Ultracapillary visualization imaging method

Abstract

The invention discloses an ultra-micro vessel visual imaging method. The method comprises the following steps: acquiring B-mode radio frequency data of an ultrasonic contrast agent; detecting contrast agent microbubbles in each frame of radio frequency data; removing motion artifacts in the data; describing the microbubbles by adopting a proper mathematical model; calculating the moving speed of the microbubbles between two adjacent frames of data, and generating a microbubble moving speed curve; and generating a corresponding index image according to the ultra-capillary related index and displaying the index image in the form of an image. The B-mode radio frequency data of the ultrasonic contrast agent is acquired, and the contrast agent microbubbles are detected by adopting a filtering means, so that noise and tissue clutter interference can be removed according to the dynamic characteristics of the input signal. For example, a space-time adaptive filter is adopted to firstly remove random noise in a space domain and then remove tissue clutters with low dynamic nature in a time dimension, so that a contrast agent microbubble signal is effectively separated from a complex signal, the image contrast is improved, and the structure of an ultra-microvessel can be displayed more clearly.

Description

Technical Field

[0001] The invention relates to the field of image processing, and in particular to an ultra-microvascular visualization imaging method. Background Art

[0002] In the field of vascular-related research, the ultramicrovascular resistance index (RI) and the ultramicrovascular pulsatility index (PI) are both very important evaluation indicators.

[0003] Ultramicrovascular resistance index (RI) is traditionally calculated using ultrasonic Doppler technology by obtaining the peak systolic velocity and the minimum diastolic velocity of the blood vessels. RI is of great significance in evaluating the elasticity and resistance of blood vessels and the blood supply status of the lesion area. In the diagnosis and monitoring of tumors, RI can reflect the distribution and blood supply of blood flow inside the tumor, and has important reference value for tumor growth, differentiation of benign and malignant tumors, judgment of malignancy, and evaluation of treatment effects. However, the new blood vessels in the tumor microenvironment have a unique network structure, and their microvessels have many abnormalities in morphology and function, such as irregular blood flow, lack of elasticity of vascular walls, abnormal branches, etc. When measuring RI based on Doppler technology, it is difficult to display or accurately measure RI due to the influence of these factors. The emergence of super-resolution technology has brought new possibilities. It can improve imaging resolution and track the movement trajectory of microbubbles more clearly. With the help of ultra-high-speed imaging methods and microbubble tracking technology, the real-time movement trajectory of microbubbles can be obtained and the velocity curve can be obtained, thereby calculating the microvascular resistance index, which provides conditions for the accurate evaluation of microvascular resistance. However, there is currently no imaging method that can display the ultra-microvascular resistance index (RI) in the region of interest in real time.

[0004] The ultramicrovascular pulsatility index (PI) is a hemodynamic parameter obtained by ultrasonic Doppler technology, which is mainly used to measure the pulsation of ultramicrovessels. It plays an important role in evaluating the elasticity and pulsation of blood vessels and the blood supply status of the lesion area. In the diagnosis and monitoring of tumors, PI helps to understand the distribution and blood supply of blood flow inside the tumor, and has important reference value for tumor growth, malignancy and treatment effect. It is also an important basis for evaluating the characteristics of tumor blood vessels. Super-resolution technology can track the movement trajectory of microbubbles more clearly, so that the microvascular network inside the tumor can be observed. Due to the characteristics of tumor vascular structure and function, the changes in its pulsatility index may be different from those of normal blood vessels. PI measurement under super-resolution technology can more accurately evaluate the pulsation characteristics of these abnormal blood vessels, but there is currently no imaging method that can display the ultramicrovascular pulsatility index of tissues in the tumor area of ​​interest in real time.

[0005] In summary, although the ultramicrovascular resistance index (RI) and ultramicrovascular pulsatility index (PI) are of great significance in vascular research, especially tumor-related research, there are still deficiencies in real-time visualization imaging. Summary of the invention

[0006] The purpose of the present invention is to provide an ultra-microvascular visualization imaging method to solve the problems raised in the above background technology.

