Ultrasound imaging method, ultrasound imaging device, and image display apparatus

Abstract

The embodiment of the application discloses an ultrasonic imaging method, an ultrasonic imaging device and an image display device. Two kinds of space-time filtering processing methods with different processing effects are adopted to process the obtained tissue structure signals, so as to filter static tissue radio frequency signals in the space radio frequency signals, extract microbubble radio frequency signals, obtain two groups of microbubble signals of first microbubble signals and second microbubble signals, and perform fusion processing. The imaging image can have the advantages of the two kinds of space-time filtering processing, the accuracy of microvessel imaging can be improved, and the imaging effect of the microvessel is improved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the field of ultrasonic imaging, and in particular to an ultrasonic imaging method, an ultrasonic imaging device and an image display apparatus. BACKGROUND

[0002] Medical ultrasonic imaging diagnostic equipment can obtain ultrasonic image information of human tissue and organ structure by using ultrasonic wave propagation in the human body. Because ultrasonic diagnosis has the advantages of safety, wide adaptability, intuition, repeatability, strong discrimination for soft tissue, flexibility and low cost, it has become the preferred technology in modern medical image diagnosis and has played an extremely important role in modern diagnostic technology. Among them, using ultrasonic imaging diagnostic equipment to check and observe microvessels and blood capillary circulation has important value for early diagnosis and treatment of various diseases, but due to the diffraction limit of ultrasonic waves in the far field, the ability of conventional ultrasonic contrast to display microvessel structure details is limited.

[0003] In order to solve the problem of displaying microvessel structure details, the current method is to use the principle of fluorescence microscopic positioning technology in optical super-resolution imaging, to obtain an image with a spatial resolution of tens of microns by introducing ultrasonic contrast agent and positioning and tracking isolated microbubbles of the ultrasonic contrast agent. In order to accurately identify and locate the microbubbles of the ultrasonic contrast agent, data processing needs to be performed on the ultrasonic signal. However, the microbubble positioning result obtained by the existing data processing method is not ideal, and the imaging effect is not ideal. SUMMARY

[0004] The following is a summary of the subject matter of the detailed description herein. This summary is not intended to limit the scope of the claims.

[0005] Embodiments of the present application provide an ultrasonic imaging method, an ultrasonic imaging device and an image display apparatus, which can improve the accuracy of microvessel imaging and improve the imaging effect of microvessels.

[0006] In a first aspect, embodiments of the present application provide an ultrasonic imaging method, comprising:

[0007] emitting a first ultrasonic wave to a target tissue, receiving an echo of the first ultrasonic wave returned by the target tissue to obtain a first ultrasonic echo signal;

[0008] obtaining a tissue structure signal of the target tissue according to the first ultrasonic echo signal, the tissue structure signal comprising a plurality of frames of spatial radio frequency signals;

[0009] performing singular value decomposition filtering processing on the tissue structure signal to extract a microbubble radio frequency signal in each frame of the spatial radio frequency signal to obtain a first microbubble signal;

[0010] perform time-domain differential filtering on the tissue structure signal to extract microbubble radio frequency signals in each frame of the spatial radio frequency signals, to obtain a second microbubble signal;

[0011] obtain target microvessel image data by fusing the first microbubble signal and the second microbubble signal;

[0012] display a microvessel image according to the target microvessel image data.

[0013] In a second aspect, an ultrasound imaging method is provided, including:

[0014] emit first ultrasound waves to a target tissue, receive echoes of the first ultrasound waves returned by the target tissue to obtain first ultrasound echo signals;

[0015] obtain a tissue structure signal of the target tissue according to the first ultrasound echo signals, the tissue structure signal including multiple frames of spatial radio frequency signals;

[0016] perform first microbubble signal processing on the tissue structure signal to extract microbubble radio frequency signals in each frame of the spatial radio frequency signals, to obtain a first microbubble signal;

[0017] perform second microbubble signal processing on the tissue structure signal to extract microbubble radio frequency signals in each frame of the spatial radio frequency signals, to obtain a second microbubble signal, wherein the first microbubble signal processing and the second microbubble signal processing have different processing effects, and the first microbubble signal processing and the second microbubble signal processing are both space-time filtering processing, the space-time filtering processing being filtering static tissue radio frequency signals in the spatial radio frequency signals according to comparison of data between multiple frames of the spatial radio frequency signals, to extract the microbubble radio frequency signals in the spatial radio frequency signals;

[0018] obtain target microvessel image data by fusing the first microbubble signal and the second microbubble signal;

[0019] display a microvessel image according to the target microvessel image data.

[0020] In a third aspect, an ultrasound imaging method is provided, including:

[0021] emit first ultrasound waves to a target tissue, receive echoes of the first ultrasound waves returned by the target tissue to obtain first ultrasound echo signals;

[0022] obtain a tissue structure signal of the target tissue according to the first ultrasound echo signals, the tissue structure signal including multiple frames of spatial radio frequency signals;

[0023] The tissue structure signal is subjected to various spatiotemporal filtering processes to extract microbubble radio frequency signals and obtain multiple sets of microbubble signals. The spatiotemporal filtering process is to filter the static tissue radio frequency signals in the spatial radio frequency signals by comparing the data between multiple frames of the spatial radio frequency signals, extract the microbubble radio frequency signals in the spatial radio frequency signals, and then obtain the microbubble signals.

[0024] The microbubble signals from each group are fused to obtain target microvascular image data;

[0025] Microvascular images are displayed based on the target microvascular image data.

[0026] Fourthly, embodiments of this application provide an ultrasonic imaging method, including:

[0027] Acquire tissue structure signals, the tissue structure signals including multiple frames of spatial radio frequency signals;

[0028] The tissue structure signal is subjected to first microbubble signal processing to extract the microbubble radio frequency signal in each frame of the spatial radio frequency signal to obtain the first microbubble signal;

[0029] The tissue structure signal is subjected to second microbubble signal processing to extract microbubble radio frequency signals from each frame of the spatial radio frequency signal, thereby obtaining the second microbubble signal. The processing effects of the first microbubble signal processing and the second microbubble signal processing are different. Both the first microbubble signal processing and the second microbubble signal processing are spatiotemporal filtering processes. The spatiotemporal filtering process filters the static tissue radio frequency signals in the spatial radio frequency signal based on the comparison of data between multiple frames of the spatial radio frequency signal, and extracts the microbubble radio frequency signal from the spatial radio frequency signal.

[0030] By fusing the first microbubble signal and the second microbubble signal, target microvascular image data is obtained.

[0031] Microvascular images are displayed based on the target microvascular image data.

[0032] Fifthly, embodiments of this application provide an ultrasonic imaging method, including:

[0033] Acquire tissue structure signals, the tissue structure signals including multiple frames of spatial radio frequency signals;

[0034] The tissue structure signal is subjected to singular value decomposition filtering to extract the microbubble radio frequency signal in each frame of the spatial radio frequency signal, thereby obtaining the first microbubble signal;

[0035] The tissue structure signal is subjected to time-domain differential filtering to extract the microbubble radio frequency signal in each frame of the spatial radio frequency signal, thereby obtaining the second microbubble signal;

[0036] By fusing the first microbubble signal and the second microbubble signal, target microvascular image data is obtained.

