High-definition micro blood flow field measuring method

Abstract

The invention provides a high-definition micro blood flow field measurement method which is characterized in that a microbubble trajectory field is highlighted based on an ultrasound contrast image sequence, then a high-resolution microbubble displacement field time sequence is obtained through speckle correlation strategy estimation, and a powerful tool is provided for real-time and non-invasive observation of capillary blood flow dynamics evolution. Compared with a traditional micro blood flow field reconstruction strategy based on microbubble positioning, the micro blood flow field reconstruction speed can be greatly increased in a non-positioning mode.

Description

Technical Field

[0001] The present application relates to the field of ultrasonic imaging technology, and in particular to a high-definition micro-blood flow field measurement method, a high-definition micro-blood flow field measurement device, an electronic device, and a computer-readable storage medium. Background Art

[0002] Microvessels play an important role in the early development and malignant progression of many diseases. For example, microangiogenesis is one of the key biomarkers of tumors. In addition, renal microvascular loss can provide an important reference for the early diagnosis of diseases such as renal ischemia and diabetic nephropathy. In particular, focusing on the functional changes of microblood flow, that is, the evolution of microhemodynamics, has key diagnostic value. Therefore, rapid imaging of microhemodynamics is of great significance for efficient clinical diagnosis.

[0003] However, traditional magnetic resonance imaging, computed tomography or ultrasound Doppler imaging have insufficient spatial resolution, high cost and low frequency of use, making it difficult to meet the actual clinical needs for microvascular monitoring.

[0004] Ultrasound microbubble contrast agent microbubbles can flow in blood vessels and vibrate in a nonlinear manner under ultrasound, generating specific harmonic characteristics that enable them to enhance blood flow signals in deeper tissues. Currently, ultrasound contrast imaging has been used as a routine blood flow imaging method in many clinical diagnoses, but it is still unable to image tiny blood vessels with high spatial resolution. In recent years, ultrasound super-resolution imaging technology based on ultrasound contrast imaging can visualize the structure and function of microvessels, and non-invasively display the rich microvascular network and blood flow velocity distribution inside organs such as mouse brain, rabbit kidney, and subcutaneous tumors, and has great application potential. However, in order to achieve high-definition reconstruction of the complete vascular network, the current ultrasound super-resolution technology relies on low-concentration microbubble contrast agent perfusion and long-term data acquisition, and the imaging efficiency is low. Therefore, to date, this technology has not been able to track changes in microhemodynamic characteristics (such as blood flow velocity and flow direction) in real time. Summary of the invention

[0005] In order to solve the problem that the prior art cannot track micro-hemodynamic characteristics in real time, the present invention provides a high-definition micro-blood flow field measurement method, a high-definition micro-blood flow field measurement device, an electronic device and a computer-readable storage medium, which achieve micro-hemodynamic imaging with micron-level spatial resolution and high refresh frame rate, shorten the time of super-resolution micro-blood flow imaging, and thus provide a powerful tool for non-invasive visualization of the dynamic evolution of microvascular hemodynamics.

[0006] The present application provides a high-definition micro blood flow field measurement method, namely: highlighting the microbubble trajectory field based on the ultrasound contrast image sequence, and then using the speckle correlation strategy to estimate the high-resolution microbubble displacement field time series, that is, the high-definition micro blood flow field; the specific steps include: for the ultrasound contrast image sequence J0, constructing K image subsequences J1 through a time sliding window k (k=1,2,…K); for each image subsequence J1 in time order k Perform high-definition processing on microbubble trajectories to obtain K image subsequences J1 k (k=1,2,…K) corresponding to K high-resolution microbubble trajectory images J2 k (k=1,2,…K); Based on K high-resolution microbubble trajectory images J2 k (k=1,2,…K), and K-1 high-resolution microbubble displacement field time series J3 are estimated by the speckle correlation strategy between adjacent frames. k (k=1,2,…K-1), which is the high-definition micro blood flow field.

[0007] In one embodiment, the specific steps of high-definition processing of microbubble trajectories include: processing each image subsequence J1 in time order. k Construct the microbubble trajectory and obtain the corresponding K confidence parameter maps J e k (k=1,2,…K); for each confidence parameter graph J in chronological order e k Perform spatial resolution enhancement to obtain the corresponding K high-resolution microbubble trajectory images J2 k (k=1,2,…K).

[0008] In one embodiment, the specific steps of constructing the microbubble trajectory include: based on the kth image subsequence J1 k , construct the corresponding time domain grayscale value fluctuation signal S for each pixel coordinate p p and / or local spatiotemporal grayscale value signal U p , further, for S p and / or U p Perform feature estimation to obtain the microbubble trajectory confidence parameter C corresponding to each pixel coordinate p p ; According to the microbubble trajectory confidence parameter C corresponding to each pixel coordinate p p and confidence parameter map J e k The preset mapping relationship of the pixel coordinate p' is used to map the k-th image subsequence J1 k Corresponding confidence parameter map J e k ; For each image subsequence J1 in time order k(k=1,2,…K) Repeat steps 1)-2) to obtain the corresponding K confidence parameter graphs J e k (k=1,2,…K).

