in ultrasound microvascular imaging.

[0043] According to the specific embodiments provided in this application, this application has the following technical effects:

[0044] The present application provides a motion correction and registration method, device, medium and product in ultrasonic microvascular imaging, which divides the ultrasonic sequence images by using the threshold of the data frame correlation coefficient and determines the reconstructed image of the static frame and the reconstructed image of the relative static frame by clustering the clustering results according to the correlation coefficient between the static frames, that is, provides a frame rate segmentation optimization registration scheme (Frame Rate Segmentation Optimization Registration), which can effectively reduce the interference caused by respiratory motion and obtain better ultrasonic super-resolution microvascular images under the condition of significantly improving the running speed. In addition, the present application has no restrictions on whether the object is static, thereby improving the versatility of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 This is a schematic diagram of a flow chart of a motion correction and registration method in ultrasound microvascular imaging in one embodiment of the present application;

[0047] Figure 2 This is a schematic diagram of the image registration process in one embodiment of the present application;

[0048] Figure 3 This is a schematic diagram of frame segmentation optimization registration results of different registration methods in one embodiment of the present application;

[0049] Figure 4 This is a schematic diagram of the results of different registration methods under the registration network in one embodiment of the present application;

[0050] Figure 5 This is a schematic diagram of the results of different registration methods under the registration network in another embodiment of the present application;

[0051] Figure 6 Schematic diagram of the comparison results of rat liver microvascular images before and after motion correction. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0053] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0054] In an exemplary embodiment, Figure 1 As shown, a motion correction and registration method in ultrasound microvascular imaging is provided, which is executed by a computer device, specifically, it can be executed by a computer device such as a terminal or a server alone, or it can be executed by a terminal and a server together. In the embodiment of the present application, the method is described by taking application to a server as an example, and includes the following S101 to S105. Among them:

[0055] S101, acquiring ultrasound sequence images that change with time; the ultrasound sequence images include: sequence images of microbubble scatterer flow and sequence images of tissue scatterer movement caused by respiratory movement;

[0056] S102, using the threshold value Corr_th2 of the data frame correlation coefficient to divide the ultrasound sequence images, and obtain reconstructed images with respiratory motion and reconstructed images without respiratory motion; the threshold value Corr_th2 of the data frame correlation coefficient is used to determine whether there is respiratory motion in the ultrasound sequence images; that is, the data frames with a value less than the threshold value Corr_th2 of the data frame correlation coefficient have respiratory motion; wherein, the respiratory motion can be divided into two stages, namely, exhalation and inhalation, and the displacement directions generated by these two stages are inconsistent, and the data frames of the motion stage are further subdivided. Therefore, dividing the ultrasound sequence images is actually dividing the data frames in the ultrasound sequence images;

[0057] S102 specifically includes:

[0058] The ultrasound sequence images are divided using the threshold of the data frame correlation coefficient;

[0059] The divided ultrasound sequence images are subjected to SVD filtering, positioning, tracking and reconstruction respectively to obtain reconstructed images with and without respiratory motion.

[0060] S103, determining the reconstructed image of the still frame and the reconstructed image of the relative still frame according to the clustering result of the clustering process based on the correlation coefficient between the still frames; the still frame is the resting state between two breaths; the relative still frame is the state between after inhalation and before exhalation; that is, performing slope analysis based on the direction of the respiratory movement, determining the slope of the inter-frame correlation curve in the respiratory movement stage, and obtaining the data frame numbers of the exhalation and inhalation states respectively;

[0061] S104, performing image registration on the reconstructed image of the still frame and the reconstructed image of the relative still frame, respectively, to obtain a reconstructed image after still frame registration and a reconstructed image after relative still frame registration; wherein the image registration process is as follows: Figure 2 As shown; the image registration method is a registration method that adopts a rigid and affine two-stage transformation followed by a symmetric normalized deformation.

[0062] S105 , superimposing the reconstructed image after the static frame registration, the reconstructed image after the relative static frame registration, and the reconstructed image without respiratory motion to obtain a registered image.

[0063] like Figure 3 The frame segmentation optimization registration results of different registration methods show that the frame segmentation optimization registration scheme can basically eliminate the artifacts caused by respiratory motion and can more clearly display the microvascular structure. In terms of image similarity evaluation, the SSIM index and MSE index are used to evaluate the registration results in the figure. The evaluation index is calculated three times and then the average value is taken. The statistical results are shown in Table 1:

[0064] Table 1 Performance comparison of microvascular model using digital segmentation optimization registration under different registration methods

[0065]

[0066]

[0067] From the values ​​of the SSIM and MSE indicators in Table 1, it can be seen that for the microvascular network model 2 with relatively complex branches, the SSIM value is improved by about 0.14 compared with the SSIM value without registration by using the registration method of rigid and affine two-stage transformation and then SyN deformation, and the MSE can be as low as about 0.436. The experimental results show that the image registration of ultrasound images using the frame number segmentation optimization registration scheme can more effectively improve the performance of super-resolution microvascular imaging and improve the imaging quality. The whole process only takes 5-6 hours, which is about 60% shorter than other algorithms, greatly reducing the time cost.