[0037] Microvascular images are displayed based on the target microvascular image data.

[0038] Sixthly, embodiments of this application provide an ultrasonic imaging method, including:

[0039] Acquire tissue structure signals, the tissue structure signals including multiple frames of spatial radio frequency signals;

[0040] The tissue structure signal is subjected to various spatiotemporal filtering processes to extract microbubble radio frequency signals and obtain multiple sets of microbubble signals. The spatiotemporal filtering process is to filter the static tissue radio frequency signals in the spatial radio frequency signals by comparing the data between multiple frames of the spatial radio frequency signals, extract the microbubble radio frequency signals in the spatial radio frequency signals, and then obtain the microbubble signals.

[0041] The microbubble signals from each group are fused to obtain target microvascular image data;

[0042] Microvascular images are displayed based on the target microvascular image data.

[0043] Seventhly, embodiments of this application provide an ultrasound imaging device, comprising:

[0044] Ultrasonic probe;

[0045] A transmitting / receiving circuit is used to control the ultrasound probe to emit ultrasound waves toward the target tissue and receive ultrasound echoes to obtain ultrasound echo signals.

[0046] The processor is used to process the ultrasound echo signal, obtain the tissue structure signal of the target tissue, and obtain the microbubble signal by performing microbubble signal processing on the tissue structure signal, and obtain the target microvascular image data based on the microbubble signal.

[0047] A display for displaying the microvascular image data;

[0048] The processor is also used to execute the ultrasonic imaging method of the first, second, or third aspect embodiments described above.

[0049] Eighthly, embodiments of this application provide an image display device, including:

[0050] A processor for acquiring and processing tissue structure signals, wherein the tissue structure signals include multiple frames of spatial radio frequency signals;

[0051] A display for displaying the microvascular image data;

[0052] The processor is also used to execute the ultrasonic imaging method of the embodiments of the fourth, fifth or sixth aspects described above.

[0053] The ultrasonic imaging method, ultrasonic imaging equipment, and image display device provided in this application employ two spatiotemporal filtering methods with different processing effects to process the acquired tissue structure signals. This filters out static tissue radio frequency signals in the spatial radio frequency signals, extracts microbubble radio frequency signals, and obtains two sets of microbubble signals: a first microbubble signal and a second microbubble signal. These signals are then fused together, combining the advantages of both spatiotemporal filtering methods. This improves the accuracy of microvascular imaging and enhances the imaging effect of microvessels. Attached Figure Description

[0054] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0055] Figure 1 This is a schematic diagram of the structure of an ultrasound imaging device provided in one embodiment of this application;

[0056] Figure 2 This is a flowchart of an ultrasonic imaging method provided in one embodiment of this application;

[0057] Figure 3 This is a flowchart of a singular value decomposition filtering process provided in one embodiment of this application;

[0058] Figure 4 yes Figure 2 A flowchart of the method steps included in step S250;

[0059] Figure 5 yes Figure 4 A flowchart of the method steps included in step S430;

[0060] Figure 6 yes Figure 5 A flowchart of the method steps included in step S520;

[0061] Figure 7 This is provided in another embodiment. Figure 2 A flowchart of the method steps included in step S250;

[0062] Figure 8 This is yet another embodiment provided. Figure 2 A flowchart of the method steps included in step S250;

[0063] Figure 9 This is a flowchart of an ultrasonic imaging method provided in another embodiment of this application;

[0064] Figure 10 This is a flowchart of an ultrasonic imaging method provided in another embodiment of this application;

[0065] Figure 11 This is a schematic diagram of the structure of an image display device provided in one embodiment of this application;

[0066] Figure 12 This is a flowchart of an ultrasonic imaging method provided in one embodiment of this application;

[0067] Figure 13 This is a flowchart of an ultrasonic imaging method provided in another embodiment of this application;

[0068] Figure 14 This is a flowchart of an ultrasonic imaging method provided in another embodiment of this application;

[0069] Figure 15 This is a schematic diagram comparing the ultrasonic imaging effect with the SVD filtering imaging effect and the DI processing imaging effect provided in one embodiment of this application. Detailed Implementation

[0070] The present application will be further described below with reference to the accompanying drawings and specific embodiments. The described embodiments should not be considered as limitations on the present application, and all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of the present application.

[0071] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0072] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0073] Medical ultrasound imaging diagnostic equipment utilizes the propagation of ultrasound waves through the human body to obtain ultrasound images of human tissues and organs. Due to its advantages such as safety, wide applicability, intuitiveness, repeatability, strong soft tissue differentiation capabilities, flexibility, and low cost, ultrasound diagnosis has become the preferred technology in modern medical imaging diagnosis and occupies a crucial position in modern diagnostic technology. In particular, the examination and observation of microvessels and vascular microcirculation using ultrasound imaging diagnostic equipment is of great value for the early diagnosis and treatment of various diseases. However, the diffraction limit of ultrasound waves in the far field limits the ability of routine clinical ultrasound contrast imaging to display detailed microvascular structures.

[0074] To address the challenge of displaying the details of microvascular structures, current approaches borrow from the principles of fluorescence microscopy in optical super-resolution imaging. By introducing an ultrasound contrast agent, isolated microbubbles of the contrast agent are located and tracked to obtain images with a spatial resolution of tens of micrometers. However, to accurately identify and locate the microbubbles of the ultrasound contrast agent, data processing of the ultrasound signal is required. Nevertheless, the microbubble localization results obtained by existing data processing methods are not ideal, resulting in suboptimal imaging effects.

[0075] Based on this, the present invention proposes an ultrasonic imaging method, an ultrasonic imaging device, an image display device, and an image processing device, which can improve the accuracy of microvascular imaging and enhance the imaging effect of microvascular imaging.

[0076] like Figure 1 The diagram shows a schematic of an ultrasound imaging device. The ultrasound imaging device 100 includes an ultrasound probe 110, a transmitting / receiving circuit 120, a processor 130, and a display 140. The transmitting / receiving circuit 120 controls the ultrasound probe 110 to transmit ultrasound waves to the target tissue and receive ultrasound echoes to obtain ultrasound echo signals. The processor 130 processes the ultrasound echo signals to obtain tissue structure signals of the target tissue and, through microbubble extraction processing of the tissue structure signals, obtains microbubble signals and, based on the microbubble signals, obtains target microvascular image data. The display 140 displays the microvascular image data.

[0077] The ultrasound probe 110 includes a transducer (not shown) composed of multiple array elements arranged in an array. These elements can be arranged in a row to form a linear array, or in a two-dimensional matrix to form a planar array; they can also form a convex array. Each element is used to emit an ultrasonic beam according to an excitation electrical signal, or to convert a received ultrasonic beam into an electrical signal. Therefore, each element can be used to achieve the mutual conversion between electrical pulse signals and ultrasonic beams, thereby enabling the emission of ultrasonic waves to target tissues in the human body (e.g., the heart, lungs, uterus, etc.), and can also be used to receive echoes of ultrasonic waves reflected back from the tissue. The elements involved in ultrasonic wave emission can be simultaneously excited by electrical signals to emit ultrasonic waves simultaneously; or the elements can be excited by several electrical signals with a certain time interval to continuously emit ultrasonic waves with a certain time interval.