[0009] In one embodiment, the specific steps of spatial resolution enhancement include: e k The local spatial domain grayscale value distribution characteristics and gradient field distribution characteristics of each pixel coordinate q in the , and the spatial domain feature parameter M corresponding to each pixel coordinate q is obtained. q ; The kth confidence parameter map J e k The spatial domain feature parameter M corresponding to each pixel coordinate q q As a weight, the gray value corresponding to each pixel coordinate q is weighted to obtain a high-resolution microbubble trajectory image J2 k ; For each confidence parameter graph J in chronological order e k (k=1,2,…K) Repeat steps 1)-2) to obtain the corresponding K high-resolution microbubble trajectory images J2 k (k=1,2,…K).

[0010] In one embodiment, the image subsequence J1 k The time domain gray value fluctuation signal S at the pixel coordinate p p and / or local spatiotemporal grayscale value signal U p The specific construction steps include: the image subsequence J1 k The time domain gray value fluctuation signal at the pixel coordinate p in the image subsequence J1 is: k The one-dimensional signal S is formed by arranging the grayscale values ​​of the pixels at the pixel coordinate p in time sequence. p ; The image subsequence J1 k The local spatiotemporal grayscale value signal at the pixel coordinate p is: the three-dimensional signal U formed by the pixel coordinate p and the grayscale values ​​of multiple pixel coordinates adjacent to the pixel point p according to the spatial relative position relationship and time sequence p .

[0011] The present application provides a high-definition micro-blood flow field measurement system, which consists of the following modules: Ultrasound contrast image subsequence construction module: for the ultrasound contrast image sequence J0, K image subsequences J1 are obtained by time sliding window construction k (k=1,2,…K); High-resolution microbubble trajectory projection module: for each image subsequence J1 in time order k Perform high-definition processing on microbubble trajectories to obtain K image subsequences J1 k(k=1,2,…K) corresponding to K high-resolution microbubble trajectory images J2 k (k=1,2,…K); High-definition micro-blood flow field reconstruction module: based on K high-resolution microbubble trajectory images J2 k (k=1,2,…K), and K-1 high-resolution microbubble displacement field time series J3 are estimated by the speckle correlation strategy between adjacent frames. k (k=1,2,…K-1), which is the high-definition micro blood flow field.

[0012] Accordingly, the present application provides an electronic device, comprising: a processor; and a memory, wherein computer program instructions are stored in the memory, and when the computer program instructions are executed by the processor, the processor executes the high-definition micro-blood flow field measurement method as described above.

[0013] Accordingly, the present application provides a computer-readable storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the processor executes any of the above-described high-definition micro-blood flow field measurement methods.

[0014] The present invention provides a high-definition micro blood flow field measurement method, a high-definition micro blood flow field measurement device, an electronic device and a computer-readable storage medium, which obtain K image subsequences J1 by constructing a time sliding window. k (k=1,2,…K), so that the spatiotemporal dynamics of microbubbles can be preserved; further, each image subsequence J1 is sorted in time order. k Perform high-definition processing on microbubble trajectories to obtain K image subsequences J1 k (k=1,2,…K) corresponding to K high-resolution microbubble trajectory images J2 k (k=1,2,…K), breaking through the diffraction limit of ultrasound imaging resolution and constructing the projection image of microbubble trajectories after weight loss; finally, in order to reconstruct the microblood flow field, based on K high-resolution microbubble trajectory images J2 k (k=1,2,…K), and K-1 high-resolution microbubble displacement field time series J3 are estimated by the speckle correlation strategy between adjacent frames. k (k=1,2,…K-1), achieving effective reconstruction of the micro blood flow field.

[0015] Different from the traditional micro blood flow field measurement method, the present invention does not rely on the microbubble positioning and tracking of sparse microbubbles to construct the flow field, but constructs a high-definition microbubble trajectory field and then uses the speckle correlation strategy to estimate the high-resolution microbubble displacement field time series, which is the high-definition micro blood flow field. This makes the present invention not limited to sparse microbubble distribution and retains rich microbubble movement information, thereby greatly improving the speed of micro blood flow velocity measurement and realizing rapid micro blood flow field measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The figure is a schematic flow chart of a high-definition micro blood flow field measurement method provided in one embodiment of the present application.

[0017] Figure 2 The figure is a schematic diagram of a high definition processing flow of microbubble trajectories provided in one embodiment of the present application.

[0018] Figure 3 Shown is a schematic flow chart of a method for constructing microbubble trajectories provided in one embodiment of the present application.

[0019] Figure 4 Shown is a schematic flow chart of a method for constructing microbubble trajectories provided in another embodiment of the present application.

[0020] Figure 5 The figure is a flow chart of a microbubble trajectory confidence estimation method provided in one embodiment of the present application.

[0021] Figure 6 Shown is a confidence parameter diagram provided in an embodiment of the present application.

[0022] Figure 7 The figure is a flow chart of a spatial resolution enhancement processing method provided in one embodiment of the present application.

[0023] Figure 8 Shown is a high-resolution microbubble trajectory image provided by an embodiment of the present application.

[0024] Fig. 9 Shown is a flow chart of a spatial resolution enhancement processing method provided in another embodiment of the present application.

[0025] Fig.10 FIG. 1 is a flow chart of a speckle displacement field estimation method provided in an embodiment of the present application.