[0068] In order to verify and evaluate the effectiveness of the motion correction and registration method in ultrasound microvascular imaging provided in this application, this application also includes:

[0069] The ultrasound microvascular network model was jointly simulated using MATLAB Ultrasound Toolbox and COMSOL Multiphysics software; and the ultrasound microvascular network model was used for evaluation.

[0070] In another exemplary embodiment, the ultrasound microvascular network model is jointly simulated using the MATLAB ultrasound toolbox and COMSOL Multiphysics software, specifically including:

[0071] S1, obtain the geometric model of microvessels;

[0072] S2, import the geometric model into COMSOL Multiphysics for finite element simulation, and obtain the blood flow velocity field in the microvessels and the displacement field caused by the movement of tissues due to respiratory movement through theoretical knowledge such as fluid mechanics and solid mechanics;

[0073] S3, based on the blood flow velocity field in the microvessels and the displacement field caused by the respiratory movement that leads to the movement of tissues, the MUST toolkit is used to simulate the ULM imaging process to obtain a joint simulation ultrasound microvascular network model; the joint simulation ultrasound microvascular network model is used to obtain a sequence of images of the flow of microbubble scatterers that changes with time and a sequence of images of the movement of tissue scatterers caused by the influence of respiratory movement.

[0074] S2 and later also include:

[0075] The ultrasound microvascular network model is optimized to make it closer to the actual situation, including the displacement of microbubbles caused by breathing and the continuous inflow of microbubbles. Finally, the simulated ultrasound image is post-processed to reconstruct the ultrasound super-resolution microvascular image. The final image is as follows: Figure 4 shown.

[0076] The ultrasound sequence images of the ULM imaging process obtained by the previous simulation are reconstructed. Due to the presence of respiratory motion, the quality of the reconstructed ULM image is degraded. Therefore, the motion of the ULM imaging data is registered to improve the quality of the motion image.

[0077] Common registration algorithms include: Affine Registration algorithm, Symmetric Normalization (SyN) registration algorithm, Elastic Registration algorithm, etc. These algorithms can process various types of medical images, including MRI, CT and ultrasound.

[0078] Affine registration adjusts the shape and position of an image through linear transformations such as translation, rotation, scaling, and shearing. It is highly efficient in dealing with simple image registration problems. In contrast, rigid registration is limited to translation and rotation operations, excluding shearing and scaling. The affine registration algorithm aims to find appropriate affine transformation parameters so that the points of the two images can correspond in space before and after registration. However, the affine registration method cannot capture the nonlinear relationship between images, does not include the factor of tissue deformation, and has certain limitations.

[0079] The symmetric normalization (SyN) registration algorithm is one of the most commonly used registration algorithms. It is a nonlinear registration method based on deformation fields that can capture complex anatomical changes. Image registration is achieved by minimizing the similarity metrics between images (such as mutual information and normalized cross-correlation) and the regularization term of the deformation field, so that the two images are as similar as possible after registration. The SyN deformation algorithm has many characteristics, such as being able to consider the transformation between the two images, being able to better handle the interchangeability and symmetry between images, capturing complex nonlinear transformations between images, being suitable for handling large image differences and deformations, and being able to adjust according to specific circumstances. It has good adaptability and flexibility in different types of image registration, being able to consider the overall transformation and local deformation between images, and having higher registration precision and accuracy compared to affine transformation.

[0080] The above registration methods each have their own advantages and disadvantages. If these registration methods are combined, sometimes it may be possible to make the best of their strengths and achieve better registration results. The following is a specific analysis of the included combinations.

[0081] According to Figure 4 The ULM image with tissue motion reconstructed in the image is registered with the ULM image reconstructed without tissue motion. The registration results include: the mapping relationship matrix estimated by linear transformation, the mapping relationship estimated by nonlinear transformation, the result of the reference image registration to the motion image, and the result of the motion image registration to the reference image. Secondly, the mapping relationship is applied to the microbubble IQ data with tissue motion displacement after filtering to obtain the motion-corrected microbubble IQ data. Finally, the corrected microbubble image is subjected to microbubble positioning, tracking and image reconstruction to obtain the motion-corrected ULM image.