[0078] The transmitting circuit can generate a transmission sequence under the control of the processor 130. The transmission sequence is used to control some or all of the multiple array elements to transmit ultrasonic waves toward the target tissue. The transmission sequence parameters include the position of the transmitting array elements, the number of array elements, and the ultrasonic beam transmission parameters (e.g., amplitude, frequency, number of transmissions, transmission interval, transmission angle, waveform, focusing position, etc.). In some cases, the transmitting circuit can phase delay the transmitted beam, so that different transmitting array elements transmit ultrasonic waves at different times, so that each transmitted ultrasonic beam can be focused in a predetermined region of interest.

[0079] The receiving circuit can receive the electrical signal of the ultrasonic echo from the ultrasonic probe 110 and process the electrical signal of the ultrasonic echo. The receiving circuit may include one or more amplifiers, analog-to-digital converters (ADCs), etc. The amplifiers are used to amplify the received ultrasonic echo electrical signal after appropriate gain compensation, and the ADCs are used to sample the analog echo signal at predetermined time intervals, thereby converting it into a digital signal. The digitized echo signal still retains amplitude, frequency, and phase information.

[0080] The image processing module of processor 130 is used to process the digitized echo signal data, such as obtaining the tissue structure signal of the target tissue and obtaining the microbubble signal by performing microbubble signal processing on the tissue structure signal, and obtaining the target microvascular image data based on the microbubble signal. The image processing module can output the microvascular image data to the display 140 of the human-computer interaction device for display. The human-computer interaction device is used for human-computer interaction, that is, receiving user input and outputting visual information; it can receive user input through a keyboard, operation buttons, mouse, trackball, etc., or it can use a touch screen integrated with the display; it outputs visual information through the display 140.

[0081] It should be understood that Figure 1The components included in the ultrasound imaging device 100 shown are merely illustrative and may include more or fewer components. This invention is not limited thereto. Figure 1 The processor 130 in the ultrasound imaging device 100 shown is also used to perform the following... Figures 2 to 10 The ultrasound imaging method or used to perform the following Figures 12 to 14 Ultrasonic imaging methods in [the context of] [the study].

[0082] Reference Figure 2 , Figure 2 The diagram shown is a flowchart of an ultrasonic imaging method according to an embodiment of this application. The ultrasonic imaging method may include the following steps:

[0083] Step S210: Emit a first ultrasonic wave to the target tissue and receive the echo of the first ultrasonic wave returned by the target tissue to obtain a first ultrasonic echo signal.

[0084] Understandably, before emitting ultrasound waves to the target tissue, an ultrasound contrast agent needs to be injected into the target tissue first, and then, for example, through... Figure 1 The transmitting / receiving circuit 120 sends a transmitting pulse with a certain amplitude and polarity to the ultrasound probe 110 to excite the ultrasound probe 110 to emit ultrasound waves toward the target tissue. After a certain delay, the transmitting / receiving circuit 120 receives the ultrasound echo, thereby obtaining the ultrasound echo signal.

[0085] Step S220: Based on the first ultrasound echo signal, obtain the tissue structure signal of the target tissue, which includes multiple frames of spatial radio frequency signals.

[0086] It should be noted that, in this embodiment, the tissue structure signal of the target tissue includes multiple frames of spatial radio frequency signals. Therefore, the subsequent steps in this embodiment are to process the multiple frames of spatial radio frequency signals. It is understood that the first ultrasonic echo signal acquired by the transmitting / receiving circuit 120 should also include multiple sets of signals, so that multiple frames of spatial radio frequency signals can be obtained from the multiple sets of signals.

[0087] Step S230: Perform singular value decomposition filtering on the tissue structure signal to extract the microbubble radio frequency signal in each frame of spatial radio frequency signal to obtain the first microbubble signal.

[0088] The Singular Value Decomposition (SVD) filtering method is used to process the tissue structure signal, that is, to batch process multiple frames of spatial radio frequency signals. Microbubble radio frequency signals can be extracted from each frame of spatial radio frequency signals based on the singular values ​​of the image matrix, so that the ultra-microvascular structure of the subsequent imaging image is more complete.

[0089] Step S240: Perform time-domain differential filtering on the tissue structure signal to extract the microbubble radio frequency signal from each frame of spatial radio frequency signal to obtain the second microbubble signal.

[0090] By using time-domain differential filtering to process tissue structure signals and extracting microbubble radio frequency signals from each frame by utilizing the difference between adjacent frames of spatial radio frequency signals, the upper-level microvessels in the image can have better continuity.

[0091] Step S250: By fusing the first microbubble signal and the second microbubble signal, the target microvessel image data is obtained.

[0092] Step S260: Display the microvascular image based on the target microvascular image data.

[0093] In this embodiment, two spatiotemporal filtering methods with different processing effects, singular value decomposition filtering and temporal difference filtering, are used to process the tissue structure signal. This filters out the static tissue radio frequency signal in the spatial radio frequency signal, extracts the microbubble radio frequency signal, and obtains two sets of microbubble signals: the first microbubble signal and the second microbubble signal. Then, they are fused. This allows the image to combine the advantages of both spatiotemporal filtering methods, which can improve the accuracy of microvascular imaging and enhance the imaging effect of microvessels.

[0094] In one embodiment of this application, the time-domain differential filtering process mentioned in step S240 includes the following steps:

[0095] By subtracting one frame of spatial radio frequency signal from the other frame of two frames with a preset interval, the statically organized radio frequency signal is filtered to obtain the microbubble radio frequency signal. The subtraction includes at least one of amplitude difference, phase difference, or frequency difference.

[0096] Understandably, the information differences between two frames of spatial radio frequency (RF) signal data can include amplitude differences, phase differences, or frequency differences. By subtracting one frame of the RF signal from the other, the differential data of the two frames of RF signals in the tissue structure signal can be calculated. Then, based on the differential data, one of the two frames of RF signals can be divided into regions to determine the dynamic and static regions of the RF signal. The dynamic region represents the microbubble RF signal, and the static region represents the static tissue RF signal. Finally, by filtering the RF signal in the static region, the microbubble RF signal is obtained. Inter-frame differential imaging (DI) processing is a simple time-domain differential processing method that utilizes the state difference between two adjacent time points—where the tissue is relatively stationary and the microbubbles are in motion—to eliminate tissue signals while retaining moving microbubble signals. In addition to acquiring signals from moving microbubbles, the DI processing method can also detect microbubble signals exhibiting behaviors such as bursting and dissolution.

[0097] Reference Figure 3 In one embodiment of this application, the singular value decomposition filtering process mentioned in step S230 includes the following steps:

[0098] Step S310: Calculate the singular values ​​of the multi-frame spatial radio frequency signals to obtain the set of singular values.

[0099] Step S320: Determine the target singular value in the singular value set according to the preset statistical parameters.

[0100] Step S330: Extract the target feature signals from each spatial radio frequency signal based on each target singular value to obtain the microbubble radio frequency signal.