[0026] Fig.11 Shown is a schematic structural diagram of a high-definition micro blood flow field measurement device provided in one embodiment of the present application.

[0027] Fig.12 Shown is a schematic structural diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0028] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] Figure 1 Shown is a high-definition micro-blood flow field measurement method provided by an embodiment of the present application. As Figure 1 shown, the preprocessing method includes:

[0030] Step 101: For the ultrasound contrast image sequence J0, K image subsequences J1 k (k = 1, 2, … K) are constructed through a time sliding window;

[0031] In one embodiment, the specific steps include.

[0032] Step 1011: Inject an ultrasound contrast agent into the imaging target to be imaged, and use an ultrasound probe to detect the area to be imaged. When a contrast enhancement signal appears, start collecting ultrasound contrast data to obtain an ultrasound contrast image sequence J0 containing T frames of two-dimensional ultrasound contrast images; it should be understood that the frequency of collecting multiple frames of contrast images can be 30 Hz or 50 Hz, and the frequency of collecting contrast images only needs to meet the specific clinical collection conditions. The embodiments of the present invention do not make specific limitations on the frequency of collecting contrast images.

[0033] In one embodiment, the method of injecting the contrast agent can be to dissolve 59 mg of SonoVue, a freeze-dried powder of sulfur hexafluoride microbubbles, in 5 mL of 0.9% sodium chloride solution and inject 0.2 mL of the contrast agent at one time. The embodiments of the present invention do not make limitations on the specific type of the contrast agent and the specific injection method.

[0034] Step 1012: Perform a sliding time window (W frames, W < T) operation on the ultrasound contrast image sequence J0; In the ultrasound contrast image sequence J0, every D frames, W consecutive ultrasound contrast images are selected to form several ultrasound contrast image subsequences J1 with the same time window size until the number of remaining consecutive ultrasound contrast image frames in the ultrasound contrast image sequence J0 is less than W frames, and finally K image subsequences J1 k (k = 1, 2, … K) are obtained. It should be understood that the selected ultrasound contrast image subsequences J1 in the ultrasound contrast image sequence J0 are continuous in time.

[0035] It should be understood that the method of selecting W continuous ultrasound contrast images every D frames in the ultrasound contrast image sequence J0 may be selecting 10 consecutive frames every 1 frame; or selecting 15 frames every 2 frames; or selecting 20 frames every 3 frames. The settings of D and W can be selected according to actual needs. For example, when the imaging frame rate is low, D and W should be smaller. The present application does not specifically limit the size of the sliding time window parameter.

[0036] Step 102: For each image subsequence J1 in time order k Perform high-definition processing on microbubble trajectories to obtain K image subsequences J1 k (k=1,2,…K) corresponding to K high-resolution microbubble trajectory images J2 k (k=1,2,…K);

[0037] Figure 2 The figure is a schematic diagram of a high-definition processing flow of microbubble trajectories provided in an embodiment of the present application, which specifically includes the following steps:

[0038] Step 201: For each image subsequence J1 in time order k Construct the microbubble trajectory and obtain the corresponding K confidence parameter maps J e k (k=1,2,…K);

[0039] Figure 3 The figure is a schematic flow chart of a method for constructing microbubble trajectories provided in an embodiment of the present application, which specifically includes the following steps:

[0040] Step 301: Select each ultrasound contrast imaging subsequence J1 k Multiple co-located pixel points at each pixel coordinate p in the image are arranged in chronological order according to the acquisition time to form a time domain grayscale value fluctuation signal S corresponding to the pixel coordinate p. p ; In one embodiment, each ultrasound contrast imaging subsequence J1 is selected k The first co-located pixel point at each pixel coordinate p forms a time domain grayscale value fluctuation signal S corresponding to the first co-located pixel point set. p For example: In the ultrasound contrast imaging subsequence J1 k Take five consecutive ultrasound contrast-enhanced image frames A, B, C, D and E, and the grayscale values ​​at the pixel coordinates (1, 1) on each frame correspond to a, b, c, d and e. Then, arrange a, b, c, d and e in the order of frames to form a one-dimensional time domain grayscale value fluctuation signal S corresponding to the pixel coordinates (1, 1). p , the dimension size is 1*5. It should be understood that the pixel coordinate p is just a reference and can be the ultrasound contrast image subsequence J1 kThe embodiment of the present invention does not limit the specific selection of the pixel coordinate p. It should be understood that as long as the time domain grayscale value fluctuation signal of each pixel coordinate can be obtained, the embodiment of the present invention does not specifically limit the specific means of obtaining the grayscale fluctuation signal.

[0041] Step 302: Calculate the time domain grayscale value fluctuation signal S at each pixel coordinate p p Perform feature estimation to obtain the microbubble trajectory confidence parameter C p , used to characterize the time domain gray value fluctuation signal S at the pixel coordinate p p The periodicity and / or randomness of fluctuations; In one embodiment, the time domain feature estimation method includes autocorrelation estimation calculation or information entropy estimation calculation. The time domain gray value fluctuation signal S at the pixel coordinate p is measured by autocorrelation estimation. p The strength of the periodicity; the time domain gray value fluctuation signal S at the pixel coordinate p is measured by information entropy estimation p It should be understood that, due to the fluidity of microbubbles, the grayscale value fluctuation signal in the time domain corresponding to the pixel coordinates of the area through which the microbubbles flow has stronger periodicity and stronger randomness. Therefore, the microbubble trajectory confidence parameter C corresponding to the pixel coordinates of the area through which the microbubbles flow is p It should be greater than the microbubble trajectory confidence parameter C corresponding to the pixel coordinates of the non-vascular area where no microbubbles flow. p .