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

[0008] A method for ultra-microvascular visualization imaging, comprising the following steps:

[0009] Step S1, acquiring B-mode radio frequency data of an ultrasound contrast agent, wherein the ultrasound contrast agent can generate an echo signal that enhances image contrast in a target area;

[0010] Step S2, using filtering means to detect contrast agent microbubbles in each frame of radio frequency data, wherein the filtering means can remove noise and tissue clutter interference;

[0011] Step S3, using motion correction means to remove motion artifacts in the data;

[0012] Step S4, defining a region of interest of microbubbles, using a suitable mathematical model to describe the microbubbles in the region, and adjusting the model parameters to fit the microbubble data;

[0013] Step S5, calculating the displacement of the same microbubble between adjacent frames, combining the time interval between two adjacent frames of radio frequency data to obtain the movement speed of the microbubble between two adjacent frames of data, and generating a microbubble movement speed curve;

[0014] Step S6, calculating the ultramicrovascular correlation index based on the microbubble migration velocity curve, generating a corresponding index image according to the ultramicrovascular correlation index and displaying it in the form of an image.

[0015] In the present invention, in step S1, B-mode radio frequency data of an ultrasound contrast agent is acquired under ultrafast ultrasound imaging. The ultrasound contrast agent is a contrast agent SonoVue containing tiny bubbles. When the contrast agent gathers in a specific area, a strong echo signal is generated under the action of ultrasound to enhance image contrast.

[0016] In the present invention, in step S2, a spatiotemporal adaptive filter is used to remove noise and tissue clutter interference according to the dynamic characteristics of the input signal. A filter is first applied to the spatial domain signal of each frame to remove random noise, and then a filter is applied to multiple frames of radio frequency data in the time dimension using time series information to remove tissue clutter with low dynamics in the time dimension, and a target tracking algorithm is used to track microbubbles in the radio frequency frame sequence, and the target tracking algorithm is a Kalman filter algorithm to obtain the motion trajectory of the microbubbles.

[0017] In the present invention, in step S3, the ultrasound probe continuously transmits and receives ultrasound waves to form multiple frames of image data, analyzes motion patterns and features in the data, dynamically adjusts or reconstructs the ultrasound image, and uses a motion correction algorithm to remove motion artifacts caused by patient movement.

[0018] In the present invention, in step S4, a suitable region of interest ROI is selected around the detected microbubbles in the image, and the ROI is a square or circular area. Grayscale value or signal intensity data is extracted in the ROI to form a two-dimensional matrix, and the least squares algorithm is used to adjust the parameters of the Gaussian function so that the fitting function is as close as possible to the actual data. The two-dimensional Gaussian function model is:

[0019]

[0020] Where I(x,y) is the intensity of the microbubble at (x,y); I0 is the maximum intensity value; (x0,z0) is the center position of the microbubble; σ z and σ z is the standard deviation of the Gaussian distribution.

[0021] In the present invention, in step S5, N frames of radio frequency data are collected by ultrafast ultrasound imaging, and the microbubble movement speed is calculated after signal filtering, motion correction, microbubble detection and positioning tracking steps; the microbubble movement speed calculation formula is as follows:

[0022]

[0023] Among them, Δt is the time interval between two adjacent frames of images, (x i ,z i ) represents the position coordinates in the i-th frame image, (x i+1 ,z i+1 ) represents the position coordinates in the i+1th frame image.

[0024] In the present invention, in step S6, the formula for calculating the ultramicrovascular resistance index RI is as follows:

[0025]

[0026] Among them, V PSV(x,z) is the maximum velocity of V(x,z) during the contraction period, V EDV (x,z) is the minimum velocity of V(x,z) at the end of diastole.

[0027] In the present invention, in step S6, the formula for calculating the ultramicrovascular pulsation index PI is as follows:

[0028]

[0029] Among them, P PSV (x,z) is the maximum value of P(x,z) during the contraction period, V EDV (x,z) is the minimum velocity of V(x,z) at the end of diastole, and MV represents the average flow velocity in one cycle.

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

[0031] 1. The present invention obtains B-mode radio frequency data of ultrasound contrast agents and detects contrast agent microbubbles by filtering, and can remove noise and tissue clutter interference according to the dynamic characteristics of the input signal. For example, a spatiotemporal adaptive filter is used to first remove random noise in the spatial domain, and then remove low-dynamic tissue clutter in the time dimension, thereby effectively separating the contrast agent microbubble signal from the complex signal, improving the image contrast, and helping to more clearly display the structure of ultra-microvessels.