[0101] It should be noted that Singular Value Decomposition (SVD) filtering is a batch processing method for multi-frame image data. The singular values ​​of the image matrix and its feature space reflect the different components and features in the image. From a physical perspective, SVD is equivalent to decomposing a matrix into a linear combination of several sub-signal spaces, where the singular values ​​are the energy levels corresponding to each sub-signal space. In the data without microbubble extraction, there are three main components: tissue signal, microbubble signal, and noise signal. Among them, the tissue signal has the strongest energy, corresponding to larger singular values; the microbubble signal is next, corresponding to medium-sized singular values; and the noise signal is the weakest, corresponding to smaller singular values. Therefore, the singular values ​​corresponding to the microbubble signals are determined using preset statistical parameters, and then the microbubble radio frequency signals are obtained through these singular values ​​and their corresponding feature vectors.

[0102] In one embodiment of this application, Figure 3 Step S320 includes the following steps:

[0103] By comparing each singular value in the singular value set with a preset threshold range, the singular values ​​that fall within the threshold range are determined as target singular values.

[0104] In this embodiment, it is assumed that the number of frames of the spatial radio frequency signal is P, and the singular values ​​obtained after the P frames of spatial radio frequency signal are filtered by SVD are denoted as λ1, λ2, ..., λ P P singular values ​​are arranged in descending order. Preset statistical parameters include a signal threshold *a* and a noise threshold *b*, where signal threshold *a* is greater than noise threshold *b*. When a singular value is greater than or equal to signal threshold *a*, it is considered a singular value corresponding to a tissue signal and should be discarded; when a singular value is less than or equal to noise threshold *b*, it is considered a singular value corresponding to a noise signal and should be discarded; when a singular value is greater than noise threshold *b* and less than signal threshold *a*, it is considered a singular value corresponding to a microbubble signal and should be retained. That is, the preset threshold range is (signal threshold *a*, noise threshold *b*). The target singular value to be retained based on this preset threshold range is denoted as... The left and right singular vectors corresponding to the target singular values ​​are denoted as follows: Where R is the number of target singular values, and R is less than P. Then, the target feature signals extracted based on each target singular value are as follows: The final obtained microbubble radio frequency signal is Where H is the transpose symbol.

[0105] In another embodiment of this application, Figure 3 Step S320 includes the following steps:

[0106] Sort the singular values ​​in the singular value set according to their size, and take a preset number of the middle part of the singular values ​​in the sorted sequence as the target singular values.

[0107] It is understood that, unlike the method in the above embodiments that compares each singular value with a preset threshold range to determine the target singular value, this embodiment first sorts each singular value according to its size, and then selects a preset number of singular values ​​from the middle portion as the target singular value. This avoids the problem of insufficient accuracy caused by an inappropriate selection of the preset threshold range. It is understood that the above two methods of determining the target singular value have different focuses, and those skilled in the art can arbitrarily choose one of them according to the actual situation in practical applications.

[0108] Reference Figure 4 In one embodiment of this application, Figure 2 Step S250 includes the following steps:

[0109] Step S410: Perform microbubble positioning processing on multiple frames of microbubble radio frequency signals in the first microbubble signal to obtain multiple frames of first microbubble positioning data.

[0110] Step S420: Perform microbubble localization processing on the multiple frames of microbubble radio frequency signals in the second microbubble signal to obtain multiple frames of second microbubble localization data.

[0111] Step S430: Perform fusion processing on multiple frames of first microbubble localization data and multiple frames of second microbubble localization data to obtain target microvascular image data.

[0112] Among them, the microbubble positioning process determines the spatial positioning of the microbubble based on the spatial distribution of the microbubble radio frequency signal, so as to obtain the microbubble positioning data corresponding to the microbubble radio frequency signal.

[0113] It is understood that in this embodiment, microbubble localization processing is first performed on the first microbubble signal and the second microbubble signal respectively, followed by fusion processing. This allows the obtained target microvascular image data to combine the advantages of both spatiotemporal filtering processes, thereby improving the accuracy of microvascular imaging and enhancing the imaging effect of microvessels. The microbubble localization processing can use existing processing methods in the prior art, which will not be described in detail in this application.

[0114] Reference Figure 5 In one embodiment of this application, Figure 4 Step S430 includes the following steps:

[0115] Step S510: Determine the microvessel level distribution data based on the first microbubble localization data of each frame and the second microbubble localization data of each frame in the corresponding frame sequence.

[0116] Specifically, step S510 includes: determining the microvessel level based on the first microbubble localization data of each frame and the second microbubble localization data of each frame in the corresponding frame sequence, thereby obtaining multi-frame microvessel level distribution data.

[0117] Step S520: Perform image processing on the microvascular level distribution data to obtain target microvascular image data.

[0118] Specifically, refer to Figure 6 , Figure 5 Step S520 includes the following steps:

[0119] Step S610: Determine the display parameters corresponding to the microvascular level distribution data of each frame to obtain multi-frame spatial display distribution data.

[0120] Step S620: Obtain target microvascular image data based on multi-frame spatial display distribution data.

[0121] It is understandable that the spatial display distribution data of each frame includes the display parameters corresponding to the microvascular level distribution data of each frame. Therefore, multi-frame tracking and accumulation of the spatial display distribution data can be performed to obtain the target microvascular image data.

[0122] Specifically, in one embodiment, the first microbubble positioning data and the second microbubble positioning data are binarized matrix data;

[0123] When the value of the target element in the first microbubble positioning data is the first data value, and the value of the element corresponding to the target element in the second microbubble positioning data is the first data value, the microvessel level is a first-level microvessel.

[0124] When the value of the target element in the first microbubble positioning data is the second data value, and the value of the element corresponding to the target element in the second microbubble positioning data is the first data value, the microvessel level is secondary microvessel.

[0125] When the value of the target element in the first microbubble positioning data is the first data value, and the value of the element corresponding to the target element in the second microbubble positioning data is the second data value, the microvessel level is grade three microvessel.

[0126] When the value of the target element in the first microbubble positioning data is the second data value, and the value of the element corresponding to the target element in the second microbubble positioning data is the second data value, the microvascular level is no microvessels.

[0127] Among them, the first data value indicates that there is a microbubble radio frequency signal at the spatial position of the target element in the spatial radio frequency signal, the second data value indicates that there is no microbubble radio frequency signal at the spatial position of the target element in the spatial radio frequency signal, and the diameter of the vessel represented by the secondary microvessel is greater than that of the vessel represented by the tertiary microvessel but less than that of the vessel represented by the primary microvessel.

[0128] In this embodiment, the corresponding binarized matrix data of the first microbubble positioning data in each frame is denoted as Location_1, and the target element in the first microbubble positioning data is denoted as Location_1(h,w). The first data value is 1, that is, when Location_1(h,w) = 1, there is a microbubble radio frequency signal at the spatial position of the target element in the spatial radio frequency signal; the first data value is 0, that is, when Location_1(h,w) = 0, there is no microbubble radio frequency signal at the spatial position of the target element in the spatial radio frequency signal. Similarly, the corresponding binarized matrix data of the second microbubble positioning data in each frame is denoted as Location_2, and the target element in the second microbubble positioning data is denoted as Location_2(h,w). The first data value is 1 or 0, respectively. Here, h represents the image depth of the target element, and w represents the image width of the target element.