[0042] Step 303: Calculate the confidence parameter C of the microbubble trajectory corresponding to each pixel coordinate p. p and confidence parameter map J e k The preset mapping relationship of the pixel coordinate p' is used to map the k-th image subsequence J1 k Corresponding confidence parameter map J e k ; It should be understood that the microbubble trajectory confidence parameter C p and confidence parameter diagram J e k The preset mapping relationship of the pixel coordinate p' in advance makes the gray value of the pixel coordinate p' in the area where the microbubbles flow through the area highlighted, and the gray value of the pixel coordinate p' in the non-vascular area where the microbubbles do not flow through the area is weakened. This is accurately conveyed to the confidence parameter map J through the preset mapping relationship. e k The corresponding position.

[0043] In the embodiment of the present invention, by setting the confidence parameter map J e kThe pixel gray value at each pixel coordinate p' is set as the corresponding microbubble trajectory confidence parameter C p， And the confidence parameter map J e k The confidence parameter C of the microbubble trajectory at all pixel coordinates p' p Normalization is performed so that the signal of the area where microbubbles flow through needs to be strengthened and the signal of the non-vascular area where microbubbles do not flow through needs to be weakened. The mapping relationship between the pixel coordinates p and the pixel coordinates p' is accurately conveyed to the confidence parameter map J. e k The corresponding pixel coordinate p' in the image is obtained, so that the microbubble trajectory is highlighted and the background and noise areas are suppressed. e k .

[0044] Step 304: For each image subsequence J1 in time order k (k=1,2,…K) Repeat steps 301-303 to obtain the corresponding K confidence parameter graphs J e k (k=1,2,…K).

[0045] Figure 4 The figure is a schematic flow chart of a method for constructing microbubble trajectories provided in an embodiment of the present application, which specifically includes the following steps:

[0046] Step 401: Based on the kth image subsequence J1 k , taking each pixel coordinate p as the center, taking a rectangular area of ​​size ω1×ω2 pixels, and arranging the pixel coordinate p in this area and the grayscale values ​​of the pixel coordinates adjacent to the pixel point p according to the spatial relative position relationship and time sequence, to form a three-dimensional signal U corresponding to each pixel coordinate p p In one embodiment, the image subsequence J1 k It contains W frames of ultrasound contrast images, with pixel coordinate p as the center, and selects ω1=ω2=L (unit: pixel), which constitutes the three-dimensional signal U corresponding to the pixel coordinate p p , the dimension size is ω1×ω2×W. ω2 and ω1 can be 4, 5, or 6. The embodiment of the present invention does not limit the size of the rectangular area.

[0047] Step 402: Calculate the three-dimensional signal U corresponding to each pixel coordinate p p Perform microbubble trajectory confidence estimation to obtain the microbubble trajectory confidence parameter C corresponding to each pixel coordinate p p ;

[0048] Figure 5 The figure is a flow chart of a microbubble trajectory confidence estimation method provided by an embodiment of the present application, and the specific steps include:

[0049] Step 501: For each pixel coordinate p corresponding to the three-dimensional signal U p Perform singular value decomposition to obtain d two-dimensional ω1×ω2 feature maps corresponding to d eigenvalues;

[0050] Step 502: Determine the characteristic value threshold k d , will be greater than k d The h two-dimensional ω1×ω2 feature graphs corresponding to the eigenvalues ​​of are taken as the signal feature graph sequence, and the number of features less than or equal to k d The (dh) two-dimensional ω1×ω2 feature maps corresponding to the (dh) eigenvalues ​​are used as the noise feature map sequence;

[0051] Step 503: construct a two-dimensional point spread function image P of size ω1×ω2 at each pixel coordinate p p ; In one embodiment, the point spread function is set to a two-dimensional Gaussian distribution, with variances σ1 and σ2 respectively, and the center of the Gaussian grayscale distribution is set to be located at the pixel coordinate p, and the grayscale value is set to be a maximum of 255. A point spread function image P at the pixel coordinate p is formed in the ω1×ω2 space range according to the set two-dimensional Gaussian distribution. p The selection of σ1 and σ2 may be based on a preset Gaussian model, or may be obtained by fitting any manually captured independent microbubble image in a single-frame ultrasound contrast imaging sequence. The present invention does not impose any specific limitation on the selection of σ1 and σ2.

[0052] Step 504: Evaluate the point spread function image P at each pixel coordinate p p The similarity between the signal feature map sequence and the noise feature map sequence is calculated based on the similarity value, and the microbubble trajectory confidence parameter C corresponding to each pixel coordinate p is calculated p .