[0032] 2. The present invention uses motion correction to remove motion artifacts in the data. By analyzing the motion patterns and features in the collected multi-frame image data, a motion correction algorithm is used to dynamically adjust or reconstruct the ultrasound image, thereby effectively reducing motion artifacts caused by patient movement (such as breathing, heartbeat, etc.) and improving the accuracy and stability of ultra-microvascular imaging.

[0033] 3. After detecting microbubbles, the present invention describes the microbubbles by defining and using a two-dimensional Gaussian function model in the region of interest of the microbubbles, and then adjusts the model parameters to fit the microbubble data. Further, the displacement of the same microbubble between adjacent frames is calculated and the moving speed is obtained, thereby generating a microbubble moving speed curve. Based on this curve, the ultramicrovascular resistance index RI and the pulsatility index PI can be accurately calculated, and the corresponding index image can be generated. It is possible to more accurately analyze the hemodynamic characteristics of ultramicrovessels and provide more valuable information for the diagnosis of diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0035] Figure 2 It is a schematic diagram of the original radio frequency data diagram and ultramicrovascular resistance index visualization image of the present invention;

[0036] Figure 3 It is a schematic diagram of the original radio frequency data diagram and ultramicrovascular pulsation index visualization image of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] See also Figure 1 , the present invention provides a technical solution:

[0039] A method for ultra-microvascular visualization imaging, comprising the following steps:

[0040] Step S1, obtaining B-mode radio frequency data of an ultrasound contrast agent, wherein the ultrasound contrast agent can generate an echo signal that enhances image contrast in a target area; obtaining B-mode radio frequency data of an ultrasound contrast agent under ultra-fast ultrasound imaging, wherein the ultrasound contrast agent is a contrast agent SonoVue containing tiny bubbles, which generates a strong echo signal under the action of ultrasound to enhance image contrast when the contrast agent gathers in a specific area.

[0041] In this step, consider a biological tissue sample containing a target area (such as a tumor area). After an ultrasound contrast agent (such as a contrast agent containing tiny bubbles) is injected, the ultrasound device transmits ultrasound to the sample. Since the tiny bubbles in the contrast agent and the surrounding tissue have different reflection characteristics to ultrasound, the contrast agent will generate strong echo signals under the action of ultrasound. These echo signals are received by the ultrasound device and converted into B-mode radio frequency data.

[0042] Step S2, using filtering means to detect contrast agent microbubbles in each frame of radio frequency data, the filtering means can remove noise and tissue clutter interference; using a spatiotemporal adaptive filter to remove noise and tissue clutter interference according to the dynamic characteristics of the input signal, first applying a filter to the spatial domain signal of each frame to remove random noise, and then using time series information to apply a filter to multiple frames of radio frequency data in the time dimension to remove tissue clutter with low dynamics in the time dimension, and using a target tracking algorithm to track the microbubbles in the radio frequency frame sequence, the target tracking algorithm is a Kalman filter algorithm, so as to obtain the motion trajectory of the microbubbles.

[0043] In this step, each frame of RF data obtained in step S1 is set as a complex signal matrix containing multiple signal components. Among them, the noise signal is manifested as random and irregular fluctuations, and the tissue clutter signal has dynamic characteristics different from the contrast agent microbubble signal, and is filtered using a spatiotemporal adaptive filter. For each frame of the spatial domain signal, it is assumed that the noise in the spatial domain signal is randomly distributed in space. First, a filter is applied to the spatial domain signal, and the random noise is identified and removed by analyzing the intensity, frequency and other characteristics of the signal at each point in space. For example, the filter can determine whether a certain point signal is noise based on statistical characteristics such as the local mean or median, and perform corresponding removal operations. In the time dimension, it is assumed that the dynamic changes of tissue clutter in multiple frames of RF data are relatively slow, while the contrast agent microbubble signal is more dynamic due to factors such as its movement in the blood flow. Filters are applied to multiple frames of RF data using time series information, and by comparing the dynamic changes of each frame signal at the same position or area, tissue clutter with low dynamics is removed, thereby highlighting the contrast agent microbubble signal for detection.