[0129] The localization results of the two sets of microbubble localization data are fused, and the fusion process includes the following four cases:

[0130] Case 1: Location_1(h,w)=1 and Location_2(h,w)=1, corresponding to the coarsest level of microvessels, then the corresponding display parameter Location(h,w)=1*Enhance_1;

[0131] Case 2: Location_1(h,w)=1 and Location_2(h,w)=1, corresponding to the microvessel level of secondary microvessels with poor SVD filtering continuity, then the corresponding display parameter Location(h,w)=1*Enhance_2;

[0132] Case 3: Location_1(h,w)=1 and Location_2(h,w)=0, corresponding to a microvessel level of DI missing grade 3 microvessels, then the corresponding display parameter Location(h,w)=1*Enhance_3;

[0133] Case 4: Location_1(h,w)=0 and Location_2(h,w)=0, corresponding to a region without microbubbles, the microvessel level is no microvessels, then the corresponding display parameter Location(h,w)=0;

[0134] Among them, Enhance_1, Enhance_2, and Enhance_3 can be represented as display brightness parameters. Different imaging effects can be achieved by optimizing the values ​​of Enhance_1, Enhance_2, and Enhance_3.

[0135] It is understandable that multi-frame tracking and accumulation are performed on the multi-frame spatial display distribution data obtained from the fusion process to obtain the super-resolution image result. Figure 15 This is a comparative schematic diagram of the ultrasound imaging effect, SVD filtering imaging effect, and DI processing imaging effect provided in one embodiment of this application. As can be seen, the ultrasound imaging method provided in this embodiment combines the advantages of both SVD filtering and DI processing methods, improving the sense of depth in microvascular imaging.

[0136] In another embodiment of this application, Figure 4 Step S430 includes the following steps:

[0137] The first microbubble localization data and the second microbubble localization data of the corresponding frame sequence are spatially weighted and summed to obtain the target microbubble localization data. The distribution of microvessels is determined based on the target microbubble localization data to obtain the target microvessel image data.

[0138] Different from the above Figure 5 The embodiments described herein obtain target microvessel image data by determining the microvessel level. In this embodiment, multiple frames of first microbubble positioning data and multiple frames of second microbubble positioning data in the corresponding frame sequence are spatially weighted and summed to obtain multiple frames of target microbubble positioning data. This makes the spatial distribution of target microbubble positioning data more continuous and smooth, resulting in better microvessel distribution and imaging effect of target microvessel image data.

[0139] In yet another embodiment of this application, Figure 4 Step S430 includes the following steps:

[0140] First microvascular image data is determined based on multiple frames of first microbubble localization data, second microvascular image data is determined based on multiple frames of second microbubble localization data, and target microvascular image data is obtained based on the first and second microvascular image data.

[0141] It is understandable that the above Figure 5 The embodiments that first determine the microvessel level and the embodiments described above that spatially weight and sum the first and second microbubble positioning data both involve fusing the two sets of microbubble positioning data before forming microvessel image data. Unlike the above two embodiments, this embodiment first forms two sets of microvessel image data based on the first and second microbubble positioning data respectively, and then fuses the two sets of microvessel image data to obtain the target microvessel image data. The first and second microvessel image data are generally brightness display data of the microvessels. Superimposing and fusing the two sets of microvessel image data allows the image to combine the advantages of two spatiotemporal filtering processes, improving the accuracy of microvessel imaging and enhancing the imaging effect.

[0142] Reference Figure 7 In one embodiment of this application, Figure 2 Step S250 includes the following steps:

[0143] Step S710: The first microbubble signal and the second microbubble signal are fused to obtain the target microbubble signal, which includes multiple frames of fused microbubble radio frequency signals.

[0144] Step S720: Perform microbubble localization processing on each frame of fused microbubble radio frequency signal in the target microbubble signal to obtain multi-frame target microbubble localization data.

[0145] Step S730: Determine the distribution of microvessels based on multi-frame target microbubble localization data to obtain target microvessel image data.

[0146] It is understandable that the above Figure 4 In one embodiment, multiple frames of microbubble radio frequency signals from the first and second microbubble signals are first processed for microbubble localization, and then the two sets of microbubble localization data are fused to obtain the target microvessel image data. In this embodiment, the first and second microbubble signals are fused first, and then microbubble localization is performed to obtain the target microvessel image data. It should be noted that both processing methods with different sequences can achieve good imaging results, and those skilled in the art can choose the appropriate method based on the actual situation.

[0147] Reference Figure 8 In one embodiment of this application, Figure 2 Step S250 includes the following steps:

[0148] Step S810: Determine the first microvascular image data based on the first microbubble signal, and determine the second microvascular image data based on the second microbubble signal.

[0149] Step S820: Obtain target microvascular image data based on the first microvascular image data and the second microvascular image data.

[0150] Understandably, this differs from the above. Figure 4 The embodiments and the above Figure 7 The embodiments provide two fusion processing methods. In this embodiment, microvascular image data is generated directly based on two sets of microbubble signals, and then the microvascular image data is fused. For example, the two sets of microvascular image data are spatially superimposed or weighted and summed to obtain the target microvascular image data.

[0151] Additionally, refer to Figure 9 , Figure 9 The diagram shown is a flowchart of another embodiment of an ultrasonic imaging method according to this application. The ultrasonic imaging method may include the following steps:

[0152] Step S910: Emit a first ultrasonic wave to the target tissue and receive the echo of the first ultrasonic wave returned by the target tissue to obtain a first ultrasonic echo signal.

[0153] Step S920: Based on the first ultrasound echo signal, obtain the tissue structure signal of the target tissue, which includes multiple frames of spatial radio frequency signals.

[0154] Step S930: Perform first microbubble signal processing on the tissue structure signal to extract the microbubble radio frequency signal in each frame of spatial radio frequency signal to obtain the first microbubble signal.

[0155] Step S940: Perform second microbubble signal processing on the tissue structure signal to extract the microbubble radio frequency signal from each frame of spatial radio frequency signal to obtain the second microbubble signal. The processing effects of the first microbubble signal processing and the second microbubble signal processing are different. Both the first microbubble signal processing and the second microbubble signal processing are spatiotemporal filtering processes. The spatiotemporal filtering process filters the static tissue radio frequency signal in the spatial radio frequency signal based on the comparison of data between multiple frames of spatial radio frequency signals in order to extract the microbubble radio frequency signal from the spatial radio frequency signal.

[0156] Step S950: By fusing the first microbubble signal and the second microbubble signal, the target microvessel image data is obtained.

[0157] Step S960: Display the microvascular image based on the target microvascular image data.

[0158] In this embodiment, two spatiotemporal filtering methods with different processing effects, first microbubble signal processing and second microbubble signal processing, are used to process the tissue structure signal. This filters out the static tissue radio frequency signal in the spatial radio frequency signal, extracts the microbubble radio frequency signal, and obtains two sets of microbubble signals, the first microbubble signal and the second microbubble signal. Then, they are fused. This allows the image to combine the advantages of the two spatiotemporal filtering methods, which can improve the accuracy of microvascular imaging and enhance the imaging effect of microvessels.

[0159] In some embodiments, the first microbubble signal processing is time-domain differential filtering or singular value decomposition filtering; the second microbubble signal processing is time-domain differential filtering or singular value decomposition filtering.