[0053] In one embodiment, the point spread function image P at the pixel coordinate p is p Respectively greater than k d The h signal feature map sequences corresponding to the characteristic values ​​of are multiplied one by one to obtain h signal feature similarity coefficient maps, and the h signal feature similarity coefficient maps are added one by one to obtain a two-dimensional ω1×ω2 signal feature similarity coefficient superposition map. It should be understood that the larger the signal feature similarity coefficient, the greater the possibility that the pixel coordinate p is located in the area where the microbubble flows. The point spread function image P at the pixel coordinate p is p Respectively less than k dMultiply and add the dh noise feature map sequences corresponding to the eigenvalues ​​to obtain dh noise feature similarity coefficient maps, and add the dh noise feature similarity coefficient maps one by one to obtain a two-dimensional ω1×ω2 noise feature similarity coefficient overlay map. It should be understood that the larger the noise feature similarity coefficient, the smaller the possibility of the existence of a microbubble trajectory signal at the pixel coordinate p and the greater the possibility of the existence of noise. Take the maximum signal feature similarity coefficient in the two-dimensional ω1×ω2 signal feature similarity coefficient overlay map as the numerator, and take the maximum noise feature similarity coefficient in the two-dimensional ω1×ω2 noise feature similarity coefficient overlay map as the denominator, and the ratio result is used as the microbubble trajectory confidence parameter C at the pixel coordinate p. p It should be understood that the microbubble trajectory confidence parameter C p The larger the value, the greater the possibility that the pixel coordinate p is located in the microbubble flow area.

[0054] Step 403: Calculate the confidence parameter C of the microbubble trajectory corresponding to each pixel coordinate p. p and confidence parameter map J e k The preset mapping relationship of the pixel coordinate p' is used to map the k-th image subsequence J1 k Corresponding confidence parameter map J e k ; It should be understood that the microbubble trajectory confidence parameter C p and confidence parameter diagram J e k The preset mapping relationship of the pixel coordinate p' in advance makes the gray value of the pixel coordinate p' in the area where the microbubbles flow through the area highlighted, and the gray value of the pixel coordinate p' in the non-vascular area where the microbubbles do not flow through the area is weakened. This is accurately conveyed to the confidence parameter map J through the preset mapping relationship. e k The corresponding position.

[0055] In the embodiment of the present invention, by setting the confidence parameter map J e k The pixel gray value at each pixel coordinate p' is set as the corresponding microbubble trajectory confidence parameter C p， And the confidence parameter map J e k The confidence parameter C of the microbubble trajectory at all pixel coordinates p' p Normalization is performed so that the signal of the area where microbubbles flow through needs to be strengthened and the signal of the non-vascular area where microbubbles do not flow through needs to be weakened. The mapping relationship between the pixel coordinates p and the pixel coordinates p' is accurately conveyed to the confidence parameter map J. e kThe corresponding pixel coordinate p' in the image is obtained, so that the microbubble trajectory is highlighted and the background and noise areas are suppressed. e k .like Figure 6 As shown in the confidence parameter diagram J e k In the image, the microbubble trajectory 602 is highlighted, the non-vascular region 601 where no microbubbles flow is weakened, and the background and noise regions are suppressed.

[0056] Step 404: For each image subsequence J1 in time order k (k=1,2,…K) Repeat steps 401-403 to obtain the corresponding K confidence parameter graphs J e k (k=1,2,…K).

[0057] Step 202: Analyze each confidence parameter graph J in chronological order e k Perform spatial resolution enhancement to obtain the corresponding K high-resolution microbubble trajectory images J2 k (k=1,2,…K);

[0058] Figure 7 FIG. 1 is a flow chart of a spatial resolution enhancement processing method provided by an embodiment of the present application, wherein the specific steps include:

[0059] Step 701: For the kth confidence parameter graph J e k Perform spatial interpolation to form an interpolation confidence parameter map J e k’ ; In one embodiment, the interpolation method may be spatial domain interpolation and frequency domain interpolation, and the interpolation multiple may be 2, 3, or 4. The embodiment of the present invention does not limit the specific method and parameters of the interpolation operation.

[0060] Step 702: For the kth interpolation confidence parameter map J e k’ The local spatial domain grayscale value distribution characteristics at each pixel coordinate q in the , and the spatial domain feature parameter M corresponding to each pixel coordinate q is obtained. q ; In one embodiment, for the interpolation confidence parameter map J e k’ For each pixel coordinate q, calculate the distance between the area to which each pixel coordinate q belongs and the background area, and convert the spatial domain feature parameter M corresponding to each pixel coordinate q into qIn one embodiment of the present invention, before calculating the distance value between the region to which each pixel coordinate q belongs and the background region, the method further comprises: setting the interpolation confidence parameter map J e k’ Binarization is performed to generate a binary interpolation confidence parameter map. Wherein, the pixel value of the microbubble area is set to 1, the pixel value of the background area is set to 0, and the microbubble area and the background area are both composed of multiple pixel blocks; the shortest distance from each pixel coordinate of the microbubble area to the background area is determined as the distance value. In one embodiment, the shortest distance can be obtained by Euclidean distance, and the embodiment of the present invention does not limit the specific algorithm of the distance. It should be understood that the distance parameter of the pixel point at the central skeleton of the microbubble flow area is greater than the distance parameter of the edge and the pixel point.