[0044] Specifically, the parameters of the spatiotemporal adaptive filter are initialized, including but not limited to the spatial filter window size, the temporal filter window size, the filter coefficient, etc. For each frame of RF data, in the spatial domain, with each pixel as the center, according to the set spatial filter window size, the signal values ​​of the surrounding pixels are obtained, and the local statistical features are calculated, including but not limited to the mean, variance, etc., and the filter coefficients are adjusted according to these features, and the signal of the central pixel is filtered to remove the random noise in the spatial domain. In the time dimension, with each pixel in each frame of data as the object, according to the set time filter window size, the signal sequence of the corresponding pixel in multiple frames of data is obtained, and the dynamic characteristics of the signal sequence are analyzed, including but not limited to calculating the signal change rate, autocorrelation function, etc., and the filter coefficients are adjusted according to the dynamic characteristics, and the signal of the pixel is filtered to remove the tissue clutter in the time dimension.

[0045] Step S3, using motion correction means to remove motion artifacts in the data, the ultrasound probe continuously transmits and receives ultrasound waves to form multiple frames of image data, analyzes the motion patterns and features in the data, dynamically adjusts or reconstructs the ultrasound image, and uses a motion correction algorithm to remove motion artifacts caused by patient movement.

[0046] In this step, it is assumed that during the continuous ultrasound imaging of biological tissues by the ultrasound probe, the acquired multiple frames of image data may generate motion artifacts due to the patient's breathing, heartbeat, or slight body movement. It is assumed that the acquired multiple frames of image data can be regarded as a signal sequence in a three-dimensional space (a two-dimensional image plane plus a time dimension).

[0047] Specifically, first, analyze the motion patterns and features in the collected multi-frame image data. For example, by selecting some landmarks with obvious features in the image (such as specific structures in the tissue or previously marked points), tracking the position changes of these landmarks in different frames, and establishing a motion model. According to the established motion model, dynamically adjust or reconstruct the ultrasound image. For example, if it is a linear motion model, the image can be corrected through transformation operations such as translation and rotation; if it is a more complex nonlinear motion model, it is necessary to use a more advanced image registration algorithm (such as a registration algorithm based on an elastic deformation model) to correct each frame of the image to a relatively static reference state, thereby removing motion artifacts caused by patient movement.

[0048] Step S4, defining a region of interest of microbubbles, using a suitable mathematical model to describe the microbubbles in the region, and adjusting the model parameters to fit the microbubble data; selecting a suitable region of interest ROI around the detected microbubbles in the image, the ROI is a square or circular area, extracting grayscale values ​​or signal intensity data in the ROI to form a two-dimensional matrix, and using the least squares algorithm to adjust the parameters of the Gaussian function so that the fitting function is as close as possible to the actual data, and the two-dimensional Gaussian function model is:

[0049]

[0050] Where I(x,y) is the intensity of the microbubble at (x,y); I0 is the maximum intensity value; (x0,z0) is the center position of the microbubble; σ x and σ z is the standard deviation of the Gaussian distribution.

[0051] In this step, it is assumed that a suitable region of interest (ROI) is selected around the detected microbubbles in the image, and this ROI can be a square or circular area. Within this ROI, the signal intensity or gray value distribution of the microbubbles has a certain regularity, which can be described by a mathematical model.

[0052] Taking the above two-dimensional Gaussian function model as an example, the specific steps are as follows:

[0053] Extract the grayscale value or signal intensity data in the ROI to form a two-dimensional matrix. Assume that this two-dimensional matrix is ​​M, and its element M(i,j) represents the grayscale value or signal intensity at the position (i,j) in the ROI. Use the least squares algorithm to adjust the parameters of the Gaussian function. The goal of the least squares method is to minimize the sum of squares of the error between the fitting function and the actual data. The specific operations are as follows:

[0054] For each data point (i, j) in a given two-dimensional matrix M, substitute it into the Gaussian function model to obtain the predicted value I pred (i,j).

[0055] Calculate the error square sum E = ∑ i,j (M(i,j)-I pred (i,j)) 2 .

[0056] By adjusting I0, x0, z0, σ x and σ z The parameters are equal to each other, so that the sum of squared errors ∑ is minimized. This can be done by taking partial derivatives of ∑ with respect to each parameter and setting the partial derivatives to zero to obtain a set of equations, and then solving this set of equations to obtain the optimal parameter values.