[0160] It is understandable that when the first microbubble signal processing is time-domain differential filtering and the second microbubble signal processing is singular value decomposition filtering, and when the first microbubble signal processing is singular value decomposition filtering and the second microbubble signal processing is time-domain differential filtering, the first microbubble signal processing and the second microbubble signal processing are different types of spatiotemporal filtering. When both the first and second microbubble signal processing are time-domain differential filtering, and when both the first and second microbubble signal processing are singular value decomposition filtering, the first and second microbubble signal processing need to use different filtering parameters, and the first and second microbubble signal processing are the same type of spatiotemporal filtering with different filtering parameters.

[0161] It should be noted that the detailed description of the time-domain differential filtering process in this embodiment can be found in [reference needed]. Figure 2 The illustrated embodiment provides a detailed description of step S240; for a detailed description of the singular value decomposition filtering process in this embodiment, please refer to [reference needed]. Figure 2 The illustrated embodiment provides a detailed description of step S230, for example, referring to...Figure 3 The detailed description of the embodiment shown; for the detailed description of step S950 in this embodiment, please refer to Figure 2 The illustrated embodiment provides a detailed description of step S250, for example, referring to... Figures 4 to 8 Specific descriptions related to this.

[0162] Additionally, refer to Figure 10 , Figure 10 The diagram shows a flowchart of another embodiment of an ultrasonic imaging method according to this application. The ultrasonic imaging method may include the following steps:

[0163] Step S1010: Emit a first ultrasonic wave to the target tissue and receive the echo of the first ultrasonic wave returned by the target tissue to obtain a first ultrasonic echo signal.

[0164] Step S1020: Based on the first ultrasound echo signal, obtain the tissue structure signal of the target tissue, which includes multiple frames of spatial radio frequency signals.

[0165] Step S1030: Perform various spatiotemporal filtering processes on the tissue structure signal to extract microbubble radio frequency signals and obtain multiple sets of microbubble signals. The spatiotemporal filtering process is to filter the static tissue radio frequency signals in the spatial radio frequency signal by comparing the data between multiple frames of spatial radio frequency signals, extract the microbubble radio frequency signals in the spatial radio frequency signal, and then obtain the microbubble signals.

[0166] Step S1040: Perform fusion processing on the microbubble signals of each group to obtain target microvascular image data.

[0167] Step S1050: Display the microvascular image based on the target microvascular image data.

[0168] Understandable, Figure 9 The illustrated embodiment applies two different spatiotemporal filtering processes—first microbubble signal processing and second microbubble signal processing—to the tissue structure signal, each with different effects. This embodiment, however, can perform multiple spatiotemporal filtering processes. For example, it can apply three microbubble signal processing methods to the tissue structure signal: first microbubble signal processing, second microbubble signal processing, and third microbubble signal processing. These first, second, and third microbubble signals are then obtained, and finally, the three sets of microbubble signals are fused to obtain the target microvascular image data, which is then displayed. This allows the image to combine the advantages of three spatiotemporal filtering processes, improving the accuracy and quality of microvascular imaging. The application of four microbubble signal processing methods to the tissue structure signal can be deduced similarly, and will not be detailed here.

[0169] The multiple spatiotemporal filtering processes used in this embodiment can be the same type of spatiotemporal filtering processes with different filtering parameters, or they can be different types of spatiotemporal filtering processes, such as time-domain difference filtering or singular value decomposition filtering.

[0170] In addition, such as Figure 11 The diagram shows a schematic of an image display device. The image display device 1100 includes a processor 1110 and a display 1120; the processor 1110 is used to acquire and process tissue structure signals, wherein the tissue structure signals include multiple frames of spatial radio frequency signals; the display 1120 is used to display microvascular image data.

[0171] It should be understood that Figure 11 The components included in the illustrated image display device 1100 are merely illustrative and may include more or fewer components. This invention is not limited thereto. Figure 11 The processor 1110 in the image display device 1100 shown is also used to execute the following... Figures 12 to 14 Ultrasonic imaging methods in [the context of] [the study].

[0172] Reference Figure 12 , Figure 12 The diagram shown is a flowchart of an ultrasonic imaging method according to an embodiment of this application. The ultrasonic imaging method may include the following steps:

[0173] Step S1210: Acquire tissue structure signal, which includes multiple frames of spatial radio frequency signal.

[0174] Step S1220: Perform first microbubble signal processing on the tissue structure signal to extract the microbubble radio frequency signal in each frame of spatial radio frequency signal to obtain the first microbubble signal.

[0175] Step S1230: Perform second microbubble signal processing on the tissue structure signal to extract the microbubble radio frequency signal from each frame of spatial radio frequency signal to obtain the second microbubble signal. The processing effects of the first microbubble signal processing and the second microbubble signal processing are different. Both the first microbubble signal processing and the second microbubble signal processing are spatiotemporal filtering processes. The spatiotemporal filtering process filters the static tissue radio frequency signal in the spatial radio frequency signal based on the comparison of data between multiple frames of spatial radio frequency signals, and extracts the microbubble radio frequency signal from the spatial radio frequency signal.

[0176] Step S1240: By fusing the first microbubble signal and the second microbubble signal, the target microvessel image data is obtained.

[0177] Step S1250: Display the microvascular image based on the target microvascular image data.

[0178] It is understandable that the ultrasonic imaging method provided in this embodiment, compared to...Figure 9 The ultrasound imaging method provided in the illustrated embodiment does not involve the steps of emitting and receiving ultrasound echoes. Instead, it only processes the acquired tissue structure signals to obtain target microvascular image data and displays the microvascular image, among other signal processing steps. Therefore, it is generally applied to applications such as... Figure 11 The image display device 1100 shown is shown.

[0179] Reference Figure 13 , Figure 13 The diagram shown is a flowchart of another embodiment of an ultrasonic imaging method according to this application. The ultrasonic imaging method may include the following steps:

[0180] Step S1310: Acquire tissue structure signal, which includes multiple frames of spatial radio frequency signal.

[0181] Step S1320: Perform singular value decomposition filtering on the tissue structure signal to extract the microbubble radio frequency signal from each frame of spatial radio frequency signal to obtain the first microbubble signal.

[0182] Step S1330: Perform time-domain differential filtering on the tissue structure signal to extract the microbubble radio frequency signal from each frame of spatial radio frequency signal to obtain the second microbubble signal.

[0183] Step S1340: By fusing the first microbubble signal and the second microbubble signal, the target microvessel image data is obtained.

[0184] Step S1350: Display the microvascular image based on the target microvascular image data.

[0185] It is understandable that the ultrasonic imaging method provided in this embodiment, compared to... Figure 2 The ultrasound imaging method provided in the illustrated embodiment does not involve the steps of emitting and receiving ultrasound echoes. Instead, it only processes the acquired tissue structure signals to obtain target microvascular image data and displays the microvascular image, among other signal processing steps. Therefore, it is generally applied to applications such as... Figure 11 The image display device 1100 shown is shown.

[0186] Reference Figure 14 , Figure 14 The diagram shows a flowchart of another embodiment of an ultrasonic imaging method according to this application. The ultrasonic imaging method may include the following steps:

[0187] Step S1410: Acquire tissue structure signal, which includes multiple frames of spatial radio frequency signal;

[0188] Step S1420: Perform various spatiotemporal filtering processes on the tissue structure signal to extract microbubble radio frequency signals and obtain multiple sets of microbubble signals. The spatiotemporal filtering process is to filter the static tissue radio frequency signals in the spatial radio frequency signals by comparing the data between multiple frames of spatial radio frequency signals, extract the microbubble radio frequency signals in the spatial radio frequency signals, and then obtain the microbubble signals.