[0061] Step 703: Transform the k-th interpolation confidence parameter map J e k’ The spatial domain feature parameter M corresponding to each pixel coordinate q q As a weight, the gray value corresponding to each pixel coordinate q is weighted to obtain a high-resolution microbubble trajectory image J2 k ; In one embodiment, the kth interpolation confidence parameter map J e k’ Each pixel coordinate q in the spatial domain feature parameter M q Multiply the pixel gray value of each pixel coordinate q to obtain the weighted pixel gray value G corresponding to each pixel coordinate q q , and then the high-resolution microbubble trajectory image J2 k The pixel gray value at each pixel coordinate q is set to the corresponding G q , thus obtaining a high-resolution microbubble trajectory image J2 k ;like Figure 8 As shown in the high-resolution microbubble trajectory image J2 k In the figure, the grayscale value of the pixel coordinate 802 at the central skeleton of the area where the microbubbles flow is enhanced, and the grayscale value of the pixel coordinate 801 at the edge and the non-vascular area where the microbubbles do not flow is weakened, so that the microbubble trajectory image becomes clearer.

[0062] Step 704: Analyze each confidence parameter graph J in chronological order e k (k=1,2,…K) Repeat steps 701-703 to obtain the corresponding K high-resolution microbubble trajectory images J2 k (k=1,2,…K).

[0063] Fig. 9FIG. 1 is a flow chart of a spatial resolution enhancement processing method provided by another embodiment of the present application, wherein the specific steps include:

[0064] Step 901: Calculate the kth confidence parameter graph J e k Perform spatial interpolation to form an interpolation confidence parameter map J e k’ ; In one embodiment, the interpolation method may be spatial domain interpolation and frequency domain interpolation, and the interpolation multiple may be 2, 3, or 4. The embodiment of the present invention does not limit the specific method and parameters of the interpolation operation.

[0065] Step 902: For the k-th interpolation confidence parameter map J e k’ The gradient field distribution characteristics at each pixel coordinate q in the image are estimated to obtain the spatial domain feature parameter M corresponding to each pixel coordinate q. q ; In one embodiment, the gradient field distribution feature is a radial symmetry feature. The radial symmetry refers to the radial symmetry of the local gradient field distribution at each pixel coordinate q. For example, the radial symmetry of the local gradient field distribution corresponding to the pixel coordinate q and the grayscale values ​​of the 12 pixel coordinates surrounding the pixel coordinate q is estimated, and the radial symmetry estimation value at the pixel coordinate q is obtained, which is the spatial domain feature parameter M. q The embodiment of the present invention does not limit the specific algorithm of the radial symmetry estimation value. It should be understood that the radial symmetry estimation value of the pixel points at the central skeleton of the microbubble flow area is greater than the radial symmetry estimation value of the pixel points at the edge and the non-vascular area where the microbubble does not flow.

[0066] Step 903: Map the kth confidence parameter graph J e k The spatial domain feature parameter M corresponding to each pixel coordinate q q As a weight, the gray value corresponding to each pixel coordinate q is weighted to obtain a high-resolution microbubble trajectory image J2 k In one embodiment, the kth interpolation confidence parameter map J e k’ Each pixel coordinate q in the spatial domain feature parameter M q Multiply the pixel gray value of each pixel coordinate q to obtain the weighted pixel gray value G corresponding to each pixel coordinate q q , and then the high-resolution microbubble trajectory image J2 k The pixel gray value at each pixel coordinate q is set to the corresponding G q , thus obtaining a high-resolution microbubble trajectory image J2 k It should be understood that in the high-resolution microbubble trajectory image J2k In the image, the pixels at the central skeleton of the area where the microbubbles flow are enhanced and the pixels at the edge are weakened, making the microbubble trajectory clearer in each frame.

[0067] Step 904: Analyze each confidence parameter graph J in chronological order e k (k=1,2,…K) Repeat steps 901-903 to obtain the corresponding K high-resolution microbubble trajectory images J2 k (k=1,2,…K).

[0068] Step 103: Based on K high-resolution microbubble trajectory images J2 k (k=1,2,…K), and K-1 high-resolution microbubble displacement field time series J3 are estimated by the speckle correlation strategy between adjacent frames. k (k=1,2,…K-1), which is the high-definition micro blood flow field.

[0069] Fig.10 FIG. 1 is a flow chart of a speckle displacement field estimation method provided in an embodiment of the present application, and the specific steps include:

[0070] Step 1001: Take the kth high-resolution microbubble trajectory image J2 k As the starting point, a data window centered around pixel coordinate j is selected as the speckle sub-image to be matched; Specifically, the data window can be set to be square, circular or elliptical, and the present invention does not specifically limit the size and form of the data window.

[0071] Step 1002: In the k+1th high-resolution microbubble trajectory image J2 k+1 Search near the corresponding pixel coordinate j to determine the best matching position of the speckle sub-image to be matched, and calculate the pixel coordinate j in the kth high-resolution microbubble trajectory image J2 k The instantaneous displacement vector v at the corresponding time point j ; Specifically, the nearby search area can be set as each pixel coordinate i in the local square area with a size of 200*200 pixels. The search method is as follows: taking each pixel coordinate i in the local square area as the center, constructing a corresponding plurality of candidate speckle sub-images; respectively estimating the similarity between the plurality of candidate speckle sub-images and the speckle sub-image to be matched, and selecting the pixel coordinate i corresponding to the candidate speckle sub-image with the highest similarity as the most matching position of the pixel coordinate j; taking the pixel coordinate i as the end point, calculating the lateral and axial displacement relative to the pixel coordinate j, and combining the two into a vector, that is, the pixel coordinate j in the kth high-resolution microbubble trajectory image J2 k The displacement vector v corresponding to the time point j .