[0082] Through different combinations of registration methods, we can obtain Figure 4 and Figure 5 The results shown. Among them, Figure 4 and Figure 5 The reference images used in the figure are all reconstructed images of the same batch of microbubble scatterers in the presence of tissue motion, and the deformation algorithms are all SyN deformation registration methods.

[0083] The effectiveness of the present application is further illustrated below by the results based on in vivo imaging data. First, the cross-sectional images of rat livers are acquired, microbubbles are injected, and post-processing and super-resolution image reconstruction are performed. Subsequently, the registration scheme is optimized by using frame segmentation, first rigid and affine transformations followed by SyN deformation registration, respiratory state data frames, motion correction, and image superposition.

[0084] Figure 6 The figure shows four groups of comparison results before and after microvascular motion correction, which are divided into two rows. The first row is the reconstructed image without motion correction, and the second row is the reconstruction result obtained by using the frame segmentation optimization registration scheme, using rigid and affine two-stage transformations and then SyN deformation registration. Figure 6 It can be seen from the red and white arrows that the use of frame segmentation optimization registration scheme can reduce the influence of motion artifacts and show clearer microvascular branches; similarly, it can be seen in the white box that the use of frame segmentation optimization registration scheme can more accurately evaluate the thickness of microvessels. Therefore, the following conclusion can be drawn from the above results: the use of frame segmentation optimization registration scheme can greatly reduce motion artifacts and obtain microvascular images with clear branches. This shows that the present application can improve the performance of ultrasound super-resolution microvascular imaging.

[0085] The results in Table 2 show that the frame segmentation optimization registration scheme can effectively reduce the interference caused by respiratory movement, reduce the average thickness of microvessels by about 2.2 times, improve the number of end points and the average porosity of blood vessels, and effectively improve the quality of ultrasound super-resolution microvascular images. At the same time, the whole process takes only 6-7 hours, which is nearly 60% shorter than other similar technologies.

[0086] Table 2 Comparison of quantitative parameters of microvascular characteristics

[0087]

[0088] It can be seen that the present application can reduce the interference of respiratory movement and obtain better ultrasonic super-resolution microvascular images under the condition of significantly improving the operating speed. That is, the present application can shorten the data processing time and improve the imaging speed.

[0089] Based on the same inventive concept, the embodiment of the present application also provides a motion correction and registration device in ultrasonic microvascular imaging for implementing the motion correction and registration method in ultrasonic microvascular imaging involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more motion correction and registration devices in ultrasonic microvascular imaging provided below can refer to the limitations of the motion correction and registration method in ultrasonic microvascular imaging above, and will not be repeated here.

[0090] In an exemplary embodiment, a motion correction and registration device for ultrasound microvascular imaging is provided, comprising:

[0091] An ultrasound sequence image acquisition module, used to acquire ultrasound sequence images that change over time; the ultrasound sequence images include: sequence images of microbubble scatterer flow and sequence images of tissue scatterer movement caused by respiratory movement;

[0092] An ultrasound sequence image segmentation module is used to segment the ultrasound sequence images using a threshold value of a data frame correlation coefficient to obtain a reconstructed image with respiratory motion and a reconstructed image without respiratory motion; the threshold value of the data frame correlation coefficient is used to determine whether the ultrasound sequence images have respiratory motion;

[0093] The module for determining the reconstructed image of a still frame and the reconstructed image of a relative still frame is used to determine the reconstructed image of a still frame and the reconstructed image of a relative still frame according to the clustering result of clustering processing of the correlation coefficients between the still frames; the still frame is the resting state between two breaths; the relative still frame is the state between after inhalation and before exhalation;

[0094] An image registration module is used to perform image registration on the reconstructed image of the still frame and the reconstructed image of the relative still frame, respectively, to obtain the reconstructed image after still frame registration and the reconstructed image after relative still frame registration;

[0095] The registered image determination module is used to superimpose the reconstructed image after static frame registration, the reconstructed image after relative static frame registration and the reconstructed image without respiratory motion to obtain a registered image.

[0096] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a motion correction and registration method in ultrasonic microvascular imaging is implemented.