[0189] Step S1430: Perform fusion processing on the microbubble signals of each group to obtain target microvessel image data;

[0190] Step S1440: Display the microvascular image based on the target microvascular image data.

[0191] It is understandable that the ultrasonic imaging method provided in this embodiment, compared to... Figure 10 The ultrasound imaging method provided in the illustrated embodiment does not involve the steps of emitting and receiving ultrasound echoes. Instead, it only processes the acquired tissue structure signals to obtain target microvascular image data and displays the microvascular image, among other signal processing steps. Therefore, it is generally applied to applications such as... Figure 11 The image display device 1100 shown is shown.

[0192] This application provides a computer storage medium storing a computer program that is applied to an ultrasound imaging device or an image display device. When the computer program is executed by a processor, it implements the ultrasound imaging method as described in the above embodiments.

[0193] This application provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the ultrasonic imaging method as described in the above embodiments.

[0194] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0195] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0196] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0197] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0198] It should also be understood that the various implementation methods provided in this application can be combined arbitrarily to achieve different technical effects.

[0199] The above provides a detailed description of the preferred embodiments of this application. However, this application is not limited to the above-described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. An ultrasonic imaging method, characterized in that, The method includes: A first ultrasonic wave is emitted toward a target tissue, and the echo of the first ultrasonic wave returned by the target tissue is received to obtain a first ultrasonic echo signal; Based on the first ultrasonic echo signal, the tissue structure signal of the target tissue is obtained, and the tissue structure signal includes multiple frames of spatial radio frequency signals; The tissue structure signal is subjected to singular value decomposition filtering to filter out the static tissue radio frequency signal in each frame of the spatial radio frequency signal, and the microbubble radio frequency signal in each frame of the spatial radio frequency signal is extracted to obtain the first microbubble signal. The tissue structure signal is subjected to time-domain differential filtering to filter out the static tissue radio frequency signal in each frame of the spatial radio frequency signal, and the microbubble radio frequency signal in each frame of the spatial radio frequency signal is extracted to obtain the second microbubble signal. By fusing the first microbubble signal and the second microbubble signal, target microvascular image data is obtained. Microvascular images are displayed based on the target microvascular image data.

2. An ultrasonic imaging method, characterized in that, The method includes: A first ultrasonic wave is emitted toward a target tissue, and the echo of the first ultrasonic wave returned by the target tissue is received to obtain a first ultrasonic echo signal; Based on the first ultrasonic echo signal, the tissue structure signal of the target tissue is obtained, and the tissue structure signal includes multiple frames of spatial radio frequency signals; The tissue structure signal is subjected to first microbubble signal processing to extract the microbubble radio frequency signal in each frame of the spatial radio frequency signal to obtain the first microbubble signal; The tissue structure signal is subjected to second microbubble signal processing to extract microbubble radio frequency signals from each frame of the spatial radio frequency signal to obtain the second microbubble signal. The processing effects of the first microbubble signal processing and the second microbubble signal processing are different. Both the first microbubble signal processing and the second microbubble signal processing are spatiotemporal filtering processes. The spatiotemporal filtering process filters the static tissue radio frequency signals in the spatial radio frequency signal based on the comparison of data between multiple frames of the spatial radio frequency signal to extract the microbubble radio frequency signal from the spatial radio frequency signal. By fusing the first microbubble signal and the second microbubble signal, target microvascular image data is obtained. Microvascular images are displayed based on the target microvascular image data.

3. The ultrasonic imaging method according to claim 2, characterized in that, The first microbubble signal processing and the second microbubble signal processing are the same type of spatiotemporal filtering processing with different filtering parameters, or they are different types of spatiotemporal filtering processing.

4. The ultrasonic imaging method according to claim 2, characterized in that, The first microbubble signal processing is time-domain differential filtering or singular value decomposition filtering; the second microbubble signal processing is time-domain differential filtering or singular value decomposition filtering.

5. An ultrasonic imaging method according to claim 1 or 4, characterized in that, The time-domain differential filtering process includes: The microbubble radio frequency signal is obtained by filtering the static organization radio frequency signal by subtracting one frame of the spatial radio frequency signal with the other frame, based on the difference between the two frames of spatial radio frequency signals with a preset interval.

6. The ultrasonic imaging method according to claim 5, characterized in that, The difference includes at least one of amplitude difference, phase difference, or frequency difference.

7. An ultrasonic imaging method according to claim 1 or 4, characterized in that, The singular value decomposition filtering process includes: Calculate the singular values ​​of the spatial radio frequency signals across multiple frames to obtain a set of singular values; The target singular value in the singular value set is determined according to preset statistical parameters; The target feature signals are extracted from each of the spatial radio frequency signals based on each of the target singular values ​​to obtain the microbubble radio frequency signals.

8. The ultrasonic imaging method according to claim 7, characterized in that, The step of determining the target singular value in the singular value set according to preset statistical parameters includes one of the following: By comparing each singular value in the singular value set with a preset threshold range, the singular values ​​that fall within the threshold range are determined as the target singular values; Alternatively, the singular values ​​in the singular value set can be sorted by size, and a predetermined number of middle singular values ​​in the sorted sequence can be obtained as the target singular value.

9. An ultrasonic imaging method according to claim 1 or 2, characterized in that, The process of fusing the first microbubble signal and the second microbubble signal to obtain target microvessel image data includes: Microbubble localization processing is performed on multiple frames of microbubble radio frequency signals in the first microbubble signal to obtain multiple frames of first microbubble localization data. Microbubble localization processing is performed on multiple frames of microbubble radio frequency signals in the second microbubble signal to obtain multiple frames of second microbubble localization data. The multi-frame first microbubble localization data and the multi-frame second microbubble localization data are fused to obtain the target microvessel image data. The microbubble positioning process involves determining the spatial location of a microbubble based on the spatial distribution of its radio frequency signals, thereby obtaining microbubble positioning data corresponding to the radio frequency signals of the microbubble.

10. The ultrasonic imaging method according to claim 9, characterized in that, The process of fusing the multiple frames of first microbubble localization data and the multiple frames of second microbubble localization data to obtain the target microvessel image data includes at least one of the following: Based on the first microbubble localization data of each frame and the second microbubble localization data of each frame in the corresponding frame sequence, the microvessel level distribution data is determined, and the microvessel level distribution data is processed to obtain the target microvessel image data. Alternatively, the multi-frame first microbubble localization data and the multi-frame second microbubble localization data of the corresponding frame sequence are spatially weighted and summed to obtain multi-frame target microbubble localization data. The distribution of microvessels is determined based on the target microbubble localization data to obtain the target microvessel image data. Alternatively, first microvascular image data can be determined based on multiple frames of the first microbubble localization data, second microvascular image data can be determined based on multiple frames of the second microbubble localization data, and the target microvascular image data can be obtained based on the first microvascular image data and the second microvascular image data.