[0072] In one embodiment, the normalized cross-correlation algorithm is used to calculate the similarity between the candidate speckle sub-image and the speckle sub-image to be matched corresponding to the pixel coordinate i. The present invention does not specifically limit the size of the nearby search area, the search method, and the calculation method of the similarity.

[0073] Step 1003: Kk adjacent high-resolution microbubble trajectory images J2 k Repeat steps 1001-1002 to obtain K-1 high-resolution microbubble displacement field time series J3 k (k=1,2,…K-1). In one embodiment, each pixel coordinate j is in the kth high-resolution microbubble trajectory image J2 k The displacement vector v corresponding to the time point j It is represented by arrows, the direction of the arrows indicates the movement angle, the length of the arrows indicates the movement speed value, and the movement vector is colored according to the instantaneous microbubble movement speed value to form a high-resolution real-time flow field video I v .

[0074] Fig.11 FIG. 1 is a schematic diagram of the structure of a high-definition micro blood flow field measurement device provided in an embodiment of the present application. Fig.11 As shown, the high-definition micro blood flow field measurement device 1000 includes: an ultrasound contrast image subsequence construction module 1101: for the ultrasound contrast image sequence J0, K image subsequences J1 are obtained by constructing through a time sliding window k (k=1,2,…K); High-resolution microbubble trajectory projection module 1102: for each image subsequence J1 in time order k Perform high-definition processing on microbubble trajectories to obtain K image subsequences J1 k (k=1,2,…K) corresponding to K high-resolution microbubble trajectory images J2 k (k=1,2,…K); High-definition micro blood flow field reconstruction module 1103: based on K high-resolution micro bubble trajectory images J2 k (k=1,2,…K), and K-1 high-resolution microbubble displacement field time series J3 are estimated by the speckle correlation strategy between adjacent frames. k (k=1,2,…K-1), which is the high-definition micro blood flow field.

[0075] It should be noted that the high-definition micro-blood flow field measurement device 1100 according to the embodiment of the present application is integrated into the electronic device 1200 as a software module and / or a hardware module. In other words, the electronic device 1200 includes the high-definition micro-blood flow field measurement device 1100. For example, the high-definition micro-blood flow field measurement device 1100 is a software module in the operating system of the electronic device 1200, or an application developed for the operating system.

[0076] In another embodiment of the present application, the high-definition micro blood flow field measurement device 1100 and the electronic device 1200 may also be separate devices (for example, a server), and the high-definition micro blood flow field measurement device 1100 may be connected to the electronic device 1200 via a wired and / or wireless network, and transmit interactive information in accordance with an agreed data format.

[0077] Fig.12 FIG. 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Fig.12 As shown, the electronic device 1200 includes: one or more processors 1201 and a memory 1203; and computer program instructions stored in the memory 1203, and when the computer program instructions are executed by the processor 1201, the processor 1201 executes the high-definition micro-blood flow field measurement method as in any of the above-mentioned embodiments. The processor 1201 may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0078] The memory 1203 may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on a computer-readable storage medium, and the processor 1101 may run the program instructions to implement the steps in the high-definition micro-blood flow field measurement method of each embodiment of the present application above and / or other desired functions. Information such as microbubble angiography images may also be stored in the computer-readable storage medium.

[0079] In one example, the electronic device 1200 includes: an input device 1202 and an output device 1204, and these components are interconnected via a bus system and / or other forms of connection mechanisms.

[0080] For example, when the electronic device is an ultrasound imaging system, the input device 1202 may be an ultrasound probe for detecting the location of microbubbles. When the electronic device is a stand-alone device, the input device 1202 may be a communication network connector for receiving collected input signals from an external removable device. In addition, the input device 1202 may also include, for example, a keyboard, a mouse, a microphone, and the like.

[0081] The output device 1204 can output various information to the outside, and may include, for example, a display, a speaker, a printer, a communication network and its connected remote output devices, etc.

[0082] Of course, to simplify, Fig.12 Only some of the components related to the present application in the electronic device 1200 are shown, and components such as a bus, an input device / output interface, etc. are omitted. In addition, according to specific application conditions, the electronic device 1200 may also include any other appropriate components.

[0083] In addition to the above methods and devices, the embodiments of the present application may also be a computer program product, including computer program instructions, which, when executed by a processor, enable the processor to execute the steps in the high-definition micro-blood flow field measurement method of any of the above embodiments.

[0084] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, etc., and may also include conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0085] In addition, an embodiment of the present application may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of the high-definition micro-blood flow field measurement method according to various embodiments of the present application described in the above-mentioned "High-definition micro-blood flow field measurement method" section of this specification.

[0086] Computer readable storage media can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0087] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, not for limitation, and the above details do not limit the present application to being implemented by adopting the above specific details.