[0097] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0098] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0099] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0100] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0101] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0102] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0103] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0104] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A motion correction and registration method in ultrasonic microvascular imaging, characterized in that: The motion correction and registration method in ultrasonic microvascular imaging comprises: Acquire ultrasound sequence images that change with time; the ultrasound sequence images include: sequence images of microbubble scatterer flow and sequence images of tissue scatterer movement caused by respiratory movement; The ultrasound sequence images are divided by using a threshold value of the data frame correlation coefficient to obtain a reconstructed image with respiratory motion and a reconstructed image without respiratory motion; the threshold value of the data frame correlation coefficient is used to determine whether the ultrasound sequence images have respiratory motion; The clustering result of clustering processing based on the correlation coefficient between the still frames determines the reconstructed image of the still frame and the reconstructed image of the relative still frame; the still frame is the resting state between two breaths; the relative still frame is the state between after inhalation and before exhalation; Performing image registration on the reconstructed image of the still frame and the reconstructed image of the relative still frame respectively, to obtain a reconstructed image after still frame registration and a reconstructed image after relative still frame registration; The reconstructed image after the still frame registration, the reconstructed image after the relative still frame registration, and the reconstructed image without respiratory motion are superimposed to obtain a registered image.

2. The motion correction and registration method in ultrasonic microvascular imaging according to claim 1, characterized in that: The method of dividing the ultrasound sequence images by using the threshold of the data frame correlation coefficient to obtain the reconstructed images with respiratory motion and the reconstructed images without respiratory motion specifically includes: The ultrasound sequence images are divided using the threshold of the data frame correlation coefficient; The divided ultrasound sequence images are subjected to SVD filtering, positioning, tracking and reconstruction respectively to obtain reconstructed images with and without respiratory motion.

3. The motion correction and registration method in ultrasonic microvascular imaging according to claim 1, characterized in that: The image registration method is a registration method that adopts a rigid and affine two-stage transformation followed by a symmetric normalized deformation.

4. The motion correction and registration method in ultrasonic microvascular imaging according to claim 1, characterized in that: The motion correction and registration method in ultrasonic microvascular imaging also includes: The ultrasound microvascular network model was jointly simulated using MATLAB Ultrasound Toolbox and COMSOL Multiphysics software; and the ultrasound microvascular network model was used for evaluation.

5. The motion correction and registration method in ultrasonic microvascular imaging according to claim 4, characterized in that: The ultrasound microvascular network model was jointly simulated using MATLAB Ultrasound Toolbox and COMSOL Multiphysics software, including: Obtaining a geometric model of microvessels; The geometric model was imported into COMSOL Multiphysics for finite element simulation to obtain the blood flow velocity field in the microvessels and the displacement field caused by the movement of the tissue due to respiratory motion. According to the blood flow velocity field in the microvessels and the displacement field caused by respiratory movement, the MUST toolkit was used to simulate the ULM imaging process and obtain a joint simulation ultrasound microvascular network model; the joint simulation ultrasound microvascular network model was used to obtain sequence images of the flow of microbubble scatterers that change with time and sequence images of the movement of tissue scatterers caused by respiratory movement.

6. The motion correction and registration method in ultrasonic microvascular imaging according to claim 5, characterized in that: The ultrasound microvascular network model was jointly simulated using the MATLAB Ultrasound Toolbox and COMSOL Multiphysics software, and then also included: Optimize the ultrasound microvascular network model.

7. A motion correction and registration device for ultrasonic microvascular imaging, characterized in that: The motion correction and registration device in the ultrasonic microvascular imaging comprises: An ultrasound sequence image acquisition module, used to acquire ultrasound sequence images that change over time; the ultrasound sequence images include: sequence images of microbubble scatterer flow and sequence images of tissue scatterer movement caused by respiratory movement; An ultrasound sequence image segmentation module is used to segment the ultrasound sequence images using a threshold value of a data frame correlation coefficient to obtain a reconstructed image with respiratory motion and a reconstructed image without respiratory motion; the threshold value of the data frame correlation coefficient is used to determine whether the ultrasound sequence images have respiratory motion; The module for determining the reconstructed image of a still frame and the reconstructed image of a relative still frame is used to determine the reconstructed image of a still frame and the reconstructed image of a relative still frame according to the clustering result of clustering processing of the correlation coefficients between the still frames; the still frame is the resting state between two breaths; the relative still frame is the state between after inhalation and before exhalation; An image registration module is used to perform image registration on the reconstructed image of the still frame and the reconstructed image of the relative still frame, respectively, to obtain the reconstructed image after still frame registration and the reconstructed image after relative still frame registration; The registered image determination module is used to superimpose the reconstructed image after static frame registration, the reconstructed image after relative static frame registration and the reconstructed image without respiratory motion to obtain a registered image.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the motion correction and registration method in ultrasonic microvascular imaging according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the motion correction and registration method in ultrasonic microvascular imaging according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the motion correction and registration method in ultrasonic microvascular imaging according to any one of claims 1 to 6 is implemented.

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