11. The ultrasonic imaging method according to claim 10, characterized in that, The step of determining the microvessel level distribution data based on the first microbubble localization data of each frame and the second microbubble localization data of each frame in the corresponding frame sequence includes: Based on the first microbubble localization data of each frame and the second microbubble localization data of each frame in the corresponding frame sequence, the microvessel level is determined, and then multi-frame microvessel level distribution data is obtained. The step of image processing the microvessel hierarchy distribution data to obtain target microvessel image data includes: The display parameters corresponding to the microvascular level distribution data of each frame are determined to obtain multi-frame spatial display distribution data; The target microvascular image data is obtained based on the multi-frame spatial display distribution data.

12. The ultrasonic imaging method according to claim 11, characterized in that, The first microbubble positioning data and the second microbubble positioning data are binary matrix data; When the value of the target element in the first microbubble positioning data is a first data value, and the value of the element corresponding to the target element in the second microbubble positioning data is a first data value, the microvessel level is a first-level microvessel. When the value of the target element in the first microbubble positioning data is the second data value, and the value of the element corresponding to the target element in the second microbubble positioning data is the first data value, the microvessel level is a secondary microvessel. When the value of the target element in the first microbubble positioning data is a first data value, and the value of the element corresponding to the target element in the second microbubble positioning data is a second data value, the microvessel level is a third-level microvessel. When the value of the target element in the first microbubble positioning data is the second data value, and the value of the element corresponding to the target element in the second microbubble positioning data is the second data value, the microvascular level is no microvessels. Wherein, the first data value indicates that the microbubble radio frequency signal exists at the spatial position corresponding to the target element in the spatial radio frequency signal, the second data value indicates that the microbubble radio frequency signal does not exist at the spatial position corresponding to the target element in the spatial radio frequency signal, and the diameter of the vessel represented by the secondary microvessel is greater than the diameter of the vessel represented by the tertiary microvessel and smaller than the diameter of the vessel represented by the primary microvessel.

13. An ultrasonic imaging method according to claim 1 or 2, characterized in that, The method of fusing the first microbubble signal and the second microbubble signal to obtain target microvessel image data includes one of the following: The first microbubble signal and the second microbubble signal are fused to obtain a target microbubble signal. The target microbubble signal includes multiple frames of fused microbubble radio frequency signals. Microbubble localization processing is performed on each frame of fused microbubble radio frequency signals in the target microbubble signal to obtain multiple frames of target microbubble localization data. The distribution of microvessels is determined based on the multiple frames of target microbubble localization data to obtain the target microvessel image data. Alternatively, first microvascular image data can be determined based on the first microbubble signal, second microvascular image data can be determined based on the second microbubble signal, and the target microvascular image data can be obtained based on the first microvascular image data and the second microvascular image data.

14. An ultrasonic imaging method, characterized in that, The method includes: A first ultrasonic wave is emitted toward a target tissue, and the echo of the first ultrasonic wave returned by the target tissue is received to obtain a first ultrasonic echo signal; Based on the first ultrasonic echo signal, the tissue structure signal of the target tissue is obtained, and the tissue structure signal includes multiple frames of spatial radio frequency signals; The tissue structure signal is subjected to various spatiotemporal filtering processes to extract microbubble radio frequency signals and obtain multiple sets of microbubble signals. The spatiotemporal filtering process is to filter the static tissue radio frequency signals in the spatial radio frequency signals by comparing the data between multiple frames of the spatial radio frequency signals, extract the microbubble radio frequency signals in the spatial radio frequency signals, and then obtain the microbubble signals. The microbubble signals from each group are fused to obtain target microvascular image data; Microvascular images are displayed based on the target microvascular image data.

15. The ultrasonic imaging method according to claim 14, characterized in that, The various spatiotemporal filtering processes include spatiotemporal filtering processes of the same type with different filtering parameters, or spatiotemporal filtering processes of different types.

16. The ultrasonic imaging method according to claim 14, characterized in that, The spatiotemporal filtering process is either time-domain difference filtering or singular value decomposition filtering.

17. An ultrasonic imaging method, characterized in that, The method includes: Acquire tissue structure signals, the tissue structure signals including multiple frames of spatial radio frequency signals; The tissue structure signal is subjected to first microbubble signal processing to extract the microbubble radio frequency signal in each frame of the spatial radio frequency signal to obtain the first microbubble signal; The tissue structure signal is subjected to second microbubble signal processing to extract microbubble radio frequency signals from each frame of the spatial radio frequency signal, thereby obtaining the second microbubble signal. The processing effects of the first microbubble signal processing and the second microbubble signal processing are different. Both the first microbubble signal processing and the second microbubble signal processing are spatiotemporal filtering processes. The spatiotemporal filtering process filters the static tissue radio frequency signals in the spatial radio frequency signal based on the comparison of data between multiple frames of the spatial radio frequency signal, and extracts the microbubble radio frequency signal from the spatial radio frequency signal. By fusing the first microbubble signal and the second microbubble signal, target microvascular image data is obtained. Microvascular images are displayed based on the target microvascular image data.

18. An ultrasonic imaging method, characterized in that, The method includes: Acquire tissue structure signals, the tissue structure signals including multiple frames of spatial radio frequency signals; The tissue structure signal is subjected to singular value decomposition filtering to filter out the static tissue radio frequency signal in each frame of the spatial radio frequency signal, and the microbubble radio frequency signal in each frame of the spatial radio frequency signal is extracted to obtain the first microbubble signal. The tissue structure signal is subjected to time-domain differential filtering to filter out the static tissue radio frequency signal in each frame of the spatial radio frequency signal, and the microbubble radio frequency signal in each frame of the spatial radio frequency signal is extracted to obtain the second microbubble signal. By fusing the first microbubble signal and the second microbubble signal, target microvascular image data is obtained. Microvascular images are displayed based on the target microvascular image data.

19. An ultrasonic imaging method, characterized in that, The method includes: Acquire tissue structure signals, the tissue structure signals including multiple frames of spatial radio frequency signals; The tissue structure signal is subjected to various spatiotemporal filtering processes to extract microbubble radio frequency signals and obtain multiple sets of microbubble signals. The spatiotemporal filtering process is to filter the static tissue radio frequency signals in the spatial radio frequency signals by comparing the data between multiple frames of the spatial radio frequency signals, extract the microbubble radio frequency signals in the spatial radio frequency signals, and then obtain the microbubble signals. The microbubble signals from each group are fused to obtain target microvascular image data; Microvascular images are displayed based on the target microvascular image data.

20. An ultrasonic imaging device, characterized in that, include: Ultrasonic probe; A transmitting / receiving circuit is used to control the ultrasound probe to emit ultrasound waves toward the target tissue and receive ultrasound echoes to obtain ultrasound echo signals. The processor is used to process the ultrasound echo signal, obtain the tissue structure signal of the target tissue, and obtain the microbubble signal by performing microbubble signal processing on the tissue structure signal, and obtain the target microvascular image data based on the microbubble signal. A display for displaying the microvascular image data; The processor is also used to perform the ultrasonic imaging method according to any one of claims 1 to 16.

21. An image display device, characterized in that, include: A processor for acquiring and processing tissue structure signals, wherein the tissue structure signals include multiple frames of spatial radio frequency signals; A display for displaying the microvascular image data; The processor is also used to perform the ultrasonic imaging method according to any one of claims 17 to 19.

Images