[0088] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used in this application refer to the words "and / or", and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used in this application refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0089] It should also be noted that in the apparatus, device and method of the present application, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0090] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0091] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

[0092] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A high-definition micro blood flow field measurement method, characterized in that: Based on the ultrasound contrast imaging sequence, the microbubble trajectory field is highlighted, and then the high-resolution microbubble displacement field time series is estimated using the speckle correlation strategy, which is the high-definition micro-blood flow field. The specific steps include: 1) For the ultrasound contrast imaging sequence J0, K image subsequences J1 are obtained by constructing a time sliding window k (k=1,2,…K); 2) For each image subsequence J1 in time order k Perform high-definition processing on microbubble trajectories to obtain K image subsequences J1 k (k=1,2,…K) corresponding to K high-resolution microbubble trajectory images J2 k (k=1,2,…K); 3) Based on K high-resolution microbubble trajectory images J2 k (k=1,2,…K), and K-1 high-resolution microbubble displacement field time series J3 are estimated by the speckle correlation strategy between adjacent frames. k (k=1,2,…K-1), which is the high-definition micro blood flow field.

2. The high definition processing of microbubble trajectories according to claim 1, comprising the following specific steps: 1) For each image subsequence J1 in time order k Construct the microbubble trajectory and obtain the corresponding K confidence parameter maps J e k (k=1,2,…K); 2) For each confidence parameter graph J in chronological order e k Perform spatial resolution enhancement to obtain the corresponding K high-resolution microbubble trajectory images J2 k (k=1,2,…K).

3. The high definition processing of microbubble trajectories according to claim 2, characterized in that: The specific steps of constructing the microbubble trajectory include: 1) based on the kth image subsequence J1 k , construct the corresponding time domain grayscale value fluctuation signal S for each pixel coordinate p p and / or local spatiotemporal grayscale value signal U p , further, for S p and / or U p Perform feature estimation to obtain the microbubble trajectory confidence parameter C corresponding to each pixel coordinate p p ; 2) According to the microbubble trajectory confidence parameter C corresponding to each pixel coordinate p p and confidence parameter map J e k The preset mapping relationship of the pixel coordinate p' is used to map the k-th image subsequence J1 k Corresponding confidence parameter map J e k ; 3) For each image subsequence J1 in time order k (k=1,2,…K) Repeat steps 1)-2) to obtain the corresponding K confidence parameter graphs J e k (k=1,2,…K).

4. The high definition processing of microbubble trajectories as described in claim 2 is characterized in that: The specific steps of spatial resolution enhancement include: 1) for the kth confidence parameter map J e k The local spatial domain grayscale value distribution characteristics and gradient field distribution characteristics of each pixel coordinate q in the , and the spatial domain feature parameter M corresponding to each pixel coordinate q is obtained. q ; 2) the kth confidence parameter map J e k The spatial domain feature parameter M corresponding to each pixel coordinate q q As a weight, the gray value corresponding to each pixel coordinate q is weighted to obtain a high-resolution microbubble trajectory image J2 k ; 3) For each confidence parameter graph J in chronological order e k (k=1,2,…K) Repeat steps 1)-2) to obtain the corresponding K high-resolution microbubble trajectory images J2 k (k=1,2,…K).

5. The high definition processing of microbubble trajectories as claimed in claim 3, characterized in that: The image subsequence J1 k The time domain gray value fluctuation signal S at the pixel coordinate p p and / or local spatiotemporal grayscale value signal U p The specific construction steps include: the image subsequence J1 k The time domain gray value fluctuation signal S at the pixel coordinate p in p The image subsequence J1 is: k The one-dimensional signal S is formed by arranging the grayscale values ​​of the pixels at the pixel coordinate p in time sequence. p ; The image subsequence J1 k The local spatiotemporal grayscale value signal U at the pixel coordinate p in p The three-dimensional signal U is formed by arranging the pixel coordinate p and the grayscale values ​​of the pixel coordinates adjacent to the pixel point p according to the spatial relative position relationship and time sequence. p .

6. A high-definition micro blood flow field measurement system, using the method described in claim 1, the system consists of the following modules: 1) Ultrasound contrast imaging subsequence construction module: For the ultrasound contrast imaging sequence J0, K image subsequences J1 are obtained through time sliding window construction k (k=1,2,…K); 2) High-resolution microbubble trajectory projection module: For each image subsequence J1 in time order k Perform high-definition processing on microbubble trajectories to obtain K image subsequences J1 k (k=1,2,…K) corresponding to K high-resolution microbubble trajectory images J2 k (k=1,2,…K); 3) High-definition micro-blood flow field reconstruction module: based on K high-resolution microbubble trajectory images J2 k (k=1,2,…K), and K-1 high-resolution microbubble displacement field time series J3 are estimated by the speckle correlation strategy between adjacent frames. k (k=1,2,…K-1), which is the high-definition micro blood flow field.

7. An electronic device comprising: processor; and a memory, wherein computer program instructions are stored in the memory, and when the computer program instructions are executed by the processor, the processor executes the high-definition micro blood flow field measurement method as claimed in any one of claims 1 to 5. A computer-readable storage medium having computer program instructions stored thereon, wherein when the computer program instructions are executed by a processor, the processor executes the high-definition micro-blood flow field measurement method as claimed in any one of claims 1 to 5.

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