[0057] Step S5, calculating the displacement of the same microbubble between adjacent frames, combining the time interval between two adjacent frames of radio frequency data to obtain the movement speed of the microbubble between two adjacent frames of data, and generating a microbubble movement speed curve; using ultra-fast ultrasound imaging to collect N frames of radio frequency data, after signal filtering, motion correction, microbubble detection and positioning tracking steps, calculate the microbubble movement speed; the microbubble movement speed calculation formula is as follows:

[0058]

[0059] Among them, Δt is the time interval between two adjacent frames of images, (x i ,z i ) represents the position coordinates in the i-th frame image, (x i+1 ,z i+1 ) represents the position coordinates in the i+1th frame image.

[0060] In this step, a series of ultrasound image frames containing microbubbles are collected under ultrafast ultrasound imaging. It is assumed that the movement of each microbubble between adjacent frames can be approximately regarded as a translational movement in a two-dimensional plane.

[0061] Specifically, N frames of RF data are collected using ultrafast ultrasound imaging, and after the previous signal filtering, motion correction, microbubble detection and positioning tracking steps, the velocity of each microbubble is calculated. For a microbubble, it is assumed that the position coordinates in the i-th frame and the i+1-th frame image are: (x i ,z i ) and (x i+1 ,z i+1 ).

[0062] The displacement of microbubbles between two adjacent frames of images is calculated according to the Euclidean distance formula:

[0063]

[0064] It is known that the time interval between two adjacent frames of images is Δt, and the moving speed of microbubbles between two adjacent frames of images is calculated as: For all N-1 pairs of adjacent images, the above calculation steps are repeated to obtain the movement speed of the microbubble between each pair of adjacent images. These speed values ​​are plotted as a function of time (expressed as frame number) to generate a microbubble movement speed curve.

[0065] Step S6, calculating the ultramicrovascular related index based on the microbubble migration velocity curve, generating a corresponding index image according to the ultramicrovascular related index and displaying it as an image; the formula for calculating the ultramicrovascular resistance index RI is as follows:

[0066]

[0067] Among them, V PSV (x,z) is the maximum velocity of V(x,z) during the contraction period, V EDV (x,z) is the minimum velocity of V(x,z) at the end of diastole.

[0068] The formula for calculating the ultramicrovascular pulsatility index PI is as follows:

[0069]

[0070] Among them, P PSV (x,z) is the maximum value of P(x,z) during the contraction period, V EDV (x,z) is the minimum velocity of V(x,z) at the end of diastole, and MV represents the average flow velocity in one cycle.

[0071] In this step, the microbubble migration velocity curve is obtained through step S5. The velocity curve reflects the hemodynamic characteristics in the ultramicrovessels. Different hemodynamic characteristics can be quantitatively represented by specific indexes.

[0072] Take the ultramicrovascular resistance index RI and pulsatility index PI as examples:

[0073] like Figure 2 As shown, for the ultramicrovascular resistance index RI: in the microbubble migration velocity curve, the maximum velocity V in the contraction period is determined by analyzing the periodic variation characteristics of the velocity curve. PSV (x,z) and the minimum velocity at end diastole V EDV (x,z), for example, in a cardiac cycle, the peak of the velocity curve usually corresponds to the maximum velocity during systole, while the trough corresponds to the minimum velocity during end-diastole.

[0074] And according to the formula Calculate the RI value.

[0075] like Figure 3 As shown, for the ultramicrovascular pulsatility index PI: Similarly, the maximum velocity V during the contraction period is determined in the microbubble migration velocity curve. PSV(x,z) and the minimum velocity at end diastole V EDV (x,z), and calculate the average flow velocity MV in one cycle. The average flow velocity MV is obtained by integrating the velocity curve in one cardiac cycle or using other statistical methods.

[0076] And according to the formula Calculate the PI value.

[0077] Repeat the above process of calculating RI and PI for each microbubble in the imaging area to obtain the corresponding index value of the ultra-microvessel. The index value corresponding to each microbubble is sequentially mapped to different colors or color levels in order from small to large, and then these colors or color levels are mapped to the position of the corresponding microbubble in the imaging area to generate and display the corresponding index image of the ultra-microvessel.

[0078] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0079] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for ultra-microvascular visualization imaging, characterized in that: The following steps are involved: Step S1, acquiring B-mode radio frequency data of an ultrasound contrast agent, wherein the ultrasound contrast agent can generate an echo signal that enhances image contrast in a target area; Step S2, using filtering means to detect contrast agent microbubbles in each frame of radio frequency data, wherein the filtering means can remove noise and tissue clutter interference; Step S3, using motion correction means to remove motion artifacts in the data; Step S4, defining a region of interest of microbubbles, using a suitable mathematical model to describe the microbubbles in the region, and adjusting the model parameters to fit the microbubble data; Step S5, calculating the displacement of the same microbubble between adjacent frames, combining the time interval between two adjacent frames of radio frequency data to obtain the movement speed of the microbubble between two adjacent frames of data, and generating a microbubble movement speed curve; Step S6, calculating the ultramicrovascular correlation index based on the microbubble migration velocity curve, generating a corresponding index image according to the ultramicrovascular correlation index and displaying it in the form of an image.

2. The method for ultra-microvascular visualization imaging according to claim 1, characterized in that: In the step S1, B-mode radio frequency data of an ultrasound contrast agent is acquired under ultrafast ultrasound imaging. The ultrasound contrast agent is SonoVue, a contrast agent containing tiny bubbles. When the contrast agent gathers in a specific area, it generates a strong echo signal under the action of ultrasound to enhance image contrast.

3. The method for ultra-microvascular visualization imaging according to claim 1, characterized in that: In step S2, a spatiotemporal adaptive filter is used to remove noise and tissue clutter interference according to the dynamic characteristics of the input signal. A filter is first applied to the spatial domain signal of each frame to remove random noise, and then a filter is applied to multiple frames of radio frequency data in the time dimension using time series information to remove tissue clutter with low dynamics in the time dimension, and a target tracking algorithm is used to track microbubbles in the radio frequency frame sequence, and the target tracking algorithm is a Kalman filter algorithm to obtain the motion trajectory of the microbubbles.

4. The method for ultra-microvascular visualization imaging according to claim 1, characterized in that: In step S3, the ultrasound probe continuously transmits and receives ultrasound waves to form multiple frames of image data, analyzes motion patterns and features in the data, dynamically adjusts or reconstructs the ultrasound image, and uses a motion correction algorithm to remove motion artifacts caused by patient movement.

5. The method for ultra-microvascular visualization imaging according to claim 1, characterized in that: In step S4, a suitable region of interest ROI is selected around the detected microbubbles in the image. The ROI is a square or circular area. Grayscale values ​​or signal intensity data are extracted in the ROI to form a two-dimensional matrix. The least squares algorithm is used to adjust the parameters of the Gaussian function so that the fitting function is as close as possible to the actual data. The two-dimensional Gaussian function model is: Where I(x,y) is the intensity of the microbubble at (x,y); I0 is the maximum intensity value; (x0,z0) is the center position of the microbubble; σ x and σ z is the standard deviation of the Gaussian distribution.

6. The method for ultra-microvascular visualization imaging according to claim 1, characterized in that: In step S5, N frames of radio frequency data are collected by ultrafast ultrasound imaging, and the microbubble movement speed is calculated after signal filtering, motion correction, microbubble detection and positioning tracking steps; the microbubble movement speed calculation formula is as follows: Among them, Δt is the time interval between two adjacent frames of images, (x i ,z i ) represents the position coordinates in the i-th frame image, (x i+1 ,z i+1 ) represents the position coordinates in the i+1th frame image.

7. The method for ultra-microvascular visualization imaging according to claim 1, characterized in that: In step S6, the formula for calculating the ultramicrovascular resistance index RI is as follows: Among them, V PSV (x,z) is the maximum velocity of V(x,z) during the contraction period, V EDV (x,z) is the minimum velocity of V(x,z) at the end of diastole.

8. The method for ultra-microvascular visualization imaging according to claim 1, characterized in that: In step S6, the formula for calculating the ultramicrovascular pulsatility index PI is as follows: Among them, P PSV (x,z) is the maximum value of P(x,z) during the contraction period, V EDV (x,z) is the minimum velocity of V(x,z) at the end of diastole, and MV represents the average flow velocity in one cycle.

Images